Novel subgroups of adult-onset diabetes and their association with outcomes: a data-driven cluster analysis of six variablesShow others and affiliations
2018 (English)In: The Lancet Diabetes and Endocrinology, ISSN 2213-8587, E-ISSN 2213-8595, Vol. 6, no 5, p. 361-369Article in journal (Refereed) Published
Abstract [en]
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, HbA1c, and homoeostatic model assessment 2 estimates of β-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.
Place, publisher, year, edition, pages
2018. Vol. 6, no 5, p. 361-369
National Category
Endocrinology and Diabetes
Research subject
Endocrinology and Diabetology
Identifiers
URN: urn:nbn:se:uu:diva-328355DOI: 10.1016/S2213-8587(18)30051-2ISI: 000430631600013OAI: oai:DiVA.org:uu-328355DiVA, id: diva2:1135251
Projects
Diabetes Mellitus at the Time for Diagnosis, Studies on Prognostic Factors
Funder
EXODIAB - Excellence of Diabetes Research in Sweden, 2009-1039Swedish Research Council, 521-2010-3490Swedish Research Council, 2010-5983Linnaeus research environment CADICS, 349-2006-237
Note
Tredjeförfattarskapet delas av tredje och fjärde författaren.
2017-08-222017-08-222018-07-20Bibliographically approved
In thesis