Towards Estimation of HIV-1 Date of Infection: A Time-Continuous IgG-Model Shows That Seroconversion Does Not Occur at the Midpoint between Negative and Positive Tests

Leitner T (2013) Towards Estimation of HIV-1 Date of Infection: A Time-Continuous IgG-Model Shows That Seroconversion Does Not Occur at the Midpoint between Negative and Positive Tests. PLoS ONE 8(4): e60906. doi:10.1371/journal.pone.0060906 Towards Estimation of HIV-1 Date of Infection: A Time- Continuous IgG-Model Shows That Seroconversion Does Not Occur at the Midpoint between Negative and Positive Tests Helena Skar 0 Jan Albert 0 Thomas Leitner 0 Cecilio Lo pez-Galndez, Centro Nacional de Microbiologa - Instituto de Salud Carlos III, Spain 0 1 Theoretical Biology and Biophysics Group, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America, 2 Department of Microbiology, Tumor and Cell Biology, Karolinska Institute, Stockholm, Sweden , 3 Clinical Microbiology , Karolinska University Hospital , Stockholm , Sweden Estimating date of infection for HIV-1-infected patients is vital for disease tracking and informed public health decisions, but is difficult to obtain because most patients have an established infection of unknown duration at diagnosis. Previous studies have used HIV-1-specific immunoglobulin G (IgG) levels as measured by the IgG capture BED enzyme immunoassay (BED assay) to indicate if a patient was infected recently, but a time-continuous model has not been available. Therefore, we developed a logistic model of IgG production over time. We used previously published metadata from 792 patients for whom the HIV-1-specific IgG levels had been longitudinally measured using the BED assay. To account for patient variability, we used mixed effects modeling to estimate general population parameters. The typical patient IgG production rate was estimated at r = 6.72[approximate 95% CI 6.17,7.33]61023 OD-n units day21, and the carrying capacity at K = 1.84[1.75,1.95] OD-n units, predicting how recently patients seroconverted in the interval 't = (31,711) days. Final model selection and validation was performed on new BED data from a population of 819 Swedish HIV-1 patients diagnosed in 2002-2010. On an appropriate subset of 350 patients, the best model parameterization had an accuracy of 94% finding a realistic seroconversion date. We found that seroconversion on average is at the midpoint between last negative and first positive HIV-1 test for patients diagnosed in prospective/cohort studies such as those included in the training dataset. In contrast, seroconversion is strongly skewed towards the first positive sample for patients identified by regular public health diagnostic testing as illustrated in the validation dataset. Our model opens the door to more accurate estimates of date of infection for HIV-1 patients, which may facilitate a better understanding of HIV-1 epidemiology on a population level and individualized prevention, such as guidance during contact tracing. - Funding: This study was supported by a grant from the National Institutes of Health (NIH) (grant no. 1R01AI087520), a postdoctoral fellowship from the Swedish Research Council to H.S. (623-2011-1100) and grants no. K2008-56X-09935-17-3, K2001-56X-0095-20-6; a grant from the Swedish International Development Cooperation Agency (grant no. SWE-2006-018); and the EU projects: SPREAD (QLK2-CT-2001-01344); EHR (LSHP-CT-2006-518211) and CHAIN (FP7/20072013) Collaborative HIV and Anti-HIV Drug Resistance Network grant agreement no. 223131. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing Interests: The authors have declared that no competing interests exist. Accurately estimating incidence of an infectious disease is vital for informed and targeted prevention, and knowing the date of infection per case is important for estimating the incidence in a population. For acute infections, like influenza, it is relatively straightforward to infer the date of infection because it occurred just shortly before the diagnosis. For chronic infections, like human immunodeficiency virus type 1 (HIV-1) infection, it is more complicated to infer the date of infection because only rarely are persons diagnosed during primary HIV-1 infection (PHI). Instead, most diagnosed persons have an established HIV-1 infection of unknown duration. Consequently, the World Health Organization (WHO), the Joint United Nations Programme on HIV/AIDS (UNAIDS), as well as national public health institutes usually simply report the number of newly diagnosed cases. Due to the current problems with HIV-1 incidence estimation, there is considerable interest in the development of assays and biomarkers that can determine if an HIV-1 infection is recent, in order to allow for estimating HIV-1 incidence in a population [1,2,3,4,5,6,7,8]. Seroconversion occurs on average 21 days after HIV-1 infection [9,10], and is thus a useful date to infer by serology. Serological assays are based on the knowledge about the development and maturation of the HIV-1 antibody response in infected persons (reviewed in [3,4,6,11]). These assays are collectively referred to as Serological Testing Algorithm for Recent HIV Seroconversion (STARHS) [4] or Recent Infection Testing Algorithm (RITA) [2]. In 1998 Janssen et al. described the first mathematical method that was specifically developed to estimate HIV-1 incidence using a cross-sectional sampling approach [12]. This method used results from a less-sensitive (or detuned) version and a standard version of an HIV-1 enzyme linked immunoassay (EIA). Since then, additional assays have been developed, such as the IgG capture BED enzyme immunoassay (BED assay) [13], the IDE-V3 assay [14], and several different avidity assays (reviewed in [15]). Adjustments of Janssens original formula have also been presented [16,17]. The BED assay, which was developed by the US Centers for Disease Control and Prevention (CDC), has been commercialized. The assay name BED signifies that it is based on a trimeric branched peptide with each branch derived from the immunodominant region of the gp41 glycoprotein of HIV-1 subtype B, circulating recombinant form (CRF) 01_AE or subtype D to overcome subtype-specific differences associated with some other assays [4]. Importantly, the BED assay, like most other serological assays, has been designed for incidence estimates in populations. At present, it provides a binary result, i.e., recent vs. long-term infection based on a cutoff value of a normalized optical density (OD-n = 0.8) in the EIA, rather than a quantitative estimation of time since seroconversion. The mean time interval from seroconversion to this cutoff value, i.e., the mean recency period, has been estimated at around 180 days, with some differences between genetic subtypes and populations [18]. The cutoff value was optimized to minimize misclassification of recent and long-term infections, but such misclassifications still occur. For instance, it is well-established that the BED assay can give a false impression of r (...truncated)