OBJECTIVE: Neurocognitive performance among older persons, including those living with HIV (people living with HIV [PLWH]), exhibits significant heterogeneity, suggesting subpopulations with differing profiles of neurocognitive impairment (NCI). Metabolic factors are associated with NCI, but their relationships to cluster-derived NCI profiles are unknown. METHOD: Participants (144 PLWH and 102 HIV uninfected) aged 50+ years completed a neuropsychological battery assessing seven cognitive domains. Latent class analysis (LCA) identified NCI profiles separately by HIV serostatus and in a combined sample. Obtained classes were examined against the Montreal Cognitive Assessment (MoCA) and diagnoses of HIV-associated neurocognitive disorders (HAND). Multinomial regression identified metabolic predictors of classification. RESULTS: LCA identified three latent classes in each participant sample: Class1Multidomain NCI (high probability of impairment across multiple domains), Class 2Learning & Recall NCI (high probability of impairment in learning and recall), and Class 3NC Unimpaired (low probability of NCI across all domains). Severity of NCI implied by classes corresponded with MoCA scores and HAND diagnoses. In analyses on the combined sample, compared to HIV-uninfected individuals, PLWH were more likely to be in Class1Multidomain NCI. Among PLWH, those with dyslipidemia and hypertension had greater odds of classification in Class 1Multidomain NCI while those with central obesity had higher odds of classification in Class 2Learning & Recall NCI; metabolic syndrome approached significance as a differential predictor. Regardless of HIV status, individuals with diabetes were more likely to be in Class 1Multidomain NCI. CONCLUSIONS: Metabolic risk factors confer heightened risk of NCI in HIV infection. Interventions to reduce metabolic risk may improve neurocognitive outcomes among PLWH.
OBJECTIVE: Neurocognitive performance among older persons, including those living with HIV (people living with HIV [PLWH]), exhibits significant heterogeneity, suggesting subpopulations with differing profiles of neurocognitive impairment (NCI). Metabolic factors are associated with NCI, but their relationships to cluster-derived NCI profiles are unknown. METHOD:Participants (144 PLWH and 102 HIV uninfected) aged 50+ years completed a neuropsychological battery assessing seven cognitive domains. Latent class analysis (LCA) identified NCI profiles separately by HIV serostatus and in a combined sample. Obtained classes were examined against the Montreal Cognitive Assessment (MoCA) and diagnoses of HIV-associated neurocognitive disorders (HAND). Multinomial regression identified metabolic predictors of classification. RESULTS: LCA identified three latent classes in each participant sample: Class1Multidomain NCI (high probability of impairment across multiple domains), Class 2Learning & Recall NCI (high probability of impairment in learning and recall), and Class 3NC Unimpaired (low probability of NCI across all domains). Severity of NCI implied by classes corresponded with MoCA scores and HAND diagnoses. In analyses on the combined sample, compared to HIV-uninfected individuals, PLWH were more likely to be in Class1Multidomain NCI. Among PLWH, those with dyslipidemia and hypertension had greater odds of classification in Class 1Multidomain NCI while those with central obesity had higher odds of classification in Class 2Learning & Recall NCI; metabolic syndrome approached significance as a differential predictor. Regardless of HIV status, individuals with diabetes were more likely to be in Class 1Multidomain NCI. CONCLUSIONS: Metabolic risk factors confer heightened risk of NCI in HIV infection. Interventions to reduce metabolic risk may improve neurocognitive outcomes among PLWH.
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