Insulin Resistance Prediction From Wearables and Routine Blood Biomarkers
Journal:
arXiv
Published Date:
Apr 30, 2025
Abstract
Insulin resistance, a precursor to type 2 diabetes, is characterized by
impaired insulin action in tissues. Current methods for measuring insulin
resistance, while effective, are expensive, inaccessible, not widely available
and hinder opportunities for early intervention. In this study, we remotely
recruited the largest dataset to date across the US to study insulin resistance
(N=1,165 participants, with median BMI=28 kg/m2, age=45 years, HbA1c=5.4%),
incorporating wearable device time series data and blood biomarkers, including
the ground-truth measure of insulin resistance, homeostatic model assessment
for insulin resistance (HOMA-IR). We developed deep neural network models to
predict insulin resistance based on readily available digital and blood
biomarkers. Our results show that our models can predict insulin resistance by
combining both wearable data and readily available blood biomarkers better than
either of the two data sources separately (R2=0.5, auROC=0.80, Sensitivity=76%,
and specificity 84%). The model showed 93% sensitivity and 95% adjusted
specificity in obese and sedentary participants, a subpopulation most
vulnerable to developing type 2 diabetes and who could benefit most from early
intervention. Rigorous evaluation of model performance, including
interpretability, and robustness, facilitates generalizability across larger
cohorts, which is demonstrated by reproducing the prediction performance on an
independent validation cohort (N=72 participants). Additionally, we
demonstrated how the predicted insulin resistance can be integrated into a
large language model agent to help understand and contextualize HOMA-IR values,
facilitating interpretation and safe personalized recommendations. This work
offers the potential for early detection of people at risk of type 2 diabetes
and thereby facilitate earlier implementation of preventative strategies.
