Systematic review and meta‐analysis: Association between liver fibrosis and subclinical atherosclerosis in nonalcoholic fatty liver disease
Bibliographic record
Abstract
BACKGROUND: Nonalcoholic fatty liver disease (NAFLD) is a liver disorder commonly associated with metabolic syndrome and cardiovascular disease (CVD). Atherosclerosis, a leading cause of CVD, has been linked to liver fibrosis. However, the evidence regarding this association is conflicting. AIM: To evaluate the link between liver fibrosis and subclinical atherosclerosis in patients with NAFLD METHODS: We conducted a comprehensive search of four databases from 1950 to February 2023 to identify eligible studies investigating the association between liver fibrosis and subclinical atherosclerosis among patients with NAFLD, utilising the PICOS framework. Two independent reviewers screened the studies; quality was assessed using the Newcastle-Ottawa Scale. Meta-analysis was performed using the DerSimonian-Liard random-effects model, and subgroup analysis was conducted based on the severity of liver fibrosis, type of subclinical atherosclerosis diagnosis and geographic region. RESULTS: The meta-analysis included 12 studies with a total of 4725 patients. Overall pooled odds ratio (OR) for subclinical atherosclerosis was 2.18 (95% CI: 1.62-2.93), indicating a significant association with liver fibrosis in NAFLD. Subgroup analysis revealed higher ORs in patients with more severe fibrosis: 1.64 (95% CI: 1.22-2.20) in ≥F1, 2.22 (95% CI: 1.37-3.62) in ≥F2, and 3.42 (95% CI: 1.81-6.46) in ≥F3. However, there was no significant difference between the West versus East and various measurements of subclinical atherosclerosis. CONCLUSIONS: Any degree of fibrosis is significantly associated with subclinical atherosclerosis, with fibrosis severity amplifying the association.
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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 | 0.015 | 0.042 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.021 | 0.039 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".