2型糖尿病骨骼肌代谢改善：GEO数据挖掘与HIIT/MICT运动干预机制解析

Mining the GEO database for skeletal muscle data from patients with type 2 diabetes, key node genes such as MSTN were identified through bioinformatics analysis as potential molecular targets for improving skeletal muscle glucose metabolism through exercise intervention. Male db/db mice at 8 weeks of age were adaptively fed for 1 week and then randomly divided into a diabetic control group (DC group), a moderate-intensity continuous training (MICT) group, and a high-intensity interval training (HIIT) group. Age-matched db/m mice served as the normal control group (NC group), with 12 mice in each group. After 1 week of adaptive training, the maximum running speed (Vmax) was measured, and a 10-week exercise intervention (5 days per week) was initiated: the HIIT group performed 2 minutes of high-intensity training at 90% Vmax (with 2 minutes of rest between sessions, repeated 10 times), while the MICT group engaged in continuous exercise at 70% Vmax (covering the same total distance as the HIIT group). Both groups included a 5-minute warm-up and a 5-minute cool-down. Vmax was re-measured every 2 weeks to dynamically adjust the exercise intensity. Body weight and random blood glucose levels were monitored weekly, and skeletal muscle tissue samples were collected at the end of the intervention.