Urinary Metabolome Analyses of Patients with Acute Kidney Injury Using Capillary Electrophoresis-Mass Spectrometry
| العنوان: | Urinary Metabolome Analyses of Patients with Acute Kidney Injury Using Capillary Electrophoresis-Mass Spectrometry |
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| المؤلفون: | Hibiki Shinjo, Tomoyoshi Soga, Yushi Kamei, Masaru Tomita, Akiyoshi Hirayama, Loki Natarajan, Shinichi Akiyama, Satsuki Ikeda, Arisa Akiba, Yuyu Kato, Rintaro Saito, Shoichi Maruyama, Minya Pu, Brian Kwan |
| المصدر: | Metabolites Metabolites, Vol 11, Iss 671, p 671 (2021) Volume 11 Issue 10 |
| بيانات النشر: | MDPI, 2021. |
| سنة النشر: | 2021 |
| مصطلحات موضوعية: | medicine.medical_specialty, Endocrinology, Diabetes and Metabolism, Urinary system, Urology, Renal function, Urine, urologic and male genital diseases, Microbiology, Biochemistry, Capillary electrophoresis–mass spectrometry, Article, law.invention, AKI, law, medicine, Molecular Biology, business.industry, urogenital system, Acute kidney injury, Area under the curve, capillary electrophoresis-mass spectrometry (CE-MS), medicine.disease, Intensive care unit, QR1-502, female genital diseases and pregnancy complications, urine, Biomarker (medicine), biomarker, business |
| الوصف: | Acute kidney injury (AKI) is defined as a rapid decline in kidney function. The associated syndromes may lead to increased morbidity and mortality, but its early detection remains difficult. Using capillary electrophoresis time-of-flight mass spectrometry (CE-TOFMS), we analyzed the urinary metabolomic profile of patients admitted to the intensive care unit (ICU) after invasive surgery. Urine samples were collected at six time points: before surgery, at ICU admission and 6, 12, 24 and 48 h after. First, urine samples from 61 initial patients (non-AKI: 23, mild AKI: 24, severe AKI: 14) were measured, followed by the measurement of urine samples from 60 additional patients (non-AKI: 40, mild AKI: 20). Glycine and ethanolamine were decreased in patients with AKI compared with non-AKI patients at 6–24 h in the two groups. The linear statistical model constructed at each time point by machine learning achieved the best performance at 24 h (median AUC, area under the curve: 89%, cross-validated) for the 1st group. When cross-validated between the two groups, the AUC showed the best value of 70% at 12 h. These results identified metabolites and time points that show patterns specific to subjects who develop AKI, paving the way for the development of better biomarkers. |
| وصف الملف: | application/pdf |
| اللغة: | English |
| تدمد: | 2218-1989 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a0a66138b6ce63fe4e1551340b95d490 http://europepmc.org/articles/PMC8540909 |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi.dedup.....a0a66138b6ce63fe4e1551340b95d490 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 22181989 |
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