Developing a blood fatty acid-based algorithm as an early predictor of insulin resistance: Applying machine learning to harmonized data from prospective cohort studies

开发基于血液脂肪酸的算法作为胰岛素抵抗的早期预测因子：将机器学习应用于前瞻性队列研究的统一数据

• 批准号: 10696711
• 负责人: Kristina Harris Jackson
• 金额: $ 32.74万
• 依托单位: OMEGAQUANT ANALYTICS, LLC
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-05-22 至 2025-05-21
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10696711
• 关键词: Affect; Algorithms; American; Area; Behavior; Behavior Therapy; Biometry; Blood; Body mass index; Businesses; Caring; Clinical; Cohort Studies; Collection; Data; Data Set; Development; Diabetes Mellitus; Disease; Disease Progression; Dryness; Early identification; Erythrocyte Membrane; Erythrocytes; Exercise; Fatty Acids; Food; Future; Glucose; Glucose Clamp; Glycosylated hemoglobin A; Goals; Health; High Prevalence; Home; Hyperglycemia; Hyperinsulinism; Individual; Insulin; Insulin Resistance; Intervention; Laboratories; Life Style; Machine Learning; Marketing; Measurement; Measures; Medical Care Costs; Metabolic; Metabolic Diseases; Metabolic syndrome; Minority; Modeling; Monitor; Non-Insulin-Dependent Diabetes Mellitus; OGTT; Pancreas; Pathway interactions; Patients; Pattern; Phase; Plasma; Prediabetes syndrome; Prevalence; Preventive measure; Price; Prospective Studies; Prospective cohort; Prospective, cohort study; Quality of life; ROC Curve; Reporting; Research Institute; Research Personnel; Risk; Risk Factors; Sampling; Screening procedure; Security; Signal Transduction; Spottings; System; Technology; Testing; Time; Underserved Population; Women' s Health; adjudication; base; burden of illness; commercialization; data access; data harmonization; diabetes risk; dietary; early screening; fasting plasma glucose; follow-up; health care availability; high risk; high risk population; improved; indexing; inner city; innovation; insulin secretion; insulin sensitivity; interest; machine learning algorithm; offspring; predictive marker; prevent; prospective; randomized, clinical trials; rural area; statistical and machine learning; type I and type II diabetes; waist circumference; willingness; willingness to pay

Project Summary Metabolic diseases, such as Metabolic Syndrome (MetSyn) and type 2 diabetes (T2DM) affect ~37% and ~10% of Americans, respectively, with a substantial impact on the health, quality of life and financial security of these individuals. Without the means to identify high risk individuals conveniently and cheaply, many will find care too little and too late: more than 80% of individuals with pre-diabetes don’t know they have it. Thus, there is a need for accessible and inexpensive early predictive biomarkers of MetSyn and/or T2DM to facilitate the early identification of high-risk individuals, providing the time necessary to make meaningful lifestyle changes to slow or prevent disease progression. Hemoglobin A1C (HbA1c) and fasting plasma glucose are traditional markers of hyperglycemia and insulin resistance, however, these markers have significant limitations in that they are measures of existing, not impending, disease. Other tests of insulin resistance (e.g., oral glucose tolerance tests and hyperinsulinemic euglycemic clamps) are inefficient, cumbersome for the patient, and expensive; thus, they are also not viable options as early screening tools. Emerging evidence suggests that erythrocyte (RBC) fatty acid (FAs) profiles may serve as an early signal of impending hyperglycemia up to 5 years before T2DM develops regardless of whether insulin sensitivity, insulin secretion and glycemic status are known. As a clinical laboratory that specializes in providing FA measurements, interpretation and customized behavioral interventions, OmegaQuant Analytics (OQA) supports a large and growing customer base of researchers, clinicians, businesses, and individuals. OQA has measured full FA profiles (28 FAs) on numerous randomized clinical trials and prospective cohort studies. Furthermore, OQA is a leader in the at-home FA testing market through its innovative dried blood spot collection system, testing almost 40,000 samples annually. Through a partnership with the Fatty Acid Research Institute (FA expertise; biostatistical support; data access), we propose to develop highly predictive metabolic indices from FA profiles using an innovative approach leveraging existing prospective cohort data. We will do this by (Aim 1) predicting future MetSyn or T2DM status from an RBC FA signature using a harmonized dataset from five leading prospective cohort studies yielding a sample of 10,264 individuals (including 1,173 minorities). Prospective statistical predictions using the harmonized data sets will yield the FA Metabolic Index (FAMI) and FA Diabetes Index (FADI). We will then (Aim 2) explore how FAMI and FADI can be leveraged to profitability by creating interpretative reports for FAMI and FADI, providing an understanding and actionable set of steps to change dietary behaviors to modify MetSyn/T2DM risk and exploring willingness of clinicians, laboratories and individuals to purchase the tests. These two simple, early-warning tests will allow for targeted intervention of individuals at high-risk for developing MetSyn and/or T2DM, ultimately leading to substantial reductions in the prevalence and societal burden of this disease.

代谢性疾病，如代谢综合征（MetSyn）和2型糖尿病（T2DM） 分别影响约37%和约10%的美国人，对健康、生活质量和 这些人的经济安全。没有办法方便地识别高风险个人， 便宜，许多人会发现照顾太少，太迟：超过80%的人与糖尿病前期不知道 因此，需要可获得且便宜的MetSyn和/或MetSyn的早期预测生物标志物。 2型糖尿病，以促进早期识别高风险的个人，提供必要的时间，使 有意义的生活方式改变，以减缓或预防疾病进展。血红蛋白A1C（HbA1c）和空腹 血浆葡萄糖是高血糖症和胰岛素抵抗的传统标志物，然而，这些标志物 重大局限性在于它们是对现有疾病而不是即将发生的疾病的衡量标准。胰岛素的其他测试 电阻（例如，口服葡萄糖耐量试验和高胰岛素正葡萄糖钳夹）是无效的， 对于患者来说很麻烦，而且价格昂贵;因此，它们作为早期筛查工具也不是可行的选择。 新出现的证据表明，红细胞（RBC）脂肪酸（FAs）谱可能作为一个早期信号， 无论胰岛素敏感性、胰岛素 分泌和血糖状态是已知的。作为一个临床实验室，专门提供FA 测量、解释和定制行为干预，OmegaQuant Analytics（OQA） 支持研究人员、临床医生、企业和个人的庞大且不断增长的客户群。OQA有 在许多随机临床试验和前瞻性队列研究中测量了完整的FA曲线（28个FA）。 此外，OQA通过其创新的干血斑， 收集系统，每年检测近40，000个样本。通过与脂肪酸研究的合作， 研究所（FA专业知识;生物统计支持;数据访问），我们建议开发高度预测性的代谢 使用创新方法利用现有前瞻性队列数据从FA配置文件中提取指数。我们将尽 这是通过（目的1）使用协调的 来自五项领先的前瞻性队列研究的数据集，产生了10，264名个体（包括1，173名 少数民族）。使用协调数据集的前瞻性统计预测将产生FA代谢指数 （FAMI）和FA糖尿病指数（FADI）。然后，我们将（目标2）探讨如何利用FAMI和FADI， 通过为FAMI和FADI创建解释性报告， 改变饮食行为以改变MetSyn/T2DM风险的步骤，并探索临床医生的意愿， 实验室和个人购买测试。这两个简单的早期预警测试将允许有针对性的 干预处于发展MetSyn和/或T2DM的高风险的个体，最终导致显著的 减少这一疾病的流行和社会负担。