Biological markers of sex-based differences in major depressive disorder and in antidepressant response
Bibliographic record
Abstract
Major depressive disorder (MDD) presents different clinical features in women and men, with women being more affected and responding differently to antidepressant treatment. Specific molecular mechanisms underlying these differences are not well studied and this narrative review aims at providing an overview of the neurobiological features underlying sex-differences in biological systems involved in MDD pathophysiology and response to antidepressant treatment, focusing on human studies. The majority of the reviewed studies were performed through candidate gene approaches, focusing on biological systems involved in MDD pathophysiology, including the stress response, inflammatory and immune, monoaminergic, neurotrophic, gamma-aminobutyric acid and glutamatergic, and oxytocin systems. The influence of the endocrine system and sex-specific hormone effects are also discussed. Genome, epigenome and transcriptome-wide approaches are less frequently performed and most of these studies do not focus on sex-specific alterations, revealing a paucity of omics studies directed to unravel sex-based differences in MDD. Few studies about sex-related differences in antidepressant treatment response have been conducted, mostly involving the inflammatory system, with less evidence on the monoaminergic system and sparse evidence in omics approaches. Our review covers the importance of accounting for sex-differences in research, optimizing patient stratification for a more precise diagnostic and individualized treatment for women and men.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".