Metabolic-Associated Fatty Liver Disease and Cognitive Performance in Type 2 Diabetes: Basal Data from the Phytate, Neurodegeneration and Diabetes (PHYND) Study
Bibliographic record
Abstract
The effect of liver fibrosis on mild cognitive impairment (MCI) and dementia risk in type 2 diabetes mellitus (T2DM) patients is unclear. Therefore, we performed a prospective cross-sectional study on 219 patients with T2DM and older than 60 years to evaluate the association between liver fibrosis, liver steatosis, and cognitive impairment. The Montreal Cognitive Assessment (MoCA) was used to screen for MCI or dementia. Liver fibrosis was estimated using the non-invasive Fibrosis-4 (FIB-4) score, and liver steatosis was assessed with the hepatic steatosis index. The mean age was 71 ± 6 years, 47% were women and according to MoCA cut-off values, 53.88% had MCI and 16.43% had dementia. A moderate or high risk of advanced fibrosis was significantly higher in patients with MCI or dementia compared to those with normal cognition (p < 0.001). After adjusting for confounders, a FIB-4 score greater than 1.54 was associated with MCI or dementia (p = 0.039). Multivariate analysis identified age over 70.5 years, antiplatelet medication use, and a FIB-4 score above 1.54 as the most relevant risk factors. Liver fibrosis, but not liver steatosis, is associated with MCI or dementia in older T2DM patients, suggesting that FIB-4 score might be a simple biomarker for the detection of 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".