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https://www.selleckchem.com/products/ehop-016.html Early and precise identification of individuals with prediabetes and type 2 diabetes (T2D) at risk for progressing to chronic kidney disease (CKD) is essential to prevent complications of diabetes. Here, we identify and evaluate prospective metabolite biomarkers and the best set of predictors of CKD in the longitudinal, population-based Cooperative Health Research in the Region of Augsburg (KORA) cohort by targeted metabolomics and machine learning approaches. Out of 125 targeted metabolites, sphingomyelin C181 and phosphatidylcholine diacyl C380 were identified as candidate metabolite biomarkers of incident CKD specifically in hyperglycemic individuals followed during 6.5 years. Sets of predictors for incident CKD developed from 125 metabolites and 14 clinical variables showed highly stable performances in all three machine learning approaches and outperformed the currently established clinical algorithm for CKD. The two metabolites in combination with five clinical variables were identified as the best set of predictors, and their predictive performance yielded a mean area value under the receiver operating characteristic curve of 0.857. The inclusion of metabolite variables in the clinical prediction of future CKD may thus improve the risk prediction in people with prediabetes and T2D. The metabolite link with hyperglycemia-related early kidney dysfunction warrants further investigation.Increased expression of pulmonary ACE2, the SARS-CoV-2 receptor, could contribute to increased infectivity of COVID-19 in patients with diabetes, but ACE2 expression has not been studied in lung tissue of subjects with diabetes. We therefore studied ACE2 mRNA and protein expression in lung tissue samples of subjects with and without diabetes that were collected between 2002 and 2020 from patients undergoing lobectomy for lung tumors. For RT-PCR analyses, samples from 15 subjects with diabetes were compared with 91 randomly chosen

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