Szczerbinski Lukasz, Mandla Ravi, Schroeder Philip, Porneala Bianca C, Li Josephine H, Florez Jose C, Mercader Josep M, Manning Alisa K, Udler Miriam S
Department of Endocrinology, Diabetology and Internal Medicine, Medical University of Bialystok, Bialystok, Poland.
Clinical Research Centre, Medical University of Bialystok, Bialystok, Poland.
medRxiv. 2023 Sep 5:2023.09.05.23295061. doi: 10.1101/2023.09.05.23295061.
The study aimed to develop and validate algorithms for identifying people with type 1 and type 2 diabetes in the All of Us Research Program (AoU) cohort, using electronic health record (EHR) and survey data.
Two sets of algorithms were developed, one using only EHR data (EHR), and the other using a combination of EHR and survey data (EHR+). Their performance was evaluated by testing their association with polygenic scores for both type 1 and type 2 diabetes.
For type 1 diabetes, the EHR-only algorithm showed a stronger association with T1D polygenic score (=3×10) than the EHR+. For type 2 diabetes, the EHR+ algorithm outperformed both the EHR-only and the existing AoU definition, identifying additional cases (25.79% and 22.57% more, respectively) and showing stronger association with T2D polygenic score (DeLong =0.03 and 1×10, respectively).
We provide new validated definitions of type 1 and type 2 diabetes in AoU, and make them available for researchers. These algorithms, by ensuring consistent diabetes definitions, pave the way for high-quality diabetes research and future clinical discoveries.
本研究旨在利用电子健康记录（EHR）和调查数据，开发并验证用于识别“我们所有人”研究计划（AoU）队列中1型和2型糖尿病患者的算法。
开发了两组算法，一组仅使用EHR数据（EHR），另一组使用EHR和调查数据的组合（EHR+）。通过测试它们与1型和2型糖尿病多基因评分的关联来评估其性能。
对于1型糖尿病，仅EHR算法与1型糖尿病多基因评分（=3×10）的关联比EHR+更强。对于2型糖尿病，EHR+算法优于仅EHR算法和现有的AoU定义，识别出更多病例（分别多25.79%和22.57%），并与2型糖尿病多基因评分显示出更强的关联（德龙检验值分别为0.03和1×10）。
我们在AoU中提供了新的经过验证的1型和2型糖尿病定义，并将其提供给研究人员。这些算法通过确保一致的糖尿病定义，为高质量的糖尿病研究和未来的临床发现铺平了道路。
