Synergistic increase in cardiovascular risk in diabetes mellitus with nonalcoholic fatty liver disease: a meta-analysis
Bibliographic record
Abstract
BACKGROUND: Nonalcoholic fatty liver disease (NAFLD) has been linked to an increased risk of cardiovascular disease (CVD). To explore the impact of diabetes mellitus (DM) as a cardiovascular risk factor, this meta-analysis quantitatively assessed the association of NAFLD and CVD in diabetic patients. METHODS: PubMed, EMBASE, and the Cochrane Library database were analyzed until the end of March 2017. Original studies analyzing the association between NAFLD and cardiovascular risk factors in the diabetic population were included. The available data related to outcome were extracted for the effect estimate using a random-effects model. The quality of the included studies was assessed using the Newcastle-Ottawa Quality Assessment Scale. RESULTS: Of the 770 initially identified studies, 11 studies involving 8346 patients were finally included. The Newcastle-Ottawa Quality Assessment Scale scores suggested that the studies included were of high quality. The pooled effects estimate showed that diabetic patients with NAFLD showed a two times increased risk for CVD compared with patients without NAFLD (odds ratio=2.20, 95% confidence interval: 1.67-2.90). Subgroup analysis also yielded a markedly increased risk, with odds ratio (95% confidence interval) values of 2.28 (1.61-3.23) and 1.90 (1.48-2.45) in cross-sectional and cohort studies, respectively. CONCLUSION: This is the first meta-analysis investigating the relationship between NAFLD and CVD independent of the impact of DM. Our findings suggested that NAFLD increases the risk of CVD in populations with comparable DM profiles. Diabetic patients diagnosed with NAFLD might benefit from a more early cardiovascular risk assessment, thereby reducing CVD morbidity and mortality.
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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 | 0.014 | 0.024 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.070 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".