Biomarkers of Progression after HIV Acute/Early Infection: Nothing Compares to CD4⁺ T-cell Count?

Area of Science:

• Immunology
• Infectious Diseases
• Biostatistics

Background:

• HIV infection progression varies significantly between individuals.
• Identifying reliable biomarkers for disease progression is essential for effective management.
• Current biomarkers lack sufficient predictive power for categorizing disease progression.

Purpose of the Study:

• To evaluate the predictive potential of various factors, including cytokines, immune activation, HLA/CCR5 genotypes, and CD4+ T-cell counts, for HIV disease progression.
• To categorize the predictive power of these variables using machine learning techniques like decision trees.
• To identify robust biomarkers for distinguishing between HIV progressors and non-progressors.

Main Methods:

• Utilized machine learning (Weka correlation-based feature selection, J48 decision tree) and non-parametric statistical methods.
• Analyzed data from 75 treatment-naïve subjects during acute/early HIV infection.
• Assessed CD4+ T-cell counts, viral load, immune activation markers, HIV-specific immune responses, HLA/CCR5 genotypes, and 39 plasma cytokines.

Main Results:

• Several cytokines (IL-10, IP-10, sIL-2Rα, TNF-α) correlated with baseline viral load, while others (IL-2, TNF-α, FGF-2, MIP-1β) correlated with CD4+ T-cell activation.
• None of the measured cytokines, immune activation markers, or HLA/CCR5 genotypes demonstrated strong predictive value for distinguishing progressors from non-progressors.
• Baseline CD4+ T-cell count was the most significant predictor, with a cut-off of 438 cells/μL showing high accuracy (0.93) and agreement (κ=0.85).

Conclusions:

• Baseline CD4+ T-cell count is a potent and reliable biomarker for predicting HIV disease progression in early infection.
• Other factors like specific cytokines, immune activation, and HLA/CCR5 genotypes have limited discriminatory power, possibly due to their variability or sampling time.
• Further research using decision tree-based approaches is warranted to identify biomarkers for post-treatment control in HIV.