Plasma biomarker discovery for early chronic kidney disease diagnosis based on chemometric approaches using LC-QTOF targeted metabolomics data

https://doi.org/10.1016/j.jpba.2017.10.036Get rights and content

Highlights

  • Multivariate data analysis of 16 metabolites from three arginine-related metabolic pathways was carried out.

  • CIT, SAM, SDMA and CNN found to be potential biomarkers for pediatric CKD.

  • CIT, SAM, SDMA and CNN add 18% accuracy for early CKD diagnosis in comparison with CNN-based classification.

  • 4 new biomarkers for preliminary CKD stage establishment prior to nephrologic assessment.

Abstract

Chronic kidney disease (CKD) is a progressive pathological condition in which renal function deteriorates in time. The first diagnosis of CKD is often carried out in general care attention by general practitioners by means of serum creatinine (CNN) levels. However, it lacks sensitivity and thus, there is a need for new robust biomarkers to allow the detection of kidney damage particularly in early stages. Multivariate data analysis of plasma concentrations obtained from LC-QTOF targeted metabolomics method may reveal metabolites suspicious of being either up-regulated or down-regulated from urea cycle, arginine methylation and arginine-creatine metabolic pathways in CKD pediatrics and controls. The results show that citrulline (CIT), symmetric dimethylarginine (SDMA) and S-adenosylmethionine (SAM) are interesting biomarkers to support diagnosis by CNN: early CKD samples and controls were classified with an increase in classification accuracy of 18% when using these 4 metabolites compared to CNN alone. These metabolites together allow classification of the samples into a definite stage of the disease with an accuracy of 74%, being the 90% of the misclassifications one level above or below the CKD stage set by the nephrologists. Finally, sex-related, age-related and treatment-related effects were studied, to evaluate whether changes in metabolite concentration could be attributable to these factors, and to correct them in case a new equation is developed with these potential biomarkers for the diagnosis and monitoring of pediatric CKD.

Introduction

Chronic kidney disease (CKD) is a major worldwide public health problem, affecting both children and adults, in which kidney function declines progressively. In clinical practice, different equations based on creatinine (CNN) concentration are used to estimate the glomerular filtration rate (GFR). This value reflects kidney function and is useful for the diagnosis of CKD and for assigning the stage or degree of the disease. Detection of CKD is considered a priority for primary care, because early treatment of CKD and its complications may delay or prevent the development of end-stage renal disease (ESRD) [1]. In clinical practice, one of the most commonly used equation in pediatrics to diagnose the disease for the first time is the Schwartz formula which is based on height, serum CNN concentration and k coefficient [2], as shown in Eq. (1).GFR  (mL min−1/1.73 m2) = k × Height  (cm)/Serum  creatinine  (mg dL−1)k coefficient has changed along the years, and is 0.45 for first year term infants, 0.55 for children and adolescent girls and 0.7 for adolescent boys currently.

Even if CNN is the classic biomarker used for the assessment of renal function in primary care attention, it has several drawbacks. Indeed, it lacks sensitivity and often reveals kidney damage when an important nephronic loss has already occurred. For that reason, in several early CKD patients a proper diagnosis by the general practitioner (GP) using the available CNN-based screening blood or urine tests is not possible until the disease progresses or more specific tests like abdominal computed tomography scan, abdominal ecography, kidney histopathology and immunohistochemistry or renal scintigraphy are carried out by nephrologists [3].

It would be ideal for GPs to be able to carry out better screening including more sensitive biomarkers for CKD in addition to CNN, as it is estimated that the majority of the population visits their GP within a 3-year period and can be subjected to screening [1]. Screening tests can be performed using either urine or blood biofluids. The utility of urinalysis is at times overestimated due to the inaccuracies in quantitatively collection of urine [4]. For that reason, blood analysis is preferred in children for diagnostic purposes. Thus, there is a need for new biomarkers (in addition to CNN) to be included in the equations used by GP in screening blood tests. Indeed, an earlier diagnosis of CKD, a better approximation to the CKD stage defined by nephrologists, monitoring of the progression of the disease and evaluation of the response to therapy are required. The early detection of CKD and the approximation of the CKD stage due to the implementation of new biomarkers in the screening tests carried out by GP would allow the early referral to the nephrologist, often leading to a better outcome of the patient.

CKD is associated with alterations in multiple metabolic pathways [5]. Arginine-creatine metabolic pathway, arginine methylation and the urea cycle were suspicious to be affected in pediatric patients with CKD and thus, some metabolites from these metabolic pathways were expected to be increased or decreased in comparison to control pediatric patients. It has to be taken into account that depending on the metabolic pathways, differences in fold change concentration of metabolites can be lower or higher. For instance, the concentration of metabolites in the central metabolism is relatively constant. Concentrations of metabolites present in secondary metabolism-related pathways may differ more in concentration, depending on environmental conditions. Indeed, all biological systems are easily perturbed by a number of intra-individual or inter-individual experimental or environmental factors, such as age, diet, growth phase, media, nutrients, pH, sex, and temperature, which should be taken into consideration. This is known as induced biological variation [6]. Central metabolic pathways include glucolysis, the pentose phosphate pathway, the tricarboxylic acid cycle, anaplerotic reactions and biosynthetic pathways of fatty acids and amino acids, and those reactions not included in central metabolic pathways are considered intermediate or secondary metabolic pathways [7]. The urea cycle is considered a central metabolic pathway, as it is part of arginine biosynthesis metabolic pathway, whereas arginine-creatine metabolic pathway and arginine methylation are not included in the central metabolic pathway, and thus are considered secondary metabolic pathways.

Metabolomics aims at studying the dynamic changes, interactions and responses to stimuli of metabolites in different metabolic pathways [8]. The feasibility of metabolomics for biomarker discovery is supported by the assumption that metabolites play an important role in biological systems and that diseases cause disruption of biochemical pathways [9]. It has to be taken into account that each biofluid contains a large number of metabolites with concentration levels that can vary by orders of magnitude. However, from a biological point of view, metabolites present in high concentrations are not necessarily more important than those at low concentrations [6].

For all these reasons, in addition to the careful planning of experiments and analytical measurements, statistical and chemometric pre-processing are essential. Indeed, chemometrics, defined as the art of extracting chemically relevant information from data produced in chemical experiments, is indispensable to obtain consistent information and discard irrelevant information [10].

The aim of this work has been to perform data analysis on the plasma concentrations of 16 metabolites from arginine-creatine, urea cycle and arginine methylation metabolic pathways in thirty-two patients at different stages of CKD and twenty-four control patients not suffering from CKD in order to find new potential biomarkers. These metabolites were selected because they were suspicious of being altered in pediatrics with CKD and were measured by means of a recently developed ion-pairing LC-QTOF-MS methodology targeted at these metabolites [11]. To turn these measurements into scores, we used complementary chemometric tools to extract the diagnostically relevant information that remain unseen with the naked eye. We have also identified the potential biomarkers for early diagnosis of CKD and checked whether these metabolites could be affected by age, sex or treatment received.

Section snippets

Chemicals and reagents

Acetonitrile used for both standard preparation and mobile phase was supplied by Scharlau (Sentmenat, Spain). In addition, LC–MS grade ammonium formate eluent additive from Fluka Analytical, Sigma-Aldrich (Steinheim, Germany) and perfluoroheptanoic acid (PFHA) from Acros Organics (New Jersey, USA) completed the mobile phase. Standard preparation required the use of LC–MS grade methanol from Scharlau (Sentmenat, Spain), ultra-high purity water obtained from pretreated tap water by means of Elix

Summary statistics

To have an approximate idea of the orders of magnitude of the concentrations of the 16 metabolites analyzed in plasma, Fig. 2 shows an image made using Matlab R2015a (Mathworks Natick, Massachusetts, United States) representing in a color scale the concentration of each analyte in each patient’s sample. In addition, a table summarizing concentration levels of each amino acid in plasma from both control and CKD pediatrics is showed in the same figure.

This picture showed a big variation because

Conclusions

Three new metabolites, CIT, SAM and SDMA, have been proposed as potential biomarkers in addition to the commonly used CNN to be implemented since they enable a better diagnosis of early stages of CKD in pediatrics. Moreover, these metabolites showed greater improvement also for the diagnosis of the rest of the stages in CKD. In addition, some gradation in the concentration of these metabolites according to the CKD stage has also been found. Therefore, it is reasonable to think of including them

Disclosures

No relevant conflicts of interest to declare.

Acknowledgements

The authors thank for technical and human support provided by SGIker of UPV/EHU and European funding (ERDF and ESF) as well as the Division of Metabolism belonging to Cruces University Hospital (Barakaldo, Spain) for supplying real samples for this study. This work was funded by the Department of Industry, Innovation, Commerce and Tourism of the Basque Government (SAI 12/25 Project) and by the Basque Government, Research Groups of the Basque University System (Project No. IT3338-10). The Basque

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