Steno Diabetes Center Copenhagen, Niels Steensens Vej 2-4, Gentofte DK-2820, Denmark.
Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Vej Building 220, Lyngby DK-2800, Denmark.
EBioMedicine. 2022 Jun;80:104032. doi: 10.1016/j.ebiom.2022.104032. Epub 2022 May 6.
Individuals with long standing diabetes duration can experience damage to small microvascular blood vessels leading to diabetes complications (DCs) and increased mortality. Precision diagnostic tailors a diagnosis to an individual by using biomedical information. Blood small molecule profiling coupled with machine learning (ML) can facilitate the goals of precision diagnostics, including earlier diagnosis and individualized risk scoring.
Using data in a cohort of 537 adults with type 1 diabetes (T1D) we predicted five-year progression to DCs. Prediction models were computed first with clinical risk factors at baseline and then with clinical risk factors and blood-derived molecular data at baseline. Progression of diabetic kidney disease and diabetic retinopathy were predicted in two complication-specific models.
The model predicts the progression to diabetic kidney disease with accuracy: 0.96 ± 0.25 and 0.96 ± 0.06 area under curve, AUC, with clinical measurements and with small molecule predictors respectively and highlighted main predictors to be albuminuria, glomerular filtration rate, retinopathy status at baseline, sugar derivatives and ketones. For diabetic retinopathy, AUC 0.75 ± 0.14 and 0.79 ± 0.16 with clinical measurements and with small molecule predictors respectively and highlighted key predictors, albuminuria, glomerular filtration rate and retinopathy status at baseline. Individual risk scores were built to visualize results.
With further validation ML tools could facilitate the implementation of precision diagnosis in the clinic. It is envisaged that patients could be screened for complications, before these occur, thus preserving healthy life-years for persons with diabetes.
This study has been financially supported by Novo Nordisk Foundation grant NNF14OC0013659.
长期患有糖尿病的个体可能会出现小血管微血管损伤，导致糖尿病并发症（DCs）和死亡率增加。精准诊断通过使用生物医学信息为个体量身定制诊断。血液小分子分析与机器学习（ML）相结合可以促进精准诊断的目标，包括更早的诊断和个体化风险评分。
我们使用 537 名 1 型糖尿病（T1D）成人队列中的数据，预测了五年内 DCs 的进展。首先使用基线时的临床危险因素计算预测模型，然后使用基线时的临床危险因素和血液衍生的分子数据计算预测模型。在两个特定于并发症的模型中预测糖尿病肾病和糖尿病视网膜病变的进展。
该模型以 0.96±0.25 和 0.96±0.06 的准确度预测糖尿病肾病的进展，分别使用临床测量值和小分子预测因子，突出的主要预测因子为蛋白尿、肾小球滤过率、基线时的视网膜病变状态、糖衍生物和酮体。对于糖尿病视网膜病变，分别使用临床测量值和小分子预测因子的 AUC 为 0.75±0.14 和 0.79±0.16，突出的关键预测因子为蛋白尿、肾小球滤过率和基线时的视网膜病变状态。构建了个体风险评分以可视化结果。
随着进一步验证，ML 工具可以促进精准诊断在临床中的实施。预计可以在并发症发生之前对患者进行筛查，从而为糖尿病患者保留健康的生活年限。
本研究得到了 Novo Nordisk 基金会 NNF14OC0013659 资助。
