Sex differences in brain aging among adults with APOE4 genetic risk and family history of Alzheimer’s disease
Bibliographic record
Abstract
Abstract Background Determining the effect of AD risk factors on brain aging in healthy middle‐aged and older women, compared to men, is critical for understanding whether there are sex differences in the pathways towards AD in cognitively intact, at risk adults. The goal of this study was to use an elastic net regression model to predict the effect of family history of AD [+FH] and the APOE‐e4 [+APOE4] genotype on brain age gap (BAG) in cognitively normal women and men. Method This cross‐sectional study used T1‐weighted structural MRIs from the Dallas Lifespan Brain Study (DLBS), South Asian Lifespan Dataset (SALD), Montreal Memory and Aging Lifespan Study (MMALS), and Pre‐symptomatic Evaluation of Experimental or Novel Treatments for Alzheimer’s Disease (PREVENT‐AD) cohorts. Data were collected from multiple sites: Dallas, USA (DLBS); Chonqing, China (SALD); Montreal, Canada (MMALS and PREVENT‐AD). 1067 cognitively normal adults (18‐89 years; 697 [65%] women) were randomly split into train and test sets. In the train set: 596 unknown FH (uFH) participants (mean [SD] age = 47.39 [18.46] years, 63% women); in the test set: 251 uFH (47.50 [19.15] years, 64% women) and 220 +FH participants (61.53 [6.04] years, 72% women). BAG, was measured as the difference between Predicted Age, estimated by the elastic net model, and Chronological Age. Result The age prediction model applied to the test set revealed greater BAG deviations in the +FH cohort compared to the uFH cohort (31% vs. 74% of the age variance, respectively; MAE = 8.22 years). A significant interaction was observed between FH status and Sex: +FH women had more advanced brain aging compared to +FH men. APOE genotype interacted with Sex in the +FH group: +APOE4 women had more positive BAG than +APOE4 men. In a sub‐cohort of individuals with AD risk factors, we observed sex differences in the role of modifiable factors of Body Mass Index and physical activity in preserving brain aging. Conclusion AD risk factors have more negative consequences to brain aging in cognitively normal women with family history than men with family history, but that modifiable factors can interact with genetic factors to potentially protect against brain aging.
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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 | 0.001 | 0.002 |
| 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) | 0.003 | 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".