Brorsson, Caroline Anna

Brunak, Søren

Dermitzakis, E.

Fernandez, J.

Franks, P.

Gudmundsdottir, Valborg

Gupta, R.

Kurbasic, A.

MacCarthy, M.

Mahajan, A.

Mari, A.

Pearson, E.

Pedersen, H. Krogh

Vinuela, A.

Online Research Database In Technology

                       General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.  • Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal   If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.     Downloaded from orbit.dtu.dk on: Dec 20, 2017Clustering on baseline clinical variables identifies subgroups of type 2 diabetespatients with different rate of progression over 18 months: a DIRECT studyBrorsson, Caroline Anna; Pedersen, Helle Krogh; Gudmundsdottir, Valborg; Mari, A.; Kurbasic, A.;Vinuela, A.; Fernandez, J.; Mahajan, A.; Gupta, Ramneek; Dermitzakis, E.; MacCarthy, M.; Franks, P.;Pearson, E.; Brunak, SørenPublished in:DiabetologiaPublication date:2017Document VersionPublisher's PDF, also known as Version of recordLink back to DTU OrbitCitation (APA):Brorsson, C. A., Pedersen, H. K., Gudmundsdottir, V., Mari, A., Kurbasic, A., Vinuela, A., ... Brunak, S. (2017).Clustering on baseline clinical variables identifies subgroups of type 2 diabetes patients with different rate ofprogression over 18 months: a DIRECT study. Diabetologia, 60(Suppl. 1), S117. [250].OP 45 Classifying diabetes249Clustering of diabetes into novel subgroups provides improved predictionof outcomeE. Ahlqvist1, A. Käräjämäki2, M. Martinell3, P. Storm1, M. Dorkhan1, P.Vikman1, R.B. Prasad1, D. Mansour Aly1, N. Shaat1, E. Lindholm1, T.Tuomi4,5, A.H. Rosengren1, L. Groop1,4;1Lund University Diabetes Centre, Lund University, Malmö, Sweden,2Primary Health Care, Vaasa Central Hospital, Vaasa, Finland, 3PublicHealth and Caring Sciences, Uppsala University, Uppsala, Sweden, 4FinnishInstitute for Molecular Medicine, Helsinki University, 5Folkhälsan ResearchCenter, Helsinki, Finland.Background and aims: The current classification of diabetes into two mainforms (T1D and T2D) has been useful in delineating T1D as an insulin-deficient form requiring insulin therapy but less useful for dissecting the het-erogeneity of T2D. A refined classification could provide a powerful tool toidentify those at greatest risk of complications already at diagnosis and tailorindividualized treatment.Materials and methods: We performed unsupervised clustering (k-meansand hierarchical) based on six variables (age, BMI, HbA1c, HOMA2-B,HOMA2-IR and GAD auto-antibodies) in ANDIS, a Swedish cohort of15,000 newly diagnosed diabetes patients. Disease progression, treatmentand development of complications were followed using medical records andnational registries. Risk of complications was analyzed using cox regressionwith the largest cluster as reference. Genetic loci known to be associated withT2D and related traits were analysed using MLE comparing each cluster to anon-diabetic cohort from the same geographical region.Results: ANDIS patients clustered into one GADA-positive cluster (re-ferred to as SAID, Severe Autoimmune Diabetes) and four GADA-negative clusters. This was replicated in three independent cohorts fromSweden and Finland. Cluster 2 (SIDD, Severe Insulin Deficient Diabetes;17.5% of patients) was characterized by early onset, insulin deficiencyand high HbA1c. During a mean follow-up of 4 years SIDD had higherHbA1c, was more likely to be prescribed insulin and develop diabeticretinopathy compared to other GADA-negative clusters. Cluster 3 (SIRD,Severe Insulin Resistant Diabetes; 15.3%) had the highest risk of kidneydisease including CKD stage 3B (HR 3.30[2.67-4.08], p=3.6x10-28) andmacroalbuminuria/end-stage renal disease (ESRD; HR 2.40[1.69-3.42],p=9.8x10-7). This was replicated in a cohort with longer follow-up(SDR, mean 11 years), where SIRD had five-fold increased risk ofESRD (HR 5.04[2.76-9.23], p=1.5x10-7). Cluster 4 (MOD, Mild ObeseDiabetes; 21.6%) and cluster 5 (MARD, Mild Age-Related Diabetes;39.1%) showed only modest metabolic derangements. Using p<0.01 ascut-off, no genetic variant was associated with all clusters. Strikingly,the T2D-associated variant, rs7903146, in the TCF7L2 gene was associ-ated with SIDD (OR 1.51[1.33-1.71], p=2.8x10-10), MOD (OR 1.38[1.21-1.56], p=25.7x10-7) and MARD (OR 1.41 [1.28-1.55], p=1.1x10-12), butnot with SIRD (OR 1.00[0.87-1.15], p=0.86). A variant in IGF2BP2(rs4402960) was associated with SIDD (OR 1.23[1.08-1.40], p=2.0x10-4)and MARD (OR 1.22[1.11-1.33], p=2.1x10-6), but not with SIRD (OR1.01[0.88-1.16], p=0.53) or MOD (OR 1.04[0.92-1.18], p=0.31).Conclusion: This study provides a first step towards a more precise, clinicallyuseful, classification of diabetes representing an important step towards preci-sion medicine in diabetes.Supported by: Swedish Research Council, ERC, Novo Nordisk Foundation,ALFDisclosure: E. Ahlqvist: None.250Clustering on baseline clinical variables identifies subgroups of type 2diabetes patients with different rate of progression over 18 months: aDIRECT studyC.A. Brorsson1, H. Krogh Pedersen1, V. Gudmundsdottir1, A. Mari2, A.Kurbasic3, A. Viñuela4, J. Fernandez5, A. Mahajan5, R. Gupta1, E.Dermitzakis4, M. MacCarthy5, P. Franks3, E. Pearson6, S. Brunak1,7, for theDIRECT Consortium;1Department of Bioinformatics, Technical University of Denmark, Lyngby,Denmark, 2Institue of Neuroscience - National Research Council, Padova,Italy, 3Lund University, Malmö, Sweden, 4University of Geneva, Geneva,Switzerland, 5University of Oxford, Oxford, 6University of Dundee,Dundee, UK, 7University of Copenhagen, Copenhagen, Denmark.Background and aims: Type 2 diabetes is characterized by a heterogeneouspresentation with varying degrees of insulin resistance and β-cell failure.Taking advantage of the detailed clinical information collected for type 2diabetes patients included in the DIRECT study, our aim was to characterizeinter-individual heterogeneity and identify subgroups with different presenta-tions of diabetic phenotypes at baseline, and investigate the effect on the rate ofprogression during follow-up. Genotyping data was used to evaluate geneticdifferences between subgroups.Materials andmethods: 836 newly diagnosed individuals were enrolledin the DIRECT study. The clustering was based on 20 clinical vari-ables from the baseline visit consisting of anthropometric, biochemicaland glycaemic modeling variables. We clustered the individuals usingan unsupervised, agglomerative clustering method. Diabetes progres-sion was assessed using individual HbA1c slopes obtained from aconditional linear mixed-model using data at 0, 9 and 18 month ad-justed for weight, diabetes medication and baseline HbA1c. An extratime-varying covariate defined to be 1 at baseline and zero for allother visits was introduced to account for presence of effect frombaseline to subsequent visits. Linear regression models with clustermembership as predictor was used to calculate effect, 95% CI andp-values for differences between subgroups. A genetic risk score(GRS) was calculated from the cumulative number of risk alleles of65 published GWAS SNPs for type 2 diabetes.Results: Full clinical data was available for 790 individuals. We identi-fied three major patient subgroups that differed significantly in regardsto their baseline characteristics. The three groups could broadly be de-scribed as insulin resistant (IR), β-cell deficient (βD), and mixed. Themost significant difference was seen in insulin sensitivity (ln(Matsuda),beta (95% CI): Mixed= -0.61 (-0.68- -0.55), p=9.9*10-68; IR=-1.3 (-1.3--1.2), p=4.9*10-203) compared to the βD group, but also insulin secre-tion, C-peptide, BMI, basal glucose, triglycerides and ALT (p<10-16).The IR group was treated with significantly more metformin comparedto the βD group (% maximum dose: IR= 5.13 (1.79-8.47), p=0.003).Investigating the rate of progression in HbA1c between baseline and 18months showed that the IR group had the fastest progression comparedto the βD group, which had the slowest progression (change in HbA1c(mmol/mol)/year: IR=0.77 (0.32-1.22), p=0.0008). There was no differ-ence for the GRS constructed of 65 GWAS SNPs.Conclusion:We have demonstrated that the newly diagnosed type 2 diabetescohort from DIRECT has a heterogeneous presentation of their diabetic phe-notype at baseline. Clustering identified three major subgroups, driven by theirlevel of insulin resistance-related traits. The subgroups showed differences intheir rate of progression over 18 months with the insulin resistant group show-ing the fastest progression.Supported by: IMI Joint Undertaking n° 115317, FP7/2007-2013 and EFPIAcompanies in kindDisclosure: C.A. Brorsson: None.251Single antibody positivity associates with two distinct phenotypes inpeople with young-onset diabetes: insights from the MY DIABETESstudyS.Misra1, A. Sebastian2, X. Liu2, L. Lupak1, E.L.Williams1, D.G. Johnston2,N.S. Oliver2;1Metabolic Medicine, Imperial College Healthcare NHS Trust, 2MetabolicMedicine, Imperial College London, London, UK.Background and aims: Pancreatic autoantibody status may be used as abiomarker to differentiate diabetes subtype. Single antibody positivity in indi-viduals presenting at older age has uncertain significance, but has not beenDiabetologia (2017) 60 (Suppl 1):S1–S608 S117

Clustering on baseline clinical variables identifies subgroups of type 2 diabetes patients with different rate of progression over 18 months: a DIRECT study

http://orbit.dtu.dk/ws/files/139260935/CLUSTER.pdf

Clustering on baseline clinical variables identifies subgroups of type 2 diabetes patients with different rate of progression over 18 months: a DIRECT study

Abstract

Similar works

Full text

Available Versions

Online Research Database In Technology