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.

Machine-learning classification of neurocognitive performance in children with perinatal HIV initiating de novo antiretroviral therapy.

OBJECTIVE: To develop a predictive model of neurocognitive trajectories in children with perinatal HIV (pHIV). DESIGN: Machine learning analysis of baseline and longitudinal predictors derived from clinical measures utilized in pediatric HIV. METHODS: Two hundred and eighty-five children (ages 2-14 years at baseline; Mage = 6.4 years) with pHIV in Southeast Asia underwent neurocognitive assessment at study enrollment and twice annually thereafter for an average of 5.4 years. Neurocognitive slopes were modeled to establish two subgroups [above (n = 145) and below average (n = 140) trajectories). Gradient-boosted multivariate regressions (GBM) with five-fold cross validation were conducted to examine baseline (pre-ART) and longitudinal predictive features derived from demographic, HIV disease, immune, mental health, and physical health indices (i.e. complete blood count [CBC]). RESULTS: The baseline GBM established a classifier of neurocognitive group designation with an average AUC of 79% built from HIV disease severity and immune markers. GBM analysis of longitudinal predictors with and without interactions improved the average AUC to 87 and 90%, respectively. Mental health problems and hematocrit levels also emerged as salient features in the longitudinal models, with novel interactions between mental health problems and both CD4 cell count and hematocrit levels. Average AUCs derived from each GBM model were higher than results obtained using logistic regression. CONCLUSION: Our findings support the feasibility of machine learning to identify children with pHIV at risk for suboptimal neurocognitive development. Results also suggest that interactions between HIV disease and mental health problems are early antecedents to neurocognitive difficulties in later childhood among youth with pHIV.

Behavioral inhibition corresponds to white matter fiber bundle integrity in older adults.

Little is known about the contribution of white matter integrity to inhibitory cognitive control, particularly in healthy aging. The present study examines the correspondence between white matter fiber bundle length and behavioral inhibition in 37 community-dwelling older adults (aged 51-78 years). Participants underwent neuroimaging with 3 Tesla MRI, and completed a behavioral test of inhibition (i.e., Go/NoGo task). Quantitative tractography derived from diffusion tensor imaging (qtDTI) was used to measure white matter fiber bundle lengths (FBLs) in tracts known to innervate frontal brain regions, including the anterior corpus callosum (AntCC), the cingulate gyrus segment of the cingulum bundle (CING), uncinate fasciculus (UNC), and the superior longitudinal fasciculus (SLF). Performance on the Go/NoGo task was measured by the number of commission errors standardized to reaction time. Hierarchical regression models revealed that shorter FBLs in the CING (p < 0.05) and the bilateral UNC (p < 0.01) were associated with lower inhibitory performance after adjusting for multiple comparisons, supporting a disconnection model of response inhibition in older adults. Prospective longitudinal studies are needed to examine the evolution of inhibitory errors in older adult populations and potential for therapeutic intervention.

Cognitive Performance and Frailty in Older HIV-Positive Adults.

OBJECTIVE: This study examined the relative contribution of cognitive status to frailty among older individuals infected with HIV+. DESIGN: Participants included 122 HIV+ individuals [mean age = 57.5 (6.6)] with a median CD4 cell count of 546. Undetectable viral load (<50 copies per mL) was observed in 94% of the sample. The sample was defined as frail (n = 21) and nonfrail (n = 101) according to the Fried phenotype criteria. Cognitive tests included measures of executive function, motor/psychomotor, language, learning, and memory. Performances were converted to standardized scores and averaged to calculate individual domain scores and a global index of cognitive function. METHODS: Logistic and hierarchical regressions were completed to separately determine the associations between clinical, demographic, and cognitive variables with regards to frailty status. RESULTS: Results of the logistic regressions revealed that lower executive function, female sex, and higher symptoms of depression were associated with frailty. The hierarchical analysis revealed no significant contribution of executive function to frailty status after accounting for female sex and symptoms of depression (Nagelkerke R = 0.15). CONCLUSIONS: These results emphasize the importance of sex distribution and mental health in explanatory models of frailty in HIV. Further, interventions targeting symptoms of depression may increase resilience in older HIV+ individuals.