Risk factor analysis for cognitive impairment in non-alcoholic fatty liver disease
Bibliographic record
Abstract
BACKGROUND: Hepatic lipid accumulation is a defining feature of liver diseases, referred to as non-alcoholic fatty liver disease (NAFLD). According to recent studies, there may be a connection between NAFLD and cognitive impairment, namely concerning memory and attention. This study aimed to define the forms of cognitive deficits associated with these risk variables and determine the factors contributing to cognitive impairment in NAFLD patients.METHODS: An analytical observational study was conducted at Wahidin Sudirohusodo Teaching Hospital in Indonesia from 2023 to 2024. A total of 126 NAFLD patients were included, and cognitive function was assessed using the Montreal Cognitive Assessment Indonesia (MoCA-INA) instrument. Statistical analysis was performed using SPSS version 25 (SPSS Inc., Chicago, IL, USA).RESULTS: Among the 126 NAFLD patients analyzed, 63 (50%) exhibited cognitive impairment, primarily as memory deficits. Obesity and dyslipidemia were significantly associated with cognitive impairment, with obesity presenting a 4.5-fold increased risk and dyslipidemia a 2.5-fold increased risk. However, hypertension and diabetes mellitus did not show significant associations with cognitive impairment.CONCLUSIONS: This study underscores the prevalence of cognitive impairment in NAFLD patients and highlights obesity and dyslipidemia as significant risk factors for cognitive decline in this population. Further longitudinal studies are needed to explore the relationship between NAFLD and cognitive impairment.
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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 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".