Dysmaturational Longitudinal Epigenetic Aging During Transition to Psychosis
Bibliographic record
Abstract
Abstract Psychosis frequently occurs during adolescence and young adulthood, possibly as a result of gene-environment interactions, mediated by epigenetic mechanisms such as DNA methylation. Methylation patterns can be leveraged to predict epigenetic age in order to identify anomalies in aging trajectories that may be associated with the emergence of psychosis. Thus, epigenetic age may provide a measurable surrogate of psychotic risk or psychosis’ emergence, and shed light on the neurodevelopmental model of psychosis. In this study, we present the first longitudinal analysis of epigenetic age trajectory during conversion to psychosis in a population at ultra-high-risk, with available genome-wide methylation DNA at two time points, at baseline and after one year of follow-up (N = 38 × 2). After predicting epigenetic age, we computed epigenetic age gap as the cross-sectional difference between real age and predicted age, and (longitudinal) epigenetic age acceleration as the derivative of predicted age with respect to time. At baseline, future converters were 2.7 years younger than nonconverters and this difference disappeared at follow-up, when some converted to psychosis. This is because during conversion to psychosis, the epigenetic age of converters accelerated by 2.8 years/year compared to nonconverters. This acceleration was robust with a strictly positive 95% confidence interval, and held its significance after adjustment for age, sex, and cannabis intake. The methylation sites most associated with aging were on genes also linked with schizophrenia and neurodevelopmental disorders. This accelerated age trajectory, following a previous deceleration, may therefore reflect dysmaturational processes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".