Comparison of different definitions of metabolic syndrome and their associations with non-alcoholic fatty liver disease: a retrospective study
Bibliographic record
Abstract
Background: Metabolic syndrome (MetS) is considered an important risk factor for non-alcoholic fatty liver disease (NAFLD). The aim of this study was to measure the prevalence of MetS based on six different MetS definitions and compare the performance of various definitions for identifying diabetes, hypertension, and dyslipidemia among NAFLD patients. Methods: The definitions compared were those developed by the World Health Organization (WHO), National Cholesterol Education Program Adult Treatment Panel III (NCEP-ATP III), International Diabetes Federation (IDF), American Association of Clinical Endocrinologists (AACE), American Heart Association/National Heart, Lung and Blood Institute (AHA/NHLBI), and Interim Joint Statement "harmonized" criteria. Receiver operator characteristic (ROC) curves were plotted for the six MetS definitions with NAFLD diagnosis. The diagnosis for NAFLD was established based on liver imaging or biopsy compatible with fatty liver disease. Results: . The most prevalent MetS component was dyslipidemia (83%), followed by hypertension (60%), obesity (61%), and diabetes (57%). The prevalence of MetS according to the WHO, NCEP/ATP-III, IDF, AACE, AHA/NHLBI, and harmonized criteria was 69%, 59%, 54%, 64%, 78%, and 79%, respectively. The highest area under the ROC curve for diabetes and hypertension was with the WHO definition (0.7405) and (0.8120), respectively. Conclusions: The prevalence of MetS in NAFLD patients varies according to the definitions of MetS employed. The modified WHO definition appeared to be most useful for the screening of MetS in NAFLD patients.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".