{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T19:59:27Z","timestamp":1792180767343,"version":"4.4.0"},"reference-count":45,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T00:00:00Z","timestamp":1769558400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Cardiovasc. Med."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Cardiovascular disease (CVD) prediction models for persons living with HIV (PLWH) depend on traditional CVD risk factors, but these underestimate true risk. We aimed to identify proteins and genetic variants and create proteo-genomic risk scores for CVD in PLWH.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We analyzed genetic and protein data from participants involved in trials for PLWH. We used state-of-the-art statistical methods for data integration, identified correlated signatures, and developed a protein score (PS) and a genetic score (GS) to predict CVD. We conducted functional enrichment analysis to explore biological functions of signatures identified in relation to CVD.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>A panel of 14 proteins and a set of 15 genetic variants were found to be better at distinguishing between CVD cases and controls than individual proteins or genetic variants. The PS or GS was each independently associated with a higher risk of CVD (OR for PS: 2.36, CI: 1.78\u20133.19; OR for GS: 4.59, CI: 3.21\u20136.80). Combining CVD-, HIV-related factors, genetics, and protein scores resulted in the most powerful discrimination with an AUC of 0.86 (CI: 0.82\u20130.90). Having a PS in the top 25% compared to the bottom 75% resulted in a 3.9 times higher risk of CVD. Having a GS in the top 25% compared to the bottom 75% resulted in a 7.3 times higher risk of CVD. For individuals with both PS and GS in the top 25% compared to others, the risk of CVD was 7.9 times higher. Functional enrichment analysis showed an upregulation of the cytokine tumor necrosis factor (TNF) and strong enrichment for inflammation related pathways such as the pathogen induced cytokine storm.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>A panel of protein biomarkers, some new (IGFBP7, HGF) and some previously known in PLWH (CLEC6A), could help identify PLWH at higher risk of developing CVD. If confirmed, these scores could be used with CVD and HIV-related factors to identify PLWH at risk for CVD who would benefit from proactive risk reduction strategies.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fcvm.2025.1726272","type":"journal-article","created":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T13:15:59Z","timestamp":1769606159000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Proteomic and genetic predictors and risk scores of cardiovascular diseases in persons living with HIV"],"prefix":"10.3389","volume":"12","author":[{"given":"Tanvi","family":"Mehta","sequence":"first","affiliation":[{"name":"Division of Biostatistics & Health Data Science, University of Minnesota","place":["Minneapolis, MN, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lillian","family":"Haine","sequence":"additional","affiliation":[{"name":"Division of Biostatistics & Health Data Science, University of Minnesota","place":["Minneapolis, MN, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jason","family":"Baker","sequence":"additional","affiliation":[{"name":"Department of Medicine, Hennepin County Medical Center","place":["Minneapolis, MN, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cavan","family":"Reilly","sequence":"additional","affiliation":[{"name":"Division of Biostatistics & Health Data Science, University of Minnesota","place":["Minneapolis, MN, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniel","family":"Duprez","sequence":"additional","affiliation":[{"name":"Division of Biostatistics & Health Data Science, University of Minnesota","place":["Minneapolis, MN, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shweta Sharma","family":"Mistry","sequence":"additional","affiliation":[{"name":"Division of 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