Association between epigenetic aging acceleration and amyloid biomarkers in bipolar disorder
Bibliographic record
Abstract
Abstract Objectives Bipolar disorder (BD) has been associated with an elevated risk of Alzheimer’s Disease (AD). We assessed AD biomarkers in BD and tested whether epigenetic aging (EA) acceleration is a potential mechanism driving variability in these markers. Design, Setting, Participants Cross-sectional study of n=59 living individuals with BD and n=20 age- and sex-equated control participants, as well as analyses of postmortem brain samples (Brodmann area 9/46) from n=46 individuals with BD. Measurements Amyloid beta (Aβ) 40 , Aβ 42 , and total Tau levels were measured in plasma from individuals with BD and controls, and Aβ 42 levels were measured in brains. EA and its acceleration (blood: GrimAge and DunedinPACE; brains: DNAmClock Cortical ) were estimated for all samples. Individuals with BD were split into quartiles with accelerated or slower EA if they were in the first or fourth quartiles for GrimAge acceleration (AgeAccelGrim), DunedinPACE, or DNAmClock Cortical acceleration (DNAmClock Cortical Accel). Results Individuals with BD showed an increase in Aβ 40 (p=.049) and a decrease in the Aβ 42/40 ratio (p=.035) compared to controls. A decrease in the Aβ 42/40 ratio was also found in individuals with BD with high versus low AgeAccelGrim (p=.028). Brain Aβ 42 levels significantly correlated with DNAmClock Cortical Accel (r 2 =.270, p=.007), with those with high EA acceleration showing higher brain Aβ 42 after controlling for confounders (p=.008). Conclusions Our results provide preliminary evidence that EA may explain the variability in AD risk in individuals with BD and could act as a target for preventing dementia and AD in BD.
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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".