Sex differences in the associations of genetic, sociodemographic and cardiovascular risk factors with depression in the Canadian Longitudinal Study on Aging (CLSA)
Bibliographic record
Abstract
Abstract Major depressive disorder (MDD) is a leading cause of morbidity and disability worldwide, with approximately twice as many women reported to have a lifetime occurrence of MDD than men. MDD is a polygenic trait, wherein hundreds to thousands of common genetic variants with small effect sizes contribute to risk of disease. This study investigated sex differences in the risk factor comorbidity and genetic architecture of MDD in over 16,000 people aged 45-85 from the Canadian Longitudinal Study on Aging (CLSA), with 21% of females (n=1,741) and 12% of males (n=1,055) coded with MDD. Polygenic risk scores (PRS) for individuals were made using sex-stratified and non-sex-specific (“both-sexes”) UK Biobank genome-wide association study summary statistics data. Odds of MDD for the sex-specific PRSs, socioeconomic, lifestyle and clinical risk factors associated with cardiovascular disease risk were assessed using a multivariable logistic regression model for each sex. Significant sex-specific risk factor associations with odds of MDD were found in females (history of ischemic heart disease (OR 1.52 (1.14-2.01), hypothyroidism (OR 1.42 (1.25-1.63), not being partnered (OR 1.34 (1.17-1.52)), having diabetes (OR 1.30 (1.11-1.52)), and higher female sex-specific autosomal PRS (OR 1.10 (1.04-1.16))) and males (high blood pressure, OR 1.35 (1.04-1.47)). Significant differences were observed in the proportion of variables that contributed to the most to each model, evaluated by relative pseudo-R 2 values. Age contributed the most to the model for both sexes (46.9% for females, 32.5% for males), wherein younger age was associated with higher odds of MDD. These results underscore the relevance for sex-disaggregating analyses of complex traits, like MDD, and the incorporation of clinical variables into models of MDD, in applications such as early detection and primary prevention.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".