PASC

SARS-CoV-2 infection and Incident Diabetes

Cardiometabolic trajectories after SARS-CoV-2 infection

The association between SARS-CoV-2 infection and incident T2DM has yet to be empirically quantified by race and/or ethnicity using rigorous methods and relevant variables for diverse populations, despite the markedly higher rates of COVID-19 and T2DM in minority communities. We will use a rigorous analytic approach that incorporates high-quality data which captures both individual susceptibility and social vulnerability in the OneFlorida+ Data Trust, a data source that includes large proportions of minority groups, to address this critical gap in knowledge. The findings from this study will directly inform recommendations for management of the rapidly growing population of patients experiencing PASC in terms of diabetes screening and cardiometabolic risk assessment in the months and years after SARS-CoV-2 infection.

This is past work with Dr. Rosette Chakkalakal, Dr. W Troy Donahoo, Dr. Mohammed K. Ali and Dr. Yi Guo.

Funding: 3R01DK120814-S1

Conceptual framework for proposed analysis.

Publications

  1. Varghese 2024 International Journal of Obesity on association of SARS-CoV-2 infection and body mass index changes.
  2. Varghese 2024 Journal of General Internal Medicine (Accepted) on association of SARS-CoV-2 infection and risk of type 2 diabetes.

Getting Involved

These projects are ideal for advanced masters or doctoral students who want hands-on experience with electronic health record data. The dataset is available to Emory graduate students as part of rotations or thesis opportunities.

  1. Healthcare utilization before and after COVID-19 infection
  2. Association of prediabetes and obesity phenotypes with incident diabetes

Pre-requisites

  1. Proficiency in R or Python
  2. Required Coursework: EPI 560 Epidemiologic Methods IV, EPI 568 Bias Analysis, Longitudinal Analysis (BIOS 502 or BIOS 525 or BIOS 526)