Metabolic Risk Factors as Differential Predictors of Profiles of Neurocognitive Impairment Among Older HIV+ and HIV− Adults: An Observational Study
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".