TY - JOUR
T1 - Four groups of type 2 diabetes contribute to the etiological and clinical heterogeneity in newly diagnosed individuals
T2 - An IMI DIRECT study
AU - Hattersley, Andrew
AU - McDonald, Timothy
AU - Teare, Harriet
AU - Ridderstrale, Martin
AU - Walker, Mark
AU - Forgie, Ian
AU - Giordano, Giuseppe N.
AU - Froguel, Philippe
AU - Pavo, Imre
AU - IMI DIRECT consortium
AU - Wesolowska-Andersen, Agata
AU - Brorsson, Caroline A.
AU - Bizzotto, Roberto
AU - Mari, Andrea
AU - Tura, Andrea
AU - Koivula, Robert
AU - Mahajan, Anubha
AU - Vinuela, Ana
AU - Tajes, Juan Fernandez
AU - Sharma, Sapna
AU - Haid, Mark
AU - Prehn, Cornelia
AU - Artati, Anna
AU - Hong, Mun Gwan
AU - Musholt, Petra B.
AU - Kurbasic, Azra
AU - De Masi, Federico
AU - Tsirigos, Kostas
AU - Pedersen, Helle Krogh
AU - Thomas, Cecilia Engel
AU - Gudmundsdottir, Valborg
AU - Banasik, Karina
AU - Jennison, Chrisopher
AU - Jones, Angus
AU - Kennedy, Gwen
AU - Bell, Jimmy
AU - Thomas, Louise
AU - Frost, Gary
AU - Thomsen, Henrik
AU - Allin, Kristine
AU - Hansen, Tue Haldor
AU - Vestergaard, Henrik
AU - Hansen, Torben
AU - Rutters, Femke
AU - Elders, Petra
AU - t'Hart, Leen
AU - Bonnefond, Amelie
AU - Canouil, Mickaël
AU - Brage, Soren
AU - Kokkola, Tarja
AU - Heggie, Alison
AU - McEvoy, Donna
N1 - 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.
PY - 2022/1/18
Y1 - 2022/1/18
N2 - 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.
AB - 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.
KW - archetypes
KW - disease progression
KW - glycaemic deterioration
KW - multi-omics
KW - patient clustering
KW - patient stratification
KW - precision medicine
KW - soft-clustering
KW - type 2 diabetes
UR - http://www.scopus.com/inward/record.url?scp=85122950661&partnerID=8YFLogxK
U2 - 10.1016/j.xcrm.2021.100477
DO - 10.1016/j.xcrm.2021.100477
M3 - Article
C2 - 35106505
AN - SCOPUS:85122950661
SN - 2666-3791
VL - 3
JO - Cell Reports Medicine
JF - Cell Reports Medicine
IS - 1
M1 - 100477
ER -