Key result
Novel six-variable nomogram effectively predicts metabolic syndrome risk in people living with HIV.
Why the study?
The prevalence of metabolic syndrome among people living with HIV receiving antiretroviral therapy is increasing worldwide, necessitating predictive tools that incorporate traditional and HIV-specific risk factors.
Can a nomogram incorporating traditional and HIV-specific risk factors accurately predict the risk of metabolic syndrome in people living with HIV receiving ART?
Cohort (n=774)
No
Can a nomogram incorporating traditional and HIV-specific risk factors accurately predict the risk of metabolic syndrome in people living with HIV receiving ART?
Answers are grounded in this paper and cite their sources.
A newly developed nomogram using six routinely collected variables can effectively predict the 1- to 3-year risk of metabolic syndrome in people living with HIV on antiretroviral therapy.
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.
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Jin et al. (2025) conducted a cohort in Metabolic syndrome in people living with HIV (n=774). Predictive nomogram (age, ART regimen, BMI, fasting blood glucose, HDL cholesterol, HIV viral load) was evaluated on Discriminative ability for predicting metabolic syndrome (C-index) (C-index 0.781 in validation cohort). A nomogram incorporating six clinical variables effectively predicted metabolic syndrome in people living with HIV after antiretroviral therapy, with a validation cohort C-index of 0.781.
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