An Augmented Aging Process in Brain White Matter in HIV

T. Kuhn; T. Kaufmann; N.T. Doan; L.T. Westlye; J. Jones; R.A. Nunez; S.Y. Bookheimer; E.J. Singer; C.H. Hinkin; A.D. Thames

. CC-BY-NC-ND 4.0 International license is made available under a The copyright holder for this preprint (which was not peer-reviewed) is the author/funder. It . https://doi.org/10.1101/265199 doi: bioRxiv preprint 5 Kuhn, T., Ph.D. 5 Here, we used a machine learning approach to quantify brain aging based on DTI. A support vector machine model trained in a large and independent training-set of healthy controls was used to predict age in HIV+ and comparable HIV-individuals. We tested for group differences in BAG between HIV+ and HIV-, and for associations with cognitive function and HIV disease factors as well as medical comorbidities within the HIV+ group. We converted raw test scores into within-sample z scores and then averaged them to create neurocognitive domain z scores. We calculated the global neurocognition score by averaging the z scores from all of the neuropsychological test variables. Given that the relationship between age and neurocognitive performance in HIV is a primary aim of this study, within-sample z scores were computed instead of demographically-adjusted T scores. IMMUNE STATUS ASSESSMENT: In the testing dataset, HIV+ participants selfreported nadir CD4+ and lifetime highest viral load were used to assess past immune status. Participants also underwent venipuncture to test current CD4+ and HIV viral load. HIV duration was calculated as the number of years since the participant's selfreported HIV diagnosis. Next, participants were classified as either 'pre-HAART' (highly active antiretroviral therapy) or 'post-HAART' based on whether their initial HIV diagnosis was before or after 1996 24 . Further, a "medical comorbidity burden' index score was computed from the medical history taken during the routine interview all participants completed during data collection. Participants were assigned a '1' if they endorsed a history of each of the following medical conditions: cerebrovascular risk factors including hypertension, heart failure, COPD, anemia, diabetes; endocrine dysfunction including thyroid disease, testosterone therapy, estrogen therapy; kidney disease. Participants were assigned a '0' for all medical conditions they did not endorse. The copyright holder for this preprint (which was not peer-reviewed) is the author/funder. It . https://doi.org/10.1101/265199 doi: bioRxiv preprint 8 Kuhn, T., Ph.D.