Amiel Castro · Frontiers in neuroendocrinology 2021 · systematic review and meta-analysis of case-control studies · n=63 studies (5,129 participants)

Variation in genes and hormones of the hypothalamic-pituitary-ovarian axis in female mood disorders - A systematic review and meta-analysis.

Cited 27 times in the scientific literature.

Level 3 - non-randomized controlled study

Systematic review of observational case-control studies

PubMed 34171352 · doi:10.1016/j.yfrne.2021.100929 · record verified 2026-08-29

What was done

This systematic review and meta-analysis evaluated hypothalamic-pituitary-ovarian (HPO) axis biomarkers (hormones and genes) in women with mood disorders. Eligible studies were case-control designs comparing healthy controls to women with premenstrual dysphoric disorder (PMDD) or depressive disorders (including major depression unrelated to reproductive transitions, pregnancy-related, postpartum, and perimenopausal depression). The review included 63 studies with a total of 5,129 participants.

What was found

The abstract reports directional findings but provides no exact numbers, effect sizes, or confidence intervals: - PMDD was accompanied by lower luteal estradiol levels compared to controls. - Women with depression unrelated to reproductive transitions showed lower testosterone levels and some evidence for lower dehydroepiandrosterone sulfate (DHEA-S) levels compared to controls. - There were no differences in HPO-related parameters between controls and women with pregnancy-related, postpartum, or perimenopausal depression.

Why it matters

This review synthesizes endocrine markers across female mood disorders, identifying specific HPO axis alterations in PMDD and non-reproductive depression while finding no baseline peripheral HPO biomarker differences in perinatal and perimenopausal depression.

Limits

All included studies were case-control designs, precluding causal inferences. The abstract reports no numerical effect sizes, variance measures, or heterogeneity statistics, and cannot account for confounding factors such as medication status, exact cycle phase verification, or assay variability.

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