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Updated: Apr 9, 2026

Development and Validation of a Methodology for Establishing Obese Rat Models with Typical Fatty Pancreas
Published on: November 11, 2025
Risk factors and clinical nomogram for fatty pancreas in adults with obesity: a case-control study
Chaoyu Pang1, Xin Gao1, Zhiyao Fan1
1Division of Pancreatic Surgery, Department of General Surgery, Qilu Hospital, Shandong University, Jinan, China.
Objectives:
The correlation between fatty pancreas (FP) and metabolic-associated steatotic liver disease (MASLD), diabetes and other metabolic diseases is still controversial, and it is crucial to recognize the risk factors of FP to prevent its clinical complications.
Methods:
Retrospective collection of clinical, pathologic, and imaging data of people with obesity attending Qilu Hospital of Shandong University from January 1, 2020 to October 1, 2022. FP was diagnosed by non-enhanced computed tomography with the ratio of pancreas/spleen <0.7. Using binary logistics regression analysis to construct an FP prediction nomogram model, bootstrap repeated sampling internal validation and temporal external validation were used to test the predictive performance of the model.
Results:
In this cohort, the average age of the participants was 32.2 years, and the average BMI was 41.3 Kg/m2. The prevalence of FP 23.3% (144/617). People with obesity with or without comorbid FP have partial differences in blood glucose, lipid. Independent factors for FP were body mass index (BMI) (OR = 1.97, 95% CI 1.29-3.01; P = 0.002), diastolic blood pressure (DBP) (OR = 1.55, 95% CI 1.03-2.33; P = 0.035), high-density lipoprotein (HDL) (OR = 1.67, 95% CI 1.11-2.52; P = 0.014), HbA1C (OR = 1.99, 95% CI 1.19-3.34; P = 0.009). This study constructed a nomogram containing these four indicators for FP, and receiver operating characteristic curve analysis indicated that the nomogram has moderate predictive performance.
Conclusions:
This study identified key risk factors and developed a validated prediction nomogram for FP in adults with obesity. This model may facilitate early identification of FP and the prevention of related metabolic disorders.
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