在我们所有人研究计划中,用于识别流行糖尿病的算法使用多基因分数验证了多基因分数
Lukasz Szczerbinski1,2,3,4,5, Ravi Mandla3,4,5,6, Philip Schroeder3,4,5
1Department of Endocrinology, Diabetology and Internal Medicine, Medical University of Bialystok, 15-276, Bialystok, Poland.
Scientific reports
|November 6, 2024
概括
新的算法准确地识别了所有我们研究计划 (AoU) 队列中的1型和2型糖尿病病例. 这些工具利用电子健康记录和调查数据,加强糖尿病研究和研究参与者识别.
科学领域:
- 生物医学信息学 生物医学信息学
- 遗传学和流行病学
- 公共卫生 公共卫生
背景情况:
- 我们所有人研究计划 (AoU) 旨在建立一个超过一百万个人的多样化数据集,以推进人类疾病研究.
- 准确识别患有特定疾病的参与者,如糖尿病,对于大型队列中的病例对照研究至关重要.
- 使用电子健康记录 (EHR) 和调查数据开发强大的算法对于最大限度地提高 AoU 数据集的实用性至关重要.
研究的目的:
- 开发和验证算法来识别AoU队列中的1型糖尿病 (T1D) 和2型糖尿病 (T2D) 的流行病例.
- 为了比较仅使用EHR数据的算法的性能与结合EHR和调查数据的算法的性能.
- 创建一个通用算法来识别没有糖尿病的个体.
主要方法:
- 开发了T1D和T2D算法,使用诊断代码,药物,实验室结果和AoU队列的调查数据.
- 为T1D和T2D创建了仅EHR和EHR+ (EHR与调查数据结合) 的算法.
- 通过评估它们与T1D和T2D的多基因分数 (PSs) 的关联来评估算法性能.
主要成果:
- 仅用于T1D的EHR算法与T1D-PS的关联比EHR+算法更强.
- 针对T2D的EHR+算法超过了仅用于EHR的算法和现有的AoU定义,识别了更多病例,并显示了与T2D-PS的改善关联.
- 开发了一种用于识别非糖尿病患者的通用算法.
结论:
- 现在可以使用验证的算法来识别AoU队列中的T1D和T2D病例.
- 开发的算法,特别是对T2D的EHR+方法,改善了病例识别和与遗传风险因素的关联.
- 这些验证的算法将提高AoU计划内的糖尿病研究的一致性和质量.
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