Prevalence of Abnormal Hepatic Biochemistry and Associated Liver Disease in UK Based South Asians With Inflammatory Bowel Disease
Bibliographic record
Abstract
Introduction: An association between Inflammatory Bowel Disease (IBD) and abnormal hepatic biochemistry has been hypothesized. Virtually no data exists for the prevalence of abnormal hepatic biochemistry in South Asian populations. The aim of our study was to investigate this in a cohort of UK based South Asian IBD patients. Methods: We conducted a retrospective study from a cohort of 106 South Asian IBD patients (51 female, mean age 42 years) over a 24-month period. Clinical data including demographics, disease characteristics and therapy were obtained from hospital records. IBD diagnosis was based on pre-defined criteria based on: symptoms, endoscopic appearance, radiological and histopathological features for Crohn's disease (CD) and ulcerative colitis (UC). Disease was phenotyped to the Montreal Classification. Abnormal hepatic biochemistry was defined as elevation above the laboratory upper limit of normal for ALT and/or ALP during the time period. Investigations performed to identify the cause of the abnormalities were noted. Chi2 and unpaired t-tests were used to compare patients with normal vs. abnormal hepatic biochemistry. Results: Serial hepatic biochemistry was available for all 106 patients (CD=29, UC=73 and IBD-U=4). Abnormal hepatic biochemistry was noted in 22/106 (21%) patients. Of these, only 9/22 patients (41%) had further investigations. Liver pathology was noted in 5/22 patients (PSC n=2, autoimmune hepatitis n=1, fatty liver disease n=1 and drug induced liver injury n=1). When comparing the groups with normal and abnormal hepatic biochemistry a significant association was found with Mercaptopurine use (p=0.025) and CD classification (p=0.05). No associations were found with biologics, cyclosporine, azathioprine, corticosteroids, 5-ASA, disease activity, UC phenotype, age, gender or diagnosis (CD or UC). Conclusion: The incidence of abnormal hepatic biochemistry in our South Asian cohort is similar to that reported in a Caucasian population (1) and 23% of our patients had associated liver disease. We noted a significant association between Mercaptopurine use and abnormal liver biochemistry, which is intriguing with previous reports of ethnicity related pharmacokinetics. Abnormal liver chemistry in IBD patients is suboptimally investigated. Further research exploring associations and etiology is much needed.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".