Liver Fibrosis and Risk of Incident Dementia in the General Population: Systematic Review With Meta‐Analysis
Bibliographic record
Abstract
Background and Aims: The relationship between liver fibrosis and the risk of developing dementia remains unclear, with studies yielding inconsistent results. This systematic review and meta-analysis seek to synthesize the available evidence. Methods: We systematically searched PubMed, Scopus, Embase, and Web of Science from their respective inception through October 2024 to identify observational studies diagnosing liver fibrosis non-invasively or via histology. The primary outcome was new-onset dementia. Risk of bias was evaluated using the Newcastle-Ottawa Scale (NOS), and pooled estimates of hazard ratios (HRs) with 95% confidence intervals (CIs) were calculated using a random-effects model. Results: ² = 76.06%). Dementia risk increased with fibrosis severity: HR 1.06 (95% CI: 0.67-1.68) in ≥F2, HR 1.32 (95% CI: 1.06-1.64) in ≥F3, and HR 1.69 (95% CI: 1.01-2.83) in F4. Geographically, the risk appeared higher in Western than Eastern countries. Women had a greater risk, and vascular dementia was more strongly associated with fibrosis than Alzheimer's disease. Sensitivity analyses confirmed the robustness of the findings, and no publication bias was observed. Conclusion: Liver fibrosis is linked to a 32% increased long-term dementia risk, independent of common demographic, social, anthropometric, and cardiometabolic factors. Fibrosis severity further increases this risk. Based on our findings, healthcare professionals should recognize the moderately increased risk of developing dementia in individuals with liver fibrosis and perform close surveillance of these patients to enable early detection and timely intervention.
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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 | 0.013 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.036 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".