Comparison of Antibody Responses to HIV Infection in Ugandan Women Infected with HIV Subtypes A and D

Longosz, Andrew F.; Morrison, Charles; Chen, Pai Lien; Brand, Hilmarie; Arts, Eric J.; Nankya, Immaculate; Salata, Robert A.; Quinn, Thomas C.; Eshleman, Susan H.; Laeyendecker, Oliver

doi:10.1089/aid.2014.0081

Cited by 16 publications

(15 citation statements)

References 23 publications

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“…in Asia, where HIV‐1 B, CRF01_AE and many B‐C recombinant forms are observed ). Development and validation of MAAs for use in East African settings may be particularly difficult, since individuals with subtype D infection have been shown to have delayed antibody maturation compared to other subtypes . We previously demonstrated that HIV diversity is a useful biomarker for cross‐sectional incidence estimation .…”

Section: Discussionmentioning

confidence: 99%

Identification and validation of a multi‐assay algorithm for cross‐sectional HIV incidence estimation in populations with subtype C infection

et al. 2018

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IntroductionCross‐sectional methods can be used to estimate HIV incidence for surveillance and prevention studies. We evaluated assays and multi‐assay algorithms (MAAs) for incidence estimation in subtype C settings.MethodsWe analysed samples from individuals with subtype C infection with known duration of infection (2442 samples from 278 adults; 0.1 to 9.9 years after seroconversion). MAAs included 1‐4 of the following assays: Limiting Antigen Avidity assay (LAg‐Avidity), BioRad‐Avidity assay, CD4 cell count and viral load (VL). We evaluated 23,400 MAAs with different assays and assay cutoffs. We identified the MAA with the largest mean window period, where the upper 95% confidence interval (CI) of the shadow was <1 year. This MAA was compared to the LAg‐Avidity and BioRad‐Avidity assays alone, a widely used LAg algorithm (LAg‐Avidity <1.5 OD‐n + VL >1000 copies/mL), and two MAAs previously optimized for subtype B settings. We compared these cross‐sectional incidence estimates to observed incidence in an independent longitudinal cohort.ResultsThe optimal MAA was LAg‐Avidity <2.8 OD‐n + BioRad‐Avidity <95% + VL >400 copies/mL. This MAA had a mean window period of 248 days (95% CI: 218, 284), a shadow of 306 days (95% CI: 255, 359), and provided the most accurate and precise incidence estimate for the independent cohort. The widely used LAg algorithm had a shorter mean window period (142 days, 95% CI: 118, 167), a longer shadow (410 days, 95% CI; 318, 491), and a less accurate and precise incidence estimate for the independent cohort.ConclusionsAn optimal MAA was identified for cross‐sectional HIV incidence in subtype C settings. The performance of this MAA is superior to a testing algorithm currently used for global HIV surveillance.

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Section: Discussionmentioning

confidence: 99%

Identification and validation of a multi‐assay algorithm for cross‐sectional HIV incidence estimation in populations with subtype C infection

et al. 2018

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“…Dominate HIV-1 strains in Kenya are clade A virus, which comprise over 50% of HIV infection, and clade D virus which covers roughly 12% to 22% of infections [ 48 – 53 ]. Clade D HIV has shown to have high levels of false-recent misclassification on antibody-based HIV incidence assays due to deferred antibody maturation and rapid antibody decline [ 25 , 26 ]. Previous studies have reported LAg-false-recent rates as high 9% to 16% in clade D HIV compared to 1.3% to 2.7% in clade A HIV.…”

Section: Discussionmentioning

confidence: 99%

“…Long-standing infection was defined as: 1) testing non-recent on LAg or 2) testing recent on LAg and from an HIV-infected person who was taking ART. Because the reported mean duration for recent infection (MDRI) for LAg for predominate HIV-1 subtypes in Kenya varies from 109 days for subtypes A and D infections combined, 172–211 days for subtype A infections, and 273–283 days for subtype D infections [ 24 – 26 ], for the purposes of this analysis recent infection was presumed to represent HIV infection acquired roughly less than one year prior to specimen collection while long-standing infection was presumed to represent HIV infection acquired roughly one year or greater prior to specimen collection.…”

Section: Methodsmentioning

confidence: 99%

Identifying Risk Factors for Recent HIV Infection in Kenya Using a Recent Infection Testing Algorithm: Results from a Nationally Representative Population-Based Survey

et al. 2016

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IntroductionA recent infection testing algorithm (RITA) that can distinguish recent from long-standing HIV infection can be applied to nationally representative population-based surveys to characterize and identify risk factors for recent infection in a country.Materials and MethodsWe applied a RITA using the Limiting Antigen Avidity Enzyme Immunoassay (LAg) on stored HIV-positive samples from the 2007 Kenya AIDS Indicator Survey. The case definition for recent infection included testing recent on LAg and having no evidence of antiretroviral therapy use. Multivariate analysis was conducted to determine factors associated with recent and long-standing infection compared to HIV-uninfected persons. All estimates were weighted to adjust for sampling probability and nonresponse.ResultsOf 1,025 HIV-antibody-positive specimens, 64 (6.2%) met the case definition for recent infection and 961 (93.8%) met the case definition for long-standing infection. Compared to HIV-uninfected individuals, factors associated with higher adjusted odds of recent infection were living in Nairobi (adjusted odds ratio [AOR] 11.37; confidence interval [CI] 2.64–48.87) and Nyanza (AOR 4.55; CI 1.39–14.89) provinces compared to Western province; being widowed (AOR 8.04; CI 1.42–45.50) or currently married (AOR 6.42; CI 1.55–26.58) compared to being never married; having had ≥ 2 sexual partners in the last year (AOR 2.86; CI 1.51–5.41); not using a condom at last sex in the past year (AOR 1.61; CI 1.34–1.93); reporting a sexually transmitted infection (STI) diagnosis or symptoms of STI in the past year (AOR 1.97; CI 1.05–8.37); and being aged <30 years with: 1) HSV-2 infection (AOR 8.84; CI 2.62–29.85), 2) male genital ulcer disease (AOR 8.70; CI 2.36–32.08), or 3) lack of male circumcision (AOR 17.83; CI 2.19–144.90). Compared to HIV-uninfected persons, factors associated with higher adjusted odds of long-standing infection included living in Coast (AOR 1.55; CI 1.04–2.32) and Nyanza (AOR 2.33; CI 1.67–3.25) provinces compared to Western province; being separated/divorced (AOR 1.87; CI 1.16–3.01) or widowed (AOR 2.83; CI 1.78–4.45) compared to being never married; having ever used a condom (AOR 1.61; CI 1.34–1.93); and having a STI diagnosis or symptoms of STI in the past year (AOR 1.89; CI 1.20–2.97). Factors associated with lower adjusted odds of long-standing infection included using a condom at last sex in the past year (AOR 0.47; CI 0.36–0.61), having no HSV2-infection at aged <30 years (AOR 0.38; CI 0.20–0.75) or being an uncircumcised male aged <30 years (AOR 0.30; CI 0.15–0.61).ConclusionWe identified factors associated with increased risk of recent and longstanding HIV infection using a RITA applied to blood specimens collected in a nationally representative survey. Though some false-recent cases may have been present in our sample, the correlates of recent infection identified were epidemiologically and biologically plausible. These methods can be used as a model for other countries with similar epidemics to inform targeted combination...

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“…Generalized antibody responses to HIV infection, such as antibody titer and avidity, tend to plateau approximately 1 year after HIV infection ( Busch et al, 2010 ). These characteristics of the antibody response are impacted by a variety of factors, including natural and drug-induced viral suppression ( Koenig et al, 2013 ; Wendel et al, 2017 ; Kassanjee et al, 2014 ), disease progression ( Laeyendecker et al, 2012b ), and HIV subtype ( Longosz et al, 2014 , 2015 ). Previous studies evaluating the banding pattern in western blots demonstrate that HIV antibody specificity evolves early in infection ( Fiebig et al, 2003 ).…”

Section: Discussionmentioning

confidence: 99%

Comprehensive Profiling of HIV Antibody Evolution

et al. 2019

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SUMMARY This study evaluates HIV antibody responses and their evolution during the course of HIV infection. A phage display system is used to characterize antibody binding to >3,300 HIV peptides in 57 adults with early- to late-stage infection. We find that the number of unique epitopes targeted (“antibody breadth”) increases early in infection and then stabilizes or declines. A decline in antibody breadth 9 months to 2 years after infection is associated with subsequent antiretroviral treatment (ART) initiation, and a faster decline in antibody breadth is associated with a shorter time to ART initiation. We identify 266 peptides with increasing antibody reactivity over time and 43 peptides with decreasing reactivity over time. These data are used to design a prototype four-peptide “serosignature” to predict duration of HIV infection. We also demonstrate that epitope engineering can be used to optimize peptide binding properties for applications such as cross-sectional HIV incidence estimation.

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Comparison of Antibody Responses to HIV Infection in Ugandan Women Infected with HIV Subtypes A and D

Cited by 16 publications

References 23 publications

Identification and validation of a multi‐assay algorithm for cross‐sectional HIV incidence estimation in populations with subtype C infection

Identification and validation of a multi‐assay algorithm for cross‐sectional HIV incidence estimation in populations with subtype C infection

Identifying Risk Factors for Recent HIV Infection in Kenya Using a Recent Infection Testing Algorithm: Results from a Nationally Representative Population-Based Survey

Comprehensive Profiling of HIV Antibody Evolution

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