AbstractThe All of Us Research Program (AoU) is an initiative designed to gather a comprehensive and diverse dataset from at least one million individuals across the USA. This longitudinal cohort study aims to advance research by providing a rich resource of genetic and phenotypic information, enabling powerful studies on the epidemiology and genetics of human diseases.

摘要美国全民研究计划（AoU）是一项旨在收集来自美国至少100万个人的全面而多样的数据集的倡议。这项纵向队列研究旨在通过提供丰富的遗传和表型信息资源来推进研究，从而对人类疾病的流行病学和遗传学进行强有力的研究。

One critical challenge to maximizing its use is the development of accurate algorithms that can efficiently and accurately identify well-defined disease and disease-free participants for case-control studies. This study aimed to develop and validate type 1 (T1D) and type 2 diabetes (T2D) algorithms in the AoU cohort, using electronic health record (EHR) and survey data.

最大限度地利用它的一个关键挑战是开发准确的算法，这些算法可以有效准确地识别明确定义的疾病和无病参与者进行病例对照研究。本研究旨在使用电子健康记录（EHR）和调查数据，在AoU队列中开发和验证1型（T1D）和2型糖尿病（T2D）算法。

Building on existing algorithms and using diagnosis codes, medications, laboratory results, and survey data, we developed and implemented algorithms for identifying prevalent cases of type 1 and type 2 diabetes. The first set of algorithms used only EHR data (EHR-only), and the second set used a combination of EHR and survey data (EHR+).

在现有算法的基础上，使用诊断代码，药物，实验室结果和调查数据，我们开发并实施了识别1型和2型糖尿病流行病例的算法。第一组算法仅使用EHR数据（仅EHR），第二组使用EHR和调查数据（EHR+）的组合。

A universal algorithm was also developed to identify individuals without diabetes. The performance of each algorithm was evaluated by testing its association with polygenic scores (PSs) for type 1 and type 2 diabetes. We demonstrated the feasibility and utility of using AoU EHR and survey data to employ diabetes algorithms.

还开发了一种通用算法来识别没有糖尿病的个体。通过测试其与1型和2型糖尿病的多基因评分（PSs）的关联来评估每种算法的性能。我们证明了使用AoU EHR和调查数据来采用糖尿病算法的可行性和实用性。

For T1D, the EHR-only algorithm showed a stronger association with T1D-PS compared to the EHR + algorithm (DeLong p-value = 3 × 10−5). For T2D, the EHR + algorithm outperformed both the EHR-only and the existing T2D definition provided in the AoU Phenotyping Library (DeLong p-values = 0.03 and 1 × 10−4, respectively), identifying 25.79% and 22.57% more cases, respectively, and providin.

对于T1D，与EHR+（DeLong p值=3×10-5）相比，仅EHR算法与T1D-PS的关联性更强。对于T2D，EHR++算法优于AoU表型库中提供的仅EHR和现有T2D定义（DeLong p值分别为0.03和1×10-4），分别识别出25.79%和22.57%的病例。

Introduction

导言

Large-scale biobanks with electronic health records (EHR) data linked to genetic information have enabled tremendous progress in the study of the genetic causes of diseases, their disease trajectories and comorbidities1. The All of Us Research Program (AoU) is a government funded biobank developed with the goal of recruiting one million individuals from across the United States to generate a diverse health database with a wealth of clinical and genomic data to fuel well-powered epidemiological and genomic studies of human diseases2.In recent years, EHR-based biobanks have increasingly been adopted as efficient sources of clinical data for studying common diseases like type 1 diabetes and type 2 diabetes3,4,5.

具有与遗传信息相关的电子健康记录（EHR）数据的大规模生物库在研究疾病的遗传原因，疾病轨迹和合并症方面取得了巨大进展1。美国全民研究计划（AoU）是一个由政府资助的生物库，旨在从美国各地招募100万人，建立一个具有丰富临床和基因组数据的多样化健康数据库，以推动对人类疾病进行有力的流行病学和基因组学研究2。近年来，基于EHR的生物库越来越多地被用作研究1型糖尿病和2型糖尿病等常见疾病的有效临床数据来源3,4,5。

The AoU cohort has the potential to become a major resource for the study of diabetes in diverse ancestries; however, there is a need to develop accurate phenotyping algorithms. In AoU, there is not yet a readily available type 1 diabetes algorithm, and the existing type 2 diabetes algorithm does not make use of all available study data sources, such as survey data.

AoU队列有可能成为研究不同祖先糖尿病的主要资源；但是，需要开发准确的表型分析算法。在AoU，还没有现成的1型糖尿病算法，现有的2型糖尿病算法没有利用所有可用的研究数据源，例如调查数据。

Furthermore, implementation of diabetes algorithms requires careful curation of additional variables, such as usage of specific sets of medications and inpatient versus outpatient setting of data capture. We therefore sought to develop diabetes algorithms and corresponding implementation code in AoU that could be made readily available to the research community and help promote high quality research of diabetes.To ensure the accuracy and precision of the phenotypic outcomes derived from EHR-based phenotyping algorithms, validation is required.

此外，糖尿病算法的实施需要仔细管理其他变量，例如特定药物的使用以及住院患者与门诊患者的数据采集设置。因此，我们试图在AoU中开发糖尿病算法和相应的实现代码，这些代码可以随时提供给研究界，并有助于促进高质量的糖尿病研究。为了确保基于EHR的表型分型算法得出的表型结果的准确性和准确性，需要进行验证。

Manual chart review has been considered the gold-standard method of the validation6; however, in many diseases where the diagnosis or phenotype assignment is not .

手动图表审查被认为是验证的金标准方法6；然而，在许多没有诊断或表型分配的疾病中。

Data availability

数据可用性

The datasets used for this study are freely available via the NIH All of Us Researcher Workbench (https://www.researchallofus.org/data-tools/workbench/) to approved researchers with institutional access. The relevant analytical code used for this analysis is available on the Researcher Workbench or from the corresponding author (Dr.

这项研究使用的数据集可以通过美国国立卫生研究院所有研究人员工作台免费获得(https://www.researchallofus.org/data-tools/workbench/)拥有机构访问权限的批准研究人员。用于此分析的相关分析代码可在研究人员工作台或相应作者（Dr。

Alisa K. Manning: amanning@broadinstitute.org) upon request..

Alisa K.Manning：amanning@broadinstitute.org)根据要求。。

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Download referencesAcknowledgementsThe All of Us Research Program is supported by the National Institutes of Health, Office of the Director: Regional Medical Centers: 1 OT2 OD026549; 1 OT2 OD026554; 1 OT2 OD026557; 1 OT2 OD026556; 1 OT2 OD026550; 1 OT2 OD 026552; 1 OT2 OD026553; 1 OT2 OD026548; 1 OT2 OD026551; 1 OT2 OD026555; IAA #: AOD 16037; Federally Qualified Health Centers: HHSN 263201600085U; Data and Research Center: 5 U2C OD023196; Biobank: 1 U24 OD023121; The Participant Center: U24 OD023176; Participant Technology Systems Center: 1 U24 OD023163; Communications and Engagement: 3 OT2 OD023205; 3 OT2 OD023206; and Community Partners: 1 OT2 OD025277; 3 OT2 OD025315; 1 OT2 OD025337; 1 OT2 OD025276.

下载参考文献致谢全美研究计划由美国国立卫生研究院院长办公室支持：地区医疗中心：1 OT2 OD026549；1 OT2 OD026554；1 OT2 OD026557；1 OT2 OD026556；1 OT2 OD026550；1 OT2 OD 026552；1 OT2 OD026553；1 OT2 OD026548；1 OT2 OD026551；1 OT2 OD026555；IAA#：AOD 16037；联邦合格卫生中心：HHSN 263201600085U；数据与研究中心：5 U2C OD023196；生物库：1 U24 OD023121；参与者中心：U24 OD023176；参与者技术系统中心：1 U24 OD023163；沟通与参与：3 OT2 OD023205；3 OT2 OD023206；；3 OT2 OD025315；1 OT2 OD025337；1 OT2 OD025276。

In addition, the All of Us Research Program would not be possible without the partnership of its participants.FundingL.S. is supported by funds from the Ministry of Education and Science of Poland within the project “Excellence Initiative—Research University,” the Ministry of Health of Poland within the project “Center of Artificial Intelligence in Medicine at the Medical University of Bialystok,” the Medical Research Agency within the project “Regional Center for Digital Medicine at the Medical University of Bialystok” (grant number 2023.ABM.02.00008) and the American Diabetes Association grant 11-22-PDFPM-03.

此外，如果没有参与者的合作，我们所有人的研究计划都是不可能的。资金。S、 由波兰教育和科学部在“卓越计划研究大学”项目中的资金支持，波兰卫生部在“比亚韦斯托克医科大学医学人工智能中心”项目中的资金支持，医学研究机构在“比亚韦斯托克医科大学数字医学区域中心”项目中的资金支持（批准号2023。ABM。02.00008）和美国糖尿病协会拨款11-22-PDFPM-03。

J.H.L. is supported by NIDDK K23 DK131345 and MGH ECOR Fund for Medical Discovery Clinical Research Award. J.C.F. is supported by NHLBI K24 HL157960. J.M.M. is supported by American Diabetes Association Innovative and Clinical Translational Award 1-19-ICTS-068, American Diabetes Association grant #11-22-ICTSPM-16, NHGRI U01HG011723, and by grant obtained by the Medical University of Bialystok from the Ministry of Science and Higher Education.

J、 H.L.得到了NIDDK K23 DK131345和MGH ECOR医学发现基金临床研究奖的支持。J、 。J、 M.M.得到了美国糖尿病协会创新与临床转化奖1-19-ICTS-068，美国糖尿病协会拨款#11-22-ICTSPM-16，NHGRI U01HG011723的支持，以及比亚韦斯托克医科大学从科学和高等教育部获得的拨款。

M.S.U.

M、 美国。

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PubMed Google ScholarContributionsL.S., R.M. and P.S. researched data, wrote, reviewed, and edited the manuscript. B.C.P., J. H. L. and J. C. F. reviewed and edited the manuscript. J. M. M., M.S.U. and A.K.M. reviewed and edited the manuscript and are the guarantors of this work and, as such, had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.Corresponding authorsCorrespondence to.

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Reprints and permissionsAbout this articleCite this articleSzczerbinski, L., Mandla, R., Schroeder, P. et al. Algorithms for the identification of prevalent diabetes in the All of Us Research Program validated using polygenic scores.

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