Researchers have identified three distinct patterns of dysglycemia in adolescents with type 1 diabetes using a combination of blinded continuous glucose monitoring data and machine-learning techniques, offering clinicians an opportunity to better tailor therapy, according to findings published in Pediatric Diabetes. “The study demonstrates that among adolescents with type 1 diabetes and
Source: CGM, machine learning reveal dysglycemia phenotypes in type 1 diabetes