Limits...
Assessment of metabolomic and proteomic biomarkers in detection and prognosis of progression of renal function in chronic kidney disease.

Nkuipou-Kenfack E, Duranton F, Gayrard N, Argilés À, Lundin U, Weinberger KM, Dakna M, Delles C, Mullen W, Husi H, Klein J, Koeck T, Zürbig P, Mischak H - PLoS ONE (2014)

Bottom Line: We identified a panel of 76 statistically significant metabolites and peptides that correlated with CKD in the training set.A classifier utilising 46 identified urinary peptide biomarkers performed statistically equivalent to the urinary and plasma metabolite classifier (ρ = -0.7752; p<0.0001 and ρ = -0.8400; p<0.0001).In conclusion, we found excellent association of plasma and urinary metabolites and urinary peptides with kidney function, and disease progression, but no added value in combining the different biomarkers data.

View Article: PubMed Central - PubMed

Affiliation: Mosaiques Diagnostics GmbH, Hannover, Germany; Department of Toxicology, Hannover Medical School, Hannover, Germany.

ABSTRACT
Chronic kidney disease (CKD) is part of a number of systemic and renal diseases and may reach epidemic proportions over the next decade. Efforts have been made to improve diagnosis and management of CKD. We hypothesised that combining metabolomic and proteomic approaches could generate a more systemic and complete view of the disease mechanisms. To test this approach, we examined samples from a cohort of 49 patients representing different stages of CKD. Urine samples were analysed for proteomic changes using capillary electrophoresis-mass spectrometry and urine and plasma samples for metabolomic changes using different mass spectrometry-based techniques. The training set included 20 CKD patients selected according to their estimated glomerular filtration rate (eGFR) at mild (59.9±16.5 mL/min/1.73 m2; n = 10) or advanced (8.9±4.5 mL/min/1.73 m2; n = 10) CKD and the remaining 29 patients left for the test set. We identified a panel of 76 statistically significant metabolites and peptides that correlated with CKD in the training set. We combined these biomarkers in different classifiers and then performed correlation analyses with eGFR at baseline and follow-up after 2.8±0.8 years in the test set. A solely plasma metabolite biomarker-based classifier significantly correlated with the loss of kidney function in the test set at baseline and follow-up (ρ = -0.8031; p<0.0001 and ρ = -0.6009; p = 0.0019, respectively). Similarly, a urinary metabolite biomarker-based classifier did reveal significant association to kidney function (ρ = -0.6557; p = 0.0001 and ρ = -0.6574; p = 0.0005). A classifier utilising 46 identified urinary peptide biomarkers performed statistically equivalent to the urinary and plasma metabolite classifier (ρ = -0.7752; p<0.0001 and ρ = -0.8400; p<0.0001). The combination of both urinary proteomic and urinary and plasma metabolic biomarkers did not improve the correlation with eGFR. In conclusion, we found excellent association of plasma and urinary metabolites and urinary peptides with kidney function, and disease progression, but no added value in combining the different biomarkers data.

Show MeSH

Related in: MedlinePlus

Correlation analysis of metabolomic and proteomic based classifier scores with follow-up eGFR.The correlation analysis is performed by using the support vector machine classification scores obtained for the test set with follow-up eGFR. A. Classifier MetaboP (plasma metabolites) ρ = −0.6009 and p = 0.0019. B. Classifier MetaboU (urinary metabolites) ρ = −0.6574 and p = 0.0005. C. Classifier Pept (urinary peptides) ρ = −0.8400 and p<0.0001.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4016198&req=5

pone-0096955-g003: Correlation analysis of metabolomic and proteomic based classifier scores with follow-up eGFR.The correlation analysis is performed by using the support vector machine classification scores obtained for the test set with follow-up eGFR. A. Classifier MetaboP (plasma metabolites) ρ = −0.6009 and p = 0.0019. B. Classifier MetaboU (urinary metabolites) ρ = −0.6574 and p = 0.0005. C. Classifier Pept (urinary peptides) ρ = −0.8400 and p<0.0001.

Mentions: The performance of above-mentioned CKD classifiers at predicting the progression of renal function was investigated using the follow-up data from the test set. The classifier MetaboP was significantly correlated with follow-up eGFR (ρ =  −0.6009, p = 0.0019, Figure 3A) and the classifier MetaboU also show a significant correlation (ρ = −0.6574, p = 0.0005, Figure 3B). The urinary peptide-based classifier was significantly correlated with the follow-up eGFR (ρ = −0.8400, p<0.0001, Figure 3C). The individual comparison of Pept with MetaboU and MetaboP (p = 0.1606 and p = 0.0879, respectively) again demonstrated no significant difference between the classifiers.


Assessment of metabolomic and proteomic biomarkers in detection and prognosis of progression of renal function in chronic kidney disease.

Nkuipou-Kenfack E, Duranton F, Gayrard N, Argilés À, Lundin U, Weinberger KM, Dakna M, Delles C, Mullen W, Husi H, Klein J, Koeck T, Zürbig P, Mischak H - PLoS ONE (2014)

Correlation analysis of metabolomic and proteomic based classifier scores with follow-up eGFR.The correlation analysis is performed by using the support vector machine classification scores obtained for the test set with follow-up eGFR. A. Classifier MetaboP (plasma metabolites) ρ = −0.6009 and p = 0.0019. B. Classifier MetaboU (urinary metabolites) ρ = −0.6574 and p = 0.0005. C. Classifier Pept (urinary peptides) ρ = −0.8400 and p<0.0001.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4016198&req=5

pone-0096955-g003: Correlation analysis of metabolomic and proteomic based classifier scores with follow-up eGFR.The correlation analysis is performed by using the support vector machine classification scores obtained for the test set with follow-up eGFR. A. Classifier MetaboP (plasma metabolites) ρ = −0.6009 and p = 0.0019. B. Classifier MetaboU (urinary metabolites) ρ = −0.6574 and p = 0.0005. C. Classifier Pept (urinary peptides) ρ = −0.8400 and p<0.0001.
Mentions: The performance of above-mentioned CKD classifiers at predicting the progression of renal function was investigated using the follow-up data from the test set. The classifier MetaboP was significantly correlated with follow-up eGFR (ρ =  −0.6009, p = 0.0019, Figure 3A) and the classifier MetaboU also show a significant correlation (ρ = −0.6574, p = 0.0005, Figure 3B). The urinary peptide-based classifier was significantly correlated with the follow-up eGFR (ρ = −0.8400, p<0.0001, Figure 3C). The individual comparison of Pept with MetaboU and MetaboP (p = 0.1606 and p = 0.0879, respectively) again demonstrated no significant difference between the classifiers.

Bottom Line: We identified a panel of 76 statistically significant metabolites and peptides that correlated with CKD in the training set.A classifier utilising 46 identified urinary peptide biomarkers performed statistically equivalent to the urinary and plasma metabolite classifier (ρ = -0.7752; p<0.0001 and ρ = -0.8400; p<0.0001).In conclusion, we found excellent association of plasma and urinary metabolites and urinary peptides with kidney function, and disease progression, but no added value in combining the different biomarkers data.

View Article: PubMed Central - PubMed

Affiliation: Mosaiques Diagnostics GmbH, Hannover, Germany; Department of Toxicology, Hannover Medical School, Hannover, Germany.

ABSTRACT
Chronic kidney disease (CKD) is part of a number of systemic and renal diseases and may reach epidemic proportions over the next decade. Efforts have been made to improve diagnosis and management of CKD. We hypothesised that combining metabolomic and proteomic approaches could generate a more systemic and complete view of the disease mechanisms. To test this approach, we examined samples from a cohort of 49 patients representing different stages of CKD. Urine samples were analysed for proteomic changes using capillary electrophoresis-mass spectrometry and urine and plasma samples for metabolomic changes using different mass spectrometry-based techniques. The training set included 20 CKD patients selected according to their estimated glomerular filtration rate (eGFR) at mild (59.9±16.5 mL/min/1.73 m2; n = 10) or advanced (8.9±4.5 mL/min/1.73 m2; n = 10) CKD and the remaining 29 patients left for the test set. We identified a panel of 76 statistically significant metabolites and peptides that correlated with CKD in the training set. We combined these biomarkers in different classifiers and then performed correlation analyses with eGFR at baseline and follow-up after 2.8±0.8 years in the test set. A solely plasma metabolite biomarker-based classifier significantly correlated with the loss of kidney function in the test set at baseline and follow-up (ρ = -0.8031; p<0.0001 and ρ = -0.6009; p = 0.0019, respectively). Similarly, a urinary metabolite biomarker-based classifier did reveal significant association to kidney function (ρ = -0.6557; p = 0.0001 and ρ = -0.6574; p = 0.0005). A classifier utilising 46 identified urinary peptide biomarkers performed statistically equivalent to the urinary and plasma metabolite classifier (ρ = -0.7752; p<0.0001 and ρ = -0.8400; p<0.0001). The combination of both urinary proteomic and urinary and plasma metabolic biomarkers did not improve the correlation with eGFR. In conclusion, we found excellent association of plasma and urinary metabolites and urinary peptides with kidney function, and disease progression, but no added value in combining the different biomarkers data.

Show MeSH
Related in: MedlinePlus