Individual responses to diabetes therapies vary widely, yet the underlying reasons remain poorly understood. A multicenter study presented at the European Association for the Study of Diabetes Conference 2025 investigated whether continuous glucose monitoring (CGM) could identify glycemic patterns that predict treatment response and complication risk.

A total of 527 patients underwent 14 days of standardized CGM. Advanced pattern recognition algorithms detected five recurring glycemic signatures: sustained hyperglycemia, high-frequency hypoglycemic excursions, postprandial spikes, early morning elevations, and chaotic fluctuations.

Each signature correlated with distinct therapeutic responses. Postprandial spike patterns responded best to incretin-based therapies, with a 32% reduction in glycemic variability. Early morning elevation patterns showed superior response to SGLT2 inhibitors, lowering morning glucose by 63 mg/dL. High-frequency hypoglycemia patterns benefited most from continuous insulin infusion, reducing hypoglycemia by 76%.

Notably, glycemic signatures predicted hospitalization for acute metabolic complications more accurately than HbA1c or disease duration (AUC 0.83 vs. 0.67, p<0.001).

These results demonstrate that signature-based therapy selection can optimize outcomes, reduce adverse events, and support personalized diabetes management. Incorporating glycemic pattern analysis into routine clinical care could transform therapeutic decision-making and improve patient safety.