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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.

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  1. 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. 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. 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. 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. 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. 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. 7.

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

    CAS  Article  Google Scholar 

  8. 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).

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Correspondence to Amélie Bonnefond or Philippe Froguel.

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The authors declare no competing interests.

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

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