Cleveland Clinic study: 71% of type 2 diabetes patients achieve A1C below 6.5% with AI precision treatment while reducing GLP-1 reliance

Type 2 diabetes is commonly managed with a one-size-fits-all approach in primary care, relying heavily on glucose-lowering medications including costly GLP-1 receptor agonists. Prolonged elevated blood sugar increases risk of serious complications such as heart disease, kidney failure, and stroke. Cleveland Clinic researchers sought to determine whether patients could reach glycemic goals while reducing dependence on these medications.

Cleveland Clinic led a 12-month clinical trial at the Twinsburg Family Health Center evaluating Twin Health's Twin Precision Treatment system. The program combines digital twin AI, wearable sensors, and a human care team to continuously track glucose, weight, blood pressure, stress, activity, and sleep, delivering personalized nutrition and exercise guidance via a smartphone app. AI-enabled predictions of individual blood glucose responses to meals informed dietary recommendations for 100 intervention-group patients versus 50 receiving standard care.

After one year, 71% of intervention participants achieved an A1C below 6.5% while taking only metformin, compared with 2.4% in the standard care group. Intervention patients lost 8.6% of body weight versus modest losses in usual care, and medication use fell sharply—including GLP-1 receptor agonist use dropping to 6%, with most program participants eliminating GLP-1s. Quality of life and treatment satisfaction were notably higher in the Twin intervention group.

• AI-driven digital twin modeling combined with wearable data and coached care can outperform usual primary care for glycemic control and weight loss.
• Many patients can sustain clinical goals while markedly de-escalating costly glucose-lowering medications, including GLP-1s.
• Personalized metabolic insights enable lasting lifestyle changes rather than indefinite medication escalation.