Masks, politics and the inexplicable superiority of South and East Asia

Introduction
Summary

• Cross-country data sets the stage
• Preclinical data and biological plausibility
• More on physical probability
• Laboratory studies
• Speculation about synergistic effects
• Could masks benefit the whole society?
• Do masks protect others or yourself?
• Psychosocial factors and the adding up of unknowns

Observational data
Clinical data

• The extrapolation problem
• A second look at the good studies and further meta-analyses
• Study quality favors the null hypothesis: meta-analysis of Xiao et al.

Consistent messaging and biological plausibility
References and Notes

Introduction

How is it possible that different health experts, organizations and governments provide such varying recommendations on mask use? For example, the World Health Organization (WHO) suggested in February of 2020 “that only those who are already sick with a respiratory illness should wear them” as did the American health authorities (reference at the end; and the US medical establishment  continues to be skeptical).

In contrast, Asian politicians urge the public to wear masks (Chan & Yuen 2020), the Austrian government plans to make mask wearing at super markets compulsory (reference) and the Austrian OEGIT (society for infectious diseases) states that wearing masks correctly can contribute to reduce rates of influenza infection (and by extension perhaps COVID19).

Before we get started, let me state my biases. I am not a fan of masks and I never considered it smart to wear them outside of crowded places. I was against wearing masks when there were less than a dozen documented cases due to my personal feelings about cost-benefits, although, now I am not sure if that was even a correct assumption. However, I am also getting sick of people downplaying their benefits, in part, perhaps due to some irrational bias against employing measures that were successful in Asia (test, trace, surveillance, masks, etc).

First, we have to distinguish two issues. Do masks work and do we have enough masks for health care workers (HCWs)? Even if masks work, some actors may see a benefit in downplaying their benefits or outright lying in order to stop a run on masks so as to protect HCWs. Lying is dumb, for obvious reasons, however.

The differences for the economy are tremendous:

• If masks work for the community and we do not have enough, we need to produce considerably more. We need to start now.
• If masks do not work for the community and we do not have enough, we only need to produce a little bit more for HCWs.

Summary

The below data is ordered from weakest to strongest:

Senescence: updated evidence

Summary

1. What is senescence?
2. Why is senescence harmful?
3. Types of senescence and the link with telomeres
4. A brief history of senescence research: seminal papers
5. Pathways leading to senescence and markers of senescence
6. Senescence controversies

7. Does senescence contribute to aging? (indirect data)
8. Does senescence increase with aging?
9. Human data suggesting that senescence contributes to aging
10. Comparative data across species
11. Comparative data within species
12. Comparative data inlong-lived mouse models
13. Age-related diseases and senescence
14. Misc data – the SASP, inflammation, plausibility and indirect evidence for causality

15. Intervening (direct data)
16. Types of interventions
17. A list of senolytic interventions
18. A word of caution about mouse healthspan studies
19. Key studies: improved healthspan
20. Key studies: improved lifespan
21. Human data and ongoing trials

22. Models of accelerated senescence
23. Disease associated with high senescent cell burden
24. Senescent cell transplantation

25. Dose response studies

26. Summary, outlook, open questions

Housekeeping note: hopefully most of the formatting errors are fixed now. I think most of them were introduced because blogspot (just like office, gmail, etc) insist to copy fully formatted text instead of supporting plain text paste per default. Once you accidentally paste formatted text it becomes hard to remove the formatting via the editor interface.

Summary
The holy grail of aging research, if you will, is a one time-treatment that can rejuvenate an aging organism. No other kind of treatment could be realistically and efficiently applied to humans. Compared to mice humans are giant, lazy slobs that don't take their medication. That is the huge problem. Unfortunately, most interventions that can extend lifespan in mice do so most efficiently when started from birth (e.g. growth hormone dwarfism). There are few interventions that lead to rejuvenation of older animals (e.g. rapamycin). Even fewer interventions allow true rejuvenation with just a short treatment or intermittent treatments. The removal of senescent cells is shaping up to be the latter which is why it is worth critically discussing the idea.

Here I would like to briefly review the evidence for the senescence theory of aging using Koch- or Braford-Hill-like criteria. I've been reading the literature through the lens of Koch & Hill for a while because there is no other way to make sense of it. We're lucky because others have already reviewed this topic from a similar angle (Yanai & Fraifeld 2018) and I just need to provide an update and, when appropriate, criticism of their approach. For example, I would like to point out that like many others Yanai & Fraifeld put too much faith in models of age-related disease and models of accelerated aging. In contrast they barely consider the concept of temporality (4) and of biological gradient (5).

Braford-Hill suggested to evaluate a correlation (or in this case hypothesis) by considering several factors like (1) strength of association, (2) consistency, (3) specificity, (4) temporality, (5) biological gradient, (6) plausibility, (7) coherence, (8) experiment, and (9) analogy.

Senescence increases with aging and this is supported by "longitudinal" data (moderate to strong evidence). This is true in many different studies and in various species. Nevertheless the data remains heterogeneous and plagued by biases. In reference to Bradford-Hill we can note that the data is generally strong (1) and more often than not consistent (2).

Slow-aging mice have reduced levels of senescence (moderate to strong evidence). While this is generally true, studies are extremely heterogeneous. Summarizing the data and going back to Bradford-Hill we can also see a biological gradient (5), for example, when an increasing degree of caloric restriction (CR) leads to an increasing suppression of senescence associated inflammation.
Fast-aging or so called progeroid mice have increased levels of senescence and removal of senescent cells improves the aging-like phenotype (strong evidence - but weak model).

All the above models have one thing in common. They fail to establish temporality (4) because - outside of experimental studies with their own shortcomings - we do not know if senescence precedes or follows aging and manipulations of lifespan. This is still a critical gap in the literature.

Senescence is associated with age-related diseases, especially CVD and metabolic disease (moderate evidence - but weak model). When we slow aging we do expect a slowing of most diseases, so per definition most anti-aging treatment would violate the concept of specificity (3). Even considering this limitation the senescence data is all over the place. The causes of senescence are too many and not well defined; the correlations between senescence and health outcomes are too numerous; and senescence is too dynamic and modulated by too many treatments; there are too many conflicting markers of senescence. Nothing is "specific" in any way, shape or form. This could be also described as a lack of coherence (7) with many outstanding questions and contradictions, although, none strong enough to sink the hypothesis.

Comparative studies across species have not studied senescence using modern-day markers. Indirect evidence supports the senescence theory (overall weak evidence). In some sense this can be described as using analogy (9) and extrapolation to make claims, as proposed by Bradford-Hill.

Senolytic therapy extends lifespan and healthspan in mice (moderate to strong evidence, good model). However, the improvement in lifespan is often small. Similarly, perhaps a bit more consistently, healthspan is improved, but this does not apply to each and every aging pathology, or even to the majority of pathologies.

Waving the pirate flag: The future of science publishing is black?

Preface: I feel like I am too scatter-brained to blog as regularly as I would like to. When I have an idea it is often easier to put it on Twitter (@Aging_Scientist) instead of writing a post. The problem with blogging is once I get obsessed with an idea, I turn into a perfectionist and start writing these long and rambling posts. But they are never "good enough" or "quite finished". Not sure how often I will blog from now one.

The era of sci-hub

Evidently it is desirable to access the scientific literature efficiently and cheaply ("open access"), because papers are written to be shared. As researchers we want to benefit the public and make our voice heard. Three types of open access (OA) schemes are well established by now (ref. 5). Green, is the publication of manuscripts in archives. Gold, is the placement of publications in journals that guarantee free access. Despite high expectations, neither approach has managed to overtake regular pay-per-view and institutional subscription based models ("paywall"). Black, can describe a service called sci-hub which copies and saves almost all biomedical papers that are being published. This “black” approach is set to revolutionize publishing, but why?

The need for a heroic effort?

If there is one thing I am particularly proud of, it is providing insights into HOW to carry out research. Whether my suggestions are useful or not, you can be the judge. Overall, this topic is related to the problem of Eroom's law in drug discovery. The latter claims that the research pace in the drug industry has slowed down, or research is becoming more and more expensive. The authors go on to speculate that the regulatory climate is one of several reasons. I will go on to suggest that the regulatory framework is even more inadequate to deal with the science of biogerontology.

Methods to accelerate research progress in biogerontology - a recap from my blog
1. We need to take Tauber's paradox and preclinical data seriously when designing human studies if we want to help our aging population. On the one hand, there is a political reason to play it slow, because any failure may lead to bad publicity and public backlash. However, from a purely intellectual point of view there is every reason to be aggressive about aging research because the risk benefit/ratio is so much better for aging interventions than any other drug. Our conservative approach has led to large studies using an inferior anti-aging agent, Metformin, and almost no useful human research on Rapamycin and derivatives (1). This fear of side-effects has also ruined human calorie-restriction research because "classic harm" and "biogerontologic benefit" were weighed incorrectly (2).

2. We have to re-consider how we carry out animal research. Regulatory bodies could leverage pre-clinical testing as a shortcut to mass screening of potential anti-aging compounds (3).

Study dump: food groups, vitamin K, sleep, nuts, B12 and phosphate

In the best case we should use epidemiology and nutrition science to guide public health policy through flexible incentives and taxes. Let's be prepared when the time comes and we can make that change real. One day, maybe by sheer luck, some politician will listen to good science. Study dump based on interesting abstracts:

Food group centric view. The author Schwingshackl works or used to work at our Viennese nutrition department. This study is a thing of beauty for everyone interested in epidemiology and our dear friends who suffer from orthorexia and will outlive us all.
A 56% reduction in relative risk for mortality with optimal intakes? Interestingly we should (or could?) see a 75% reduction by very naive multiplication (RR estimated at nadir in Figure 2). At a quick glance I do not see if the authors truly prove diminishing returns or not, though.:

With increasing intake (for each daily serving) of whole grains (RR: 0.92; 95% CI: 0.89, 0.95), vegetables (RR: 0.96; 95% CI: 0.95, 0.98), fruits (RR: 0.94; 95% CI: 0.92, 0.97), nuts (RR: 0.76; 95% CI: 0.69, 0.84), and fish (RR: 0.93; 95% CI: 0.88, 0.98), the risk of all-cause mortality decreased; higher intake of red meat (RR: 1.10; 95% CI: 1.04, 1.18) and processed meat (RR: 1.23; 95% CI: 1.12, 1.36) was associated with an increased risk of all-cause mortality in a linear dose-response meta-analysis. A clear indication of nonlinearity was seen for the relations between vegetables, fruits, nuts, and dairy and all-cause mortality. Optimal consumption of risk-decreasing foods results in [ONLY] 56% reduction of all-cause mortality, whereas consumption of risk-increasing foods is associated with a 2-fold increased risk of all-cause mortality.

Optimal consumption (the smallest serving with significant results and no further substantial change in risk or no further data for larger amounts) of risk-decreasing foods [3 servings whole grains/d (RR = 0.79), 3 servings vegetables/d (RR = 0.89), 3 servings fruit/d (RR = 0.90), 1 serving nuts/d (RR = 0.85), 1 serving legumes/d (RR = 0.90), and 2 servings fish/d (RR = 0.90)] results in a 56% reduction 

Could be a problem:  "We rated the quality of meta-evidence for the 12 food groups. The NutriGrade meta-evidence rating was “very low” for eggs; “low” for refined grains, vegetables, fruits, and SSBs; “moderate” for nuts, legumes, dairy, fish, red meat, and processed meat; and “high” for whole grains"

Schwingshackl, Lukas, et al. "Food groups and risk of all-cause mortality: a systematic review and meta-analysis of prospective studies." The American Journal of Clinical Nutrition 105.6 (2017): 1462-1473.

In defense of being underweight

It is common wisdom to claim that underweight people are at increased risk of death and disease. Epidemiologists, doctors and nutritionists would tend to favour this position but certainly not biogerontologists (1) because weight-loss is very similar to calorie restriction (CR), which is one of the most robust life-extending interventions known. Now the question is which science best informs health policy in an aging, obese world and were do these differences in opinion come from? This review published in 2014 by Luigi Fontana, an expert in human CR, and the distinguished epidemiologist Frank Hu is worth a read as primer (1).

First of all, weight-loss, low bodyweight, low adiposity and CR are not one and the same. CR is, however, associated with initial weight-loss, low bodyweight and adiposity. Mice can be obese but still in a CR-state because they lost weight from their individual ad libitum set point. To define our working hypothesis I favour the idea that low body-weight in healthy people is suggestive of a CR-like state (i.e. mild CR). Being thin is a similar phenotype to CR and it seems like a plausible idea.

As is often the case reality is more complicated than our assumptions. Existing associations may be real but exaggerated or not as well supported as we thought, which is the case for cancer & obesity (4). We know that adiposity is harmful, but recently the controversial idea of an "obesity paradox" was suggested. Observational studies have found that being somewhat overweight could be healthy, but before making a final judgement we have to consider other study types that are in disagreement. What is more, new evidence suggests even the very observational studies are flawed.

The three strongest study designs informing our opinion on CR and leanness are epidemiology, biomarker studies and animal experiments*. One may wonder why I would mention human and animal studies in the same breath if everyone knows that human studies are superior. However, the point is that human studies are only superior if all else is equal. A well-designed, controlled mouse experiment measures healthspan across 100% of the animal's lifespan. If these experiments can be replicated in diverse non-human species the data cannot be ignored anymore. In contrast, observational studies follow human subjects only across 25% of their lifespan and usually much shorter. They are also uncontrolled which can lead to technically insurmountable biases (residual confounding, self-selection, etc).

*other type of circumstantial evidence exists e.g. the Okinawan population, see (1)

Just to drive this point home: One could claim that the CR studies and BMI epidemiology measure something completely different. A low BMI is healthy if you arrived at it per force (e.g. experimental imposition in animal experiments and elite dieters), but not if you were always thin, i.e. "self-selected" (what the observational studies measure). In reality I think there is an intersection between the two at a CR-like state. It is also important to address the BMI literature because for better or worse it is often trotted out as a counter-argument to do human CR.

Historically, animal and biomarker studies have favoured the biogerontologist's view (thinner is better, see [1]) while epidemiology did not. Therefore in this post I will mostly discuss how observational studies have recently shifted closer to the biogerontologist's view. In addition I will mention some studies not discussed in (1), including self-selected bodyweight in animals and the anorexia nervosa literature.

Aging across the tree of life?

Just read a beautiful paper and I am not disagreeing. Just criticizing their approach and conclusions to learn something for myself. So let's see. Aging is conserved in different species, right? The authors (2, 3) claim that there are big problems with this assumption and I will discuss the two articles together.

Here we contrast standardized patterns over age for 11 mammals, 12 other vertebrates, 10 invertebrates, 12 vascular plants and a green alga..Although it has been predicted that evolution should inevitably lead to increasing mortality and declining fertility with age after maturity, there is great variation among these species

 Furthermore:

More sophisticated analyses including both shape and pace have confirmed the importance of slow, negligible, and negative aging [44].One of the most striking findings in recent years is that demographic aging appears to be far from universal [3,39]
......
This finding is crucial and paradigm-shifting because it implies that there is no single, universal aging pathway. At most, there might be a pathway that is shared when aging is present but can be turned off.