Universal DNA methylation age across mammalian tissues
Bibliographic record
Abstract
ABSTRACT Aging is often perceived as a degenerative process resulting from random accrual of cellular damage over time. Despite this, age can be accurately estimated by epigenetic clocks based on DNA methylation profiles from almost any tissue of the body. Since such pan-tissue epigenetic clocks have been successfully developed for several different species, we hypothesized that one can build pan-mammalian clocks that measure age in all mammalian species. To address this, we generated data using 11,754 methylation arrays, each profiling up to 36 thousand cytosines in highly-conserved stretches of DNA, from 59 tissue-types derived from 185 mammalian species. From these methylation profiles, we constructed three age predictors, each with a single mathematical formula, termed universal pan-mammalian clocks that are accurate in estimating the age (r>0.96) of any mammalian tissue. Deviations between epigenetic age and chronological age relate to mortality risk in humans, mutations that affect the somatotropic axis in mice, and caloric restriction. We characterized specific cytosines, whose methylation levels change with age across most mammalian species. These cytosines are greatly enriched in polycomb repressive complex 2-binding sites, are located in regions that gradually lose chromatin accessibility with age and are proximal to genes that play a role in mammalian development, cancer, human obesity, and human longevity. Collectively, these results support the notion that aging is indeed evolutionarily conserved and coupled to developmental processes across all mammalian species - a notion that was long-debated without the benefit of this new compelling evidence. SUMMARY This study identifies and characterizes evolutionarily conserved cytosines implicated in the aging process across mammals and establishes pan mammalian epigenetic clocks.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".