Evidence mapPaperPMID 40745504Full record

ArticleInsights into imaging2025

Individualized prediction of post-acute pancreatitis diabetes mellitus by combining lipid metabolism and anatomical features.

Ling Ling Tang, Qi Zhang, Shuang Yi Song, Nian Liu, Qing Lin Du, Shu Ting Zhong, Xiao Hua Huang

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Article in Insights into imaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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7 authors.

Ling Ling TangDepartment of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, China.
Qi ZhangSchool of Medical Imaging, North Sichuan Medical College, Nanchong, China.
Shuang Yi SongSchool of Medical Imaging, North Sichuan Medical College, Nanchong, China.
Nian LiuDepartment of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, China.
Qing Lin DuSchool of Medical Imaging, North Sichuan Medical College, Nanchong, China.
Shu Ting ZhongSchool of Medical Imaging, North Sichuan Medical College, Nanchong, China.
Xiao Hua HuangDepartment of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, China. 15082797553@163.com.ORCID http://orcid.org/0000-0002-3490-4142

Funding

Bureau of Science & Technology and Intellectual Property Nanchong City No.20SXQT0303Scientific Research and Development Plan Project of Affiliated Hospital of North Sichuan Medical College 2023JC047 and 2023MPZK013Scientific Research and Development Plan Project of North Sichuan Medical College CBY22-QNA30
6 · The paper itself

Abstract

objectivesTo investigate the lipid metabolism and anatomical risk factors of post-acute pancreatitis diabetes mellitus (PPDM) and their value in individualized prediction. MATERIALS AND

methodsA continuous retrospective analysis was conducted on 241 patients with acute pancreatitis (AP) treated in our hospital from January 2017 to December 2021. The type and angle of the pancreaticobiliary junction were measured on magnetic resonance cholangiopancreatography (MRCP) images, and baseline lipid metabolism indicators were collected. We evaluated the risk factors of PPDM using univariate and multivariate Cox proportional hazard analysis, established quantitative prediction models for PPDM, and evaluated the predictive value of lipid metabolism and features of the pancreaticobiliary junction.

resultsOverall, 85 of 241 eligible patients (35.27%) ultimately developed PPDM. Univariate and multivariate analyses showed B-P type in pancreaticobiliary junction (p = 0.017), the angle of junction (p = 0.041), non-high-density lipoprotein (p = 0.029), alcohol index (p < 0.001), body mass index (p = 0.042), inflammatory frequency (p = 0.016), fasting blood glucose (p = 0.002), concomitant hypertension (p < 0.001) were important predictive factors for the occurrence of PPDM. The model that integrated imaging features of the pancreaticobiliary junction has a higher predictive performance than models without imaging features, with an AUC of 0.882 (95% CI, 0.836-0.930). The AUC of the combined model was 0.886 (95% CI, 0.841-0.932), and there was no statistical difference in AUC between the combined model and the pancreaticobiliary junction model (p = 0.340).

conclusionThe lipid metabolism and morphological characteristics of the pancreaticobiliary junction are additional risk factors for PPDM, and the quantitative prediction model shows moderate predictive performance. CRITICAL RELEVANCE STATEMENT: The type and angle of the pancreaticobiliary junction based on MRCP are independent predictors of PPDM, which can quantitatively predict risk in the early stage. KEY POINTS: PPDM has an increasing incidence and poor prognosis, which requires early monitoring. Larger angles and B-P type in the pancreaticobiliary junction are risk factors for PPDM. Quantitative prediction of PPDM risk allows for early personalized prevention and treatment.

Indexed as

Acute pancreatitisDiabetesLipid metabolismMagnetic resonance cholangiopancreatographyPancreaticobiliary junction

Identifiers

PMID40745504
PMCPMC12314159

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.