There have been very few efforts to subgroup type 2 diabetes
none of which have been implemented in the clinic.
Added value of this study In this study, a data-driven cluster analysis of six simple variables measured at diagnosis in adult patients with newly diagnosed diabetes (n=14755) identified five replicable clusters of patients with significantly different characteristics and risk of diabetic complicationshttps://docs.google.com/document/d/1_a0s4hdxcuoLo3xkQzaArl10un0kp4Gc4JAEDCFDvKQ/edit. These included a cluster of very insulin-resistant individuals with significantly higher risk of diabetic kidney disease than the other clusters, a cluster of relatively young insulin deficient individuals with poor metabolic control (high HbA1c), and a large group of elderly patients with the most benign disease course. Implications of all the available evidence This new substratification could change the way we think about type 2 diabetes and help to tailor and target early treatment to patients who would benefit most, thereby representing a first step towards precision medicine in diabetes.
Novel subgroups of adult-onset diabetes and their association with outcomes: a data-driven cluster analysis of six variables 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 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. Introduction Diabetes is the fastest increasing disease worldwide and a substantial threat to human health.1 Existing treatment strategies have been unable to stop the progressive course of the disease and prevent development of chronic diabetic complications. One explanation for these shortcomings is that diagnosis of diabetes is based on measurement of only one metabolite, glucose, but the disease is heterogeneous with regard to clinical presentation and progression. Diabetes classification into type 1 and type 2 diabetes relies primarily on the presence (type 1 diabetes) or absence (type 2 diabetes) of autoantibodies against pancreatic islet β-cell antigens and age at diagnosis (younger for type 1 diabetes). With this approach, 75–85% of patients are classified as having type 2 diabetes. A third subgroup, latent autoimmune diabetes in adults (LADA; affecting <10% of people with diabetes), defined by the presence of glutamic acid decarboxylase antibodies (GADA), is phenotypically indistinguishable from type 2 diabetes at diagnosis, but becomes increasingly similar to type 1 diabetes over time.2 With the introduction of gene sequencing in clinical diagnostics, several rare monogenic forms of diabetes were described, including maturityonset diabetes of the young and neonatal diabetes.3,4 Existing treatment guidelines are limited by the fact they respond to poor metabolic control when it has developed, but do not have means to predict which patients will need intensified treatment. Evidence suggests that early treatment is crucial for prevention of life-shortening complications because target tissues seem to remember poor metabolic control decades later (so-called metabolic memory).5,6 A refined classification could provide a powerful tool to identify at diagnosis those at greatest risk of complications and enable individualised treatment regimens in the same way as genetic diagnosis of monogenic diabetes guides clinicians to optimal treatment.7 With this aim, we present a novel diabetes classification based on unsupervised, data-driven cluster analysis of six commonly measured variables and compare it metabolically, Lancet Diabetes Endocrinol 2018; 6: 361–69 Published Online March 1, 2018 http://dx.doi.org/10.1016/ S2213-8587(18)30051-2 See Comment page 348 *Contributed equally Lund University Diabetes Centre, Department of Clinical Sciences, Lund University, Skåne University Hospital, Malmö, Sweden (E Ahlqvist PhD, P Storm PhD, M Dorkhan PhD, P Vikman PhD, R B Prasad PhD, D M Aly MSc, P Almgren MSc, Y Wessman MSc, N Shaat PhD, P Spégel PhD, Prof H Mulder PhD, E Lindholm PhD, Prof O Melander PhD, O Hansson PhD, Prof Å Lernmark PhD, A H Rosengren PhD, Prof L Groop PhD); Department of Primary Health Care, Vaasa Central Hospital, Vaasa, Finland (A Käräjämäki MD, K Lahti MD); Diabetes Center, Vaasa Health Care Center, Vaasa, Finland (A Käräjämäki, K Lahti); Department of Public Health and Caring Sciences, Uppsala University, Uppsala, Sweden (M Martinell MD); Lund University Diabetes Centre, Department of Clinical Sciences, Skåne University Hospital (A Carlsson PhD), and Department of Chemistry, Centre for Analysis and Synthesis (P Spégel), Lund University, Lund, Sweden; Clinical Research and Trial Center, Lund University Hospital, Sweden (U Malmqvist PhD); Folkhälsan Research Center, Helsinki, Finland (T Forsén PhD, T Tuomi PhD); Abdominal Center, Endocrinology, Helsinki University Central Hospital, Research Program for Diabetes and Obesity (T Tuomi), and Finnish Institute for Molecular Medicine (Prof L Groop, T Tuomi), University of Helsinki, Helsinki, Finland; and Department of Neuroscience Articles 362 www.thelancet.com/diabetes-endocrinology Vol 6 May 2018 and Physiology, Wallenberg Center for Molecular and Translational Medicine, University of Gothenburg, Gothenburg, Sweden (A H Rosengren) Correspondence to: Prof Leif Groop, Lund University Diabetes Centre, Malmö 21428, Sweden leif.groop@med.lu.se genetically, and clinically to the current classification in four separate populations from Sweden and Finland. Methods Study populations We used data from five cohorts: All New Diabetics in Scania (ANDIS), the Scania Diabetes Registry (SDR), All New Diabetics in Uppsala (ANDIU), Diabetes Registry Vaasa (DIREVA), and Malmö Diet and Cancer CardioVascular Arm (MDC-CVA). The ANDIS project aims to recruit all incident cases of diabetes within Scania County in Sweden (about 1 200000 inhabitants). All health-care providers in Scania were invited; the current registration covered the period from Jan 1, 2008, to Nov 3, 2016, during which 177 clinics registered 14 625 patients (>90% of eligible patients) aged 0–96 years within a median of 40 days (IQR 12–99) after diagnosis. Median follow-up for this cohort was 4·01 years (IQR 2·02–6·00). Between 1996 and 2009, SDR recruited more than 7400 individuals with diabetes of all types from Scania County, 1466 of whom were recruited within 2 years after diagnosis and had all data necessary for clustering.8 Median follow-up for this cohort was 11·05 years (IQR 8·33–14·56). Of the remaining three cohorts, ANDIU is a project similar to ANDIS in the Uppsala region (about 300000 inhabitants) in Sweden and provided complete data on all clustering variables for 844 patients; DIREVA is from western Finland (roughly 170000 inhabitants) and includes 5107 individuals with diabetes recruited from 2009 to 2014; and MDC-CVA includes 3300 individuals randomly selected from the larger Malmö Diet and Cancer study, to which all men and women born between 1923 and 1950 from the city of Malmö, southern Sweden, were invited to participate.9 The ANDIS and SDR study protocols were approved by the regional ethics review committee in Lund (ANDIS: 584/2006 and 2012/676; SDR: LU 35-99), DIREVA was approved by the ethics committee in Vasa (6/2007), and ANDIU was approved by the regional ethics review committee in Uppsala (2011/155). All participants gave written informed consent. Measurements In ANDIS, blood samples were drawn at registration, and fasting plasma glucose was analysed after overnight fasting with the HemoCue Glucose System (HemoCue AB, Ängelholm, Sweden). C-peptide concentrations were measured with an electro-chemiluminescence immunoassay on Cobas e411 (Roche Diagnostics, Mannheim, Germany) or a radioimmunoassay (Human C-peptide RIA; Linco, St Charles, MO, USA; or Peninsula Laboratories, Belmont, CA, USA). In ANDIS and SDR, GADA was measured with an ELISA (reference <11 U/mL10) or with radiobinding assays using ³⁵S-labelled protein11 (positive cutoff: 5 relative units or 32 IU/mL). The radiobinding assays had 62–88% sensitivity and 91–99% specificity, and the ELISA assay had 72% sensitivity and 99% specificity (Combinatorial Autoantibody or Diabetes/ Islet Autoantibody Standardization Programs 1998–2013). In ANDIU, GADA was measured at Laboratory Medicine in Uppsala (ref <5 U/mL). In DIREVA, GADA was measured with an ELISA (RSR, Cardiff, UK; positive cutoff 10 IU/mL). Zinc transporter 8 autoantibodies (ZnT8A) were measured with a radiobinding assay, as previously described.12 HbA1c was measured at diagnosis with the Variant II Turbo HbA1c Kit 2.0 (Bio-Rad Laboratories, Copenhagen, Denmark). Measurements of HbA1c, alanine aminotransferase, ketones, and serum creatinine over time were obtained from the Clinical Chemistry database. Genotyping Genotyping of ANDIS participants was done on frozen DNA samples prepared from blood with Gentra Puregene Blood Kits (Qiagen, Hilden, Germany) using iPlex (Sequenom, San Diego, CA, USA) or TaqMan For more on the ANDIS project see http://andis.ludc.med.lu.se For more on the ANDIU project see http://www.andiu.se Research in context Evidence before this study National guidelines maintain information about diabetes classification, but this classification has not been much updated during the past 20 years, and very few attempts have been made to explore heterogeneity of type 2 diabetes. We searched PubMed up to Jan 1, 2017, using the Medical Subject Heading terms “diabetes mellitus”, “type 2”, and “classification”. We identified several calls from expert groups for a revised classification, but few efforts to subgroup type 2 diabetes, none of which have been implemented in the clinic. Added value of this study In this study, a data-driven cluster analysis of six simple variables measured at diagnosis in adult patients with newly diagnosed diabetes (n=14755) identified five replicable clusters of patients with significantly different characteristics and risk of diabetic complications. These included a cluster of very insulin-resistant individuals with significantly higher risk of diabetic kidney disease than the other clusters, a cluster of relatively young insulin deficient individuals with poor metabolic control (high HbA1c), and a large group of elderly patients with the most benign disease course. Implications of all the available evidence This new substratification could change the way we think about type 2 diabetes and help to tailor and target early treatment to patients who would benefit most, thereby representing a first step towards precision medicine in diabetes.
none of which have been implemented in the clinic.
Added value of this study In this study, a data-driven cluster analysis of six simple variables measured at diagnosis in adult patients with newly diagnosed diabetes (n=14755) identified five replicable clusters of patients with significantly different characteristics and risk of diabetic complicationshttps://docs.google.com/document/d/1_a0s4hdxcuoLo3xkQzaArl10un0kp4Gc4JAEDCFDvKQ/edit. These included a cluster of very insulin-resistant individuals with significantly higher risk of diabetic kidney disease than the other clusters, a cluster of relatively young insulin deficient individuals with poor metabolic control (high HbA1c), and a large group of elderly patients with the most benign disease course. Implications of all the available evidence This new substratification could change the way we think about type 2 diabetes and help to tailor and target early treatment to patients who would benefit most, thereby representing a first step towards precision medicine in diabetes.
Novel subgroups of adult-onset diabetes and their association with outcomes: a data-driven cluster analysis of six variables 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 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. Introduction Diabetes is the fastest increasing disease worldwide and a substantial threat to human health.1 Existing treatment strategies have been unable to stop the progressive course of the disease and prevent development of chronic diabetic complications. One explanation for these shortcomings is that diagnosis of diabetes is based on measurement of only one metabolite, glucose, but the disease is heterogeneous with regard to clinical presentation and progression. Diabetes classification into type 1 and type 2 diabetes relies primarily on the presence (type 1 diabetes) or absence (type 2 diabetes) of autoantibodies against pancreatic islet β-cell antigens and age at diagnosis (younger for type 1 diabetes). With this approach, 75–85% of patients are classified as having type 2 diabetes. A third subgroup, latent autoimmune diabetes in adults (LADA; affecting <10% of people with diabetes), defined by the presence of glutamic acid decarboxylase antibodies (GADA), is phenotypically indistinguishable from type 2 diabetes at diagnosis, but becomes increasingly similar to type 1 diabetes over time.2 With the introduction of gene sequencing in clinical diagnostics, several rare monogenic forms of diabetes were described, including maturityonset diabetes of the young and neonatal diabetes.3,4 Existing treatment guidelines are limited by the fact they respond to poor metabolic control when it has developed, but do not have means to predict which patients will need intensified treatment. Evidence suggests that early treatment is crucial for prevention of life-shortening complications because target tissues seem to remember poor metabolic control decades later (so-called metabolic memory).5,6 A refined classification could provide a powerful tool to identify at diagnosis those at greatest risk of complications and enable individualised treatment regimens in the same way as genetic diagnosis of monogenic diabetes guides clinicians to optimal treatment.7 With this aim, we present a novel diabetes classification based on unsupervised, data-driven cluster analysis of six commonly measured variables and compare it metabolically, Lancet Diabetes Endocrinol 2018; 6: 361–69 Published Online March 1, 2018 http://dx.doi.org/10.1016/ S2213-8587(18)30051-2 See Comment page 348 *Contributed equally Lund University Diabetes Centre, Department of Clinical Sciences, Lund University, Skåne University Hospital, Malmö, Sweden (E Ahlqvist PhD, P Storm PhD, M Dorkhan PhD, P Vikman PhD, R B Prasad PhD, D M Aly MSc, P Almgren MSc, Y Wessman MSc, N Shaat PhD, P Spégel PhD, Prof H Mulder PhD, E Lindholm PhD, Prof O Melander PhD, O Hansson PhD, Prof Å Lernmark PhD, A H Rosengren PhD, Prof L Groop PhD); Department of Primary Health Care, Vaasa Central Hospital, Vaasa, Finland (A Käräjämäki MD, K Lahti MD); Diabetes Center, Vaasa Health Care Center, Vaasa, Finland (A Käräjämäki, K Lahti); Department of Public Health and Caring Sciences, Uppsala University, Uppsala, Sweden (M Martinell MD); Lund University Diabetes Centre, Department of Clinical Sciences, Skåne University Hospital (A Carlsson PhD), and Department of Chemistry, Centre for Analysis and Synthesis (P Spégel), Lund University, Lund, Sweden; Clinical Research and Trial Center, Lund University Hospital, Sweden (U Malmqvist PhD); Folkhälsan Research Center, Helsinki, Finland (T Forsén PhD, T Tuomi PhD); Abdominal Center, Endocrinology, Helsinki University Central Hospital, Research Program for Diabetes and Obesity (T Tuomi), and Finnish Institute for Molecular Medicine (Prof L Groop, T Tuomi), University of Helsinki, Helsinki, Finland; and Department of Neuroscience Articles 362 www.thelancet.com/diabetes-endocrinology Vol 6 May 2018 and Physiology, Wallenberg Center for Molecular and Translational Medicine, University of Gothenburg, Gothenburg, Sweden (A H Rosengren) Correspondence to: Prof Leif Groop, Lund University Diabetes Centre, Malmö 21428, Sweden leif.groop@med.lu.se genetically, and clinically to the current classification in four separate populations from Sweden and Finland. Methods Study populations We used data from five cohorts: All New Diabetics in Scania (ANDIS), the Scania Diabetes Registry (SDR), All New Diabetics in Uppsala (ANDIU), Diabetes Registry Vaasa (DIREVA), and Malmö Diet and Cancer CardioVascular Arm (MDC-CVA). The ANDIS project aims to recruit all incident cases of diabetes within Scania County in Sweden (about 1 200000 inhabitants). All health-care providers in Scania were invited; the current registration covered the period from Jan 1, 2008, to Nov 3, 2016, during which 177 clinics registered 14 625 patients (>90% of eligible patients) aged 0–96 years within a median of 40 days (IQR 12–99) after diagnosis. Median follow-up for this cohort was 4·01 years (IQR 2·02–6·00). Between 1996 and 2009, SDR recruited more than 7400 individuals with diabetes of all types from Scania County, 1466 of whom were recruited within 2 years after diagnosis and had all data necessary for clustering.8 Median follow-up for this cohort was 11·05 years (IQR 8·33–14·56). Of the remaining three cohorts, ANDIU is a project similar to ANDIS in the Uppsala region (about 300000 inhabitants) in Sweden and provided complete data on all clustering variables for 844 patients; DIREVA is from western Finland (roughly 170000 inhabitants) and includes 5107 individuals with diabetes recruited from 2009 to 2014; and MDC-CVA includes 3300 individuals randomly selected from the larger Malmö Diet and Cancer study, to which all men and women born between 1923 and 1950 from the city of Malmö, southern Sweden, were invited to participate.9 The ANDIS and SDR study protocols were approved by the regional ethics review committee in Lund (ANDIS: 584/2006 and 2012/676; SDR: LU 35-99), DIREVA was approved by the ethics committee in Vasa (6/2007), and ANDIU was approved by the regional ethics review committee in Uppsala (2011/155). All participants gave written informed consent. Measurements In ANDIS, blood samples were drawn at registration, and fasting plasma glucose was analysed after overnight fasting with the HemoCue Glucose System (HemoCue AB, Ängelholm, Sweden). C-peptide concentrations were measured with an electro-chemiluminescence immunoassay on Cobas e411 (Roche Diagnostics, Mannheim, Germany) or a radioimmunoassay (Human C-peptide RIA; Linco, St Charles, MO, USA; or Peninsula Laboratories, Belmont, CA, USA). In ANDIS and SDR, GADA was measured with an ELISA (reference <11 U/mL10) or with radiobinding assays using ³⁵S-labelled protein11 (positive cutoff: 5 relative units or 32 IU/mL). The radiobinding assays had 62–88% sensitivity and 91–99% specificity, and the ELISA assay had 72% sensitivity and 99% specificity (Combinatorial Autoantibody or Diabetes/ Islet Autoantibody Standardization Programs 1998–2013). In ANDIU, GADA was measured at Laboratory Medicine in Uppsala (ref <5 U/mL). In DIREVA, GADA was measured with an ELISA (RSR, Cardiff, UK; positive cutoff 10 IU/mL). Zinc transporter 8 autoantibodies (ZnT8A) were measured with a radiobinding assay, as previously described.12 HbA1c was measured at diagnosis with the Variant II Turbo HbA1c Kit 2.0 (Bio-Rad Laboratories, Copenhagen, Denmark). Measurements of HbA1c, alanine aminotransferase, ketones, and serum creatinine over time were obtained from the Clinical Chemistry database. Genotyping Genotyping of ANDIS participants was done on frozen DNA samples prepared from blood with Gentra Puregene Blood Kits (Qiagen, Hilden, Germany) using iPlex (Sequenom, San Diego, CA, USA) or TaqMan For more on the ANDIS project see http://andis.ludc.med.lu.se For more on the ANDIU project see http://www.andiu.se Research in context Evidence before this study National guidelines maintain information about diabetes classification, but this classification has not been much updated during the past 20 years, and very few attempts have been made to explore heterogeneity of type 2 diabetes. We searched PubMed up to Jan 1, 2017, using the Medical Subject Heading terms “diabetes mellitus”, “type 2”, and “classification”. We identified several calls from expert groups for a revised classification, but few efforts to subgroup type 2 diabetes, none of which have been implemented in the clinic. Added value of this study In this study, a data-driven cluster analysis of six simple variables measured at diagnosis in adult patients with newly diagnosed diabetes (n=14755) identified five replicable clusters of patients with significantly different characteristics and risk of diabetic complications. These included a cluster of very insulin-resistant individuals with significantly higher risk of diabetic kidney disease than the other clusters, a cluster of relatively young insulin deficient individuals with poor metabolic control (high HbA1c), and a large group of elderly patients with the most benign disease course. Implications of all the available evidence This new substratification could change the way we think about type 2 diabetes and help to tailor and target early treatment to patients who would benefit most, thereby representing a first step towards precision medicine in diabetes.
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