Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.


Clustering for a better prediction of type 2 diabetes mellitus

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.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Rent or buy this article

Prices vary by article type



Prices may be subject to local taxes which are calculated during checkout


  1. American Diabetes Association. 2. Classification and Diagnosis of Diabetes: Standards of Medical Care in Diabetes-2021. Diabetes Care 44 (Suppl. 1), 15–33 (2021).

    Article  Google Scholar 

  2. 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).

    Article  Google Scholar 

  3. 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).

    Article  Google Scholar 

  4. 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).

    Article  Google Scholar 

  5. 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).

    Article  Google Scholar 

  6. Wagner, R. et al. Pathophysiology-based subphenotyping of individuals at elevated risk for type 2 diabetes. Nat. Med. (2021).

    Article  PubMed  Google Scholar 

  7. Bonnefond, A. & Froguel, P. Rare and common genetic events in type 2 diabetes: what should biologists know? Cell Metab. 21, 357–368 (2015).

    Article  CAS  Google Scholar 

  8. Morley, J. E. Diabetes and aging: epidemiologic overview. Clin. Geriatr. Med. 24, 395–405 (2008).

    Article  Google Scholar 

Download references


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).

Author information

Authors and Affiliations


Corresponding authors

Correspondence to Amélie Bonnefond or Philippe Froguel.

Ethics declarations

Competing interests

The authors declare no competing interests.

Supplementary information

Rights and permissions

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bonnefond, A., Froguel, P. Clustering for a better prediction of type 2 diabetes mellitus. Nat Rev Endocrinol 17, 193–194 (2021).

Download citation

  • Published:

  • Issue Date:

  • DOI:

This article is cited by


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing