Endogenous Sex Hormones (FSH, Oestradiol, Testosterone and SHBG) and Type 2 Diabetes Risk in Postmenopausal Women: A Systematic Review and Meta-Analysis

Abstract

Background/Objectives: Menopause is accompanied by substantial changes in endogenous sex hormones that influence metabolic regulation. However, the associations of specific hormones with type 2 diabetes (T2D) risk in postmenopausal women remain inconsistent. This study aimed to quantify the relationships between incident T2D and follicle-stimulating hormone (FSH), oestradiol, testosterone, and sex hormone-binding globulin (SHBG), and to examine cross-sectional differences in hormone concentrations between postmenopausal women with and without T2D. Methods: MEDLINE, Embase and Cochrane CENTRAL were searched from database inception to 21 June 2024. Eligible studies included prospective cohort, nested case–control and case–control designs. Associations with incident T2D were pooled using Hartung–Knapp–Sidik–Jonkman random-effects meta-analysis. Both categorical and continuous estimates were extracted, prioritising maximally adjusted models. Risk of bias was assessed using ROBINS-E and the Newcastle–Ottawa Scale. Results: Sixteen studies (18 articles; 𝑛 = 16,180) were included. Higher SHBG was consistently associated with lower T2D risk in cohort analyses (RR 0.55; 95% CI 0.38–0.72; I² ≈ 0%). Higher FSH was also associated with lower risk (high vs. low: HR 0.55, 95% CI 0.29–0.81), although continuous estimates showed heterogeneity. Higher oestradiol was associated with increased T2D risk (RR 1.61, 95% CI 1.18–2.03; I² ≈ 6%), while testosterone was not significantly associated with incident T2D (RR 1.11, 95% CI 0.73–1.50). Cross-sectional analyses indicated lower SHBG and higher testosterone in women with T2D. Conclusions: Endogenous hormone profiles and SHBG concentrations are associated with T2D in postmenopausal women, with the most consistent evidence for an inverse association between SHBG and incident T2D. Because the available evidence is observational and partly heterogeneous, these findings should be interpreted as associations rather than causal or clinically predictive effects. Standardised measurement, repeated pre-diagnostic sampling and external validation are required before these biomarkers can be considered for routine risk stratification.