Four groups of type 2 diabetes contribute to the etiological and clinical heterogeneity in newly diagnosed individuals: An IMI DIRECT study
Agata Wesolowska‐Andersen, Caroline Brorsson, Roberto Bizzotto, Andrea Mari, Andrea Tura, Robert W. Koivula, Anubha Mahajan, Ana Viñuela, Juan Fernández Tajes, Sapna Sharma, Mark Haid, Cornelia Prehn, Anna Artati, Mun‐Gwan Hong, Petra B. Musholt, Azra Kurbasic, Federico De Masi, Kostas Tsirigos, Helle Pedersen, Valborg Guðmundsdóttir, Cecilia Engel Thomas, Karina Banasik, C Jennison, Angus G. Jones, Gwen Kennedy, Jimmy D. Bell, E. Louise Thomas, Gary Frost, Henrik F. Thomsen, Kristine H. Allin, Tue H. Hansen, Henrik Vestergaard, Torben Hansen, Femke Rutters, Petra J. M. Elders, Leen M. ‘t Hart, Amélie Bonnefond, Mickaël Canouil, Søren Brage, Tarja Kokkola, Alison Heggie, Donna McEvoy, Andrew T. Hattersley, Timothy J. McDonald, Harriet Teare, Martin Ridderstråle, Mark Walker, Ian Forgie, Giuseppe N. Giordano, Philippe Froguel, Imre Pávó, Hartmut Ruetten, Oluf Pedersen, Emmanouil T. Dermitzakis, Paul W. Franks, Jochen M. Schwenk, Jerzy Adamski, Ewan R. Pearson, Mark I. McCarthy, Søren Brunak
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.