Nonalcoholic Fatty Liver Disease Increases Cardiovascular Risk in Inflammatory Bowel Diseases
Bibliographic record
Abstract
Background: Nonalcoholic fatty liver disease (NAFLD) is strongly associated with cardiovascular disease in the general population. Both conditions seem more frequent in patients with inflammatory bowel disease (IBD). We aimed to assess the effect of NAFLD and liver fibrosis on intermediate-high cardiovascular risk in IBD. Methods: and liver stiffness measurement by TE ≥8 kPa, respectively. Cardiovascular risk was assessed with the atherosclerotic cardiovascular disease (ASCVD) risk estimator and categorized as low if <5%, borderline if 5%-7.4%, intermediate if 7.5%-19.9%, and high if ≥20% or if previous cardiovascular event. Predictors of intermediate-high cardiovascular risk were investigated by multivariable logistic regression analysis. Results: Of 405 patients with IBD included, 278 (68.6%), 23 (5.7%), 47 (11.6%), and 57 (14.1%) were categorized as at low, borderline, intermediate, and high ASCVD risk, respectively. NAFLD and significant liver fibrosis were found in 129 (31.9%) and 35 (8.6%) patients, respectively. After adjusting for disease activity, significant liver fibrosis and body mass index, predictors of intermediate-high ASCVD risk were NAFLD (adjusted odds ratio [aOR] 2.97, 95% CI, 1.56-5.68), IBD duration (aOR 1.55 per 10 years, 95% CI, 1.22-1.97), and ulcerative colitis (aOR 2.32, 95% CI, 1.35-3.98). Conclusions: Assessment of cardiovascular risk should be targeted in IBD patients with NAFLD, particularly if they have longer IBD duration and ulcerative colitis.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".