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

> **NIH NIH R01** · GEORGE WASHINGTON UNIVERSITY · 2024 · $457,519

## Abstract

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.

## Key facts

- **NIH application ID:** 10885015
- **Project number:** 5R01HL156518-04
- **Recipient organization:** GEORGE WASHINGTON UNIVERSITY
- **Principal Investigator:** ALI AHMED
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $457,519
- **Award type:** 5
- **Project period:** 2021-09-16 → 2026-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10885015

## Citation

> US National Institutes of Health, RePORTER application 10885015, Magnesium supplement and vascular health: Machine learning from the longitudinal medical record (5R01HL156518-04). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10885015. Licensed CC0.

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