Wesolowska-Andersen, A.* ; Brorsson, C.A.* ; Bizzotto, R.* ; Mari, A.* ; Tura, A.* ; Koivula, R.W.* ; Mahajan, A.* ; Viñuela, A.* ; Tajes, J.F.* ; Sharma, S. ; Haid, M. ; Prehn, C. ; Artati, A. ; Hong, M.G.* ; Musholt, P.B.* ; Kurbasic, A.* ; De Masi, F.* ; Tsirigos, K.D.* ; Pedersen, H.K.* ; Gudmundsdottir, V.* ; Thomas, C.E.* ; Banasik, K.* ; Jennison, C.* ; Jones, A.* ; Kennedy, G.* ; Bell, J.* ; Thomas, L.* ; Frost, G.* ; Thomsen, H.* ; Allin, K.* ; Hansen, T.H.* ; Vestergaard, H.* ; Hansen, T.* ; Rutters, F.* ; Elders, P.* ; t'Hart, L.* ; Bonnefond, A.* ; Canouil, M.* ; Brage, S.* ; Kokkola, T.* ; Heggie, A.* ; McEvoy, D.* ; Hattersley, A.* ; McDonald, T.A.* ; Teare, H.* ; Ridderstråle, M.* ; Walker, M.* ; Forgie, I.* ; Giordano, G.N.* ; Froguel, P.* ; Pavo, I.* ; Ruetten, H.* ; Pedersen, O.* ; Dermitzakis, E.* ; Franks, P.W.* ; Schwenk, J.M.* ; Adamski, J. ; Pearson, E.* ; McCarthy, M.I.* ; Brunak, S.*
Four groups of type 2 diabetes contribute to the etiological and clinical heterogeneity in newly diagnosed individuals: An IMI DIRECT study.
Cell Rep. Med. 3:100477 (2022)
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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Publication type
Article: Journal article
Document type
Scientific Article
Thesis type
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Keywords
Archetypes ; Disease Progression ; Glycaemic Deterioration ; Multi-omics ; Patient Clustering ; Patient Stratification ; Precision Medicine ; Soft-clustering ; Type 2 Diabetes; Genome-wide Association; Insulin-resistance; Cell Dysfunction; Fat-content; Onset; Normalization; Mechanisms; Generation; Subgroups; Atlas
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Language
english
Publication Year
2022
Prepublished in Year
0
HGF-reported in Year
2022
ISSN (print) / ISBN
2666-3791
e-ISSN
2666-3791
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Volume: 3,
Issue: 1,
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Article Number: 100477
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Cell Press
Publishing Place
Radarweg 29, 1043 Nx Amsterdam, Netherlands
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0000-00-00
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0000-00-00
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Reviewing status
Peer reviewed
POF-Topic(s)
30201 - Metabolic Health
30505 - New Technologies for Biomedical Discoveries
Research field(s)
Genetics and Epidemiology
Enabling and Novel Technologies
PSP Element(s)
G-505600-003
A-630710-001
G-500600-001
Grants
Innovative Medicines Initiative 2 Joint Undertaking
European Union
Novo Nordisk Foundation
Wellcome Trust New Investigator Award
NIDDK
Wellcome Trust
Innovative Medicines Initiative Joint Undertaking
Copyright
Erfassungsdatum
2022-06-08