Epigenetic age acceleration is related to cognitive decline in the elderly: Results of the Austrian Stroke Prevention Study
Bibliographic record
Abstract
Aim Epigenetic clocks, quantifying biological age through DNA methylation (DNAmAge), have emerged as potential indicators of brain aging. As the variety of DNAmAge algorithms grows, consensus on their efficacy in predicting age‐related changes is lacking. This study aimed to explore the intricate relationship between diverse DNAmAge algorithms and structural and cognitive markers of brain aging. Methods Within a cohort of 796 elderly patients (mean age, 65.8 ± 7.9 years), we scrutinized 11 DNAmAge algorithms, including Horvath, Hannum, Zhang's clocks, PhenoAge, GrimAge, DunedinPACE, and principal component (PC)–based PCHorvath, PCHannum, PCPhenoAge, and PCGrimAge. We evaluated their association with baseline cognition and cognitive decline, assessed through follow‐up evaluations at three (T1) and six (T2) years postbaseline. Additionally, we examined their relationship with structural magnetic resonance imaging markers of brain aging, including white matter. Results Zhang's clock was the best predictor of decline in memory ( β = −0.04) and global cognition ( β = −0.03), whereas PCGrimAge was the best predictor of speed decline ( β = −0.17). The DNAmAge algorithms were the second‐best predictors in explaining cognitive variability after education in memory and global cognition ( R 2 partial = 1.66% to 2.82%) and the best predictors for speed decline ( R 2 partial = 2.13%). PC‐trained DNAmAge algorithms outperformed their respective original version. Conclusion DNAmAge algorithms are strong and independent predictors of cognitive decline in the normal elderly population and explain additional variability in cognitive decline beyond that accounted for by conventional risk factors.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".