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Novel subgroups of ...
Novel subgroups of adult-onset diabetes and their association with outcomes: a data-driven cluster analysis of six variables
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Ahlqvist, E. (author)
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Storm, P. (author)
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Karajamaki, A. (author)
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Martinell, M. (author)
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Dorkhan, M. (author)
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Carlsson, A. (author)
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Vikman, P. (author)
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Prasad, R. B. (author)
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Aly, D. M. (author)
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Almgren, P. (author)
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Wessman, Y. (author)
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Shaat, N. (author)
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Spegel, P. (author)
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Mulder, H. (author)
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Lindholm, E. (author)
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Melander, O. (author)
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Hansson, O. (author)
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Malmqvist, U. (author)
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Lernmark, A. (author)
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Lahti, K. (author)
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Forsen, T. (author)
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Tuomi, T. (author)
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- Rosengren, Anders H., 1978 (author)
- Gothenburg University,Göteborgs universitet,Institutionen för neurovetenskap och fysiologi,Institute of Neuroscience and Physiology
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Groop, L. (author)
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(creator_code:org_t)
- Elsevier BV, 2018
- 2018
- English.
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In: Lancet Diabetes & Endocrinology. - : Elsevier BV. - 2213-8587. ; 6:5, s. 361-369
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https://helda.helsin...
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https://gup.ub.gu.se...
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https://doi.org/10.1...
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Abstract
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- 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.
Subject headings
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Endokrinologi och diabetes (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Endocrinology and Diabetes (hsv//eng)
Keyword
- insulin
- mechanisms
- mutations
- mellitus
- risk
- complications
- participants
- definition
- antibodies
- variants
- Endocrinology & Metabolism
Publication and Content Type
- ref (subject category)
- art (subject category)
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- By the author/editor
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Ahlqvist, E.
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Storm, P.
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Karajamaki, A.
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Martinell, M.
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Dorkhan, M.
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Carlsson, A.
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show more...
-
Vikman, P.
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Prasad, R. B.
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Aly, D. M.
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Almgren, P.
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Wessman, Y.
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Shaat, N.
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Spegel, P.
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Mulder, H.
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Lindholm, E.
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Melander, O.
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Hansson, O.
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Malmqvist, U.
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Lernmark, A.
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Lahti, K.
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Forsen, T.
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Tuomi, T.
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Rosengren, Ander ...
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Groop, L.
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show less...
- About the subject
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- MEDICAL AND HEALTH SCIENCES
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MEDICAL AND HEAL ...
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and Clinical Medicin ...
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and Endocrinology an ...
- Articles in the publication
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Lancet Diabetes ...
- By the university
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University of Gothenburg