Machine Learning Analysis Reveals Novel Neuroimaging and Clinical Signatures of Frailty in HIV.

BACKGROUND: Frailty is an important clinical concern for the aging population of people living with HIV (PLWH). The objective of this study was to identify the combination of risk features that distinguish frail from nonfrail individuals. SETTING: Machine learning analysis of highly dimensional risk features was performed on a clinical cohort of PLWH. METHODS: Participants included 105 older (average age = 55.6) PLWH, with at least a 3-month history of combination antiretroviral therapy (median CD4 = 546). Predictors included demographics, HIV clinical markers, comorbid health conditions, cognition, and neuroimaging (ie, volumetrics, resting-state functional connectivity, and cerebral blood flow). Gradient-boosted multivariate regressions were implemented to establish linear and interactive classification models. Model performance was determined by sensitivity/specificity (F1 score) with 5-fold cross validation. RESULTS: The linear gradient-boosted multivariate regression classifier included lower current CD4 count, lower psychomotor performance, and multiple neuroimaging indices (volumes, network connectivity, and blood flow) in visual and motor brain systems (F1 score = 71%; precision = 84%; and sensitivity = 66%). The interactive model identified novel synergies between neuroimaging features, female sex, symptoms of depression, and current CD4 count. CONCLUSIONS: Data-driven algorithms built from highly dimensional clinical and brain imaging features implicate disruption to the visuomotor system in older PLWH designated as frail individuals. Interactions between lower CD4 count, female sex, depressive symptoms, and neuroimaging features suggest potentiation of risk mechanisms. Longitudinal data-driven studies are needed to guide clinical strategies capable of preventing the development of frailty as PLWH reach advanced age.

Influence of Demographic and Psychological Factors on Attitudes Toward Sport Betting Among Young Adults in Southwest Nigeria.

Prediction and wager on the outcome of games/matches are known as sports betting. Positive attitudes toward sports betting may, in the long run, predict sports betting behavior and gambling-related problems. This study, therefore, examined the influence of gender, age, sports betting knowledge and peer-based gambling on attitudes toward sports betting among young adults in Nigeria. The ex-post facto design was adopted in this cross-sectional survey. The participants were 749 students (42.9% females) of a large tertiary institution in southwest Nigeria. A standardized questionnaire comprising demographics and measures of attitudes toward sports betting, sports betting knowledge and perception of peer gambling behavior was utilized in the study. Results revealed that attitudes toward sports betting are positive for older males who are knowledgeable about sports betting and have friends who gamble. Sports betting knowledge and peer-based gambling stood out as significant predictors of attitudes toward sports betting. The social circles of young adults may be the starting points for initiatives to develop healthy attitudes toward sports betting and preventing gambling-related problems.