Magnesium supplement and vascular health: Machine learning from the longitudinal medical record

镁补充剂和血管健康：从纵向病历中进行机器学习

• 批准号: 10489843
• 负责人: ALI AHMED
• 金额: $ 41.53万
• 依托单位: GEORGE WASHINGTON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-16 至 2025-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10489843
• 关键词: Address; Adult; Adverse event; Affect; American; Atherosclerosis; Benefits and Risks; Blood; Blood Vessels; Calcium; Cardiac; Characteristics; Clinical; Clinical effectiveness; Data; Databases; Diabetes Mellitus; Diagnosis; Dietary Magnesium; Effectiveness; Electronic Health Record; Eligibility Determination; Enzymes; Equilibrium; Functional disorder; Goals; Health; Health Benefit; Heart failure; Hospitalization; Human; Hypomagnesemia; Impairment; Individual; Inflammation; Insulin Resistance; Intake; Investigation; Knowledge; Laboratory Animals; Link; Long-Term Effects; Longterm Follow-up; Machine Learning; Magnesium; Magnesium Deficiency; Marketing; Measures; Medical Records; Methodology; Mg supplementation; Minerals; Modality; Modeling; Observational Study; Oral; Outcome; Pathway interactions; Patients; Pattern; Pharmacoepidemiology; Pilot Projects; Polypharmacy; Population; Public Health; Randomized Controlled Trials; Reporting; Risk; Risk Factors; Safety; Sample Size; Serum; Serum Magnesium Level; Structural Models; System; Techniques; Technology; Testing; Time; United States Department of Veterans Affairs; United States Food and Drug Administration; United States National Institutes of Health; Veterans; Veterans Health Administration; Weight; Work; active comparator; base; cohort; cost; deep learning; deep learning model; design; diabetes risk; dietary supplements; endothelial dysfunction; follow-up; high risk; improved; improved outcome; individual patient; insulin sensitivity; interest; mortality; mortality risk; multiple chronic conditions; novel; personalized decision; phenotypic data; pill; precision medicine; predictive modeling; prospective; randomized controlled design; risk prediction model; therapeutic effectiveness; tool

Project Summary/Abstract Over half of adult Americans use dietary supplements. However, little is known about their safety and effectiveness as these products are not approved by the US Food and Drug Administration (FDA) and post-marketing surveillance is limited to adverse events. The NIH Office of Dietary Supplements (ODS) seeks to fill in that gap and has identified electronic health record (EHR) data as a potential tool to advance that goal. Preliminary data from our pilot study sponsored by the NIH ODS that used advanced machine/deep learning techniques suggest that magnesium supplements may lower the risk of heart failure (HF) in patient with diabetes mellitus (DM) and may improve outcomes in those with HF. Both HF and DM affect the health and outcomes of millions of Americans. DM is a risk factor for HF and adversely affects outcomes in those with HF. Magnesium is an integral part of over 300 human enzyme systems, which are impaired in magnesium deficiency. Findings from our study suggest that a low dietary magnesium intake is associated with a higher risk of incident HF, especially among those with DM. However, less is known about this relationship in patients with HF. The Specific Aims 1 and 2 of the proposed projects are to test the hypotheses that a new prescription for oral magnesium supplement is associated with a lower risk of incident HF in those with DM and of mortality and hospitalization in patients with HF. Although magnesium is inexpensive and relatively safe, its long-term effects may vary for individual patients. Thus, instead of recommending it to millions of patients, it would be ideal to recommend to individuals who are most likely to benefit. Thus, our Specific Aim 3 is to develop and validate a novel explainable deep learning-based risk prediction model to determine with precision the optimal clinical setting under which an individual may derive clinical benefits from magnesium supplementation given their individual characteristics including multimorbidity and polypharmacy. These aims will be achieved by interrogating the Veterans Affairs (VA) national EHR data that includes over 2 million Veterans with DM and 1 million with HF with ~20 years of longitudinal data on magnesium supplements, serum magnesium, and outcomes. We will use a new-user design, marginal structural model (propensity score weighting) with machine- learning-based estimation and stability analyses to minimize confounding and account for potential biases. The prediction model for individual risk/benefit will be validated using the Cerner Health Facts® data for generalizability in non-Veteran populations. The findings of proposed study will generate new evidence that will have direct clinical implications and those of Aim 3 specifically will provide a novel precision medicine tool to individualize magnesium supplement use.

项目总结/摘要 超过一半的美国成年人使用膳食补充剂。然而，人们对其安全性知之甚少 由于这些产品未经美国食品和药物管理局批准， (FDA)上市后监测仅限于不良事件。美国国立卫生研究院膳食办公室 补充资料（ODS）试图填补这一空白，并确定了电子健康记录（EHR）数据 作为推进这一目标的潜在工具。初步数据来自我们的试点研究， 使用先进机器/深度学习技术的NIH ODS表明，镁 补充剂可以降低糖尿病（DM）患者发生心力衰竭（HF）的风险， 可以改善HF患者的预后。HF和DM均影响患者的健康和结局， 数百万美国人。DM是HF的危险因素，对HF患者的结局有不良影响。 镁是超过300种人体酶系统的组成部分，这些酶系统在体内受损。 镁缺乏症我们的研究结果表明，低膳食镁摄入量 与HF事件的高风险相关，尤其是在糖尿病患者中。然而， 在HF患者中了解这种关系。具体目标1和2 项目是测试假设，一个新的处方口服镁补充剂是 与糖尿病患者发生HF的风险较低以及死亡率和住院率较低相关 在HF患者中。虽然镁是廉价和相对安全，其长期影响 可能因患者个体而异。因此，它不会推荐给数百万患者， 最适合推荐给最有可能受益的个人。因此，我们的具体目标3是 开发并验证一种新的可解释的基于深度学习的风险预测模型，以确定 精确确定个体可获得临床获益的最佳临床环境 从镁补充剂考虑，他们的个人特点，包括多morphine 和多药疗法这些目标将通过审问退伍军人事务部（VA）来实现。 国家EHR数据，包括超过200万患有DM的退伍军人和100万患有HF的约20 镁补充剂、血清镁和结果的多年纵向数据。我们 将使用新用户设计，边际结构模型（倾向评分加权）与机器- 基于学习的估计和稳定性分析，以最大限度地减少混淆， 潜在的偏见将使用Cerner验证个体风险/获益的预测模型 Health Facts®数据在非退伍军人群体中的普遍性。拟议研究的结果 将产生新的证据，这些证据将具有直接的临床意义，特别是Aim 3的意义。 将提供一种新的精确医学工具，以个性化镁补充剂的使用。