Abstract

The presentation and underlying pathophysiology of type 2 diabetes (T2D) is complex and heterogeneous. Recent studies attempted to stratify T2D into distinct subgroups using data-driven approaches, but their clinical utility may be limited if categorical representations of complex phenotypes are suboptimal. We apply a soft-clustering (archetype) method to characterize newly diagnosed T2D based on 32 clinical variables. We assign quantitative clustering scores for individuals and investigate the associations with glycemic deterioration, genetic risk scores, circulating omics biomarkers, and phenotypic stability over 36 months. Four archetype profiles represent dysfunction patterns across combinations of T2D etiological processes and correlate with multiple circulating biomarkers. One archetype associated with obesity, insulin resistance, dyslipidemia, and impaired β cell glucose sensitivity corresponds with the fastest disease progression and highest demand for anti-diabetic treatment. We demonstrate that clinical heterogeneity in T2D can be mapped to heterogeneity in individual etiological processes, providing a potential route to personalized treatments.

Original languageEnglish
Article number100477
JournalCell Reports Medicine
Volume3
Issue number1
Early online date4 Jan 2022
DOIs
Publication statusPublished - 18 Jan 2022

Bibliographical note

Funding Information:
The work leading to this publication has received support from the Innovative Medicines Initiative Joint Undertaking under grant agreement 115317 (DIRECT), resources of which are composed of financial contribution from the European Union’s Seventh Framework Programme ( FP7/2007-2013 ) and EFPIA companies’ in-kind contribution. The Novo Nordisk Foundation is acknowledged (grants NNF17OC0027594 and NNF14CC0001 ). E.P. holds a Wellcome Trust New Investigator Award ( 102820/Z/13/Z ). M.I.C. holds grants from NIDDK ( U01-DK105535 ) and the Wellcome Trust ( 090532 , 098381 , 106130 , 203141 , and 212259 ). M.I.C. was a Wellcome investigator. K.B. and S. Brunak received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement 115881 (RHAPSODY). This research was funded, in whole or in part, by the Wellcome Trust (grants 102820/Z/13/Z , 090532 , 098381 , 106130 , 203141 , and 212259 ).

Data and code availability
Data: Requests for access to IMI DIRECT data, including data presented here, can be made to the Lead Contact. All data are available without restriction in a secure environment.
Code: Our manuscript does not report any novel custom code. The software for the main clustering method is available as an R package and was published in reference 10 and 11.
Any additional information required to reanalyze the data reported in this paper is available from the Lead Contact upon request.

Keywords

  • archetypes
  • disease progression
  • glycaemic deterioration
  • multi-omics
  • patient clustering
  • patient stratification
  • precision medicine
  • soft-clustering
  • type 2 diabetes

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)

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