This overview examines the integration of machine learning (ML) approaches into diabetes prediction and diagnosis, highlighting the evolution from classical statistical methods to advanced data-driven ...

Leveraging cutting-edge metabolomics, this study unveils a cost-effective and accurate diabetes prediction model that could revolutionize early detection and prevention strategies. Study: Novel type 2 ...

Researchers at at Massachusetts General Hospital have developed a blood test that examines over 200 proteins to assess an individual’s biological aging rate. According to the research, the test — ...

MASLD is prevalent in T2DM patients, with a 65% occurrence rate, and poses a higher risk for severe liver diseases. The study analyzed 3,836 T2DM patients, identifying key predictors like BMI, ...

More information: Ahmed A. Metwally et al, Prediction of metabolic subphenotypes of type 2 diabetes via continuous glucose monitoring and machine learning, Nature Biomedical Engineering (2024). DOI: ...

Data from continuous glucose monitors can predict nerve, eye and kidney damage caused by type 1 diabetes, University of Virginia Center for Diabetes Technology researchers have found. That suggests ...

Non-communicable diseases (NCDs) such as cardiovascular diseases, diabetes, cancer, and chronic respiratory conditions are the leading cause of death globally, accounting for 74% of all deaths ...

RICHMOND, Va. (WRIC) — A research study from the University of Virginia Center for Diabetes Technology suggests that data from continuous glucose monitors (CGMs) can predict the development of serious ...

Time-in-range computed from virtual CGM data predicts retinopathy, neuropathy similarly to glycated hemoglobin data. (HealthDay News) — Fourteen-day continuous glucose monitoring (CGM) traces added to ...