A model for predicting high BMI of people living with HIV after receiving antiretroviral therapy

• Published in: Therapeutic advances in chronic disease Vol. 13; pp. 204062232211027 - 20406223221102750
• Main Authors: Qian, Zhe, Wu, Houji, Wu, Yihua, Liao, Wei, Yu, Tao, Xu, Xuwen, Peng, Jie, Cai, Shaohang
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 2022; SAGE PUBLICATIONS, INC; SAGE Publishing
• Subjects: Acquired immune deficiency syndrome; AIDS; Antiretroviral drugs; Body mass index; Drug therapy; HIV; Human immunodeficiency virus; Hyperlipidemia; Original Research; Regression analysis; human immunodeficiency virus; liver steatosis; hyperlipidemia; infection; high BMI; acquired immunodeficiency syndrome
• ISSN: 2040-6223; 2040-6231
• DOI: 10.1177/20406223221102750

Objective: The objective of this study was to evaluate the characteristics of high body mass index (BMI) and normal weight people living with HIV after antiretroviral therapy (ART) and establish a model. Methods: A total of 290 people living with HIV after 1 year of ART treatment were enrolled and divided into two groups based on whether their BMI index was <24 or ⩾24 at week 48. The demographic, clinical data were collected and analyzed. Multivariable logistic regression analysis was performed. A model was established and use to predict the occurrence of certain diseases. Results: A total of 290 people living with HIV were included in this study; 200 had a normal BMI (BMI < 24) and 90 were high BMI (BMI ⩾ 24) after 1-year ART. Their baseline characteristics were significantly different in relation to age (p = 0.007), sex distribution (p = 0.040), ART regimen (p = 0.040), alanine aminotransferase levels (p < 0.001), and three major serum lipid levels: triglycerides (p < 0.001), cholesterol (p = 0.011), and low-density lipoprotein (p = 0.005). A multivariate logistic regression analysis resulted in the development of a model for the diagnosis of high BMI and hyperlipidemia. The model score is an independent risk factor for hyperlipidemia (odds ratio = 2.674, p = 0.001) and high BMI (p < 0.001). The model score is significantly correlated with the controlled attenuation parameter (CAP) value (r = 0.230, p < 0.001) and can be used to divide the severity of liver steatosis based on CAP value. Conclusions: This study demonstrated a easy-to-use model to detect high BMI, hyperlipidemia, and liver steatosis in people living with HIV without risk factors for BMI changing at baseline after 1 year of ART treatment.