Impact of HIV-1 genetic diversity on disease progression: a prospective cohort study in Guangxi

Abstract

The high proportion of AIDS cases and mortality rates in Guangxi underscores the urgency to investigate the influence of HIV-1 genetic diversity on disease progression in this region. Newly diagnosed HIV-1 patients were enrolled from January 2016 to December 2021, and the follow-up work and detection of CD4+T lymphocytes were carried out every six months until December 2022. Multivariate logistic regression was used to analyze the factors affecting pre-treatment CD4+T lymphocyte counts, while local weighted regression models (LOESS) and generalized estimating equation models (GEE) were conducted to assess factors influencing CD4+T Lymphocyte Recovery. Cox regression analysis was utilized to examine the impact of subtypes on survival risk. Additionally, HIV-1 env sequences were utilized for predicting CXCR4 and CCR5 receptors. The study encompassed 1867 individuals with pol sequences and 281 with env sequences. Our findings indicate that age over 30, divorced/widowed, peasant, heterosexual infection, CRF01_AE, long-term infection, and Pre-treatment Viral load >10000 copies/ml were factors associated with higher risk for pre-treatment CD4+T lymphocyte decline. Specifically, male gender, age over 30, heterosexual infection (HETs), long-term infection, CRF01_AE, and Pre-treatment CD4 T cell counts below 350/µL were identified as risk factors impeding CD4+T lymphocyte recovery. Pre-treatment CD4+T lymphocyte counts and recovery in individuals infected with CRF01_AE were lower compared to CRF07_BC and CRF55_01B. Additionally, CRF01_AE and CRF08_BC subtypes exhibited higher mortality rates than CRF07_BC, CRF55_01B, and other subtypes. Notably, CRF01_AE demonstrated the highest percentage of CXCR4 affinity ratios. This research unveils the intricate influence of HIV-1 gene diversity on CD4+T lymphocyte dynamics and clinical outcomes. It highlights the multifaceted nature of HIV infection in Guangxi, providing novel insights into subtype-specific disease progression among HIV-infected individuals in this region.

Keywords: CD4+T lymphocyte; HIV-1; disease progression; genetic diversity; subtype.

Figure 1

Effects of subtypes on Pre-Treatment CD4+T lymphocytes. (A) Effects of subtypes on Pre-Treatment CD4+T lymphocytes among all population; (B) Effects of subtypes on Pre-Treatment CD4+T lymphocytes between HET and MSM population. *Represents P<0.05, **represents P<0.01, ***represents P<0.001. ns represents P>0.05.

Figure 2

Effects of different subtypes on CD4+T lymphocyte recovery post-Treatment. LOESS model was utilized to predict post-treatment CD4+T lymphocytes recovery, color indicates different subtypes.

Figure 3

Effects of subtypes on survival risk. (A) The COX regression model was utilized to evaluate the influence of various HIV-1 subtypes on survival risk; (B) The univariate COX regression was used to analysis the survival risk across subtypes incorporating gender, age, marital status, education, occupation, infection route, pre-treatment CD4 T cell counts, pre-treatment Viral load, clinical stage, drug resistance, treatment regimen, duration time for initiated treatment and treatment. ns represents P>0.05.

Figure 4

Prediction of receptors selection and mutation within CRF01_AE, CRF07_BC and CRF55_01B. (A) Prediction of receptors selection in various subtypes; (B) Comparative analysis of CD4+T cell counts between CXCR4 and CCR5 group; (C) Comparative analysis of V3 region sequences within CRF01_AE, CRF07_BC and CRF55_01B. *Represents P<0.05.