Dataset Information

Genetic Risk Score in Diabetes Associated With Chronic Pancreatitis Versus Type 2 Diabetes Mellitus.

ABSTRACT:

Introduction

Diabetes mellitus (DM) is a complication of chronic pancreatitis (CP). Whether pancreatogenic diabetes associated with CP-DM represents a discrete pathophysiologic entity from type 2 DM (T2DM) remains uncertain. Addressing this question is needed for development of specific measures to manage CP-DM. We approached this question from a unique standpoint, hypothesizing that if CP-DM and T2DM are separate disorders, they should be genetically distinct. To test this hypothesis, we sought to determine whether a genetic risk score (GRS) based on validated single nucleotide polymorphisms for T2DM could distinguish between groups with CP-DM and T2DM.

Methods

We used 60 T2DM single nucleotide polymorphisms to construct a weighted GRS in 1,613 subjects from the North American Pancreatitis Study 2 and 2,685 subjects from the Multi-Ethnic Study of Atherosclerosis, all of European origin.

Results

The mean GRS was identical between 321 subjects with CP-DM and 423 subjects with T2DM (66.53 vs 66.42, P = 0.95), and the GRS of both diabetic groups was significantly higher than that of nondiabetic controls (n = 3,554, P < 0.0001). Exploratory analyses attempting to enrich the CP-DM group for pancreatogenic diabetes, such as eliminating diabetes diagnosed before CP, requiring pancreas-specific comorbidities, or removing those with a family history of diabetes, did not improve the ability of the GRS to distinguish between CP-DM and T2DM.

Discussion

Recognizing that we lacked a gold standard to define CP-DM, our study suggests that CP-DM may be a subtype of T2DM, a notion that should be tested in future, large prospective studies.

SUBMITTER: Goodarzi MO

PROVIDER: S-EPMC6708663 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature

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Publications

Genetic Risk Score in Diabetes Associated With Chronic Pancreatitis Versus Type 2 Diabetes Mellitus.

Goodarzi Mark O MO Nagpal Tanvi T Greer Phil P Cui Jinrui J Chen Yii-Der I YI Guo Xiuqing X Pankow James S JS Rotter Jerome I JI Alkaade Samer S Amann Stephen T ST Baillie John J Banks Peter A PA Brand Randall E RE Conwell Darwin L DL Cote Gregory A GA Forsmark Christopher E CE Gardner Timothy B TB Gelrud Andres A Guda Nalini N LaRusch Jessica J Lewis Michele D MD Money Mary E ME Muniraj Thiruvengadam T Papachristou Georgios I GI Romagnuolo Joseph J Sandhu Bimaljit S BS Sherman Stuart S Singh Vikesh K VK Wilcox C Mel CM Pandol Stephen J SJ Park Walter G WG Andersen Dana K DK Bellin Melena D MD Hart Phil A PA Yadav Dhiraj D Whitcomb David C DC

Clinical and translational gastroenterology 20190701 7

<h4>Introduction</h4>Diabetes mellitus (DM) is a complication of chronic pancreatitis (CP). Whether pancreatogenic diabetes associated with CP-DM represents a discrete pathophysiologic entity from type 2 DM (T2DM) remains uncertain. Addressing this question is needed for development of specific measures to manage CP-DM. We approached this question from a unique standpoint, hypothesizing that if CP-DM and T2DM are separate disorders, they should be genetically distinct. To test this hypothesis, w ...[more]

PMID: 31232720

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Project description:Background: Previous studies have constructed prediction models for type 2 diabetes mellitus (T2DM), but machine learning was rarely used and few focused on genetic prediction. This study aimed to establish an effective T2DM prediction tool and to further explore the potential of genetic risk scores (GRS) via various classifiers among rural adults. Methods: In this prospective study, the GRS for a total of 5,712 participants from the Henan Rural Cohort Study was calculated. Cox proportional hazards (CPH) regression was used to analyze the associations between GRS and T2DM. CPH, artificial neural network (ANN), random forest (RF), and gradient boosting machine (GBM) were used to establish prediction models, respectively. The area under the receiver operating characteristic curve (AUC) and net reclassification index (NRI) were used to assess the discrimination ability of the models. The decision curve was plotted to determine the clinical-utility for prediction models. Results: Compared with the individuals in the lowest quintile of the GRS, the HR (95% CI) was 2.06 (1.40 to 3.03) for those with the highest quintile of GRS (P trend < 0.05). Based on conventional predictors, the AUCs of the prediction model were 0.815, 0.816, 0.843, and 0.851 via CPH, ANN, RF, and GBM, respectively. Changes with the integration of GRS for CPH, ANN, RF, and GBM were 0.001, 0.002, 0.018, and 0.033, respectively. The reclassifications were significantly improved for all classifiers when adding GRS (NRI: 41.2% for CPH; 41.0% for ANN; 46.4% for ANN; 45.1% for GBM). Decision curve analysis indicated the clinical benefits of model combined GRS. Conclusion: The prediction model combined with GRS may provide incremental predictions of performance beyond conventional factors for T2DM, which demonstrated the potential clinical use of genetic markers to screen vulnerable populations. Clinical Trial Registration: The Henan Rural Cohort Study is registered in the Chinese Clinical Trial Register (Registration number: ChiCTR-OOC-15006699). http://www.chictr.org.cn/showproj.aspx?proj=11375.

Dataset Information

Genetic Risk Score in Diabetes Associated With Chronic Pancreatitis Versus Type 2 Diabetes Mellitus.

Introduction

Methods

Results

Discussion

Publications

Genetic Risk Score in Diabetes Associated With Chronic Pancreatitis Versus Type 2 Diabetes Mellitus.

Similar Datasets

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