Origin of Sex-Biased Mental Disorders: An Evolutionary Perspective
Bibliographic record
Abstract
Sexual dimorphism or sex bias in diseases and mental disorders have two biological causes: sexual selection and sex hormones. We review the role of sexual selection theory and bring together decades of molecular studies on the variation and evolution of sex-biased genes and provide a theoretical basis for the causes of sex bias in disease and health. We present a Sexual Selection-Sex Hormone theory and show that male-driven evolution, including sexual selection, leads to: (1) increased male vulnerability due to negative pleiotropic effects associated with male-driven sexual selection and evolution; (2) increased rates of male-driven mutations and epimutations in response to early fitness gains and at the cost of late fitness; and (3) enhanced female immunity due to antagonistic responses to mutations that are beneficial to males but harmful to females, reducing female vulnerability to diseases and increasing the thresholds for disorders such as autism. Female-driven evolution, such as reproduction-related fluctuation in female sex hormones in association with stress and social condition, has been shown to be associated with increased risk of certain mental disorders such as major depression disorder in women. Bodies have history, cells have memories. An evolutionary framework, such as the Sexual Selection-Sex Hormone theory, provides a historical perspective for understanding how the differences in the sex-biased diseases and mental disorders have evolved over time. It has the potential to direct the development of novel preventive and treatment strategies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".