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

Ahlqvist, Emma LU ; Storm, Petter LU ; Käräjämäki, Annemari; Martinell, Mats; Dorkhan, Mozhgan LU ; Carlsson, Annelie LU ; Vikman, Petter LU ; Prasad, Rashmi B. LU ; Aly, Dina Mansour LU and Almgren, Peter LU , et al. (2018) In The Lancet Diabetes and Endocrinology
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, HbA1c, and homoeostatic model assessment 2 estimates of β-cell function and insulin resistance), and were related to prospective... (More)

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.

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The Lancet Diabetes and Endocrinology
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Elsevier
external identifiers
  • scopus:85042583803
ISSN
2213-8587
DOI
10.1016/S2213-8587(18)30051-2
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English
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71c089dc-2059-41d5-9c96-52ecd9c390e2
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2018-03-08 14:39:59
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2018-10-16 04:06:08
@article{71c089dc-2059-41d5-9c96-52ecd9c390e2,
  abstract     = {<p>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<sub>1c</sub>, 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.</p>},
  author       = {Ahlqvist, Emma and Storm, Petter and Käräjämäki, Annemari and Martinell, Mats and Dorkhan, Mozhgan and Carlsson, Annelie and Vikman, Petter and Prasad, Rashmi B. and Aly, Dina Mansour and Almgren, Peter and Wessman, Ylva and Shaat, Nael and Spégel, Peter and Mulder, Hindrik and Lindholm, Eero and Melander, Olle and Hansson, Ola and Malmqvist, Ulf and Lernmark, Åke and Lahti, Kaj and Forsén, Tom and Tuomi, Tiinamaija and Rosengren, Anders H. and Groop, Leif},
  issn         = {2213-8587},
  language     = {eng},
  month        = {03},
  publisher    = {Elsevier},
  series       = {The Lancet Diabetes and Endocrinology},
  title        = {Novel subgroups of adult-onset diabetes and their association with outcomes : A data-driven cluster analysis of six variables},
  url          = {http://dx.doi.org/10.1016/S2213-8587(18)30051-2},
  year         = {2018},
}