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January 23, 2026

P0617Prevalence and Risk Factors of Sarcopenia in Patients with Ulcerative Colitis

Key Points

This research aims to investigate the prevalence of sarcopenia diagnosed by CT in ulcerative colitis patients and identify risk factors.
Retrospective data collection from 331 ulcerative colitis patients at a hospital.
Evaluation of abdominal CT images to assess skeletal muscle index for sarcopenia diagnosis.
Multicollinearity analysis and variable selection using LASSO algorithm for predictive modeling.
Establishment of a logistic regression model to predict sarcopenia based on identified factors.
Sarcopenia prevalence in ulcerative colitis patients found to be 69.5%.
Eight predictive factors identified, including age, gender, serum albumin levels, and BMI.
Logistic model showed AUC of 0.812 in training set, indicating good discriminatory power.

Abstract

Abstract Background Sarcopenia is recognised as being associated with disease activity and clinical prognosis in patients with ulcerative colitis. However, the prevalence of sarcopenia diagnosis among ulcerative colitis patients differs considerably across clinical studies. This study aimed to explore the prevalence of CT-diagnosed sarcopenia and its risk factors using uniform diagnostic criteria, and to establish a predictive model for sarcopenia in ulcerative colitis patients. Methods This retrospective study collected data from ulcerative colitis patients hospitalised at the Second Affiliated Hospital of Xi’an Jiaotong University. Following inclusion and exclusion criteria, 331 patients were selected as study subjects, and their clinical records were compiled. Abdominal CT images at the L3 level were analysed to calculate skeletal muscle index for sarcopenia diagnosis, determining the prevalence of sarcopenia among ulcerative colitis patients. Multicollinearity analysis was performed on clinical data. Where multicollinearity existed, variables were selected using the LASSO algorithm on the training set. A logistic regression model was established to predict sarcopenia diagnosis. Model discriminatory power was evaluated via area under the curve (AUC) of the receiver operating characteristic (ROC) curve on both training and testing sets. Results (1) The prevalence of sarcopenia diagnosed by CT in ulcerative colitis patients was 69.5% (230/331).(2) Compared with patients without sarcopenia, the LASSO algorithm identified eight key predictive factors for sarcopenia diagnosis: age, gender, occupation, BMI, red blood cell count, albumin, creatinine, and CA125. Logistic multivariate regression analysis indicated that advanced age OR (95% CI): 1.064 (1.031–1.098), female OR (95% CI): 8.606 (3.093–23.943) and reduced serum albumin levels OR (95% CI): 0.918 (0.851–0.990) as independent risk factors for sarcopenia.(3) A predictive model for sarcopenia diagnosis was established based on gender, age, albumin, and BMI. In the training set, the model demonstrated an AUC of 0.812 (0.757–0.868), sensitivity of 67.5%, and specificity of 84.4%, indicating satisfactory discriminatory capability. In the validation cohort, the AUC was 0.798 (0.052–0.696), sensitivity 78.9%, and specificity 75%, indicating moderate discriminatory ability. Conclusion (1) The prevalence of sarcopenia is high among ulcerative colitis patients, with a CT-based diagnosis rate of 69.5%.(2) Female, advanced age, and lower albumin levels constitute independent risk factors for sarcopenia in ulcerative colitis patients.(3) The diagnostic prediction model established based on gender, age, albumin, and BMI demonstrates sound clinical applicability. Conflict of interest: Ms. Jiang, Gemeng: No conflict of interest

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