Epigenetic age dysregulation in individuals with bipolar disorder and schizophrenia
Bibliographic record
Abstract
Bipolar disorder (BD) and schizophrenia (SCZ) are debilitating disorders that are associated with significant burden and reduced quality of life. In this study, we leveraged microarray data derived from both the Illumina HumanMethylation450 platform to investigate the epigenetic age of individuals with SCZ (n = 40), BD (n = 40), and healthy controls (n = 38), across five epigenetic clocks. Various statistical metrics were used to identify discrepancies between epigenetic and chronological age across the three groups. We observed a significant increase in epigenetic age compared to chronological age in the BD group. Mean epigenetic age acceleration was also higher in individuals with bipolar disorder compared to healthy controls across four different epigenetic clocks (p<0.05). Despite the study's relatively small sample size, these findings suggest that both individuals with bipolar disorder and schizophrenia may have epigenetic markers associated with a premature aging phenotype, which could be suggestive of negative outcomes associated with the disease. In our future studies, we hope to elucidate this finding further by elucidating the precise link between epigenetic age, symptomatology and disease progression.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".