التفاصيل البيبلوغرافية
العنوان: |
Urinary Metabolome Analyses of Patients with Acute Kidney Injury Using Capillary Electrophoresis-Mass Spectrometry |
المؤلفون: |
Rintaro Saito, Akiyoshi Hirayama, Arisa Akiba, Yushi Kamei, Yuyu Kato, Satsuki Ikeda, Brian Kwan, Minya Pu, Loki Natarajan, Hibiki Shinjo, Shin’ichi Akiyama, Masaru Tomita, Tomoyoshi Soga, Shoichi Maruyama |
المصدر: |
Metabolites; Volume 11; Issue 10; Pages: 671 |
بيانات النشر: |
Multidisciplinary Digital Publishing Institute |
سنة النشر: |
2021 |
المجموعة: |
MDPI Open Access Publishing |
مصطلحات موضوعية: |
AKI, capillary electrophoresis-mass spectrometry (CE-MS), biomarker, urine |
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. |
نوع الوثيقة: |
text |
وصف الملف: |
application/pdf |
اللغة: |
English |
Relation: |
Frontiers in Metabolomics; https://dx.doi.org/10.3390/metabo11100671 |
DOI: |
10.3390/metabo11100671 |
الاتاحة: |
https://doi.org/10.3390/metabo11100671 |
Rights: |
https://creativecommons.org/licenses/by/4.0/ |
رقم الانضمام: |
edsbas.6937CC2F |
قاعدة البيانات: |
BASE |