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.


Diagnosing diabetes mellitus from smartphone-based vascular signals

A need exists for rapid, cheap and noninvasive diagnostic tests for type 2 diabetes mellitus (T2DM). Now, a smartphone-based photoplethysmography screening test has been reported to detect T2DM based on a novel digital vascular biomarker, distinct from blood glucose, analysed with deep learning.

This is a preview of subscription content

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.


  1. 1.

    Pinchevsky, Y., Butkow, N., Raal, F. J., Chirwa, T. & Rothberg, A. Demographic and clinical factors associated with development of type 2 diabetes: a review of the literature. Int. J. Gen. Med. 13, 121–129 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  2. 2.

    Cowie, C. C. Diabetes diagnosis and control: missed opportunities to improve health: the 2018 Kelly West Award Lecture. Diabetes Care 42, 994–1004 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  3. 3.

    Saeedi, P. et al. Global and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045: Results from the International Diabetes Federation Diabetes Atlas, 9th edition. Diabetes Res. Clin. Pract. 157, 107843 (2019).

    Article  Google Scholar 

  4. 4.

    The Expert Committee on the Diagnosis & Classification of Diabetes Mellitus. Report of the Expert Committee on the Diagnosis and Classification of Diabetes Mellitus. Diabetes Care 20, 1183–1197 (1997).

    Article  Google Scholar 

  5. 5.

    The International Expert Committee. International Expert Committee report on the role of the A1C assay in the diagnosis of diabetes. Diabetes Care 32, 1327–1334 (2009).

    Article  PubMed Central  Google Scholar 

  6. 6.

    Colagiuri, S. et al. Glycemic thresholds for diabetes-specific retinopathy: implications for diagnostic criteria for diabetes. Diabetes Care 34, 145–150 (2011).

    Article  PubMed  Google Scholar 

  7. 7.

    Avram, R. et al. A digital biomarker of diabetes from smartphone-based vascular signals. Nat. Med. 26, 1576–1582 (2020).

    CAS  Article  PubMed  Google Scholar 

  8. 8.

    Chowienczyk, P. J. et al. Photoplethysmographic assessment of pulse wave reflection: blunted response to endothelium-dependent beta2-adrenergic vasodilation in type II diabetes mellitus. J. Am. Coll. Cardiol. 34, 2007–2014 (1999).

    CAS  Article  PubMed  Google Scholar 

  9. 9.

    Silva, I. V. G., de Figueiredo, R. C. & Rios, D. R. A. Effect of different classes of antihypertensive drugs on endothelial function and inflammation. Int. J. Mol. Sci. 20, 3458 (2019).

    CAS  Article  PubMed Central  Google Scholar 

  10. 10.

    Lamacchia, O. & Sorrentino, M. R. Diabetes mellitus, arterial stiffness and cardiovascular disease: clinical implications and the influence of SGLT2i. Curr. Vasc. Pharmacol. (2020).

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Author information



Corresponding author

Correspondence to David C. Klonoff.

Ethics declarations

Competing interests

DCK is a consultant to Dexcom, Eoflow, Fractyl, Lifecare, Novo, Roche, Samsung and Thirdwayv.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Klonoff, D.C. Diagnosing diabetes mellitus from smartphone-based vascular signals. Nat Rev Endocrinol 16, 681–682 (2020).

Download citation


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