Novel plasma peptide markers involved in the pathology of CKD identified using mass spectrometric approach
Chronic kidney disease (CKD) may progress to end-stage renal disease (ESRD) at different pace. Early markers of disease progression could facilitate and improve patient management. However, conventional blood and urine chemistry have proven unable to predict the progression of disease at early stage...
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| Main Authors: | , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
5 August 2019
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| In: |
Journal of molecular medicine
Year: 2019, Volume: 97, Issue: 10, Pages: 1451-1463 |
| ISSN: | 1432-1440 |
| DOI: | 10.1007/s00109-019-01823-8 |
| Online Access: | Verlag, Volltext: https://doi.org/10.1007/s00109-019-01823-8
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| Author Notes: | Prathibha R. Gajjala, Heike Bruck, Heidi Noels, Georg Heinze, Francesco Ceccarelli, Andreas Kribben, Julio Saez-Rodriguez, Nikolaus Marx, Walter Zidek, Joachim Jankowski, Vera Jankowski |
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| 245 | 1 | 0 | |a Novel plasma peptide markers involved in the pathology of CKD identified using mass spectrometric approach |c Prathibha R. Gajjala, Heike Bruck, Heidi Noels, Georg Heinze, Francesco Ceccarelli, Andreas Kribben, Julio Saez-Rodriguez, Nikolaus Marx, Walter Zidek, Joachim Jankowski, Vera Jankowski |
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| 520 | |a Chronic kidney disease (CKD) may progress to end-stage renal disease (ESRD) at different pace. Early markers of disease progression could facilitate and improve patient management. However, conventional blood and urine chemistry have proven unable to predict the progression of disease at early stages. Therefore, we performed untargeted plasma peptidome analysis to select the peptides involved in progression, which are suitable for long prospective studies in future. The study consists of non-CKD (n = 66) and CKD (n = 106) patients with different stages. We performed plasma peptidomics on these subjects using chromatography and mass spectrometric approaches. Initially, we performed LC-ESI-MS and applied least absolute shrinkage and selection operator logistic regressions to select the peptides that are differentially expressed and we generated a peptidomic score for each subject. Later, we identified and sequenced the peptides with MALDI-MS/MS and also performed univariate and multivariate analyses with the clinical variables and peptidomic score to reveal their association with progression of renal disease. A logistic regression model selected 14 substances showing different concentrations according to renal function, of which seven substances were most likely occur in CKD patients. The peptidomic model had a global P value of < 0.01 with R2 of 0.466, and the area under the curve was 0.87 (95% CI, 0.8149-0.9186; P < 0.0001). The predicted score was significantly higher in CKD than in non-CKD patients (2.539 ± 0.2637 vs − 0.9382 ± 0.1691). The model was also able to predict stages of CKD: the Spearman correlation coefficient of the linear predictor with CKD stages was 0.83 with concordance indices of 0.899 (95% CI 0.863-0.927). In univariate analysis, the most consistent association of peptidomic score in CKD patients was with C-reactive protein, sodium level, and uric acid, which are unanticipated substances. Peptidomic analysis enabled to list some unanticipated substances that have not been extensively studied in the context of CKD but were associated with CKD progression, thus revealing interesting candidate markers or mediators of CKD of potential use in CKD progression management.Key messages• Conventional blood and urine chemistry have proven unable to predict the progression of disease at early stages of chronic kidney disease (CKD).• We performed untargeted plasma peptidome analysis to select the peptides involved in progression.• A logistic regression model selected 14 substances showing different concentrations according to renal function.• These peptides are unanticipated substances that have not been extensively studied in the context of CKD but were associated with CKD progression, thus revealing markers or mediators of CKD of potential use in CKD progression management. | ||
| 650 | 4 | |a Chronic kidney disease | |
| 650 | 4 | |a Disease progression | |
| 650 | 4 | |a End-stage renal disease | |
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| 650 | 4 | |a Systems medicine | |
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