Diabetes is a chronic disease resulting from the pancreatic inability to produce insulin in reaction to blood glucose levels. There are two main types of diabetes. Type 1 diabetes is characterized by absolute insulin deficiency, whereas Type 2 is characterized by insulin resistance. In 2014, approximately $422$ million people worldwide were living with Type 1 or 2 diabetes, with $30$ million people being affected in the US alone. High levels of blood glucose observed in diabetes lead to increased risk of adverse health effects including retinopathy, cardiovascular disease, lower extremity amputations cognitive dysfunction, and premature morbidity and mortality. Thus, the primary treatment goal in diabetes is glucose control. However, a normal glucose profile is non-constant, with typical normal values varying between $70$-$120$ mg/dL and peaks associated with meal intakes. The highly non-linear and non-stationary nature of glucose profiles is due to a wide range of environmental factors including time, quantity and composition of meals, physical activity time, intensity and type, stress, and sleep quality.

Continuous Glucose Monitors (CGMs) are small wearable devices that measure the interstitial glucose levels continuously throughout the day, with some monitors taking measurements as often as every 5 minutes. Data from these monitors provide a detailed quantification of the variation in glucose levels during the course of the day, and thus CGMs play an increasing role in clinical practice. For more on CGMs, see Rodbard (2016) “Continuous Glucose Monitoring: A Review of Successes, Challenges, and Opportunities.”.

Out group focuses on developing statistical methods to aid analyses and interpretation of CGM data. Some selected research products are below.

CGM datasets

Our team has released Awesome-CGM, a list of public Continuous Glucose Monitoring (CGM) datasets. To cite the most recent collection:

  • Xinran Xu, Neo Kok, Junyan Tan, Mary Martin, David Buchanan, Elizabeth Chun, Rucha Bhat, Shaun Cass, Eric Wang, Sangaman Senthil, & Irina Gaynanova. (2024). IrinaStatsLab/Awesome-CGM: Updated release with additional public CGM dataset and enhanced processing (v2.0.0). Zenodo.DOI

Additionally, we released Glucobench, a collection of pre-processed public CGM data for forecasting (prediction of future glucose values) with implementation of multiple benchmark ML models. To cite Glucobench specifically

Software products:

Relevant publications: