美国加州大学Geoffrey H. Tison团队近日取得一项新成果。经过不懈努力，他们的最新研究揭示了基于智能手机提供血管信号的糖尿病数字生物标志物。 这一研究成果发表在2020年8月17日的《自然-医学》上。

考虑到糖尿病对血管产生多方面的影响，研究人员猜想基于智能手机的光电容积描记法可为糖尿病提供广泛可用的数字生物标记。

在本研究中，研究人员开发了一个深度神经网络（DNN），利用基于智能手机的光电容积描记法从最初5870例（“主要队列”）参与者中检测出了糖尿病，然后研究人员在另外7780例人群（“同期”队列）和来自三所医院181名潜在患者（“诊所队列”） 中进行了验证。

对于常见糖尿病，在主要队列中DNN的曲线下面积为0.766（95％置信区间：0.750-0.782；敏感性75％，特异性65％），在同期人群中为0.740（95％置信区间：0.723-0.758；敏感性81％，特异性54％）。

当将DNN的结果（称为DNN分数）与年龄、性别、种族/民族和体重指数一起纳入回归分析时，曲线下面积为0.830，而DNN分数仍可独立预测糖尿病。DNN在临床队列中的表现与其他验证数据集相似。在具有血红蛋白A1c的人群中，连续DNN评分与血红蛋白A1c之间存在显著正相关（P≤0.001）。

这些发现表明，基于智能手机的光电容积描记法可提供易于获得的、低创伤的糖尿病数字生物标志物。

研究人员表示，糖尿病患者在全球范围内迅速增加，可能会从2019年的4.51亿增加到2045年的6.93亿。2型糖尿病的潜伏性会导致诊断延迟并增加发病率。

附：英文原文

Title: A digital biomarker of diabetes from smartphone-based vascular signals

Author: Robert Avram, Jeffrey E. Olgin, Peter Kuhar, J. Weston Hughes, Gregory M. Marcus, Mark J. Pletcher, Kirstin Aschbacher, Geoffrey H. Tison

Issue&Volume: 2020-08-17

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.

DOI: 10.1038/s41591-020-1010-5

Source: https://www.nature.com/articles/s41591-020-1010-5