With the success of anti-retroviral therapy (ART), cardiovascular disease (CVD) and other diseases of aging are now critical health problems facing HIV+ people. HIV+ people have a 50% higher risk of acute myocardial infarction (AMI) compared to uninfected people. Among HIV+ people, traditional risk factors, ART, HCV, and metabolic abnormalities are all CVD risk factors. However, these risk factors do not fully explain the excess risk of CVD in HIV nor, when incorporated into risk prediction models, acceptably predict CVD risk in HIV+ people. Current approaches designed to elucidate the underlying mechanism for excess CVD risk and mortality among HIV+ people while also identifying those at greatest CVD risk are limited in two major ways: 1) prior studies focused on a small number or even a single specific biomarker to explain what is likely a very complicated mechanism, and 2) such studies typically only assessed whether the incorporation of one specific biomarker would substantially improve CVD risk prediction. We hypothesize that large-scale proteomics will identify important new CVD biomarkers/mediators, and biological pathways in HIV. Aptamer proteomics (SOMAscan, SomaLogic, Boulder CO) allows for rapid quantification of over 1100 proteins in a small volume of blood, making this proteomic technology ideal for large cohort studies. We have already utilized SOMAscan in the Heart and Soul and HUNT3 cohorts of > 2000 subjects with stable CHD to identify 200 proteins prognostic of CVD and mortality events. We have applied bioinformatic approaches to these proteins to derive and externally validate a 9-protein CVD risk model which is superior to risk models using traditional risk factors. We hypothesize that by using large-scale proteomics, we can identify novel protein biomarkers that (1) will be associated with incident CVD and mortality events in HIV, (2) will generate new information about biological pathways unique to HIV-associated CVD, and (3) can be used to improve CVD risk prediction in HIV+ people. Importantly, results from this study may identify novel proteins that could serve as new targets for pharmacologic therapies to treat CVD in HIV. We will leverage the existing Veteran Aging Cohort Study Biomarker Cohort (VACS BC) , a longitudinal cohort of 1525 HIV+ and 853 HIV- veterans, all of whom have stored plasma, inflammatory biomarker data, and measurements of immune function along with adjudicated CVD outcomes. Using this existing infrastructure, we plan to use SOMAscan technology to measure the levels of 1130 proteins and link this new data to existing VACS data to accomplish the following aims:
Aim 1 a) to discover a broad range of individual protein biomarkers predictive of CVD and mortality events in HIV;
and Aim 1 b) to elucidate biological pathways of CVD in HIV by applying pathway analysis to the prognostic proteins discovered in Aim 1a;
Aim 2) to identify a small multi-protein panel of biomarkers among HIV+ people to predict CVD outcomes and mortality events;
and Aim 3) to evaluate the relative prognostic utility of a proteomics risk prediction model compared to traditional, Framingham based clinical risk models in HIV.

Public Health Relevance

Individiuals with HIV have higher rates of cardiovascular disease (CVD), and traditional risk factors do not fully explain this higher risk, nor do they predict CVD risk in this patient population. In this proposal, we will use large-scale proteomics technology to identify novel protein markers that (1) will be associated with incident CVD and mortality events in HIV, (2) will generate new information about biological pathways unique to HIV- associated CVD, and (3) can be used to improve CVD risk prediction in HIV+ people. Findings from this study will help to identify new proteins that can be used to develop new treatments for CVD in HIV.

National Institute of Health (NIH)
National Heart, Lung, and Blood Institute (NHLBI)
Research Project (R01)
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Special Emphasis Panel (ZHL1-CSR-F (M2))
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Wong, Renee P
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University of California San Francisco
Internal Medicine/Medicine
Schools of Medicine
San Francisco
United States
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