Article

Heterogeneity in phenotype, disease progression and drug response in type 2 diabetes

Details

Citation

Nair ATN, Wesolowska-Andersen A, Brorsson C, Rajendrakumar AL, Hapca S, Gan S, Dawed AY, Donnelly LA, McCrimmon R, Doney ASF, Palmer CNA, Viswanathan M, Anjana RM, Hattersley AT, Dennis JM & Pearson ER (2022) Heterogeneity in phenotype, disease progression and drug response in type 2 diabetes. Nature Medicine, 28, pp. 982-988. https://doi.org/10.1038/s41591-022-01790-7

Abstract
Type 2 diabetes (T2D) is a complex chronic disease characterized by considerable phenotypic heterogeneity. In this study, we applied a reverse graph embedding method to routinely collected data from 23,137 Scottish patients with newly diagnosed diabetes to visualize this heterogeneity and used partitioned diabetes polygenic risk scores to gain insight into the underlying biological processes. Overlaying risk of progression to outcomes of insulin requirement, chronic kidney disease, referable diabetic retinopathy and major adverse cardiovascular events, we show how these risks differ by patient phenotype. For example, patients at risk of retinopathy are phenotypically different from those at risk of cardiovascular events. We replicated our findings in the UK Biobank and the ADOPT clinical trial, also showing that the pattern of diabetes drug monotherapy response differs for different drugs. Overall, our analysis highlights how, in a European population, underlying phenotypic variation drives T2D onset and affects subsequent diabetes outcomes and drug response, demonstrating the need to incorporate these factors into personalized treatment approaches for the management of T2D.

Keywords
Type 2 diabetes

Journal
Nature Medicine: Volume 28

StatusPublished
FundersNational Institute for Health Research
Publication date31/05/2022
Publication date online09/05/2022
Date accepted by journal22/03/2022
URLhttp://hdl.handle.net/1893/34306
ISSN1078-8956
eISSN1546-170X

People (1)

People

Dr Simona Hapca

Dr Simona Hapca

Lecturer, Computing Science