Impact of steatotic liver disease and non‐alcoholic steatohepatitis on cognitive impairment in people living with <scp>HIV</scp>: A cross‐sectional study
Bibliographic record
Abstract
INTRODUCTION: The link between fatty liver diseases and cognitive impairment among people living with HIV (PLWH) remains unclear. We investigated the association of steatotic liver disease (SLD), advanced liver fibrosis and non-alcoholic steatohepatitis (NASH) with significant activity and liver fibrosis with cognitive impairment in PLWH. METHODS: Cognitive performance was assessed for PLWH aged ≥50 years on stable antiretroviral therapy (ART) with the Thai-validated version of the Montreal Cognitive Assessment (MoCA), and a cut-off of <25/30 was used to define cognitive impairment. SLD and NASH with significant activity and liver fibrosis were defined as having a controlled attenuation parameter value ≥248 dB/m and a FibroScan-AST (FAST) score ≥0.67, respectively. Multivariable logistic regression was employed to investigate the association of cognitive impairment with SLD or NASH. RESULTS: Of the 319 PLWH (63.3% male and 98% had HIV-1 RNA ≤50 copies/mL) included, 74 (38%) had SLD. NASH with significant activity and liver fibrosis was present in 66 (20.1%) participants. Some 192 (60.2%) participants had cognitive impairment. In a multivariable analysis, NASH with significant activity and liver fibrosis was significantly associated with cognitive impairment (adjusted odds ratio [aOR] 2.01, 95% CI 1.02-3.98, p = 0.04), after adjusting for HIV-related parameters, age, sex, body mass index, employment status, education, income level, smoking, alcohol use, diabetes mellitus, hypertension and HIV-related parameters. The association of a lone diagnosis of SLD and cognitive impairment was not statistically significant. CONCLUSIONS: NASH with significant activity and liver fibrosis was associated with lower cognitive performance, even after controlling for demographics and HIV disease parameters. Additional research is needed to better understand the underlying mechanisms.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".