Immune age and biological age as determinants of vaccine responsiveness among elderly populations: the Human Immunomics Initiative research program.

The Human Immunomics Initiative (HII), a joint project between the Harvard T.H. Chan School of Public Health and the Human Vaccines Project (HVP), focuses on studying immunity and the predictability of immuneresponsiveness to vaccines in aging populations. This paper describes the hypotheses and methodological approaches of this new collaborative initiative. Central to our thinking is the idea that predictors of age-related non-communicable diseases are the same as predictors for infectious diseases like COVID-19 and influenza. Fundamental to our approach is to differentiate between chronological, biological and immune age, and to use existing large-scale population cohorts. The latter provide well-typed phenotypic data on individuals' health status over time, readouts of routine clinical biochemical biomarkers to determine biological age, and bio-banked plasma samples to deep phenotype humoral immune responses as biomarkers of immune age. The first phase of the program involves 1. the exploration of biological age, humoral biomarkers of immune age, and genetics in a large multigenerational cohort, and 2. the subsequent development of models of immunity in relation to health status in a second, prospective cohort of an aging population. In the second phase, vaccine responses and efficacy of licensed COVID-19 vaccines in the presence and absence of influenza-, pneumococcal- and pertussis vaccines routinely offered to elderly, will be studied in older aged participants of prospective population-based cohorts in different geographical locations who will be selected for representing distinct biological and immune ages. The HII research program is aimed at relating vaccine responsiveness to biological and immune age, and identifying aging-related pathways crucial to enhance vaccine effectiveness in aging populations.

A Generalized Entropy Measure of Within-Host Viral Diversity for Identifying Recent HIV-1 Infections.

There is a need for incidence assays that accurately estimate HIV incidence based on cross-sectional specimens. Viral diversity-based assays have shown promises but are not particularly accurate. We hypothesize that certain viral genetic regions are more predictive of recent infection than others and aim to improve assay accuracy by using classification algorithms that focus on highly informative regions (HIRs).We analyzed HIV gag sequences from a cohort in Botswana. Forty-two subjects newly infected by HIV-1 Subtype C were followed through 500 days post-seroconversion. Using sliding window analysis, we screened for genetic regions within gag that best differentiate recent versus chronic infections. We used both nonparametric and parametric approaches to evaluate the discriminatory abilities of sequence regions. Segmented Shannon Entropy measures of HIRs were aggregated to develop generalized entropy measures to improve prediction of recency. Using logistic regression as the basis for our classification algorithm, we evaluated the predictive power of these novel biomarkers and compared them with recently reported viral diversity measures using area under the curve (AUC) analysis.Change of diversity over time varied across different sequence regions within gag. We identified the top 50% of the most informative regions by both nonparametric and parametric approaches. In both cases, HIRs were in more variable regions of gag and less likely in the p24 coding region. Entropy measures based on HIRs outperformed previously reported viral-diversity-based biomarkers. These methods are better suited for population-level estimation of HIV recency.The patterns of diversification of certain regions within the gag gene are more predictive of recency of infection than others. We expect this result to apply in other HIV genetic regions as well. Focusing on these informative regions, our generalized entropy measure of viral diversity demonstrates the potential for improving accuracy when identifying recent HIV-1 infections.

Virologic outcomes of first-line HAART and associated factors among Chinese patients with HIV in three sentinel antiretroviral treatment sites.

OBJECTIVE: To evaluate HIV drug resistance (HIVDR) among Chinese patients with HIV receiving first-line highly active antiretroviral therapy (HAART). METHODS: Based on the WHO HIVDR surveys, a prospective cohort study with 12-month follow-up was conducted to estimate the prevalence of HIV RNA<1000 copies/ml and HIVDR. RESULTS: A total of 341 study subjects naïve to prior antiretroviral therapy (ART) were followed up for a median of 12.1 months. The overall mortality rate was 9.9 per 100 person-years. The median of CD4 counts increased from 182 cells/mm(3) at baseline to 268 cells/mm(3) at 12 months (P<0.0001). Of patients with plasma HIV-1 RNA concentrations ≥1000 copies/ml at 12 months, the proportions of resistance to non-nucleoside reverse transcriptase drugs, nucleoside/nucleotide reverse transcriptase inhibitors, and protease inhibitor drugs were 34.2%, 23.7% and 0%, respectively. The overall proportion of HIV RNA<1000 copies/ml was 85.7% at 12 months. Occupation of farmer (AOR=0.3, 95% CI: 0.08, 0.94; P=0.0393) and HAART counselling and instruction through telephone (AOR=2.8, 95% CI: 1.4, 5.6; P=0.0047) were significantly associated with HIV RNA<1000 copies/ml. CONCLUSION: Our study demonstrated that the community-based ART had significant effects on viral suppression and immune recovery. HIVDR should be monitored in the long term to guide informed decisions on preventing HIVDR and choices of first- and second-line regimens.