Predicting complications of diabetes with longitudinal metabolic trajectories

利用纵向代谢轨迹预测糖尿病并发症

• 批准号: 10605337
• 负责人: Evan L Reynolds
• 金额: $ 9万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-04-08 至 2024-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10605337
• 关键词: Affect; Age; Algorithms; American Indians; Body Weight decreased; Characteristics; Chronic Kidney Failure; Clinical Data; Complex; Complication; Complications of Diabetes Mellitus; Data; Data Scientist; Data Set; Databases; Development; Diabetes Mellitus; Diagnosis; Disease; Exercise; Future; Goals; Health system; Heterogeneity; Impairment; Incidence; Individual; Integrated Health Care Systems; Intervention; Knowledge; Lead; Longevity; Measurement; Medical; Mentors; Metabolic; Metabolic Control; Metabolic dysfunction; Metabolic syndrome; Non-Insulin-Dependent Diabetes Mellitus; Patients; Peripheral Nervous System Diseases; Pharmaceutical Preparations; Phase; Phenotype; Physiology; Pima Indian; Population; Populations at Risk; Prevalence; Quality of life; Research Personnel; Risk; Risk Factors; Sample Size; Statistical Methods; Symptoms; Techniques; Testing; Time; Training; United States; United States Department of Veterans Affairs; Veterans; analytical tool; cohort; complex data; data warehouse; feature extraction; flexibility; improved; innovation; large scale data; large-scale database; machine learning algorithm; machine learning prediction algorithm; metabolic phenotype; metabolic profile; nerve damage; novel; prevent; rate of change; renal damage; repository; weight loss intervention

ABSTRACT The prevalence of diabetes is increasing worldwide, with an estimated 463 million cases in 2019 (31 million cases in the United States). Patients with diabetes suffer from a number of common and morbid complications, including peripheral neuropathy (PN) and chronic kidney disease (CKD). In addition to diabetes, the metabolic syndrome (MetS) has been firmly established as a risk factor for these complications. Unfortunately, interventions that improve metabolic risk factors for patients with long-term metabolic impairment do not completely prevent or reverse diabetic complications, which may be the result of the current timing of interventions after metabolic risk factors have been present for many years. Importantly, very few studies have assessed the longitudinal association between the MetS and diabetic complications; therefore, the ideal timing of these interventions is unknown. Metabolic profiles worsen as age increases and there is a substantial amount of heterogeneity between patient metabolic trajectories. What remains unknown is how changes in specific aspects of longitudinal metabolic profiles (such as rate of change, cumulative effects, and changes during specific risk-periods) affect the progression of these complications. It is also unknown whether the onset of diabetic complications can be accurately predicted using longitudinal metabolic trajectories. Machine learning algorithms are flexible and powerful analytic tools for making predictions with complex data; therefore, they offer an ideal approach to predict diabetic complications using complex characteristics from metabolic trajectories. Our initial aims are to (1) determine the association between metabolic trajectories, PN, and CKD, and (2) develop a machine learning algorithm to predict these complications with detailed characteristics of metabolic trajectories. We will accomplish these initial aims using three complementary and powerful databases with differential metabolic complexity and sample size. First, we will determine if metabolic trajectories are associated with and are predictive of diabetic complications using two cohorts of Pima American Indians that have undergone detailed longitudinal metabolic and diabetic complication phenotyping. Then, to determine if our results can be implemented in a large-scale, integrated United States health system, our final aim is to (3) develop a comprehensive machine learning algorithm to predict complications for patients with diabetes using the Veterans Affairs Corporate Data Warehouse database, which contains detailed longitudinal medical information on over 3.1 million Veterans with diabetes from 1999-2021. Results from this study will provide a novel understanding of the associations between metabolic trajectories and diabetic complications. Ultimately, this data will inform future disease modifying interventions that reverse the metabolic trajectories that are most predictive of diabetic complications.

摘要 全世界的糖尿病患病率正在增加，2019年估计有4.63亿例(3100万例 美国的案例)。糖尿病患者患有一些常见和病态的并发症， 包括周围神经病(PN)和慢性肾脏病(CKD)。除了糖尿病，新陈代谢 综合征(METS)已被确定为这些并发症的危险因素。不幸的是， 改善长期代谢受损患者代谢危险因素的干预措施 完全预防或逆转糖尿病并发症，这可能是目前的时机所致 代谢危险因素后的干预已存在多年。重要的是，很少有研究 评估了甲硫氨酸和糖尿病并发症之间的纵向关联；因此，理想的时机 这些干预措施中有哪些是未知的。代谢谱随着年龄的增长而恶化，而且有大量的 患者代谢轨迹之间的异质性程度。目前尚不清楚的是， 纵向代谢特征的特定方面(如变化率、累积效应和变化 在特定的风险期)影响这些并发症的进展。目前也不清楚是否会出现 使用纵向代谢轨迹可以准确地预测糖尿病并发症的发生。机器 学习算法是灵活而强大的分析工具，用于对复杂数据进行预测；因此， 它们提供了一种利用代谢的复杂特征来预测糖尿病并发症的理想方法 轨迹。我们最初的目标是(1)确定代谢轨迹、PN和CKD之间的关联， 以及(2)开发了一种机器学习算法来预测这些并发症，其详细特征为 代谢轨迹。我们将利用三个相辅相成和强大的工具来实现这些初步目标 具有不同代谢复杂性和样本量的数据库。首先，我们将确定新陈代谢 使用两组PIMA的轨迹与糖尿病并发症相关并可预测 接受过详细的纵向代谢和糖尿病并发症表型鉴定的美洲印第安人。 然后，为了确定我们的结果是否可以在大规模的、综合的美国卫生系统中实施， 我们的最终目标是(3)开发一个全面的机器学习算法来预测患者的并发症 使用退伍军人事务部企业数据仓库数据库，该数据库包含详细的 1999-2021年超过310万名患有糖尿病的退伍军人的纵向医疗信息。由此产生的结果 这项研究将对代谢轨迹和糖尿病之间的关系提供新的理解 并发症。最终，这些数据将为未来逆转新陈代谢的疾病修改干预措施提供信息 最能预测糖尿病并发症的轨迹。