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

项目摘要/摘要 超过一半的美国成年人使用膳食补充剂。然而，人们对它们的安全性知之甚少。 和有效性，因为这些产品没有得到美国食品和药物管理局的批准 (FDA)和上市后监测仅限于不良事件。美国国立卫生研究院饮食办公室 补充剂(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。初步数据来自我们由 使用先进的机器/深度学习技术的NIH ods表明，镁 补充剂可降低糖尿病患者心力衰竭(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健康和预后 数百万美国人。糖尿病是心力衰竭的危险因素，对心力衰竭患者的预后有不利影响。 镁是300多种人体酶系统的组成部分，这些酶系统在 缺镁。我们的研究结果表明，饮食中低镁摄入量 与发生心力衰竭的风险较高有关，特别是在糖尿病患者中。然而，更少的是 已知心衰患者存在这种关系。建议的具体目标1和2 项目是测试一种口服补镁新处方的假设 糖尿病患者发生心力衰竭的风险较低，死亡率和住院风险较低 在心力衰竭患者中。虽然镁价格便宜，而且相对安全，但它的长期影响 可能会因个别患者而有所不同。因此，它不会向数百万患者推荐它，而是 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基于深度学习的可解释风险预测模型，以确定 准确地说，个人可以从中获得临床益处的最佳临床环境 来自镁补充剂，因为它们的个体特征包括多发病 和多家药房。这些目标将通过审问退伍军人事务部(VA)来实现 国家电子病历数据，包括200多万患有糖尿病的退伍军人和100万患有~20岁的心力衰竭的退伍军人 多年来关于镁补充剂、血清镁和结果的纵向数据。我们 将使用新用户设计、边际结构模型(倾向分数加权)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和考虑 潜在的偏见。对个人风险/收益的预测模型将使用Cerner进行验证 健康事实®数据，用于非退伍军人群体的概括性。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的那些 将提供一种新颖的精准医学工具，使镁补充剂的使用个性化。

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.