Development and validation of a nomogram for predicting the outcome of metabolic syndrome among people living with HIV after antiretroviral therapy in China.

Background: The prevalence of metabolic syndrome among people living with HIV (PLWH) is increasing worldwide. This study aimed to develop and validate a nomogram to predict the risk of metabolic syndrome in PLWH receiving antiretroviral therapy (ART) in China, accounting for both traditional and HIV-specific risk factors. Methods: A retrospective cohort study was conducted among PLWH receiving ART at a designated treatment center in Yinzhou District, China. A total of 774 patients were randomly assigned to development and validation cohorts in a 5:5 ratio. Predictive variables were identified using the least absolute shrinkage and selection operator and multivariable Cox regression analysis. The model's discriminative ability was assessed using the C-index and the area under the receiver operating characteristic curve (AUC). Calibration was evaluated through calibration plots, and clinical utility was assessed using decision curve analysis (DCA). Results: The nomogram incorporated age, ART regimen, body mass index, fasting blood glucose, high-density lipoprotein cholesterol, and HIV viral load as predictive factors. The C-index was 0.726 in the development cohort and 0.781 in the validation cohort, indicating strong discriminative ability. AUC values for predicting metabolic syndrome at 1, 2, and 3 years were 0.732, 0.728, and 0.737 in the development cohort, and 0.797, 0.803, and 0.783 in the validation cohort. Calibration plots showed strong concordance between predicted and observed outcomes, while DCA affirmed the model's clinical applicability. Conclusion: A user-friendly nomogram incorporating six routinely collected variables was developed and internally validated, which can effectively predict metabolic syndrome in PLWH following ART.