A metabolomics-based approach for predicting stages of chronic kidney disease
| Autor: | Tatsuya Fujisawa, Toshihiko Ozawa, Toshiaki Kamachi, Masahiro Kohno, Noriaki Tanaka, Tatsunari Yoshida, Toshihiro Kobayashi, Hiroyuki Yanai, Yuriko Matsumura, Kouichi Fujiwara, Atsuo Iwasawa |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2014 |
| Předmět: |
Adult
Male medicine.medical_specialty Metabolite medicine.medical_treatment Biophysics Urology Renal function urologic and male genital diseases Kidney Biochemistry Mass Spectrometry chemistry.chemical_compound Metabolomics Internal medicine medicine Humans Risk factor Cystatin C Renal Insufficiency Chronic Molecular Biology Aged Aged 80 and over Creatinine biology Cell Biology Middle Aged medicine.disease female genital diseases and pregnancy complications Endocrinology Early Diagnosis chemistry Multivariate Analysis biology.protein Linear Models Female Hemodialysis Kidney disease Chromatography Liquid Glomerular Filtration Rate |
| Zdroj: | Biochem Biophys Res Commun.. 445(2):412-416 |
| Popis: | Chronic kidney disease (CKD) is a major epidemiologic problem and a risk factor for cardiovascular events and cerebrovascular accidents. Because CKD shows irreversible progression, early diagnosis is desirable. Renal function can be evaluated by measuring creatinine-based estimated glomerular filtration rate (eGFR). This method, however, has low sensitivity during early phases of CKD. Cystatin C (CysC) may be a more sensitive predictor. Using a metabolomic method, we previously identified metabolites in CKD and hemodialysis patients. To develop a new index of renal hypofunction, plasma samples were collected from volunteers with and without CKD and metabolite concentrations were assayed by quantitative liquid chromatography/mass spectrometry. These results were used to construct a multivariate regression equation for an inverse of CysC-based eGFR, with eGFR and CKD stage calculated from concentrations of blood metabolites. This equation was able to predict CKD stages with 81.3% accuracy (range, 73.9-87.0% during 20 repeats). This procedure may become a novel method of identifying patients with early-stage CKD. |
| Databáze: | OpenAIRE |
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