Early-life exposures and risk of multiple gynecological diseases: evidence from a large community-based study of 272,706 women
摘要
Early-life exposures may influence long-term reproductive health, but comprehensive population-based evidence remains limited. This study aimed to evaluate the associations between si
This large observational association study used data from 272,706 women derived from the UK Biobank, a population-based cohort resource. Six gynecological disorders—uterine fibroids (UF), polycystic ovary syndrome (PCOS), endometriosis, genital prolapse, female infertility, and premenstrual syndrome (PMS)—were identified from hospital inpatient records, first occurrence records, and self-reports. Multivariable logistic regression was used as the primary analysis to estimate adjusted odds ratios (aORs), with Cox models as supplementary analyses. The primary adjusted model included age at recruitment, ethnicity, educational attainment, Townsend deprivation index, and smoking status. A Bonferroni-adjusted significance threshold of P < 0.0014 was applied.
ResultsAmong 272,706 women, the prevalence of the six non-neoplastic gynecological diseases ranged from 0.07% for PMS to 8.51% for UF. After Bonferroni correction, LRAU during early life was associated with higher odds of UF, PCOS, endometriosis, genital prolapse, and PMS. Earlier menarche was associated with higher odds of UF, PCOS, endometriosis, and genital prolapse. Maternal smoking around birth was associated with endometriosis, genital prolapse, and PMS. Not being breastfed as a baby was associated with endometriosis and PMS. Birth weight was associated with genital prolapse. Findings were generally consistent across Cox models and sensitivity analyses, although PMS estimates should be interpreted cautiously because of limited cases.
ConclusionsThese findings suggest associations between selected early-life factors and adult non-neoplastic gynecological diseases. Some exposures are potentially modifiable, whereas others are non-modifiable. Together, these factors may help identify individuals at higher risk and inform future studies on risk stratification, but their potential preventive implications require further causal validation.