Our paper, "Differential risk assessment in persons at risk of type 2 diabetes using urinary peptidomics", has been carefully evaluated and selected by a panel of experts in recognition of its scientific quality, innovation, and contribution to the field.

On behalf of the journal Metabolism, the Editor-in-Chief, Dr. Christos Mantzoros, congratulated us and praised our outstanding work and contribution.

Individuals at increased risk of type 2 diabetes have recently been classified into six prediabetes clusters, which stratify the risk of progression to diabetes and diabetes complications (low-risk clusters: 1, 2 and 4, high-risk clusters: 3, 5 and 6). Individuals in cluster 6 have an elevated risk of nephropathy and all-cause mortality despite delayed onset of diabetes. The urinary peptidome classifiers CKD273 (chronic kidney disease, CKD), HF2 (heart failure, HF) and CAD238 (coronary artery disease, CAD) are based on unique urinary peptide patterns and have shown potential for identifying individuals at risk for CKD and cardiovascular pathologies. This observational study investigates whether peptidome classifiers can differentiate complication risks across the prediabetes clusters and if a novel combination of peptides can distinguish high-risk from low-risk prediabetes clusters.