Digital biomarkers for interstitial glucose prediction in healthy individuals using wearables and machine learning.

ABSTRACT: A personalized low-glycemic diet, maintaining stable blood glucose levels, aids in weight reduction and managing (pre-)diabetes and migraines in individuals. However, invasiveness, high cost, and limited lifecycle of continuous glucose monitoring (CGM) devices restrict their widespread use. To address these issues, we investigated machine learning (ML) approaches for glucose monitoring using data from non-invasive wearables. Our study comprised two phases involving healthy participants: The main study included two experimental sessions lasting 7-8 h with two standardized test meals, totaling over 1550 interstitial glucose (IG) measurements with CGM, and high-frequency multimodal data collected by two different non-invasive sensor devices. The follow-up study involved more than 14,400 IG me

SUBMITTER: Huang X 

PROVIDER: S-EPMC12361560 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature