Type 2 diabetes affects hundreds of millions globally, and its prevalence is rising. A major precursor to this condition is insulin resistance (IR), where the body’s cells do not respond properly to insulin, a hormone crucial for regulating blood sugar. Detecting IR early is key, as lifestyle changes can often reverse it and prevent or delay the onset of type 2 diabetes. However, current methods for accurately measuring IR, like the “gold standard” euglycemic insulin clamp or the Homeostatic Model Assessment for Insulin Resistance (HOMA-IR), which requires specific insulin blood tests, are often invasive, expensive, or not readily available in routine check-ups. These steps create significant barriers to early detection and intervention, especially for those unknowingly at risk.
What if we could leverage data already available to many people, such as data from wearable devices and common blood tests, to estimate IR risk? In “Insulin Resistance Prediction From Wearables and Routine Blood Biomarkers”, we explore a suite of machine learning models that have the potential of predicting IR using wearable data (e.g., resting heart rate, step count, sleep patterns) and routine blood tests (e.g., fasting glucose, lipid panel). This approach shows strong performance across the studied population (N=1,165) and an independent validation cohort (N=72), particularly in high-risk individuals, such as people with obesity and sedentary lifestyles. Additionally, we introduce the Insulin Resistance Literacy and Understanding Agent (an IR prototype agent), built on the state-of-the-art Gemini family of LLMs to help understand insulin resistance, facilitating interpretation and safe personalized recommendations. This work offers the potential for early detection of people at risk of type 2 diabetes and thereby facilitates earlier implementation of preventative strategies. The models, predictions, and the Insulin Resistance Literacy and Understanding Agent described in this research are intended for informational and research purposes only.
