Nonalcoholic fatty liver disease contributes to subclinical atherosclerosis: A systematic review and meta‐analysis
Bibliographic record
Abstract
Nonalcoholic fatty liver disease (NAFLD) is associated with an increased risk of atherosclerotic cardiovascular disease. In our meta‐analysis, we aimed to assess the correlation of NAFLD and four surrogate markers of subclinical atherosclerosis. PubMed, Embase, and the Cochrane Library were searched up until April 2017. Original studies investigating the association between NAFLD and subclinical atherosclerosis were included. The outcome data were extracted and pooled for the effect estimate by using a random‐effects model. We used the Newcastle‐Ottawa Quality Assessment Scale to assess the quality of the included studies. Of the 434 initially retrieved studies, 26 studies involving a total of 85,395 participants (including 29,493 patients with NAFLD) were included in this meta‐analysis. The Newcastle‐Ottawa Quality Assessment Scale scores suggested the included studies were of high quality. The pooled effects estimate showed that subjects with NAFLD exhibited a significant independent association with subclinical atherosclerosis compared to the non‐NAFLD group (odds ratio, 1.60; 95% confidence interval, 1.45‐1.78). Subgroup analysis suggested that the presence of NAFLD yielded a remarkable higher risk of increased carotid artery intima‐media thickness/plaques, arterial stiffness, coronary artery calcification, and endothelial dysfunction with odds ratios (95% confidence interval) of 1.74 (1.47‐2.06), 1.56 (1.24‐1.96), 1.40 (1.22‐1.60), and 3.73 (0.99‐14.09), respectively. Conclusion: Our meta‐analysis revealed a close link between NAFLD and subclinical atherosclerosis in light of four different indices. Patients with NAFLD might benefit from screening and surveillance of early atherosclerosis, which would facilitate the prediction of potential cardiovascular disease burden, risk stratification, and appropriate intervention in the long term. ( Hepatology Communications 2018;2:376‐392)
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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 | 0.012 | 0.023 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.037 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".