Histological validation of a type 1 diabetes clinical diagnostic model for classification of diabetes.

Alice L J Carr ,Clive Wasserfall ,Christine S Flaxman ,Lauric A Ferrat ,Seth A Sharp ,Desmond Schatz ,Amanda L Posgai ,Laura M Jacobsen ,Daniel J Perry ,Irina Kusmartseva ,Martha Campbell‐Thompson ,Maria Beery ,Srikar Chamala ,Angus G Jones ,Sarah J Richardson ,Mark A Atkinson ,Anita Lynam ,Beverley M Shields ,Todd M Brusko ,Richard A Oram

doi:10.1111/dme.14361

Abstract

Misclassification of diabetes is common due to an overlap in the clinical features of type 1 and type 2 diabetes. Combined diagnostic models incorporating clinical and biomarker information have recently been developed that can aid classification, but they have not been validated using pancreatic pathology. We evaluated a clinical diagnostic model against histologically defined type 1 diabetes. We classified cases from the Network for Pancreatic Organ donors with Diabetes (nPOD) biobank as type 1 (n=111) or non-type 1 (n=42) diabetes using histopathology. Type 1 diabetes was defined by lobular loss of insulin-containing islets along with multiple insulin-deficient islets. We assessed the discriminative performance of previously described type 1 diabetes diagnostic models, based on clinical features (age at diagnosis, BMI) and biomarker data [autoantibodies, type 1 diabetes genetic risk score (T1D-GRS)], and singular features for identifying type 1 diabetes by the area under the curve of the receiver operator characteristic (AUC-ROC). Diagnostic models validated well against histologically defined type 1 diabetes. The model combining clinical features, islet autoantibodies and T1D-GRS was strongly discriminative of type 1 diabetes, and performed better than clinical features alone (AUC-ROC 0.97 vs. 0.95; P=0.03). Histological classification of type 1 diabetes was concordant with serum C-peptide [median <17pmol/l (limit of detection) vs. 1037pmol/l in non-type 1 diabetes; P<0.0001]. Our study provides robust histological evidence that a clinical diagnostic model, combining clinical features and biomarkers, could improve diabetes classification. Our study also provides reassurance that a C-peptide-based definition of type 1 diabetes is an appropriate surrogate outcome that can be used in large clinical studies where histological definition is impossible. Parts of this study were presented in abstract form at the Network for Pancreatic Organ Donors Conference, Florida, USA, 19-22 February 2019 and Diabetes UK Professional Conference, Liverpool, UK, 6-8 March 2019.

Highlights

Correct classification of diabetes type is crucial for appropriate management reduction of long-term complications
The model combining clinical features, islet autoantibodies and type 1 diabetes genetic risk score (T1D-GRS) was strongly discriminative of type 1 diabetes, and performed better than clinical features alone (AUC-ROC 0.97 vs. 0.95; P = 0.03)
Our study provides reassurance that a C-peptidebased definition of type 1 diabetes is an appropriate surrogate outcome that can be used in large clinical studies where histological definition is impossible

Summary

Introduction

Correct classification of diabetes type is crucial for appropriate management reduction of long-term complications. A fundamental difference between type 1 and type 2 diabetes is that the former is characterized by rapid progression to endogenous insulin deficiency due to autoimmune b-cell destruction. This difference forms the basis of differences in their treatment and management [1,2,3], this aetiopathological definition is difficult to apply in clinical practice. Clinical features are predominately used for classification of diabetes type, with only age at diagnosis and BMI having evidence for clinical utility at onset [4]. Diabetic Medicine published by John Wiley & Sons Ltd on behalf of Diabetes UK

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Conclusion