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Novel subgroups of adult-onset diabetes and their association with outcomes: a data-driven cluster analysis of six variables

Journal article
Authors E. Ahlqvist
P. Storm
A. Karajamaki
M. Martinell
M. Dorkhan
A. Carlsson
P. Vikman
R. B. Prasad
D. M. Aly
P. Almgren
Y. Wessman
N. Shaat
P. Spegel
H. Mulder
E. Lindholm
O. Melander
O. Hansson
U. Malmqvist
A. Lernmark
K. Lahti
T. Forsen
T. Tuomi
Anders H. Rosengren
L. Groop
Published in Lancet Diabetes & Endocrinology
Volume 6
Issue 5
Pages 361-369
ISSN 2213-8587
Publication year 2018
Published at Institute of Neuroscience and Physiology
Pages 361-369
Language en
Links https://doi.org/10.1016/S2213-8587(...
Keywords insulin, mechanisms, mutations, mellitus, risk, complications, participants, definition, antibodies, variants, Endocrinology & Metabolism
Subject categories Endocrinology and Diabetes

Abstract

Background Diabetes is presently classified into two main forms, type 1 and type 2 diabetes, but type 2 diabetes in particular is highly heterogeneous. A refined classification could provide a powerful tool to individualise treatment regimens and identify individuals with increased risk of complications at diagnosis. Methods We did data-driven cluster analysis (k-means and hierarchical clustering) in patients with newly diagnosed diabetes (n=8980) from the Swedish All New Diabetics in Scania cohort. Clusters were based on six variables (glutamate decarboxylase antibodies, age at diagnosis, BMI, HbA(1c), and homoeostatic model assessment 2 estimates of beta-cell function and insulin resistance), and were related to prospective data from patient records on development of complications and prescription of medication. Replication was done in three independent cohorts: the Scania Diabetes Registry (n=1466), All New Diabetics in Uppsala (n=844), and Diabetes Registry Vaasa (n=3485). Cox regression and logistic regression were used to compare time to medication, time to reaching the treatment goal, and risk of diabetic complications and genetic associations. Findings We identified five replicable clusters of patients with diabetes, which had significantly different patient characteristics and risk of diabetic complications. In particular, individuals in cluster 3 (most resistant to insulin) had significantly higher risk of diabetic kidney disease than individuals in clusters 4 and 5, but had been prescribed similar diabetes treatment. Cluster 2 (insulin deficient) had the highest risk of retinopathy. In support of the clustering, genetic associations in the clusters differed from those seen in traditional type 2 diabetes. Interpretation We stratified patients into five subgroups with differing disease progression and risk of diabetic complications. This new substratification might eventually help to tailor and target early treatment to patients who would benefit most, thereby representing a first step towards precision medicine in diabetes.

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