Prevalence of Non-alcoholic Fatty Liver Disease in Patients With Inflammatory Bowel Disease: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Non-alcoholic fatty liver disease (NAFLD) has emerged as one of the significant comorbidities in patients with inflammatory bowel disease (IBD). This systematic review and meta-analysis aimed to synthesize existing evidence on NAFLD prevalence in IBD patients and explore sources of heterogeneity. A comprehensive literature search was conducted across PubMed/MEDLINE, Embase, Web of Science, and Scopus from January 2016 to September 2025, including studies reporting NAFLD prevalence in adult IBD patients diagnosed through imaging, biopsy, or validated biomarkers. Two independent reviewers screened studies and extracted data on patient demographics, IBD subtypes, and NAFLD diagnostic methods. Quality assessment was performed using the Newcastle-Ottawa Scale. Thirty-five studies encompassing over 47 million IBD patients were included. The pooled prevalence of NAFLD in IBD patients was 26% (95% CI: 23-29%), with substantial heterogeneity (I² = 99.9%). Subgroup analysis revealed a higher prevalence in cross-sectional studies (40.9%) compared to retrospective studies (19.5%). Crohn's disease patients demonstrated higher NAFLD prevalence (26.1%) than ulcerative colitis patients (17.0%). Studies published from 2020 onwards reported a slightly higher prevalence (27.5%) compared to earlier studies (24.8%). The findings indicate that approximately one in four IBD patients has concurrent NAFLD, representing a substantial comorbidity burden. These results underscore the importance of systematic NAFLD screening in IBD patients and integrated multidisciplinary care addressing both gastrointestinal and metabolic complications.
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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 | 0.013 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.034 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".