A digital biomarker of diabetes from smartphone-based vascular signals.

ABSTRACT: The global burden of diabetes is rapidly increasing, from 451 million people in 2019 to 693 million by 2045¹. The insidious onset of type 2 diabetes delays diagnosis and increases morbidity². Given the multifactorial vascular effects of diabetes, we hypothesized that smartphone-based photoplethysmography could provide a widely accessible digital biomarker for diabetes. Here we developed a deep neural network (DNN) to detect prevalent diabetes using smartphone-based photoplethysmography from an initial cohort of 53,870 individuals (the 'primary cohort'), which we then validated in a separate cohort of 7,806 individuals (the 'contemporary cohort') and a cohort of 181 prospectively enrolled individuals from three clinics (the 'clinic cohort'). The DNN achieved an area un

SUBMITTER: Avram R 

PROVIDER: S-EPMC8483886 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature