Epigenetic clock acceleration is linked to age-at-onset of idiopathic and <i>LRRK2</i> Parkinson’s disease
Bibliographic record
Abstract
Abstract Parkinson’s disease is a clinically and genetically heterogeneous movement disorder with highly variable age-at-onset. DNA methylation (DNAm) age is an epigenetic clock that could reflect biological aging. Studies of DNAm-age acceleration (difference between DNAm-age and chronological age) are pertinent to neurodegenerative diseases (e.g., Parkinson’s disease), for which aging is the strongest risk-factor. We assessed DNAm-age in idiopathic Parkinson’s disease (n=96) and a longitudinal LRRK2 cohort at four time-points over a 3-year period (n=220), including manifesting (n=91) and non-manifesting (n=129) G2019S-carriers. A highly variable age-at-onset was observed in both the idiopathic cohort (26-77 years) and manifesting G2019S-carriers (39-79 years). Increased DNAm-age acceleration was significantly associated with younger onset in idiopathic and LRRK2 -related Parkinson’s disease, suggesting that every 5-year increase in DNAm-age acceleration is linked to about 6-year earlier onset. At an individual level, DNAm-age acceleration remained steady over a 3-year period for most G2019S-carriers, indicating that it might serve as a stable biomarker of biological aging. Future studies should evaluate the stability of DNAm-age acceleration over longer time-periods, especially for phenoconverters from non-manifesting to manifesting subjects. In conclusion, DNAm-age acceleration is linked to disease onset, and could be used in disease-modifying clinical trials of prodromal Parkinson’s disease.
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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 | 0.001 | 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) | 0.002 | 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".