Emma Ahlqvist1, Petter Storm1, Annemari Käräjämäki2, Mats Martinell3, Mozhgan Dorkhan1, Annelie Carlsson4, Petter Vikman1, Rashmi B Prasad1, Dina Mansour Aly1, Peter Almgren1, Ylva Wessman1, Nael Shaat1, Peter Spégel5, Hindrik Mulder1, Eero Lindholm1, Olle Melander1, Ola Hansson1, Ulf Malmqvist6, Åke Lernmark1, Kaj Lahti2, Tom Forsén7, Tiinamaija Tuomi8, Anders H Rosengren9, Leif Groop10. 1. Lund University Diabetes Centre, Department of Clinical Sciences, Lund University, Skåne University Hospital, Malmö, Sweden. 2. Department of Primary Health Care, Vaasa Central Hospital, Vaasa, Finland; Diabetes Center, Vaasa Health Care Center, Vaasa, Finland. 3. Department of Public Health and Caring Sciences, Uppsala University, Uppsala, Sweden. 4. Lund University Diabetes Centre, Department of Clinical Sciences, Skåne University Hospital, Lund University, Lund, Sweden. 5. Lund University Diabetes Centre, Department of Clinical Sciences, Lund University, Skåne University Hospital, Malmö, Sweden; Department of Chemistry, Centre for Analysis and Synthesis, Lund University, Lund, Sweden. 6. Clinical Research and Trial Center, Lund University Hospital, Sweden. 7. Folkhälsan Research Center, Helsinki, Finland. 8. Folkhälsan Research Center, Helsinki, Finland; Abdominal Center, Endocrinology, Helsinki University Central Hospital, Research Program for Diabetes and Obesity, University of Helsinki, Helsinki, Finland; Finnish Institute for Molecular Medicine, University of Helsinki, Helsinki, Finland. 9. Lund University Diabetes Centre, Department of Clinical Sciences, Lund University, Skåne University Hospital, Malmö, Sweden; Department of Neuroscience and Physiology, Wallenberg Center for Molecular and Translational Medicine, University of Gothenburg, Gothenburg, Sweden. 10. Lund University Diabetes Centre, Department of Clinical Sciences, Lund University, Skåne University Hospital, Malmö, Sweden; Finnish Institute for Molecular Medicine, University of Helsinki, Helsinki, Finland. Electronic address: leif.groop@med.lu.se.
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 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.
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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