Four groups of type 2 diabetes contribute to the etiological and clinical heterogeneity in newly diagnosed individuals: An IMI DIRECT study

Imre Pavo; IMI DIRECT consortium

doi:10.1016/j.xcrm.2021.100477

Four groups of type 2 diabetes contribute to the etiological and clinical heterogeneity in newly diagnosed individuals: An IMI DIRECT study

Imre Pavo, IMI DIRECT consortium

Research output: Contribution to journal › Article › peer-review

62 Citations (SciVal)

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 language	English
Article number	100477
Journal	Cell Reports Medicine
Volume	3
Issue number	1
Early online date	4 Jan 2022
DOIs	https://doi.org/10.1016/j.xcrm.2021.100477
Publication status	Published - 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.

Funding

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 ). 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.

Keywords

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

ASJC Scopus subject areas

General Biochemistry,Genetics and Molecular Biology

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1016/j.xcrm.2021.100477Licence: CC BY

Cite this

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title = "Four groups of type 2 diabetes contribute to the etiological and clinical heterogeneity in newly diagnosed individuals: An IMI DIRECT study",

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.",

keywords = "archetypes, disease progression, glycaemic deterioration, multi-omics, patient clustering, patient stratification, precision medicine, soft-clustering, type 2 diabetes",

author = "Andrew Hattersley and Timothy McDonald and Harriet Teare and Martin Ridderstrale and Mark Walker and Ian Forgie and Giordano, {Giuseppe N.} and Philippe Froguel and Imre Pavo and {IMI DIRECT consortium} and Agata Wesolowska-Andersen and Brorsson, {Caroline A.} and Roberto Bizzotto and Andrea Mari and Andrea Tura and Robert Koivula and Anubha Mahajan and Ana Vinuela and Tajes, {Juan Fernandez} and Sapna Sharma and Mark Haid and Cornelia Prehn and Anna Artati and Hong, {Mun Gwan} and Musholt, {Petra B.} and Azra Kurbasic and {De Masi}, Federico and Kostas Tsirigos and Pedersen, {Helle Krogh} and Thomas, {Cecilia Engel} and Valborg Gudmundsdottir and Karina Banasik and Chrisopher Jennison and Angus Jones and Gwen Kennedy and Jimmy Bell and Louise Thomas and Gary Frost and Henrik Thomsen and Kristine Allin and Hansen, {Tue Haldor} and Henrik Vestergaard and Torben Hansen and Femke Rutters and Petra Elders and Leen t'Hart and Amelie Bonnefond and Micka{\"e}l Canouil and Soren Brage and Tarja Kokkola and Alison Heggie and Donna McEvoy",

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{\textquoteright}s Seventh Framework Programme ( FP7/2007-2013 ) and EFPIA companies{\textquoteright} 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.",

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doi = "10.1016/j.xcrm.2021.100477",

language = "English",

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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

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AU - Brorsson, Caroline A.

AU - Bizzotto, Roberto

AU - Mari, Andrea

AU - Tura, Andrea

AU - Koivula, Robert

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AU - Musholt, Petra B.

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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

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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.

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Four groups of type 2 diabetes contribute to the etiological and clinical heterogeneity in newly diagnosed individuals: An IMI DIRECT study

Abstract

Bibliographical note

Funding

Keywords

ASJC Scopus subject areas

UN SDGs

Access to Document

Other files and links

Fingerprint

Electrospray Time-of-Flight Mass Spectrometer (Open-Access)

Cite this

Four groups of type 2 diabetes contribute to the etiological and clinical heterogeneity in newly diagnosed individuals: An IMI DIRECT study

Abstract

Bibliographical note

Funding

Keywords

ASJC Scopus subject areas

UN SDGs

Access to Document

Other files and links

Fingerprint

Equipment

Electrospray Time-of-Flight Mass Spectrometer (Open-Access)

Cite this