Development and validation of a predictive model for metabolic syndrome in a large cohort of people living with HIV.

Background: The global prevalence of metabolic syndrome (MetS) in people living with HIV (PLWH) is on the rise in the post era of antiretroviral therapy (ART). Nevertheless, there are no validated predictive models available for assessing the risk of MetS in this specific population.

Methods: This study included PLWH who participated in annual follow-ups at Southern Medical University Nanfang Hospital from September 2022 to November 2023. Participants enrolled in this study were divided into the training set and validation set based on the follow-up duration. We employed both multivariate logistic regression and lasso regression to develop three distinct prediction models. Subsequently, the optimal model was determined through comprehensive analyses, including receiver operating characteristic (ROC) curve analysis, calibration curve, and decision curve analysis (DCA). Ultimately, we generated a nomogram for the optimal model and analyzed the correlation between the model score and the components of MetS.

Results: A total of 1017 participants were included in this study, with 814 in the training set and 203 in the validation set. The ultimate prediction model of MetS risk in PLWH incorporated five factors: age, CD8 + T cell counts, controlled attenuation parameter (CAP), gamma-glutamyl transferase (γ-GT) and lactate dehydrogenase (LDH). The area under the ROC curve (AUC) of the model in the training set and validation set was 0.849 and 0.834, respectively. Furthermore, we revealed a significant correlation between the model score and the MetS components. Additionally, the model score revealed significant group differences in MetS and related metabolic disorders.

Conclusions: This study established a potential model for predicting MetS in PLWH.