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

Matthew M. Cousins, Jacob Konikoff, Oliver B. Laeyendecker, Connie Celum, Susan P. Buchbinder, George R. Seage, Gregory D Kirk, Richard D Moore, Shruti Hemendra Mehta, Joseph Bernard Margolick, Joelle Brown, Kenneth H. Mayer, Beryl A. Koblin, Darrell Wheeler, Jessica E. Justman, Sally L. Hodder, Thomas C Quinn, Ron Brookmeyer, Susan Eshleman

Research output: Contribution to journalArticle

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.

Original languageEnglish (US)
Pages (from-to)115-121
Number of pages7
JournalJournal of Clinical Microbiology
Volume52
Issue number1
DOIs
StatePublished - Jan 2014

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Freezing
Biomarkers
HIV
Incidence
CD4 Lymphocyte Count
Viral Load
Immunoenzyme Techniques
HIV-2
Genome
Infection

ASJC Scopus subject areas

  • Microbiology (medical)

Cite this

HIV diversity as a biomarker for hiv incidence estimation : Including a high-resolution melting diversity assay in a multiassay algorithm. / Cousins, Matthew M.; Konikoff, Jacob; Laeyendecker, Oliver B.; Celum, Connie; Buchbinder, Susan P.; Seage, George R.; Kirk, Gregory D; Moore, Richard D; Mehta, Shruti Hemendra; Margolick, Joseph Bernard; Brown, Joelle; Mayer, Kenneth H.; Koblin, Beryl A.; Wheeler, Darrell; Justman, Jessica E.; Hodder, Sally L.; Quinn, Thomas C; Brookmeyer, Ron; Eshleman, Susan.

In: Journal of Clinical Microbiology, Vol. 52, No. 1, 01.2014, p. 115-121.

Research output: Contribution to journalArticle

Cousins, Matthew M. ; Konikoff, Jacob ; Laeyendecker, Oliver B. ; Celum, Connie ; Buchbinder, Susan P. ; Seage, George R. ; Kirk, Gregory D ; Moore, Richard D ; Mehta, Shruti Hemendra ; Margolick, Joseph Bernard ; Brown, Joelle ; Mayer, Kenneth H. ; Koblin, Beryl A. ; Wheeler, Darrell ; Justman, Jessica E. ; Hodder, Sally L. ; Quinn, Thomas C ; Brookmeyer, Ron ; Eshleman, Susan. / HIV diversity as a biomarker for hiv incidence estimation : Including a high-resolution melting diversity assay in a multiassay algorithm. In: Journal of Clinical Microbiology. 2014 ; Vol. 52, No. 1. pp. 115-121.
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AU - Celum, Connie

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AU - Kirk, Gregory D

AU - Moore, Richard D

AU - Mehta, Shruti Hemendra

AU - Margolick, Joseph Bernard

AU - Brown, Joelle

AU - Mayer, Kenneth H.

AU - Koblin, Beryl A.

AU - Wheeler, Darrell

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AU - Quinn, Thomas C

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