Antipsychotic Polypharmacy and Epigenetic Age Acceleration in Schizophrenia
Bibliographic record
Abstract
Abstract Schizophrenia spectrum disorders (SSD) are debilitating psychiatric illnesses that require extensive pharmacologic, cognitive, and functional management. SSD patients are often prescribed different medications, most commonly antipsychotics, which bear numerous side effects. Recently, accumulating evidence has shown epigenetic aging changes in SSD. However, the effects of antipsychotic medications on this phenomenon remain unexplored. We investigated whether antipsychotic medications are associated with epigenetic age acceleration (EAA) in 153 SSD patients. EAA was estimated using six different epigenetic clocks, based on the methylation patterns of peripheral blood cells. The analysis revealed some evidence of aging deceleration based on the Hannum DNAm Age in individuals on antipsychotic polypharmacy, relative to their monopharmacy counterparts (mean difference=–0.59 years, p=0.0109), which was only nearing significance after adjusting for multiple comparisons (padjusted=0.0654). In sex-specific analysis, only females displayed significantly decelerated epigenetic aging in the polypharmacy group in three of the six clocks. Furthermore, we observed no dose-dependent effects of antipsychotics on EAA in all clocks using three dose standardization methods (daily defined dose, chlorpromazine equivalents, and percent of maximum allowed dose). The findings suggest that antipsychotic treatment may modulate biological aging in SSD; however, this effect is not dose-dependent. Moreover, there appears to be an interplay between sex, polypharmacy, and epigenetic aging. These findings contribute to our understanding of the biological effects of antipsychotic treatment, and future research in this area is key for weighing the benefits and the risks of pharmacological management of SSD.
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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) | 0.002 | 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".