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.

项目摘要/摘要 超过一半的成年美国人使用饮食补充剂。但是，对他们的安全知之甚少 和有效性，因为这些产品未获得美国食品和药物管理局的批准 （FDA）和上市后的监视仅限于不良事件。 NIH饮食办公室 补充剂（ODS）试图填补该空白，并确定了电子健康记录（EHR）数据 作为促进该目标的潜在工具。我们的试点研究的初步数据由 使用高级机器/深度学习技术的NIH ODS表明镁 补充剂可能会降低糖尿病患者（DM）患者心力衰竭（HF）的风险 可以改善HF患者的预后。 HF和DM都会影响 数百万美国人。 DM是HF的危险因素，并不利地影响HF患者的结果。 镁是300多个人类酶系统中不可或缺的一部分，这些酶系统受损 镁缺乏症。我们研究的发现表明，饮食中镁的摄入量低是 与较高的事件HF风险相关，尤其是在患有DM的人群中。但是，少是 在HF患者中知道这种关系。提议的特定目的1和2 项目是为了检验的假设，即口服镁补充剂的新处方 与DM和死亡率和住院的患者发生的HF发生较低风险有关 在HF患者中。尽管镁廉价且相对安全，但其长期影响 个别患者可能会有所不同。那就是，它没有将其推荐给数百万患者，而是 非常适合推荐最有可能受益的个人。那，我们的特定目标3是 开发和验证一种可解释的基于深度学习的风险预测模型的新颖新颖 精确的最佳临床环境，个人可以获得临床益处 从补充镁的个人特征（包括多个多发菌） 和多剂。这些目标将通过审问退伍军人事务（VA）来实现 国家EHR数据包括超过200万拥有DM的退伍军人和100万的HF，约为20 多年的镁补充剂，血清镁和结果的纵向数据。我们 将使用机器的新用户设计，边缘结构模型（倾向得分加权） 基于学习的估计和稳定性分析，以最大程度地减少混杂并说明 潜在偏见。个人风险/利益的预测模型将使用Cerner验证 HealthFacts®数据，用于非退伍军人人口的普遍性。拟议研究的发现 将产生新的证据，这些证据将具有直接的临床含义，而目标3则具体的证据 将提供一种新型的精密药物工具来个性化镁补充剂的使用。