The interaction of polygenic risk for depression and age on white blood cell count
Bibliographic record
Abstract
Abstract Background Polygenic risk for depression is associated with elevated white blood cell count (WBC), providing support for a pro-inflammatory causal mechanism of depression. However, the important roles of biological sex and age on depression and inflammation have not been accounted for in these analyses. Methods We calculated polygenic risk scores for depression (PRS dep ) in 362,074 individuals from the UK Biobank (aged 39-72, 53.7% female) and 22,965 individuals from the Canadian Longitudinal Study on Aging (CLSA; aged 45-86, 50.3% female). We tested for main effects of PRS dep , sex, and non-linear age, as well as their interactions, on 25 blood-based measures (i.e., from complete blood counts, metabolic and lipid panels, and inflammatory tests). Associations significant in both cohorts were carried forward for further sex-stratified and mediation modelling. Results PRS dep was significantly associated with 13 biomarkers in the UK Biobank, with five replicating in the CLSA (WBC, granulocyte, and lymphocyte counts, and C-reactive protein and triglyceride levels), with standardized effect sizes ranging from β=0.006 (C-reactive protein, p =7.50×10 −4 ) to β=0.03 (WBC, p =1.71×10 −4 ). Sex-specific effects of PRS dep were observed for C-reactive protein levels (male; Stouffer’s p =2.40 ×10 −5 ) and triglyceride levels (female; Stouffer’s p =5.19 ×10 −3 ). Further differentiation was observed in the female subgroups based on post-menopausal status for WBC (Stouffer’s p =3.83 ×10 −3 ) and neutrophil/granulocyte (Stouffer’s p =5.05 ×10 −3 ) counts. Bi-directional mediation was also observed between all five biomarkers and a depression diagnosis, with up to 12.6% of the association between PRS dep and triglyceride levels mediated through depression. Conclusions We have shown that effects of PRS dep on multiple peripheral biomarkers, beyond WBC, remain significant even when accounting for important interactions of biological sex and age, providing insight into the pro-inflammatory etiology of depression.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".