Tracing molecular and social pathways to sex differences in depression: An empirical analysis of the Canadian Longitudinal Study on Aging (CLSA)
Bibliographic record
Abstract
Variations in gene expression patterns and cortico-striatal circuit morphology have been linked to sex differences in depression. We investigated gene-by-environment (GE) interactions between dopamine gene expression in these brain regions and the social environment on depressive symptomatology (CESD-10 scale) in 45-75 y.o. CLSA participants. We computed two biologically-informed genetic scores related to the D4 dopamine receptor (DRD4): 1. predicted prefrontal (PFC) expression using PrediXcan machine learning; 2. expression-based polygenic risk scores for the co-expression gene network in the striatum (STR-ePRS). Using latent profile analysis, we classified individuals based on social network size, support, cohesion, and participation in 3 distinct profiles: low-, medium-, and high-social network support (18/40/42%). In the full sample, GE interactions were significant for STR-ePRS--with effects driven by men--but not for PFC expression. Analyses by sex revealed significant G*E effects for both DRD4 scores in men (n=7217) but not in women (n=6733). Lower STR-ePRS scores were associated with worse depressive symptomatology for men in the low-support profile. Higher PFC DRD4 expression was associated with worse depressive symptoms for men in the low-support profile but milder symptoms in the medium- and high-support groups. The DRD4 gene network overlapped with gene sets related to cardiometabolic health (blood pressure, BMI, diabetes), suggesting common pathways for physical and mental well-being. Our findings implicate DRD4 and related gene networks as potential moderators of social environmental influences in depression, particularly for male seniors with low social support, consistent with previously reported sex differences in reward sensitivity and decision-making.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".