Mehta Tanvi, Haine Lillian, Baker Jason, Reilly Cavan, Duprez Daniel, Mistry Shweta, Steffen Brian, Jain Mamta K, Arenas-Pinto Alejandro, Polizzotto Mark, Staub Therese, Safo Sandra E
University of Minnesota, Minneapolis, MN, USA.
Hennepin County Medical Center, Minneapolis, MN, USA.
medRxiv. 2025 May 13:2025.05.08.25327219. doi: 10.1101/2025.05.08.25327219.
Cardiovascular diseases (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.
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.
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. Combining CVD-, HIV-related factors, genetics, and protein scores resulted in the most powerful discrimination. 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.
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.
针对感染人类免疫缺陷病毒（HIV）的人群（PLWH）的心血管疾病（CVD）预测模型依赖于传统的CVD风险因素，但这些因素会低估真实风险。我们旨在识别蛋白质和基因变异，并为PLWH创建CVD的蛋白质基因组风险评分。
我们分析了参与PLWH试验的参与者的基因和蛋白质数据。我们使用了最先进的统计方法进行数据整合，识别相关特征，并开发了蛋白质评分（PS）和基因评分（GS）来预测CVD。我们进行了功能富集分析，以探索与CVD相关的特征的生物学功能。
发现一组14种蛋白质和一组15个基因变异在区分CVD病例和对照方面比单个蛋白质或基因变异更好。PS或GS各自独立地与较高的CVD风险相关。结合CVD、HIV相关因素、遗传学和蛋白质评分产生了最强的区分能力。功能富集分析显示细胞因子肿瘤坏死因子（TNF）上调，并且病原体诱导的细胞因子风暴等炎症相关途径有强烈富集。
一组蛋白质生物标志物，有些是新的（IGFBP7、HGF），有些是PLWH中先前已知的（CLEC6A），可以帮助识别发生CVD风险较高的PLWH。如果得到证实，这些评分可与CVD和HIV相关因素一起用于识别有CVD风险且将从积极的风险降低策略中受益 的PLWH。
