Triglyceride glucose waist circumference and non alcoholic fatty liver disease: a systematic review and meta analysis
Bibliographic record
Abstract
BACKGROUND: Insulin resistance (IR) plays a noticeable role in the pathogenesis of non-alcoholic fatty liver disease (NAFLD). The triglyceride glucose-waist circumference (TyG-WC) index, a novel measure for assessing IR, may hold significant predictive value for NAFLD. However, the relationship between TyG-WC and the risk of NAFLD remains elusive. To investigate this association, this comprehensive meta-analysis was conducted. METHODS: A systematic electronic search was conducted using the PubMed, Embase, and Web of Science databases from their inception until July 2024 to identify observational studies assessing the relationship between TyG-WC and the risk of NAFLD. Joanna Briggs Institute's critical appraisal checklist was utilized to evaluate the quality of cross-sectional studies, while the Newcastle-Ottawa Scale (NOS) score was used to assess cohort studies. The principal summary outcomes included the mean difference (MD) and the corresponding 95% confidence interval (CI). RESULTS: In total, 10 studies comprising 38,518 participants were included in this meta-analysis, of whom 37% were diagnosed with NAFLD. The analysis revealed a significant MD between NAFLD and non-NAFLD cases (MD, 137.41; [95% CI, 121.52-153.31]). CONCLUSION: A significant MD was identified between NAFLD and non-NAFLD cases. The TyG-WC index was found to be positively correlated with the risk of NAFLD, suggesting that it may serve as a potential indicator for NAFLD.
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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 | 0.011 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.032 |
| Bibliometrics | 0.008 | 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".