Non‐alcoholic fatty liver disease's prevalence and impact on alanine aminotransferase associated with metabolic syndrome in the Chinese
Bibliographic record
Abstract
BACKGROUND AND AIM: Non-alcoholic fatty liver disease (NAFLD) is becoming a major public health hazard in China. The present study aimed to estimate the prevalence of NAFLD, NAFLD with abnormal serum alanine aminotransferase (ALT) levels, and determine the potential associations of ALT levels with the components of metabolic syndrome (MetS) in the absence or presence of NAFLD in Chinese adults. METHODS: A population-based cross-sectional survey was conducted with 2226 participants. Physical examinations, laboratory tests and hepatic ultrasounds were performed. Individuals were further stratified into higher or lower ALT subgroups with the upper quartiles of ALT in this population. The MetS was identified according to the criteria of the Chinese Joint Committee for Developing Chinese Guidelines (JCDCG). RESULTS: The standardized prevalence of NAFLD was 23.3% (NAFLD with abnormal ALT levels, 3.1%), 26.5% (NAFLD with abnormal ALT levels, 5.1%) in males, and 19.7% (NAFLD with abnormal ALT levels, 0.9%) in females. Multivariate logistic analysis revealed that higher ALT was significantly associated with elevated triglyceride (TG) in the non-NAFLD participants, independent of age, smoking status, drinking status, and other MetS-related measures with odds ratios (95% confidence intervals) of 3.4 (1.6-7.1) and 2.3 (1.4-3.7) in males and females, respectively. On the other hand, the higher ALT was statistically associated with elevated TG and hyperglycemia in the NAFLD cases with odds ratios of 2.2 to 2.5 (P<0.05). CONCLUSIONS: The prevalence of NAFLD has become epidemic in Shanghai adults. NAFLD combined with ALT levels may be used to identify the individuals at the different risk levels of metabolic disorders.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".