Systematic Review With Meta-analysis: Epidemiology of Nonalcoholic Fatty Liver Disease in Patients With Inflammatory Bowel Disease
Bibliographic record
Abstract
BACKGROUND: Nonalcoholic fatty liver disease (NAFLD) is increasingly identified in patients with inflammatory bowel disease (IBD), but there are few systematic reviews and meta-analyses of the studies of NAFLD in IBD patients. METHODS: MEDLINE, Web of Science, Cochrane Library, and Scopus were searched (until August 2018) to identify observational studies that reported the prevalence and risk factors for NAFLD in IBD patients. Pooled prevalence, odds ratios (OR), mean difference (MD), and 95% confidence intervals (95% CI) were calculated. Study quality was assessed using the modified Newcastle-Ottawa scale. RESULTS: Of the 662 citations evaluated, 19 studies (including 5620 subjects) reported the prevalence of NAFLD in IBD population and were included for the analysis. The overall pooled prevalence was 27.5% (95% CI, 20.7%-34.2%). The prevalence was higher in older patients (MD = 8.22; 95% CI, 6.22-10.22), type 2 diabetes (OR = 3.85; 95% CI, 2.49-5.95), hypertension (OR = 3.18; 95% CI, 2.36-4.28), obesity (OR = 2.79; 95% CI, 1.73-4.50), insulin resistance (OR = 6.66; 95% CI, 1.28-34.77), metabolic syndrome (OR = 4.96; 95% CI, 3.05-8.05), chronic kidney disease (OR = 4.83; 95% CI, 1.79-13.04), methotrexate use (OR = 1.76; 95% CI, 1.02-3.06), surgery for IBD (OR = 1.28; 95% CI, 1.02-1.62), and longer duration of IBD (MD = 5.60; 95% CI, 2.24-8.97). CONCLUSIONS: We found that NAFLD was not uncommon in the IBD population. Older age, metabolic risk factors, methotrexate use, prior surgery, and longer duration of IBD are predictors for the presence of NAFLD in IBD. Screening of NAFLD might be recommended among IBD patients with the aforementioned factors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.008 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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; both teacher heads agree on what is shown here.
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".