Complex phenotypic and genetic clustering of individuals who are potentially at increased risk of type 2 diabetes mellitus (T2DM) can enable the identification of individuals who are likely to develop T2DM and vascular complications. Precision medicine for prediabetes should improve prevention programmes and reduce mortality.
Subscribe to Journal
Get full journal access for 1 year
only $4.92 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Tax calculation will be finalised during checkout.
Rent or Buy article
Get time limited or full article access on ReadCube.
All prices are NET prices.
American Diabetes Association. 2. Classification and Diagnosis of Diabetes: Standards of Medical Care in Diabetes-2021. Diabetes Care 44 (Suppl. 1), 15–33 (2021).
Gourgari, E., Wilhelm, E. E., Hassanzadeh, H., Aroda, V. R. & Shoulson, I. A comprehensive review of the FDA-approved labels of diabetes drugs: Indications, safety, and emerging cardiovascular safety data. J. Diabetes Complications 31, 1719–1727 (2017).
Tabák, A. G. et al. Trajectories of glycaemia, insulin sensitivity, and insulin secretion before diagnosis of type 2 diabetes: an analysis from the Whitehall II study. Lancet 373, 2215–2221 (2009).
Ahlqvist, E. et al. Novel subgroups of adult-onset diabetes and their association with outcomes: a data-driven cluster analysis of six variables. Lancet Diabetes Endocrinol. 6, 361–369 (2018).
Udler, M. S. et al. Type 2 diabetes genetic loci informed by multi-trait associations point to disease mechanisms and subtypes: A soft clustering analysis. PLoS Med. 15, e1002654 (2018).
Wagner, R. et al. Pathophysiology-based subphenotyping of individuals at elevated risk for type 2 diabetes. Nat. Med. https://doi.org/10.1038/s41591-020-1116-9 (2021).
Bonnefond, A. & Froguel, P. Rare and common genetic events in type 2 diabetes: what should biologists know? Cell Metab. 21, 357–368 (2015).
Morley, J. E. Diabetes and aging: epidemiologic overview. Clin. Geriatr. Med. 24, 395–405 (2008).
A.B. and P.F. are supported by grants from the French National Research Agency (ANR-10-LABX-46 (European Genomics Institute for Diabetes) and ANR-10-EQPX-07-01 (LIGAN-PM)); from the European Research Council (ERC GEPIDIAB – 294785, to PF; ERC Reg-Seq – 715575, to A.B.); and from the National Center for Precision Diabetic Medicine — PreciDIAB, which is jointly supported by the French National Agency for Research (ANR-18-IBHU-0001), by the European Union (FEDER), by the Hauts-de-France Regional Council and by the European Metropolis of Lille (MEL).
The authors declare no competing interests.
About this article
Cite this article
Bonnefond, A., Froguel, P. Clustering for a better prediction of type 2 diabetes mellitus. Nat Rev Endocrinol 17, 193–194 (2021). https://doi.org/10.1038/s41574-021-00475-4