Incidence and Predictors of Nonalcoholic Fatty Liver Disease by Serum Biomarkers in Patients with Inflammatory Bowel Disease
Bibliographic record
Abstract
BACKGROUND: Patients with inflammatory bowel disease (IBD) are at high risk for non-alcoholic fatty liver disease (NAFLD). Longitudinal data on incident NAFLD are lacking. We employed non-invasive methods to study incidence and predictors of NAFLD. METHODS: This was a retrospective study of IBD patients without known liver disease followed at IBD clinic of McGill University. NAFLD was defined as Hepatic Steatosis Index (HSI) ≥36 and absence of alcohol intake. Advanced liver fibrosis was diagnosed by FIB-4 ≥2.67. Active IBD was defined as partial Mayo score ≥3 for ulcerative colitis, Harvey Bradshaw Index ≥ 5 or flare during follow-up. Kaplan-Meier and Cox regression analyses were used to investigate incidence and predictors of NAFLD development. RESULTS: Three hundred twenty-one consecutive patients (median age 33.7 yr, 47% males) were observed for a median of 3.2 years (interquartile range 1.5-6). Over 1181.2 persons-year (PY), 108 (33.6%) patients developed NAFLD, accounting for an incidence rate of 9.1/100 PY (95% confidence interval [CI], 7.4-10.9). 7 (2.2%) patients developed advanced liver fibrosis, accounting for an incidence rate of 0.5/100 PY (95% CI, 0.2-1.1). Development of NAFLD was predicted by disease activity (adjusted hazard ratio [aHR] = 1.58; 95% CI, 1.08-2.33, P = 0.02), disease duration (aHR = 1.12; 95% CI, 1.03-1.23, P = 0.01), and prior surgery for IBD (aHR = 1.34; 95% CI, 1.04-1.74, P = 0.02). CONCLUSIONS: NAFLD is a frequent comorbidity in patients with IBD. These patients can also develop advanced liver fibrosis. Disease activity, duration of IBD and prior surgery are predictors of NAFLD development. This should represent one more incentive to achieve and maintain early clinical remission. Further prospective studies are of interest.
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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 | 0.005 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".