Int Urol Nephrol DOI 10.1007/s11255-013-0600-2
NEPHROLOGY - REVIEW
Metabolomics insights into pathophysiological mechanisms of nephrology Aihua Zhang • Hui Sun • Shi Qiu • Xijun Wang
Received: 4 June 2013 / Accepted: 31 October 2013 Ó Springer Science+Business Media Dordrecht 2013
Abstract Kidney diseases (KD), a major public health problem that affects about 10 % of the general population, manifest in progressive loss of renal function, which ultimately leads to complete kidney failure. However, current approaches based on renal histopathological results and clinical parameters lack sensitivity and are not sufficient to characterize the category and progression of nephrology or to predict nephrology progression risk reliably or to guide preventive interventions. The high incidence and financial burden of KD make it imperative to diagnose KD at early stages when therapeutic interventions are far more effective. Nowadays, the appearance of metabolomics (the highthroughput measurement and analysis of metabolites) has provided the framework for a comprehensive analysis of KD and serves as a starting point for generating novel molecular diagnostic tools for use in nephrology. Changes in the concentration profiles of a number of small-molecule metabolites found in either blood or urine can be used to localize kidney damage or assess kidneys suffering from injury. The power of metabolomics allows unparalleled opportunity to query the molecular mechanisms of KD. Novel metabolomics technologies have the ability to provide a deeper understanding of the disease beyond classical histopathology, redefine the characteristics of the disease state, and identify novel approaches to reduce renal failure.
A. Zhang (&) H. Sun S. Qiu X. Wang National TCM Key Lab of Serum Pharmacochemistry, Key Laboratory of Chinmedomics, Department of Pharmaceutical Analysis, and Key Metabolomics Platform of Chinese Medicines, Heilongjiang University of Chinese Medicine, Heping Road 24, Harbin 150040, China e-mail: [email protected] X. Wang e-mail: [email protected]
This review gives an overview of its application to important areas in clinical nephrology, with a particular focus on biomarker discovery. Great strides forward are being made in breaking down important barriers to the successful prevention and treatment of this devastating disorder. Keywords Metabolomics Kidney disease Biomarkers Metabolites Nephrology
Introduction Kidney diseases (KD) have complex pathogenesis; its diagnostic and treatment decisions are mainly based on kidney histology, a limited set of serologic markers, and clinical manifestations [1, 2]. Currently, no sufficiently sensitive or specific tests are present to detect early disease, predict disease progression, or monitor treatment response. Therefore, the discovery of a specific, reliable diagnostic and prognostic biomarker for nephrology is imperative. The early detection of KD is pivotal for successful patient treatment and management. Recent development of a variety of analytic platforms has raised hopes for identifying novel metabolite markers, and recent efforts have led to several promising novel markers of KD [3]. The advent of advanced metabolomics technologies’ development is bringing these goals into focus [4]. Metabolomics, one of the relative newcomers of the omics techniques, has the power to yield not only specific biomarkers but also insight into the pathophysiology of disease [5]. The general procedure in which metabolomics used for diagnosis and biomarker discovery was shown in Fig. 1. Metabolomics is a global metabolic profiling framework that utilizes high-resolution analytics with multivariate data
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Fig. 1 The general procedures in metabolomics can be used for biomarker discovery
analysis tools to derive an integrated picture of both endogenous and xenobiotic metabolisms [6]. It is now moving from a discovery phase in small studies to a validation phase in much larger numbers of patients with diseases [7]. The advantage of metabolomics is that it could reveal changes in both phenotype and genotype [8]. Advantages over the traditional renal biopsy include accessibility, safety, the possibility of serial sampling, and the potential for noninvasive prognostic and diagnostic monitoring of disease and an individual’s response to treatment [9]. Ideally, such metabolomics assays would be applicable to noninvasively obtained body fluids, enabling not only diagnosis of at risk patients, but also asymptomatic screening, monitoring disease recurrence, and response to treatment. The development of metabolomics tests to detect KD would be of tremendous benefit to both patients and the healthcare system [10–12]. A deeper understanding of global perturbations in biochemical pathways and useful biomarkers could provide valuable insights about mechanisms of KD. Studies of metabolite profile in blood, kidney tissue, and urine have been used to identify early biomarkers for kidney disease [13, 14]. Detailed characterization of nephrology at the metabolites’ level could yield the needed integrative knowledge to better understand its molecular mechanisms, to develop more reliable biomarker sets for diagnosis and personalized treatment. This review provides why we need new biomarkers of KD, and how biomarkers are discovered and should be evaluated, highlights the urinary biomarker as a noninvasive method to detect either morphological or biochemical changes in KD.
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Bringing metabolomics into the forefront of nephrology research Kidney diseases manifest in progressive loss of renal function, which ultimately leads to complete kidney failure [15]. Multiple factors involved in the pathogenesis of KD have made the mechanisms underlying the origins, and progression of nephrology is not fully understood. One of the important factors contributing to this problem is the lack of an early KD diagnostic test. Therefore, new biomarkers of KD are needed. The advent of advanced metabolomics technologies is bringing these goals into focus [16]. Metabolomics—the nontargeted measurement of all metabolites—is beginning to show promise in biomarker discovery and aiding the choice of therapy for patients [17]. Small-molecule metabolites produced by the body have an important role in biological systems and represent attractive candidates to understand KD phenotypes [18]. Metabolomics can present a holistic picture of the metabolites alterations and provide biomarkers that could revolutionize disease characterization and detection. The development of metabolomics assays that can diagnose disease accurately, or that can augment current methods of evaluation, would be a significant advance [19]. Ideally, metabolomics would be applicable to noninvasively obtained body fluids, enabling not only diagnosis of at risk patients, but also asymptomatic screening, monitoring disease recurrence and response to treatment. It holds promise for early diagnosis, increased choice of therapy, and the identification of new metabolic pathways that could potentially be targeted in kidney disease, which will lead to
Int Urol Nephrol
personalized therapies. The discovery of new biomarkers will improve the design of clinical trials and identify patients at an earlier stage of the disease. The focus of future metabolomics studies in kidney disease is to identify reliable early biomarkers for molecular diagnosis and prediction of disease progression.
Metabolomics to study kidney disease Acute kidney injury Acute kidney injury (AKI) is a prevalent and devastating condition associated with significant morbidity and mortality. Despite marked improvements in clinical care, the outcomes for AKI subjects have shown limited improvement in the past 50 years [20]. A major factor inhibiting clinical progress in this field has been the inability to accurately predict and diagnose early kidney dysfunction. The current gold standard clinical and biochemical criteria for diagnosis of AKI rely on urine output and serum creatinine, which are insensitive, nonspecific, and late markers of disease. A study was performed by Liu et al. [21] to further understand the metabolomic changes of AKI. AKI could be characterized by oxidative stress and changes in lipid metabolism through metabolomic investigation. Rapid LC/MS-based metabolic profiling of serum demonstrated in a pilot study that metabolomics could provide novel indicators of AKI [22]. Increases in acylcarnitines and amino acids and a reduction in serum levels of arginine and several lysophosphatidylcholines were observed in patients with AKI. Increases in homocysteine and pyroglutamate have been recognized as biomarkers of renal disease, and acylcarnitines represent biomarkers of defective fatty acid oxidation. Chronic kidney disease Chronic kidney disease (CKD) is the end point of a number of renal and systemic diseases. Metabolites such as creatinine and urea are established kidney function markers. High-throughput metabolomic studies have not been reported in large general population samples spanning normal kidney function and CKD [23]. A metabolomics approach was applied to establish a human CKD serum metabolic profile [24]. Significant endogenous metabolites that contributed to distinguish CKD in different stages included the products of glycolysis, amino acids, organic osmolytes, and so on. Based on these metabolites, the model for diagnosing patients with CKD achieved the sensitivity and specificity of 100 %. The identified metabolic biomarkers may provide useful information for the diagnosis of CKD, especially in early stages.
Renal cell carcinoma Renal cell carcinoma (RCC) whose incidence is increasing is the most prevalent malignancy of the kidney. Its survival rates are very low. The disease regularly progresses asymptomatically and is frequently metastatic upon presentation, thereby necessitating the development of an early method of detection. Early detection is currently by far the most-promising approach to reduce RCC mortality. RCC tumors had elevated levels of lactate, glutamate, pyruvate, glutamine, and creatine, but decreased levels of acetate, malate, and amino acids including valine, alanine, and aspartate [25]. An LC–MS-based method has been carried out in conjunction with multivariate data analysis to discriminate the global serum profiles of RCC patients and healthy controls [26]. The feasibility of serum metabolomics for the diagnosis and staging of RCC has also been evaluated. One-hundred percent sensitivity in detection has been achieved, and a satisfactory clustering between the early stage and advanced-stage patients is observed. As a result, 30 potential metabolites for RCC are identified. RCC is found to be closely related to disturbed phospholipid catabolism, sphingolipid metabolism, phenylalanine metabolism, etc.,. NMR-based metabolomic approach had been applied in order to monitor the alterations of plasma metabolic profile in RCC patients [27]. Alterations in the levels of LDL/ VLDL, NAC, lactate, and choline were observed between RCC patients and controls discriminating these groups in PCA plots. A global metabolite-profiling approach was applied to characterize the metabolite pool of RCC and normal renal tissue [28]. Differentially regulated metabolites, such as vitamin E derivates and metabolites of glucose, fatty acid, and inositol phosphate metabolism determined by the metabolic profile of RCC, provide leads for the characterization of novel pathways in RCC. Results suggested a key role for metabolic pathways involving arachidonic acid, free fatty acids, proline, uracil and the tricarboxylic acid cycle, and illustrate the potential of metabolomics methods to uncover the metabolic phenotype of cancer. Autosomal dominant polycystic kidney disease Autosomal dominant polycystic kidney disease (ADPKD) is the most commonly inherited kidney disease and affects one in 1,000 individuals. ADPKD is an inherited systemic disease characterized by renal cyst expansion, resulting in renal failure. Ultrasound is most often used to diagnose ADPKD; such a modality is only useful late in the disease after macroscopic cysts are present. With the progression of renal damage, the accumulation of uremic compounds is recently reported to subsequently cause further renal damage and hypertension. Finding uremic toxins and sensitive markers for detecting the early stage of ADPKD is
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necessary to clarify its pathophysiological process and to prevent its progression. Among the compounds, increases in 5-methyl-20 -deoxycytidine, glucosamine, ectoine, allantoate, a-hydroxybenzoate, phenaceturate, and 3-phenylpropionate and decreases in 2-deoxycytidine, decanoate and 10-hydroxydecanoate were newly identified in the ADPKD [29]. Using functional score analysis and the KEGG pathway database, Taylor et al. [30] had identified several biologically relevant metabolic pathways that are altered very early in this disease, the most highly represented being the purine and galactose metabolism pathways. In addition, they also identify several specific candidate biomarkers, including allantoic acid and adenosine, which are augmented in the urine of young cystic mice. These markers and pathway components may prove useful as a noninvasive means of diagnosing cystic KD and to suggest novel therapeutic approaches. Diabetic nephropathy Diabetic nephropathy (DN) is a leading cause of end-stage renal disease. Currently, microalbuminuria is an only noninvasive marker available for the diagnosis of diabetic nephropathy. However, some patients with microalbuminuria have advanced renal pathological changes for which therapy is less effective than earlier stages of the disease. Therefore, novel biomarkers for earlier diagnosis of diabetic nephropathy are crucially required. Zhao et al. [31] had performed a metabolomic analysis of perfused renal cortex samples from the diabetic rats. A number of abnormal metabolites in the diabetic kidney had been identified. Metabolic profiling was used to explore new serum biomarkers with high sensitivity and specificity for DN diagnosis, through comprehensive analysis of serum metabolites [32]. The prevalence of renal stone disease is increasing, although it remains higher in men than in women when matched for age. Garcia-Perez et al. [33] had applied two orthogonal analytical platforms in parallel to characterize the urinary metabolic signatures related to the loss of the renal chloride-oxalate exchanger in slc26a6 null mice. Key discriminating metabolites included oxalate, trimethylamine-N-oxide, etc.,. Urinary metabolites showed a sex-specific pattern included trimethylamine, trimethylamine-n-oxide, citrate, spermidine, guanidinoacetate, and 2-oxoisocaproate. Gender-dependent metabolic expression of the consequences of slc26a6 deletion might have relevance to the difference in prevalence of renal stone formation in men and women. Immunoglobulin A nephropathy Immunoglobulin A nephropathy is the most common cause of chronic renal failure among primary glomerulonephritis
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patients. The ability to diagnose immunoglobulin A nephropathy remains poor. Renal biopsy is an inconvenient, invasive, and painful examination, and no reliable biomarkers have been developed for use in routine patient evaluations. A study, done by Sui et al. [34], had identified useful biomarkers of immunoglobulin A nephropathy to establish a human immunoglobulin A nephropathy metabolic profile. Compared with the healthy controls, patients had higher levels of phenylalanine, myo-inositol, lactate, L6 lipids, L5 lipids, and L3 lipids. Investigating the metabolic changes in the evaluation process of renal fibrosis may enhance the understanding of its pathogenesis. A metabolomics study was performed to study the metabolic changes in serum samples of renal interstitial fibrosis (RIF) rats [35]. The changed metabolites such as valine, isoleucine, lactate, 3-hydroxybutyrate, alanine, acetate, acetoacetate, pyruvate, and glutamate represent potential biomarkers. Energy metabolism seemed to be impaired at the early stage of fibrosis but enhanced at a late stage, suggests that metabolomics can provide novel insights into the pathogenesis of RIF. Primary renal hypouricemia Primary renal hypouricemia (PRH) refers to a rare condition of increased renal urate clearance, caused by an isolated inborn error of membrane transport of urate in the renal proximal tubule. Several cases of exercise-induced acute renal failure and urolithiasis have been reported. Tzovaras et al. [36] had assessed tubular function in PRH using NMR-based metabolomic urine analysis. Individuals with primary hypouricemia were differentiated from healthy individuals in the orthogonal signal correction/partial least-squares-discriminant analysis models of the NMR data with a statistically significant separation. The components that contributed to this separation were the lower levels of hippurate, creatinine, and trimethylaminoxide, and the higher levels of phenylalanine, alanine, glycine, glutamate, acetate, and of an unidentified metabolite observed in hypouricemic subjects compared with controls. Kidney cancer Kidney cancer is the seventh most common cancer in the world; its incidence is increasing, and it is frequently metastatic at presentation, at which stage, patient survival statistics are grim. In addition, there are no useful biofluid markers for this disease, such that diagnosis is dependent on imaging techniques that are not generally used for screening. Kim et al. [37] had used metabolomics techniques to identify metabolites in kidney cancer patients’ urine. It found that quinolinate, 4-hydroxybenzoate, and gentisate are differentially expressed, and these metabolites
Int Urol Nephrol
are involved in common pathways of specific amino acid and energetic metabolism, consistent with high tumor protein breakdown and utilization. Further evaluation of the global metabolomics analysis, as well as confirmation of the specific potential biomarkers using a larger sample size, will lead to new avenues of kidney cancer diagnosis and therapy [38–40].
Conclusions and future perspectives Metabolomics, the nontargeted interrogation of small molecules in a biological sample, is an ideal technology for identifying diagnostic biomarkers. In this review, we describe important general aspects in biomarker discovery using metabolomics and the drawbacks of existing biomarkers, and discuss currently published studies on KD, and provide an overview of opportunities in the field. We also highlight several metabolomic studies of biomarkers of KD selected from the plethora of studies performed. Many of these markers have been confirmed across multiple studies and can detect KD earlier than the traditional clinical chemistry and histopathology methods. With this economical, fast, and highly scalable metabolomics technology, we now have a powerful tool that enables advancing the next generation of personalized diagnostics and therapeutics. Despite this power of metabolomics has the potential to provide insights into complex pathophysiological processes and reveal novel diagnostic biomarkers as well as therapeutic drug targets, the actual status of metabolomics to KD monitoring is still in its early stage and has not yet reached the mature stage of clinical application. To date, no specific biomarker has sufficient full-scale testing to qualify but may be the result of the presence of any malignancy. It required for valid metabolomic profiling and biomarkers. Furthermore, little information exists on the relative strength of various biomarkers for their prediction of mortality. The development of combinatorial markers is an attractive goal. A multimarker approach might refine prognosis in patients on KD, but this concept needs to be properly evaluated in large longitudinal studies and clinical trials. To achieve this, we need to develop international collaboration. Advancements in this field will eventually lead to personalized medicine to treat KD patients. Acknowledgments This work was supported by grants from the Key Program of Natural Science Foundation of State (Grant No. 90709019, 81173500, 81302905, 81102556, 81202639), National Key Technology Research and Development Program of the Ministry of Science and Technology of China (Grant No. 2011BAI03B03, 2011BAI03B06, 2011BAI03B08), Key Science and Technology Program of Heilongjiang Province, China (Grant No. GC06C501, GA08C303, GA06C30101), Foundation of Heilongjiang University
of Chinese Medicine (Grant No. 201209), National Key Subject of Drug Innovation (Grant No. 2009ZX09502-005). Conflict of interest The authors have declared that they have no competing interests.
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NEPHROLOGY - REVIEW
Metabolomics insights into pathophysiological mechanisms of nephrology Aihua Zhang • Hui Sun • Shi Qiu • Xijun Wang
Received: 4 June 2013 / Accepted: 31 October 2013 Ó Springer Science+Business Media Dordrecht 2013
Abstract Kidney diseases (KD), a major public health problem that affects about 10 % of the general population, manifest in progressive loss of renal function, which ultimately leads to complete kidney failure. However, current approaches based on renal histopathological results and clinical parameters lack sensitivity and are not sufficient to characterize the category and progression of nephrology or to predict nephrology progression risk reliably or to guide preventive interventions. The high incidence and financial burden of KD make it imperative to diagnose KD at early stages when therapeutic interventions are far more effective. Nowadays, the appearance of metabolomics (the highthroughput measurement and analysis of metabolites) has provided the framework for a comprehensive analysis of KD and serves as a starting point for generating novel molecular diagnostic tools for use in nephrology. Changes in the concentration profiles of a number of small-molecule metabolites found in either blood or urine can be used to localize kidney damage or assess kidneys suffering from injury. The power of metabolomics allows unparalleled opportunity to query the molecular mechanisms of KD. Novel metabolomics technologies have the ability to provide a deeper understanding of the disease beyond classical histopathology, redefine the characteristics of the disease state, and identify novel approaches to reduce renal failure.
A. Zhang (&) H. Sun S. Qiu X. Wang National TCM Key Lab of Serum Pharmacochemistry, Key Laboratory of Chinmedomics, Department of Pharmaceutical Analysis, and Key Metabolomics Platform of Chinese Medicines, Heilongjiang University of Chinese Medicine, Heping Road 24, Harbin 150040, China e-mail: [email protected] X. Wang e-mail: [email protected]
This review gives an overview of its application to important areas in clinical nephrology, with a particular focus on biomarker discovery. Great strides forward are being made in breaking down important barriers to the successful prevention and treatment of this devastating disorder. Keywords Metabolomics Kidney disease Biomarkers Metabolites Nephrology
Introduction Kidney diseases (KD) have complex pathogenesis; its diagnostic and treatment decisions are mainly based on kidney histology, a limited set of serologic markers, and clinical manifestations [1, 2]. Currently, no sufficiently sensitive or specific tests are present to detect early disease, predict disease progression, or monitor treatment response. Therefore, the discovery of a specific, reliable diagnostic and prognostic biomarker for nephrology is imperative. The early detection of KD is pivotal for successful patient treatment and management. Recent development of a variety of analytic platforms has raised hopes for identifying novel metabolite markers, and recent efforts have led to several promising novel markers of KD [3]. The advent of advanced metabolomics technologies’ development is bringing these goals into focus [4]. Metabolomics, one of the relative newcomers of the omics techniques, has the power to yield not only specific biomarkers but also insight into the pathophysiology of disease [5]. The general procedure in which metabolomics used for diagnosis and biomarker discovery was shown in Fig. 1. Metabolomics is a global metabolic profiling framework that utilizes high-resolution analytics with multivariate data
123
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Fig. 1 The general procedures in metabolomics can be used for biomarker discovery
analysis tools to derive an integrated picture of both endogenous and xenobiotic metabolisms [6]. It is now moving from a discovery phase in small studies to a validation phase in much larger numbers of patients with diseases [7]. The advantage of metabolomics is that it could reveal changes in both phenotype and genotype [8]. Advantages over the traditional renal biopsy include accessibility, safety, the possibility of serial sampling, and the potential for noninvasive prognostic and diagnostic monitoring of disease and an individual’s response to treatment [9]. Ideally, such metabolomics assays would be applicable to noninvasively obtained body fluids, enabling not only diagnosis of at risk patients, but also asymptomatic screening, monitoring disease recurrence, and response to treatment. The development of metabolomics tests to detect KD would be of tremendous benefit to both patients and the healthcare system [10–12]. A deeper understanding of global perturbations in biochemical pathways and useful biomarkers could provide valuable insights about mechanisms of KD. Studies of metabolite profile in blood, kidney tissue, and urine have been used to identify early biomarkers for kidney disease [13, 14]. Detailed characterization of nephrology at the metabolites’ level could yield the needed integrative knowledge to better understand its molecular mechanisms, to develop more reliable biomarker sets for diagnosis and personalized treatment. This review provides why we need new biomarkers of KD, and how biomarkers are discovered and should be evaluated, highlights the urinary biomarker as a noninvasive method to detect either morphological or biochemical changes in KD.
123
Bringing metabolomics into the forefront of nephrology research Kidney diseases manifest in progressive loss of renal function, which ultimately leads to complete kidney failure [15]. Multiple factors involved in the pathogenesis of KD have made the mechanisms underlying the origins, and progression of nephrology is not fully understood. One of the important factors contributing to this problem is the lack of an early KD diagnostic test. Therefore, new biomarkers of KD are needed. The advent of advanced metabolomics technologies is bringing these goals into focus [16]. Metabolomics—the nontargeted measurement of all metabolites—is beginning to show promise in biomarker discovery and aiding the choice of therapy for patients [17]. Small-molecule metabolites produced by the body have an important role in biological systems and represent attractive candidates to understand KD phenotypes [18]. Metabolomics can present a holistic picture of the metabolites alterations and provide biomarkers that could revolutionize disease characterization and detection. The development of metabolomics assays that can diagnose disease accurately, or that can augment current methods of evaluation, would be a significant advance [19]. Ideally, metabolomics would be applicable to noninvasively obtained body fluids, enabling not only diagnosis of at risk patients, but also asymptomatic screening, monitoring disease recurrence and response to treatment. It holds promise for early diagnosis, increased choice of therapy, and the identification of new metabolic pathways that could potentially be targeted in kidney disease, which will lead to
Int Urol Nephrol
personalized therapies. The discovery of new biomarkers will improve the design of clinical trials and identify patients at an earlier stage of the disease. The focus of future metabolomics studies in kidney disease is to identify reliable early biomarkers for molecular diagnosis and prediction of disease progression.
Metabolomics to study kidney disease Acute kidney injury Acute kidney injury (AKI) is a prevalent and devastating condition associated with significant morbidity and mortality. Despite marked improvements in clinical care, the outcomes for AKI subjects have shown limited improvement in the past 50 years [20]. A major factor inhibiting clinical progress in this field has been the inability to accurately predict and diagnose early kidney dysfunction. The current gold standard clinical and biochemical criteria for diagnosis of AKI rely on urine output and serum creatinine, which are insensitive, nonspecific, and late markers of disease. A study was performed by Liu et al. [21] to further understand the metabolomic changes of AKI. AKI could be characterized by oxidative stress and changes in lipid metabolism through metabolomic investigation. Rapid LC/MS-based metabolic profiling of serum demonstrated in a pilot study that metabolomics could provide novel indicators of AKI [22]. Increases in acylcarnitines and amino acids and a reduction in serum levels of arginine and several lysophosphatidylcholines were observed in patients with AKI. Increases in homocysteine and pyroglutamate have been recognized as biomarkers of renal disease, and acylcarnitines represent biomarkers of defective fatty acid oxidation. Chronic kidney disease Chronic kidney disease (CKD) is the end point of a number of renal and systemic diseases. Metabolites such as creatinine and urea are established kidney function markers. High-throughput metabolomic studies have not been reported in large general population samples spanning normal kidney function and CKD [23]. A metabolomics approach was applied to establish a human CKD serum metabolic profile [24]. Significant endogenous metabolites that contributed to distinguish CKD in different stages included the products of glycolysis, amino acids, organic osmolytes, and so on. Based on these metabolites, the model for diagnosing patients with CKD achieved the sensitivity and specificity of 100 %. The identified metabolic biomarkers may provide useful information for the diagnosis of CKD, especially in early stages.
Renal cell carcinoma Renal cell carcinoma (RCC) whose incidence is increasing is the most prevalent malignancy of the kidney. Its survival rates are very low. The disease regularly progresses asymptomatically and is frequently metastatic upon presentation, thereby necessitating the development of an early method of detection. Early detection is currently by far the most-promising approach to reduce RCC mortality. RCC tumors had elevated levels of lactate, glutamate, pyruvate, glutamine, and creatine, but decreased levels of acetate, malate, and amino acids including valine, alanine, and aspartate [25]. An LC–MS-based method has been carried out in conjunction with multivariate data analysis to discriminate the global serum profiles of RCC patients and healthy controls [26]. The feasibility of serum metabolomics for the diagnosis and staging of RCC has also been evaluated. One-hundred percent sensitivity in detection has been achieved, and a satisfactory clustering between the early stage and advanced-stage patients is observed. As a result, 30 potential metabolites for RCC are identified. RCC is found to be closely related to disturbed phospholipid catabolism, sphingolipid metabolism, phenylalanine metabolism, etc.,. NMR-based metabolomic approach had been applied in order to monitor the alterations of plasma metabolic profile in RCC patients [27]. Alterations in the levels of LDL/ VLDL, NAC, lactate, and choline were observed between RCC patients and controls discriminating these groups in PCA plots. A global metabolite-profiling approach was applied to characterize the metabolite pool of RCC and normal renal tissue [28]. Differentially regulated metabolites, such as vitamin E derivates and metabolites of glucose, fatty acid, and inositol phosphate metabolism determined by the metabolic profile of RCC, provide leads for the characterization of novel pathways in RCC. Results suggested a key role for metabolic pathways involving arachidonic acid, free fatty acids, proline, uracil and the tricarboxylic acid cycle, and illustrate the potential of metabolomics methods to uncover the metabolic phenotype of cancer. Autosomal dominant polycystic kidney disease Autosomal dominant polycystic kidney disease (ADPKD) is the most commonly inherited kidney disease and affects one in 1,000 individuals. ADPKD is an inherited systemic disease characterized by renal cyst expansion, resulting in renal failure. Ultrasound is most often used to diagnose ADPKD; such a modality is only useful late in the disease after macroscopic cysts are present. With the progression of renal damage, the accumulation of uremic compounds is recently reported to subsequently cause further renal damage and hypertension. Finding uremic toxins and sensitive markers for detecting the early stage of ADPKD is
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necessary to clarify its pathophysiological process and to prevent its progression. Among the compounds, increases in 5-methyl-20 -deoxycytidine, glucosamine, ectoine, allantoate, a-hydroxybenzoate, phenaceturate, and 3-phenylpropionate and decreases in 2-deoxycytidine, decanoate and 10-hydroxydecanoate were newly identified in the ADPKD [29]. Using functional score analysis and the KEGG pathway database, Taylor et al. [30] had identified several biologically relevant metabolic pathways that are altered very early in this disease, the most highly represented being the purine and galactose metabolism pathways. In addition, they also identify several specific candidate biomarkers, including allantoic acid and adenosine, which are augmented in the urine of young cystic mice. These markers and pathway components may prove useful as a noninvasive means of diagnosing cystic KD and to suggest novel therapeutic approaches. Diabetic nephropathy Diabetic nephropathy (DN) is a leading cause of end-stage renal disease. Currently, microalbuminuria is an only noninvasive marker available for the diagnosis of diabetic nephropathy. However, some patients with microalbuminuria have advanced renal pathological changes for which therapy is less effective than earlier stages of the disease. Therefore, novel biomarkers for earlier diagnosis of diabetic nephropathy are crucially required. Zhao et al. [31] had performed a metabolomic analysis of perfused renal cortex samples from the diabetic rats. A number of abnormal metabolites in the diabetic kidney had been identified. Metabolic profiling was used to explore new serum biomarkers with high sensitivity and specificity for DN diagnosis, through comprehensive analysis of serum metabolites [32]. The prevalence of renal stone disease is increasing, although it remains higher in men than in women when matched for age. Garcia-Perez et al. [33] had applied two orthogonal analytical platforms in parallel to characterize the urinary metabolic signatures related to the loss of the renal chloride-oxalate exchanger in slc26a6 null mice. Key discriminating metabolites included oxalate, trimethylamine-N-oxide, etc.,. Urinary metabolites showed a sex-specific pattern included trimethylamine, trimethylamine-n-oxide, citrate, spermidine, guanidinoacetate, and 2-oxoisocaproate. Gender-dependent metabolic expression of the consequences of slc26a6 deletion might have relevance to the difference in prevalence of renal stone formation in men and women. Immunoglobulin A nephropathy Immunoglobulin A nephropathy is the most common cause of chronic renal failure among primary glomerulonephritis
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patients. The ability to diagnose immunoglobulin A nephropathy remains poor. Renal biopsy is an inconvenient, invasive, and painful examination, and no reliable biomarkers have been developed for use in routine patient evaluations. A study, done by Sui et al. [34], had identified useful biomarkers of immunoglobulin A nephropathy to establish a human immunoglobulin A nephropathy metabolic profile. Compared with the healthy controls, patients had higher levels of phenylalanine, myo-inositol, lactate, L6 lipids, L5 lipids, and L3 lipids. Investigating the metabolic changes in the evaluation process of renal fibrosis may enhance the understanding of its pathogenesis. A metabolomics study was performed to study the metabolic changes in serum samples of renal interstitial fibrosis (RIF) rats [35]. The changed metabolites such as valine, isoleucine, lactate, 3-hydroxybutyrate, alanine, acetate, acetoacetate, pyruvate, and glutamate represent potential biomarkers. Energy metabolism seemed to be impaired at the early stage of fibrosis but enhanced at a late stage, suggests that metabolomics can provide novel insights into the pathogenesis of RIF. Primary renal hypouricemia Primary renal hypouricemia (PRH) refers to a rare condition of increased renal urate clearance, caused by an isolated inborn error of membrane transport of urate in the renal proximal tubule. Several cases of exercise-induced acute renal failure and urolithiasis have been reported. Tzovaras et al. [36] had assessed tubular function in PRH using NMR-based metabolomic urine analysis. Individuals with primary hypouricemia were differentiated from healthy individuals in the orthogonal signal correction/partial least-squares-discriminant analysis models of the NMR data with a statistically significant separation. The components that contributed to this separation were the lower levels of hippurate, creatinine, and trimethylaminoxide, and the higher levels of phenylalanine, alanine, glycine, glutamate, acetate, and of an unidentified metabolite observed in hypouricemic subjects compared with controls. Kidney cancer Kidney cancer is the seventh most common cancer in the world; its incidence is increasing, and it is frequently metastatic at presentation, at which stage, patient survival statistics are grim. In addition, there are no useful biofluid markers for this disease, such that diagnosis is dependent on imaging techniques that are not generally used for screening. Kim et al. [37] had used metabolomics techniques to identify metabolites in kidney cancer patients’ urine. It found that quinolinate, 4-hydroxybenzoate, and gentisate are differentially expressed, and these metabolites
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are involved in common pathways of specific amino acid and energetic metabolism, consistent with high tumor protein breakdown and utilization. Further evaluation of the global metabolomics analysis, as well as confirmation of the specific potential biomarkers using a larger sample size, will lead to new avenues of kidney cancer diagnosis and therapy [38–40].
Conclusions and future perspectives Metabolomics, the nontargeted interrogation of small molecules in a biological sample, is an ideal technology for identifying diagnostic biomarkers. In this review, we describe important general aspects in biomarker discovery using metabolomics and the drawbacks of existing biomarkers, and discuss currently published studies on KD, and provide an overview of opportunities in the field. We also highlight several metabolomic studies of biomarkers of KD selected from the plethora of studies performed. Many of these markers have been confirmed across multiple studies and can detect KD earlier than the traditional clinical chemistry and histopathology methods. With this economical, fast, and highly scalable metabolomics technology, we now have a powerful tool that enables advancing the next generation of personalized diagnostics and therapeutics. Despite this power of metabolomics has the potential to provide insights into complex pathophysiological processes and reveal novel diagnostic biomarkers as well as therapeutic drug targets, the actual status of metabolomics to KD monitoring is still in its early stage and has not yet reached the mature stage of clinical application. To date, no specific biomarker has sufficient full-scale testing to qualify but may be the result of the presence of any malignancy. It required for valid metabolomic profiling and biomarkers. Furthermore, little information exists on the relative strength of various biomarkers for their prediction of mortality. The development of combinatorial markers is an attractive goal. A multimarker approach might refine prognosis in patients on KD, but this concept needs to be properly evaluated in large longitudinal studies and clinical trials. To achieve this, we need to develop international collaboration. Advancements in this field will eventually lead to personalized medicine to treat KD patients. Acknowledgments This work was supported by grants from the Key Program of Natural Science Foundation of State (Grant No. 90709019, 81173500, 81302905, 81102556, 81202639), National Key Technology Research and Development Program of the Ministry of Science and Technology of China (Grant No. 2011BAI03B03, 2011BAI03B06, 2011BAI03B08), Key Science and Technology Program of Heilongjiang Province, China (Grant No. GC06C501, GA08C303, GA06C30101), Foundation of Heilongjiang University
of Chinese Medicine (Grant No. 201209), National Key Subject of Drug Innovation (Grant No. 2009ZX09502-005). Conflict of interest The authors have declared that they have no competing interests.
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