A Novel Metabolic Signature To Predict the Requirement of Dialysis or Renal Transplantation in Patients with Chronic Kidney Disease.
J Proteome Res
; 18(4): 1796-1805, 2019 04 05.
Article
em En
| MEDLINE
| ID: mdl-30817158
ABSTRACT
Identification of chronic kidney disease patients at risk of progressing to end-stage renal disease (ESRD) is essential for treatment decision-making and clinical trial design. Here, we explored whether proton nuclear magnetic resonance (NMR) spectroscopy of blood plasma improves the currently best performing kidney failure risk equation, the so-called Tangri score. Our study cohort comprised 4640 participants from the German Chronic Kidney Disease (GCKD) study, of whom 185 (3.99%) progressed over a mean observation time of 3.70 ± 0.88 years to ESRD requiring either dialysis or transplantation. The original four-variable Tangri risk equation yielded a C statistic of 0.863 (95% CI, 0.831-0.900). Upon inclusion of NMR features by state-of-the-art machine learning methods, the C statistic improved to 0.875 (95% CI, 0.850-0.911), thereby outperforming the Tangri score in 94 out of 100 subsampling rounds. Of the 24 NMR features included in the model, creatinine, high-density lipoprotein, valine, acetyl groups of glycoproteins, and Ca2+-EDTA carried the highest weights. In conclusion, proton NMR-based plasma fingerprinting improved markedly the detection of patients at risk of developing ESRD, thus enabling enhanced patient treatment.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Diálise Renal
/
Transplante de Rim
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Insuficiência Renal Crônica
/
Metaboloma
/
Metabolômica
Tipo de estudo:
Etiology_studies
/
Prognostic_studies
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Risk_factors_studies
Limite:
Aged
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Female
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Humans
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Male
/
Middle aged
Idioma:
En
Revista:
J Proteome Res
Assunto da revista:
BIOQUIMICA
Ano de publicação:
2019
Tipo de documento:
Article
País de afiliação:
Alemanha