# Machine Learning and Longitudinal Analyses of Metformin Response Among Veterans

> **NIH VA IK2** · VA EASTERN COLORADO HEALTH CARE SYSTEM · 2022 · —

## Abstract

Type 2 diabetes mellitus is a chronic disease that may be amenable to precision medicine approaches
because it affects a large, diverse segment of the population. In fact, the VA and American Diabetes
Association guidelines recommend individualization of diabetes management, yet initial diabetes treatment is
rarely individualized in current routine clinical care. The vast majority of diabetes patients are initially treated
with metformin, and over a quarter of these patients fail to respond to metformin alone, leading to delays in
achievement of early glycemic control and potentially avoidable risk of diabetes complications. There is a
paucity of validated strategies to individualize initial diabetes treatment. Thus, precision medicine approaches,
which attempt to match optimal disease management strategies to characteristics of an individual patient, are
ideal to address the evidence gap to systematically guide individualized diabetes care. Individualized or
precision medicine treatment is common in cardiovascular disease care, where clinical risk prediction tools
guide drug selection (e.g., anticoagulation in atrial fibrillation) and treatment intensity (e.g., cholesterol goals on
statin therapy). We propose a similar approach for diabetes treatment individualization based on prediction
tools that estimate risk of diabetes complications and glycemic response to metformin.
 The overall goals of this career development award (CDA) are to develop and validate prediction
tools that inform individualized diabetes care. The first two aims of the proposal are designed to determine
patient characteristics at the onset of treatment that predict diabetes-related complications (Aim 1) and
glycemic response to metformin (Aim 2). In Aim 3, we will evaluate whether these prediction models can inform
treatment approaches to achieve improvements in long-term diabetes complications. We will leverage large VA
data repositories to create two independent cohorts of Veterans with type 2 diabetes to complete the aims of
this proposal and form the basis of additional future observational studies. This study is innovative in that it
leverages real-world clinical data from Veterans to generate evidence to guide precision medicine
interventions; uses machine learning approaches and longitudinal methods to capture information from
repeated measurements in routine clinical care to make maximal use of electronic health record data; and
focuses on prediction tools based on data available at the time of diabetes diagnosis to guide initial treatment.
 The career development plan aligns research aims with training aims in order to prepare the
applicant to undertake a research career focused on applications of precision medicine to improve
Veteran health. The training goals of the proposal are focused in three areas that share a common theme of
maximizing longitudinal VA clinical data for clinically-relevant observational research: 1) machine learning
approaches applied to a cl...

## Key facts

- **NIH application ID:** 10463653
- **Project number:** 5IK2CX001907-04
- **Recipient organization:** VA EASTERN COLORADO HEALTH CARE SYSTEM
- **Principal Investigator:** SRIDHARAN RAGHAVAN
- **Activity code:** IK2 (R01, R21, SBIR, etc.)
- **Funding institute:** VA
- **Fiscal year:** 2022
- **Award amount:** —
- **Award type:** 5
- **Project period:** 2019-04-01 → 2024-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10463653

## Citation

> US National Institutes of Health, RePORTER application 10463653, Machine Learning and Longitudinal Analyses of Metformin Response Among Veterans (5IK2CX001907-04). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10463653. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*