Machine learning selected smoking-associated DNA methylation signatures that predict HIV prognosis and mortality

• Type of Material: Article
• Authors: Xinyu Zhang, Yale School of Medicine; Ying Hu, National Cancer Institute; Bradley E. Aouizerat, New York University; Gang Peng, Yale School of Public Health; Vincent Marconi, Emory University; Michael J. Corley, University of Hawaii; Todd Hulgan, Vanderbilt University; Kendall J. Bryant, National Institute on Alcohol Abuse and Alcoholism; Hongyu Zhao, Yale School of Public Health; John H. Krystal, Yale School of Medicine; Amy C. Justice, VA Connecticut Healthcare System; Ke Xu, Yale School of Medicine
• Language: English
• Date: 2018-12-13
• Publisher: BMC (part of Springer Nature)
• Publication Version: Final Published Version
• Copyright Statement: © 2018 The Author(s).
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1186/s13148-018-0591-z
• Title of Journal or Parent Work: Clinical Epigenetics
• ISSN: 1868-7075
• Volume: 10
• Issue: 1
• Start Page: 155
• End Page: 155
• Grant/Funding Information: The project was supported by the National Institute on Drug Abuse [R03 DA039745 (Xu), R01 DA038632 (Xu), R01DA047063 (Xu and Aouizerat), R01DA047820(Xu and Aouizerat)] and the National Center for Post-Traumatic Stress Disorder, USA.
• Keywords: Ensemble machine learning; CIGARETTE-SMOKING; DIAGNOSIS; INFLAMMATION; Tobacco smoking; LUNG-CANCER; Mortality; Oncology; LIFE EXPECTANCY; ALL-CAUSE; INDIVIDUALS; SELF-REPORTED SMOKING; Science & Technology; TOBACCO SMOKING; Life Sciences & Biomedicine; IDENTIFICATION; HIV frailty; DNA methylation
• Research Categories: Health Sciences, Oncology; Psychology, Social; Biology, Biostatistics

Abstract

Background: The effects of tobacco smoking on epigenome-wide methylation signatures in white blood cells (WBCs) collected from persons living with HIV may have important implications for their immune-related outcomes, including frailty and mortality. The application of a machine learning approach to the analysis of CpG methylation in the epigenome enables the selection of phenotypically relevant features from high-dimensional data. Using this approach, we now report that a set of smoking-associated DNA-methylated CpGs predicts HIV prognosis and mortality in an HIV-positive veteran population. Results: We first identified 137 epigenome-wide significant CpGs for smoking in WBCs from 1137 HIV-positive individuals (p < 1.70E-07). To examine whether smoking-associated CpGs were predictive of HIV frailty and mortality, we applied ensemble-based machine learning to build a model in a training sample employing 408,583 CpGs. A set of 698 CpGs was selected and predictive of high HIV frailty in a testing sample [(area under curve (AUC) = 0.73, 95%CI 0.63~0.83)] and was replicated in an independent sample [(AUC = 0.78, 95%CI 0.73~0.83)]. We further found an association of a DNA methylation index constructed from the 698 CpGs that were associated with a 5-year survival rate [HR = 1.46; 95%CI 1.06~2.02, p = 0.02]. Interestingly, the 698 CpGs located on 445 genes were enriched on the integrin signaling pathway (p = 9.55E-05, false discovery rate = 0.036), which is responsible for the regulation of the cell cycle, differentiation, and adhesion. Conclusion: We demonstrated that smoking-associated DNA methylation features in white blood cells predict HIV infection-related clinical outcomes in a population living with HIV.