Examining Liver Function in Adults with Diabetes in the United States
Bibliographic record
Abstract
PURPOSE: Hyperglycemia is the hallmark of various types of diabetes and considered to be a risk factor for several chronic disorders including liver function. Though liver is a dynamic organ, incessant glucotoxicity can lead to altered liver function. The goals of the present study were to examine the association between diabetes with liver functions amongst adults in the United States. METHODS: We analyzed 14,948 adults with diabetes in the National Health and Nutrition Examination Survey (NHANES) conducted from 2007 to 2016. Diabetes and prediabetes were defined in accordance with the American Diabetes Association 2021 guidelines. The association of demographic characteristics with glycemic levels was analyzed using the Chi-square test. A multivariate logistic regression model was constructed to evaluate the associations of glycemic levels with abnormal liver enzyme levels. Regression model was adjusted for age, sex, and ethnicity. The statistical analyses were performed using STATA ver. 14. A. p value of ≤0.05 or≤0.001 was considered statistically significant. RESULTS: A total of 14,948 adults (20 years and above) were included in this study. Sample mean age was 45.5±0.33 yrs., 54% were female, 53% were non-Hispanic White, and 60% had some college or graduate level education. In the overall sample, 19% adults were diabetic and 34% were pre-diabetic. Pre-diabetic glycemic levels were associated over one and half times higher odds of ALT (OR: 1.45, 95% CI: 1.31, 1.60, p<0.001) and over 1.3 times of higher odds AST (OR: 1.30, 95% CI: 1.14, 1.49,p<0.001). On the other hand, diabetic glycemic levels were associated with close to one and half times higher odds of ALT (OR: 1.37, 95% CI: 1.18, 1.59,p<0.001) and AST (OR: 1.48, 95% CI: 1.24, 1.76, p<0.001). On regression analysis, after adjustment, pre-diabetic and diabetic status was associated with high ALT (OR: 1.21, 95%CI: 1.11, 1.32, p<0.001), AST (OR: 1.14, 95%CI: 1.04, 1.27, p<0.05), ALP (OR: 1.40, 95%CI: 1.16, 1.68, p<0.001) and GGT (OR: 1.42, 95%CI: 1.24, 1.63, p<0.001). CONCLUSIONS: Our results indicated that diabetes is significantly associated with liver function. This observed association deserves further exploration to understand the longitudinal impact of diabetes on liver function.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".