Metabolic Enzyme-Inspired Molecular Clusters for Advanced Type 2 Diabetic Complications by Alleviating Immunosuppression

Type 2 diabetes mellitus is the most prevalent disease in the world, with one-tenth of the population suffering from the disease, and the most critical challenges are its complications that induce high disability and mortality rates. The state-of-the-art therapeutic agents can manage glucose but fail to prevent renal failure as well as neurodegeneration with immunosuppression. Herein, we developed a deep learning design strategy that exploits the ‘size-fitting effect’ to engineer an atomic-precision metal cluster for preventing diabetic complications by targeting metabolic abnormality and immunosuppression. The designed AuZn cluster achieves almost 100% α-amylase inhibition and 88% α-glucosidase inhibition, resulting in the normalized glycated hemoglobin and sustained glucose control. The intrinsic redox properties reduce oxidative stress damage, promoting β-cell regeneration and metabolic stress alleviation. Consequently, the renal function, the most prevalent complications, shows that glomerular filtration can be restored to normal levels without urinary protein, while the clinical dulaglutide is not show any improvement. The key marker during early neurocognitive disorders, the amyloid precursor protein (APP) induced by complications, can be effectively suppressed, and diabetes induced organelle degeneration in neurons can be restored.