Pharmacotherapy guidelines for type 2 diabetes (T2D) emphasize patient-centered care, but applying this approach effectively in outpatient practice remains challenging. Data-driven treatment ...
A new machine-learning tool can identify diabetes and prediabetes using ECG data, according to research recently published online in BMJ Innovations. “The motivation for the study was to search for ...
A University of Virginia Center for Diabetes Technology-developed algorithm—paired with a continuous glucose monitor—can help users better manage their type 2 diabetes by recommending insulin-dose ...
A landmark study by the German Diabetes Center (DDZ), published in The Lancet Diabetes & Endocrinology, sheds new light on the heterogeneity of type 2 diabetes. The researchers have employed an ...
A machine learning model for optimizing treatment of patients with poorly controlled type 2 diabetes
Cardiorenal-protective sodium-glucose cotransporter-2 inhibitors (SGLT-2i) and glucagon-like peptide-1 receptor agonists (GLP-1RA) lack selection guidance. We aimed to build a SGLT-2i/GLP-1RA Decision ...
The latest type 2 diabetes (T2D) management guidance from the American Association of Clinical Endocrinology (AACE) covers newer diabetes medications, comorbidities, and — for the first time — ...
A new paper surveying advances in diabetes pathogenesis and treatment explores the complex factors contributing to the onset and progression of the disease, suggesting that an understanding of these ...
After decades of neglect, malnutrition-related diabetes, or type 5, is finally getting the research, recognition, and ...
A new study led by Marc D. Breton, PhD, found that a University of Virginia Center for Diabetes Technology-developed algorithm – paired with a continuous glucose monitor – can help users better manage ...
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