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HIV diversity as a biomarker for HIV incidence estimation: including a high-resolution melting diversity assay in a multiassay algorithm.

ABSTRACT: Multiassay algorithms (MAAs) can be used to estimate cross-sectional HIV incidence. We previously identified a robust MAA that includes the BED capture enzyme immunoassay (BED-CEIA), the Bio-Rad Avidity assay, viral load, and CD4 cell count. In this report, we evaluated MAAs that include a high-resolution melting (HRM) diversity assay that does not require sequencing. HRM scores were determined for eight regions of the HIV genome (2 in gag, 1 in pol, and 5 in env). The MAAs that were evaluated included the BED-CEIA, the Bio-Rad Avidity assay, viral load, and the HRM diversity assay, using HRM scores from different regions and a range of region-specific HRM diversity assay cutoffs. The performance characteristics based on the proportion of samples that were classified as MAA positive by duration of infection were determined for each MAA, including the mean window period. The cross-sectional incidence estimates obtained using optimized MAAs were compared to longitudinal incidence estimates for three cohorts in the United States. The performance of the HRM-based MAA was nearly identical to that of the MAA that included CD4 cell count. The HRM-based MAA had a mean window period of 154 days and provided cross-sectional incidence estimates that were similar to those based on cohort follow-up. HIV diversity is a useful biomarker for estimating HIV incidence. MAAs that include the HRM diversity assay can provide accurate HIV incidence estimates using stored blood plasma or serum samples without a requirement for CD4 cell count data.

SUBMITTER: Cousins MM

PROVIDER: S-EPMC3911463 | biostudies-literature | 2014 Jan

REPOSITORIES: biostudies-literature

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HIV diversity as a biomarker for HIV incidence estimation: including a high-resolution melting diversity assay in a multiassay algorithm.

Cousins Matthew M MM Konikoff Jacob J Laeyendecker Oliver O Celum Connie C Buchbinder Susan P SP Seage George R GR Kirk Gregory D GD Moore Richard D RD Mehta Shruti H SH Margolick Joseph B JB Brown Joelle J Mayer Kenneth H KH Koblin Beryl A BA Wheeler Darrell D Justman Jessica E JE Hodder Sally L SL Quinn Thomas C TC Brookmeyer Ron R Eshleman Susan H SH

Journal of clinical microbiology 20131023 1

Multiassay algorithms (MAAs) can be used to estimate cross-sectional HIV incidence. We previously identified a robust MAA that includes the BED capture enzyme immunoassay (BED-CEIA), the Bio-Rad Avidity assay, viral load, and CD4 cell count. In this report, we evaluated MAAs that include a high-resolution melting (HRM) diversity assay that does not require sequencing. HRM scores were determined for eight regions of the HIV genome (2 in gag, 1 in pol, and 5 in env). The MAAs that were evaluated i ...[more]

PMID: 24153134

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Project description:BackgroundMulti-assay algorithms (MAAs) are used to estimate population-level HIV incidence and identify individuals with recent infection. Many MAAs use low viral load (VL) as a biomarker for long-term infection. This could impact incidence estimates in settings with high rates of early HIV treatment initiation. We evaluated the performance of two MAAs that do not include VL.MethodsSamples were collected from 219 seroconverters (infected < 1 year) and 4376 non-seroconverters (infected > 1 year) in the HPTN 071 (PopART) trial; 28.8% of seroconverter samples and 73.2% of non-seroconverter samples had VLs ≤ 400 copies/mL. Samples were tested with the Limiting Antigen Avidity assay (LAg) and JHU BioRad-Avidity assays. Antibody reactivity to two HIV peptides was measured using the MSD U-PLEX assay. Two MAAs were evaluated that do not include VL: a MAA that includes the LAg-Avidity assay and BioRad-Avidity assay (LAg + BR) and a MAA that includes the LAg-Avidity assay and two peptide biomarkers (LAg + PepPair). Performance of these MAAs was compared to a widely used MAA that includes LAg and VL (LAg + VL).ResultsThe incidence estimate for LAg + VL (1.29%, 95% CI: 0.97-1.62) was close to the observed longitudinal incidence (1.34% 95% CI: 1.17-1.53). The incidence estimates for the other two MAAs were higher (LAg + BR: 2.56%, 95% CI 2.01-3.11; LAg + PepPair: 2.84%, 95% CI: 1.36-4.32). LAg + BR and LAg + PepPair also misclassified more individuals infected > 2 years as recently infected than LAg + VL (1.2% [42/3483 and 1.5% [51/3483], respectively, vs. 0.2% [6/3483]). LAg + BR classified more seroconverters as recently infected than LAg + VL or LAg + PepPair (80 vs. 58 and 50, respectively) and identified ~ 25% of virally suppressed seroconverters as recently infected.ConclusionsThe LAg + VL MAA produced a cross-sectional incidence estimate that was closer to the longitudinal estimate than two MAAs that did not include VL. The LAg + BR MAA classified the greatest number of individual seroconverters as recently infected but had a higher false recent rate.

Dataset Information

HIV diversity as a biomarker for HIV incidence estimation: including a high-resolution melting diversity assay in a multiassay algorithm.

Publications

HIV diversity as a biomarker for HIV incidence estimation: including a high-resolution melting diversity assay in a multiassay algorithm.

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