IP-10 Levels as an Accurate Screening Tool to Detect Acute HIV Infection in Resource-Limited Settings.

Acute HIV infection (AHI) is the period prior to seroconversion characterized by high viral replication, hyper-transmission potential and commonly, non-specific febrile illness. AHI detection requires HIV-RNA viral load (VL) determination, which has very limited access in low-income countries due to restrictive costs and implementation constraints. We sought to identify a biomarker that could enable AHI diagnosis in scarce-resource settings, and to evaluate the feasibility of its implementation. HIV-seronegative adults presenting at the Manhiça District Hospital, Mozambique, with reported-fever were tested for VL. Plasma levels of 49 inflammatory biomarkers from AHI (n = 61) and non-HIV infected outpatients (n = 65) were determined by Luminex and ELISA. IP-10 demonstrated the best predictive power for AHI detection (AUC = 0.88 [95%CI 0.80-0.96]). A cut-off value of IP-10 ≥ 161.6 pg/mL provided a sensitivity of 95.5% (95%CI 85.5-99.5) and a specificity of 76.5% (95%CI 62.5-87.2). The implementation of an IP-10 screening test could avert from 21 to 84 new infections and save from US$176,609 to US$533,467 to the health system per 1,000 tested patients. We conclude that IP-10 is an accurate biomarker to screen febrile HIV-seronegative individuals for subsequent AHI diagnosis with VL. Such an algorithm is a cost-effective strategy to prevent disease progression and a substantial number of further HIV infections

Challenges of diagnosing acute HIV-1 subtype C infection in African women: performance of a clinical algorithm and the need for point-of-care nucleic-acid based testing.

Background. Prompt diagnosis of acute HIV infection (AHI) benefits the individual and provides opportunities for public health intervention. The aim of this study was to describe most common signs and symptoms of AHI, correlate these with early disease progression and develop a clinical algorithm to identify acute HIV cases in resource limited setting. Methods. 245 South African women at high-risk of HIV-1 were assessed for AHI and received monthly HIV-1 antibody and RNA testing. Signs and symptoms at first HIV-positive visit were compared to HIV-negative visits. Logistic regression identified clinical predictors of AHI. A model-based score was assigned to each predictor to create a risk score for every woman. Results. Twenty-eight women seroconverted after a total of 390 person-years of follow-up with an HIV incidence of 7.2/100 person-years (95%CI 4.5–9.8). Fifty-seven percent reported ≥1 sign or symptom at the AHI visit. Factors predictive of AHI included age <25 years (OR = 3.2; 1.4–7.1), rash (OR = 6.1; 2.4–15.4), sore throat (OR = 2.7; 1.0–7.6), weight loss (OR = 4.4; 1.5–13.4), genital ulcers (OR = 8.0; 1.6–39.5) and vaginal discharge (OR = 5.4; 1.6–18.4). A risk score of 2 correctly predicted AHI in 50.0% of cases. The number of signs and symptoms correlated with higher HIV-1 RNA at diagnosis (r = 0.63; p<0.001). Conclusions. Accurate recognition of signs and symptoms of AHI is critical for early diagnosis of HIV infection. Our algorithm may assist in risk-stratifying individuals for AHI, especially in resource-limited settings where there is no routine testing for AHI. Independent validation of the algorithm on another cohort is needed to assess its utility further. Point-of-care antigen or viral load technology is required, however, to detect asymptomatic, antibody negative cases enabling early interventions and prevention of transmission

Impact of CCR5delta32 Host Genetic Background and Disease Progression on HIV-1 Intrahost Evolutionary Processes: Efficient Hypothesis Testing through Hierarchical Phylogenetic Models

The interplay between C-C chemokine receptor type 5 (CCR5) host genetic background, disease progression, and intrahost HIV-1 evolutionary dynamics remains unclear because differences in viral evolution between hosts limit the ability to draw conclusions across hosts stratified into clinically relevant populations. Similar inference problems are proliferating across many measurably evolving pathogens for which intrahost sequence samples are readily available. To this end, we propose novel hierarchical phylogenetic models (HPMs) that incorporate fixed effects to test for differences in dynamics across host populations in a formal statistical framework employing stochastic search variable selection and model averaging. To clarify the role of CCR5 host genetic background and disease progression on viral evolutionary patterns, we obtain gp120 envelope sequences from clonal HIV-1 variants isolated at multiple time points in the course of infection from populations of HIV-1–infected individuals who only harbored CCR5-using HIV-1 variants at all time points. Presence or absence of a CCR5 wt/Δ32 genotype and progressive or long-term nonprogressive course of infection stratify the clinical populations in a two-way design. As compared with the standard approach of analyzing sequences from each patient independently, the HPM provides more efficient estimation of evolutionary parameters such as nucleotide substitution rates and dN/dS rate ratios, as shown by significant shrinkage of the estimator variance. The fixed effects also correct for nonindependence of data between populations and results in even further shrinkage of individual patient estimates. Model selection suggests an association between nucleotide substitution rate and disease progression, but a role for CCR5 genotype remains elusive. Given the absence of clear dN/dS differences between patient groups, delayed onset of AIDS symptoms appears to be solely associated with lower viral replication rates rather than with differences in selection on amino acid fixation