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

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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 broad and diverse sample of the U.S. population.