Objective: The potential for changing HIV-1 virulence has significant implications for the AIDS epidemic, including changing HIV transmission rates, rapidity of disease progression, and timing of ART. Published data to date have provided conflicting results. Design: We conducted a meta-analysis of changes in baseline CD4⁺ T-cell counts and set point plasma viral RNA load over time in order to establish whether summary trends are consistent with changing HIV-1 virulence. Methods: We searched PubMed for studies of trends in HIV-1 prognostic markers of disease progression and supplemented findings with publications referenced in epidemiological or virulence studies. We identified 12 studies of trends in baseline CD4⁺ T-cell counts (21 052 total individuals), and eight studies of trends in set point viral loads (10 785 total individuals), spanning the years 1984–2010. Using random-effects meta-analysis, we estimated summary effect sizes for trends in HIV-1 plasma viral loads and CD4⁺ T-cell counts. Results: Baseline CD4⁺ T-cell counts showed a summary trend of decreasing cell counts [effect = −4.93 cells/μl per year, 95% confidence interval (CI) −6.53 to −3.3]. Set point viral loads showed a summary trend of increasing plasma viral RNA loads (effect = 0.013 log₁₀ copies/ml per year, 95% CI −0.001 to 0.03). The trend rates decelerated in recent years for both prognostic markers. Conclusion: Our results are consistent with increased virulence of HIV-1 over the course of the epidemic. Extrapolating over the 30 years since the first description of AIDS, this represents a CD4⁺ T cells loss of approximately 148 cells/μl and a gain of 0.39 log₁₀ copies/ml of viral RNA measured during early infection. These effect sizes would predict increasing rates of disease progression, and need for ART as well as increasing transmission risk.

Evolutionary theory hypothesizes that intermediate virulence maximizes pathogen fitness as a result of a trade-off between virulence and transmission, but empirical evidence remains scarce. We bridge this gap using data from a large and long-standing HIV-1 prospective cohort, in Uganda. We use an epidemiological-evolutionary model parameterised with this data to derive evolutionary predictions based on analysis and detailed individual-based simulations. We robustly predict stabilising selection towards a low level of virulence, and rapid attenuation of the virus. Accordingly, set-point viral load, the most common measure of virulence, has declined in the last 20 years. Our model also predicts that subtype A is slowly outcompeting subtype D, with both subtypes becoming less virulent, as observed in the data. Reduction of set-point viral loads should have resulted in a 20% reduction in incidence, and a three years extension of untreated asymptomatic infection, increasing opportunities for timely treatment of infected individuals.

Trends in HIV virulence have been monitored since the start of the AIDS pandemic, as studying HIV virulence informs our understanding of HIV epidemiology and pathogenesis. Here, we model changes in HIV virulence as a strictly evolutionary process, using set point viral load (SPVL) as a proxy, to make inferences about empirical SPVL trends from longitudinal HIV cohorts. We develop an agent-based epidemic model based on HIV viral load dynamics. The model contains functions for viral load and transmission, SPVL and disease progression, viral load trajectories in multiple stages of infection, and the heritability of SPVL across transmissions. We find that HIV virulence evolves to an intermediate level that balances infectiousness with longer infected lifespans, resulting in an optimal SPVL∼4.75 log₁₀ viral RNA copies/mL. Adaptive viral evolution may explain observed HIV virulence trends: our model produces SPVL trends with magnitudes that are broadly similar to empirical trends. With regard to variation among studies in empirical SPVL trends, results from our model suggest that variation may be explained by the specific epidemic context, e.g. the mean SPVL of the founding lineage or the age of the epidemic; or improvements in HIV screening and diagnosis that results in sampling biases. We also use our model to examine trends in community viral load, a population-level measure of HIV viral load that is thought to reflect a population's overall transmission potential. We find that community viral load evolves in association with SPVL, in the absence of prevention programs such as antiretroviral therapy, and that the mean community viral load is not necessarily a strong predictor of HIV incidence. Virulence can be defined as disease severity; virulence of a pathogen may evolve as the rates of host mortality and transmission are balanced. HIV virulence trends are estimated using set point viral load, a proxy for the rate of HIV disease progression. To assess the capacity for HIV virulence to evolve and to place published virulence trends in an evolutionary context, we developed an evolutionary model based on HIV viral load dynamics. Our model reveals that HIV virulence evolves to an optimal set point (∼4.75 log10 copies/mL) similar to levels seen in natural populations. In comparing published trends to model-based trends, we infer: a) published trends are consistent with HIV adaptation to the human population; and b) variation among published trends may be explained by epidemic context or by sampling biases resulting from improvements in HIV screening and diagnosis. We also assess trends in HIV community viral load, defined as the mean viral load of all reported viral loads in a community, and thought to reflect a population's transmission potential. We find that community viral load evolves in association with set point, in the absence of prevention programs like antiretroviral therapy, and does not necessarily predict HIV incidence.

This review summarizes the use of genetic similarity clusters to understand HIV transmission and inform prevention efforts.

There are global increases in the use of HIV antiretroviral therapy (ART), guided by clinical benefits of early ART initiation and the efficacy of treatment as prevention of transmission. Separately, it has been shown theoretically and empirically that HIV virulence can evolve over time; observed virulence levels may reflect an adaptive balance between infected lifespan and per-contact transmission rate. However, the potential effects of widespread ART usage on HIV virulence are unknown. To predict these effects, we used an agent-based stochastic model to simulate evolutionary trends in HIV virulence, using set point viral load as a proxy for virulence. We calibrated our model to prevalence and incidence trends of South Africa. We explored two distinct ART scenarios: (1) ART initiation based on HIV-infected individuals reaching a CD4 count threshold; and (2) ART initiation based on individual time elapsed since HIV infection (a scenario that mimics “universal testing and treatment” (UTT) aspirations). In each case, we considered a range in population uptake of ART. We found that HIV virulence is generally unchanged in scenarios of CD4-based initiation. However, with ART initiation based on time since infection, virulence can increase moderately within several years of ART rollout, under high coverage levels and early treatment initiation (albeit within the context of epidemics that are rapidly decreasing in size). Sensitivity analyses suggested the impact of ART on virulence is relatively insensitive to model calibration. Our modeling study suggests that increasing HIV virulence driven by UTT is likely not a major public health concern, but should be monitored in sentinel surveillance, in a manner similar to transmitted resistance to antiretroviral drugs.

HIV infections show great variation in the rate of progression to disease, and the role of viral genetic factors in this variation had remained poorly characterized until recently. Now a series of four studies [1–4] published within a year has filled this important gap and has demonstrated a robust effect of the viral genotype on HIV virulence.