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Metabolites in biofluids can serve as biomarkers for diagnosing diseases and monitoring body conditions. Among the available biofluids, interstitial fluid (ISF) in the skin has garnered considerable attention owing to its advantages, which include inability to clot, easy access to the skin, and possibility of incorporating wearable devices. However, the scientific understanding of skin ISF composition is limited. In this study, we aimed to compare metabolites between skin dialysate containing metabolites from the skin ISF and venous blood (plasma) samples, both collected under resting states. We collected forearm skin dialysate using intradermal microdialysis alongside venous blood (plasma) samples from 12 healthy young adults. We analyzed these samples using capillary electrophoresis–fourier transform mass spectrometry–based metabolomics (CE–FTMS). Significant positive correlations were observed in 39 metabolites between the skin dialysate and plasma, including creatine (a mitochondrial disease biomarker), 1-methyladenosine (an early detection of cancer biomarker), and trimethylamine N-oxide (a posterior predictor of heart failure biomarker). Based on the Human Metabolome Technologies database, we identified 12 metabolites unique to forearm skin dialysate including nucleic acids, benzoate acids, fatty acids, amino acids, ascorbic acid, 3-methoxy-4-hydroxyphenylethyleneglycol (an Alzheimer's disease biomarker), and cysteic acid (an acute myocardial infarction biomarker). We show that some venous blood biomarkers may be predicted from skin dialysate or skin ISF, and that these fluids may serve as diagnostic and monitoring tools for health and clinical conditions. • Skin interstitial fluid offers non-clotting, easy access from the skin surface. • Identified 12 key metabolites in skin dialysate by using CE–FTMS–based metabolomics. • Skin dialysate positively correlates with plasma in 39 metabolites. [ABSTRACT FROM AUTHOR]

Diabetes is a complex and heterogeneous disease, making the prediction of the risks of diabetic complications challenging. Novel adult-onset diabetes subgroups have been studied using cluster analysis, but its application in East Asians remains unclear. We conducted a retrospective cohort study to elucidate the clinical utility of cluster-based subgroup analysis in the Japanese population. Cluster analysis based on anti-glutamate decarboxylase antibody (GAD antibody) levels, age at diagnosis, body mass index (BMI), hemoglobin A1c (A1c), and homeostatic model assessment 2 estimates of β-cell function and insulin resistance was performed in 1520 diabetic patients. The risk of developing diabetic complications was analyzed using Kaplan–Meier analysis and the Cox proportional hazards model. By cluster analysis, we identified five distinct subgroups of adult-onset diabetes in the Japanese population. The risk of diabetic complications varied greatly among the clusters. Patients with severe autoimmune diabetes or severe insulin deficiency diabetes were at an increased risk of diabetic retinopathy, and those with severe insulin resistant diabetes (SIRD) had the highest risk of developing diabetic kidney disease (DKD). After adjusting for uncorrectable and correctable risk factors, SIRD was found to be an independent risk factor for DKD. In conclusion, we identified five subgroups of adult-onset diabetes and the risk factors for diabetic complications in the Japanese population. This new classification system can be effective in predicting the risk of diabetic complications and for providing optimal treatment. [ABSTRACT FROM AUTHOR]