tag:blogger.com,1999:blog-18089424783354271222024-03-17T00:15:10.332-07:00Biogerontology and HealthThe Art of SurvivalKismethttp://www.blogger.com/profile/08714147243460910596noreply@blogger.comBlogger124125tag:blogger.com,1999:blog-1808942478335427122.post-50437880545139391772024-03-17T00:14:00.000-07:002024-03-17T00:14:37.744-07:00AGI is a loaded gun - should we pull the trigger?<p>Recently I had an "elevator thought" for an analogy that best explains my views on <a href="https://en.wikipedia.org/wiki/Existential_risk_from_artificial_general_intelligence" target="_blank">X-risks</a> of any sort. Should we promote or slow down AI research? Will advanced AIs, superintelligences and AGI spell the end of humanity? These are questions which while not in need of an urgent answer at least become increasingly plausible to entertain.</p><p>Imagine AGI as a very special game of Russian roulette. You have a gun with 7 chambers. Five of these are empty, while one chamber carries a regular bullet. If the regular bullet fires, it's immediate death. If you pull the trigger when the barrel faces an empty chamber, you are still dead, albeit not instantly. Give or take 50 years and your life is over. However, in contrast to a normal game of Russian roulette, the gun is also loaded with another special bullet. When this bullet fires nothing bad happens. To the contrary, it will allow you to fulfill all or most of your dreams.</p><p><br /></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiGFPWLOH98AiX0n9vQrzjHL8FkDeHD6jA2pwz9fcBUdUxXmT_R8xMEZDKLUsLoU34w9CVxot3WzrVptri4E0o5od-N3uwZms0lx5OGNWTWPD875-wHqfoHo0bW1UhWoersD37uIvr7eryLXvgEoDQ2Do_8flmD7BOOyFuxOyeOSGZSpA2uFYNstj2mUhF3/s512/revolver1.jpg" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="512" data-original-width="512" height="320" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiGFPWLOH98AiX0n9vQrzjHL8FkDeHD6jA2pwz9fcBUdUxXmT_R8xMEZDKLUsLoU34w9CVxot3WzrVptri4E0o5od-N3uwZms0lx5OGNWTWPD875-wHqfoHo0bW1UhWoersD37uIvr7eryLXvgEoDQ2Do_8flmD7BOOyFuxOyeOSGZSpA2uFYNstj2mUhF3/s320/revolver1.jpg" width="320" /></a></div><br /><p>Facing the options of immediate death, death in the not-too-distant future or fulfillment of your dreams, would you pull the trigger? I know my answer.</p><p>But what should be our collective response to this?</p><p>As researchers our duty is <i>not </i>to tell people whether they should or should not build AGI, and whether they should take risks. Our duty is to make the gun as safe possible. To count the number of chambers. Are there really 7, could it be more or less? How many bullets are there really and are any of them blanks? How hard do you have to pull and for how long before the gun actually fires? What does it really mean to "fulfill your dreams"?</p><p>As society we are faced with the difficult choice of actually pulling the trigger once researchers have explained the probabilities we are facing, with all the caveats that will always remain on such estimated probabilities. What are our moral choices here? If you ask me, we do not owe the future the continued existence of humanity. We owe it to our parents, our siblings, our friends, lovers, wifes, husbands, our children, mates, pets, teachers, doctors and lawyers, we owe them to make the world better and do it now before they turn to dust.</p><p><b>Against panic, in favour of reasonable discourse</b></p><p></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhiap7AtLvTgWnEcbZusHWN1ayAGVa_Mzjx4KgdOcQiDjiWTJXaZSeZacpbhZJ5obbPw3noVPEJcmZX9m1-eNywHO9bGsI_Xqg72uUyqKnN3fkgr1jssXlAI21X81-maaVD5xkLMUWgQyxVtPMY6pQ5oLaDxQzaCHuGFIx10l1IksaC8EyulNHyO7oefn1m/s642/polls.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="350" data-original-width="642" height="217" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhiap7AtLvTgWnEcbZusHWN1ayAGVa_Mzjx4KgdOcQiDjiWTJXaZSeZacpbhZJ5obbPw3noVPEJcmZX9m1-eNywHO9bGsI_Xqg72uUyqKnN3fkgr1jssXlAI21X81-maaVD5xkLMUWgQyxVtPMY6pQ5oLaDxQzaCHuGFIx10l1IksaC8EyulNHyO7oefn1m/w400-h217/polls.png" width="400" /></a></div><br /><div class="separator" style="clear: both; text-align: center;"><span style="text-align: left;"><a href="https://twitter.com/AISafetyMemes/status/1759231062646104165">https://twitter.com/AISafetyMemes/status/1759231062646104165</a></span></div><div class="separator" style="clear: both; text-align: center;"><span style="text-align: left;"><br /></span></div>You might have seen similar tweets in your timeline recently. I saw them and they made me really concerned. I did expect the public sentiment to be against AI, since fear of change is an ingrained human tendency, but I still decided to actually take a look at the data. It quickly became clear to me that <a href="https://de.wikipedia.org/wiki/Eliezer_Yudkowsky" target="_blank">Yudkowite doomers</a> are misrepresenting the data to further their own agenda. If you believe that AI is going to destroy human civilization very soon then distorting statistics is probably the least of your worries.<p></p><p>For example, let us look at this poll from August 2023:<br /><a href="https://www.pewresearch.org/short-reads/2023/08/28/growing-public-concern-about-the-role-of-artificial-intelligence-in-daily-life/">https://www.pewresearch.org/short-reads/2023/08/28/growing-public-concern-about-the-role-of-artificial-intelligence-in-daily-life/</a></p><p>If you ask a vague question, you indeed do get a vague answer that is driven by fear. Not entirely surprising. However, when you start enumerating the use cases of AI you notice that most people actually see AI tools as beneficial! A clear majority thinks AI helps more than it hurts (251 vs 213 points) and AI comes out ahead in 5 out of 8 questions.</p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEh2UX-ZdBcxtRvg7botOXyAeajFqpCTy-CS1J4S5OuOD340hUGRVZ5xszH4vByq9BxVe8liXTttH9dKtdoUZ2-3VZjXUMpVZVad6OZQ3YN7SBZDCNCPInYuriXshsKnZMS5aRlf80HqQNeshw-AamY9r3NNdFO2erJ6fqE27FIYIF4meIcaAUXt0ku1Cq6u/s1124/pew.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="1124" data-original-width="840" height="640" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEh2UX-ZdBcxtRvg7botOXyAeajFqpCTy-CS1J4S5OuOD340hUGRVZ5xszH4vByq9BxVe8liXTttH9dKtdoUZ2-3VZjXUMpVZVad6OZQ3YN7SBZDCNCPInYuriXshsKnZMS5aRlf80HqQNeshw-AamY9r3NNdFO2erJ6fqE27FIYIF4meIcaAUXt0ku1Cq6u/w478-h640/pew.png" width="478" /></a></div><br /><p><br /></p><p></p><div class="separator" style="clear: both; text-align: center;"><br /></div><br /><br /><p></p><p><br /></p>Kismethttp://www.blogger.com/profile/08714147243460910596noreply@blogger.com0tag:blogger.com,1999:blog-1808942478335427122.post-41900591685390161162024-03-03T20:14:00.000-08:002024-03-03T20:18:58.380-08:00A new Evidence Based Pyramid for the 21st Century<p>A: Choose your battles carefully.<br />B: Okay.<br />A: Which battle are you going to choose?<br />B: All of them.</p><p>After telling mouse researchers that they need to be more careful with the health of <a href="https://biogerontolgy.blogspot.com/2023/12/how-long-should-lab-mice-live-in-good.html" target="_blank">their control mice</a>, we will now proceed to tell clinicians and policymakers that they are wrong and need to modify the guidelines they teach in med school.</p><p><br />The idea of an improved EBM pyramid has been on my mind and bugging me for a while. And I am not saying this because I consider myself a mouse researcher, but the commonly published EBM pyramid is much too focused on human evidence.</p><p>(Just to reiterate for those who did not get the opening joke, this is my own personal view. I may be wrong. However, I am confident that a large minority of people agrees with me but may not dare to speak out or does not consider this battle important enough.)</p><p>We all have seen a variation of the evidence-based pyramid. What many, however, do not realize is that this version is very skewed, if not outright wrong. The version you have seen basically discounts the value of animal research to zero, which is not consistent with the reality. It is obvious why, all else being equal, humans are a better model of human aging than rats and horses. However, all things are never equal between study designs.</p><p>There are many subtle ways in which animal studies are better than RCTs and observational studies. The most important one being the length of the study. Small pets, research and companion animals are thought to live and age faster than humans on many dimensions. Therefore we as researchers are able to perform studies across the whole (remaining) lifespan of an organism. RCTs usually last 3-10 years and observational studies realistically between 4-15 years, with a lot of variability at the tail ends. This means human studies only last for one tenth of the species lifespan, or less. Thus they often cannot uncover long term effects and effects on aging per se, unless these occur via "fast effects" like partial rejuvenation or via reduction of non age-related mortality (resilience?).</p><p>Enter an unbiased evidence pyramid that fairly considers the totality of the evidence.</p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhhiDzl-jQ-2s3_tTV7Smxzzo-UEJckFgvXhLFNKo-h7uN88YZti-729GL-Yd7KG9LcsNVUICmGsBRH4Q1WyEmCI7eLvOi_t8A7OGk_iDyjo2cg4j2JAeZeLm3-jPZlJ_MzB-vPGIeb2MVl8Uh8Uj3cnwO-xSv7PVQxxGY19ujxn8Gu2OYONKB5EcNqG7cg/s1914/pyramid%204.png" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="993" data-original-width="1914" height="332" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhhiDzl-jQ-2s3_tTV7Smxzzo-UEJckFgvXhLFNKo-h7uN88YZti-729GL-Yd7KG9LcsNVUICmGsBRH4Q1WyEmCI7eLvOi_t8A7OGk_iDyjo2cg4j2JAeZeLm3-jPZlJ_MzB-vPGIeb2MVl8Uh8Uj3cnwO-xSv7PVQxxGY19ujxn8Gu2OYONKB5EcNqG7cg/w640-h332/pyramid%204.png" width="640" /></a></div><br /><div class="separator" style="clear: both; text-align: center;"><br /></div>A few notes.<div><br /></div><div>This pyramid is designed in the context of aging research so we are mostly looking at things like lifespan, all-cause mortality and morbidity, CVD, cancer, neurodegeneration and age-related disease incidence as well as relevant surrogate endpoints.<br /><div><br /></div><div>It could be argued that many people working in the field know that the EBM pyramid is a simplification. Perhaps people think it is easier to teach the current version than reality. I am not so sure that this is the true motive. I get the impression that doctors do often look down on the basic sciences. Teaching the current EBM pyramid will just propagate this arrogance.<div><br /></div><div>Neither the new nor old EBM pyramid draws a good distinction between surrogate and hard outcome RCTs. For example, it is almost impossible to design a phase I study that will give a good readout on hard outcomes unless you are testing parachutes. However, an RCT with soft outcomes is not in any way superior to a cohort study or mouse study with hard outcomes. Although, here again we would have to distinguish between validated and not-yet-validated, or even invalid, surrogates ("soft outcomes"). Say, blood pressure vs. some oxidized DNA bases in blood lymphocytes or ORAC/TBARS.</div><div><br /></div><div>Similarly, neither pyramid accounts for the garbage in garbage out principle. A meta-analysis of RCTs with mortality or cancer as a secondary outcome may be weaker than a single, well-designed RCT with these as primary outcomes (we have seen this recently with vitamin D and aspirin). A meta-analysis of weak surrogate outcomes might be totally worthless.</div><div><br /></div><div>For negative data, I would prefer human over animal studies and for positive data the other way around. For example, if a cohort study shows harm and an animal study shows benefit you may not want to take the chance. If an animal study shows benefit and the cohort study shows nothing, I might consider the animal data at least as powerful as the cohort studies since cohort studies could fail to capture subtle effects that take the whole lifespan to accrue, or they might be biased (e.g. in the case of BMI literature).<div><br /></div></div></div></div>Kismethttp://www.blogger.com/profile/08714147243460910596noreply@blogger.com0tag:blogger.com,1999:blog-1808942478335427122.post-75572388887737930562024-01-19T21:53:00.000-08:002024-01-25T05:13:31.295-08:00Community Longevity Survey 2024 - Bryan Johnson, Transhumanism and more<p>In 2022 as COVID was winding down we started building a <a href="https://www.meetup.com/singapore-longevity-and-health-meetup/" target="_blank">longevity community in Singapore</a> which has now become one of the largest and longest-running such communities thanks to all our members, speakers and organizers.</p><p>Recently we had an event to celebrate the success of our meetup and brainstorm ideas to improve our community. I took this opportunity to design a little survey. If you have any suggestions you can always contact me on twitter at Aging_scientist. The first part deals with attitudes towards lifespan extension and the second part is a narrative review of Bryan Johsonon's impact on our meetup.</p><p><b>How long do you want to live?</b><br />This question was intentionally copied from the Donner et al. 2016 paper, which I think is important and amazing. Re-reading their paper, I would just like to point out one minor issue: please for the love of god, use tables next time instead of reporting numerical values in text. All the links to external data are dead! That much for indefinite lifespans...</p><p>Okay, let us compare the results as well as we can.</p><p><span id="docs-internal-guid-99a54acb-7fff-b93c-b42a-7e8529662087"><img alt="Forms response chart. Question title: Q1: How long do you want to live?. Number of responses: 36 responses." height="269" 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QQQCC5AoCS4mXGGUzgjdCkNQtxxe1cZMmSw67Yj5vO1/THHHCMaStFDqzz86U9/Mv7/zz//wrLNTKSQitej2Pt8yy03G1UszKEO8zU0BDJo8BB54401xj+7VQzRn5kraGjFlcLCJdKsadPw5d59d7Pc1a2H/Oc//zECNPl5ufLnP59SrMsaeJgyJVMKCgqNn3XtepukT5xg+IQOc6BEQyIL710gZ5xxuuPj26uiOIUs3nnnXUno1dsIT2gwIkglk+uvu1ZmzZphqQRiD5TomM2ZPbNYkEU7rM+bPjFDli2/3+h/UlJvSRkzylL1ZNeuz6RXYm9jzmn1ksKlS0Sf235oNZn77lsmaRMzjB+1bHmZ5MzPkmrVqoWb+n1We2UarZ6Sk5MtNWpUD1/LvGVTjx7dZdy4sY4VdHQLqKQ+/eSDDz40znUKzXjNXX6OAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIFC6AgRKovSOFChp3qyZsbjeqNHfLNUazLcyByxC/+5UacOp8sTQIYOlf/Ldgbe/MfdZK0QsLciXhg0bugq89trr0jupr1E946KLLpT52fOMbWWcDg0gaMBg1qw5xo9HjBgmyf3vDjd96623pevtdxr/rRU2NGChwROnQ0Mgd93Vw7WtOVCSOWWSETpxOzSwMW9edngLInsARH8+bep0yctfYlzCbYsi8/XNjk7hGXOgRH+uAYrWrVu59nHVquelf/JA4+c339ReMjMnW7aXMd8vUhhHz9dgT8+eiaIhDntb+7MOGJAsgwcPdJ1H5qoyoW2ZtKJN6DCPQ+r4cYad29zInDrdqN6jR6S2UX4cOQ0BBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAIM4CBEqiBNXtTx5buVL2798vB387aFRfeP+DD8LVRLTqSI/u3YwtYqpWrVrsLuaAhf4wUuURDWvodidjxow1rnPmmWdKQUGe1K1TJ1DvzcEEDQYsyc+VmjVrOl5D7zlz5mxZcO9C4+fDhw81AiLlypVzvac5CGIPRqhPj569RLf60SogWfPmSKtWLSNez+1G5iDDtGmZ0qVzp4gO5vBO/fr1ZEl+nlEFRo8vv/xKEnsnyYcffmRssbN8WaExFpEODWZMmjRFCgvvM5rZwzPmQIluKaPBHb2v2+EVtrH3cdKkdOnc6VbXsJLbfb799lujUsimTe8YYR57FRn7eToHzEEQu7VWkUnPmGScFqkKS6BJSmMEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAgTIhQKAkjsOgQYPXX39DUlPTZPfu3caV3baJMYcItFpIXu5i+dvfznPtjblahDaaPWuGdOhwS6DeBwmU6NY0Q4eNkNWrXzTukZ+fK1e2uSLi/SJdXyutTEzPkAceWGFcQ8MbrVu1lC5dOsuFFzY3gi2RwirmGwcNlGiIJaFXkhQVFRlBCg2NhCpt+N2Kx/7gGvAZPTrF+OeOHTvIlMkZUqlSJeO/YwmUOAV9NNixcNFimT59Zrgbata5cydp1fIyqVWrlq9wiTnU41XpJHQj83P269dHRo8aGe7Djp07pU9SP6Mqih4aFLr11g7Srl1bOfeccyxVVgJNVBojgAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCBx2AQIlJTAE77//gfRO6iNaxcStEoQ5UOJVLSTURd1SRquG6GFf3PfzGEECJeZQhJ9r29s4PZMGO1InTJTnnltV7JK6lU6bKy6X9u1vlObNm0nlypVdbxs0UGIPx6x48P5wFRLzOOhWRbm5i6RGjeqejxypqki8AyXamX379sns2XOl8L5lxhZE5kODHJdccrF0uOVmadHiUjnhhBMc+2+viuP5kLYGTnNuw4YNMmLkGNmxY0exy+l2SrrFUNu210n9+vV9hV6C9on2CCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIlI0CgpARctVLJtKnTJS9/iXH1AQOSZfiwIZY7RRMoefzxJ2TY8N8rRNi3lPHzGKUZKLFvLRPq38GDB2Xt2nWSPT9H1q/f4NhtDZf07Zskd95xu2OwJGig5Oeff5aUlHvkyaeeNu7nFigJYhpp/EoiUKL91kolWmVE7V5++ZViwRJto1VSEnslSO/eiVKzZg3XOednvtjbdO16m6RPnCDHHHOM5Uf79v0sD65YIfn5BfLFF184XloDRro1UItLL/VdiSaaPnIOAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggEB8BAiUxMex2FVWrXpe+icPNP7dKahg3mrFb4USc4ghSPgh1LloAyVaZaVjh1uk5p9q+tbSYMOtHTuKbufjdug2PpveeUf+8Y+35I031siWLVssTa+55mrJnDJJTjzxRMu/Bw2U/PDDD5I8YJCsWbPWuI5boKRly8skZ36WVKtWzfM5165bJ3fd1cNop/2cM3umUY1Gj5IKlJg7pRVLiorelzf/8Q/D7r33iiwBk8aNz5fZs2bKX/96Zvg08/w5q0EDufrqq6TiMRU9nzXUoPYZteXmm9sXC5SEfq5hod27P5d/vPVWeEz37NkTvr5uczRq5HBJ6JUgFStU8H1fGiKAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIlL4AgRIf5v/973/lp59+MlqWK1deqlU7wXP7Dq/wx65dn0mvxN6iIY+6depIQUGenHnm/xb/nbplDql07NhBpkzOMCpS+D2CBEo07JGU1Fc2bNxoBCWWLysUDb6U5KHhgwcffEgW3LtQ9u/fb9xKq7sMHjzQEkAIGijRrXYSeiVJUVFRsWfRih89evYSbdOoUSMpZFNTJQAAIABJREFUWJJbLMDi9MwrVjwkKWPHGT+yj0VpBErsffrxxx+NCixZWfMlFOKwh450e5rb7+hmBE+ChGeiHXMNmHz88T9l7rwseeGF1cZldC4tWrRALmvRItrLch4CCCCAAAIIIIAAAggggAACCCCAAAII/B97dwJnc9n/f/wtg7GOXRpCTKGyjf0uZSlJd0WNoiiytBAhOyEUSnWn7CoKkXRL9atsd2RXoqSQZZJ9GWYsY5j///oeZjPbd+acmXO+53Uej3nInOt7XZ/P8zo9HmXergsBBBBAAAEEEEAAAQSyQIBASTqQPXE9TVRUlF7q3Vfff7/UqmDGjGlq0vjuVKt57/1JeuONCdaY5K7RSasVO4ESE6IZ9soImeCEeY0d+5rahD2a1hLJvm+CBadPn1Fs7GUrkFOgYIEUT6gw17rMX/CZBgwYZM1VOzRU06ZNUeHCQXFz2w2UJDwNJulVPIcOHdYznbto+/bfrbDDRx/NVGitWqn2mdTGXOXywvPPxT3jzkCJ8TBhkUuXLlnzFyhQIMUTQsz7P/ywyjoZx3y+kgaVEoaYgoOD9eEHM2Q8MvJKGLLKmTOnChYsmOJVNuY0lQEDB+vLL5dk+LObkRp5BgEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAg4wIEStJh9+fOnXr66Wd08OBBpecH8SYEYIIf5qQN80op/DH74080bNhwa4wJa7z66gjlzp072YrMiSEvvNBDa9autd5//7131aLFfemoPn6InUCJecoEO/r3H2hNcO+99+iN8WOt4IDdV8LTTsyzH3/8UaonVCSsM7nTW+wESsxemCDOm2++ZZV9X/N79eab45UvXz7r9zGXLmnUqDH66KNZ1u87dnxKgwYNTPVKloT1JRdCcWegxJzUMmjwUH3++SKrPnMF0OOPP5biFiRc21wxM3fObNWuXdsaf+7cOQ0cONg6ycS8hr8yVB06tE8xCJLaPic87SQ9J7sk/Cxl5HQdu585xiOAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIZE6AQEk6/MwJC336vKz/+/Y7a/Tzzz2rl3r3SjF08Oeff6rTM1114MCBVE+92Ld/vzp27Kw9e/bI/PB/zJhRevSR1tf8gN+EIj5b+LkGDRpiXVdifoA/bepklSpVMh3Vxw+xGygx9XXt8qxMoMa8Xnyxu3r06J5i3yacMXnyFBUtUlSPPto6LhxjN7Sx+aef9NRTnaxTNsw1OzNnTFORIkXiGkkYKKlXr67+887bKlmyRLIWCffCDBg5crjaP/lEorEJ1wsKCtJ77/0nxcCL+SwMHTY8LuCRNKBiJnZnoMTM98EHH2nkq6OsmtMK9iT8TCUXfvr662/Us1dv63OUVq9mvSNHjmrc+Df0RLu21l7kyJHDqiPhyS7msztlyqQUT9hJGup59tmu6t/vZVufXQYjgAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCGStAIGSdHovX7FS3bo9Z/0g3vwAvXPnTlawJOGJHeYH59u2bbOuivnll63WzCZYMXLEcOXNm/ealZJe72J+wD98+DA9+O8HdN1111njTRjjy8VfasTIUYqIiLC+l9GTJewGSpIGWUzfHZ9+Ss891y1RwMPUdPjwESt4cPUkjR49XlDPF3vIXIdiXj//vEUdO3W2erjq16N7d+XLF+9i1tuzZ691KsqmzZut5zo/00n9B/RLFGJJGCgxYxo2aKDRo0eqfPnyccZmLnPVjbk652ogpm7dOpr47jsqUSJx+MQYvzXh7bgTZcz7r44crqbNmiZa99ixY3r7nf/ok0/mWuuY/fpg5nTVrFkj0d66O1CSNNhjTvgYOKCfihcvnmhdswejx7wWd7VMcmGXpIEY0+uwoYOtoErC03HMNUU//fyzBg8aavmZk1imT5ui+vXrWWsa38lTpmrcuDes35t5Rr06Qs2aNY377F79/JprnczJKFf33sxz112N0vlvHsMQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBLJDgEBJOtVN6ODddyfqP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" width="640" /></span></p><p><span>Donner results:<br />to 85, 65% of respondents<br />to 120 and beyond, 57% (replication cohort)<br />indefinitely 1% (not their data, reference to NYT survey)</span></p><p><span>Our results:<br /></span>to 85, 33% of respondents<br />to 120 and beyond, 66%<br />indefinitely 22%<br /><br />Based on this I would guess that our baseline data looks better, i.e. people in the longevity meetup desire longer lives more than the average person. One would expect such selection effects. Also very likely that many of our members are geroscience-pilled. They know that radical lifespan extension would be only possible with radical healthspan extension, since the two are almost the same over longer timescales. Hence, indefinite is philosophically a very sound answer.</p><p><span id="docs-internal-guid-e85c95e4-7fff-9b2a-5b96-4aa26e5c9b45"><img alt="Forms response chart. Question title: Q2: How long do you want to live? Provided mental and physical youthfulness.
. Number of responses: 35 responses." height="269" 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" width="640" /></span></p><p><span>Donner results:</span></p><p><span>to 120 and above 80%<br />infinite 42%<br /></span></p><p>Our results:<br />to 120 and beyond, 89%<br />indefinitely 43%</p><p><span>Again we are doing somewhat better than an average sample but the correct phrasing of the question helps to narrow down the difference. Many studies now have found that you MUST phrase this question the way we did to know "what people want" otherwise they will tell you "what they expect to get". Two very different topics.</span></p><p><b>The strange and polarizing case of Bryan Johnson</b></p><p><span id="docs-internal-guid-3f829889-7fff-b856-ae34-c8d761d7e0af"><img alt="Forms response chart. Question title: I found the Longevity and Health meetup through
. Number of responses: 36 responses." height="304" 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" width="640" /></span></p><p><span>This question was designed with the Bryan issue in mind. You will see why and how in a moment. When I first heard of Bryan I was neutral. Many researchers I know, in contrast, have a rather negative view of him. He is a crook, a scammer, he makes us look bad as "the billionaire who wants to live forever". Well, I am not going to disagree fully. If the last meme turns into a divisive political issue, which could happen, it would be catastrophic for sure. However, as with all polarizing figures there are also many positive aspects to this persona.</span></p><p><span>I really like Bryan's honesty. He does come across as a genuine transhumanist who believes in the power of longevity research and could help the community grow. But as a scientist I wanted to try and provide some numbers, back up my intuition that Bryan might be good for the field at leat somewhat.</span></p><p>I thought to myself. Been there, done that. People looked down on Aubrey de Grey before as well, but I remain convinced that he actually did benefit the field. For one, he got me interested in longevity research. Without Aubrey we would not have the Singapore Longevity Meetup as it is today. In fact, many people around me also got interested in longevity research because they have transhumanist, immortalist and radical longevity dreams. They just don't necessarily broadcast this. Given this, I came to the conclusion that <b>hype sustains the field</b>. Too little hype and the field runs cold: little funding, especially non-conventional funding, few outside experts wanting to join, no grassroots communities, etc. On the other hand, of course, too much hype and the field will run hot and burn down.</p><p>So to back up this intuition I would like to provide three numbers. (1) When Bryan gave our community a shoutout we got dozens of new members joining our telegram. (2) Based on the above survey at least 10% of our community found us thanks to Bryan. (3) Furthermore, he also supported grassroots efforts to create a new community in Singapore focused on his "don't die" ideas. Looking at the size and composition of this community, I am definitely impressed. I would consider their members longevity-adjacent. If we do stick with this somewhat charitable interpretation, we have to conclude that Bryan helped to <i>double </i>the local longevity community, since their meetup appears to be roughly as popular as ours with little overlap in participants. (Note: I am not sure if there were any regular longevity-adjacent meetups post-COVID and pre-Bryan apart from our meetup. Maybe there were and I just do not know them.)</p><p>My final remarks and conclusion here would be that Bryan appeals to a segment of the population that we fail to reach. He appeals to well-off, health-conscious people who want more than just regular medical care and nutrition advice. Basically, <b>he got the health enthusiasts</b> interested in longevity, the way <b>VitaDAO got the crypto enthusiasts</b> interested in longevity. At least speaking for the Singapore situation.</p><p><b>Who are our members and what do they want?</b></p><p><span id="docs-internal-guid-9c3a56e0-7fff-cd98-4337-558a459a21c5"><img alt="Forms response chart. Question title: I identify as …
. Number of responses: 36 responses." height="304" 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" width="640" /></span></p><p>The large number of self-declared biohackers again speaks to the popularity and importance of figures like Bryan Johson. Our meetup is very popular with health professionals I would say and another thing I notice again and again is the large number of tech-adjacent people coming to our meetups.</p><p>Some suggestions for the meetup included networking, lots of different physical and social activities on top of science. This is another reason why the Bryan movement is successful. People like simple events and activities. People like community, exercise, socializing, hands-on activities and the outdoors. His meetups heavily feature these elements.</p><p><b>References</b></p><p>Donner, Yoni, et al. "Great desire for extended life and health amongst the American public." Frontiers in genetics 6 (2016): 353.</p>Kismethttp://www.blogger.com/profile/08714147243460910596noreply@blogger.com0tag:blogger.com,1999:blog-1808942478335427122.post-29624240191234992572023-12-12T22:10:00.000-08:002023-12-12T22:21:15.402-08:00How long should lab mice live in a good lifespan study?<p><b>The impact of short-lived controls on the interpretation of
lifespan experiments and progress in geroscience. </b>Pabis et al. 2023<br /><a href="https://www.biorxiv.org/content/10.1101/2023.10.08.561459v1">https://www.biorxiv.org/content/10.1101/2023.10.08.561459v1</a></p><p class="MsoNormal">
<b>ABSTRACT<br />
</b>Although lifespan extension remains the gold standard for assessing
interventions hypothesized to impact the biology of aging, there are important
limitations to this approach. Our reanalysis of lifespan studies from multiple
sources suggests that the use of short-lived control cohorts tends to
exaggerate the relative efficacy of putative longevity interventions. Moreover,
due to the high cost and long timeframes of mouse studies, it is rare that a
particular longevity intervention will be independently replicated by multiple
groups.<o:p></o:p></p>
<p class="MsoNormal">To facilitate identification of successful interventions, we
propose an alternative approach. The level of confidence we can have in an
intervention is proportional to the degree of lifespan extension above the
strain- and species-specific upper limit of lifespan, which we can estimate
from comparison to historical controls. In the absence of independent
replication, a putative mouse longevity intervention should only be considered
with high confidence when control lifespans are close to 900 days or if the final
lifespan of the treated group is considerably above 900 days. Using this
“900-day rule” we identified several candidate interventions from the
literature that merit follow-up studies.<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiX-qdXoWDLZpmdDVnqAekdpl2r-gg_INXY-GVcorUyzZD6sXN-KW_HDQmwp-MB34ov9W3hJJPvJOAmZQ5Ca3CdFauSpMhFIGCB1Fh3FyyaqKWGj1PqrMCQ3YBHmALHoiGnxdnbttqXfRn7Erqnw68E-FvnmXnz3sE0sg1oavKqg3SpDLMYJ8VnWLs4514l/s1024/DALL%C2%B7E%202023-12-13%2014.04.37%20-%20A%20black%20and%20white%20sketch%20of%20two%20mice%20side%20by%20side.%20One%20mouse%20is%20visibly%20happy,%20with%20a%20big%20smile%20and%20bright%20eyes.%20The%20other%20mouse%20is%20unhappy,%20with%20a%20fr.png" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="1024" data-original-width="1024" height="400" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiX-qdXoWDLZpmdDVnqAekdpl2r-gg_INXY-GVcorUyzZD6sXN-KW_HDQmwp-MB34ov9W3hJJPvJOAmZQ5Ca3CdFauSpMhFIGCB1Fh3FyyaqKWGj1PqrMCQ3YBHmALHoiGnxdnbttqXfRn7Erqnw68E-FvnmXnz3sE0sg1oavKqg3SpDLMYJ8VnWLs4514l/w400-h400/DALL%C2%B7E%202023-12-13%2014.04.37%20-%20A%20black%20and%20white%20sketch%20of%20two%20mice%20side%20by%20side.%20One%20mouse%20is%20visibly%20happy,%20with%20a%20big%20smile%20and%20bright%20eyes.%20The%20other%20mouse%20is%20unhappy,%20with%20a%20fr.png" width="400" /></a></div><div class="separator" style="clear: both; text-align: center;">lean and healthy mice are superior. credit: ChatGPT</div><br /><p></p>
<p class="MsoNormal"><b>Introduction - what is the predictive power of the 900-day rule?<o:p></o:p></b></p>
<p class="MsoNormal">The question we want to investigate in this follow-up post
to our preprint is whether there is an optimal cutoff for mouse control
lifespans that defines a robust study. Robust here means simply a result that
can replicated by other investigators. For this analysis, we consider the ITP
study as the replication study and all other studies as hypothesis-generating. <o:p></o:p></p>
<p class="MsoNormal">We believe that studies reporting positive results against a
long-lived control population (lifespan > cutoff) are more likely to be
robust. The question is whether we can test this systematically by varying the
cutoff which could allow us to define an unbiased cutoff. In our previous work
we proposed that controls should live 900 days (± 50 days). This is based on a
biological argument, and several assumptions, rather than a statistical one.
Below we will try to make a more statistical case based on the ratio of false
positives to false negatives.<o:p></o:p></p>
<p class="MsoNormal">One major caveat of this analysis is that the data quality
and the number of published mouse studies is low making it difficult to <i>systematically</i>
evaluate different cutoffs. Especially because defining a successful mouse
study is quite subjective. Therefore we choose a more narrative approach.</p>
<p class="MsoNormal"><b>Results<o:p></o:p></b></p>
<p class="MsoNormal">If we use a cutoff for control mice at 900 days (± 50 days),
we find two true positives (rapamycin, NDGA) and one maybe positive (Aspirin)
in the DrugAge dataset that also replicate in the ITP. We consider NDGA a true positive because it works
rather consistently in male but not female HET3 mice across ITP cohorts (tested
four times). Aspirin only worked once and only in males (tested three times) so
it is difficult to classify.<o:p></o:p></p>
<p class="MsoNormal">If we use a cutoff for control mice at 800 days (± 50 days),
we find the same true positives as above. However, in addition we find four
false positives (curcumin, green tea, nicotinamide riboside [NR] derivatives,
and metformin). Classifying metformin and NR derivatives is difficult because
several studies showed no benefit or even life-shortening in the case of
metformin. If we exclude metformin and NR from our list of false positives we
still have two additional false positives in total (curcumin, green tea).<o:p></o:p></p>
<p class="MsoNormal">Fisetin and resveratrol deserve their own discussion since
they were not in the DrugAge and/or the ITP dataset that we used for our
manuscript.<o:p></o:p></p>
<p class="MsoNormal">In a small study, fisetin was reported to extend the median lifespan
of male and female C57BL/6 mice from an estimated 745 days to 873 days (Yousefzadeh
et al. 2018). Recently after submission of our manuscript, the ITP reported that
fisetin fails to extend the lifespan of HET3 mice. This would be consistent
with the notion that the initial lifespan result was not robust, although could
be also attributed to a strain difference since HET3 mice show more limited
senescence-related pathology (Harrison et al. 2023).<o:p></o:p></p>
<p class="MsoNormal">Baur et al. (2006) found that resveratrol extends the
lifespan of obese mice whose median lifespan was just shy of 770 days. This
finding was not replicated in the ITP nor by Pearson et al. (2008) when
comparing treated animals with controls that had a median lifespan of around
840 days. Again, this would be consistent with the notion that the initial
lifespan result was not robust due to short-lived controls.<o:p></o:p></p>
<p class="MsoNormal"><b>Summary<o:p></o:p></b></p>
<p class="MsoNormal">Overall applying relaxed lifespan criteria we find 3-6 false
positives (curcumin, green tea, fisetin, NR derivatives, and possibly
resveratrol, metformin), 2 true positives (rapamycin, NDGA) and one maybe
positive (aspirin). As a reminder, a false positive here is defined as a drug that works outside of the ITP but not in the ITP. All false positives are eliminated using the 900-day rule
with no additional false negatives. If we consider Aspirin a “hit” in the ITP,
then the 900-day rule would produce 1 false negative.</p>
<p class="MsoNormal"><b>Notes, limitations and additions<o:p></o:p></b></p>
<p class="MsoNormal">Although the ITP was the first study to show that rapamycin
extends mouse lifespan we can nevertheless compare the published rapamycin
studies with the ITP result using our methodology as if the order of publication was reversed. Rapamycin (and arguably
NDGA) are the only drugs that increase lifespan in at least one sex in the ITP
and in long-lived B6 or hybrid mice outside of the ITP.<o:p></o:p></p>
<p class="MsoNormal">For convenience and to exclude potential biases we heavily
rely on DrugAge to define “all other studies”. When appropriate we try to
reference other relevant studies that may not have been reported in DrugAge,
although the discussion of these studies here is not exhaustive.<o:p></o:p></p>
<p class="MsoNormal">Somewhat in line with the 900-day rule, although this one is
hard to call, is the story of ACE inhibitors. Captopril extends male lifespan
in the ITP. Enalapril has non-significant benefits. Similarly, ramipril appears
to have small, non-significant benefits in long-lived F1 hybrid mice (Spindler
2016). Simvastatin itself also has marginal, inconsistent benefits in these F1 mice and later went on to fail in the ITP.<o:p></o:p></p>
<p class="MsoNormal"><o:p>Again, as we emphasize in our manuscript, a study violating the 900-day rule is not worthless, it is simply flawed. Most studies ever published are flawed in one way or another. Such studies can still provide valuable insights.</o:p></p>
<p class="MsoNormal"><b>References<br /></b>Yousefzadeh, Matthew J., et al. "Fisetin is a
senotherapeutic that extends health and lifespan." EBioMedicine 36 (2018):
18-28.</p><p class="MsoNormal"><o:p></o:p></p>
<p class="MsoNormal">Harrison, David E., et al. "Astaxanthin and meclizine
extend lifespan in UM-HET3 male mice; fisetin, SG1002 (hydrogen sulfide donor),
dimethyl fumarate, mycophenolic acid, and 4-phenylbutyrate do not significantly
affect lifespan in either sex at the doses and schedules used."
GeroScience (2023): 1-22.<o:p></o:p></p>
<p class="MsoNormal">Baur, J. A., Pearson, K. J., Price, N. L., Jamieson, H. A.,
Lerin, C., Kalra, A., Prabhu, V. V., Allard, J. S., Lopez-Lluch, G., Lewis, K.,
Pistell, P. J., Poosala, S., Becker, K. G., Boss, O., Gwinn, D., Wang, M.,
Ramaswamy, S., Fishbein, K. W., Spencer, R. G., … Sinclair, D. A. (2006).
Resveratrol improves health and survival of mice on a high-calorie diet.
Nature, 444(7117), Article 7117. https://doi.org/10.1038/nature05354<o:p></o:p></p>
<p class="MsoNormal">Spindler, Stephen R., Patricia L. Mote, and James M. Flegal.
"Combined statin and angiotensin-converting enzyme (ACE) inhibitor
treatment increases the lifespan of long-lived F1 male mice." Age 38
(2016): 379-391.<br /><br />Succesful ITP interventions can be seen here at a glance:<br /><a href="https://www.nia.nih.gov/research/dab/interventions-testing-program-itp/supported-interventions">https://www.nia.nih.gov/research/dab/interventions-testing-program-itp/supported-interventions</a><o:p></o:p></p>
<p class="MsoNormal"><br /></p>
<p class="MsoNormal"><o:p> </o:p></p>Kismethttp://www.blogger.com/profile/08714147243460910596noreply@blogger.com0tag:blogger.com,1999:blog-1808942478335427122.post-57324611815572518132023-11-09T23:16:00.000-08:002023-11-09T23:16:10.335-08:00The Longevity Singularity<p>The idea behind this concept is very simple and I am sure not very novel. Nevertheless, I think we so far lacked a pithy name for this idea.</p><p></p><div class="separator" style="clear: both; text-align: center;"><div class="separator" style="clear: both; text-align: center;"><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEi5TIss2W5iVSpil3cWc17wikFQLDu-YfKbZSuwyMiuQfsi49yJ4txlxM_UtuNrhkGe_55ypty19Asd4tDnnz7EnNDqClh35NgKIZqgQGJc7PZxqY8q6M6jf0k1-gFVz9XPKEZHkmXWqY_PTmooG0GDvdu-lY84aHPQXGo0vs8cUVtj8SXmpFA8P9vHYm9G/s768/sing.png" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="753" data-original-width="768" height="314" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEi5TIss2W5iVSpil3cWc17wikFQLDu-YfKbZSuwyMiuQfsi49yJ4txlxM_UtuNrhkGe_55ypty19Asd4tDnnz7EnNDqClh35NgKIZqgQGJc7PZxqY8q6M6jf0k1-gFVz9XPKEZHkmXWqY_PTmooG0GDvdu-lY84aHPQXGo0vs8cUVtj8SXmpFA8P9vHYm9G/s320/sing.png" width="320" /></a></div><br /><div class="separator" style="clear: both; text-align: center;"><br /></div></div></div>Why could it be that we are closer to curing or slowing aging then it seems? Clearly, the biology is very, very poorly understood. The naive engineer's view on "biological repair" may help circumvent some of these issues but it surely won't get us the whole way to biological immortality or even radical lifespan extension. So what gives?<p></p><p><b>The Longevity Singularity argument</b> says that we need to get one longevity drug to market. Not two, not three and definitely not zero. Just one will do the trick. Once people realize how cost-effective longevity treatments are, companies, governments and academia will rush to develop more drugs and work on the underlying biology. CEOs will see the dollars on their accounts, governments will see the popularity a real anti-aging drug would ensure them with an aging population and academics will be lured by Nature publications and Nobel prizes.</p><p>All we need for now is to understand aging biology just well enough to deliver one drug. Most other efforts should be dedicated to getting this drug to market. </p><p><i>A thought experiment</i></p><p>Rapamycin or some rapalog could be that kind of drug, if we are lucky. One can imagine a world where the next rapalog is developed in TAME2, delivers significant improvements on multi-morbidity outcomes and is then soon approved for age-related diseases. A Nobel prize is awarded for the discovery of the mTOR pathway and to the Interventions Testing Program which first showed that rapamycin works in mice. Soon after companies rush to develop me-toos and a whole slew of anti-anabolic drugs. Indications are widened from aging to age-prevention. They all turn out to "work", even though they barely slow aging. Insurance companies are going bankrupt in a run on life insurance policies triggering a small recession, which is solved by some legislative magic. Insurers start paying people for being healthy and taking longevity drugs. Somehow these drugs tap into a conserved "rejuvenation-like" pathway, which is later discovered as the NIH pours trillions of dollars into basic research. Their effects on cancer are a bonus. This is when the fun really starts and we begin to discover dozens of new pathways, drugs start trickling in around 2040, if AI has not taken over the world by then, that is.</p><p><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgIj58hy3V4rHdSig9FOOacOojVC4bpBNxNehuXaHErnFx4zXFfgLCoQutbY50o9uWO6_B5gnCvJO1Vyr0VHpaIpKtniSa0FyUqchmBR3BfbghtO2KjR0aTMq-ngKsTYR1QqmtKVklBUaAKYFxJfa2cOSGD0HbrihSIjg5A9C7nG-gn3lcUXXabg9N1wr9K/s1522/expon.png" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="827" data-original-width="1522" height="348" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgIj58hy3V4rHdSig9FOOacOojVC4bpBNxNehuXaHErnFx4zXFfgLCoQutbY50o9uWO6_B5gnCvJO1Vyr0VHpaIpKtniSa0FyUqchmBR3BfbghtO2KjR0aTMq-ngKsTYR1QqmtKVklBUaAKYFxJfa2cOSGD0HbrihSIjg5A9C7nG-gn3lcUXXabg9N1wr9K/w640-h348/expon.png" width="640" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;"></td></tr></tbody></table><br /></p><div class="separator" style="clear: both; text-align: center;"><br /></div><br /><br /><p></p><p><br /></p>Kismethttp://www.blogger.com/profile/08714147243460910596noreply@blogger.com0tag:blogger.com,1999:blog-1808942478335427122.post-32031043817102942302023-09-25T20:32:00.004-07:002023-10-05T23:46:53.660-07:00The empty promise of Public Health Science (PHS)<p>What follows is an opinion piece that rises above the level of a "showerthought" and hence merits a whole blog post. So for a start, I am wondering whether this issue is trivially obvious to others like it has become to me over the years? Or whether what I am going to say is actually controversial for other aging researchers? Presumably, this will be <i>highly</i> controversial outside of the aging field. That is a given.</p><p>Someone once jokingly said that I like to start fights and so I always choose controversial topics. This is not true. I want us all to be friends and cure aging. No, I mean seriously. All I want is for the foundations of our field to be solid.</p><p><b>Geroscience vs Public Health Science (PHS)</b></p><p>Something I noticed over time is that there exists a qualitative distinction between geroscience (longevity research) and public health science (1). Geroscience is superior by far. We are talking orders of magnitude here.</p><p>Public Health Science (PHS) here is used as an umbrella term including nutritional and exercise science, as well as epidemiology and related subjects. PHS is related to geroscience because the major goal of PHS is also the extension of healthy lifespan and because many longevity doctors have a strong background in PHS. This sounds good, right?</p><p>However, here comes the catch. PHS offers small gains at a high cost whereas geroscience offers large gains for cheap. The cornerstones of PHS include healthy diet and exercise. No doubt, both are tremendously important. The only problem is that people do not want to exercise nor do they want to have a healthy diet. These are not free nor are they cheap, in terms of opportunity cost. If these lifestyle interventions were easy, the obesity pandemic would not exist.</p><p>I think I understand why many colleagues struggle to see it the same way. We scientists often come from privileged backgrounds and forget that not everyone has the willpower, money, time, ability and desire to invest hours upon hours per week to exercise and to abstain from delicious yet destructive habits. Many of us are also genetically privileged in one way or another, which makes it hard to empathise with people who have different struggles. For example, I am genetically lean so I will never fully understand the problems of people who already at a young age struggle with their weight and other vices. This is a privilege that I did not deserve nor choose in any way, since I had no say in who became my parents.</p><p>Be that as it may, a healthy life is tough. It has a cost, let us be honest here. What do you get in return for this investment? Well, maybe 5-10 years of lifespan and a couple more years of healthspan, depending on how "all in" you go, what the comparison group is and assuming that the underlying data is robust (2). In exchange for decades of, what to many people is, suffering<span style="color: red;">.</span></p><p>Let us compare this with geroscience. We have found drugs and interventions that can extend the healthy lifespan of mice by 30% or so, which is an order of magnitude more than exercise in the same animal model (3).</p><p>If you look at the promises of geroscience, on the low end we are rivaling the benefits of the antibiotic revolution. A pill, cheap, easy to consume with no compliance issues. Compare this to a time investment of 5 hrs per week for exercise, which barely even extends lifespan.</p><p><b>The dark secret of nutritional science and PHS</b></p><p>Even worse, PHS often makes outright wrong claims - no gains instead of small gains. Data is often based on weak recommendations rooted exclusively in flawed epidemiology. Time and time again controlled trials have proven that epidemiologic conclusions are tentative at best. Antioxidants, multivitamins, B-vitamins (via the homocysteine hypothesis) and recently vitamin D flunked in all relevant primary prevention studies that used hard outcomes. Vitamin D, as bad as it sounds, is probably still the success story of vitamin epidemiology, with exceedingly tiny but somewhat robust benefits on cancer and all-cause mortality.</p><p>So is epidemiology worthless? I believe it is not. That would be over-correcting too far after our bad experiences with clinical translation of "epidemiologic hypotheses". Personally, I still use epidemiology to guide my decisions, even though I recognize its limitations are more severe than I could have ever anticipated. </p><p><b>It is all about fairness</b></p><p>Again, I am sure I will be misrepresented as saying that exercise is worthless or worse. That is not what I am saying. I am, however, indeed saying that the benefits of exercise and diet are exaggerated and often based on weak evidence. The implication of this is important because it means there is a misallocation of funds that is more severe than we have believed. It is, of course, consensus among aging researchers that we are under-funded, but the reality seems to be even worse.</p><p>Since PHS enjoys a reputation that is too good and not commensurate with what it can deliver or has delivered, it means that this field, and probably others too, are taking away resources that should be dedicated to geroscience.</p><p>Geroscience is unfortunately a niche research field, albeit a growing one. What our field needs is an increased fraction of GDP spent on all research, medical research specifically and a re-allocation of resources from PHS and other medical sciences towards geroscience. <span>This would be the ethical, wise, longterm minded and utilitarian decision.</span></p><p><span>Please take this option seriously.</span></p><p><span><b>References and notes</b></span></p><p><span>(1) To spell out the difference for those unfamiliar with geroscience. PHS aims to extend lifespan by optimizing health and preventing disease whereas geroscience aims to extend lifespan by slowing aging.</span></p><p><span>(2) The benefits of exercise and diet are most likely highly exaggerated because they are based on epidemiologic studies that are subject to confounding and reverse causation. Almost every nutritional intervention suggested to be beneficial by epidemiology either failed in controlled trials or produced results that were much more modest than expected. How many life years you can gain from a healthy lifestyle is very much an open question. For example, exercise, assuming the data is sound, is only associated with a lifespan extension of 2-3 years but you spend around 1.5 of those years <i>doing</i> exercise! This can only really be worth it if someone enjoys exercising...</span></p><p><span><a href="https://jamanetwork.com/journals/jamainternalmedicine/fullarticle/648593">https://jamanetwork.com/journals/jamainternalmedicine/fullarticle/648593</a></span></p><p><span>(3) </span> <span>You will have to believe me here that exercise fails to extend lifespan in common animal models, because I am still working on a long article reviewing the human and animal exercise data. If you can show me two different publications using mice where exercise produced more than 10% lifespan extension I will buy you a beer!</span></p><p><br /></p>Kismethttp://www.blogger.com/profile/08714147243460910596noreply@blogger.com0tag:blogger.com,1999:blog-1808942478335427122.post-58060870710445996152023-09-04T14:55:00.009-07:002023-09-05T02:52:47.034-07:00Blogging ARDD2023 - vitaDAO, longevity medicine, senescence, clocks and more<p>This is a follow up post to my last conference report from the <a href="http://biogerontolgy.blogspot.com/2023/08/blogging-at-longevity-summit-2023.html" target="_blank">Longevity Summit 2023</a> which I wrote just two weeks ago. Below are my impressions and <i>inspirations </i>from the <a href="https://agingpharma.org/" target="_blank">ARDD</a>. Do not consider this a conference report or summary proper. Also, I hope no one comes away with a bad impression of the conference after my blog post, because it certainly was an amazing conference as always. However, I would much rather talk about relevant controversies and problems rather than reiterate all the good things.<br /></p><p><b>The food, once again - lifestyle changes made hard</b><br />As every year the catering leaves a lot to be desired. How can there be so few options for healthy food at a longevity conference? Some days there were just cookies and pastries during the breaks. Most of these not even vegan and the only way to get healthy food was, in fact, to ask for the "vegan option". It can't be so hard to offer fruits and veggies throughout the <i>whole</i> day.</p><p>This is also a pertinent illustration of a wider issue. People claim that lifestyle and exercise are our best tools to fight aging for now. Not only would I argue that the evidence for these claims is much weaker than we are led to believe (working on an article on this topic!). In addition, there is no easy way to implement lifestyle changes for most of us. If even doctors and longevity researchers cannot get it right at a conference, then probably no one can. This goes to highlight that we badly need drugs and intermittent interventions, like senolytics or partial reprogramming, to tackle aging because compliance with lifestyle changes is close to zero.</p><p>We need to fund more research into drugs and interventions rather than more research into lifestyle changes or exercise!</p><p>If we want exercise or lifestyle to work we need <i>radical changes</i> in the whole food environment and out of the box ideas, and lots of money and/or political will. One good example that I find promising is to pay people for exercising, as suggested by a colleague from BioAge whose name escapes me now. Another friend also proposed the "gamification" of exercise. This can be done using apps, tokens, rewards and by helping people to find the kind of sports they particularly enjoy.</p><p><b>How about some taurine with your food?<br /><br /></b></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjXDZtR2sG1b7xOXZSwce2FLkZeaFGY52UMxaYsNPZRtebM2wM4-bp7aWFg1iBVm6NxZ7w6kaM3HBexx5M9N6xC24QuLqHqOEXzWnthLA8Gke2C5HOxQnJs99mMk7plPEqFy9iJUfJorQeS5uKXpqOUC-Lyg2yT8MUc3LkyyYFBkwp8dh0pr6e5Wd5uW7ns/s920/ARDD_twitter.jpg" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="259" data-original-width="920" height="180" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjXDZtR2sG1b7xOXZSwce2FLkZeaFGY52UMxaYsNPZRtebM2wM4-bp7aWFg1iBVm6NxZ7w6kaM3HBexx5M9N6xC24QuLqHqOEXzWnthLA8Gke2C5HOxQnJs99mMk7plPEqFy9iJUfJorQeS5uKXpqOUC-Lyg2yT8MUc3LkyyYFBkwp8dh0pr6e5Wd5uW7ns/w640-h180/ARDD_twitter.jpg" width="640" /></a></div>It seems that old mice have a real problem with the absorption of taurine! Also, the story was published in <a href="https://www.science.org/doi/abs/10.1126/science.abn9257" target="_blank">Science</a> and not Nature.<p></p><p><b>VitaDAO at the ARDD</b><br />My DAO colleagues were present at ARDD with a booth and with several talks. Great to meet you in person.</p><p><b></b></p><div class="separator" style="clear: both; text-align: center;"><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEg7rsqjEUcZSCfNi-q26gbvWudDJ3imy09rBxrVEMkU0dQRnG28sgJIohqMXaa9YlIxSN_2g66O95bedco_ZMgrZSPRZchtN0q5dYkri9i334fpCvpDrAOdZTk9cS4HM2RwAVAfITfr_JodYy0Pngw8AMgYILpJ4VVu5WUXa-pFLYo6oWQ7izxJCgkrXAv4/s4032/F4sZs4NWEAAKpkb.jpg" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="3024" data-original-width="4032" height="300" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEg7rsqjEUcZSCfNi-q26gbvWudDJ3imy09rBxrVEMkU0dQRnG28sgJIohqMXaa9YlIxSN_2g66O95bedco_ZMgrZSPRZchtN0q5dYkri9i334fpCvpDrAOdZTk9cS4HM2RwAVAfITfr_JodYy0Pngw8AMgYILpJ4VVu5WUXa-pFLYo6oWQ7izxJCgkrXAv4/w400-h300/F4sZs4NWEAAKpkb.jpg" width="400" /></a></div><div class="separator" style="clear: both; text-align: center;"><br /></div></div>(I am the person who does not know how to smile if anyone is wondering)<div><br />Stay tuned for a special epigenetics-themed episode of the Aging Science podcast recorded during ARDD. It was fun to meet people in person to talk about science! Meanwhile enjoy the last released episode where I <a href="https://www.vitadao.com/blog-article/decoding-longevity-dna-mutations-mole-rats-and-cancer-prevention-with-prof-vera-gorbunova-on-the-aging-science-podcast-by-vitadao" target="_blank">interviewed Vera Gorbunova</a>.<br /><p><b>Persulfidation is weird, very weird<br /></b>Apparently highly persulfidated proteins still behave quite similarly to regular proteins but they may be more stable(?) showing less phase separation and persulfidation also helps cells to resolve stress granules. This also links with the hydrogen sulfide story, which I hope will be validated in mice soon (not looking promising, sadly, on the intervention side of things).</p><p><b>Moderates vs radicals<br /></b>Some people, especially at ARDD rather than longevity summit, were quite skeptical whether Bryan Johnson should be the poster child for longevity. If I recall correctly some of the panelists were also quite unconvinced by his supplement regimen, going so far as to say that he is merely "stimulating the economy", with his supplement regimen. This is probably true for many of the supplements but not all. Nir Barzilai also raised the issue of negative synergy. I am on the fence regarding this.</p><p>Be that as it may, it reminds me of the eternal debate between moderates and radicals. Should we talk about immortality at all? While the idea naturally follows from first principles*, if you dare to think about it, this does not come naturally to most people. In fact, it is rather jarring to hear talk of immortality in a world where we cannot cure COVID-19, dry eyes nor solve homelessness.</p><p>Either way, I prefer to be honest. I will always say that we should extend both lifespan and healthspan; and that we should strive to give people the choice to live as long as they want. You won't see me politicising and downplaying longevity science any time soon to get more funding, for better or worse.<br /><br />*once we accept that 1. the longevity dividend is true, 2. modest lifespan extension is feasible and desirable because of (1), and 3. freedom of choice holds, it follows that if we can scale the longevity dividend from 80 to 90 years, we can scale it from 100 to 120 and to 10000 years. At no point of this can we draw a non-arbitrary boundary that would not violate (3).</p><p><b>Poster sessions and networking</b><br />For those who may not know, the talks at conferences are almost a side-show, especially if they end up online. Plenty of collaborations are forged in the fires of the shortest lunch breaks, during poster sessions, through networking, socializing, etc.</p><p><b>My 5 cents on the topic of personalised medicine</b><br />This paragraph is<b> </b>inspired by a couple of discussions I had at ARDD. When it comes to screening and personalised medicine I tend to be more pessimistic than most others at the conference, whereas I am more optimistic about supplements and drugs.</p><p>There is this idea that genetics will allow us to find the perfect individualised diet or to optimize targetted therapies. The main problem is that running such studies becomes cost prohibitive. We barely have the money to run primary prevention trials with treatments that would benefit the vast majority of the population, now imagine how much more expensive those studies (and treatments) would be if the target population was only 10% or 1% of all people. Recruitment will be harder if you have to screen 100x more people, but most importantly pharma companies will not be able to recoup the money for these kinds of drugs unless they are very very expensive. </p><p>Maybe just as with mendelian randomization, it is only a matter of time. Large enough GWAS studies have ushered us into the era of mendelian randomization so we finally have a tool that fills the vast, almost infinite, gap between observational studies and controlled trials. Maybe GWAS studies will help us define hyper-responders to vegan diets, keto, intermittent fasting, rapamycin, metformin, who knows? It is certainly worth trying but we are not even close if you ask me.</p><p>I really do believe that the aging process is highly conserved and universal between individuals and we should be able to find universal drugs that can slow aging, which then will trigger a multi trillion dollar race to develop more such drugs, which will be the true "longevity escape velocity".</p><p>Another topic that is receiving a lot of positive attention from basic scientists is screening. However, cancer screening will be only useful if we find a way to slow aging that fails to slow cancer rates or actually increases them. This is not as absurd as it sounds, think eg of telomerase as potential tradeoff between cancer and improved regeneration capacity. Doctors like to emphasize the risks of screening which involve over-diagnosis leading to harms from surgery and biopsy up to and including death. However, there is another issue, which as always is Taeuber's paradox. Screening for cancer will not save any lives because cancer is a disease of older people who will just succumb to some other disease. If you were to reduce cancer mortality by a couple of percent this should not extend lifespan, as far as we know from first principles. Lo and behold this is what studies show if you do not believe me. The lifespan benefits are a rounding error:</p><p><i>"The findings of this meta-analysis suggest that colorectal cancer screening with sigmoidoscopy may extend life by approximately 3 months; lifetime gain for other screening tests appears to be unlikely or uncertain."</i><a href="https://jamanetwork.com/journals/jamainternalmedicine/article-abstract/2808648"><br />https://jamanetwork.com/journals/jamainternalmedicine/article-abstract/2808648</a></p><p>I do see a future where regular MRI screening combined with blood tests and watchful waiting, when needed, allows us to cure every cancer in its early stages but this future is still pretty much science fiction. We have to make it a reality, although it is not going to be easy or cheap.</p><p>I have been wrong before, so it would not be the first time if I underestimated a new technology!</p><p><b>Kejun Albert Ying talks about adaptAge and damAge clocks<br /></b>He came up with a perfect metaphor for adaptive vs non-adaptive epigenetic changes. Imagine non-adaptive epigenetic changes like a cane or glasses. They might be a very strong marker for aging and will predict a person's age very well, but just removing the cane will not make anyone healthier, it could actually make the situation worse. The cane for sure is not a causal driver of aging.</p><p>Current epigenetic clocks seem to measure both adaptive and non-adaptive changes, causal and non-causal changes. However, that is not as problematic as it sounds because a treatment that allowed patients to walk without a cane would be great and you could still monitor efficacy just by looking at cane usage! The current clocks might be useful to predict which treatments will be beneficial even if reversal of these clocks is non-causal. The jury is out on this question. What is more, we need to understand how much noise these clocks measure, which is another interesting controversy. How can clocks be reversible if they measure stochastic noise? This kind of damage is considered very hard to reverse.</p><p>Albert also mentioned ClockBase, a comprehensive database with epigenetic clock data for many or most GEO datasets. (I could not get it to work when I tried. Also it is slow. Vadim please invest in better servers if the raw data is not downloadable.)<a href="http://gladyshevlab.org:3838/ClockBase/#section4"><br />http://gladyshevlab.org:3838/ClockBase/#section4</a></p><p><b>The near future of mouse lifespan studies</b><br />Several groups at ARDD mentioned they are running their own multi-compound mouse lifespan studies. The Vadim Gladyshev lab is one of the labs testing multiple drugs, most likely based on genetic signatures that are predicted to extend longevity. Another group that shall go unnamed mentioned they are running studies in HET3 mice -- which is an excellent choice.</p><p><b>Toren Finkel on novel therapeutics - TFEB, senesence and NAD</b><br />Great to see more people targeting TFEB for longevity. Toren presented data on a small drug called G300 which is a TFEB activator that increases lysotracker signal at 30 nM, so quite potent. If I recall correctly, it was targeting Cullin4 and DAF7 which degrade nuclear TFEB making it quite short-lived. It works in a mouse model of AMD and clinical trials will begin in 2024.</p><p>The whole TFEB issue, and a talk I had with Adam Antebi, reminds me of an unresolved question in our field. For some reason, we made zero progress testing novel mTOR inhibitors in the ITP, hence we do not know which ones will work best in mice. One camp claims that mTORC1 selective inhibitors will have fewer side-effects and will work better because mTORC2 inhibition is harmful -- with good data to back up this hypothesis. On the other hand, there are highly potent ATP-competitive dual mTOR inhibitors that are also strong TFEB activators like torin-1. If the TFEB story is to be believed, these could have additional benefits over rapamycin. In vivo veritas and yet we have no data to resolve this, what a shame.</p><p><b>Nir Barzilai on proteomic clocks and senescence</b><br />By now it is pretty clear that you can construct an aging clock from any sufficiently rich dataset. The reason early projects languished was probably because they were looking for single markers. During his talk Nir went through his recent data on proteomic clocks. One thing that struck me was that only(?) 20% of SASP proteins were significantly associated with age in one of his cohorts (probably looking at the SenMayo SASP, don't quote me on the details). I am not entirely sure if that was significant, maybe the null hypothesis was closer to 0%. Either way, it is quite a low number and overall I feel like proteomics clocks are very inconsistent between studies. Not sure if transcriptomics is much better. Maybe that is one of the reasons why epigenetic clocks enjoy such a success? Whatever they measure, they do it at least with quite some reliability and inter-study consistency.</p><p>Joao Passos also gave a great talk later highlighting why Nir's results are the way they are. He does not seem to believe in the canonical SASP anymore, if I understand it correctly. Maybe there are many different senescent cell types with different SASPs and these could be different between people? After his talk I still do not fully understand the matter, doesn't there have to be some consistency if senescence is a real phenomenon that can be (partially) ameliorated with dasatinib+quercetin or ablation of p16 positive cells?</p><p>Well, maybe senescence is not real? By not real, I mean smoke, mirrors and hype with only something small but real behind it all. That might be closer to the position of senescence skeptics like Rich Miller, although this might be going too far if you ask me. Perhaps understandable after his disappointment with fisetin replication...</p><p><b>Jan Vijg and DNA damage</b><br />Most of Jan's talk was pretty standard for him, so I will not bore you with the details. You can find his talks on youtube I believe. Perhaps worth pointing out is that some of his data would make me believe that there is a selective mechanism to reduce SNVs during reprogramming, whether direct or indirect. One potential implication would be that epigenetic reprogramming is not as epigenetic as we thought and some of the benefits could be due to reduced mutation burden.</p><p><b>UnTAMING rapalogs for longevity (not today)<br /></b>Every time I go to ARDD I am thrilled to see so many amazing companies dedicated to tackling age-related disease, even Nestle appears to be doing basic & translational aging research these days, but on the other hand I am equally disappointed that no one is targeting <i>aging per se</i>. Obviously, if you are funding early-stage companies with VC money that is out of the question. If your fund only manages 100M then you cannot fund a phase III study for 50M even if you wanted for diversification purposes. However, some players do have the money to fund phase III studies and still refuse to do so. Which is surprising because it is not like big pharma has no appetite to set on fire large sums of money for hopelessly impossible diseases. One just has to think about the billions poured down the drain trying to slow Alzheimer's disease, which finally may be yielding real therapeutics, by the way. How would the world look if pharma invested billions in phase III studies to slow aging?</p><p>It is not like we have no candidate drugs whatsoever -- an excuse that I often hear. One could always argue that big pharma is not investing because the early-stage pipeline is empty, but this is only partly true. Perhaps we really do not have enough drug candidates to fund billions worth of phase III studies, but we do have a candidate that should be worth at least a couple hundred million in clinical development.</p><p>Indeed, the failure to develop rapamycin or patentable rapalogs for age-related multi-morbidity is probably the biggest disappointment of the last decade. Although there are people pushing to develop rapalogs, most famously Joan Mannick, they are limited because investors only fund medium-sized studies focusing on well-defined age-related conditions. What we need instead, however, is a large rapalog study with mortality and composite morbidity outcomes as a primary outcome, just the way TAME was envisioned for metformin.</p><p><b>Doctors vs basic scientists -- longevity medicine to the rescue?</b><br />I have mixed feelings about longevity medicine. There is the eternal question whether money spent on biohacking and longevity medicine would be better spent on basic and translational aging research, which is probably the case. On the other hand, realistically this is probably a pot that was never destined for research. As someone jokingly said: it is much preferable if the rich buy MRI scanners rather than yachts. If it is a choice between conspicuous consumption and self-improvement, the latter always wins.</p><p>Regardless of whether we <i>should</i> there is the question whether we <i>can</i>? Can longevity medicine deliver on its promise? I would say that the benefits of current longevity medicine approaches are overblown because most people do not understand just how heavy the health penalty imposed by Taeuber's paradox is. You cannot put out wildfire with a couple of fire extinguishers. Trying to promote the health of the elderly without addressing the underlying cause, which is aging, becomes a game of futile whack-a-mole. What good is your exercise regimen and penis rejuvenation (I am looking at you Bryan Johnson) if there is no primary prevention for cancer? Because cancer cachexia will not care about your muscles when it devours them and more.</p><p>No doubt longevity medicine can deliver some quality of life improvements which are undeniably important, but the net benefits on age-related mortality and morbidity will be smaller than it would seem. Remember, that you cannot simply consider the benefits on mortality or hard outcomes, suggested by epidemiology or RCTs, to be additive. It never works that way, as you quickly run into diminishing returns. Nevertheless a small fire extinguisher is better than none.</p><p>Also can we even agree on what is healthy and what longevity medicine should deliver? Looking back at the discussions of case reports at ARDD I do not think we can. I am not a doctor, so take this with a grain of salt, but when I hear doctors who suggest dropping vitamin D while adding psyllium husk (for constipation? was there really no whole food alternative?), I cannot but disapprove. I have reviewed the vitamin D literature extensively and although I totally agree that vitamin D is overhyped, there remains no doubt that the risk-benefit ratio is positive and everyone should supplement with around 2000 IU per day or get tested and supplement appropriately.<a href="https://biogerontolgy.blogspot.com/2022/05/vitamin-d-supplementation-update.html"><br />https://biogerontolgy.blogspot.com/2022/05/vitamin-d-supplementation-update.html<br /></a><br />The field is not at a stage where we can have much agreement on anything and IMHO never will be until we invest at least one order of magnitude more in health science (as a percentage of GDP) and 2 orders of magnitude more in aging research.</p><p><b>Doctors vs basic scientists -- differences in mindset<br /></b>During the longevity panel I was reminded of how I think differently as translational mouse bigerontologist compared to doctors. For some reasons doctors love epidemiology. No matter how weak the study design, how tiny the cohort or trial, a doctor will always choose human data over animal data, no matter what, disregarding all of evolutionary biology along the way.</p><p>Unfortunately epidemiological studies are so heavily biased that we have to consider them hypothesis-generating. You can obviously guide clinical decisions based on epidemiology if there is no other choice, or if there are other concordant lines of evidence, but it is NOT desirable. Again and again epidemiology has led us astray, e.g. when it predicted health benefits of antioxidants or of vitamin D. If these ever materialized, they were tiny compared to the predictions of epi. Given these issues, I would love to see a head-to-head comparison. It is entirely possible that on average epidemiology translates into RCT success as well as do animal studies, or worse!</p><p>Animal studies are insanely powerful because they can be randomized and well-controlled, which epidemiology can never achieve. Animal studies are interventional studies at heart and these are methodically superior to epidemiology. On the other hand, their validity is obviously lower since the model organism is non-human. However, I don't see a trivial way to quantify the importance of "genetic similarity" vs "methodical superiority". </p><p>Repeat after me: mouse studies are not the lowest quality of evidence. Different studies have their own strengths and limitations!</p></div>Kismethttp://www.blogger.com/profile/08714147243460910596noreply@blogger.com0tag:blogger.com,1999:blog-1808942478335427122.post-89971321920705770682023-08-22T01:48:00.003-07:002023-08-22T02:06:59.954-07:00Blogging at the Longevity Summit 2023 - speculation, news and views<p>Here are my comments from the Longevity Summit in Dublin.</p><p><b>The infinite deleteriome?</b></p><p>Vadim Gladyshev believes there is an infinite number of damage types, what he calls "side effects or consequences of living", to paraphrase. For the same reason he thinks that the Hallmarks of aging are misleading because they give the false impression that we know all the age-related pathologies and damage types. While Vadim is right that there must be an infinite number of consequences, we still do not know how many of those damage types are relevant during a normal human lifespan or when they become relevant if we were to extend lifespan to hundreds of years. Aubreys whole argument was always that we need to address a couple of damage types to make a dent in human aging, and then address the remaining ones in the future. Both have a point and there is a lot of space between "only 10 hallmarks of aging need to be addressed" and "we need to solve all consequences of the infinite deleteriome".</p><p><b>Biomarkers of aging are not enough</b></p><p>The panel on Friday briefly touched on the topic of biomarkers. Perhaps this is the right place to remind everyone that we will <i>never</i> have biomarkers that can replace hard outcomes. Even the best biomarkers for other diseases like LDL-C rarely if ever replace phase III studies for primary prevention. The best we can expect from biomarkers like epigenetic age is allowing us to run cheaper phase II studies where we pre-select promising compounds to run in phase III studies.</p><p>Ultimately, we will use composite outcomes as in the TAME trial and eventually, in the far away future, functional rejuvenation together with biomarkers.</p><p><b>The pipeline is empty - Matt Kaeberlein</b></p><p>Matt thinks that aging research is stuck because we have not found anything better than caloric restriction (CR). While I agree with Matt in a broad sense, he is somewhat overly pessimistic in his talk. If you take a look at his slides he is comparing CR with drugs (rapamycin) and intermittent treatments like senolytics, reprogramming and parabiosis. The breakthrough with these treatments is not the degree of lifespan extension with these interventions, but the fact that they are clinically feasible, whereas CR is not and never will be - unless perhaps you count semaglutide as CR.</p><p>On the other hand it is true that we are "looking under the lamppost" so to speak since we are so over-focused on growth signalling in longevity research. Indeed, I also had the same thoughts a while ago:</p><p><a href="https://biogerontolgy.blogspot.com/2020/09/cr-plus-cr-adjacent-cr-independent-what.html">https://biogerontolgy.blogspot.com/2020/09/cr-plus-cr-adjacent-cr-independent-what.html</a></p><p><b>The eternal problem of spontaneous necrolysis</b></p><p>The Andrei Seluanov talk on cancer reminded me of this unsolved issue in mouse research. Usually we want to perform some sort of necropsy on animals, but this is made difficult since animals are often found dead in the cage -- this means their tissues will be damaged by necrolysis. It amazes me, however, that we still have not found any technical solution to this problem! One can imagine an AI vision system and robotics to preserve dead animals, or having a technician on call instead of robotics. To be fair, there are other issues with using mice that died spontaneously, although I do not think they are insurmountable. (Mainly people prefer to study cross-sectional cohorts of the same chronologic age.)</p><p>Room for a multimillion-dollar startup? Or there is probably a company doing this already that I am not aware of...</p><p><b>Is the reversibility of epigenetic age the same as rejuvenation?</b></p><p>Aubrey raised an issue that was on my mind as well. Are epigenetic clocks reliably measuring age-related changes if stress can increase biological age? Steve Horvath and Vadim Gladyshev mentioned that pregnancy, smoking**, high-fat diet, etc can indeed increase epigenetic age. We know for a fact that most of these stresses do not affect aging rate per se and may not even have a huge impact on mortality. There are two ways to explain these findings:</p><p>Hypothesis 1: acute acceleration of epigenetic age is real age-related damage that is repaired. An analogy would be DNA modifications (like 8-OHdG) vs mutations.</p><p>Hypothesis 2: acute acceleration of epigenetic aging is not real age-related damage, but instead a shortcoming of the method which includes highly variable sites or because of some fundamental flaws.</p><p>If hypothesis 1 is correct we should screen for gain or loss of repair under stress. This would allow us to identify drivers of epigenetic aging.<br /><br /></p><p><b>Finally lifespan data on partial epigenetic reprogramming and parabiosis - vindication?</b></p><p>This data might not have escaped your notice, but it certainly did mine. David Sinclair, Vadim Gladyshev and Matt Kaeberlein presented some new lifespan data on these rejuvenation therapies.</p><p>Two papers report lifespan of parabiotic mice. One study by Vadim found a 5wk lifespan extension after 3mo of parabiosis (Zhang et al. 2021) while another manuscript published in Rejuv Res did not (Yankova et al. 2022). This is not necessarily bad, since 3 mo is a rather short-term treatment, although I am sure everyone hoped for better efficacy.</p><p>One study published as a preprint in biorxiv reports lifespan for mice subjected to partial reprogramming using adenoviral, doxycycline-inducible Oct-Sox-Klf (Cano et al. 2023). The final median LS was a respectable 1000+ days even though treatment was started in mice that were roughly 900 days old! This work was sponsored by Rejuvenate Bio so we need to await independent replication - as we should even if it were academic work (1).</p><p>Their mice are so healthy, which makes the data even more amazing:</p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjUmu3nqCtcTlHAZmVhYRlspUabVrMd6YKyX43MBJecPomG0ucvEe3D8xFT3KCMrAoCaYz8si4TX6kIlG2DzwOhC4C6xXsKVjHdf_jMnFNn6HsmstI9Dp556IoZ9zGAOK9-ZMdpS-KtEpppDp8l2yCecwZC-b92fXkDNJ5s1gFuzDVAIwqWmlTcJt-xc0Q4/s546/LSC2.jpg" imageanchor="1" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" data-original-height="385" data-original-width="546" height="226" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjUmu3nqCtcTlHAZmVhYRlspUabVrMd6YKyX43MBJecPomG0ucvEe3D8xFT3KCMrAoCaYz8si4TX6kIlG2DzwOhC4C6xXsKVjHdf_jMnFNn6HsmstI9Dp556IoZ9zGAOK9-ZMdpS-KtEpppDp8l2yCecwZC-b92fXkDNJ5s1gFuzDVAIwqWmlTcJt-xc0Q4/s320/LSC2.jpg" width="320" /></a></div><br /><p></p><div class="separator" style="clear: both; text-align: center;"><br /></div><p></p><p><b>The future of partial reprogramming</b></p><p>David Sinclair correctly points out that the FDA will not look kindly at viral reprogramming, which combines multiple issues between vector safety and actual payload toxicity. He and others propose to use chemical cocktails instead of viral delivery of OSK(M) or similar genes. Although this is not new per se, the regulatory angle was something I had not considered.</p><p><b>Premature death in mice?</b></p><p>One of the audience members raised the question whether animals may be euthanized too aggressively? Modern veterinary care standards indeed emphasize euthanasia for humane reasons and are always getting stricter. While this is generally beneficial, it could lead to problems. Imagine you have animals that are sick but not close to death, e.g. because of dermatitis. If you remove these mice too early this will decrease power. However, it seems unlikely that these procedures would mask the efficacy of longevity treatments if you believe that healthspan and lifespan are linked (as I do, despite many recent debates on the matter). On the other hand, could it be that modern euthanasia practice limits the power of frailty studies since frail mice are culled too quickly? A rigorous analysis of this problem would be desirable.</p><p><b>Early comparative biology and non-traditional model systems</b></p><p>Steve Austad says that now may be the time to revisit some early comparative biology with modern tools and less of a bias to see everything as related to oxidative stress. The question, to me, is if we just replace one set of biases with another one? Inflammation and DNA damage is now en vogue, will it always be?</p><p>Steve talks about the famous caloric restriction experiment in wild-caught mice and whether they respond to CR. He thinks that the experiment was not a complete failure since max LS was extended. He thinks we can utilize wild mice or make our current mice "more wild", use pet mice, etc. Vera and Steve mention some of the issues of working with wild mice: they bite more often than our docile lab strains, can be more fierce and escape their cages very skilfully.</p><p>Steve thinks we are underestimating the age of bowhead whales.</p><p><b>Why did myostatin gene therapies fail if they are so promising?</b></p><p>Matt Scholz of OISIN and many others at the conference are quite bullish on myostatin therapies. Makes me wonder why myostatin-based therapies have failed in the clinical until now? Perhaps one charitable explanation is that these therapies were often tested for dystrophies and other muscle-wasting diseases rather than sarcopenia which are very hard targets since they have a specific pathomechanism?</p><p><b>Bryan Johnson - a blueprint for transhumanism?</b></p><p>It turns out that Bryan Johson is actually a hardcore transhumanist, and a member of Foresight Institute. He was speaking about the importance of aligning AI, not destroying the biosphere, not dying, and not killing each other. The idea is to live long enough to witness superintelligence. I came away with a much better opinion of him than before the interview. The only disappointment were the fawning questions. Even if you like Bryan, maybe it would not hurt to ask critical questions? This is especially strange considering that I heard some very critical comments from participants.</p><p><b>Eric Verdin speaking at the Longevity Summit</b></p><p>Which goes to show that we can work together even if we do not agree on all matters, just as with Bryan Johson. I have written extensively on this topic when I <a href="https://biogerontolgy.blogspot.com/2021/10/let-us-talk-about-immortality.html" target="_blank">slammed Eric</a> for his exceedingly conservative views about the word "immortality". Given this, one could find it somewhat surprising that Eric would attend a conference hosted by Aubrey de Grey. May I remind everyone that this is THE conference for transhumanists and immortalists. We literally had a booth where you could get your copy of the following book signed by the authors: "The Death of Death: The Scientific Possibility of Physical Immortality and its Moral Defense"!</p><p>Amusing for sure!</p><p><b>References</b></p><p>Gene Therapy Mediated Partial Reprogramming Extends Lifespan and Reverses Age-Related Changes in Aged Mice. Carolina Cano Macip, Rokib Hasan, Victoria Hoznek, Jihyun Kim, Louis E. Metzger IV, Saumil Sethna, Noah Davidsohn bioRxiv 2023.01.04.522507; doi: https://doi.org/10.1101/2023.01.04.522507</p><p>Zhang, Bohan, et al. "Multi-omic rejuvenation and lifespan extension upon exposure to youthful circulation." bioRxiv (2021): 2021-11.</p><p>Yankova, Tatiana, et al. "Three-Month Heterochronic Parabiosis Has a Deleterious Effect on the Lifespan of Young Animals, Without a Positive Effect for Old Animals." Rejuvenation Research 25.4 (2022): 191-199.</p><p>https://www.science.org/content/article/two-research-teams-reverse-signs-aging-mice</p><p>(1) Not being an expert in viral delivery, I am actually somewhat surprised that their system works at all since it relies on two adenoviruses and hence requires a cell to be infected by both. They only present partial single-lane western blots to prove expression of reprogramming factors. Hopefully reviewers will push them to provide more than that.</p><p>**smoking might accelerate aging, but there is also an interesting question of whether ANYTHING at all can accelerate aging. If the damage that drives aging is multifaceted it should be as hard to shorten lifespan via aging as it would be to lengthen lifespan by slowing aging. It is also very possible that even the most powerful mutations or interventions, even those that extend lifespan, only affect some aspects of aging. Hence, it would be no huge surprise if X promoted death and impaired autophagy, but failed to affect epigenetic clocks or vice versa. The jury is still out on whether progerias faithfully recapitulate aging, aspects of aging or nothing much.</p><p><b>Conference selfie</b></p><p></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhIqx5nAZUn2q6dDt2KknMWT_04rYiSiPbkdlujvHFjOs3ooI0Nc1rkgrJ-ikKBouQappQ2m-UmbKyTAEYGWOy95rk1xMswTJkfzD2yUXtkm_9uXG4za5O_gOzLrShLP5tMIYoo7V93OZN9aLLNiTAcnkxXL9U6RK1MVx7k3XjEtpL0w25rqInqQELHWfaD/s4608/IMG_20230820_185039.jpg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="4608" data-original-width="3456" height="320" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhIqx5nAZUn2q6dDt2KknMWT_04rYiSiPbkdlujvHFjOs3ooI0Nc1rkgrJ-ikKBouQappQ2m-UmbKyTAEYGWOy95rk1xMswTJkfzD2yUXtkm_9uXG4za5O_gOzLrShLP5tMIYoo7V93OZN9aLLNiTAcnkxXL9U6RK1MVx7k3XjEtpL0w25rqInqQELHWfaD/s320/IMG_20230820_185039.jpg" width="240" /></a></div><br /><b><br /></b><p></p>Kismethttp://www.blogger.com/profile/08714147243460910596noreply@blogger.com0tag:blogger.com,1999:blog-1808942478335427122.post-38560019099193588072023-03-31T08:30:00.001-07:002023-03-31T08:30:00.250-07:00An Interview with Rich Miller - the VitaDAO Aging Science podcast #2<p>Check out the podcast <a href="https://podcasters.spotify.com/pod/show/vitadao/episodes/The-Mouse-Longevity-Chronicles-Unraveling-Aging-with-Dr--Richard-Miller-during-The-Aging-Science-podcast-by-VitaDAO-e20po1l" target="_blank">here</a>. </p><p>In this podcast, I (@aging_scientist) spoke with Dr. Rich Miller about the ins and outs of mouse aging.</p><p>The specific topics we discussed, roughly in this order, included the Interventions Testing Program, rapamycin, mecilizine and other lifespan-extending drugs. We talked about the importance of genetically heterogenous mouse stocks and the issues with fast-aging progeroid mouse models. We also covered his more recent work on aging rate indicators, and the difference between classic biomarkers and aging rate indicators. Finally, we talked about the importance of lifespan research, misuses of the word healthspan and the emerging use of frailty indices in mice.</p><p>I did not make it easy for Rich asking some difficult questions, most of which he answered quite well, even if I may disagree here or there. All in all, it was great to interview one of my favorite aging researchers!</p><p><b>About Rich Miller – short biography</b></p><p>Rich Miller got his BA degree at Haverford College, and then an MD and PhD at Yale. He received postdoctoral training at Harvard and Sloan-Kettering and was a faculty member at Boston University before moving to Michigan in 1990.</p><p>Now he is a principal investigator at the University of Michigan where his lab studies long lived mice and tries to understand why mice and other animals live as long as they do. He has been instrumental in facilitating the birth of the interventions testing program (ITP) which was the program that first discovered that rapamycin extends the lifespan of mice. </p><p>If you want to know one cool piece of trivia about Rich. I once checked and it appears that he has coauthored more mouse longevity studies than a small European country like the Netherlands. This attests to both his experience but also to the lack of mouse research in Europe.</p><p>full article: <a href="https://www.vitadao.com/blog-article/the-mouse-longevity-chronicles-unraveling-aging-with-dr-richard-miller-during-the-aging-science-podcast-by-vitadao">https://www.vitadao.com/blog-article/the-mouse-longevity-chronicles-unraveling-aging-with-dr-richard-miller-during-the-aging-science-podcast-by-vitadao</a></p>Kismethttp://www.blogger.com/profile/08714147243460910596noreply@blogger.com0tag:blogger.com,1999:blog-1808942478335427122.post-21226249642366732462023-03-21T04:59:00.000-07:002023-03-21T04:59:16.075-07:00An Interview with Alaattin Kaya - the VitaDAO Aging Science podcast #1<p>Finally, my new project has launched. In this amazing collaboration with VitaDAO we will interview different aging researchers, trying to ask difficult and controversial questions. Below is the first episode.</p><p><b>Exploring the Frontiers of Science: Breakthroughs, Funding Challenges, and Replication Crisis with Alaattin Kaya during The Aging Science podcast by VitaDAO</b></p><p>In this podcast, I (@aging_scientist) had the pleasure of interviewing Asst. Prof Alaattin Kaya (@akaya_lab).</p><p>We talked about recent breakthroughs in the field, the difficulties of getting funding for risky and novel research, funding agencies, “fishing expeditions”, the importance of overexpression genetic screens in aging research, novel mechanisms of action, and the replication crisis.</p><p>We also talked about yeast as a model organism and had a clear consensus:</p><p>“Of course, yeah, [it’s] everybody loves yeast.” (Alaattin Kaya)</p><p>It was a fun podcast! Since we really liked Alaattin’s overexpression screens (see below for more) we want to help him fund his amazing research. So if you know of any philanthropists or funding agencies that are willing to fund moonshots in aging research, please do reach out to us or directly to him.<br />link: <a href="https://vimm.vcu.edu/laboratory-groups/alaattin-kaya-phd/">https://vimm.vcu.edu/laboratory-groups/alaattin-kaya-phd/</a></p><p>check out the podcast <a href="https://podcasters.spotify.com/pod/show/vitadao/episodes/Exploring-the-Frontiers-of-Science-Breakthroughs--Funding-Challenges--and-Replication-Crisis-with-Alaattin-Kaya-during-The-Aging-Science-podcast-by-VitaDAO-e1vp3mk" target="_blank">here</a></p><p>Read the full article:<br /><a href="https://vitadao.medium.com/exploring-the-frontiers-of-science-breakthroughs-funding-challenges-and-replication-crisis-with-858069204593">https://vitadao.medium.com/exploring-the-frontiers-of-science-breakthroughs-funding-challenges-and-replication-crisis-with-858069204593</a></p>Kismethttp://www.blogger.com/profile/08714147243460910596noreply@blogger.com0tag:blogger.com,1999:blog-1808942478335427122.post-8187737180597983872022-10-15T02:34:00.004-07:002022-10-15T02:42:11.836-07:00Iron and aging - evolutionary musings<p><a href="https://novoslabs.com/iron-mineral/" target="_blank">This is an awesome article someone wrote for</a> NOVOS labs and I want to put it into context here. Read it if you have any questions why and how iron is harmful to health and longevity.</p><p><b>Iron metabolism in an evolutionary context<br /></b>Get ready for a profound evo-devo take. Since this is speculation, please do take this with a grain of salt. Over the years I developed an idea which can be summarized as a trade-off between feeling "good" and being healthy. I know this is not what you all wanted to hear. Everyone wants to feel healthy and <i>be </i>healthy, but often times the reality is different.</p><p>Here are just a few examples. No time to reference any. (If you want to read a scientific article with too many references, just follow the top link.)</p><p>Low blood pressure is linearly associated with better CVD outcomes but very low levels are also associated with orthostatic syncope and potentially weakness. Low blood pressure would be also horrible for homo sapiens specimens during most of their evolutionary history as it greatly increases the risk of hypovolemic shock after trauma – and let us keep in mind that trauma due to accidents, murder, conflict and war is a major cause of death for most mammals or humans.</p><p>Caloric restriction is another example. For most, except a small number of elite dieters and those with eating disorders, the feeling of hunger is unpleasant or at least mildly annoying. However, the feeling of hunger may drive the benefits of caloric restriction.</p><p>Iron is perhaps the best example of this hypothesis if you ask me. Low iron stores, while probably preventing cancer and aging, predispose to severe anemia even with moderate, chronic blood losses. This is not just significant due to trauma, but can also be an issue with menstruation or parasite infection or any number of other conditions that increase (internal) blood loss. In the wild, you got anemia, you die.</p><p>Low thyroid and IGF-1 signaling is another example, perhaps more speculatively. My limited understanding is that somewhat higher thyroid levels are rather pleasant and that hypothyroidism symptoms include things like tiredness and depression. See e.g.:</p><p><i>‘Endless energy, unexplained weight loss—the initial symptoms of an overactive thyroid are often seen as “good.” That’s why hyperthyroidism is one of the most underdiagnosed endocrine conditions.’</i></p><p><a href="https://www.endocrineweb.com/conditions/hyperthyroidism">https://www.endocrineweb.com/conditions/hyperthyroidism</a></p><p>This explains why our species developed an unhealthy setpoint for IGF1, thyroid levels, blood pressure and body fat levels. Put in laymen’s terms: live fast, get fat*, die young, kind of holds true – even if it is a simplification of reality. On the other hand, in evolutionary terms, antagonistic pleiotropy predicts that certain traits that have benefits during early adulthood are harmful during later parts of life, when evolutionary selection forces are weak, and thereby drive aging.</p><p>*when nutrients are over-abundant</p><p><b>Can we be healthy and happy?<br /></b>Now the question is if we can “hack” that connection between wellbeing and health. It may very well be that we can do so by using modern technology. It seems like there exists a zone between very-healthy-quite-unhappy and very-unhealthy-and-content that maximizes the two target outcomes while only leaving a few of the benefits on the table. Careful and prudent monitoring of energy levels and target blood levels could be the solution. Ferritin is the best example. In general, levels around 50-80 ug/L are considered safe, but already yield strong anti-cancer benefits in some studies. What one would like to do is regularly monitor ferritin levels and just play around with levels between 30 to 100 ug/L, or even higher if you feel like you are predisposed to depression, fatigue and brain fog.</p><p>Finally, a biohacker’s dream come true?<br /><br /><br /></p><p></p><div class="separator" style="clear: both; text-align: center;"><br /></div><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/a/AVvXsEjsF44FTkEggMkcshhhQBS_ScqFE_KSS3ABFyaBZa3du2nS_vDLA9VUrq9zf_5kCcKQqYRXjNlLsMxFvZXNL6khAnO7Y3ML7ECOK8z6OYBsw5rmvl8UNifsQxX8f6Y9-UFYbaCFMyULGInJCWN6kAcFNwQ6fpH7sXbgGEhz845JBg3KgUOb0QlISjvFhg" style="margin-left: 1em; margin-right: 1em;"><img alt="" data-original-height="512" data-original-width="512" height="400" src="https://blogger.googleusercontent.com/img/a/AVvXsEjsF44FTkEggMkcshhhQBS_ScqFE_KSS3ABFyaBZa3du2nS_vDLA9VUrq9zf_5kCcKQqYRXjNlLsMxFvZXNL6khAnO7Y3ML7ECOK8z6OYBsw5rmvl8UNifsQxX8f6Y9-UFYbaCFMyULGInJCWN6kAcFNwQ6fpH7sXbgGEhz845JBg3KgUOb0QlISjvFhg=w400-h400" width="400" /></a></div><br /><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/a/AVvXsEgFEhnDPecPv6llqUYGuf8Nl-HsMPPtwP-WZVDT8vZ4NhkN3Svj4yxA47n8Pf1QAAtsf8yQxZ1a5ZKXl8Sf5lCdLWBDGvtbuGbVzFunDMs04C_ICrJZPJih2jCiAf5WT3XC4SA3r-U2dH3htXjNASyImP5bgxGlf0wWHd6nybavjpbfpJeroDbxUra56w" style="margin-left: 1em; margin-right: 1em;"><img alt="" data-original-height="512" data-original-width="512" height="400" src="https://blogger.googleusercontent.com/img/a/AVvXsEgFEhnDPecPv6llqUYGuf8Nl-HsMPPtwP-WZVDT8vZ4NhkN3Svj4yxA47n8Pf1QAAtsf8yQxZ1a5ZKXl8Sf5lCdLWBDGvtbuGbVzFunDMs04C_ICrJZPJih2jCiAf5WT3XC4SA3r-U2dH3htXjNASyImP5bgxGlf0wWHd6nybavjpbfpJeroDbxUra56w=w400-h400" width="400" /></a></div><br /><div class="separator" style="clear: both; text-align: center;"><span style="text-align: left;">The sword of Damocles made of iron, with great power comes great responsibility, as interpreted by stable diffusion AI.</span></div><br /><p></p>Kismethttp://www.blogger.com/profile/08714147243460910596noreply@blogger.com0tag:blogger.com,1999:blog-1808942478335427122.post-77697726745947397602022-10-06T07:02:00.004-07:002022-10-06T07:03:58.603-07:00Is aging a disease?<p><i>got a cold, am bored, so I thought I will blog about a cool discussion on twitter</i></p><p>While to me it is strikingly obvious that aging is a disease there are valid reasons to disagree. In particular, I do like the view that sees aging as a treatable and preventable state which is the cause of age-related diseases or the view that would describe aging as a "disease-like" condition but not a disease per se. All these views have merit.</p><p>Although classifying aging as disease is very important to me it is not <i>that </i>important. It is not the hill I am willing to die on, you could say. Nevertheless a brief coverage of the twitter debate that was had.</p><p><b>What does the popular opinion say?</b></p><p></p><div class="separator" style="clear: both; font-weight: bold; text-align: center;"><a href="https://blogger.googleusercontent.com/img/a/AVvXsEgL0SuUfH9viFu4_6y96mfSmuumA4H85R2ASDQ6xfmwINgvTfpyWD7QEyAqzUYBJPN1wdXz9XasgW_Kwn0DCjJSiYvCQMNExrd51yRZTEU_dVO3_dWnjpDMWad-6cJ6qSp41fZhET30TCiutbJwAMGOfkQKMS8qKeEl4XJPRNBB9LR3AxK6VQSfqlIxIg" style="margin-left: 1em; margin-right: 1em;"><img alt="" data-original-height="574" data-original-width="1021" height="360" src="https://blogger.googleusercontent.com/img/a/AVvXsEgL0SuUfH9viFu4_6y96mfSmuumA4H85R2ASDQ6xfmwINgvTfpyWD7QEyAqzUYBJPN1wdXz9XasgW_Kwn0DCjJSiYvCQMNExrd51yRZTEU_dVO3_dWnjpDMWad-6cJ6qSp41fZhET30TCiutbJwAMGOfkQKMS8qKeEl4XJPRNBB9LR3AxK6VQSfqlIxIg=w640-h360" width="640" /></a></div><div class="separator" style="clear: both; font-weight: bold; text-align: center;"><br /></div><div class="separator" style="clear: both; font-weight: bold; text-align: center;"><a href="https://blogger.googleusercontent.com/img/a/AVvXsEh3nE0DmJvN7hu_s49wgwKtMwRqGhsH0ojA5bHdwEQPxRSMyR9RT9V9njlYHsbC-JRq9JhFe3pHmRmkDEU97yyyMcpJEnxHFMdRuxS06gWbKIqn6wk1Rx0ccgiTakU_gWBwWxXKA0keBSQDrX7sgaVUri8RHrBo_7ggBE4Bh4HRmlCEo9f9joJVFp7cSw" style="margin-left: 1em; margin-right: 1em;"><img alt="" data-original-height="477" data-original-width="1043" height="293" src="https://blogger.googleusercontent.com/img/a/AVvXsEh3nE0DmJvN7hu_s49wgwKtMwRqGhsH0ojA5bHdwEQPxRSMyR9RT9V9njlYHsbC-JRq9JhFe3pHmRmkDEU97yyyMcpJEnxHFMdRuxS06gWbKIqn6wk1Rx0ccgiTakU_gWBwWxXKA0keBSQDrX7sgaVUri8RHrBo_7ggBE4Bh4HRmlCEo9f9joJVFp7cSw=w640-h293" width="640" /></a></div><p style="font-weight: bold;"><b><br /></b></p><p>We learn several things here. The answer depends on the sample you study, and, since this is twitter, your friends and followers. We can conclude that Alex is most likely popular with young entrepreneurs, immortalists, tech bros, biohackers, etc. who are into radical lifespan extension. Dudley Lamming strikes me as a moderate and academic, so his poll might be a bit more representative for this part of the population. I am positively surprised that he is joining our side in the debate and also by the numbers in the poll. One third in favour of the disease notion, more than he expected, apparently.</p>It would not have been the first time to read disappointing opinion polls from aging researchers. I am reminded of a pretty shocking survey. I think it was the below one (Cohen et al 2020), but there might have been an even worse one.<p></p><p></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/a/AVvXsEgrmQKBrnpzb4ygouIQWQiFf4mWhhWJGNGooxwJP_21XhzOJO5Cc-0U8e0UZb4ywUVC1zJbsBv09PMLukapjd2wPntpDh_LLAqzfbmjGu9PJ4DJ-QhtisTqiUPF5ps33JkSoWHuUIsxaSSHatqHXkM-QGxinsnh-r9EzogVfSNa6JvE2BTyupP2xNDA-Q" style="margin-left: 1em; margin-right: 1em;"><img alt="" data-original-height="230" data-original-width="706" height="130" src="https://blogger.googleusercontent.com/img/a/AVvXsEgrmQKBrnpzb4ygouIQWQiFf4mWhhWJGNGooxwJP_21XhzOJO5Cc-0U8e0UZb4ywUVC1zJbsBv09PMLukapjd2wPntpDh_LLAqzfbmjGu9PJ4DJ-QhtisTqiUPF5ps33JkSoWHuUIsxaSSHatqHXkM-QGxinsnh-r9EzogVfSNa6JvE2BTyupP2xNDA-Q=w400-h130" width="400" /></a></div><br />Given the existence of Taeuber's paradox, these results do make me speechless. There should be 100% strong agreement on this point, at least among aging researchers. Not only is aging horrifying on visceral, personal level, no, targeting aging is also the only medically viable path forward to increase healthspan and lifespan in developed nations. Everything else is so inefficient and low impact that it is a rounding error -- except perhaps stopping the few big killers we still let loose, and the murderer we recently invited into our house, obesity, smoking and alcoholism.<p></p><p>Lamming alluded to other opinion polls which I could not find at a glance, although the book chapter by Janac, Clarke and Gems (2017) comes close as it has survey data (some of which is rather low quality, unfortunately, due to poor wording).</p><p><b>The fear of patholog-ization?<br /></b>This is in my opinion one of the key reasons why we so far refused to call aging a disease.</p><p>We see quite often that people with certain conditions oppose classification of these as a disease, even though they would benefit from such a change (e.g. due to better insurance coverage), whenever they identify with their condition or fear stigmatization. Given our history with things like homosexuality caution is obviously warranted.</p><p>Many (or some?) people in the transgender community oppose defining aspects of their condition as a disease, although eventually gender dysphoria did end up in the DSM defined as a treatable condition.</p><p>Similarly, many handicapped people feel like their handicap made them appreciate life in a different way or makes them unique. Often the word dignitiy is used and sadly it is often coopted by political conservatives, but that is a story for another day. I am sure many older people will argue that aging just changed them, but did not make them worse, just made them appreciate life in a different way. However, there is no relation between these feelings and the disease question. One can suffer from a disease, e.g. acne, hairloss, aging, whatnot, and feel totally fine.</p><p>On the other hand, we should not neglect a large number of people who are actually suffering because of their handicaps and would embrace the disease label. I, for example, have suffered from chronic joint and finger pain that at some point was so bad I was thinking about quitting science. A very minor handicap compared to others, but one that I could do without, one that made no positive contribution to my life whatsoever and just made me a worse person overall. </p><p><b>Normal cannot be a disease state?<br /></b>Charles Brenner makes this case on twitter, but I always found this argument very weak. So what if everyone has a disease?<br /></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/a/AVvXsEjuCApf0sgTztFn3l1AcbHC7BgNACP077B7N8uvvzgioMfojLZQ1Hw4e_N7vHU7EqVPwNQyy-5Nki0aA-4KGuT-2zXiMngrWkh3qOpLUd1wGpTIXWBENTPySlduoBf3o3zS9h6DCle59Mv8aYIoPKyEnIjUib38rpTWcRmDd4jzQ8IzuRKTVX13gAjTNA" style="margin-left: 1em; margin-right: 1em;"><img alt="" data-original-height="183" data-original-width="1012" height="117" src="https://blogger.googleusercontent.com/img/a/AVvXsEjuCApf0sgTztFn3l1AcbHC7BgNACP077B7N8uvvzgioMfojLZQ1Hw4e_N7vHU7EqVPwNQyy-5Nki0aA-4KGuT-2zXiMngrWkh3qOpLUd1wGpTIXWBENTPySlduoBf3o3zS9h6DCle59Mv8aYIoPKyEnIjUib38rpTWcRmDd4jzQ8IzuRKTVX13gAjTNA=w640-h117" width="640" /></a></div><br /><br /><p></p><p>In a society where every child starts to smoke at age 1, COPD and lung cancer will eventually reach a prevalence of 100%. Are they not diseases in this society anymore? What about atherosclerosis and cancer? We know that the underlying pathologies like fatty streaks, arterial calcification, DNA mutations in driver genes, clonally expanded precancerous cells increase exponentially, often start in childhood and reach levels of close to 100% -- arguing that if humans lived a little longer the ultimate outcome would be a cancer and CVD prevalence of close to 100%. Yet cancer and CVD are considered diseases but aging is not?</p><p>For me, the <b>main defining characteristic of a disease</b> has to be a meaningful deviation from the norm and an objective deterioration in quality of life that often, but not always, leads to a subjective deterioration in quality of life.</p><p></p><div class="separator" style="clear: both; text-align: center;"><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/a/AVvXsEhFeTJkqQCb1kQaoPc_ZDzh6mBaDgrNKCdRbfHpANEfFqsUupoy_FgduxLR-oeFA-RWo9pzGUdHKsBKWX4blAij6xMmkaPHYhYfylj26SloI8YMELyNvfFCY19DMtvUq_1590llkeudgxHmQkkFxGTk-Txf_gm7RLle-ju8rE7gqWABkDg8w5NRM5OUFg" style="margin-left: 1em; margin-right: 1em;"><img alt="" data-original-height="234" data-original-width="1015" height="149" src="https://blogger.googleusercontent.com/img/a/AVvXsEhFeTJkqQCb1kQaoPc_ZDzh6mBaDgrNKCdRbfHpANEfFqsUupoy_FgduxLR-oeFA-RWo9pzGUdHKsBKWX4blAij6xMmkaPHYhYfylj26SloI8YMELyNvfFCY19DMtvUq_1590llkeudgxHmQkkFxGTk-Txf_gm7RLle-ju8rE7gqWABkDg8w5NRM5OUFg=w640-h149" width="640" /></a></div><br /><br /></div><p></p><p><i>A deviation from the norm (1)</i><br />It is trivial to define a healthy person as someone in their early twenties. This would allow us to define aging or at the very least old age as a disease. It also address Brenner's claim and creates the "counterfactual" that Morgan Levine is asking for. If we distinguish the process of aging, akin to the fatty streak, from the result, which is old age, akin to atherosclerosis, we can perhaps arrive at a reasonable conclusion (which will be politically none the easier, I do not envy anyone who goes out calling old age a disease).</p><p>The issue of diagnostic criteria Levine mentions is a bit of a red herring. It is like saying what I do not see is not real. So in that sense it is actually a good point, because what we think of as disease, while guided by science, is socially constructed. I just want to change the social construction.<br /><br /><i>Objective deterioration in quality of life</i><br />This is trivially true for aging.<br /><br /><i>A subjective deterioration in quality of life<br /></i>If I break my leg I can still be happy after I got my cast and I am still a human. Nonetheless it is a medical condition that objectively decreases my quality of life, just like aging. Maybe we could settle to call aging a medical condition if that makes the skeptics happy, but in reality aging is more like osteoporosis, it is both a disease and a risk factor for worse outcomes.<br />Here it is important to point out that a disease should decrease subjective quality of life in at least a subset of people. Since psychological responses to diseases are quite different, many, even quite severe, diseases will not decrease subjective quality of life.</p><p></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/a/AVvXsEg_uLCVDudQ7j5u4mCz7SikXDlduoaYuamVnIlLgXqzW-d3KbVKLNl3QTEAribBmhmI9-p93YRgXbF0MTiOPQydd7SdqtVYRmaHXn6Fgi17Ow64GFgt7hPbmCfp4_YoJzVIeRY0JXapMmhdC03xxnDUuTmoiCA8CF5cjkv5mmdABsH8QvzFkSJG3AyqbA" style="margin-left: 1em; margin-right: 1em;"><img alt="" data-original-height="306" data-original-width="1021" height="192" src="https://blogger.googleusercontent.com/img/a/AVvXsEg_uLCVDudQ7j5u4mCz7SikXDlduoaYuamVnIlLgXqzW-d3KbVKLNl3QTEAribBmhmI9-p93YRgXbF0MTiOPQydd7SdqtVYRmaHXn6Fgi17Ow64GFgt7hPbmCfp4_YoJzVIeRY0JXapMmhdC03xxnDUuTmoiCA8CF5cjkv5mmdABsH8QvzFkSJG3AyqbA=w640-h192" width="640" /></a></div><br />Finally, Steven Austad was highlighting the similarities between aging and other processes like menopause or development (I think). However, these are not as harmful as is aging, except perhaps menopause, which, as far as I am concerned, can be called a disease. He suggested the the argument could be won by a reductio ad absurdum but never provided any. If we widen the meaning of the word "disease" enough to include aging, something bad would happen, but what? Puberty is clearly not a disease, even if mortality rates change during puberty. The mortality rates change due to aging, I'd say rather than due to what we commonly understand as puberty (sexual maturation).<p></p><p><b>Summary<br /></b>I think it would be acceptable to call aging a disease. It is clear, not totally wrong scientifically and it sends a message. <b>The construction of disease is highly arbitrary to begin with.</b> If we as a society decide that it is a disease, then that will be it.</p><p>It might be slightly more accurate to say that aging is a slow, pervasive and harmful process, similar to atherosclerosis, that leads to a syndrome that occurs at some point during old age (or might be synonymous with old age) that then leads to death.</p><p>One commenter tweeted what I think is a nice way to end this post:</p><p></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/a/AVvXsEjrDtkeNcnTfu8v-VuS1bsrAE0m7-1JB-R1Tx94bFzN_tfGwUTgG0XtuYxnZR75yuUzp52yX1iMJzWrDiuAAB5Jb7afiDHVQFRLaH4Rp4R68Q4w5j5uqi7BA4MlEOCFRlf8VAmLcUol0M0gqlgUKq0p4o6rGCdN7nG5HCKtJVrEno8t075tktSUTTCUTQ" style="margin-left: 1em; margin-right: 1em;"><img alt="" data-original-height="280" data-original-width="1016" height="176" src="https://blogger.googleusercontent.com/img/a/AVvXsEjrDtkeNcnTfu8v-VuS1bsrAE0m7-1JB-R1Tx94bFzN_tfGwUTgG0XtuYxnZR75yuUzp52yX1iMJzWrDiuAAB5Jb7afiDHVQFRLaH4Rp4R68Q4w5j5uqi7BA4MlEOCFRlf8VAmLcUol0M0gqlgUKq0p4o6rGCdN7nG5HCKtJVrEno8t075tktSUTTCUTQ=w640-h176" width="640" /></a></div><br /><br /><p></p><p><b>References and </b><b>notes</b></p><p>Cohen, Alan A., et al. "Lack of consensus on an aging biology paradigm? A global survey reveals an agreement to disagree, and the need for an interdisciplinary framework." Mechanisms of ageing and development 191 (2020): 111316.</p><p>Janac, Sarah, Brian Clarke, and David Gems, eds. Chapter 2 Aging: Natural Or Disease? A View from Medical Textbooks. Royal Society of Chemistry, 2017.</p><p>(1) I believe if we had no reference to norms at all, as appealing as this is, it would lead to pretty wild conclusions, which while exciting sound rather absurd. For example, a cognitive disability may be defined via X, Y, Z and an IQ lower than a certain number, but then would an IQ of lower than 200 not also be a "pathology" since there are people who have such an IQ (or at least somewhere in the ballpark)? It is an interesting question.</p>
<blockquote class="twitter-tweet"><p dir="ltr" lang="en">Hi all - <a href="https://twitter.com/DrMorganLevine?ref_src=twsrc%5Etfw">@DrMorganLevine</a> <a href="https://twitter.com/LeonidG77901288?ref_src=twsrc%5Etfw">@LeonidG77901288</a> and I (<a href="https://twitter.com/LammingLab?ref_src=twsrc%5Etfw">@LammingLab</a>) will taking on "Is aging a disease" today. I will be taking the affirmative (a minority position polling at aging conferences would suggest), and I think <a href="https://twitter.com/DrMorganLevine?ref_src=twsrc%5Etfw">@DrMorganLevine</a> and <a href="https://twitter.com/LeonidG77901288?ref_src=twsrc%5Etfw">@LeonidG77901288</a> will be taking the negative</p>— Lamming Lab (@LammingLab) <a href="https://twitter.com/LammingLab/status/1577299223640416256?ref_src=twsrc%5Etfw">October 4, 2022</a></blockquote> <script async="" charset="utf-8" src="https://platform.twitter.com/widgets.js"></script>Kismethttp://www.blogger.com/profile/08714147243460910596noreply@blogger.com0tag:blogger.com,1999:blog-1808942478335427122.post-85394453629705905212022-09-27T00:35:00.004-07:002022-12-09T21:29:47.519-08:00Why study aging and not something else?<p><i>(unfinished placeholder post for the future)</i></p><p>In this post I want to explain in a bit more detail why aging research is the most important scientific field of the 21st century. If you are looking for the <a href="https://www.meetup.com/singapore-longevity-and-health-meetup/" target="_blank">Singapore Longevity and Health Meetup</a> you can find us on meetup.com and on telegram.</p><p><b>Studying and targeting aging is the most efficient use of resources</b></p><p>One day I hope that every highschool biology textbook will have a page dedicated to the biology of aging. Here I would like to explain why it should be so.</p><p>The 20th century brought unprecedented prosperity and well-being for many. Average life expectancy saw a dramatic increase doubling globally from 32 to 66 years, settling somewhere around 80 to 90 years in developed nations. Indeed, life expectancies in developed nations are so high now that the rate of aging has become the limiting factor for human health- and lifespan.</p><p>This brings us to the most important scientific study you have never heard of and the most important paradox in all of medicine that is rarely discussed. I am not even exaggerating, not one bit. It was in 1977 when Nathan Keyfitz drew attention to a demographic fact that became known as the Taeuber paradox. Although I am sure the literature on this dates back to pre-70s, this is the eponymous paper that gave the idea a name. Keyfitz wrote those pithy lines that you should never forget: "What difference would it make if cancer were eradicated? An examination of the Taeuber paradox".</p><p>To understand the answer to this question, we must think in the following terms. Most diseases of the elderly are directly caused by aging and due to “competing age-related risks” for these various diseases the eradication of any one disease would only have a surprisingly small benefit. Put in the simplest way, if cancer does not kill you so will cardiovascular disease (Olshansky et al. 2016; Keyfitz 1977). This necessitates a totally new approach to health and longevity provided by modern aging research.</p><p>Already in the 70s Keyfitz went on to answer the question that eradicating all cancer - a monumental, unprecedented feat - would do almost nothing to advance human lifespan and, as it turns out, very little to improve healthspan.</p><p>The implications of this are so serious that it is worth digesting every syllable of his thesis. I know it is shocking to see a long-upheld worldview crumble, but the truth is that most of the disease-centered medical research we are doing in the developed world to target age-related diseases (in contrast to diseases of e.g. poverty) is running into a wall; <b>our research is close to useless</b> because it will fail to extend human lifespan and healthspan (1). Only when we target aging directly to slow all dozens or hundreds of age-related diseases at once will be able to improve the human condition. In fact, it is very possible that the Taeuber paradox is at the heart of <a href="https://en.wikipedia.org/wiki/Eroom%27s_law#:~:text=Eroom's%20law%20is%20the%20observation,first%20observed%20in%20the%201980s." target="_blank">Eroom's law</a>, as pharma companies were forced to slowly shift towards addressing age-related diseases, while still using the old disease-centric tools.<br /></p><p>This phenomenon is widely known among aging researchers but not often by this name. It is important to attach a name to an important law because it makes it easier to remember. This is the most important fact in all of modern medicine and it must be remembered.</p><p></p><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/a/AVvXsEiO8juv4rtJo7EWREMB7GjxGbzwxpmfnsv4muWPqGHjcw_ALItLRfi8Y5B-idVCbzQzj9f6PyEtGH5u-wnmbpdvTCv-fKy2W89q6JJCr2rhTiqVFt1Xg_aJP5edrY2rXqqznO0pJ-jmK0_Sh0iC4MiI-AlBavzhm6WnXojSzRYlTcnOO80JQoxX-107vA" style="margin-left: auto; margin-right: auto;"><img alt="" data-original-height="1005" data-original-width="1006" height="400" src="https://blogger.googleusercontent.com/img/a/AVvXsEiO8juv4rtJo7EWREMB7GjxGbzwxpmfnsv4muWPqGHjcw_ALItLRfi8Y5B-idVCbzQzj9f6PyEtGH5u-wnmbpdvTCv-fKy2W89q6JJCr2rhTiqVFt1Xg_aJP5edrY2rXqqznO0pJ-jmK0_Sh0iC4MiI-AlBavzhm6WnXojSzRYlTcnOO80JQoxX-107vA=w400-h400" width="400" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;"></td></tr></tbody></table><br /><br /><p></p><p><b>Studying and targeting aging is ethical</b></p><p>Everyone ages and many of us - those lucky enough - grow old. Unfortunately, aging leads to a lot of suffering whether as a consequence of age-related diseases directly impairing quality of life or due to the loss of a loved one. If we could slow aging then we would reduce the amount of suffering per unit of time. We know from human and animal studies that those who live longer generally spend a larger part of their life in good health. <a href="http://biogerontolgy.blogspot.com/2021/07/the-symmetry-error-are-benefits-of.html" target="_blank">There is no "law of symmetry"</a> suggesting that an extension of lifespan has to be fully offset by an extension of suffering at the end of life. Lifespan extension is a pure good in a chaotic world.</p><p>Even if you consider the health of the environment and the planet as a factor, it is better to extend human lifespan rather than shorten it. Only people who have a real stake in the future will care about long-term problems like climate change.</p><p>We all deserve the choice to live our life the way we want to.</p><p>Our goal is to extend human lifespan by one year, then another, and another. There is no limit, there is no law saying we cannot or should not continue to extend human lifespan. Any amount of lifespan extension no matter how small, as long as it was achieved by slowing the intrinsic rate of aging, will have enormous benefits to the taxpayer and to everyone growing old. We will try to extend human lifespan as much as possible. One has to be realistic, one has to have dreams and one has to have longterm goals.</p><p><b>References</b></p><p>1. As stated this limitation does not necessarily apply to diseases of poverty like e.g. Malaria, worm infections, HIV, etc because they are per definition cheap to target, since they occur in resource poor-countries that are constrained by their limited budgets and because they usually afflict children and adults of all ages. There are many unexplored implications as well. One creative way of breaking out of (or ignoring) Taeuber's confines, in developed nations, is to target mental diseases and the hedonic steady state, because these issues afflict people of all ages and because they target a completely orthogonal problem (i.e. the fact that no matter how objectively useful a treatment is, no matter how good or bad our life, the brain tends to return to some sort of hedonic setpoint; basically this is the concept of the hedonic treadmill).</p>Kismethttp://www.blogger.com/profile/08714147243460910596noreply@blogger.com0tag:blogger.com,1999:blog-1808942478335427122.post-18021187246845619022022-08-31T15:42:00.007-07:002022-09-01T00:52:04.809-07:00Live blogging ARDD2022 - aging is not the enemy?<p><b>Aging is not the enemy<br /></b>As Matt Kaeberlein put it, we need to be more precise in our language. At the very least say that biological age is the enemy and not "aging", implying old people. I would add: be precise and honest. Another example, one he alluded to, we should not conflate strong and marginal lifespan-extending interventions (e.g. rapamycin with exercise). We need to be clear on these distinctions, just as we need to be clear that the work of current aging-related biotechs, as amazing as it is, still remains a far cry from real interventional biogerontology.</p><p><b>How do biotech people think?<br /></b>I came away with a better understanding of the way small and big biotech companies like Altos or Cambrian operate on a conceptual level. Despite my disagreements I have a lot of respect for the current batch of biotechs. Basically, there are two approaches towards getting real anti-aging therapies. One is to operate within the current regulatory framework and maybe reform it from the inside, while the other one is to upend and change the frameworks, e.g. the work of Matt Kaeberlein on dogs or Nir Barzilai's TAME study.</p><p>The way our VC panelists explained it makes actually a lot of sense. The idea is to assign the value of longevity to be zero so it can be sold to the more conservative VC investors. For them, things have to work and make money even if they do not extend lifespan. Aging is perceived as being very risky and a quick clinical proof of concept is necessary for pharma to become interested.</p><p>However, this leads to some very interesting disagreements and misunderstandings between the old guard of biogerontologists and the researchers at these new biotechs. I like to put it this way: many of the Altos people, to use one example, are researchers doing aging-related work and not aging researchers (obviously there are plenty of exceptions). Thus they have completely different views than "we" do and have never seen or read many of the seminal papers that we know by heart. When I say "we" I count myself as part of the old guard, or aligned with their views, because I grew up on a diet of mouse studies from the 80s and worshipped survival curves.</p><p>However, there is nothing wrong with these young upstarts having different opinions. I totally agree that the field needs fresh blood and just because I disagree with them doesn't mean they're wrong or that we are enemies. <b>Edit:</b> Also I am sure not all biogerontologists of the 90s worshipped mouse survival as much as I do today, but I do get the impression that Walford, Weindruch, Spindler, Rich Miller, even Aubrey de Grey all believed (or do still believe) that lifespan extension in the mouse is the gold standard for preclinical. So maybe it is not quite appropriate to talk about a homogenous "we" the oldschool-influenced biogerontologists, although I do think there is some kind of divide.</p><p>This difference in the way we think about aging explains my many disagreements with Morgan Levine and the discussion of exercise is a very typical example. As a (mouse) gerontologist I am hard-pressed to call an intervention anti-aging if it fails to meaningfully slow aging in a model as simple as the mouse.</p><p><b>Somewhat against the single disease focus of biotech<br /></b>Now having explained how biotech people may think, on to the actual topic. The problem with their way of doing things. The focus on targeting single diseases. The rationale for the TAME trial is that a composite endpoint of age-related diseases is more powerful (more events in a trial) because all of these endpoints should be affected by slowed aging. If this is true, which we generally do believe as a field, then targeting single diseases is inefficient per definition.</p><p>Imagine drug X slows five age-related diseases by 10% or reduces their incidence by 10%, each of those diseases led to 50 events. Because they all change together you get a reduction of 25 events out of a total of 250. If you ran the study just targeting a single disease with the same compound it would need to be 5x larger. To be fair, it is not like biotech companies get a choice, because the TAME style design is not acceptable to the FDA (yet).</p><p>Nevertheless, we should be honest about the shortcomings of the single disease approach and given those issues, as a field, we need to focus a little bit more on reforming the drug approval process instead of celebrating biotech just because they are doing something, which is better than nothing. Again, I am a friend of pharma, but we need more. I fully disagree that their approach <i>alone </i>will be enough.</p><p>I know there are other complexities, as treatments may not slow all age-related diseases across the board. It is very possible that you can find a condition that responds more to a class of drug and ride to FDA approval on the back of such a condition. Like diabetes for metformin.</p><p><b>How then do we cure aging?</b><br />That was the question I was asked at the bar. I, of course, do not know the answer to this, just like everyone else, but I do have an idea on a conceptual level. First of all, let us be bold. We can say that we want to increase human lifespan and healthspan, but do add that our longterm goal is to eliminate aging as well. Investors and the general public are neither dumb nor overly conservative, contrary to popular opinion in the field.</p><p>I found it very interesting that Anu Wartiovaara in today's talk used the words "I want to cure mitochondrial disease". It seems that all researchers are allowed to dream big except aging researchers. This has to stop. We can and should dream of a world without cancer, aging and war. Now we can take the first steps towards this goal.<br /></p><p>We need to create a virtuous cycle of exponential growth in investment until most of the medical R&D across the globe is spent on aging research. For this to happen, we need biopharma to be successful, but even more so we need people like Matt Kaeberlein to pressure regulators and politicians by showing that drugs can extend the lifespan of companion animals, we need people like Nir Barzilai who will show that treatments can slow age-related conditions in reasonably sized trials. We need a first success or something that will be perceived as a success by the public.</p><p><b>Healthspan is not the gold standard<br /></b>Back to the topic of being precise and my constant disagreements with Morgan Levine and other researchers like her (1). In fact, I am getting a de ja vu of having had this debate on twitter a million times already. So to be precise, healthspan is not the gold standard, because it is simply a surrogate for lifespan extension. We study healthspan because studying healthspan and lifespan together is too expensive. Only marginally beneficial treatments* can show differential effects on healthspan and lifespan. This will never happen with robust treatments. Since aging causes the diseases of aging and these cause the suffering of aging, the postponement of aging per definition improves healthspan. If you ask me, this is trivial if not a tautology.</p><p>Today it was Gordon Lithgow from the Buck institute who was arguing against the recent healthspan hype; or at least that is the way I am going to interpret it. We need to make animals live longer to be on the right track and I totally agree. In animal models we have to aim for the best possible outcomes because most treatments are likely to be less effective in humans. If you start already low in animals, you will end up with nothing of value once you get into the clinic. A million things improve the healthspan of mice, but only few treatments reliably extend their lifespan.</p><p>*the reduced healthspan of women which seems to happen despite increased lifespan is one such example. You might think it is a counter-example to my claim but far from it. For an interventional biogerontologist the difference in lifespans between men and women is marginal at best. I am NOT saying this is a useless phenomenon. I am just saying to be clear on the difference between real anti-aging and marginal treatments. As long as we have nothing better than exercise and the study of sex-differences, it is totally justifiable and necessary to investigate these.</p><p><b>Aging is really hard but...</b><br />If you compare the number of successful GWAS hits for cardiovascular disease and aging it appears that aging yields fewer hits. This is consistent with the idea that aging is really, really difficult, if you ask me. There are also issues with the endpoints in those GWAS studies because things like (parental) lifespan are still often driven by effects on healthspan and not aging per se, as Joris Deelen pointed out. He is also more keen to study aging per se rather than healthspan, if I may add. It is therefore not a surprise that no one wants to target aging per se and we fool ourselves into thinking that exercise is an anti-aging treatment. The true revolution is yet to come, but it will be big when it does and the world will never be the same. Hopefully, within our lifetimes my favorite curse will become reality: may you live in interesting times.</p><p><b>Notes and corrections</b></p><p>1. That happens when you use names for illustrative purposes. As it turns out Morgan Levine is a trained biogerontologist. Perhaps another way of putting it is: The "new guard" whether they are trained as gerontologists or not, is not as focused on chasing lifespan extension in mice as interventional biogerontologists of the old days. As people have observed at the conference, the field used to be all caloric restriction and most of that work was in mice and the 20-30% lifespan extension by CR in highly inbred mouse models was our holy grail. Now there are people emphasizing healthspan as the way forward instead, or focusing on specific age-related diseases in the clinic and doing real cell biology, reprogramming, clocks and all that cool stuff. Many of these things are necessary for biogerontology to progress and some of the things are not biogerontology itself.<br />I did not want to single out a single person. My point was more that pharma companies simply hire good researchers to do good work rather than just biogerontologists. In addition, many out-of-field researchers are pivoting towards aging research, so there is an influx of new people giving rise to an old guard and a new one. Unfortunately, some of these non-biogerontologists or "new guard" gerontologists are almost hostile to concepts like lifespan extension and the longterm goal of significant lifespan extension (I had quite a lot of disagreements with the way Vittorio Sebastiano was <a href="http://biogerontolgy.blogspot.com/2022/04/controversial-and-exciting-talks-report.html" target="_blank">talking about healthspan</a> at the vitaDAO symposium I was covering).</p>Kismethttp://www.blogger.com/profile/08714147243460910596noreply@blogger.com0tag:blogger.com,1999:blog-1808942478335427122.post-89024331202969114712022-08-29T15:26:00.001-07:002022-08-29T15:26:27.465-07:00Live Blogging the ARDD2022 conference<p>The first day of the ARDD2022 conference started with several different workshops and I would like to share my initial impressions of these. It was without a doubt an amazing day with many great panelists!</p><p>However, even though I enjoyed it a lot, I would nevertheless like to write something critical or provocative. It is simply more interesting to talk about our disagreements than to give a boring conference recap and recite the geroscience hypothesis once again (which by the way should be renamed to the geroscience paradigm, according to one panelist, which I would endorse).</p><p>In the morning the conference started with a very informative <b>workshop on web3</b> and the excellent work of <a href="https://www.vitadao.com/" target="_blank">vitaDAO </a>to get more decentralized science funding for everyone. Unfortunately, due to a meeting, I could not attend the first half of the workshop so I do not have much to say on this.</p><p>What I do like about vitaDAO in general (and impetus for that matter) is the reduced level of bureaucracy allowing for faster funding. The panelists also pointed out how NFTs would make fractional ownership easier, which I think is great. It is often hard for an individual to invest in companies doing aging research. Nevertheless, people are still skeptical. One of my colleagues said they will take vitaDAO more seriously as a source of funding if they are still around in a couple of years. Hence stability and continuity should be an important "proof of principle" to achieve for this new project to convince the skeptics. Finally, one interesting question from the audience was "how do you defend a patent on the blockchain"? While defending IP, according to the panelists, would still work the same as normally, I am a bit worried about coordination. If the IP is owned by many small actors, will they be able to coordinate in order to do so?</p><p>Then in the end the panelists discussed some juicy almost philosophical questions:</p><p></p><ul style="text-align: left;"><li>what is the importance of cosmetic applications?</li><li>we do not like the term anti-aging due to the snake oil connotation and we should be cautious not to oversell the science (talking about living forever). Although one panelist correctly pointed out that biological immortality is more or less <a href="http://biogerontolgy.blogspot.com/2021/10/let-us-talk-about-immortality.html" target="_blank">a real phenomenon</a>. </li></ul><p></p><p><br /></p><p>Still struggling a bit with gender diversity in some aspects.</p><p><span style="white-space: pre;"> </span></p><p>Later during the day, <b>Felipe Sierra</b> from the Hevolution Foundation walked us through his criticisms of the "hallmarks of aging". More or less in agreement, I think that the hallmarks are just a rough guide. He thinks that loss of resilience is the most important outcome of aging**, which reminded me of an idea I was mulling over. Treatments that improve resilience may increase lifespan without slowing aging. While these treatments may be good in terms of being low hanging fruits, they could lead the field astray because they fail to target aging per se. It is unlikely that we can increase resilience considerably in the face of damage accumulation and aging. Some longevity benefits are possible but the treatments may not be additive. I am worried that rapamycin is one such treatment because even short courses of rapamycin are beneficial, which is inconsistent with slowed aging or damage accumulation theories. Maybe it is consistent with rejuvenation (optimistically) or indeed "it only affects resilience" or maybe it is programmed aging, or a combination of multiple effects.</p><p>Ultimately he thinks we are not very good at linking the hallmarks with physiologic and pathophysiologic outcomes. Also he is very interested in defining halmarks of health rather than focusing on disease -- and he does not believe that aging is a disease, which is one of my favourite controversies.</p><p>We have come very far in this debate and it seems the overton window has shifted quite a bit. <b>Evelyne Bischof</b>, another panelist, for example, disagreed because she thinks aging is a disease. Even <b>Nir Barzilai</b> entertained the notion that aging might be a disease. I doubt we could have had such conversations 10 years ago! Finally we can start saying what we think...</p><p>Another problem with Felipe's focus on resilience is that a loss of resilience may not be a universal marker on the tissue or cell level. For cancer cells, clonally selected populations and senescenct cells it is actually increased resilience (or resistance?) to environmental (apoptotic) stressors that is the problem! Finally, he was not quite convinced by telomeres as a hallmark of aging - and <a href="http://biogerontolgy.blogspot.com/2022/03/telomeres-vs-dna-damage-battle-of-aging.html" target="_blank">I used to agree with this until recently</a>, but after taking another look at the literature I am somewhat more positive.<br /><br /><b>Joan Mannick </b>talked about the phase II and III trials of the mTOR inhibitor RTB101 to restore the antiviral interferon-1 response which is lost during aging. Apparently it worked in phase II to decrease (PCR?) diagnosed infections in the elderly but failed in phase III on a very soft endpoint of self-diagnosed(?) respiratory tract infections. The data they showed on incidence of severe infections seemed quite promising. Hopefully they can go back into phase III and prove that this was not a lucky post-hoc analysis.<br /></p><p><b>Nir Barzilai </b>was bullish on SGLT2 inhibitors and I am starting to believe him.</p><p><b>The things I disagree with for the most part</b></p><p><b>James Kirkland</b> today played the role of a "reasonable conservative" and said all the things I disagree with. While none of them were outrageous, many of them were wrong, in my opinion. For example, he asked whether longevity should be still considered the "gold standard" in aging research because he doubts that lifespan correlates with healthspan. Igf-1 reduction was one of the negative examples he chose. Well, first of all, he is wrong because in mouse models longevity and healthspan almost always go hand in hand. Rich Miller, a major figure in the NIA ITP, agrees and anyone who takes even a casual look at the literature should do the same. Just think of rapamycin, caloric restriction or dwarfism, all these interventions extend lifespan and have well-documented health benefits in mice (even if the data is imperfect).</p><p>I think many people who claim that healthspan does not follow lifespan simply do not know how to read a mouse lifespan study (sorry). Most of the so-called examples are from mouse models that show only modest lifespan extension that has never been replicated under rigorous conditions (e.g. weak Igf-1 reduction models or other weird models instead of GH dwarfs). This is a topic <a href="http://biogerontolgy.blogspot.com/2021/07/the-symmetry-error-are-benefits-of.html" target="_blank">that I wrote about extensively</a> if you are interested in more counterarguments. I am also not convinced by his example of the Bolivian Laron dwarfs. The human data is way too preliminary to make such a sweeping conclusion.</p><p>Kirkland also questions whether people actually want to live to 120. This is because he is asking the wrong people (the very sick elderly) the wrong questions (how long do you want to live?). To the contrary, when you ask the right question roughly 30% of want to live forever (1). That is extremely radical.</p><p>If I recall correctly it was also him who suggested running smaller trials on severe conditions to first prove that a drug is safe enough for large studies. I think this is problematic, because there are often no severe hereditary conditions sufficiently similar to aging and because it slows down research a lot. Longevity drugs should be immediately trialed in small studies in healthy volunteers. We need to think big. Aging is a difficult target and it will require a heroic effort to target -- and, at any rate, there are millions of healthy volunteers that would absolutely love to be part of such trials.</p><p>He also thinks that it is dangerous to talk about combinations when we don't have evidence for single compounds. This is again a problematic approach because (human) aging is so multifactorial that it may never be possible to target it with just a single drug. What then? Secondly, polypharmacy is pretty standard in the real world. Might as well test it in clinical trials from the get-go. Third, combination therapies have statistical advantages and lead to more efficient use of resources. Fourth, combination therapies can potentially reduce and not enhance the side-effects of treatments. I think we need to shy away from such absolutes. </p><p>In a similarly misguided "primum non nocere" spirit <b>Joan Mannick</b> argues we should not use drugs off-label. However, Matt Kaeberlein would likely disagree (<a href="https://rapamycinstudy.org/faqs/" target="_blank">UW Rapamycin Study</a>) as would I. There is a place for RCTs and a place for observational trials of patients who use drugs off-label. Both study designs can teach us something, however, off-label use will give us data much faster. In either case people will experiment with their own health and we might as well learn from it.</p><p>In one of the panels, <b>Eric Verdin</b> once again showed that he is a reasonable conservative and not a visionary. He was arguing that nutrition, diet, lifestyle and exercise should have a bigger role in aging research, I strongly disagree for multiple reasons. Exercise only leads to very modest health benefits in animal models - arguing that rapamycin and other drugs will produce better bang for the buck. In addition these treatments, by and large, have nothing to do with aging research. A healthy lifestyle is a question of public health policy. We do not need biogerontology for that and the incentives to promote healthy living have to be implemented on a national level by politicians. Finally, as <b>Matt Kaeberlein</b> points out - who in contrast to Verdin is more of a visionary - exercise is not harmless. It is just that the harms of exercise are not well documented and downplayed: risk of injury, pain and discomfort (DOMS etc), waste of time (not everyone actually enjoys exercise and this is okay!). Telling people to "just eat healthy" is simply not realistic and our conference demonstrates the irony of this suggestion. A room full of longevity and health researchers, yet the breakfast catering consisted of croissants and chocolate pastries. </p><p>All in all, I would argue that lifestyle is very important but should only play a tiny role in our discourse. Similarly, the fight against ultraprocssed foods is also very important but has little to do with aging per se.</p><p><br /></p><p>1. Asked “If doctors developed a pill that enabled you to live forever at your current age, would you take it?” a surprising number of people turned out to be hardcore life extensionists: "There were no differences by age...Among young adults, 40.0% indicated they would not take the pill, 34.2% indicated they would take the pill, and 25.8% indicated they were unsure."</p><p>Barnett, Michael D., and Jessica H. Helphrey. "Who wants to live forever? Age cohort differences in attitudes toward life extension." Journal of Aging Studies 57 (2021): 100931.</p><p>**presumably in a less trivial and tautologic way than you imagine - since aging is defined as a loss of resilience to begin with, this statement at first sounds a bit trivial</p>Kismethttp://www.blogger.com/profile/08714147243460910596noreply@blogger.com0tag:blogger.com,1999:blog-1808942478335427122.post-55760329028370578182022-07-08T06:35:00.001-07:002022-07-08T06:35:00.228-07:00Coffeehouse notes - June<p><b>Underpaid PhD students</b></p><p><i>probably just whining</i></p><p><i></i></p><div class="separator" style="clear: both; text-align: center;"><i><a href="https://blogger.googleusercontent.com/img/a/AVvXsEgc841PHn_q6siJBXU2Pjmrv01e9jqCByeLCFUHT8-fv-Tw_rI492W11B3bXgNGHX1zXzK3ZCYJQueceiZ0OHST-JO5nqaGhK6GJdgE7pvVNrIgW4787NgSaeG2CnF8EiEIZHTVJXdeb4NuVIYIDYOBt7HkHS64qyIXivV1msvo3ksu1aAVsbBw7EmFIw" style="margin-left: 1em; margin-right: 1em;"><img alt="" data-original-height="199" data-original-width="745" height="170" src="https://blogger.googleusercontent.com/img/a/AVvXsEgc841PHn_q6siJBXU2Pjmrv01e9jqCByeLCFUHT8-fv-Tw_rI492W11B3bXgNGHX1zXzK3ZCYJQueceiZ0OHST-JO5nqaGhK6GJdgE7pvVNrIgW4787NgSaeG2CnF8EiEIZHTVJXdeb4NuVIYIDYOBt7HkHS64qyIXivV1msvo3ksu1aAVsbBw7EmFIw=w640-h170" width="640" /></a></i></div><i><br /><br /></i><p></p><p>Unbelievable how many times I heard people say a degree or PhD is "a chance" so it is okay to be underpaid! What utter nonsense. Any decent job is a chance, since you can put it on your CV and develop your professional skills. Stop with the Stockholm syndrome already.<br />It is unbelievable that PhD students and, in some places, technicians <a href="https://www.nature.com/articles/d41586-022-01392-w" target="_blank">cannot afford a lower middle-class life</a> without the support of their parents or lucky top-up scholarships. And that is just the beginning of it, undergrads in science and many postgrads have it horrible as well. A first step would be to unionize, but all this will do is stop further deterioration and provide some modest benefits. Nonetheless a PhD student union is essential.<br />Even if you <i>can </i>make bank after your PhD, the costs of tuition leading up to the degree and the abysmal pay have many downstream effects:</p><p></p><ul style="text-align: left;"><li>lack of equal opportunity is unfair towards underprivileged students and thus unethical</li><li>the winner takes it all system punishes the unlucky ones, e.g. imagine you have to drop out due to illness or burnout midway through a degree (tuition should be replaced by a real success-taxes)</li></ul><p></p><ul style="text-align: left;"><li>the quality of the average PhD, professor and scientific article suffers because:</li><li>smart people drop out as they cannot afford tuition, cost of living and the opportunity costs of not working</li><li>smart people who want to support a family go to industry</li><li>smart people who are egocentric go to industry</li><li>smart people who want a comfortable life go to industry</li></ul><p><b>Lifespan testing - what is slowed aging?</b><br />This idea developed based on a twitter discussion I had, where someone was doubting the use of mean lifespan differences to measure effects on aging. Finally, getting a chance to dig up references and back up my intuition. Testing some sort of mean lifespan effects remains the gold standard, at least in mouse lifespan studies. The reason is both maximum lifespan testing as well as mortality doubling time require larger sample sizes and are too sensitive to outliers (see e.g. Lombard and Miller 2014). </p><p><b>Lifespan testing - best practices</b><br />Currently reviewing lifespan studies and it is no secret that these are of rather variable quality. In some 80% of the studies I reviewed, the authors forgot at least one of the basics: reporting the strain, gender, and weight of your mice; the sample size, as well as median or mean and max lifespan in a table. Almost none provide the full mortality data underlying the survival curve which can be used for estimating mortality doubling time and other parameters.</p><p><b>Dirty money - clean science?<br /></b>I was alerted to a new player who will be funding aging research. The Hevolution foundation which is supported by the Saudi royal family. It seems they have been already heavily involved in funding the TAME trial. Many aging researchers will start asking themselves the question whether it is ethical to take their money now. As for me, I have been thinking about similar issues for many years already - due to my exposure to SE/E-Asia so maybe you will find my thinking insightful.</p><p>Most countries in the world are not democracies and it makes no sense to sanction them all the time and refuse cooperation. This is not how you survive in a globalized world. Free trade, travel and the exchange of ideas benefit both the poor/oppressed people living in said countries as well as your own citizens. Sanctions, refusal to cooperate and the threat of resignation will be sharper weapons when they are used sparingly and only in response to acute changes, i.e. rate and direction of change, rather than absolute levels of "human rights" or "freedom".</p><p>A redditor discussing the news summed it up perfectly with this witty quote:</p><blockquote style="border: none; margin: 0px 0px 0px 40px; padding: 0px; text-align: left;"><p><i>Behavioral geneticist David T. Lykken wrote:</i></p><p><i>"If you can find me some rich villains that want to contribute to my research - Khaddaffi, the Mafia, whoever - the worse they are, the better I'll like it. I'm doing a social good by taking their money... Any money of theirs that I spend in a legitimate and honorable way, they can't spend in a dishonorable way"</i></p></blockquote><blockquote><p><i></i></p></blockquote><p><i></i></p><p><a href="https://www.rapamycin.news/t/hevolution-foundation-announced-saudi-royalty-puts-billions-in-longevity-research/1574">https://www.rapamycin.news/t/hevolution-foundation-announced-saudi-royalty-puts-billions-in-longevity-research/1574</a><br /><a href="https://www.reddit.com/r/longevity/comments/v6ugqg/saudi_arabia_plans_to_spend_1_billion_a_year/">https://www.reddit.com/r/longevity/comments/v6ugqg/saudi_arabia_plans_to_spend_1_billion_a_year/</a></p><p><b>Chinchilla law of scaling<br /></b></p><p><i>what a time to be alive</i></p><p>As a disclaimer, I am not in any way knowledgeable about ML/AI, but I do like to follow the news and the general trends because they are relevant to me. By training, I am a biogerontologist, and in my spare time a bit of an armchair futurist and progress "researcher". Alphafold has certainly shown that ML is more than just vaporware. ML is coming to biology. AGI is inescapable (when no one knows).</p><p>Many moons ago, when I took a look at the GPT-3 training dataset I immediately thought to myself - would it be better if it trained on more data? Could libgen and sci-hub provide advantages to a company or actor willing to use pirated material? When I asked someone (who I presume was knowledgeable), though, I was told that it is not as simple as that. The systems are not limited by the size of the corpus.</p><p><a href="https://www.lesswrong.com/posts/midXmMb2Xg37F2Kgn/new-scaling-laws-for-large-language-models" target="_blank">Turns out they are</a>. Apparently these models are quite over-parameterized and it is more compute-efficient to train them on larger datasets with more tokens rather than further increasing model size. It is pretty cool to see how much low-hanging fruit there is left in the ML field.</p><p>Anyone got access to DALL-E2</p><p><b>Masking at conferences - yes or no?</b></p><p><i>quality vs quantity of life debate incoming</i></p><p>I am not really keen to engage the specifics of this debate as it will depend on the country and the conference, however, I do want to make a broader point. COVID has slowly morphed into a War on Extroverts. I know it is incomprehensible for most scientists, since science attracts hardcore introverts, that one could suffer due to social restrictions even as "mild" as "having to wear a mask for three years" -- which pre-pandemic no one would have considered "mild" and that kind of imagery was only reserved for art depicting a dystopian future. It is hard for me to comprehend that it would be hard to comprehend for someone that normal people clamor for normalcy.</p><p>Mental suffering is real suffering. Just like depression is not all in your head, neither is social isolation due to the current situation, which is still not even close to normal.</p><p>Either way, the masking war will be lost sooner or later since the population at large will not put up with this. (And, no, I am not against masks. I wore one when it still got you strange looks on the bus and wrote articles about the <a href="http://biogerontolgy.blogspot.com/2020/04/masks-politics-and-inexplicable.html" target="_blank">evidence base for masking</a>) We have to find other and better ways to protect everyone involved.</p><p>However, apart from that I want to make a philosophical remark here. We - the scientists - promised that the trillions spent on science will buy us a better future, which is why we got that money. No one wants to live in a dystopian future where the air is unfit to breathe. I cannot work as an aging researcher under these circumstances and neither should you. Why extend a life whose quality deteriorates every year? Are we that impotent?</p><p>The point is not that we refuse to save lives. The point is that BOTH quality and quantity of life matter. </p><p></p><div class="separator" style="clear: both; text-align: center;"><br /></div><div class="separator" style="clear: both; text-align: center;"><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/a/AVvXsEg35vA4maLd-AUFM-fDCVK_TUsw-lwf5HS9rT-9qtMNSbu-OMMyVcNYFkX0c86pVo5DLhlAdkur_gFZTKfsFN3NB77lwW4SKePeFqPUqHW4QwZUlT4vLcpzq45DLCqr_hb7QK_Z-eA-y1HazDB20WBgiRa_hKj8fLbgTr14o2A8Iuj7g7O3LgVowrthOw" style="margin-left: 1em; margin-right: 1em;"><img alt="" data-original-height="864" data-original-width="1144" height="483" src="https://blogger.googleusercontent.com/img/a/AVvXsEg35vA4maLd-AUFM-fDCVK_TUsw-lwf5HS9rT-9qtMNSbu-OMMyVcNYFkX0c86pVo5DLhlAdkur_gFZTKfsFN3NB77lwW4SKePeFqPUqHW4QwZUlT4vLcpzq45DLCqr_hb7QK_Z-eA-y1HazDB20WBgiRa_hKj8fLbgTr14o2A8Iuj7g7O3LgVowrthOw=w640-h483" width="640" /></a></div></div><p></p><p>Obviously putting on your mask sporadically will do nothing to prevent COVID. It has to be worn consistently throughout social interactions to do anything and that will diminish your quality of life. If someone disagrees with this, they are a bad-faith actor or troll and not worth time to interact with. Okay, having established that: yes, we should wear masks and we have done so for almost two years. But we cannot do so forever and we only do it as long as the risk/benefit ratio merits it. Extroverts have sacrificed plenty during the pandemic.</p><p><b>Why are you doing science?</b></p><p><i>It's all about the Pinkerian promise of a better life.</i></p><p>Generalizations and stereotypes can be useful, as long as we use them productively and not to judge the individual. This entry is another example of how scientists seem to be disconnected from the life of normal people. </p><p>I always wondered why I do not get along with many scientists. Of course, I have met many cynical people who had no passion for science. It would be understandable if I did not get along with that kind of person -- because I do love my science. However, why is it so hard for me to connect with the workaholics churning out high-impact paper after high-impact paper? Should we not have a common topic and language? After plenty of get-togethers, wine outings, etc I had a realization of what bothers me and perhaps impairs my ability. It is something that I sense is absent in science and scientists: a passion for life, zest, verve, esprit. Perhaps understandably science favors the bookish introvert, and there is nothing wrong with this per se, unless these scientists lose touch with the reality of normal people.</p><p>This little dialogue encapsulated what I felt for a long time:<br />Me: "Why do you do science?"<br />Them: "Oh, I really enjoy understanding things and playing around. It is my nature."<br />Me: "Okay, I see. As for me, I do science mainly to improve the life of the people and to better society."<br />Them: "No, that is not all why I do science."</p><p>Okay, maybe this is why you are doing science. However, this is not the reason why you are <i>permitted </i>to do science. A well-run mouse lifespan study costs half a million dollars. A decent microscope costs half a million dollars. This is more than anyone living a comfortable lower-middle-class life in Austria will ever have on their account. This is money that could have been spent to better the lives of those who cannot afford the basic necessities of life. Do you think we - the people - give you money to play around while we clean your toilets, bag your groceries and drive you to your appointments, flights and meetings?</p><p>You promised us a better future. We - the people - will destroy you and your science if you do not deliver, whether you think this is fair or not is irrelevant. That is how the world works. Science has to deliver and science has to communicate what it has achieved.</p><p>If you prefer, though, there is a more poetic way of looking at it. Science is the only glimmer of hope on a vast canvas of darkness. It is the only way to realize the Pinkerian promise of a better future. The only thing that sets us apart from the dust, the dirt, the cockroaches and worms crawling beneath us.</p><p>If you prefer, there is also a cynical way of looking at it. Doing good science can, to some extent, also benefit yourself.</p><p><b>References and notes</b></p><p>Miller, Richard A., and David B. Lombard. "Aging, disease, and longevity in mice." Annual Review of Gerontology and Geriatrics 34.1 (2014): 93-138.</p>Kismethttp://www.blogger.com/profile/08714147243460910596noreply@blogger.com0tag:blogger.com,1999:blog-1808942478335427122.post-43378250815571132042022-06-22T07:39:00.023-07:002022-06-22T07:39:00.222-07:00How long do you want to live?<p>Some important white guy once quipped they only believe statistics they have manipulated themselves. There is no better way to demonstrate this problem than the question "how long do you want to live?", whose misuse has been haunting me to this day.</p><p>This question by and large measures two things, and neither of them is a desire for lifespan extension. The first one is the respondent's level of depression and the second one is their aversion to frailty and general level of optimism.</p><p><b>I want to live to 80</b><br />If you ask someone "how long do you want to live" there will be two common classes of pessimistic answers. One gives a number in the 30 to 40 range when you ask a young person and the other a number around 80. Here we will demonstrate why these answers are wrong per definition because they <i>evade </i>the question (1) and why it is foolish to even ask this question to begin with, without providing any further explanation.</p><p>Let us deal with the answer of around 80. How could it possibly be that so many people choose a number within 10 to 20% of the average life expectancy in the year 2020 if they could have chosen an infinite number of different life expectancies? The natural numbers range between 0 and infinity, so it is an extraordinary coincidence that answers would cluster around a certain value, even more extraordinary that this value is exactly the human average life expectancy. This suggests the obvious, people expect certain things to happen at certain ages. What happens around the age of 80? People grow old, frail and die. No one wants that, at least not the frail part.</p><p>However, this clearly does not address the question. We did not ask "how long do you want to live after you have grown old and frail?", we asked "how long do you want to live?"</p><p>Although there was no a priori reason to mistake the question we asked for the more specific and pessimistic one, it is somewhat - although not completely - understandable to mistake the two. It is still a bit baffling to me that people would answer in the most pessimistic way, because humans do have the ability to answer abstract questions using their <i>positive </i>imagination. When asked "do you want to have the ability to fly?" or "would you like to go to Mars?" people will rarely answer "a jetpack is too cumbersome and expensive, so no" or "the flight to Mars takes too long and the isolation would be horrendous for my mental health". Neither of these answers is wrong per se, but they all make the most pessimistic assumptions possible. If we asked these questions, the majority of answers would never cluster around the most pessimistic interpretation of such a question.</p><p>On the other hand, it makes sense to assume that people grow frail at the age of 80. This is our lived experience. However, as you may remember the goal of biogerontology is to extend the healthy human lifespan. So as biogerontologists, to determine public support for our research, we want to know how long people desire to live if they are healthy. If I wanted to portray aging research in a bad light, I would indeed conduct surveys just asking "how long do you want to live?"</p><p>Given our cultural baggage, currently, there is no safe way whatsoever of asking the how-long question. These how-long questions are fundamentally unable to uncouple the desire to live from assumptions about the future that are largely extrapolations of the present and status quo. Anyone conducting such a survey is either misinformed or intentionally trying to sabotage longevity research.</p><p>It is biologically impossible to extend human lifespan without extending healthspan. So if you answer 200 that means you want to live some 180 healthy years. If you so choose, you can off yourself at 180 giving you double the healthy years and zero suffering. Who would choose fewer options in favor of more options if they had that chance?</p><p>Better albeit imperfect ways of asking the how-long question:</p><p></p><ul style="text-align: left;"><li>"Do you want to be healthy for as long as possible?"</li><li>"Assuming you could maintain your current health and looks, how long would you want to live?"</li><li>"Given the option of taking a pill that allows you to stay young and live for as long as you desire, without any side-effects, would you take such a pill?"</li></ul><div>The ultimate way of asking this question might be different:</div><div><ul style="text-align: left;"><li>which one of these two views do you find more reasonable? Which one do you agree with more?</li><li>A. I want my life to have a clear ending at a certain predetermined age. I do not wish to have the option of changing this age.</li><li>B. I want to live my life day-to-day as long as I am happy. I can choose to live for as long or as short as I want.</li></ul></div><p></p><p><b>I want to live to 30 or 40<br /></b>Obviously, saying "I want to kill myself" is not really an option in surveys or at a dinner party. Neither is "my life is going really badly and I do not want the pain to continue". However, it is pretty clear that a large number of people do not want to live longer because they are in pain or suffering from depression. Again, though, this fails to answer the question since their response can be rephrased as "I can only endure this pain for another X years and not more" and this is clearly not an answer to the how-long question. Alternative questions would be:</p><p></p><ul style="text-align: left;"><li>"assuming we could cure depression and keep you reasonably happy <i>and </i>you could maintain your current health and looks, how long would you want to live?"</li><li>"how long would you want to live if you were guaranteed to be as healthy and as happy as you have been during the best time of your life? (i.e. think about the best year in your life)"</li></ul><p></p><p><b>What have we learned?</b></p><p>I am not saying there are no people who genuinely want to die at 30, 40 or 80. That would be arrogant. However, I do want to show that these people are rare and not actually identified by asking "how long do you want to live?". In fact, most people mistake this question (see ref 2). Which brings me to the big problem: why do we <a href="https://www.axios.com/2018/11/28/longevity-poll-living-longer-100" target="_blank">keep asking this dumb question</a> in surveys (3) and claiming that it has any meaning?</p><p>As easy as it is to fool the public with statistics, it is equally easy to fool yourself, if you ask questions with a predetermined narrative. For a long time, biogerontologists have believed that people do not desire a long life, since they have been asking foolish how-long questions designed to give the conservative answers they masochistically crave. This narrative has to end. I think it is time for biogerontologists to stand up with pride and say: everyone wants to live longer and, regardless of that, our science is the only hope for humanity to avert a demographic catastrophe and the only hope to drastically increase healthspan. We are the science of the future.</p><p><b>References and notes</b></p><p>(1) We can argue whether the answers are fundamentally illogical because they evade the question or whether they just answer a special, extremely pessimistic version of the question. Either way the answers will be biased and useless because of the way the question is phrased. There is no right way of asking the question, but there are ways that are fundamentally more flawed than others.</p><p>(2) Above we have developed an intuition for why and how people mistake the how-long question. On top of that, we also have hard data showing this is exactly what happens. The answers get much more optimistic if you stipulate good healthspan which is a biologically reasonable assumption (also <a href="http://biogerontolgy.blogspot.com/2021/10/let-us-talk-about-immortality.html" target="_blank">discussed here</a>).</p><p>Asked “If doctors developed a pill that enabled you to live forever at your current age, would you take it?” a surprising number of people turned out to be hardcore life extensionists: "There were no differences by age...Among young adults, 40.0% indicated they would not take the pill, 34.2% indicated they would take the pill, and 25.8% indicated they were unsure."</p><p>The older people were community dwelling (n~310) and the younger participants (n= 593) were psychology students. Surprisingly no cohort effect was seen, as I would have expected the younger participants to be more positive towards life extension. However, it does look like there is a trend and the study may have been underpowered to detect differences (only 23-32% of older adults would take the pill).</p><p>At first glance, everything makes sense to me. In this 2011 article, the numbers, on a similar but different question, were 21% for the young and 12% for the older people. We see a suggestive cohort effect (older generations being less positive about lifespan extension)vand a normalization of lifespan extension research because the numbers are much higher in the more recent survey.</p><p>Barnett, Michael D., and Jessica H. Helphrey. "Who wants to live forever? Age cohort differences in attitudes toward life extension." Journal of Aging Studies 57 (2021): 100931.</p><p>(3) This survey at least tried having some sort of "DEPENDS ON LIFE QUALITY" question, but I speculate that even outright stating "how long would you want to live in good health" is not enough because people will naturally make assumptions about the unknowable "I will get bored and will want to die" or they will be unable to "suspend their disbelief" so to say. They will actually refuse to take the premise for granted, because it is so at odds with their experience. We need better ways of asking these questions.</p><p>Of course, the only <i>rational</i> answer if you fear that you will get bored, is "I want to live forever with the option of ending my life [when I get bored]" instead of "I want to live X years [making an a priori assumption about the time you will get bored, i.e. the unknowable future.]". While humans are not rational, I do feel with the right question design we could reveal that a relative or absolute majority of people is, indeed, able to think rationally about aging. (That is not to say that being purely rational is the only way to go about life, although I do think it is <i>usually </i>the best way.)</p>Kismethttp://www.blogger.com/profile/08714147243460910596noreply@blogger.com0tag:blogger.com,1999:blog-1808942478335427122.post-10990298454196468892022-05-28T08:14:00.001-07:002022-05-29T08:25:52.574-07:00Vitamin D supplementation - an update<p><b>Should healthy people supplement vitamin D?</b><br />As the organizer of the "<a href="https://www.meetup.com/singapore-longevity-and-health-meetup/" target="_blank">Singapore Longevity and Health Meetup</a>" I feel almost obliged to write about supplements. When people get interested in longevity and join such a meetup, it is because they want to be healthier now and not in a faraway future. In contrast, we, as scientists, deal with that future which is years or decades away. Of course many of us scientists also started as traditional pill-popping life extensionists because we had this passion for life and for health. Eventually, however, we came to realize that lifespan extension will require significant medical breakthroughs and that is why we end up focusing on basic research rather than supplements, diets or exercise.</p><p>It bears repeating. The best way to make radical and significant lifespan extension happen within our lifetimes is to <a href="https://www.sens.org/get-involved/donate/#:~:text=Donate%20to%20SENS%20Research%20Foundation,a%20PayPal%20account%20to%20donate." target="_blank">donate to research</a> or convince politicians, philanthropists, etc. to invest money in aging research and <a href="https://crowdfundedcures.org/" target="_blank">reform patent</a> and FDA regulations so that aging can be targeted as if it were a disease. This has to be the foundation.</p><p>Meanwhile, while we wait for scientists to come up with some real anti-aging interventions, there is nothing wrong with optimizing one's health to reap the small life extension benefits that this brings. Between acarbose, metformin and rapamycin we are getting closer to having approved drugs that can moderately slow down the rate of aging. They work decently in mouse models, but as a human, taking any of these still constitutes a high risk/high reward game, and those inclined to do so, should consult with their doctor. Supplements and dietary interventions, in contrast, have a place, as lower risk, lower reward interventions.</p><p>When I think of supplements and aging, I try to rationalize these within some sensible framework like e.g. Bruce Ames triage theory. It stands to reason that the average intake of micronutrients is only sufficient to optimize reproduction and not long-term health. The only problem with this theory is that the average intake of micro- and macronturients could be either<i> too low </i>or <i>too high</i> for optimal health (for example, it might be too high for [animal] protein, total calories, iron and simple carbohydrates). It is always easy to make an evolutionary argument but we cannot infer the direction of effects from this argument. In order to find out we need to actually test micronutrient supplements for their clinical benefits.</p><p><b>Summary and TL;DR - what happened when we tested vitamin D in large trials?</b><br />Although vitamin D was considered an up-and-coming superstar micronutrient just 5 to 10 years ago, many controlled trials later, it did not live up to the hype. Nevertheless, controlled trials do suggest a small, borderline significant reduction in cancer and/or all-cause mortality. Given the promising data from other study types, the well-established safety of vitamin D, the prevalence of population-wide insufficiency and the cheap cost of supplemental vitamin D, there is only one rational answer to that question. Yes, we should supplement around 2000IU/d (or get tested) in order to reach blood levels of around 30ng/ml.</p><p><b>Vitamin D and health - the basics<br /></b>Vitamin D is usually measured as 25-hydroxyvitamin D (25(OH)D) in blood, which is a relatively stable, semi-inactive precursor to the active "hormone" 1,25-dihydroxyvitamin D (1,25(OH)2D; calcitriol). It is produced from 7-dehydrocholesterol in skin through a couple of intermediate steps. Since sun exposure can quickly produce large amounts of vitamin D equivalent to 10000 IU of supplemental vitamin D or more, many people have mistakenly argued that large levels of supplementation have to be safe and beneficial. This, however, does not necessarily follow. It only tells us that large levels of supplementation have no acute side-effects in the healthy. Since evolution does not optimize for long-term health, this is all we can conclude. What we really need is a scientific answer to that question.</p><p>As has been known since the early 20th century, vitamin D is necessary for bone health and severe deficiency is a cause of rickets and other bone disorders. It was only quite recently that extra-skeletal effects of vitamin D have received attention, although studies hinted at such effects long ago.<br /><br />Off the top of my head, I still remember what exactly put vitamin D on my radar. It was this meta-analysis of randomized trials by <b>Autier and Gandini (2007)</b> showing a 7% reduction in mortality (10). That 7% figure, I still remember it. It was a remarkable finding because, as aging researchers, we obsess over mortality rather than single disease outcomes. Measuring mortality in studies is a way to respect Taeuber's paradox, albeit an imperfect one. For example, it does not matter whether an intervention prevents cancer, if it increases risk from other diseases or the survivors end up dying from cardiovascular disease instead. Conrad Taeuber was one of the first demographers who noticed that all age-related diseases increase at the same time, thus any treatment which only mitigates one cause would be highly inefficient (Keyfitz et al. 1977).</p><p><b>Recent studies and meta-analyses</b><br />(this started out as a reddit post for r/longevity commenting on a <a href="https://www.reddit.com/r/longevity/comments/ubiy2e/three_simple_treatments_may_reduce_invasive/" target="_blank">recently published study</a> but it quickly got too long.)<br /><br />Let's not get too excited about this study (i.e. the <b>DO-HEALTH trial</b>). While this is definitely a promising signal, the study was quite small by the standards of prevention trials, tested multiple outcomes and only looked at cancer as a secondary endpoint (2). The authors found that vitamin D, exercise and omega 3 acids each led to 30% fewer invasive cancers in healthy older adults and the effects were cumulative so you can combine these treatments to increase the benefits.</p><p>Why is this not good enough? This kind of analysis is commonly known as a post hoc fishing expedition. As we all know, if you are not careful the ocean can swallow you and your scientific hypotheses at any time. How do we prevent this from happening?</p><p>So first of all, the original DO-HEALTH study failed for every single <i>pre-specified</i> endpoint (1). Vitamin D, omega-3 fatty acids or exercise did not help older adults (n=2157 participants) to be healthier, as far as we can tell based on the statistics.</p><p class="MsoNormal"></p><blockquote><i>The 6 primary outcomes were change in systolic and diastolic
blood pressure (BP), Short Physical Performance Battery (SPPB), Montreal
Cognitive Assessment (MoCA), and incidence rates (IRs) of nonvertebral
fractures and infections over 3 years...These findings do not support the use of vitamin D... for these clinical outcomes among relatively healthy older adults.</i></blockquote><o:p></o:p><p></p><p>What the authors published after the main results were reported is a "pre-defined exploratory analysis" of the original study. The authors make a convincing case that they have got a good hypothesis because earlier studies showed benefits of vitamin D supplementation on cancer.</p><p>The <b>VITAL study</b> with over 25000 participants found a significant 17% reduction in metastatic or fatal cancers in those randomized to vitamin D although there were "no significant differences for [overall] cancer incidence" (4). There was also a suggestive reduction in cancer mortality (HR = 0.83; 95% CI, 0.67-1.02]; P = .08).</p><p></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/a/AVvXsEhZDGa-XyKbzo1a_JjOrJHqwZ-RVpEDD0lBcFZt_5XuVSS5POb4vrRT5zcSOX66O3XbZjnxLGOCbTW4-bPxb_ZnBUy9f9b6r54VT_HEDlsjac3U6-WCvFc-ELug63V-vFNzMCZyPNKeafJhVF8rRSrzvave-ZrOkrXMh0N2GDkaGI-DkZ40TlXud0Jmfg" style="margin-left: 1em; margin-right: 1em;"><img alt="" data-original-height="378" data-original-width="780" height="310" src="https://blogger.googleusercontent.com/img/a/AVvXsEhZDGa-XyKbzo1a_JjOrJHqwZ-RVpEDD0lBcFZt_5XuVSS5POb4vrRT5zcSOX66O3XbZjnxLGOCbTW4-bPxb_ZnBUy9f9b6r54VT_HEDlsjac3U6-WCvFc-ELug63V-vFNzMCZyPNKeafJhVF8rRSrzvave-ZrOkrXMh0N2GDkaGI-DkZ40TlXud0Jmfg=w640-h310" width="640" /></a></div><b>Figure.</b> invasive and fatal cancer combined after vitamin D treatment (2000 IU/day)<br /><br /><p></p><p>The effects of vitamin D may be so small that even VITAL did not have the power to pick them up. If we do not want to sail blindly on the vast ocean of speculative ideas we need to systematically review and quantify all vitamin D trials. Luckily, someone's done that work for us. A meta-analysis of RCTs by Keum et al. including VITAL (Manson et al. 2018) showed that "vitamin D supplementation significantly reduced total cancer mortality [by 13%] but did not reduce total cancer incidence" (5).</p><p></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/a/AVvXsEitnW7ucMYFvdc5vNBX9P7P8ZiCdsW9q_xzs0kjumUm7X4tTbN5HO_NLUlu9_LLhBaklG0kTWJ46LCGelDRAgfVQA83iUBv1Q_gDWNUTenVUGObMHLHFgbctLuj9P9hQ2w2ad4jXECWGnXLleD4W4MaBiWMXf0tBDid0gqc8eqG2Esh2-qcgVAwbBcUPg" style="margin-left: 1em; margin-right: 1em;"><img alt="" data-original-height="619" data-original-width="614" height="640" src="https://blogger.googleusercontent.com/img/a/AVvXsEitnW7ucMYFvdc5vNBX9P7P8ZiCdsW9q_xzs0kjumUm7X4tTbN5HO_NLUlu9_LLhBaklG0kTWJ46LCGelDRAgfVQA83iUBv1Q_gDWNUTenVUGObMHLHFgbctLuj9P9hQ2w2ad4jXECWGnXLleD4W4MaBiWMXf0tBDid0gqc8eqG2Esh2-qcgVAwbBcUPg=w635-h640" width="635" /></a></div><br />Crucially, "there was a statistically significant 7% reduction in total mortality in our meta-analysis where cancer death (n = 1591) accounted for 33% of total death (n = 4872)" which brings us back 15 years to the meta-analysis by Autier and Gandini. Maybe it was not so wrong back then? The cancer effect is real, at least in the sense that it is reproducible. A meta-analysis by Guo, Huang, Fan et al. 2022 finds exactly the same thing.<p></p><p><b>Vitamin D and mortality - do we have a consensus?<br /></b>Now that we have established a potential beneficial effect on cancer mortality, what is the net impact on all-cause mortality? Keum et al. already suggested a benefit, but that is not the only line of evidence we have to consider. Given the vast amount of available data, different experts have performed dozens if not hundreds of meta-analyses, sometimes arriving at different conclusions. I particularly like the approach taken by Liu, Meng, Tian 2021, which is to consider prospective epidemiology, RCTs and mendelian randomization (MR) studies together. </p><p></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/a/AVvXsEhxez8dO1raWWcoz_O0cCzR5VFXVHV9O4VMQ5h-izkIgXHglZj2Lxj2DR2RPLKkQLgXEukb90JGlrU-ojR_GXggrPJd5pn5gI9fXM3DRn7Igi_iV93S7zCvEJDORhq1l5VwUkx1fwInBawRFlG7WYHSAcJsVAWLjZpQyCW0-SeCbsOoBF2xgeoZ4R-WMg" style="margin-left: 1em; margin-right: 1em;"><img alt="" data-original-height="590" data-original-width="1902" height="198" src="https://blogger.googleusercontent.com/img/a/AVvXsEhxez8dO1raWWcoz_O0cCzR5VFXVHV9O4VMQ5h-izkIgXHglZj2Lxj2DR2RPLKkQLgXEukb90JGlrU-ojR_GXggrPJd5pn5gI9fXM3DRn7Igi_iV93S7zCvEJDORhq1l5VwUkx1fwInBawRFlG7WYHSAcJsVAWLjZpQyCW0-SeCbsOoBF2xgeoZ4R-WMg=w640-h198" width="640" /></a></div><br />Both RCTs and MR argue against an effect on cancer incidence. MR data is inconsistent with the RCT data on cancer mortality, however, a quick look at the paper suggests that this is only based on the UK biobank study. While this is a great cohort to do MR, I think we can do better. More and larger analyses with improved genetic tools would be useful. Also, I have no clue whether vitamin D is suitable for MR. I trust the experts, but it seems a bit concerning that many of the MR studies were only based on two genes that regulate vitamin D levels.<br /><br />The all-cause mortality data looks the most promising because it is consistent between RCTs, epidemiology and mendelian randomization (MR). However, here also several caveats apply. The MR data is based on a 2014 study using 3 cohorts, while a sample size of 100k is okay, we can do better. Pooled MR studies of iron biomarkers and longevity, including UK Biobank and other cohorts, have gone up to sample sizes of 1 million and such updated MR studies are slowly coming in (8). Furthermore, not all meta-analyses of RCTs show reduced all-cause mortality (see eg ref 6) after vitamin D treatment. Therefore I am worried that their reading of the literature is a bit too optimistic, and evidently not everyone agrees with such a rosy interpretation of the data (3).<p></p><div><b>Conclusion and my own thoughts - should we take vitamin D?</b></div><div>First of all, many people have low vitamin D levels, leaving them at risk of adverse health outcomes. Although exact cut-offs remain controversial, we do know that moderate deficiency of <20ng/ml (50nmole/L) is <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4723156/" target="_blank">prevalent even in tropical countries</a> like Singapore - with a whopping 40% of the population falling into this category. These levels are associated with increased mortality in epidemiologic studies. It is relatively uncontroversial to suggest that people should fix their low vitamin D levels even though we do not know the optimal level. However, in the absence of RCT data we can look at prospective epidemiology and Mendelian randomization studies (8) both suggesting an optimal level around 20-30ng/ml (50-80nmole/L), which is close, albeit a bit lower, than the numbers I recall from meta-analyses published almost 10 years ago.</div><div><br /></div><div>Second of all, the controlled trials performed to date are consistent with a small benefit or no benefit (which is equivalent to no risk) and this has to be taken into consideration. However, it turns out that doctors are notoriously bad at evaluating risk-benefit ratios (3) and what we need here to make sense of the data is a gambler's mindset instead. Would you pay 4 cents or less per day for a non-zero chance of 5 to 10% reduced cancer and all-cause mortality with no potential downside? You would have to be a fool not take vitamin D, or to refuse a vitamin D blood test, if you ask me.</div><div><br /></div><div>A 5 to 10% reduction in all-cause mortality or cancer mortality, while it would save trillions of dollars, would be very difficult to detect even in studies that have over 20k participants like VITAL. </div><p>Nevertheless, although the data has shown some promise it remains hard to rule out no effect. Inconsistencies between studies are also worrying, how can we trust the data from the DO-HEALTH trial showing reduced cancer incidence when VITAL and meta-analyses only suggest benefits for cancer mortality?</p><p>There are some studies we can do relatively quickly to clarify the situation, but barring another large primary prevention trial the data will always remain speculative. Some things that would help are patient-level meta-analyses, better Mendelian randomization (MR) with larger cohorts and better genetic tools (7), longer-term follow-up of trial participants with better case ascertainment, and more consistent meta-analytic insight on the mortality data in vitamin D trials.</p><p>It will be also interesting to see stratification by vitamin D baseline levels and BMI. These seem to be the factors most often associated with differential outcomes. Indeed, one reason why the DO-HEALTH trial looked better than VITAL for cancer, might have been the lower vitamin D levels of the participants. Finally, another issue that has plagued n3 trials over the last decades, and was a problem in VITAL as well, is the enrollment of very healthy participants (9), which pushes down the effect sizes and reduces statistical power. It may well be that healthy people derive less benefit from these interventions, which is consistent with diminishing returns.</p><p><b>References and comments</b></p><p><i>this is very much a quick and dirty write-up. I have not read many of the cited papers in much detail, but the conclusions are generally straightforward, to me, after having followed the field for a decade on and off.</i></p><div>1. Bischoff-Ferrari, Heike A., et al. "Effect of vitamin D supplementation, omega-3 fatty acid supplementation, or a strength-training exercise program on clinical outcomes in older adults: the DO-HEALTH randomized clinical trial." Jama 324.18 (2020): 1855-1868.<br /><a href="https://jamanetwork.com/journals/jama/fullarticle/2772758#:~:text=The%20DO%2DHEALTH%20trial%20tested,major%20comorbidities%20aged%2070%20years">https://jamanetwork.com/journals/jama/fullarticle/2772758#:~:text=The%20DO%2DHEALTH%20trial%20tested,major%20comorbidities%20aged%2070%20years</a></div><p>2. Bischoff-Ferrari, Heike A., et al. "Combined Vitamin D, Omega-3 Fatty Acids, and a Simple Home Exercise Program May Reduce Cancer Risk Among Active Adults Aged 70 and Older: A Randomized Clinical Trial." Frontiers in Aging (2022): 33.</p><p><span>3. Bouillon, Roger, et al. "The health effects of vitamin D supplementation: Evidence from human studies." Nature Reviews Endocrinology (2021): 1-15.</span></p><p>4. Chandler, Paulette D., et al. "Effect of vitamin D3 supplements on development of advanced cancer: a secondary analysis of the VITAL randomized clinical trial." JAMA network open 3.11 (2020): e2025850-e2025850.</p><p>5. Keum, N., et al. "Vitamin D supplementation and total cancer incidence and mortality: a meta-analysis of randomized controlled trials." Annals of Oncology 30.5 (2019): 733-743.</p><p>6. Barbarawi, Mahmoud, et al. "Vitamin D supplementation and cardiovascular disease risks in more than 83 000 individuals in 21 randomized clinical trials: a meta-analysis." JAMA cardiology 4.8 (2019): 765-776.<br />6b. Zhang, Y., et al. "Association of vitamin D or calcium supplementation with cardiovascular outcomes and mortality: a meta-analysis with trial sequential analysis." The journal of nutrition, health & aging 25.2 (2021): 263-270.</p><p>Zhang, Yu, et al. "Association between vitamin D supplementation and mortality: systematic review and meta-analysis." Bmj 366 (2019).</p><p>Liu, Di, et al. "Vitamin D and Multiple Health Outcomes: An Umbrella Review of Observational Studies, Randomized Controlled Trials, and Mendelian Randomization Studies." Advances in Nutrition (2021).<br /><a href="https://academic.oup.com/advances/advance-article/doi/10.1093/advances/nmab142/6433621?login=true">https://academic.oup.com/advances/advance-article/doi/10.1093/advances/nmab142/6433621?login=true</a></p><p>7. "Depending on their number, these SNPs can explain from 2% to 10% of the variance in 25OHD levels."<br />"The identification of over 150 25OHD-associated genetic variants in 2020, which explain a considerable portion of the variance in 25OHD levels (~10.5%)43, has enabled a deeper understanding of the genetic determinants contributing to variation in circulating 25OHD levels. These newly identified SNPs will probably enable improved instrumentation of vitamin D in Mendelian randomization studies." from ref 3</p><p>8. Sofianopoulou, Eleni, et al. "Estimating dose-response relationships for vitamin D with coronary heart disease, stroke, and all-cause mortality: observational and Mendelian randomisation analyses." The Lancet Diabetes & Endocrinology 9.12 (2021): 837-846.<br /><a href="https://pubmed.ncbi.nlm.nih.gov/34717822/">https://pubmed.ncbi.nlm.nih.gov/34717822/</a><br />Comment: Okay technically the authors provide strong evidence for a 40nmole/L cut-off below which vitamin D deficiency is particularly harmful, but, as far as I can tell, 80nmole/L seems closer to the optimum. The mortality data from MR is somewhat confusing with a RR of 0.99 across the whole sample, but when stratified the RR never reaches higher than 0.98 (Fig 2). Same story with finer stratification, only 2 out of 12 data points show a RR above 1.</p><p></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/a/AVvXsEhFlSrXJZE1orJcPvc6qpmv2ZmHpOj8UtOSjX6NEFCLzj1AOH4vD9IAzPbV0zE5Xvt8EKgnY459aZ1goU60wanqwtEvUbfnwlmMqkggElcN1xw4SHN447q_qJyaeSIy9vIc8CXjAcPFJnKPgQAyRchuuVYkgLudL6vw8qYP5rsD1txL7qwCXeM7LSGH6w" style="margin-left: 1em; margin-right: 1em;"><img alt="" data-original-height="554" data-original-width="712" height="499" src="https://blogger.googleusercontent.com/img/a/AVvXsEhFlSrXJZE1orJcPvc6qpmv2ZmHpOj8UtOSjX6NEFCLzj1AOH4vD9IAzPbV0zE5Xvt8EKgnY459aZ1goU60wanqwtEvUbfnwlmMqkggElcN1xw4SHN447q_qJyaeSIy9vIc8CXjAcPFJnKPgQAyRchuuVYkgLudL6vw8qYP5rsD1txL7qwCXeM7LSGH6w=w640-h499" width="640" /></a></div><br /><br /><p></p><p>9. "In the 2017–2020 megatrials (that is, VITAL, ViDA and D2d), overall mortality was much lower than shown in the previous meta-analyses... and did not show an effect of vitamin D supplementation on overall mortality" from ref 3</p><p>10. Arch Intern Med. 2007 Sep 10;167(16):1730-7.<br />Vitamin D supplementation and total mortality: a meta-analysis of randomized controlled trials.<br />Autier P, Gandini S.<br />Comment: My understanding is that many earlier meta-analyses before the existence of isolated vitamin D megatrials were pooling data also from calcium+vitamin D arms. It is somewhat reassuring that meta-analyses including and excluding calcium trials tend to arrive at similar conclusions regarding cancer and all-cause mortality. Unfortunately, there is no one right way to pool studies. Negative meta-analyses often include studies with large bolus vitamin D and ergocalciferol, although there is reasonable evidence that these work worse than daily supplemental vitamin D.</p>Kismethttp://www.blogger.com/profile/08714147243460910596noreply@blogger.com0tag:blogger.com,1999:blog-1808942478335427122.post-77800086337696119252022-04-25T10:33:00.002-07:002022-04-28T00:33:42.208-07:00Controversial and exciting talks - a report from the Crypto meets Longevity Symposium<p><i>Brought to you by the @Aging_Scientist on twitter</i></p><p>This will be the last post in my coverage of the "<a href="https://www.vitadao.com/" target="_blank">VitaDAO </a>– <a href="https://www.youtube.com/watch?v=GJ-rJAfjhBE&t=2s" target="_blank">Crypto meets Longevity Symposium</a>" (see here for <a href="http://biogerontolgy.blogspot.com/2022/04/vitadao-crypto-meets-longevity-symposium.html" target="_blank">part 1</a>). I will summarize several talks from the VitaDAO symposium, although fewer than I aimed for, because it took way too long to write these as it is. And as always I will provide my comments on the state of the art. All errors are mine.</p><p>My personal highlight from the symposium was the talk by Savva Kerdemelidis from Crowd Funded Cures who introduced the pay-for-success approach as a solution to market distortions in the pharma business. Finally, the talk by Vittorio Sebastiano inspired me to write about the abuses of the word "healthspan". I think it is essential that we keep biogerontology honest and stop confusing laypeople by claiming that healthspan is somehow magically different from lifespan because it is not. These two are, in most cases, inseparable. More on this below!</p><p><b>Prof. Marco Demaria - University of Groningen<br />Heterogeneity in Senescence: from Mechanisms to Interventions</b></p><p>The goal is to delay multiple diseases by targeting aging. Telomeres shorten and cells grown in culture reach the Hayflick limit, similar things happen in vivo. We see activation of p53 and p16 expression leading to a stable generally irreversible state that is called senescence. Not all senescence (sen) is bad, because it is of course also a tumor suppressor mechanism. The factors secreted by senescent cells (SASP), are growth stimulatory, angiogenic, and pro-inflammatory. This is also called sterile inflammation, which is inflammation when no pathogen is present.</p><p>We see more p16 staining in aging skin and colon, while in the brain, for example, we see elevations of GATA4 and p16.</p><p>Then Demaria talks about the, by now, classic p16 reporter mouse model where p16 was tagged with an RFP, luciferase, HSV-Tk cassette. This allows to visualize senescent cells by FACS sorting via the RFP protein, through visible light by luminescence and to destroy them by ganciclovir (GCV). 6 months of GCV treatment improves running distance and grip strength.</p><p>Senescent cells accumulate in most tissues and they are at the center of several aging mechanisms. Treatments can be senolytics vs senomorphic, i.e. either kill the cells or reduce their harmful SASP secretion. Many drugs exist but senescent cells are heterogeneous e.g. dependent on tissue of origin, stress type etc. so not all drugs will be universal.</p><p></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/a/AVvXsEjOOTFndh6Fb2-_g8GqDj4LX1F9aQfZZVTGDrFGh_haJYTbR0wYdm97kvccK3ih6dmZuZIhF46B2zd8Vibmw4fUlCxgAePV415ZT3MeyRbSVe5OoyPMq-BHzmHFWLcMSO0RJ_jdLw9UndaLvqghOVMQzR83mGWam13YbrWCjtS1mreirhcSnkyF5vJLiw" style="margin-left: 1em; margin-right: 1em;"><img alt="" data-original-height="862" data-original-width="1394" height="396" src="https://blogger.googleusercontent.com/img/a/AVvXsEjOOTFndh6Fb2-_g8GqDj4LX1F9aQfZZVTGDrFGh_haJYTbR0wYdm97kvccK3ih6dmZuZIhF46B2zd8Vibmw4fUlCxgAePV415ZT3MeyRbSVe5OoyPMq-BHzmHFWLcMSO0RJ_jdLw9UndaLvqghOVMQzR83mGWam13YbrWCjtS1mreirhcSnkyF5vJLiw=w640-h396" width="640" /></a></div><br /><br /><p></p><p>Using an overlapping transcript signature from the different sen models, his team tried to predict drugs that hit all of the pathways (the candidates are proprietary). One of them administered in 3x 3-week cycles decreases luminescence expression and improves grip strength.</p><p>Senescent cell accumulation is also seen in the cochlea of aging mice and ablation leads to amelioration of hearing loss.</p><p>If high oxygen partial pressure promotes sen, can hypoxia interfere with sen? 20% oxygen is the standard cell culturing protocol whereas in the body cells are exposed to between 1 and 9% O2, with a mean around 5%. Thus for his experiments 1% was considered hypoxia and 5% normoxia.</p><p>1% vs 5% although cells do enter sen they do not produce the SASP, and this effect is mediated via mTOR. Roxadustat, an EPO inducer, also reduces the SASP but the effect looks more modest to me. In mice the drug inhibits mTOR and increases grip strength.</p><p>During the discussion Demaria mentions that luminescence only shows senescent cells near the body surface, and their abundance is estimated at 3-5% by RFP FACS sorting. In fat it can be higher. Human sen in blood is hard to measure, usually only really feasible via biopsy or in autopsy study. As of now it seems the number of sen cells does not correlate with physiologic function in situ.</p><p>Ideally one would like to copy the oxygenation levels of the original tissues when doing experiments but this is expensive to do. Other issues in experiments are too much glucose and 1D culture.</p><p>Finally, we should keep in mind there are beneficial components of SASP, while NFKB-driven targets may be bad, others may promote immune surveillance or tissue repair and we do not want to mess this up chronically. In vitro early SASP is the good one, late one is bad, but this seems like an over-simplification.</p><div><p class="MsoNormal"><b>Prof. Irina Conboy - UC Berkley<br />
Rejuvenation by dilution<br />
</b>The goal is to boost regeneration rather than just preventing damage. In
old animals regeneration is somehow inhibited, especially in the brain. You can
see that parabiosis promotes regeneration in the brain, e.g. looking at BrdU or
Ki67+ staining as markers of neurogenesis, which would be decreased in old
animals. Conboy already found this in 2003, she says, but was then rejected by
Nature. Later she collaborated with Wyss-Coray who replicated all her findings
and then it was published some 7 years later.<o:p></o:p></p>
<p class="MsoNormal">Now they are exploring neutral blood exchange, with “blood”
that is neither old nor young. Old mice explore more after NBE and become more
curious, beta-galactosidase staining decreases as well. When they did
proteomics they noticed there is a 1<sup>st</sup> wave during which
old-associated proteins are decreased followed by a 2<sup>nd</sup> wave when
there is an increase of young proteins normally suppressed during aging. As far
as brain neurogenesis is concerned the senolytic ABT263 is less effective than
the blood exchange. Conboy also saw less fibrosis and more regeneration in the
liver. Right now they are exploring other hallmarks of aging to see if these
are affected by blood exchange.<o:p></o:p></p>
<p class="MsoNormal">This is a promising field because apheresis in humans is
already approved and it sidesteps many issues aging research faces, i.e. we do
not need to fully understand what is happening with thousands of proteins as
long as the blood exchange works. Some team members are working on innovative
exchange fluids not just albumin, to improve the protocol. However, so far
there is no long term data testing whether these proteomic changes are
sustained.<o:p></o:p></p>
<p class="MsoNormal">Conboy mentions she is now working for Rejuvenation Research
as an editor (please submit your manuscripts) and that, in her opinion, lipids do
not change much with age as do proteins (addressing Max’ question). Although clearly some lipids <a href="https://www.nature.com/articles/s41591-019-0719-5" target="_blank">do change</a> in human plasma during aging.<o:p></o:p></p><p class="MsoNormal"><b>Prof. Vittorio Sebastiano – Stanford University<br />
Repurposing the Principles of Reproduction and Embryonic Development to Stall
and Reverse Aging<o:p></o:p></b></p><p class="MsoNormal">The idea is to hijack our knowledge of embryology and
development to reverse cell aging. The species itself does not age, so it
should be possible to figure out why. This is important because there will be 2
billion people by 2050 at risk of age related diseases and frailty.<o:p></o:p></p><p class="MsoNormal"> Sebastiano discloses
that he is the Cofounder of Turn Biotechnologies who works on “epigenetic
reprogramming of age (ERA™)”. This is something to keep in mind that I want to
point out, and I am sure it applies to Conboy just like everyone else these
days. All of the big names work in companies that are selling or developing
products related to their research. Everyone is biased in some way, so we
should never take all their words as gospel.<o:p></o:p></p><p class="MsoNormal">Old cells have a disorganized epigenetic program. Embryonic
stem cells and germ cells undergo massive reprogramming during development
which includes a lot of demethylation and histone changes.<o:p></o:p></p><p class="MsoNormal">Apparently cloned sheep and other animals reach a normal age,
which is proof that the somatic mutations were not limiting to lifespan.
Although here I would like to comment about the reasons why the species itself
does not age. Clearly, something profound is happening, that, if we could
harness it, would forever revolutionize aging research. This has not escaped
our notice, but can we ever harness it and is it really due to reprogramming?
The other candidate rejuvenating mechanism is selection, which is my preferred
mechanism (1).<o:p></o:p></p><p class="MsoNormal"></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/a/AVvXsEjJCclpsoYVurpubyA6psJAjIKHjViSUH-dZWaT4lBN9z2Tofq7FVd-ylbWQUnBEzjCz7lL-mH5Pr9YDLVCLx7fEaEwJfxbABt7rN70It1F41SPvfVaYWDtbc34h00a6ZyEBU6QqD8mi1vxQ55uDeS0Dwfp1RWXg2ORE03f4xfAdgrYxq4oTGSDq-eXiw" style="margin-left: 1em; margin-right: 1em;"><img alt="" data-original-height="470" data-original-width="737" height="255" src="https://blogger.googleusercontent.com/img/a/AVvXsEjJCclpsoYVurpubyA6psJAjIKHjViSUH-dZWaT4lBN9z2Tofq7FVd-ylbWQUnBEzjCz7lL-mH5Pr9YDLVCLx7fEaEwJfxbABt7rN70It1F41SPvfVaYWDtbc34h00a6ZyEBU6QqD8mi1vxQ55uDeS0Dwfp1RWXg2ORE03f4xfAdgrYxq4oTGSDq-eXiw=w400-h255" width="400" /></a></div><br /><br /><p></p><p class="MsoNormal">Sebastiano continues, asking if we can separate loss of
identity from epigenomic rejuvenation? He thinks we can. His lab uses mRNA as a
platform for delivery of reprogramming factors because mRNA-based reprogramming
of fibroblasts to iPSCs is well-established in his lab. He characterized reprogramming
at different stages (it takes around 12d) to figure out how long it would take
to do partial reprogramming. When they reprogrammed human cells from old donors
for around 4 days several markers of aging improved e.g. autophagosome
formation and membrane potential. As a control, administering just GFP mRNAs does
not help. A youthful transcriptome was restored and the epigenetic clock
suggested the cells were younger.<o:p></o:p></p><p class="MsoNormal">Using a skin explant burn wound model in mice they seem
improved wound healing after after 8d of doxycycline induced reprogramming (prelim
data). Proliferation of keratinocytes seems to be responsible here.<o:p></o:p></p><p class="MsoNormal">Can they also rescue stem cells? The experiment goes roughly
as follows: Keep isolated stem cells in culture for 48h, divide into two groups,
reprogram, transplant back into an injured muscle. Contralateral injection of
untreated cells serves as a control that shares the same systemic environment
(a nod to Irina Conboy). Regenerative capacity of treated cells was as good as that
of young stem cells. No sign of tumorigenesis but more and thicker muscle fibres.<o:p></o:p></p><p class="MsoNormal">Similar experiment, 30d after injury they measured muscle
force and the reprogrammed stem cells allowed regeneration that led to 40% more
muscle force than control stem cells. Works the same with human stem cells in
immunocompromised mice.<o:p></o:p></p><p class="MsoNormal">They are using the 4 Yamanaka factors plus 2 more, i.e.
nanog and lin28. Interestingly, this is different from the Altos Labs publication that was using
4 and from the Sinclair publication with just 3. Still trying to figure out
which one is most important. Ratio matters but the reprogramming seems to work
with all cell types. Sebastiano argues that c-myc is not always bad because it silences
retrotransposons and drives mitochondrial biogenesis.<o:p></o:p></p><p class="MsoNormal"><b>Prof. Vittorio Sebastiano – discussion of safety and the
desire for immortality (or lack thereof)<o:p></o:p></b></p><p class="MsoNormal">During the discussion Irina Conboy mentions that complete
reprogramming gives 100% teratoma formation. Sounds scary. How to make it safer?
Timing, dosage, ratio. Plus, mRNAs are safer than other approaches because they
have a 16-24h half-life.<o:p></o:p></p><p class="MsoNormal">Finally, Sebastiano states he is not interested in making
people immortal. On top of that he is not even interested in defeating the
biological limits of lifespan. He just wants to impact healthspan. Now that is
a very disappointing statement because it is scientifically untenable. On the
bright side, we can learn that being a bigshot at Stanford does not confer
immunity from the naturalistic fallacy. I really wish the interviewer/moderator
at least offered some pushback here. Because aren’t humans also animals, and
thus part of the natural world? And did we not already triple our natural
lifespan? What exactly is the “natural” limit of lifespan anyhow and does Sebastiano
disagree with the use of man-made antibiotics?<o:p></o:p></p><p class="MsoNormal">Not only is the statement vague and contradictory as
demonstrated above, it also flies in the face of decades of research. It is
impossible to significantly extend healthspan without extending lifespan. If
you do not want to believe me because I am an outsider or perhaps a shrill
“extremist”, will you believe Rich Miller who said exactly the same thing <a href="https://www.youtube.com/watch?v=42PzfNs9egA" target="_blank">inthe interview</a> I shared in my <a href="http://biogerontolgy.blogspot.com/2022/04/april-and-may-events-and-news-vitadao.html" target="_blank">other post</a>? All robust models of healthspan extension are
also long-lived.<o:p></o:p></p><p class="MsoNormal">For those who do not know him. Miller is a white man with a
white beard who has worked at the National Institute of Aging for decades. It
does not get more “establishment” than that. In addition, he is one of the best
interventional mouse gerontologists in the world. Rich Miller has authored and
co-authored more mouse aging papers than a small European country. He is the
grand wizard of animal welfare and good mouse husbandry, as his labs have
consistently produced some of the healthiest mice and most rigorous mouse data
we have ever seen.<br />
<!--[endif]--><o:p></o:p></p><p class="MsoNormal">I know there is a push towards healthspan in aging research
and, to a large extent, it is totally valid. However, just like many other
biogerontologists I know that this push has happened for the wrong reasons; to
gain political favor with the elites because many among us believe that the
word “lifespan” is too scary for politicians and laypeople. I disagree. Instead,
I think we need to champion healthspan on its own merits, without distortions,
fallacies and half-truths. Without pitting healthspan as the opposite to
lifespan. Way too often biogerontologists have turned into enemies fighting
about words like lifespan, healthspan and biological immortality, instead of
fighting the common enemy which is aging and age-related diseases. We want to
study healthspan because it is a correlate of longevity that can be measured
quickly and easily, which could allow us to save money, test more drugs,
running smaller but more efficient studies, not because of what is “natural”.<o:p></o:p></p><p class="MsoNormal">
</p><p class="MsoNormal">Given the evidence, I am not scared to say that we aim to
extend lifespan and healthspan as much as we can. They go hand in hand.<o:p></o:p></p><p class="MsoNormal">For a more in-depth discussion you can read one of my older posts where I try to debunk the myth of "bad lifespan extension" that does not extend healthspan (<a href="http://biogerontolgy.blogspot.com/2021/07/the-symmetry-error-are-benefits-of.html" target="_blank">The Symmetry Error</a>). There are also a couple of posts I wrote criticizing the conservative mindset of present-day biogerontology (<a href="http://biogerontolgy.blogspot.com/2021/10/let-us-talk-about-immortality.html" target="_blank">Let us talk about Immortality</a>, <a href="http://biogerontolgy.blogspot.com/2022/01/revamping-biogerontology-for-new-decade.html" target="_blank">Revamping biogerontology for a new decade</a>).</p><p class="MsoNormal"></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/a/AVvXsEhbYcJstVZgdYCzH2xNOZZxwNA2dxmiNZBIHsKO7vFmquvtsjnWQOGizBCuWmNiZMapk8omP6sRSikKsQTDa5Oj0SOBdbRJYgjJQH5ccFlyMsd3cCmAPMe2phT_6HDcoQE3R73-_KgjUDw3mPyPz_WPlAsrU5IqL7uGc7938Impa_CC1P-6vrZi1N2bFw" style="margin-left: 1em; margin-right: 1em;"><img alt="" data-original-height="752" data-original-width="1659" height="290" src="https://blogger.googleusercontent.com/img/a/AVvXsEhbYcJstVZgdYCzH2xNOZZxwNA2dxmiNZBIHsKO7vFmquvtsjnWQOGizBCuWmNiZMapk8omP6sRSikKsQTDa5Oj0SOBdbRJYgjJQH5ccFlyMsd3cCmAPMe2phT_6HDcoQE3R73-_KgjUDw3mPyPz_WPlAsrU5IqL7uGc7938Impa_CC1P-6vrZi1N2bFw=w640-h290" width="640" /></a></div><br /><br /><p></p><p class="MsoNormal"><b>Savva Kerdemelidis - Crowd Funded Cures<br />
Pay-for-success x IP-NFT Pilot: A New Financial Model for Repurposing
Off-Patent Rapamycin<o:p></o:p></b></p><p class="MsoNormal">Kerdemelidis goal is to develop medicines where the patent
system has failed e.g repurposing (finding a new use for an old drug). To do so
he funded the Crowd Funded Cures Charity. It certainly is a pressing issue as US
healthcare costs went from 4 to 16% in the 20<sup>th</sup> century (private and
public). There is also Erooms law, an observation suggesting that drug development
becomes more and more expensive. Patents cause distortions for drugs that allow
monopoly pricing. Sometimes this is good, sometimes it fails.<o:p></o:p></p><p class="MsoNormal">1.5T$ in cost savings over 10y from repurposing should be
possible but there exists no mechanism to capture this. Similar to the tragedy
of the commons. Diets and supplements also have these issues as do antibiotics
and neglected diseases. Then he gives a couple of examples. For example, paricalcitol
was “shown effective against late-stage pancreatic cancer”, but pharma will not
fund large studies to prove it. I suppose he forgot to add a “possibly” to this
sentence, because as it stands this is an oxymoron. The talk might have benefitted from
more precise language here.<br />
<!--[if !supportLineBreakNewLine]--><br />
<!--[endif]--><o:p></o:p></p><p class="MsoNormal">Actually, he says, there are mechanisms to support repurposing,
e.g. the FDA allows exclusivity of repurposed drugs but this is hard to enforce.
Propranolol was shown effective for anxiety, while normally it is used for hypertension.
Doctors can just prescribe it off-label or somehow make the diagnosis fit
hypertension.<o:p></o:p></p><p class="MsoNormal">This leads to so-called financial orphan drugs.<o:p></o:p></p><p class="MsoNormal">Drug development is cheap, R&D effort is 3 billion dollars
to bring a new drug to the market and it usually takes 15 yrs. In contrast, repurposing
costs on average 30 millions and takes 3 yrs.<o:p></o:p></p><p class="MsoNormal">Examples include low-dose naltrexone for pain, which is now off-patent for addiction. Simvastatin for multiple sclerosis. Dichloroacetate (DCA)
and IV vitamin C for some cancers. Also, impressively, fluvoxamine for COVID
(this one has decent evidence IIRC). I noticed, interestingly many of these are
also (in)famous drugs that are often promoted by cranks and self-proclaimed maverick
doctors, which may be a state that is fueled by promising phase II trials with
no follow-up.<o:p></o:p></p><p class="MsoNormal">Then another famous example: Psilocybin mushrooms for
depression and other mental illnesses.<o:p></o:p></p><p class="MsoNormal">Prescribing requires big trials and RCTs, off label prescribing
rarely is done based on smaller studies. What can we do about this?<o:p></o:p></p><p class="MsoNormal">90% of clinical trials are funded by private capital because
government and academia do not want to take the risk.<o:p></o:p></p><p class="MsoNormal">However, with already approved drugs the investor has no way
to recoup costs after the trial due to off-label use. In his proposed system, the
insurer could act as a “success payer” who would offer to pay for a <u>successful</u>
trial demonstrating benefit. In principle this is very simple. The insurer just
has to calculate cost savings from the approval and payout part of these
future savings. <o:p></o:p></p><p class="MsoNormal"></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/a/AVvXsEjHzfkusr2oyc5AIDVLF6PUkO7lbuHzFHBuKjaa9Q_FtyC3N1_I4LSDZ_I8-zkJDrHxVNympyrK-s0Z3p4f1r_tiz5Ex5Xwrxs37cJkujaoq-UL41Yh1iv5fIvrjpBsPcfEwyMdh3_-Hizi3Bcyso-0epyt6IaMi1N5E8w_AzVpVRTowsRnZoDbNJ6b4Q" style="margin-left: 1em; margin-right: 1em;"><img alt="" data-original-height="889" data-original-width="1349" height="422" src="https://blogger.googleusercontent.com/img/a/AVvXsEjHzfkusr2oyc5AIDVLF6PUkO7lbuHzFHBuKjaa9Q_FtyC3N1_I4LSDZ_I8-zkJDrHxVNympyrK-s0Z3p4f1r_tiz5Ex5Xwrxs37cJkujaoq-UL41Yh1iv5fIvrjpBsPcfEwyMdh3_-Hizi3Bcyso-0epyt6IaMi1N5E8w_AzVpVRTowsRnZoDbNJ6b4Q=w640-h422" width="640" /></a></div><br /><br /><p></p><p class="MsoNormal">As a proof of principle he wants to fund a rapamycin plus exercise
phase II pilot study with the main outcome being change in the 30s chair stand
test. I did not fully get how this is going to work, though.<o:p></o:p></p><p class="MsoNormal"><b>Discussion and comments: what can we do about financial
orphan drugs?<o:p></o:p></b></p><p class="MsoNormal">As mentioned by Kerdemelidis, most drugs are off-patent
already so we have a nice treasure trove at our disposal. I love the idea. It
is not the first time I thought about market inefficiencies in pharma and how
to solve them but I would have never come up with such an ingenious idea. My suggestion
would have been clumsy, albeit workable. The government could impose a tax on
certain classes of drugs which is directly used in order to fund drugs that are
not economically viable. For example, income from a tax on mainstream antibiotic
sales could be paid out to companies that developed new antibiotics. Or an
exponential tax on me-too drugs could be used to fund more novel drugs, etc.<o:p></o:p></p><p class="MsoNormal">The solution to get more pharma investment for aging is to reclassify aging as a disease or as a treatable condition as per the FDA and
EMA. This will get us <i>new</i> drugs. However, even once this is resolved we
will still want to promote repurposing studies for <i>old</i> drugs that could
be effective for aging. This is where his pay-for-success model shines.<o:p></o:p></p><p class="MsoNormal">
</p><p class="MsoNormal">Finally, I remain a bit worried about first-mover (dis)advantage.
Since all the insurers reap the benefits from the approval and the published
studies, each individual institution benefit from waiting for the other insurers to act
as a “success payer” first. This should be solvable by a consortium, one would hope,
though.<o:p></o:p></p><p class="MsoNormal"><i>It was fun to write about the science that I love.</i></p><p class="MsoNormal">1. Several selection-based mechanisms operate to ensure the viability and fitness of the (future) fetus and child. For example, on the father's side we have competition between sperm which will select for cells that are able to produce ATP efficiently via aerobic glycolysis, capable of efficient chemotaxis to find the egg cell, expression of the right enzymes to penetrate the zona pellucida etc. On the mother's side, I speculate, there are similar mechanisms to select for a healthy egg cell to mature. In addition, we have the well-described mitochondrial bottleneck which allows a single mitochondrial genotype to become fixed, thus allowing evolution to act on this. Act, it does. Evolution is cruel and indifferent, but effective. The rate of spontaneous abortions rises to 75% in pregnant women 45yo of age (Andersen et al. 2000) and this does not count pre-implantation failure which is also very high and post-partum selection that would act, e.g. on aneuploid, weak, sick or unfit individuals, in the wild.<br /></p><p class="MsoNormal">The level of pre-implantation selection can be naively estimated from <a href="https://www.acog.org/womens-health/faqs/having-a-baby-after-age-35-how-aging-affects-fertility-and-pregnancy#:~:text=What%20are%20the%20chances%20of,get%20pregnant%20per%20menstrual%20cycle." target="_blank">fertility statistics</a>:</p><p class="MsoNormal">"What are the chances of pregnancy as a woman gets older? For healthy couples in their 20s and early 30s, around 1 in 4 women will get pregnant in any single menstrual cycle. By age 40, around 1 in 10 women will get pregnant per menstrual cycle."</p><p class="MsoNormal">If we assumed the couple had sex every 3 days this would give a failure rate of 39 in 40 for young and 99 out of 100 encounters for older women. This should give us an intuition for the severity of selection. Given the importance of fertility for species survival the high failure rates likely serve <i>some </i>purpose, although this remains speculation. Hopefully, I can write something more rigorous on this topic in the future.</p><p class="MsoNormal">see e.g. Zaidi, Arslan A., et al. "Bottleneck and selection in the germline and maternal age influence transmission of mitochondrial DNA in human pedigrees." Proceedings of the National Academy of Sciences 116.50 (2019): 25172-25178.</p></div>Kismethttp://www.blogger.com/profile/08714147243460910596noreply@blogger.com0tag:blogger.com,1999:blog-1808942478335427122.post-21758515869959534532022-04-20T10:42:00.002-07:002022-04-21T09:59:05.302-07:00April and May events and news - VitaDAO, ARDD22, Singapore and more<p>This is my personal selection of events of interest to professional biogerontologists and those who are following the science of aging. Do let me know if there are any additions to be made.</p><p></p><ul style="text-align: left;"><li><b>Grants</b></li><li>Impetus grant <a href="https://impetusgrants.org/news-and-updates/bounties-for-round-2-of-impetus-grants" target="_blank">round 2 application</a> will open at the beginning of May and close at the end of May. This time around it is not an open application as in round 1, but a call for specific projects that align with topics such as interventional human studies involving omics endpoints or validation of methylation clocks (direly needed). Go check it out.</li><li><b>Upcoming events<br /></b></li><li>updates for the below events will be posted on the <a href="https://www.vitadao.com/" target="_blank">VitaDAO webpage</a>.</li><li>28th of April - The Science of Biostasis and Cryopreservation.<br />Dr. Emil Kendziorra, Founder & CEO at Tomorrow Biostasis and Kai Micah Mills, Founder & CEO Cryopets</li><li>12th Of May - Technologies for Longevity Medicine.<br />Will be Cohosted with ARK from Kings College - speakers TBD</li><li><b>Recorded events and seminars</b></li><li>The first V<b>itaDAO crypto longevity symposium</b> was held online on the 13th of April and is available on <a href="https://www.youtube.com/watch?v=GJ-rJAfjhBE" target="_blank">youtube </a>for those who have missed it. The advent of online-only and hybrid conferences that are being streamed and uploaded to youtube is one of the big changes heralded by the pandemic. It seems like we have lost the battle against the virus, but <a href="http://biogerontolgy.blogspot.com/2021/11/a-longbet-about-coronavirus-is-world.html" target="_blank">won the war.</a> Life is slowly returning to normal, with minor improvements.</li><li><b>Regular online events, talks, podcasts and webinars</b></li><li>Numerous online events are a trend that was probably accelerated by COVID.</li><li>With the discontinuation of Aging Science Talks hosted by Mair and Lamming there is a lack of regular weekly events. This is partly compensated for by the amazing <b>NUS Healthy Longevity Webinar</b> hosted by Brian Kennedy and Andrea Maier, which will continue throughout April and May with livestreams every Thursday that are <a href="https://www.youtube.com/watch?v=KhWHXU0GTOA&list=PL7bfV-ASE8DiUbH1fYfN47A4hNwIVcpGh" target="_blank">uploaded to youtube</a> afterwards. <a href="https://nus-sg.zoom.us/webinar/register/2616395806861/WN__sypkX6ZSomc7cGAkK3LbA" target="_blank">Sign-up here</a>. Prof Henrich Jasper from the Buck institute is the upcoming speaker for the 21st of April.</li><li>Although not quite the same thing, Eleanor Sheekey from the "The Sheekey Science Show" regularly posts <a href="https://www.youtube.com/watch?v=Y5ivE6-TwdI" target="">videos </a>about aging that are quite accessible to laypeople. Lifespan.io also has very <a href="https://www.youtube.com/watch?v=4rMiPeCPdkg" target="_blank">good content</a> on youtube as does Live Long world. For example this <a href="https://www.youtube.com/watch?v=42PzfNs9egA" target="_blank">fantastic video with Rich Miller</a>, who needs no introduction for anyone who has done serious mouse longevity work.</li><li><b>Conferences</b></li><li>The Aging Research and Drug Discovery Meeting (<a href="http://agingpharma.org/" target="_blank">ARDD2022</a>) registration is live. The conference will be held in beautiful Copenhagen between the 29th of August and 2nd September. <span>I have fond memories of the city which encompasses everything that is great about Europe, starting from amazing architecture, over bike infrastructure to terrific public transport</span>. Submission deadline for short talks is the 15th of July and for posters it is the 15th of August. ARDD aims to bring together researchers, drug developers and investors. Speakers will include Vadim Gladyshev, Matt Kaeberlein, Laura Niedernhofer, Linda Partdrige, Judith Campisi, Anu Suomalainen and many more.<br /></li><li>The <a href="https://www.grc.org/systems-aging-conference/2022/" target="_blank">Gordon Research Conference</a> "Systemic Processes, Omics Approaches and Biomarkers in Aging" will take place from the 29th of May 29 until the 3rd of June. As always it is in the US and the application deadline is the 1st of May.</li><li>The German <a href="https://cms2.vcongress.de/dgfa2021/home" target="_blank">DGFA conference</a> will be taking place offline from the 23rd to 24th of June 2022. Max attendance of 100. The deadline for abstract submission is 2nd May.</li><li><b>News</b></li><li>My approach is not to chase the news too much. It is well established that the daily news cycle is toxic. As far as science is concerned, if it is published in Nature, I will hear about it one way or another. On top of that I am trying to keep up with the interesting news about longevity, progress studies and futurism without having to read about the war all the time. Therefore lesswrong, twitter and the Nature editorial page are out for the last months (all too political), while <a href="https://www.reddit.com/r/longevity/" target="_blank">reddit/longevity</a> is in. This subreddit is an amazing resource, but only if you have the ability to filter out all the bullshit at a glance.</li><li><b>Offline events</b></li><li>We are starting the <a href="https://www.meetup.com/singapore-longevity-and-health-meetup/" target="_blank">Singapore Longevity Meetup</a> now that the COVID rules have been relaxed.</li></ul><div><div class="separator" style="clear: both; text-align: center;"><br /></div></div><div><br /></div><div><b>Notes from the VitaDAO Crypto meets Longevity Symposium</b></div><div><br /></div><div><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/a/AVvXsEgDyw0GpT_oEDg5Ij5KOjAvoArClfGq8GbIxFpQcZkNr7x_2IIZ34bu8UGGHDOREy8LoO9jwU6kgLvEgeyDZaT4x6LEOVl9NkE3FvbDZscHcJQACEOw4oI46D6HzgAKyWtzhDw_hAU2Mnw00p6JkTvGntTBCXsP3iAVO7alZBWaowleK_PmH5X0sErYKA" style="margin-left: 1em; margin-right: 1em;"><img alt="" data-original-height="637" data-original-width="500" height="400" src="https://blogger.googleusercontent.com/img/a/AVvXsEgDyw0GpT_oEDg5Ij5KOjAvoArClfGq8GbIxFpQcZkNr7x_2IIZ34bu8UGGHDOREy8LoO9jwU6kgLvEgeyDZaT4x6LEOVl9NkE3FvbDZscHcJQACEOw4oI46D6HzgAKyWtzhDw_hAU2Mnw00p6JkTvGntTBCXsP3iAVO7alZBWaowleK_PmH5X0sErYKA=w313-h400" width="313" /></a></div><div class="separator" style="clear: both; text-align: left;"><br /></div><div class="separator" style="clear: both; text-align: center;"><p class="MsoNormal" style="text-align: left;">I (the @Aging_Scientist on twitter) will summarize a couple
of the talks from the VitaDAO symposium, although fewer than I aimed for, because
it took way too long to write these as is. And as always I will provide my
comments on the state of the art. All errors are mine. <o:p></o:p></p></div><p class="MsoNormal"><b>Prof. Marco Demaria - University of Groningen<br />
Heterogeneity in Senescence: from Mechanisms to Interventions<br />
</b>The goal is to delay multiple diseases by targeting aging. Telomeres shorten with time and cells grown in
culture reach the Hayflick limit, similar things happen in vivo. We see activation
of p53 and p16 expression leading to a stable generally irreversible state that
is called senescence. Not all senescence (sen) is bad, because it is of course
also a tumor suppressor mechanism. The factors secreted by senescent cells (SASP), are
growth stimulatory, angiogenic, pro-inflammatory. This is also called sterile inflammation,
which is inflammation when no pathogen is present.<b><o:p></o:p></b></p>
<p class="MsoNormal">We see more p16 staining in aging skin and colon, while in
the brain, for example, we see elevations of GATA4 and p16.<o:p></o:p></p>
<p class="MsoNormal">Then Demaria talks about the, by now, classic p16 reporter
mouse model where p16 was tagged with an RFP, luciferase and HSV-Tk cassette. This
allows to visualize senescent cells by FACS sorting via the RFP protein, through
visible light by luminescence and destroying them by ganciclovir treatment (GCV). 6 months
of GCV treatment improves running distance and grip strength in mice.<o:p></o:p></p>
<p class="MsoNormal">Senescent cells accumulate in most tissues and they are at
the center of several aging mechanisms. Treatments can be senolytics vs senomorphic,
i.e. either kill the cells or reduce their harmful SASP secretion. Many drugs
exist but senescent cells are heterogeneous e.g. dependent on the tissue of origin,
stress type etc. so not all drugs will be universal.<o:p></o:p></p>
<p class="MsoNormal">Using an overlapping transcript signature from the different
sen models, his team tried to predict drugs that hit all of the pathways (the candidates
are proprietary). One of them administered in 3x 3-week cycles decreased luminescence
expression and improved grip strength.<o:p></o:p></p>
<p class="MsoNormal">Senescent cell accumulation is also seen in the cochlea of
aging mice and ablation leads to amelioration of hearing loss.<o:p></o:p></p>
<p class="MsoNormal">If high oxygen partial pressure promotes sen, can hypoxia
interfere with sen? 20% oxygen is the standard cell culturing protocol whereas
in the body cells are exposed to between 1 and 9% O2, with a mean of around 5%.
Thus for his experiments 1% was considered hypoxia and 5% normoxia.<o:p></o:p></p>
<p class="MsoNormal">At 1% vs 5% although cells do enter sen they do not produce the
SASP, and this effect is mediated via mTOR. Roxadustat, an EPO inducer, also
reduces the SASP but the effect looks more modest to me than the effect of hypoxia. In mice the drug
inhibited mTOR and increased grip strength.<o:p></o:p></p>
<p class="MsoNormal">During the discussion Demaria mentions that luminescence
only shows senescent cells near the body surface, and their abundance is
estimated at 3-5% by RFP FACS sorting. In fat it can be higher. Human sen in
blood is hard to measure, usually only really feasible via biopsy or in autopsy
study. As of now it seems the number of sen cells in a tissue cannot be predicted based on the characteristics of the tissue and its physiology.<o:p></o:p></p>
<p class="MsoNormal">Ideally one would like to copy the oxygenation levels of the
original tissues when doing experiments but this is expensive to do. Other
issues in experiments are too much glucose and 1D culture.<o:p></o:p></p>
<p class="MsoNormal">Finally, we should keep in mind there are beneficial components
of SASP, while NFKB-driven targets may be bad, others may promote immune surveillance
or tissue repair and we do not want to mess this up chronically. In vitro early
SASP is the good one, late one is bad, but this seems like an over-simplification.<o:p></o:p></p>[to be continued in another post]</div><p></p>Kismethttp://www.blogger.com/profile/08714147243460910596noreply@blogger.com0tag:blogger.com,1999:blog-1808942478335427122.post-33630863882168597562022-04-10T01:37:00.002-07:002022-04-10T11:10:25.045-07:00VitaDAO – Crypto meets Longevity Symposium<p>One of my colleagues is involved with <a href="https://www.vitadao.com/" target="_blank">VitaDAO</a>, and based on what I have seen from them it seems like a <a href="https://vitadao.medium.com/how-vitadao-works-61bbf861fe96" target="_blank">cool project</a>. Since I have not been involved with any anti-aging communities for a while, I decided to write a bit about one of their upcoming events. As far as I can tell, VitaDAO is like a neat web 3.0 version of <a href="https://www.longecity.org/forum/" target="_blank">longecity</a>, that tries to combine longevity research with crypto, crowd-funding, discord and other techy stuff that is appealing to young people (which is <i>kind of</i> in line with my vision for <a href="http://biogerontolgy.blogspot.com/2022/01/revamping-biogerontology-for-new-decade.html" target="_blank">modern biogerontology</a>). So far I am not sure, however, if the crypto-tech aspect is madness or genius, but only time will tell.</p><p><b>VitaDAO – Crypto meets Longevity Symposium</b><br />Date: Wednesday 13th of April starting 4pm CET<br />Duration: 6-7 hours<br /><a href="https://www.meetup.com/vitadao/events/285057395/" target="_blank">Meetup link</a></p><p>The organizers aim to "to educate the Crypto field on Longevity, and the Longevity Biotech sector on Blockchain technology", while I aim to break my eternal writer's block. The biology aspect, for sure, is solid and worth writing about. The symposium will cover conference staples such as senolytics, small molecules, fasting-mimicking diets, and the eternally hot, controversial topic of parabiosis ("young blood"), recently joined by another controversial upstart reprogramming. I will explain why these topics matter and add some of my current thoughts about the state of the art.</p><p><b>Senescence and senolytics</b><br />As it has become clear, most species accumulate so-called senescent cells when they grow older. These cells are arrested in a half-dysfunctional state, secreting harmful cytokines and other molecules that promote inflammation or might just generally contribute to entropic noise. This has earned them the endearing name "zombie cells" and removal of such cells in mouse models is one of the more robust healthspan promoting interventions, with some impact on lifespan as well. If you want to delve into this in a bit more detail, quite a bit more detail, in fact, I have written about <a href="http://biogerontolgy.blogspot.com/2020/03/senescence-updated-evidence.html" target="_blank">senescence </a>on this blog before.</p><p>Here, I would like to comment on the clinical potential of senolytics and translational pitfalls. It appears that aging researchers and biotech/pharma companies are still trapped in 20th-century-thinking always looking for the "age-related disease most likely to benefit from senolytics". While this kind of study design may be a necessary evil, it is somewhat inconsistent with modern biogerontology. If senescence played a moderate role in dozens of age-related diseases, it would be very difficult to detect changes in disease-specific outcomes in a typical small phase II study. In fact, we need something like the <a href="https://www.afar.org/tame-trial" target="_blank">TAME trial</a> or a TAME pilot for senolytics, i.e. a study that focuses on composite outcomes.</p><p><b>Fasting-mimicking diets</b><br />The cornerstone of 20th century biogerontology is the finding by Clive McCay that caloric restriction extends the lifespan of white rats (1). In fact, almost every modern intervention (or mutation) that extends lifespan in mice is related to caloric restriction. It is anti-anabolism all the way down, <a href="http://biogerontolgy.blogspot.com/2020/09/cr-plus-cr-adjacent-cr-independent-what.html" target="_blank">as I have argued</a>. Most anti-aging interventions slow proliferation, growth, maturation or anabolic processes. While this is a useful paradigm, we are definitely trapped and need to escape from doing just this one thing. First of all, we need radically different treatments to significantly delay aging. Aubrey de Grey was never wrong on that account. Second, anti-anabolism carries certain risks, like increasing frailty in the elderly.</p><p>The other problem with caloric restriction (CR) is publication and model bias. More and more, we have found out that CR is just not that robust, partly because too often we have studied CR in ugly, unprofessional ways (2). I have discussed the evidence <a href="http://biogerontolgy.blogspot.com/2021/12/caloric-restriction-and-fasting-does-it.html" target="_blank">here</a>, and since then I became even more skeptical about the quality of the mouse studies (why? - that shall be left for a future post). Luckily, it seems like rapamycin and other interventions may be a more robust version of CR! It makes sense, as semi-starvation is very stressful for the body and hence older mice, for example, often fail to adapt to CR. It might be better to activate or inhibit the central nutrient-sensing pathways without the actual stress. Interestingly, the human <a href="http://biogerontolgy.blogspot.com/2017/08/in-defense-of-being-underweight.html" target="_blank">epidemiology </a>(or rather mendelian randomization) recently turned out to support CR somewhat, and when I saw the data I triumphantly titled my post "<a href="http://biogerontolgy.blogspot.com/2021/08/biogerontologists-score-thin-is-healthy.html" target="_blank">Biogerontologists Score - Thin is Healthy!</a>"</p><p>The other other problem with CR is that no one will do it. Obesity pandemic and all that, you know. Thus people like Valter Longo and others are searching for more <i>palatable </i>approaches. Many ideas have emerged including fasting, time-restricted feeding, moderate protein restriction, single amino acid restriction etc. They all do have varying support from mouse models and epidemiology. I think it is worthwhile research, <span>even though it is never going to make a big dent. Small molecule drugs are a better candidate for anti-aging therapies because compliance is easier.</span></p><p><b>Small molecules</b><br />Small molecules are defined as chemical substances with a mass of below ~900 Da. Why not big molecules you may ask? Small molecules are useful because they have good oral availability, in contrast to biologics and big molecules, are cheaper to produce and easier to use in screening libraries, although I am not sure if the latter is due to an accident of history or a real advantage (5). </p><p>Historically, they have been the mainstay of big pharma and have saved millions of lives. By and large, and despite all the controversies, the story of big pharma has been a story of "good capitalism" (3). When the interests of money and of the common people align, great things can happen. If you are interested in the pharma field please read <a href="https://www.science.org/blogs/pipeline" target="_blank">Derek Lowe's blog</a>, he is witty, insightful and sometimes writes about aging research.</p><p>For what it is worth, rapamycin has a molecular weight of 914 Da, dasatinib of 488 Da and quercetin of 302 Da (together they form the famous senolytic cocktail D+Q). Clearly, small molecules are of <i>some </i>use to biogerontologists.</p><p>Recently one thing on my mind was the limitation of small molecules, as useful as they are. Is there even a hypothetical combination of small molecules that could extend the lifespan of C. elegans to be quasi-immortal given infinite screening power? (4) I do not think so. Of course you may argue it is pointless to think about the far future and quasi-immortality (<a href="http://biogerontolgy.blogspot.com/2021/10/let-us-talk-about-immortality.html" target="_blank">I disagree</a>, "Let us talk about Immortality"), but the issues run deeper. There may be therapies that are fundamentally more effective than small molecules on a 1-to-1 basis, say, e.g. viral delivery or cell therapies. It is just something to keep in mind <span>and I think parabiosis and "young blood" are such candidates (6).</span></p><p><b>Parabiosis and "young blood"</b><br />A procedure that connects the blood circulation of two different animals is called parabiosis. Why would you want to do that? Parabiosis was invented in the 19th century and later used by 20th-century <a href="https://en.wikipedia.org/wiki/Parabiosis" target="_blank">physiologists</a> to great effect showing, for example, that hormones <a href="https://www.nature.com/articles/nm1010-1097" target="_blank">produced by obese mice</a> could induce starvation in a linked-up control mouse. This hormone turned out to be leptin. It was not until the 2000s when the technique was rediscovered by the group of Thomas Rando at Stanford and his then-student (postdoc?) Irina Conboy, who found that linking up a young organism with an old one might lead to rejuvenation of the old due to circulating factors in young blood. Although this finding still has not lead to any tangible breakthroughs in mice, much less humans, as far as I can tell, it nevertheless made my list of <a href="http://biogerontolgy.blogspot.com/2021/01/roughly-decade-of-progress-in.html" target="_blank">key findings of the last decade</a> (2010-2020). The promise is there and it could become really big. </p><p>The most recent development in this field is called neutral blood exchange (Mehdipour et al. 2021). It is based on the idea that, rather than transferring a protective factor from the young to the old, parabiosis simply serves to dilute a pro-inflammatory, pro-aging milieu in the old organism. In this case, blood plasma was removed and replaced with albumin containing solution (a procedure that is well established in the clinic and called <a href="https://www.msdmanuals.com/home/blood-disorders/blood-transfusion/apheresis" target="_blank">apheresis</a>). This study, however, again highlights what I dislike about the parabiosis field specifically and mouse aging research more broadly. A claim is made that healthspan is improved, but only limited, non-standardized evidence is provided. Instead I would like to see the same measure of healthspan used in every mouse study, and on top of that you can do whatever you want. So, please, just use the Whitehead frailty index.</p><p>Personally, I also like that parabiosis is conceptually simple, side-stepping many difficult questions, and related to the iron accumulation hypothesis of aging. We know that body iron stores are elevated during aging, especially in middle-aged men, and bloodletting appears to protect from cancer. This is a "dilution" so to say. We know that blood donation might be healthy for that reason (might, because it is still controversial due to healthy user bias). However, I am wondering if anyone looked at the effects of plasma donation on mortality and age-related diseases?</p><p><b>Reprogramming the epigenome<br /></b>The massive success of the epigenetic clock(s) in predicting chronologic age hinted at the possibilities here. Something clearly changes in the epigenome with age, although figuring out causality was never going to be easy. David Sinclair was one of the first researchers who wanted to test this experimentally in animals using so-called ICE mice, which are supposed to harbor non-mutagenic DNA breaks that disturb local epigenetic marks (Hayano et al. 2019), but as much of his work this also remains <a href="https://pubpeer.com/publications/96E06671D862488A142F6CD127FEF4?utm_source=Chrome&utm_medium=BrowserExtension&utm_campaign=Chrome" target="_blank">controversial</a> (8).</p><p>There are two types of reprogramming, both useful for the aging researcher. First came the reprogramming of somatic cells into induced pluripotent stem cells. A technique that has its use both as study tool, because it greatly improves in vitro models of disease and, eventually, might help to produce cells, tissues and organs for replacement therapies. In 2012 this culminated with a <a href="https://www.nobelprize.org/prizes/medicine/2012/press-release/" target="_blank">Nobel prize</a> for Gurdon and Yamanaka. Later on, and that is what we are talking about here, scientists figured that if epigenetic damage accumulated with aging, reprogramming, or what they term partial reprogramming, might be able to revert this in a living organism.</p><p>Recently some studies started emerging to support this idea. It does not matter if ICE mice are a good progeroid model showing premature aging, if you can actually rejuvenate mice with partial reprogramming. Most of the data was done in cell culture (and not without its own <a href="https://pubpeer.com/publications/E006ECA3F03CA48839B631F5682C7A" target="_blank">controversy</a>) and there is also some promising data from a progeroid model (Ocampo et al. 2016, also magically attracting <a href="https://pubpeer.com/publications/48BDF08C260CC217F4CB3C8CF27EAB?utm_source=Chrome&utm_medium=BrowserExtension&utm_campaign=Chrome" target="_blank">criticism</a>). However, on this blog, we prefer to look at healthy, well-husbanded mice as the gold standard. </p><p>The Altos Labs publication by Browder et al. 2022 was supposed to do that. It received quite a lot of attention on science twitter and it is one of the best studies the field has produced so far (for better or worse). From the beginning, when you see supposedly groundbreaking data, you can ask yourself why was this not published in Nature or Science if it is so great? (7) At any rate, this one was published in Nature Aging which is also among the best aging journals in the world.</p><p>There are two ways of reading a paper. One is to have a quick glance and the other is to look at it in varying levels of detail. As researchers we have to take these shortcuts because we cannot read every paper that comes out in detail or else we would spend 26 hours per day just assessing literature. When this publication first came out, I had a quick look and immediately noticed a major weakness. Except for figure 5, it is all biomarker work (epigenetics, transcriptomics, metabolomics and lipidomics), so we have no clue whether it works or not! The next question is if the biomarker work is any good? Let us have a look.</p><p>The authors tested two long-term reprogramming strategies, induction from 15 to 22 months and from 12 to 22 months. Reprogramming is achieved through cyclical expression of the classic reprogramming transcription factors Oct3/4, Sox2, Klf4, c-Myc (abbreviated with OSKM) under a doxycycline-inducible promoter. Expression is induced for 2 days followed by a 5 day break.</p><p>No difference in survival was seen, which is reported as a positive outcome when it is not, although to be fair their study was underpowered to detect any differences.</p><p>Next, the authors show epigenetic rejuvenation using “Lifespan Uber Correlation (LUC) clocks, which are constructed on the basis of CpGs whose rate of change correlates with maximum life span across mammals” in 2 out of 6 tissues, whereas a more conventional clock only shows rejuvenation in skin (and several of the p-values would barely pass a Bonferroni correction for multiple testing). A priori there is no reason to think that an endogenous signaling pathway would preferentially induce changes in methylation of CpG islands that have been selected by evolution <i>across </i>species rather than those that more strongly change <i>within </i>species. Clearly, the LUC clocks are not appropriate for this task unless the authors can show within species validation (10). They are not invalid, but as of now, we have to assume they are inferior to conventional clocks.</p><p>It stands to reason that the LUC clock was chosen to improve the look of the manuscript. Indeed using standard elastic net (EN) clocks we can only see rejuvenation in the skin after long-term reprogramming and during short-term reprogramming the kidney cannot decide whether it is older or younger.</p><p></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/a/AVvXsEi9xOwrcApJklDVdYtKTCrIEmpvI3mb4gQWEyZEvijBLaFfpV_a9iKixuGU1Vmd34j_zMefR4Yyto8S9myvLGfQdQT1BiPMGQGGdztZK1eRToHDXCSCC7i8s0YZ4BXZvlNDkV0fK7qsuAkKmzwK_FPsTvGtgmIwwWIiGSouQ0nRVFBsTO-yiQBrpdgpjg" style="margin-left: 1em; margin-right: 1em;"><img alt="" data-original-height="1080" data-original-width="1177" height="586" src="https://blogger.googleusercontent.com/img/a/AVvXsEi9xOwrcApJklDVdYtKTCrIEmpvI3mb4gQWEyZEvijBLaFfpV_a9iKixuGU1Vmd34j_zMefR4Yyto8S9myvLGfQdQT1BiPMGQGGdztZK1eRToHDXCSCC7i8s0YZ4BXZvlNDkV0fK7qsuAkKmzwK_FPsTvGtgmIwwWIiGSouQ0nRVFBsTO-yiQBrpdgpjg=w640-h586" width="640" /></a></div><p><b>Fig S6</b> from Browder et al: note the kidney is misbehaving again</p>Afterwards the authors focus a lot on the skin, because if I am honest, it looks like the treatment only really works in skin. This makes sense since these protocols were largely developed and validated in vitro on fibroblasts.<p></p><p>The RNAseq data is also not very convincing. The authors state that “In the skin, for example, we observed a clear separation of skin samples from control and doxycycline-treated mice”, but that is not immediately obvious from the shown PCA plot, perhaps, because the sample size is at the very low end of what is acceptable for this type of analysis. Also a bit frustrating is the reliance on PCA plots and differential expression analysis of hand-selected pathways to begin with. Is there nothing more elegant like a mouse transcriptomic aging clock or signature? I am aware of transcriptomic clock work in humans or C. elegans, but at a quick glance I could not find anything (published) in mice, although I do recall that Vera Gorbunova presented <a href="https://youtu.be/q0ZYfVoV0s0?t=783" target="_blank">a clock derived from cross-species data</a> (9).</p><p>The other recent publication that made a splash in the field was Lu et al. (2020) from the Sinclair lab, who showed that reprogramming with OSK, leaving out the oncogenic c-Myc, could restore mouse vision at 12 but not at 18 months of age. Without going into much detail, they showed that reprogramming reversed the age-related transcriptomic signature seen in the eye (retinal ganglion cells), which to me seems like the data that Browder et al. should have shown.</p><p>I like crazy ideas, however, reprogramming still needs some more work. Rigorous replication and validation is key so the paradigm does not get stuck with mediocre healthspan and lifespan studies like telomerase research did <a href="http://biogerontolgy.blogspot.com/2022/03/telomeres-vs-dna-damage-battle-of-aging.html" target="_blank">for years</a>. Again I am frustrated that the authors fail to discuss the shortcoming of their paper, instead we have to rely on <a href="https://pubpeer.com/" target="_blank">pubpeer </a>and twitter for critical discussion.</p><p><b>References and notes</b></p><p><b>epistemic status:</b> relatively quick write-up and brainstorming, my expertise is in mouse longevity</p><p>1/ McDonald, Roger B., and Jon J. Ramsey. "Honoring Clive McCay and 75 years of calorie restriction research." The Journal of nutrition 140.7 (2010): 1205-1210.</p><p>2/ have you ever seen the funnel plots of CR mouse studies? Or the reporting quality and sample sizes of a typical mouse aging or CR study? Sadly, it is pitiful.</p><p><span>3/ vioxx <a href="https://www.drugwatch.com/vioxx/#:~:text=Number%20of%20Patients%20Affected%20by%20Vioxx&text=Out%20of%20those%20patients%20who,FDA%20led%20to%20those%20deaths." target="_blank">killed 60 000 people</a>, whereas the coronavirus vaccine saved <a href="https://www.ecdc.europa.eu/en/news-events/who-ecdc-nearly-half-million-lives-saved-covid-19-vaccination" target="_blank">half a million people</a>, just in Europe and the benefits continue to accrue. You can do the math.</span></p><p><span>4/ let's define quasi-immortality as a lifespan of 10 years without abusing any of the Dauer stage pathways and no massive loss in fecundity or vitality.</span></p><p><span>5/ I do not know how one would go about screening biologicals, considering that many of these require a systemic response.</span></p><p><span>6/ </span>1-to-1 basis means small molecule vs other intervention in terms of treatment cost, developmental cost or efficacy. It may well be that for every billion dollars spent on small molecules, one could achieve more if the same money was spent on non-conventional therapies.</p><p>7/ one obvious alternative explanation is that failing to publish in the top-tier journals has nothing to do with the manuscript quality, Nature and Science suck, they tend to publish overhyped papers and miss a lot of outstanding work that ends up in Elife, Cell Metabolism or Aging Cell.</p><p>8/ It was argued that he failed to convincingly demonstrate lack of mutagenic DNA damage in his model.</p><p>9/ I was not very impressed with the validation given that MR and CR are life-extending but score very low.</p><p>10/ The LUC clocks were never developed with the goal of being the superior clock. As stated in the original Horvath publication:<br /><i></i></p><blockquote><i>"We used LUC-related CpGs to develop novel epigenetic age estimators for mice. These LUC clocks <b>do not outperform existing mouse clocks</b> in terms of accuracy of chronological age. Rather, these clocks are meant to address a more elusive aim: the measurement of biological age that correlates with lifespan. The LUC clocks are based on the premise that LUC-related CpGs, being also linked to lifespan, are more informative of biological age than purely age-related CpGs </i>[even this is a stretch because intraspecies life extension mechanisms and interspecies life extension mechanisms are often quite different]<i>. However, <b>this hypothesis requires validation.</b>"</i></blockquote><i></i><br />Keep in mind, the authors did indeed provide minimal validation using GHRKO and CR mice, showing that these are younger with the LUC-based clock. Unfortunately, there is no validation on skin and kidney that I am aware of.<p></p><p>Mehdipour, Melod, et al. "Plasma dilution improves cognition and attenuates neuroinflammation in old mice." GeroScience 43.1 (2021): 1-18.</p><p>Whitehead, Jocelyne C., et al. "A clinical frailty index in aging mice: comparisons with frailty index data in humans." Journals of Gerontology Series A: Biomedical Sciences and Medical Sciences 69.6 (2014): 621-632.</p><p>DNA Break-Induced Epigenetic Drift as a Cause of Mammalian Aging<br />doi: 10.1101/808659 doi: 10.2903/j.efsa.2012.2904 issn: 1831-4732 <br />Hayano et al.</p><p>Ocampo, Alejandro, et al. "In vivo amelioration of age-associated hallmarks by partial reprogramming." Cell 167.7 (2016): 1719-1733.</p><p>Browder, Kristen C., et al. "In vivo partial reprogramming alters age-associated molecular changes during physiological aging in mice." Nature Aging 2.3 (2022): 243-253.</p><p>Lu, Yuancheng, et al. "Reprogramming to recover youthful epigenetic information and restore vision." Nature 588.7836 (2020): 124-129.</p><div><b>Referenced blog posts</b></div><div>equally amused, impressed and embarrassed by all the stuff I have written:</div><div><br /></div><div><a href="http://biogerontolgy.blogspot.com/2022/01/revamping-biogerontology-for-new-decade.html" target="_blank">Revamping biogerontology for a new decade</a>. January 30, 2022.<br /><a href="http://biogerontolgy.blogspot.com/2020/03/senescence-updated-evidence.html" target="_blank">Senescence: updated evidence</a>. March 25, 2020.<br /><a href="http://biogerontolgy.blogspot.com/2020/09/cr-plus-cr-adjacent-cr-independent-what.html" target="_blank">CR Plus, CR adjacent, CR independent - what the heck?</a> September 04, 2020<br /><a href="http://biogerontolgy.blogspot.com/2021/12/caloric-restriction-and-fasting-does-it.html" target="_blank">Caloric Restriction and Fasting - does it work?</a> December 21, 2021<br /><a href="http://biogerontolgy.blogspot.com/2021/08/biogerontologists-score-thin-is-healthy.html" target="_blank">Biogerontologists Score - Thin is Healthy! </a>August 29, 2021<br /><a href="http://biogerontolgy.blogspot.com/2021/10/let-us-talk-about-immortality.html" target="_blank">Let us talk about Immortality.</a> October 12, 2021<br /><a href="http://biogerontolgy.blogspot.com/2021/01/roughly-decade-of-progress-in.html" target="_blank">Roughly a decade of progress in biogerontology</a>. January 03, 2021.<br /><a href="http://biogerontolgy.blogspot.com/2017/08/in-defense-of-being-underweight.html" target="_blank">In defense of being underweight.</a> August 23, 2017.<br /><a href="http://biogerontolgy.blogspot.com/2022/03/telomeres-vs-dna-damage-battle-of-aging.html" target="_blank">Telomeres vs DNA damage - battle of the aging theories.</a> March 26, 2021.</div><div><br /></div>Kismethttp://www.blogger.com/profile/08714147243460910596noreply@blogger.com0tag:blogger.com,1999:blog-1808942478335427122.post-48642180509851156812022-03-26T04:00:00.001-07:002022-03-26T04:00:25.909-07:00Telomeres vs DNA damage - battle of the aging theories<p><i>even though everyone has their favorite pet theory of aging, we need to remember that aging is multifactorial</i></p><p>This is a brief commentary on one of the recent NUS aging webinars "<a href="https://www.youtube.com/watch?v=P4_xKBBFR0M&list=PL7bfV-ASE8DiUbH1fYfN47A4hNwIVcpGh&index=45" target="_blank">AgeX Therapeutics: Targeting Biological Ageing | Dr Michael D. West</a>".</p><p></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/a/AVvXsEiCQNeZxPp3uuCONrtwfr8_XoGsGNwR7QDFfk71DybHLLxcX2SnbrFYK7oDTuD7hohFu1r9rWvivcO-gwSFrVe7EPECKi5F9UcJDxeJWR6qed5oOPit5apm06LOIJpiPMQDwT9IN-DiYy6KvBPmEMCkfya_iW_KKMEUkvaxl45sCHuK4UDYZDd1M2nOCg" style="margin-left: 1em; margin-right: 1em;"><img alt="" data-original-height="579" data-original-width="1339" height="173" src="https://blogger.googleusercontent.com/img/a/AVvXsEiCQNeZxPp3uuCONrtwfr8_XoGsGNwR7QDFfk71DybHLLxcX2SnbrFYK7oDTuD7hohFu1r9rWvivcO-gwSFrVe7EPECKi5F9UcJDxeJWR6qed5oOPit5apm06LOIJpiPMQDwT9IN-DiYy6KvBPmEMCkfya_iW_KKMEUkvaxl45sCHuK4UDYZDd1M2nOCg=w400-h173" width="400" /></a></div><br /><br /><p></p><p>I have never been a fan of the telomere hypothesis/theory.
Not saying it is wrong, but it did get hyped up a lot. This should not be very surprising since it is very appealing to laypeople (and thus potentially investors) and then there was the Nobel prize putting it into the spotlight. It is interesting that
the speaker would mention DNA damage as a potentially weaker theory. In
fact, both theories suffer from the same issues (1).</p><p class="MsoNormal">
Neither theory has received a particularly strong backing from interventional mouse
studies, contrary to assertions in the video.</p><p class="MsoNormal">In addition, we should not forget that many other types of evidence are relevant to assess the strength of a theory. The types of evidence that are used in biogerontology include:</p><p class="MsoNormal"></p><ul style="text-align: left;"><li>comparative biology – long-lived species should display conserved mechanisms of aging</li><li>long-lived mouse mutants – if your trait is age-associated it should be changed in these mice</li><li>mouse intervention studies</li><li>observational studies in humans (longitudinal change with chronologic aging; change associated with health outcomes)</li><li>interventional studies in humans</li></ul><p></p>
<p class="MsoNormal">Let us compare the two theories in light of these principles. Strength of the DNA damage theory:<br />
1/ Conceptually cleaner and safer: linear (or at least monotonous) relationship
between DNA damage and aging can be expected (6). Telomeres are a beast that has to
be tamed through temporal or spatial expression of telomerase. Do not want to
have it active in cancer cells.<br />
2/ Better human data since we have overwhelming evidence that there are
age-related diseases caused by DNA damage and <i>longer </i>telomeres, i.e. cancer. Apart from that, overall I
would say both theories have decent support from observational studies in
humans.<br />
3/ has actual support from long-lived models like dwarf mice (Page 2009, Dominick 2017) (3)<br />4/ I feel that the comparative data for the DNA damage theory is stronger, although this could be my own bias due to more familiarity with the data. </p>
<p class="MsoNormal">Strength of the telomere theory:<br />
1/ better support from mouse intervention studies, but not by much (5). The best
study I could find was de Jesus et al. 2012 (7). While the control mice in this
study were healthy, the treatment group consisted of only 17 animals and the
study as such was never repeated by any other lab. For comparison, the seminal
work on rapamycin was performed on 150 controls and 300 animals in the
intervention group and was by now replicated in perhaps a thousand animals in
different labs across the globe. Sure, the NIA ITP is run by the best mouse
aging labs in the world, but others can do it too. See for example Zhang et al.
2012 for absolutely stunning lifespan data in FGF21-transgenic mice with good
median lifespans for the controls and 77 treated animals vs 67 controls. While
FGF21 is being studied by conventional pharma companies I have not seen much
hype in the aging field about this finding and no startup has yet formed to
harness this potential – as far as I can tell. <br />
<br />
The DNA damage theory is hard to test specifically since we lack the tools to
do so. If we included e.g. Nrf2 data as indirect evidence for the DNA damage
theory the data would be roughly on par but still weak for both theories (2).</p><p class="MsoNormal"><o:p><b>References and notes</b></o:p></p><p class="MsoNormal"><o:p><b>epistemic status: </b>quick draft, late to the party, half-baked</o:p></p>
<p class="MsoNormal">1/ Let us ignore the multistress resistance theory which seems like a better-supported superset of the DNA damage theory.</p><p class="MsoNormal">2/ weak is relative, I do not consider the DNA damage theory to be weak, just exceedingly hard to test. Also the Nrf2 experimental data is not particularly good, but substantially better than nothing I would say. We have nothing (in mice) that resembles direct evidence for the DNA damage theory. We do, however, have overwhelming evidence from progeroid models that DNA damage is important to aging. While I put less stock into these models than the average biogerontologist, it is a moot point in the context of this blog post. As far as I can tell, there are decent telomere attrition mouse models suggesting telomeres matter leading to a "draw". Although, again, I feel like the DNA damage progeroid models are a tad bit better.</p>
<p class="MsoNormal"><span lang="DE">3/ Page, Melissa M.,
et al. </span>"Mechanisms of stress resistance in Snell dwarf mouse
fibroblasts: enhanced antioxidant and DNA base excision repair capacity, but no
differences in mitochondrial metabolism." Free Radical Biology and
Medicine 46.8 (2009): 1109-1118.<br /><br /><span>Dominick, Graham, et al. "mTOR regulates the expression of DNA damage response enzymes in long‐lived Snell dwarf, GHRKO, and PAPPA‐KO mice." Aging Cell 16.1 (2017): 52-60.<br /><b>Note: </b>DSB repair seems to be the currently favored mechanism in comparative studies, so these dwarf studies are somewhat contradictory because they highlight different pathways.</span></p><p class="MsoNormal">4/ Zhang, Yuan, et al. "The starvation hormone, fibroblast growth factor-21, extends lifespan in mice." elife 1 (2012): e00065.</p><p class="MsoNormal">5/ To my great surprise the mouse data is actually much better than I remembered it, not that I was ever very familiar with the telomere field.</p><p class="MsoNormal">6/ Obviously "linear" is an over-simplification between hormesis and feedback mechanisms, which were also (likely) the death knell for antioxidant studies.</p><p class="MsoNormal">7/ Bernardes de Jesus, Bruno, et al. "Telomerase gene therapy in adult and old mice delays aging and increases longevity without increasing cancer." EMBO molecular medicine 4.8 (2012): 691-704.</p>Kismethttp://www.blogger.com/profile/08714147243460910596noreply@blogger.com0tag:blogger.com,1999:blog-1808942478335427122.post-32071781922616664352022-02-27T10:00:00.003-08:002022-02-27T10:33:55.606-08:00A prediction market for the NIA’s mouse Interventions Testing Program<p><i>there was an attempt to write two posts per month</i></p><p><b>Why bet on these outcomes? Background Information.</b></p><p>The 20th century brought unprecedented prosperity and well-being for many. Average life expectancy saw <a href="https://ourworldindata.org/life-expectancy" target="_blank">a dramatic increase</a> doubling globally from 32 to 66 years, settling somewhere around 80 to 90 years in developed nations. Indeed, life expectancies in developed nations are so high now that the rate of intrinsic aging has become the limiting factor for human health- and lifespan. This, however, poses a tremendous challenge for both the pension system and disease-centered biomedical research.</p><p>Most diseases of the elderly are directly caused by aging and due to competing age-related risks for these various diseases the eradication of any one disease would only have surprisingly small benefits, i.e. during aging risk of death increases in parallel for most diseases.</p><p>This phenomenon is also known as Taeuber paradox and its importance to modern biomedical research cannot be overstated. Put in the simplest way, if cancer does not kill you so will cardiovascular disease (Olshansky et al. 2016; Keyfitz 1977).</p><p>Therefore treatments that can decelerate the rate of aging are the only available path to significantly increase human lifespan going forward. Before drugs and treatments can be considered for larger clinical studies in humans, pilot trials in rodents must be performed as a necessary prerequisite. This is why the National Institute on Aging established the Interventions Testing Program which “is a multi-institutional study investigating treatments with the potential to extend lifespan and delay disease and dysfunction in mice”.</p><p>So far it is the largest systematic effort to test potentially life-extending compounds in mice that has been performed. This is an attempt to give the biogerontology community, interested experts and laypeople an opportunity to predict outcomes of this important experiment.</p><p>There are two good reasons to establish a prediction market. First of all, prediction markets may outperform expert opinion. Second of all, as of now we have no way to know the public (expert) opinion. It would be valuable to figure out what the biogerontology community thinks about the different compounds even if in the end they were unable to correctly predict the outcome of the lifespan studies.</p><p><b>The bets</b></p><p></p><ul style="text-align: left;"><li><b>Cohort 12: C2016</b></li><li>for a <a href="https://onlinelibrary.wiley.com/doi/full/10.1111/acel.13328" target="_blank">historical comparison</a>: nicotinamide riboside, candesartan cilexetil, geranylgeranylacetone, and MIF098 all failed. 17-a-estradiol & Canagliflozin failed because they are not robust across genders (male only). If we were to stipulate that the prediction markets work the first four compounds should have received a low score and the second two a somewhat higher score.<br /><br /></li><li><b>Cohort 13: C2017</b></li></ul><ol style="text-align: left;"><li><a href="https://manifold.markets/KamilPabis/will-13butanediol-significantly-ext" target="_blank">Will 1,3-butanediol significantly extend the lifespan of UM-HET3 mice tested by the NIA’s Interventions Testing Program? (Cohort 13: C2017)</a></li><li><a href="https://manifold.markets/KamilPabis/will-captopril-significantly-extend" target="_blank">Will Captopril significantly extend the lifespan of UM-HET3 mice tested by the NIA’s Interventions Testing Program? (Cohort 13: C2017)</a></li><li><a href="https://manifold.markets/KamilPabis/will-lleucine-significantly-extend" target="_blank">Will L-Leucine significantly extend the lifespan of UM-HET3 mice tested by the NIA’s Interventions Testing Program? (Cohort 13: C2017)</a></li><li><a href="https://manifold.markets/KamilPabis/will-pb125-significantly-extend-the" target="_blank">Will PB125 significantly extend the lifespan of UM-HET3 mice tested by the NIA’s Interventions Testing Program? (Cohort 13: C2017)</a></li><li><a href="https://manifold.markets/KamilPabis/will-sulindac-significantly-extend" target="_blank">Will Sulindac significantly extend the lifespan of UM-HET3 mice tested by the NIA’s Interventions Testing Program? (Cohort 13: C2017)</a></li><li><a href="https://manifold.markets/KamilPabis/will-syringaresinol-significantly-e" target="_blank">Will Syringaresinol significantly extend the lifespan of UM-HET3 mice tested by the NIA’s Interventions Testing Program? (Cohort 13: C2017)</a></li><li><a href="https://manifold.markets/KamilPabis/will-phase-ii-rapamycinacarbose-sig" target="_blank">Will Phase II - Rapamycin/Acarbose significantly extend the lifespan of UM-HET3mice tested by the NIA’s Interventions Testing Program? (Cohort 13: C2017)</a></li></ol><p></p><div><br /></div><p><b>Further reading and notes</b></p><p>I decided against splitting the bets by gender to make the number of bets more manageable. This is acceptable because the key outcome is robust lifespan extension across both genders which - to be fair - is a high bar, so far only passed by rapamycin. Phase II tests will be included because there is potential for negative interaction. Also I decided against numerical predictions simply because I could not find a decent prediction market that allows this and because I do not see the benefits of the added complexity. This post or future posts will be updated with more recent cohorts. Results from Cohort 13 should be published soon so this one is my priority for now.<br />I am considering writing a companion post about the different drugs being studied by the NIA ITP and the rationale behind them. May be updated.</p><p><a href="https://www.nia.nih.gov/research/dab/interventions-testing-program-itp">https://www.nia.nih.gov/research/dab/interventions-testing-program-itp</a></p><p><a href="https://daily.jstor.org/how-accurate-are-prediction-markets/">https://daily.jstor.org/how-accurate-are-prediction-markets/</a></p><p>Olshansky, S. Jay. "Articulating the case for the longevity dividend." Cold Spring Harbor Perspectives in Medicine 6.2 (2016): a025940.</p><p>Keyfitz, Nathan. "What difference would it make if cancer were eradicated? An examination of the Taeuber paradox." Demography 14.4 (1977): 411-418.</p><p>Nadon, Nancy L., et al. "NIA interventions testing program: investigating putative aging intervention agents in a genetically heterogeneous mouse model." EBioMedicine 21 (2017): 3-4.</p><p><a href="https://manifold.markets/markets">https://manifold.markets/markets</a></p><p><b>Resolution Criteria</b></p><p>Peer-reviewed publication or official pre-print from the NIA ITP. Maximum lifespan will be used and Mean lifespan only should the NIA ITP team not publish max LS data (highly unlikely). If p-value for maximum lifespan change for the pooled cohort is not provided the following criteria will be used for resolution: significant MLS increase in both genders OR <0.01 in one gender and a non-significant numerical increase of at least 5% in the other gender. We relax criteria because the analysis split by gender will obviously have lower power than a pooled analysis. The cut-off is based on the rapamycin 4.7 ppm data, which we want to define as "barely making the cut", see also Table 1:</p><p><a href="https://onlinelibrary.wiley.com/doi/full/10.1111/acel.12194">https://onlinelibrary.wiley.com/doi/full/10.1111/acel.12194</a></p><div><br /></div>Kismethttp://www.blogger.com/profile/08714147243460910596noreply@blogger.com0tag:blogger.com,1999:blog-1808942478335427122.post-77441882896221481902022-01-30T07:27:00.003-08:002022-02-18T02:53:09.759-08:00Revamping biogerontology for a new decade<p><i>a much delayed New Year's post</i></p><p><b>Biogerontology must engage with science-fiction<br /></b>Physicists have a long and productive tradition of engaging with pseudoscience, futurism and science-fiction (2). Some of them talk about wormholes and faster than light travel (3), what is possible and what is impossible, and how we can get closer to these dreams. Others, like NASA, talk about the Science of Star Trek (1). In fact, every single pop-sci book about physics I read in my childhood talked about space ships going to the stars and time travel. Who was not inspired by science fiction when they were young! Why can't we do the same? Where are the children's books talking about a 300-year-old woman getting a new, young kidney; where are the youtube videos about biological immortality?</p><p>Humans have dreamed about eternal youth and interstellar travel for centuries. Although no one laughs when NASA talks about the concepts behind future interstellar travel, even if this is centuries in the future, when we talk about extremely long lifespans and discuss concepts of maintaining youth forever, we are denigrated as dreamers, crackpots or we're politically "damaging the cause" (8).</p><p>But you better believe that biogerontologists are the ones well equipped to talk about immortality and rejuvenation in all shapes and forms.</p><p>One thing that I wanted to make clear, but perhaps failed to convey <a href="http://biogerontolgy.blogspot.com/2021/10/let-us-talk-about-immortality.html" target="_blank">in my last blog post</a>, was that we have to inspire people if we want a new generation of scientists to thrive. Science exists to make our dreams come true: the dream of a better, longer, healthier life. The dream of understanding, taking pride and stock in this fleeting world. The <i>aspiration </i>to live as long as we want - which is equivalent to "[biological] immortality". If we never talk about these dreams and outline potential paths to realize some of them, science will whither, because passionate activists and researchers will go elsewhere. </p><p>Do NOT overpromise, but let's be honest about our ultimate goals and let us weigh in with our expertise. Many of us, if not most, carry these dreams in their hearts. </p><p></p><p><b>Biogerontology must engage with the arts<br /></b>Recently I stumbled upon a Steven Weinberg obituary and immediately thought to myself if we could modify his famous quote as follows:</p><p></p><p></p><p></p><p></p><p></p><blockquote style="font-style: italic;">The effort to understand and stop aging is one of the very few things that lifts human life a little above the level of farce, and gives it some of the grace of tragedy. This is the effort to allow people a choice: life or death, when and how long.</blockquote><p>Thinking about this a little more, I started to wonder: why do we have so many famous writers talking about particle physics and cosmology out of all things in the world? Why is there beautiful prose written about black holes, stars, time travel and all topics even farther away than radical lifespan extension, but so little on topics closer to everyday life? (5) Oh, and besides, Steven Weinberg is dead. He died on July the 23rd of 2021. Aging claims so many great minds. Do you really want great, productive scientists and artists to die? Wouldn't it be horrible towards the deceased's family to claim that death and aging is okay and just a natural part of life?</p><p>If John Lenon could imagine a world without heaven, borders and wars 50 years ago in a song, why can't we imagine a world without aging in a song or pop-sci article today? (4) Do we live in a world without war and religious strife? No! Lenon's achievement is not world peace; but he did touch and inspire millions of people with his anti-war anthem. Now imagine a world without aging, it is easy if you try.</p><p></p><p><b>Biogerontology must engage with pop culture<br /></b>I am not an artist so I can't be the catalyst for this kind of change. However, I can help a bit with another issue. Do you know why the grassroots movement to slow climate change, fight pollution, promote veg*anism and reduce ecological damage is thriving? (6) It is successful not only because it is appealing to young people on a visceral level ("it is our future that you are stealing"). It is because the movement is able to express its dreams in a way that is appealing to young people; through pop culture: cynical, satirical, entertaining, artistic, bitter, pithy or light-hearted.</p><p>The difference is day and night. Just google "aging memes", these are hot garbage, and compare them to the amazing "climate change memes" you will find online. We are complete buffoons when it comes to advertising and selling our own brand. Almost every <i>modern </i>aging researcher knows we are right, but how do we convince others? Part of the strategy is having young people move the overtone window. The process will not always be clean, tidy and truthful - that is a fact of life when laypeople communicate in pithy punchlines - but, overall, it will be effective and it will eventually help science (7).</p><p></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/a/AVvXsEicrXppZkU2nODmcEbkSeqB2DhYmUlkFNNIJkHBJg3j6adf6sK3Way023402KQudU6qroNtqYEz8dCsuNjBwcX0QlI5kth6aRiqIoeN9uqIjWtZ-4sNCd-JxODEIrJ4Yb_DF2q0BThvqv6dFGMYbIiQ8zCKdKM26XmCsknt7ZOHIjTdZjl6jcd3Auddtw" style="margin-left: 1em; margin-right: 1em;"><img alt="" data-original-height="871" data-original-width="1528" height="364" src="https://blogger.googleusercontent.com/img/a/AVvXsEicrXppZkU2nODmcEbkSeqB2DhYmUlkFNNIJkHBJg3j6adf6sK3Way023402KQudU6qroNtqYEz8dCsuNjBwcX0QlI5kth6aRiqIoeN9uqIjWtZ-4sNCd-JxODEIrJ4Yb_DF2q0BThvqv6dFGMYbIiQ8zCKdKM26XmCsknt7ZOHIjTdZjl6jcd3Auddtw=w640-h364" width="640" /></a></div><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/a/AVvXsEh16JYKFjeodNzz2i3mmZtX8Zs6XKJjLB6HW7ZQb8dIxmHBDwmSFJL10k4_lsJ5SCBmJ0uOsjhWf7EuvPBVWtnuWcpmbX_VuKV4vMOEyd5s6g33edBG6JcCOdaHo1gHfdtZKGtJ9lxpQf5Muk5AhxLoZo4qBXaFFe9xxTOx26Win-UKOW3xgOhuLTg2NA" style="margin-left: 1em; margin-right: 1em;"><img alt="" data-original-height="744" data-original-width="731" height="400" src="https://blogger.googleusercontent.com/img/a/AVvXsEh16JYKFjeodNzz2i3mmZtX8Zs6XKJjLB6HW7ZQb8dIxmHBDwmSFJL10k4_lsJ5SCBmJ0uOsjhWf7EuvPBVWtnuWcpmbX_VuKV4vMOEyd5s6g33edBG6JcCOdaHo1gHfdtZKGtJ9lxpQf5Muk5AhxLoZo4qBXaFFe9xxTOx26Win-UKOW3xgOhuLTg2NA=w393-h400" width="393" /></a></div><br /><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/a/AVvXsEjKlKBQxYyJfA1xAHQ3jUYVGjtchP1Xnmh7QaomykoHLWm_-510KCeTHfH5TsTzLcdsJhQwyg-F8TykJRs3TUqTvjkxglPuTpK6QzFATDOiVgwVVS2pTEdNiXYxVKntHUFBU7A9HLq5f3Ki1nA6toYKhY_IJiVEGIdHvTOlxDAYuygbOBOUMTmhZdVrQw" style="margin-left: 1em; margin-right: 1em;"><img alt="" data-original-height="584" data-original-width="987" height="236" src="https://blogger.googleusercontent.com/img/a/AVvXsEjKlKBQxYyJfA1xAHQ3jUYVGjtchP1Xnmh7QaomykoHLWm_-510KCeTHfH5TsTzLcdsJhQwyg-F8TykJRs3TUqTvjkxglPuTpK6QzFATDOiVgwVVS2pTEdNiXYxVKntHUFBU7A9HLq5f3Ki1nA6toYKhY_IJiVEGIdHvTOlxDAYuygbOBOUMTmhZdVrQw=w400-h236" width="400" /></a></div><br /><p></p><p><b>We are young and we are coming to upend your world<br /></b>Biogerontology and science in general has been the domain of old white men for way too long. We the scientists failed to change that. While I have no problem tapping the wisdom of venerable professors, I do think we need fresh faces, new ideas, radical changes; we need inspiring communication, we need art, memes, we need exaggerations, jokes and most of all we need the truth; we need a healthy mix of different strategies. I know it is scary, but don't fear us, because we are on your side. Let us consult John Lenon for a closing remark. He would be an old man now, yet he would agree with us:</p><p><i></i></p><p></p><blockquote><p></p><p><i>You may say I'm a dreamer</i></p><p></p><p><i>But I'm not the only one</i></p><p><i>I hope someday you'll join us</i></p><p><i>And the world will be as one</i></p></blockquote><p><i></i></p><p></p><p>A world where we can openly say that we are trying to defeat aging and then see how far we go. A world where dreams are not dismissed out of hand, but instead are used as inspiration to craft long-term plans and goals.</p><p><b>References and notes</b><br /><i>minor edits for clarity</i></p><p>1. <a href="https://www.nasa.gov/topics/technology/features/star_trek.html">https://www.nasa.gov/topics/technology/features/star_trek.html</a></p><p>2. And biologists talk about free will, metaphysics and religion all the time, also not their field per se!</p><p>3. <a href="https://www.youtube.com/watch?v=-2c0P2CEU9A">https://www.youtube.com/watch?v=-2c0P2CEU9A</a></p><p>4. Not getting too much into details. There are of course many theories. Cognitive dissonance is one. As I suggested in my post, if we people admitted that death is pointless, they fear death or at the very least do not want to die, then they would have to face certain hard truths about the world - and they cannot handle this. People will rather invent reasons why death and aging is useful.</p><p>5. The smallest of steps towards immortality would produce unprecedented increases in human healthspan due to a phenomenon known as Taeuber's paradox.</p><p>5b. That may be changing slowly with people like Aubrey de Grey or, more recently, David Sinclair reach "pop star" or "cult" status.</p><p>6. Let us not get into the endless debate about consumer vs government responsibility. Yes, I do agree that political change has a better leverage than going for changes in consumer behavior - and that thus many veg*an activists may not be acting as perfectly effective altruists. Regardless, the anti-climate change movement is <strike>fucking </strike>quite successful.</p><p>7. While climate change activists are not always truthful, they are definitely more reasonable than climate change deniers and, I believe, having a positive impact on the discourse.</p><p>8. You may have your preferred strategy and consider us naive. That is your right. However, what I do not get is, people wasting time, energy and political capital in order to attack their allies. We have a common enemy: diseases, suffering, science denialism, frauds. Let's focus on that.</p><p></p>Kismethttp://www.blogger.com/profile/08714147243460910596noreply@blogger.com0tag:blogger.com,1999:blog-1808942478335427122.post-55897289669208352582021-12-21T06:46:00.000-08:002021-12-21T06:46:00.854-08:00Caloric Restriction and Fasting - does it work?<p>Caloric Restriction and Fasting - does it work? At least in mice?</p><p>It is unfortunate that I have to ask this question in 2021, but that's life - full of surprises, some of them unpleasant. Caloric Restriction (CR) is known to extend lifespan of many mouse strains and of other species like spiders, butterflies and dogs (2). This assumption has been the cornerstone of modern biogerontology and has informed, if not driven, most of the research that has been performed in our field so far (4). However, more and more studies show that the CR state is fragile, easily disturbed, and not as robust as we once thought. In particular, there have been studies in wild-derived mice, <a href="http://biogerontolgy.blogspot.com/2021/12/a-replication-attempt-of-failed-cr.html" target="_blank">the ILSXISS inbred panel</a>, and now this work by Lamming, casting doubt on the CR hypothesis.</p><p>You may ask what is the CR hypothesis? When I say "CR works" or "does not work" I mean something specific. No one cares if CR is a lab curiosity that helps a couple dozen of strains live longer. We study CR because we hope it will be robust across species and because we're hoping CR in humans would either lead to non-zero lifespan (LS) extension or would pave the way for future CR-mimetic therapies (1). Perhaps we should call this the strong CR hypothesis. We know the weak CR hypothesis is true as something interesting is happening in these animals and life extension is real even if not robust across strains and species.</p><p>Today we will critically evaluate the "strong" CR hypothesis in light of a new publication by Heidi Pak from the Lamming lab (5). Without a long introduction, let me say that there is an ongoing debate about the ability of different "time-restricted" feeding protocols to extend mouse lifespan independently of CR. To make the confusion perfect different protocols and concepts go by easily distinguishable names like every other day feeding, intermittent fasting, time restricted feeding (let's call all of these fasting*). The major unresolved question is as follows. Are any, all or some benefits of CR due to fasting*?</p><p>For a long time I was a strong believer that only calories matter, but now I am not so sure. Right now I can imagine that <i>some </i>of the benefits of CR are indeed due to fasting.</p><p>A key observation, at least for me, that led to renewed interest (or reframing) of fasting* research was the eating behaviour of our little gluttonous furry friends. As pointed out by Pak et al: <i>"CR animals rapidly consume their entire daily meal within ~2 h, and then fast for ~22 h until their next meal"</i>. It took me a while until I came to recognize this semi-trivial observation for its importance. In practical terms this also means we have two possibilities of studying fasting*: reduce calories while modifying the window during which the food is consumed, or keep calories constant while decreasing the same window (as was done more often in the past). The authors use both strategies, although the first approach takes center stage.</p><p><b>Abstract - TL;DR</b><br />Emerging data suggests that fasting* in mice contributes to the health benefits we see in animals that eat less. Inconsistent or flawed study designs, however, still limit the conclusions we can draw. While Pak et al., as discussed here, strengthens the fasting hypothesis, the use of dietary dilution - which I speculate is toxic - remains a major methodical limitation of this study. Nevertheless, it is intriguing to see that a calorie-restricted diet diluted with 50% cellulose fails to improve lifespan and mouse frailty. If true, this might weaken the CR hypothesis yet again because it shows that we do not even have a handle on the most basic aspects of a decade-old protocol, <span>but other than that it is not a major attack on the strong CR hypothesis. CR could be real even if we have to rename it to "chronic intermittent fasting".</span></p><p><b>The Lamming paper: fasting* could mediate the benefits of CR</b></p><p><i></i></p><blockquote><i>We find that fasting is required for CR-induced changes in insulin sensitivity and fuel selection. Additionally, fasting alone without reduced energy intake is sufficient to recapitulate the metabolic phenotypes and transcriptional signature of a CR diet. Finally, we show that fasting is required for CR-induced improvements in glucose metabolism, frailty and lifespan in C57BL/6J male mice. Our results overturn the long-held belief that the beneficial effects of a CR diet in mammals are mediated solely by the reduction of caloric intake, and highlight fasting as an important component of the metabolic and geroprotective effects of CR.</i></blockquote><p></p><p>A long-held belief overturned, or is it? (13)</p><p>In total five different dietary regimes were studied by Pak et al. 2021 (5). The authors used two special dietary approaches to reduce the effect of CR-induced fasting. One involves dilution of the standard (ad libitum) diet with 50% indigestible cellulose (diluted AL) and the other a contraption that dispenses food in regular intervals during the dark period when mice are active (MF.cr). Both lead to 30% restriction, but should allow the mice to snack throughout the day. Then we also have standard CR and AL conditions. On top of that, in some experiments, we have time-restricted AL (TR.al), i.e., mice ate only during a 3h window (7).</p><p>All restricted diets led to improved glucose tolerance and reduced bodyweight in B6 mice but only CR proper (with the associated fasting periods) led to improved insulin tolerance. In contrast, in DBA/2J mice CR did not lead to changes in insulin tolerance and there were no differences between feeding groups. This is an interesting choice of secondary mouse model since this is one of the few classical strains where CR fails to extend lifespan.</p><p>In addition the authors performed metabolomics which is hard to interpret because we know little about the expected changes in contrast to transcriptomics. As far as I can tell, looking at these PCA plots in the supplementary data diluted AL is generally different from AL and again different from CR. To me they look distinct even in the liver, although the authors say the following: <i>"CR-fed mice had a distinct metabolomic signature from AL-fed mice </i><i>[in muscle]</i><i>; however, unlike in the liver, Diluted AL-fed mice had a distinct signature from AL-fed mice"</i>. MF.cr animals were not studied for some reason. Not clear this strengthens the main point.</p><p>Then following that, the authors switch to studying time-restricted AL feeding (TR.al). They perform transcriptomics and state that "Fasting and calorie restriction result in highly similar transcriptomic signatures in liver and white adipose tissue" without providing any clear quantification. But assuming that it is true, what does it tell us? Shouldn't this analysis include diluted AL or MF.cr to show that it produces a response similar to AL? I am very confused by this choice. The authors also noted that TR.al led to a small caloric deficit. This issue has plagued most of the published fasting* research and was often used by critics as an argument to discount the effects of fasting.</p><p>Most importantly, the fact that fasting drives some of the metabolic changes during CR is not a sufficiently strong argument to imply that it also drives the lifespan benefits of CR. As we have seen, many of the candidate (metabolic) changes during CR make no contribution to lifespan or a very minor one. For example, the quantitative role of insulin sensitivity during CR remains questionable, among other reasons, because low insulin by itself fails to robustly extend lifespan (6). Similarly, in the <a href="http://biogerontolgy.blogspot.com/2021/12/a-replication-attempt-of-failed-cr.html" target="_blank">ILSXISS replication effort</a>, there was no relationship between glucose tolerance under CR and lifespan extension.</p><p><b>Dilution induced CR: impact on lifespan and healthspan</b></p><p>Knowing this, the authors move on to hard outcomes like healthspan and lifespan, but this is where it gets interesting. "[the] MF.cr regimen is similar to a regimen previously shown to extend the lifespan of mice", yet the authors refrain from using it in this study. Sure, cost is the obvious limiting factor because the setup appears technically challenging at first, but either way this is an important shortcoming (as the authors acknowledge).</p><p>When someone explains dietary dilution to me, my immediate gut reaction is "this must be toxic". I mean, come on, have you ever tried eating Walden Farms zero-calorie sauces? I experimented with these once and quickly found out how hard it is to suppress your gag reflex confronted with something so otherworldly. Even though some people swear by it, I just couldn't. Now imagine half of your diet, almost 1kg, per day is made up of these sauces! Jokes aside, it is a safe intuition that (3) massive dietary dilution could be unhealthy and this is something the authors need to actively disprove.</p><p>The authors (or reviewers), to their credit, do realize this. They successfully rule out some forms of toxicity, for example, <i>"when accounting for the amount of cellulose consumed, Diluted AL-fed mice absorbed digestible macronutrients similarly to AL-fed mice </i>[but micronutrients were not investigated]<i>"</i> and gut barrier integrity measured using fluorescent dextran was also comparable between the groups.</p><p>Other signs however point in the direction of dietary-dilution-is-killing mice. The frailty profiles between AL and diluted AL are not similar although they should be if the null hypothesis is true: <i>"the specific deficits of AL-fed and Diluted AL-fed mice that contributed to their equivalent frailty varied, with Diluted AL-fed mice developing kyphosis, and AL mice having declining grip strength and body composition".</i> To be fair that assay is probably too noisy for such a strong assumption.</p><p>Perhaps more importantly, if you ask me, the diet seems to be outright killing the mice. Diluted AL does not merely offset the benefits of CR, it actually shortens the mean lifespan of mice. As if this was not problematic enough, the difference emerges at around 250 days, which is way too early to have anything to do with aging. Finally, "the incidence of cancer in Diluted AL-fed mice was low, perhaps due to their shorter lifespan" - which is also consistent with a benefit of diluted AL, and induced CR, on cancer incidence that is overridden by acute toxicity.</p><p>Another indication that something is off with the diluted AL diet is the large behavioral change of the mice, who appeared rather unhappy with the diet and ended up "shredding" their pellets (looking for some other food source? [8]) These mice also lost even more weight (including lean and fat mass) than mice subjected to 30% CR.</p><p><b>Summary<br /></b>While this is undoubtedly a great paper I remain a bit stumped by the many "convenient" choices. Diluted AL was used instead of MF.cr which would be the superior protocol that still reduces the period of fasting. Several experiments with restricted AL mice were performed that would have benefitted from an MF.cr / diluted AL control. Overall while the authors advance an interesting hypothesis, I do think the paper fails to upset the established consensus in the way the ILSXISS inbred panel did. If the CR benefits were <i>only </i>or<i> predominantly</i> due to fasting this would be indeed a huge sensation. Right now we do not know.</p><p>I would have preferred if the authors looked at other markers of the CR state like IGF-1 and also hunger signaling. The non-physiological nature of diluted AL raises a lot of intriguing issues in relation to hunger. One far fetched hypothesis is that calorie sensing relies more heavily on sensing the "volume" or "mass" of the consumed food rather than the calories itself (9). If this were true, it could explain why diluted AL mice performed so poorly, since the volume of the consumed food would be quite high. If the food was diluted by 50% then the mice need to raise their intake by roughly 40% to reach a caloric intake of 70% relative to ad libitum. That is a tremendous increase.</p><p>This reminds me of a story: I once got a rather terse mail when I suggested to a researcher that their data was interesting but strangely at odds with my intuition and thus felt incomplete, scoffing "the data is the data", but this could not be farther from the truth in biology. All our data is subject to implicit and explicit biases, and we test so few conditions that there is a lot of room for genuine disagreement and professional intuition. This is exactly such a case, this study seems "wrong", not in the sense of being incorrect, but rather like a piece of a puzzle that is annoyingly at odds with the other pieces. Reconciling such inconvenient studies can be very worthwhile.</p><p>The fact that Solon-Biet (10) arrived at a similar conclusion also using dietary dilution, while the only known study of MF.cr showed lifespan extension (11), may be part of the answer. The fact that time restricted feeding leads to smaller health benefits than CR is consistent with the idea that only some of the CR benefits are due to fasting (12) and inconsistent with the data shown by Lamming and Solon-Biet using dilution approaches.</p><p>Ultimately, we may be dealing with more than one puzzle here, with pieces yet to be found and assembled.</p><div><b>References and notes</b></div><div><br /></div><div>*) fasting in this article is an umbrella term and shorthand, where appropiate the exact protocol is mentioned</div><div><br /></div><div>1. I am starting to wonder if CR-mimetics and anti-anabolic pathways could work in humans even if CR is not robust. It's certainly possible but a failure of CR would constitute a major downgrade for the Bayesian prior here.</div><div><br /></div><div>2. not all data is as experimentally rigorous as one would hope, though.<br />Nakagawa, Shinichi, et al. "Comparative and meta‐analytic insights into life extension via dietary restriction." Aging cell 11.3 (2012): 401-409.</div><div><br /></div><div>3. part of the intution goes as follows we know that (ultra-)processing of foods is generally a problem as are other extraordinary deviations from the diet an organism evolved to deal with. We also know that not all types of fibre are beneficial at all doses, e.g.: </div><div><br /></div><div>Bonithon-Kopp, Claire, et al. "Calcium and fibre supplementation in prevention of colorectal adenoma recurrence: a randomised intervention trial." The Lancet 356.9238 (2000): 1300-1306.</div><div><br /></div><div>4. these days, luckily, we are getting more and more crazy out of the left field ideas.</div><div><br /></div><div>5. Pak, Heidi H., et al. "Fasting drives the metabolic, molecular and geroprotective effects of a calorie-restricted diet in mice." Nature Metabolism (2021): 1-15.</div><div><br /></div><div>6. Nelson, James F., et al. "Probing the relationship between insulin sensitivity and longevity using genetically modified mice." Journals of Gerontology Series A: Biomedical Sciences and Medical Sciences 67.12 (2012): 1332-1338.</div><div><br /></div><div>7. "Mice in the TR.al group were entrained to eat comparably to the AL-fed group within a 3-h period during the first 2 weeks, and food was always removed 3 h after the start of feeding. "</div><div><br /></div><div>8. "Due to shredding behaviour of the Diluted AL group, we performed a comprehensive food consumption where we measured average shredded food and food left in the hopper. We found an average of 23% of the food was shredded on the bed of the cage. We accounted for this value to calculate for Diluted AL food consumption presented in this paper."</div><div><br /></div><div>9. Non-caloric effects via food sensing would not be totally unexpected for sure:</div><div>Libert, Sergiy, et al. "Regulation of Drosophila life span by olfaction and food-derived odors." Science 315.5815 (2007): 1133-1137.</div><div><br /></div><div>10. would have to re-read to check if they had a non-dilution approach</div><div>Solon-Biet, S. M. et al. The ratio of macronutrients, not caloric intake, dictates cardiometabolic health, aging and longevity in ad libitum-fed mice. Cell Metab. 19, 418–430 (2014).</div><div>Also note this beautiful study that Lamming dug up totally at odds:<br />Kokkonen, G. C. & Barrows, C. H. The effect of dietary cellulose on life span and biochemical variables of male mice. Age 11, 7–9 (1988).</div><div><br /></div><div>11. unfortunately the control mice are a bit shortlived at around 770d mean LS</div><div>Nelson, W. & Halberg, F. Meal-timing, circadian rhythms and life span of mice. J. Nutr. 116, 2244–2253 (1986).</div><div><br /></div><div>12. Mitchell, Sarah J., et al. "Daily fasting improves health and survival in male mice independent of diet composition and calories." Cell metabolism 29.1 (2019): 221-228.<br /><a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6326845/">https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6326845/</a><br /><br />12a. Mice are not very bright but you can train them using food: <i>"The MF mice learned quickly that food would not be continuously available, and thus, tended to gorge, spending hours each day without food. "</i></div><div><div><br /></div><div>Note that the eating window of ML (fasted) mice was half that of controls but still higher than CR. Thus this study cannot disprove the hypothesis that an isocaloric reduction of the eating window to CR levels would lead to further LS extension and that most CR benefits are due to fasting.</div></div><div><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiQnQVFpMkKcxdbbq9C6BVOgs9bcWAkU7aVWbtaH3v-Wd-XuDY75HI6epEObMaov65pCAWOSD4NrdHTKzdqDvsWwSgCl08KOJ3mp5QTb3hKd4myhLCysWefBC3J9zAg_pQ6oN6oWKBd0Dc1/" style="margin-left: 1em; margin-right: 1em;"><img alt="" data-original-height="162" data-original-width="293" height="177" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiQnQVFpMkKcxdbbq9C6BVOgs9bcWAkU7aVWbtaH3v-Wd-XuDY75HI6epEObMaov65pCAWOSD4NrdHTKzdqDvsWwSgCl08KOJ3mp5QTb3hKd4myhLCysWefBC3J9zAg_pQ6oN6oWKBd0Dc1/" width="320" /></a></div><br />Another strength of that study: <i>"The bodyweight gain trajectory of NIA-MF mice was significantly lower compared to NIA-AL controls (p=3.48E-05), while no significant difference was observed between WIS-MF and WIS-AL mice (p=0.55) (Fig. S1, upper panels)"</i></div><div><br /></div><div>Given that both WIS and NIA groups show similar LS extension after MF (fasting) argues against so called crpto CR effects.</div><div><br /></div><div><br /></div><div>13. Actually I like the conservative phrasing of the abstract. However, this is not why this paper is so controversial and interesting. It is the cellulose dilution lifespan study that is key. The cellulose data, if true, would suggest 100% of the CR effect is due to fasting.</div><p></p>Kismethttp://www.blogger.com/profile/08714147243460910596noreply@blogger.com0