Risk Factors for Chronic Kidney Disease in Patients With Crohn’s Disease
Bibliographic record
Abstract
BACKGROUND: Patients with inflammatory bowel diseases (IBD), including Crohn's disease (CD), are at risk of complications, including kidney disease. It is important to identify IBD patients at higher risk of chronic kidney disease (CKD) to improve prevention and treatment. Here, we investigated the clinical and metabolomic characteristics of CD patients who develop CKD. METHODS: We identified adult CD patients with (CD + CKD, n = 87) and selected CD patients without CKD (CD controls) matched by age, race, and gender. We collected data on demographic characteristics (age, smoking status, ethnicity, gender), IBD characteristics (diagnosis, Montreal classification, medication use, IBD-related surgeries, perianal disease), and kidney-related factors (primary sclerosing cholangitis, end-stage renal disease, hypertension, diabetes, organ transplantation, and nephrolithiasis). Univariate and multivariate analyses were conducted and odds ratios were calculated to identify risk factors for CKD. Serum samples were collected for untargeted metabolomic analysis. RESULTS: Chronic kidney disease was far more common in CD patients than UC patients. Crohn's disease patients with kidney stones had a 10-fold higher risk of developing CKD than those without kidney stones. Crohn's disease patients with more than 2 IBD-related surgeries had a 7.3-fold higher risk of developing CKD than those who had not undergone surgery. There was no relationship between the number of biologics used or mesalamine use and the risk of CKD. The serum of CD + CKD patients had elevated levels of pro-inflammatory metabolites and those linked to kidney injury. CONCLUSIONS: We recommend regular kidney function monitoring and ensuring proper hydration to prevent or manage potential kidney-related complications in CD patients. Patients with resections and kidney stones are particularly vulnerable.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".