Abstract
Original language | English |
---|---|
Article number | 100477 |
Journal | Cell reports. Medicine |
Volume | 3 |
Issue number | 1 |
DOIs | |
Publication status | Published - 18 Jan 2022 |
Keywords
- archetypes
- disease progression
- glycaemic deterioration
- multi-omics
- patient clustering
- patient stratification
- precision medicine
- soft-clustering
- type 2 diabetes
Access to Document
Other files and links
Cite this
- APA
- Author
- BIBTEX
- Harvard
- Standard
- RIS
- Vancouver
}
In: Cell reports. Medicine, Vol. 3, No. 1, 100477, 18.01.2022.
Research output: Contribution to journal › Article › Academic › peer-review
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 - 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 - Gudmundsdottir, Valborg
AU - Thomas, Cecilia Engel
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
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
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 ). 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). Conceptualization: A.W.-A. C.A.B. M.I.C. E.P. and S.B.; formal analysis: A.W.-A. C.A.B. R.B. A. Mari, A.T. R.K. A. Mahajan, A.V. J.F.T. S.S. A.K. C.J. A.J. H.K.P. V.G. and C.E.T.; clinical investigation: A.J. A. Hattersley, A. Heggie, D.M. F.R. P.E. L.t'H. K.A. T.H.H. H.V. T.H. H. Thomsen, and M.R.; molecular data investigation: M.H. C.P. A.A. M.-G.H. P.B.M. G.K. J.B. L.T. G.F. T.M. and T.K.; project administration: F.D.M. K.T. I.F. R.K. G.N.G. I.P. H.R. O.P. M.W. E.P. S. Brage, and P.W.F.; resources: H. Teare; writing – original draft: A.W.-A. C.A.B. M.I.C. E.P. and S.B.; writing – reviewing & editing: R.B. A. Mari, A.T. M.H. K.B. M.I.C. E.P. and S.B.; funding acquisition: M.W. O.P. E.D. P.W.F. J.M.S. J.A. E.R.P. M.I.C. and S.B. The views expressed in this article are those of the authors and not necessarily those of the NHS, the NIHR, or the Department of Health. M.I.C. has served on advisory panels for Pfizer, Novo Nordisk, and Zoe Global; has received honoraria from Merck, Pfizer, Novo Nordisk, and Eli Lilly; and has received research funding from Abbvie, Astra Zeneca, Boehringer Ingelheim, Eli Lilly, Janssen, Merck, Novo Nordisk, Pfizer, Roche, Sanofi Aventis, Servier, and Takeda. As of June 2019, M.I.C. is an employee of Genentech and a holder of Roche stock. S.B. is holder of stock in Intomics, Hoba Therapeutics, Novo Nordisk, and Lundbeck and holds managing board memberships in Proscion and Intomics. Publisher Copyright: © 2021 The Authors
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 - https://doi.org/10.1016/j.xcrm.2021.100477
DO - https://doi.org/10.1016/j.xcrm.2021.100477
M3 - Article
C2 - 35106505
SN - 2666-3791
VL - 3
JO - Cell reports. Medicine
JF - Cell reports. Medicine
IS - 1
M1 - 100477
ER -