Performance of a Limiting-Antigen Avidity Enzyme Immunoassay for Cross-Sectional Estimation of HIV Incidence in the United States

Background: A limiting antigen avidity enzyme immunoassay (HIV-1 LAg-Avidity assay) was recently developed for cross-sectional HIV incidence estimation. We evaluated the performance of the LAg-Avidity assay alone and in multi-assay algorithms (MAAs) that included other biomarkers. Methods and Findings: Performance of testing algorithms was evaluated using 2,282 samples from individuals in the United States collected 1 month to >8 years after HIV seroconversion. The capacity of selected testing algorithms to accurately estimate incidence was evaluated in three longitudinal cohorts. When used in a single-assay format, the LAg-Avidity assay classified some individuals infected >5 years as assay positive and failed to provide reliable incidence estimates in cohorts that included individuals with long-term infections. We evaluated >500,000 testing algorithms, that included the LAg-Avidity assay alone and MAAs with other biomarkers (BED capture immunoassay [BED-CEIA], BioRad-Avidity assay, HIV viral load, CD4 cell count), varying the assays and assay cutoffs. We identified an optimized 2-assay MAA that included the LAg-Avidity and BioRad-Avidity assays, and an optimized 4-assay MAA that included those assays, as well as HIV viral load and CD4 cell count. The two optimized MAAs classified all 845 samples from individuals infected >5 years as MAA negative and estimated incidence within a year of sample collection. These two MAAs produced incidence estimates that were consistent with those from longitudinal follow-up of cohorts. A comparison of the laboratory assay costs of the MAAs was also performed, and we found that the costs associated with the optimal two assay MAA were substantially less than with the four assay MAA. Conclusions: The LAg-Avidity assay did not perform well in a single-assay format, regardless of the assay cutoff. MAAs that include the LAg-Avidity and BioRad-Avidity assays, with or without viral load and CD4 cell count, provide accurate incidence estimates

A Comparison of Two Measures of HIV Diversity in Multi-Assay Algorithms for HIV Incidence Estimation

Background: Multi-assay algorithms (MAAs) can be used to estimate HIV incidence in cross-sectional surveys. We compared the performance of two MAAs that use HIV diversity as one of four biomarkers for analysis of HIV incidence. Methods: Both MAAs included two serologic assays (LAg-Avidity assay and BioRad-Avidity assay), HIV viral load, and an HIV diversity assay. HIV diversity was quantified using either a high resolution melting (HRM) diversity assay that does not require HIV sequencing (HRM score for a 239 base pair env region) or sequence ambiguity (the percentage of ambiguous bases in a 1,302 base pair pol region). Samples were classified as MAA positive (likely from individuals with recent HIV infection) if they met the criteria for all of the assays in the MAA. The following performance characteristics were assessed: (1) the proportion of samples classified as MAA positive as a function of duration of infection, (2) the mean window period, (3) the shadow (the time period before sample collection that is being assessed by the MAA), and (4) the accuracy of cross-sectional incidence estimates for three cohort studies. Results: The proportion of samples classified as MAA positive as a function of duration of infection was nearly identical for the two MAAs. The mean window period was 141 days for the HRM-based MAA and 131 days for the sequence ambiguity-based MAA. The shadows for both MAAs were <1 year. Both MAAs provided cross-sectional HIV incidence estimates that were very similar to longitudinal incidence estimates based on HIV seroconversion. Conclusions: MAAs that include the LAg-Avidity assay, the BioRad-Avidity assay, HIV viral load, and HIV diversity can provide accurate HIV incidence estimates. Sequence ambiguity measures obtained using a commercially-available HIV genotyping system can be used as an alternative to HRM scores in MAAs for cross-sectional HIV incidence estimation

Cross-Sectional HIV Incidence Estimation: Techniques and Challenges

Tracking and surveillance of the HIV epidemic depend on accurate estimation of the number of new infections in the population. The rate at which these infections occur, known as the incidence, is also critical for effectively designing, targeting, and evaluating prevention efforts. Incidence can be estimated through cross-sectional surveys by using biomarkers, such as HIV viral load, CD4 cell count, and recently developed serologic assays, which define and mark people in an early disease stage. The total number of individuals found in this stage, that is possessing markers of recent infection, gives a snapshot of how the epidemic is progressing. We explore how these biomarkers should best be combined to define this early disease stage by examining how the definition influences the bias and variability of the cross-sectional incidence estimator. These calculations depend on estimating the probability that persons will remain in the early disease stage $t$ years after seroconversion. We present two different approaches for estimating this probability curve. Once we have defined viable methods for combining these biomarkers we derive the sample sizes needed to conduct one or more cross-sectional surveys and explore how missing biomarker data should be handled in the context of implementing these surveys

Cross-Sectional HIV Incidence Estimation: Techniques and Challenges

Tracking and surveillance of the HIV epidemic depend on accurate estimation of the number of new infections in the population. The rate at which these infections occur, known as the incidence, is also critical for effectively designing, targeting, and evaluating prevention efforts. Incidence can be estimated through cross-sectional surveys by using biomarkers, such as HIV viral load, CD4 cell count, and recently developed serologic assays, which define and mark people in an early disease stage. The total number of individuals found in this stage, that is possessing markers of recent infection, gives a snapshot of how the epidemic is progressing. We explore how these biomarkers should best be combined to define this early disease stage by examining how the definition influences the bias and variability of the cross-sectional incidence estimator. These calculations depend on estimating the probability that persons will remain in the early disease stage $t$ years after seroconversion. We present two different approaches for estimating this probability curve. Once we have defined viable methods for combining these biomarkers we derive the sample sizes needed to conduct one or more cross-sectional surveys and explore how missing biomarker data should be handled in the context of implementing these surveys

Trapping Crystal Nucleation of Cholesterol Monohydrate: Relevance to Pathological Crystallization

Crystalline nucleation of cholesterol at the air-water interface has been studied via grazing incidence x-ray diffraction using synchrotron radiation. The various stages of cholesterol molecular assembly from monolayer to three bilayers incorporating interleaving hydrogen-bonded water layers in a monoclinic cholesterol·H(2)O phase, has been monitored and their structures characterized to near atomic resolution. Crystallographic evidence is presented that this multilayer phase is similar to that of a reported metastable cholesterol phase of undetermined structure obtained from bile before transformation to the triclinic phase of cholesterol·H(2)O, the thermodynamically stable macroscopic form. According to grazing incidence x-ray diffraction measurements and crystallographic data, a transformation from the monoclinic film structure to a multilayer of the stable monohydrate phase involves, at least initially, an intralayer cholesterol rearrangement in a single-crystal-to-single-crystal transition. The preferred nucleation of the monoclinic phase of cholesterol·H(2)O followed by transformation to the stable monohydrate phase may be associated with an energetically more stable cholesterol bilayer arrangement of the former and a more favorable hydrogen-bonding arrangement of the latter. The relevance of this nucleation process of cholesterol monohydrate to pathological crystallization of cholesterol from cell biomembranes is discussed

Number of samples classified as assay positive using the LAg-Avidity assay alone.

<p>Samples from the MACS, ALIVE, and HIVNET 001 cohorts (N = 1,780) were tested using the LAg-Avidity assay (LAg). Four assay cutoffs were evaluated: 0.5, 1.0, 1.5, and 3.0 optical density units (OD-n); samples were classified as assay positive if they were below the assay cutoff. The number and percentage of samples that were assay positive are presented separately for individuals with different durations of HIV infection (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0082772#s2" target="_blank">Methods</a>). N indicates the number of samples in each group.</p

Performance characteristics of MAAs that include the LAg-Avidity assay and HIV viral load, with and without CD4 cell count.

<p>Samples from the MACS, ALIVE, and HIVNET 001 cohorts (N = 1,780) were tested using MAAs that included the LAg-Avidity assay and HIV viral load, with and without CD4 cell count. The cutoffs used for the LAg-Avidity assay (1.0 or 1.5 normalized optical density units [OD-n]) and the cutoff used for CD4 cell count (200 cells/mm³) are recommended by the assay manufacturer. The cutoff used for HIV viral load (VL, 1,000 copies/mL) was previously suggested for use with the Lag-Avidity assay along with self-report of antiretroviral treatment <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0082772#pone.0082772-Brookmeyer6" target="_blank">[35]</a>. Samples were classified as MAA positive if they met the criteria of each component assay. In the table, CD4 cell count testing is listed first in the MAA, since that testing must be performed at the time of sample collection. The number and percentage of samples that were MAA positive are presented separately for individuals with different durations of HIV infection (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0082772#s2" target="_blank">Methods</a>). N indicates the number of samples in each group. The mean window period and shadow for each MAA are shown.</p

Proportion of samples classified as assay positive using the LAg-Avidity assay alone or with HIV viral load, as a function of the duration of HIV infection.

<p>Probability curves were generated by analyzing samples from three cohort studies (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0082772#s2" target="_blank">Methods</a>). (A) Probability curves generated using the LAg-Avidity assay with four different assay cutoffs (0.5, 1.0, 1.5, and 3.0 normalized optical density units [OD-n]). Samples were classified as assay positive if the LAg-Avidity assay result was below the assay cutoff. (B) Probability curves generated using the MAAs shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0082772#pone-0082772-g002" target="_blank">Figure 2</a>. Samples were classified as MAA positive if results from each of the component assays met the requirements of the MAA.</p