Type 2 diabetes is a complex, multifactorial disease with varying presentation and underlying pathophysiology. Recent studies using data-driven cluster analysis have led to a stratification of type 2 diabetes into novel subgroups based on six clinical measurements. Whether these subgroups truly correspond to the underlying phenotypic differences is nevertheless unclear. Here, we apply an unsupervised, data-driven clustering method (Similarity Network Fusion) to characterize type 2 diabetes in two independent cohorts involving 1,134 subjects in total based on integrated plasma lipidomics and peptidomics data without pre-selection. Logistic regression was then used to explore clustering based on ≥ 180 circulating lipids and 1,195 protein biomarkers, alongside clinical signatures. Two subgroups were identified, one of which associated with elevated C-peptide levels, diabetic complications and more severe insulin resistance compared to the other. GWAS analysis against 403 type 2 diabetes risk variants revealed associations of several SNPs with clusters and altered molecular profiles. We thus demonstrate that heterogeneity in type 2 diabetes can be captured by circulating omics alone using an unsupervised bottom-up approach. Such multi-omics signatures could reflect pathological mechanisms underlying type 2 diabetes and thus may help inform on precision medicine approaches to disease management.

G.A.R. has received grant funding from, and is a consultant for, Sun Pharmaceuticals Inc. K.S. is CEO of Lipotype. K.S. and C.K. are shareholders of Lipotype. M.JG. is an employee of Lipotype. M.K.H. is an employee of Janssen Research &

G.R. was supported by a Wellcome Trust Investigator Award (212625/Z/18/Z), MRC Programme grant (MR/R022259/1) Diabetes UK (BDA/15/0005275, BDA 16/0005485) grants and a start-up grant from the CR-CHUM, University of Montreal. This project has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement no. 115881 (RHAPSODY). This Joint Undertaking receives support from the European Union Horizon 2020 research and innovation programme and EFPIA. This work is supported by the Swiss State Secretariat for Education Research and Innovation (SERI) under contract no. 160097.

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The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

The Hoorn Diabetes Care System (DCS) cohort is a prospective cohort with currently over 14,000 individuals with routine care data. The Ethical Review Committee of the VU University Medical Center, Amsterdam approved the study. In 2008-2014, additional blood sampling was done in 5,500 participants, who provided written informed consent. These samples were used for this study. The Genetics of Diabetes Audit and Research Tayside Study (GoDARTS) is a cohort of ~8,000 patients with T2D. The study was approved by the Tayside Medical Ethics Committee and all individuals provided informed consent.

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All data produced in the present study are available upon reasonable request to the authors.