A digital biomarker of diabetes from smartphone-based vascular signals
Robert Avram, Jeffrey E Olgin, Peter Kuhar, J Weston Hughes, Gregory M Marcus, Mark J Pletcher, Kirstin Aschbacher, Geoffrey H Tison, Robert Avram, Jeffrey E Olgin, Peter Kuhar, J Weston Hughes, Gregory M Marcus, Mark J Pletcher, Kirstin Aschbacher, Geoffrey H Tison
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 under the curve for prevalent diabetes of 0.766 in the primary cohort (95% confidence interval: 0.750-0.782; sensitivity 75%, specificity 65%) and 0.740 in the contemporary cohort (95% confidence interval: 0.723-0.758; sensitivity 81%, specificity 54%). When the output of the DNN, called the DNN score, was included in a regression analysis alongside age, gender, race/ethnicity and body mass index, the area under the curve was 0.830 and the DNN score remained independently predictive of diabetes. The performance of the DNN in the clinic cohort was similar to that in other validation datasets. There was a significant and positive association between the continuous DNN score and hemoglobin A1c (P ≤ 0.001) among those with hemoglobin A1c data. These findings demonstrate that smartphone-based photoplethysmography provides a readily attainable, non-invasive digital biomarker of prevalent diabetes.
Conflict of interest statement
Competing Interests: Dr. Olgin has received research funding from Samsung and iBeat. Dr. Marcus has received research funding from Medtronic, Jawbone, and Eight. Dr. Aschbacher received funding from Jawbone Health Hub. Peter Kuhar is an employee of Azumio. Dr. Tison has received research grants from Janssen Pharmaceuticals and Myokardia and is an advisor to Cardiogram, Inc. None of the remaining authors have potential conflicts of interest. Azumio provided no financial support for this study and only provided access to the data. Data analysis, interpretation and decision to submit the manuscript were performed independently from Azumio.
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Source: PubMed