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: 10301239
• 负责人: ALI AHMED
• 金额: $ 45.96万
• 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/10301239
• 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 observed; 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; 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.

项目概要/摘要 超过一半的美国成年人使用膳食补充剂，但人们对其安全性知之甚少。 和有效性，因为这些产品未经美国食品和药物管理局批准 (FDA) 和上市后监测仅限于不良事件。 补充品 (ODS) 旨在填补这一空白，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。 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 可能会改善心力衰竭患者的预后。心力衰竭和糖尿病都会影响患者的健康和预后。 数以百万计的美国人认为，糖尿病是心力衰竭的一个危险因素，并对心力衰竭患者的预后产生不利影响。 镁是 300 多种人体酶系统的重要组成部分，这些酶系统在 我们的研究结果表明，膳食镁摄入量低会导致镁缺乏。 与较高的心力衰竭风险相关，尤其是对于患有糖尿病的患者而言。 已知心力衰竭患者的这种关系。所提议的具体目标 1 和 2。 项目旨在测试以下假设：口服镁补充剂的新处方是 与糖尿病患者发生心力衰竭、死亡和住院的风险较低相关 尽管镁价格便宜且相对安全，但其长期影响。 因此，它不会推荐给数百万患者，而是会因个体患者而异。 因此，我们的具体目标 3 是推荐给最有可能受益的个人。 开发并验证一种新颖的可解释的基于深度学习的风险预测模型，以确定 精确地确定个人可以获得临床益处的最佳临床环境 考虑到他们的个体特征（包括多发病），需要补充镁 这些目标将通过询问退伍军人事务部（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 的退伍军人，其中约 20 我们对镁补充剂、血清镁和结果进行了多年的纵向数据。 将使用新用户设计、边际结构模型（倾向得分加权）和机器- 基于学习的估计和稳定性分析，以最大限度地减少混杂因素并考虑 个人风险/收益的预测模型将使用 Cerner 进行验证。 Health Facts® 数据在非退伍军人群体中的普遍适用性 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，特别是目标 3 的意义 将提供一种新颖的精准医疗工具来个性化镁补充剂的使用。