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Dina Mansour Aly

Research student

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

Author

  • Emma Ahlqvist
  • Petter Storm
  • Annemari Käräjämäki
  • Mats Martinell
  • Mozhgan Dorkhan
  • Annelie Carlsson
  • Petter Vikman
  • Rashmi B. Prasad
  • Dina Mansour Aly
  • Peter Almgren
  • Ylva Wessman
  • Nael Shaat
  • Peter Spégel
  • Hindrik Mulder
  • Eero Lindholm
  • Olle Melander
  • Ola Hansson
  • Ulf Malmqvist
  • Åke Lernmark
  • Kaj Lahti
  • Tom Forsén
  • Tiinamaija Tuomi
  • Anders H. Rosengren
  • Leif Groop

Summary, in English

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. Funding: Swedish Research Council, European Research Council, Vinnova, Academy of Finland, Novo Nordisk Foundation, Scania University Hospital, Sigrid Juselius Foundation, Innovative Medicines Initiative 2 Joint Undertaking, Vasa Hospital district, Jakobstadsnejden Heart Foundation, Folkhälsan Research Foundation, Ollqvist Foundation, and Swedish Foundation for Strategic Research.

Department/s

  • Genomics, Diabetes and Endocrinology
  • Division of Clinical Genetics
  • PediatricCeliaki/Diabetes
  • Breastcancer-genetics
  • Diabetes - Cardiovascular Disease
  • Centre for Analysis and Synthesis
  • Diabetes - Molecular Metabolism
  • Cardiovascular Research - Hypertension
  • Diabetes and Celiac Unit
  • Diabetes - Islet Patophysiology
  • EXODIAB: Excellence of Diabetes Research in Sweden
  • EpiHealth: Epidemiology for Health

Publishing year

2018-05

Language

English

Pages

361-369

Publication/Series

The Lancet Diabetes and Endocrinology

Volume

6

Issue

5

Document type

Journal article

Publisher

Elsevier

Topic

  • Endocrinology and Diabetes

Status

Published

Research group

  • Genomics, Diabetes and Endocrinology
  • PediatricCeliaki/Diabetes
  • Diabetes - Cardiovascular Disease
  • Diabetes - Molecular Metabolism
  • Cardiovascular Research - Hypertension
  • Diabetes and Celiac Unit
  • Diabetes - Islet Patophysiology

ISBN/ISSN/Other

  • ISSN: 2213-8587