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Risk Factors for NAFLD and Development of Advanced Fibrosis in Inflamatory Bowel Disease Patients

2015· article· en· W2977438840 on OpenAlexaboutno aff
Hillary Bownik, Rotoyna Carr, Arpan Patel, Ann Tierney, Kimberly A. Forde, Caroline Kerner, Gary R. Lichtenstein

Bibliographic record

VenueThe American Journal of Gastroenterology · 2015
Typearticle
Languageen
FieldMedicine
TopicLiver Disease Diagnosis and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineInternal medicineGastroenterologyMetabolic syndromeInflammatory bowel diseaseNonalcoholic fatty liver diseaseUlcerative colitisSteatosisDiabetes mellitusInsulin resistanceFatty liverDiseaseObesityEndocrinology

Abstract

fetched live from OpenAlex

Introduction: Nonalcoholic fatty liver disease (NAFLD) is a common cause of hepatic steatosis in patients with inflammatory bowel disease (IBD). Both metabolic syndrome (MetS) and intestinal inflammation are implicated in NAFLD pathogenesis. We examined whether MetS and IBD severity increase the risk of NAFLD severity in patients with NAFLD and IBD. Methods: A retrospective electronic medical record analysis of patients with IBD and NAFLD seen in our health system from Jan 1997-Dec 2011 was conducted. Patients were identified as having “MetS” (>3 of the following: hypertension, hyperlipidemia, BMI>30mg/kg2, and diabetes/insulin resistance) or “non-MetS.” NAFLD severity was assessed using the BARD score (score 2 or greater has an OR for advanced fibrosis of 17(85% CI 9.2-31.9) and a NPV of 96%.) IBD phenotype was assessed using the Montreal classification. IBD severity was determined by the Harvey Bradshaw Index (HBI) for Crohn's Disease (CD), and the Simple Clinical Colitis Activity Index (SCCAI) for Ulcerative Colitis (UC). Patient demographics, medications, and serology were analyzed. Statistical analysis was performed using Fisher Exact test, Mann-Whitney U test, or χ2 analysis where appropriate with P value. Results: 83 patients were included in our analysis (24 UC, 59 CD). Mean age was 52 with an average of 12.2 years between IBD diagnosis and NAFLD diagnosis and mean follow up of 16 yrs. 23% of patients had MetS. IBD patients with MetS were significantly older at the time of IBD dx (44 vs 33, p=0.005) and NAFLD dx (55 vs 46, p=0.018) and had higher BMI (34.4 vs 28.8, p < 0.001). IBD patients with MetS had higher ALT (57 U/L vs. 38 U/L, p=0.007); AST (56 U/L vs. 36 U/L, p=0.002), & Hb (14.1 v 13.1, p=0.017). Comparing the MetS patients to NonMetS patients, there was no significant difference between IBD phenotype, IBD medications (i.e. steroids, anti-TNFs, & immunomodulators), and IBD severity. MetS patients had significantly higher prevalence of advanced fibrosis based on BARD scores (95% vs 65%, p=0.009). IBD disease severity as assessed by HBI and SCCAI scores (even comparing severe disease only) was not associated with NAFLD severity. Conclusion: NAFLD is increasingly recognized as a cause of hepatic steatosis in IBD patients. IBD disease severity was not associated with advanced NAFLD. However, the presence of MetS is a risk factor for advanced liver fibrosis and should prompt hepatology referral and evaluation in IBD patients.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.012
GPT teacher head0.256
Teacher spread0.243 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2015
Admission routes1
Has abstractyes

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