2002-LB: New Model for Type 1 Diabetes Differential Screening

Introduction & Objective: Determining whether a patient has type 1 (T1D), type 2 (T2D) or monogenic diabetes is an important diagnostic and therapeutic concern. Recent successes in T1D disease-modifying therapies to impact the course of early-stage T1D have ignited the consideration of the need...

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Bibliographic Details
Published in:	Diabetes (New York, N.Y.) Vol. 73; p. 1
Main Authors:	Mendizabal, Leire, Saso-Jiménez, Laura, Apaolaza, Naiara, Martínez, Rosa, Zulueta, Mirella, Urrutia, Inés M, Simon, Laureano, Castano, Luis
Format:	Journal Article
Language:	English
Published:	New York American Diabetes Association 01-06-2024
Subjects:	Correlation analysis Diabetes Diabetes mellitus (insulin dependent) Diabetes mellitus (non-insulin dependent) Mathematical models Regression analysis Susceptibility
Online Access:	Get full text
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Summary:	Introduction & Objective: Determining whether a patient has type 1 (T1D), type 2 (T2D) or monogenic diabetes is an important diagnostic and therapeutic concern. Recent successes in T1D disease-modifying therapies to impact the course of early-stage T1D have ignited the consideration of the need for and feasibility of population screening to identify those at increased risk of T1D. Our objective was to develop a non-invasive method for early differential screening of individuals with increased susceptibility to T1D based on a Polygenic Risk Score (PRS). Methods: We analyzed a retrospective cohort of 1,911 individuals (475 controls, 486 T1D, 464 T2D, 486 MODY) from Hospital Cruces (Bilbao, Spain). Training (70%) and validation (30%) sets were established. A total of 306 SNPs were selected after exhaustive exploration of databases published to date of SNPs associated with T1D and T2D. Chi square test and Pearson correlation analysis preceded development of a multinomial logistic regression model and a combination of binomial models. Models' performance was assessed by receiver operating characteristic (ROC) curve in training and validation data sets. Results: We developed a Multinomial PRS Model that differentially discerns T1D susceptibility from susceptibility to other diabetes types and controls with an AUC of 0.87, 81% sensitivity and 83% specificity. Conclusions: This new tool for T1D differential screening shows the possibility to discriminate susceptibility to T1D versus no susceptibility and susceptibility to other diabetes types. It may aid in management of clinical and population strategies to best assist those at increased T1D susceptibility.
ISSN:	0012-1797 1939-327X
DOI:	10.2337/db24-2002-LB