Chen Suling, Xu Yuyuan, Jiang Yuanhui, Chen Hongjie, Wu Xiaoxuan, Qian Zhe, Xu Xuwen, Zhong Huiqun, Peng Jie, Cai Shaohang
Department of Infectious Diseases, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China.
Key Laboratory of Infectious Diseases Research in South China, Ministry of Education, Guangzhou, China.
Virol J. 2024 Dec 19;21(1):321. doi: 10.1186/s12985-024-02592-8.
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.
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.
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.
This study established a potential model for predicting MetS in PLWH.
在抗逆转录病毒疗法（ART）的后时代，全球人类免疫缺陷病毒（HIV）感染者（PLWH）中代谢综合征（MetS）的患病率正在上升。然而，目前尚无经过验证的预测模型可用于评估这一特定人群中MetS的风险。
本研究纳入了2022年9月至2023年11月在南方医科大学南方医院参加年度随访的PLWH。根据随访时间将纳入本研究的参与者分为训练集和验证集。我们采用多变量逻辑回归和套索回归开发了三种不同的预测模型。随后，通过综合分析确定最佳模型，包括受试者工作特征（ROC）曲线分析、校准曲线和决策曲线分析（DCA）。最终，我们为最佳模型生成了列线图，并分析了模型得分与MetS各组分之间的相关性。
本研究共纳入1017名参与者，其中训练集814名，验证集203名。PLWH中MetS风险的最终预测模型纳入了五个因素：年龄、CD8 + T细胞计数、受控衰减参数（CAP）、γ-谷氨酰转移酶（γ-GT）和乳酸脱氢酶（LDH）。该模型在训练集和验证集的ROC曲线下面积（AUC）分别为0.849和0.834。此外，我们揭示了模型得分与MetS各组分之间存在显著相关性。此外，模型得分在MetS及相关代谢紊乱方面显示出显著的组间差异。
本研究建立了一个预测PLWH中MetS的潜在模型。
