# Randomized Controlled Trial of a Six-Month Mindfulness-Based Intervention for Type 2 Diabetes

> **NIH NIH R01** · PENNSYLVANIA STATE UNIV HERSHEY MED CTR · 2022 · $472,098

## Abstract

ABSTRACT
New-onset diabetes and severe diabetic ketoacidosis (DKA) have frequently been reported in patients infected
with COVID-19, even in the absence of a known history of diabetes. However, the extent to which COVID-19
viral infection triggers or accelerates the development of hyperglycemia, diabetes and DKA remains unclear.
Therefore, there is an urgent public health need for epidemiologic studies of diabetes incidence and severity
and its potential association with COVID-19 and SARS-CoV-2 in diverse patient populations. The overall
objective of this study is to determine the incidence, severity, and risk factors for new-onset diabetes and DKA
in patients with COVID-19 infection. We plan to use Artificial Intelligence (AI)-based technology consisting of
novel interpretable machine learning predictive models to study COVID-19 and risk factors for diabetes in
large, diverse and multi-resolution datasets, including de-identified patient data from the TriNetX Research
Network of multiple health care organizations (HCOs), and from the global CoviDIAB Registry of COVID-19
related diabetes. We will investigate the predictive value of diabetes risk factors such as frequency and
severity of COVID-19 infection, age, sex, race, body mass index (BMI), pre-existing health conditions, family
history of diabetes, use of glucocorticoids, and social determinants of health (SDOH). Specific Aim 1:
Determine (a) the incidence of new onset diabetes, (b) incidence of DKA, (c) severity of hyperglycemia and
DKA at onset, (d) timing of diabetes onset, and (e) risk factors for new onset diabetes among patients with
COVID-19, as compared to two control groups: (1) Non-COVID-19 patients diagnosed with influenza, and (2)
Non-COVID-19 patients without influenza. Specific Aim 2: Apply novel Artificial Intelligence-based technology
consisting of interpretable machine learning models to patient databases, TriNetX Research Network and the
global CoviDIAB Registry of COVID-19 related diabetes, to predict the development of (a) new onset diabetes,
(b) DKA, and (c) severe DKA following COVID-19 infection, compared to the two control groups, as defined in
Aim 1. Specific Aim 3: Develop a grant proposal by using the results from the proposed study to inform the
design of a future prospective multicenter randomized controlled trial (RCT) of a lifestyle and/or pharmacologic
intervention for patients at high risk for new onset diabetes related to COVID-19, identified via EHRs from
multiple healthcare systems serving diverse patient populations. Our study findings and predictive ML models
will provide evidence on what may be the best sites to capture national patient representation, variables of
interest, clinical outcomes, and sample size for different age groups, regions, and pre-existing conditions. We
envision this study will lead to a multicenter clinical trial to study new onset diabetes in COVID-19, that will be
highly successful as its design will be based on data from a broa...

## Key facts

- **NIH application ID:** 10631839
- **Project number:** 3R01DK119379-03S1
- **Recipient organization:** PENNSYLVANIA STATE UNIV HERSHEY MED CTR
- **Principal Investigator:** NAZIA T. RAJA-KHAN
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $472,098
- **Award type:** 3
- **Project period:** 2019-04-15 → 2025-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10631839

## Citation

> US National Institutes of Health, RePORTER application 10631839, Randomized Controlled Trial of a Six-Month Mindfulness-Based Intervention for Type 2 Diabetes (3R01DK119379-03S1). Retrieved via AI Analytics 2026-07-29 from https://api.ai-analytics.org/grant/nih/10631839. Licensed CC0.

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