Microglial function moderates the relation between depression risk factors and depression outcomes across the life course in females
Bibliographic record
Abstract
Abstract Background Depression has an enormous socio-economic burden and is twice as common in women compared to men. Microglia are exceptionally responsive to environmental stimuli and their phenotype differs substantially by sex. We hypothesized microglial function would moderate the relation between depression risk factors and depressive outcomes in a sex-specific manner. Methods We used expression quantitative trait loci and single nucleus RNA-sequencing resources to generate polygenic scores (PGS) representative of individual variation in microglial function in the fetal (GUSTO; N=239-315, and ALSPAC; N=928-1461) and adult periods (UK Biobank; N=54753-72682). We stratified our analyses by sex and tested the interaction effects of these PGS with prenatal maternal depression symptoms and adult stressors, well-characterized depression risk factors. We used internalizing (early childhood) or depressive symptoms (late childhood and adulthood) as outcomes. Results The fetal microglia PGS moderated the association between maternal prenatal depressive symptoms and female offspring internalizing symptoms at 4 (GUSTO; beta=-0.25, 95%CI -0.44 to - 0.06, P=0.008) and 7 years (GUSTO; beta=-0.16, 95%CI -0.318 to -0.008, P=0.04), and depressive symptoms at 8.5-10 years (GUSTO; beta = -0.15, 95%CI = -0.25 to -0.03, P= 0.01) and 24 years (ALSPAC; beta=0.1, 95%CI 0.008 to 0.19, P=0.03). The adult microglial PGS moderated the relation between BMI (UK Biobank; beta=0.001, 95%CI 0.0009 to 0.003, P=7.74E-6) and financial insecurity (UK Biobank; beta=0.001, 95%CI 0.005 to 0.015, P=2E-4) with depressive symptoms in females. There were no significant interactions in males. Conclusion Our results illustrate an important role for microglial function in the conferral of sex-dependent depression risk.
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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".