Sex‐ and Age‐Specific Metabolic Outcomes of Prolactin Normalisation in Hyperprolactinaemia

BACKGROUND: Hyperprolactinaemia has been linked to adverse metabolic profiles, but the metabolic consequences of prolactin (PRL) normalisation remain insufficiently characterised. OBJECTIVE: To evaluate changes in anthropometric, blood pressure, and metabolic parameters following PRL normalisation, with analyses stratified by sex and age. METHODS: We retrospectively analysed 445 patients (286 women, 159 men) treated with cabergoline who had paired clinical and biochemical measurements before and after PRL normalisation. Pre-post differences were assessed overall and stratified by sex and age (< 50 vs. ≥ 50 years). Additional multivariable models examined independent predictors of metabolic changes. RESULTS: Men exhibited more severe baseline disease, including higher PRL levels, greater prevalence of macroadenomas, hypopituitarism, and cardiometabolic comorbidities. After PRL normalisation, body weight, BMI, and blood pressure showed no clinically relevant changes. Small but statistically significant reductions were observed in total cholesterol (185 → 181 mg/dL; p < 0.001), LDL-C (110 → 106 mg/dL; p = 0.038), and triglycerides (91 → 82 mg/dL; p < 0.001). These lipid changes, however, were not independently explained by sex, age, PRL dynamics, cabergoline exposure, or gonadal status in multivariable analyses. Fasting glucose and HDL-C remained largely unchanged. By contrast, the TyG index decreased significantly across sexes and age groups, and multivariable modelling identified higher initial cabergoline dose and baseline hypogonadism as independent determinants of greater TyG reduction. CONCLUSIONS: PRL normalisation with cabergoline was associated with modest improvements in conventional lipid fractions and a more consistent reduction in the TyG index, suggesting a preferential impact on insulin-resistance-related pathways. The absence of independent effects of sex, age, PRL dynamics, or cabergoline exposure on lipid changes underscores the need for cautious interpretation of statistically significant but small absolute shifts.