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Schork, A. ; Fritsche, A. ; Schleicher, E.D.* ; Peter, A.* ; Heni, M. ; Stefan, N. ; Jumpertz von Schwartzenberg, R. ; Guthoff, M. ; Mischak, H.* ; Siwy, J.* ; Birkenfeld, A.L. ; Wagner, R.

Differential risk assessment in persons at risk of type 2 diabetes using urinary peptidomics.

Metabolism 167:156174 (2025)
Verlagsversion DOI PMC
Open Access Gold (Paid Option)
Creative Commons Lizenzvertrag
OBJECTIVE: 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. Clusters 1, 2 and 4 are low-risk clusters while clusters 3, 5 and 6 are high-risk clusters; 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. METHODS: Urine peptidome analysis was performed on spot urine samples from individuals across 6 prediabetes clusters (n = 249) and 19 individuals with screen-detected diabetes (study cohorts at University Hospital Tübingen, Germany from 11/2004 to 11/2012). Predefined urinary classifiers were calculated for each participant. Lasso regression analysis was used to identify an optimal combination of peptides distinguishing low- Schlesinger et al. (2022), Wagner et al. (2021) [1,2,4] and high-risk (Rooney et al., 2021; Wagner, 2023; Latosinska et al., 2021 [3,5,6]) clusters. RESULTS: The predefined urinary peptidome classifiers CKD273, HF2 and CAD238 differed significantly across prediabetes clusters, particularly with elevated values in cluster 6 compared to the healthiest cluster 2. CKD273, HF2 and CAD238 were inversely associated with insulin sensitivity indexes. Machine Learning identified a combination of 112 urinary peptides that differentiated low-risk from high-risk prediabetes clusters (AUC-ROC 0.868 (95 % CI 0.755-0.981)). CONCLUSIONS: Urinary peptidome classifiers support the increased risk of CKD and suggest an elevated risk of heart failure and coronary artery disease in the high-risk prediabetes cluster 6. Urine peptidomics show promising potential as a tool for identifying high-risk prediabetes individuals and guiding early preventive interventions.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Korrespondenzautor
Schlagwörter Cad238 ; Ckd273 ; Cardiovascular Mortality ; Chronic Kidney Disease ; Cluster Analysis ; Hf2 ; Prediabetes ; Risk Stratification ; Urine Peptidome; Insulin Sensitivity; Kidney; Proteome; Disease; Intervention; Diagnosis; Improves
ISSN (print) / ISBN 0026-0495
e-ISSN 1532-8600
Zeitschrift Metabolism: clinical and experimental
Quellenangaben Band: 167, Heft: , Seiten: , Artikelnummer: 156174 Supplement: ,
Verlag Elsevier
Verlagsort 1600 John F Kennedy Boulevard, Ste 1800, Philadelphia, Pa 19103-2899 Usa
Nichtpatentliteratur Publikationen
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Diabetes Research and Metabolic Diseases (IDM)
Förderungen TUEFF program of the Medical Faculty of Tuebingen University
German Diabetes Association (Deutsche Diabetesgesellschaft, DDG)
German Federal Ministry for Education and Research
German Center for Diabetes Research (Deutsches Zentrum fur Diabetesforschung, DZD)