S1379 Prevalence and Epidemiological Characteristics of Non Alcoholic Fatty Liver Disease in the U.S. Population
Bibliographic record
Abstract
Introduction: Non-alcoholic fatty liver disease is a biologically and clinically heterogeneous multisystem disorder that affects one-quarter of the global adult population and causes substantial social and economic implications. They may present as isolated hepatic steatosis, non-alcoholic steatohepatitis, hepatic fibrosis, cirrhosis, liver cancer, end-stage liver disease, and death. There is limited literature describing the national prevalence and epidemiological characteristics of NAFLD in the US population. The primary outcome of the study was to evaluate the concurrent prevalence and lifestyle diseases among NAFLD. Methods: A retrospective cross-sectional study using the NHANES database from 2015-to 2018 was conducted. The datasets were downloaded from the NHANES web site and combined using SAS software (Version 9.4). Proper weighting procedures for weighting multiple years of NHANES data were employed for this study. We included participants that aged ≥18 years and had completed data from the NHANES questionnaires. Univariate and multivariate logistic regression analysis was conducted to evaluate the prevalence and epidemiology of NAFLD and the association of NAFLD with lifestyle disorders Results: Of the total 255,968 sample size, the total number of people identified with NAFLD was 717 (0.26%). NAFLD was more prevalent in older (median: 62 years), males, Mexican American and other Hispanics, and in those with median household income >$100,000. People with NAFLD had a higher prevalence odds of having Diabetes Mellitus (OR: 10.40, 95% CI: 10.37-10.42 p < 0.001), cancer (OR: 2. 10, 955 CI: 2.09-2.10, p < 0.001), depression (OR: 2.528, 95% CI: 2.52-2.53 p < 0.001), hypersomnia (OR: 1.36, 95% CI: 1.34-1.36 p < 0.001), obesity (OR: 1.08, 95% CI: 1.08-1.08, p < 0.001), low dietary fibre intake (OR: 1.18, 95% CI: 1.18-1.19), a sedentary lifestyle (OR: 1.47, 95% CI: 1.46-1.472, p < 0.001) Conclusion: People with NAFLD had a higher association of having lifestyle disorders including diabetes, obesity, depression, hypersomnia, low dietary fibre intake, and a sedentary lifestyle. Cancer was also found to be higher among people with NAFLD. Our study summarises the epidemiological characteristics of NAFLD in the US population. Early identification and risk mitigation strategies with active lifestyle might reduce the burden of NAFLD associated disorders.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".