Epigenetic age predicts depressive symptoms during the COVID-19 pandemic in the Canadian Longitudinal Study on Aging: importance of biological sex
Bibliographic record
Abstract
Epigenetic age is a biological metric of overall health and may predict mental health responses to unprecedented stressors. We sought to determine whether epigenetic age acceleration can predict older adults' trajectory of depressive symptoms before and during the COVID-19 pandemic, and whether sex differences exist. Data from baseline (2012-2015), first follow-up (2015-2018), and COVID-19 Baseline survey (April-May 2020) and COVID-19 Exit survey (September-December 2020) of the Canadian Longitudinal Study on Aging were used. Epigenetic age was measured at the study baseline, and depressive symptoms were assessed at each of the four time points using the 10-item Center for Epidemiological Studies Depression Scale (CESD-10). Sex-stratified mixed linear models examined the effect of epigenetic age (measured by DNAmAge and Hannum Age) on changes in CESD-10. The mean participant chronological age at study entry was 63±10 years (46% female). Unexpectedly, younger epigenetic age predicted increases in depressive symptoms from first follow-up to COVID-19 Baseline survey (p's < 0.05) in females only. Higher epigenetic age was not related to changes in CES-10 score during that time period (p's > 0.05). These findings suggest epigenetic age is a biological factor that can identify females at risk for greater negative effects of major life stressors on mental health.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".