Detection of Recent HIV-1 Infection Using a New Limiting-Antigen Avidity Assay: Potential for HIV-1 Incidence Estimates and Avidity Maturation Studies

BACKGROUND: Accurate and reliable laboratory methods are needed for estimation of HIV-1 incidence to identify the high-risk populations and target and monitor prevention efforts. We previously described a single-well limiting-antigen avidity enzyme immunoassay (LAg-Avidity EIA) to detect recent HIV-1 infection. METHODS: We describe here further optimization and characterization of LAg-Avidity EIA, comparing it to the BED assay and a two-well avidity-index (AI) EIA. Specimen sets included longitudinal sera (n = 393), collected from 89 seroconverting individuals from 4 cohorts representing 4 HIV-1 subtypes, and sera from AIDS patients (n = 488) with or without TB co-infections from 3 different cohorts. Ninety seven HIV-1 positive specimens were purchased commercially. The BED assay, LAg-Avidity EIA, AI-EIA and HIV serology were performed, as needed. RESULTS: Monitoring quality control specimens indicated high reproducibility of the LAg-Avidity EIA with coefficient of variation of <10% in the dynamic range. The LAg-Avidity EIA has an overall mean duration of recency (ω) of 141 days (95% CI 119-160) at normalized optical density (ODn) cutoff of 1.0, with similar ω in different HIV-1 subtypes and populations (132 to 143 days). Antibody avidity kinetics were similar among individuals and subtypes by both the LAg-Avidity EIA and AI-EIA compared to the HIV-IgG levels measured by the BED assay. The false recent rate among individuals with AIDS was 0.2% with the LAg-Avidity EIA, compared to 2.9% with the BED assay. Western blot profiles of specimens with increasing avidity confirm accurate detection of recent HIV-1 infections. CONCLUSIONS: These data demonstrate that the LAg-Avidity EIA is a promising assay with consistent ω in different populations and subtypes. The assay should be very useful for 1) estimating HIV-1 incidence in cross-sectional specimens as part of HIV surveillance, 2) identifying risk factors for recent infections, 3) measuring impact of prevention programs, and 4) studying avidity maturation during vaccine trials.