A methylation clock is an empirical construct. There is no understanding of physiology or metabolism built into the process. The clock is engineered to do the best job predicting (in the case of the GRIM-Age clock, for example) future mortality and morbidity based on methylation patterns. The whole process is agnostic about biological mechanism.
It is a legitimate question whether a drug or diet that sets back the methylation clock has actually increased life expectancy. Maybe methylation is a downstream consequence of aging, like grey hair or wrinkled skin. We would hardly expect a skin cream or hair dye to increase life expectancy.
For me, personally, this is an easy question. I have devoted much of my professional career since 1996 to opposing the “selfish gene” version of evolution and promoting multilevel selection. I have collected evidence that aging is a systemic phenomenon, centrally controlled, and that epigenetics (including methylation) is the primary way in which aging is enforced on the body. I was poised to believe that methylation clocks measure something real and important even before the first clocks appeared [2013].
Many other scientists looking at anti-aging interventions have been happy to take a practical approach, not invoking theory at all, but accepting the impressive correlations of aging clocks with other measures of biological age as good enough reason to trust that any intervention able to sett back the methylation clocks is probably setting back biological age.
Morgan Levine is a biostatistician par excellence. As a post-doc working with Steve Horvath, she developed what I consider to be the best, most usefull methylation clock..With her own research group at Yale, she has continued to innovate, with a promising approach based on the mathematics of principal component analysis [my write-up last September]
In a recent preprint, Morgan Levine has deeply questioned whether methylation clocks can be trusted in the way that so many of us have trusted them. Although young and just at the beginning of her career, she has done more than anyone except Horvath himself to advance methylation clock technology. For her to question the foundational value of her own work is a gesture of courage and deep intellectual honesty.
Before the post-doctoral work with Horvath that created the PhenoAge clock, Levine studied evolutionary biology of aging with the incomparable authority, Caleb Finch. She has her own ideas about the evolutionary origins of aging, and they are rooted in classical evolutionary theory. She sees the cause of aging as somatic evolution and accumulation of damage. She is deeply influenced by Peter Medawar’s [1952] hypothesis that what happens late in life is outside the influence of natural selection.
And so she raises the deep question: how much of epigenetic change associated with age is a driver of aging, and how much is a response to the body’s increasingly damaged state?
“Though the connection between risk and time may appear probabilistic on the surface, the emerging pathology is rooted in the molecular and cellular remodeling of the organismal system over its lifetime. Such changes likely result from accumulated damage, selection pressures at the level of cells, compensatory mechanisms, and/or the unintended consequences of a biological program. However, alterations to a complex system must abide by a hierarchical structure2, initiating at lower levels of biological organization (e.g. molecules) prior to manifesting at the higher levels in which they are typically observed (e.g. tissue and organ dysregulation and failure, and eventually death)3. Thus, to delay, prevent, or even reverse the maladies currently awaiting us in late life, we must discover how to decipher and remodel the molecular fingerprint of aging.”
Here, Levine is raising the most basic questions about the origin and meaning of methylation changes with age. Levine proceeds from her strongest skill—She is master of a wide array of sophisticated statistical tools.
Part of the classification has to do with Yamanaka programming, which is about stem cell vs differentiated cell. Another part comes from Framingham Heart study patients, and which CpGs change with age in FHS subjects. She distinguishes sites that increase methylation with age from others that decrease methylation with age. She associates some CpGs with cancer.
She maps Shores, Islands, and Open Seas. CpG islands are promoter regions of the genome that have lots of CpGs in close proximity. Shores are regions on the boundary of CpG islands, and Open Seas are regions in which CpGs exist as isolated, disconnected units.
Having divided 4779 CpGs into 12 groups, she can ask, How much of each group is represented in each of the most commonly used methylation clocks?
And which modules are performing best and most consistently across clocks?
Conclusions
Levine entertains the idea of “epigenetic drift” as part of the story, however she recognizes that the changes that underpin the most reliable clocks are not “drift” but clearly directional. She asks, to what extent do methylation changes cause aging and to what extent are they responses to various, incidental results of the aging process?
“If DNAm changes were purely reflecting entropic alterations or epigenetic drift, we would expect to see a bias against changes in CpGs that start around 0.5 (corresponding to random chance of methylation at a given site) . However, what we observe is actually a regression away from the mean, in which these heterogeneous populations of cells are systematically losing DNAm with time. This suggests that the green-yellow module’s notable pattern of epigenetic aging is unlikely to stem from noise or aberrant DNAm changes with age. Instead, DNAm changes may reflect cellular selection pressure or clonal expansion in which the cells without DNAm at these CpGs are able to outcompete (proliferate more than) the ones with DNAm . Alternatively, it could reflect a regulated compensatory mechanism that gets initiated with aging, or a continuation of a developmental program that is not turned-off . These scenarios have different implications for our understanding of epigenetic changes. The first would suggest that individual cells are not changing DNAm patterns with age, but rather the changes that are observed in bulk data are happening at the level of cell populations, shifting prevalence of cells with heterogenous states. The second and third scenarios, on the other hand, would suggest within cell DNAm changes, perhaps as a response to extracellular environment or signaling changes with aging (e.g. integrated stress response (ISR) ), or as an extended developmental program that fails to be extinguished—somewhat aligned with the hyper-function theory of aging . In moving forward, single-cell DNAm data may help distinguish individual vs. population changes.”
Here she references “Integrated Stress Response” as a theory of the aging metabolism. She also refers to Mikhail Blagosklonny’s idea that developmental programs have a momentum that spills over into aging phenotypes.
Morgan Levine is a brilliant scientist, facing the harshest possible self-criticism of her work. Her conclusions are tentative and open-ended.
A more definitive, empirical approach
The big, interesting question may not require theoretical analysis. What we want to know is whether we have been justified in using methylation clocks to indicate whether aging has been slowed or reversed. The most direct answer to that question comes from Harold Katcher’s rats, — the most successful example of setting back methylation age in a whole animal. Currently, Katcher has two sets of 8 rats that are the same chronological age; one group has been treated with E5 and has a much lower methylation age. He is waiting to see how long each group lives. So far, 2 of the untreated rats have died, and all 8 of the treated rats are still alive. Of course, the treated rats look and act much younger, and have physiological characteristics of younger rats. Over the coming months, the survival test will produce an answer to the important question whether a younger methylation age implies a younger biological age, in a form that is independent of theory.
from ScienceBlog.com https://ift.tt/YHEPjqV
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