Changes in serum metabolites with the stage of chronic kidney disease: Comparison of diabetes and non-diabetes

Blood biomarkers improve the prediction of prevalent and incident severe chronic kidney disease

BACKGROUND

The prevalence of chronic kidney disease (CKD) is high. Identification of cases with CKD or at high risk of developing it is important to tailor early interventions. The objective of this study was to identify blood metabolites associated with prevalent and incident severe CKD, and to quantify the corresponding improvement in CKD detection and prediction.

METHODS

Data from four cohorts were analyzed: Singapore Epidemiology of Eye Diseases (SEED) (n = 8802), Copenhagen Chronic Kidney Disease (CPH) (n = 916), Singapore Diabetic Nephropathy (n = 714), and UK Biobank (UKBB) (n = 103,051). Prevalent CKD (stages 3-5) was defined as estimated glomerular filtration rate (eGFR) < 60 mL/min/1.73 m2; incident severe CKD as CKD-related mortality or kidney failure occurring within 10 years. We used multivariable regressions to identify, among 146 blood metabolites, those associated with CKD, and quantify the corresponding increase in performance.

RESULTS

Chronic kidney disease prevalence (stages 3-5) and severe incidence were 11.4% and 2.2% in SEED, and 2.3% and 0.2% in UKBB. Firstly, phenylalanine (Odds Ratio [OR] 1-SD increase = 1.83 [1.73, 1.93]), tyrosine (OR = 0.75 [0.71, 0.79]), docosahexaenoic acid (OR = 0.90 [0.85, 0.95]), citrate (OR = 1.41 [1.34, 1.47]) and triglycerides in medium high density lipoprotein (OR = 1.07 [1.02, 1.13]) were associated with prevalent stages 3-5 CKD. Mendelian randomization analyses suggested causal relationships. Adding these metabolites beyond traditional risk factors increased the area under the curve (AUC) by 3% and the sensitivity by 7%. Secondly, lactate (HR = 1.33 [1.08, 1.64]) and tyrosine (HR = 0.74 [0.58, 0.95]) were associated with incident severe CKD among individuals with eGFR < 90 mL/min/1.73 m2 at baseline. These metabolites increased the c-index by 2% and sensitivity by 5% when added to traditional risk factors.

CONCLUSION

The performance improvements of CKD detection and prediction achieved by adding metabolites to traditional risk factors are modest and further research is necessary to fully understand the clinical implications of these findings.

Exploration of Metabolomic Markers Associated With Declining Kidney Function in People With Type 2 Diabetes Mellitus

Background

Metabolomics, the study of small molecules in biological systems, can provide valuable insights into kidney dysfunction in people with type 2 diabetes mellitus (T2DM), but prospective studies are scarce. We investigated the association between metabolites and kidney function decline in people with T2DM.

Methods

The Edinburgh Type 2 Diabetes Study, a population-based cohort of 1066 men and women aged 60 to 75 years with T2DM. We measured 149 serum metabolites at baseline and investigated individual associations with baseline estimated glomerular filtration rate (eGFR), incident chronic kidney disease [CKD; eGFR <60 mL/min/(1.73 m)2], and decliner status (5% eGFR decline per year).

Results

At baseline, mean eGFR was 77.5 mL/min/(1.73 m)2 (n = 1058), and 216 individuals had evidence of CKD. Of those without CKD, 155 developed CKD over a median 7-year follow-up. Eighty-eight metabolites were significantly associated with baseline eGFR (β range -4.08 to 3.92; PFDR < 0.001). Very low density lipoproteins, triglycerides, amino acids (AAs), glycoprotein acetyls, and fatty acids showed inverse associations, while cholesterol and phospholipids in high-density lipoproteins exhibited positive associations. AA isoleucine, apolipoprotein A1, and total cholines were not only associated with baseline kidney measures (PFDR < 0.05) but also showed stable, nominally significant association with incident CKD and decline.

Conclusion

Our study revealed widespread changes within the metabolomic profile of CKD, particularly in lipoproteins and their lipid compounds. We identified a smaller number of individual metabolites that are specifically associated with kidney function decline. Replication studies are needed to confirm the longitudinal findings and explore if metabolic signals at baseline can predict kidney decline.

Urine myo-inositol as a novel prognostic biomarker for diabetic kidney disease: a targeted metabolomics study using nuclear magnetic resonance

BACKGROUND

As a leading cause of chronic kidney disease, clinical demand for noninvasive biomarkers of diabetic kidney disease (DKD) beyond proteinuria is increasing. Metabolomics is a popular method to identify mechanisms and biomarkers. We investigated urinary targeted metabolomics in DKD patients.

METHODS

We conducted a targeted metabolomics study of 26 urinary metabolites in consecutive patients with DKD stage 1 to 5 (n = 208) and healthy controls (n = 26). The relationships between estimated glomerular filtration rate (eGFR) or urine protein-creatinine ratio (UPCR) and metabolites were evaluated. Multivariate Cox analysis was used to estimate relationships between urinary metabolites and the target outcome, end-stage renal disease (ESRD). C statistics and time-dependent receiver operating characteristics (ROC) were used to assess diagnostic validity.

RESULTS

During a median 4.5 years of follow-up, 103 patients (44.0%) progressed to ESRD and 65 (27.8%) died. The median fold changes of nine metabolites belonged to monosaccharide and tricarboxylic acid (TCA) cycle metabolites tended to increase with DKD stage. Myo-inositol, choline, and citrates were correlated with eGFR and choline, while mannose and myo-inositol were correlated with UPCR. Elevated urinary monosaccharide and TCA cycle metabolites showed associations with increased morality and ESRD progression. The predictive power of ESRD progression was high, in the order of choline, myo-inositol, and citrate. Although urinary metabolites alone were less predictive than serum creatinine or UPCR, myo-inositol had additive effect with serum creatinine and UPCR. In time-dependent ROC, myo-inositol was more predictive than UPCR of 1-year ESRD progression prediction.

CONCLUSION

Myo-inositol can be used as an additive biomarker of ESRD progression in DKD.

Untargeted plasma 1H NMR-based metabolomic profiling in different stages of chronic kidney disease

Chronic kidney disease (CKD) is a serious public health issue affecting thousands of people worldwide. CKD diagnosis is usually made by Estimated Glomerular Filtration Rate (eGFR) and albuminuria, which limit the knowledge of the mechanisms behind CKD progression. The aim of the present study was to identify changes in the metabolomic profile that occur as CKD advances. In this sense, 77 plasma samples from patients with CDK were evaluated by 1D and 2D Nuclear Magnetic Resonance Spectroscopy (NMR). The NMR data showed significant changes in the metabolomic profile of CKD patients and the control group. Principal component analysis (PCA) clustered CKD and control patients into three distinct groups, control, stage 1 (G1)-stage 4 (G4) and stage 5 (G5). Lactate, glucose, acetate and creatinine were responsible for discriminating the control group from all the others CKD stages. Valine, alanine, glucose, creatinine, glutamate and lactate were responsible for the clustering of G1-G4 stages. G5 was discriminated by calcium ethylenediamine tetraacetic acid, magnesium ethylenediamine tetraacetic acid, creatinine, betaine/choline/trimethylamine N-oxide (TMAO), lactate and acetate. CKD G5 plasma pool which was submitted in MetaboAnalyst 4.0 platform (MetPA) analysis and showed 13 metabolic pathways involved in CKD physiopathology. Metabolic changes associated with glycolysis and gluconeogenesis allowed discriminating between CKD and control patients. The determination of involved molecules in TMAO generation in G5 suggests an important role in this uremic toxin linked to CKD and cardiovascular diseases. The aforementioned results propose the feasibility of metabolic assessment of CKD by NMR during treatment and disease progression.

Nuclear Magnetic Resonance-Based Metabolomics Approach Revealed the Intervention Effect of Using Complementary and Alternative Medicine (CAM) by CKD Patients

Chronic kidney disease (CKD) is the end point of a number of systemic chronic diseases. The prevalence of CKD is increasing worldwide and recent epidemiological studies are showing the high prevalence of renal failure in CKD patients using complementary and alternative medicines (CAMs). Clinicians believe that biochemical profiles of CKD patients using CAM (referred here to as CAM-CKD) may be different compared to those on standard clinical treatment and should be managed differently. The present study aims to explore the potential of the NMR-based metabolomics approach to reveal the serum metabolic disparity between CKD and CAM-CKD patients with respect to normal control (NC) subjects and if the differential metabolic patterns can provide rationale for the efficacy and safety of standard and/or alternative therapies. Serum samples were obtained from 30 CKD patients, 43 CAM-CKD patients, and 47 NC subjects. The quantitative serum metabolic profiles were measured using 1D ¹H CPMG NMR experiments performed at 800 MHz NMR spectrometer. The serum metabolic profiles were compared using various multivariate statistical analysis tools available on MetaboAnalyst (freely available web-based software) such as partial least-squares discriminant analysis (PLS-DA) and random forest (a machine learning) classification method. The discriminatory metabolites were identified based on variable importance in projection (VIP) statistics and further evaluated for statistical significance (i.e., p < 0.05) using either Student t-test or ANOVA statistics. PLS-DA models were capable of clustering CKD and CAM-CKD with considerably high values of Q ² and R ². Compared to CAM-CKD patients, the sera of CKD patients were characterized by (a) elevated levels of urea, creatinine, citrate, glucose, glycerol, and phenylalanine and phenylalanine-to-tyrosine ratio (PTR) and (b) decreased levels of various amino acids (such leucine, isoleucine, valine, and alanine), high-density lipoproteins, lactate, and acetate. These changes suggested that CKD patients manifest severe oxidative stress, hyperglycemia (with dampened glycolysis), increased protein energy wasting, and reduced lipid/membrane metabolism. Statistically significant and strong positive correlation of PTR with serum creatinine levels suggested the role of oxidative stress in the progression of kidney disease. Significant differences in metabolic patterns between CKD and CAM-CKD patients were observed. With respect to NC subjects, the serum metabolic changes were more aberrant in CKD patients compared to CAM-CKD patients. The aberrant metabolic changes in CKD patients with manifestations of higher oxidative stress compared to CAM-CKD patients could explain clinical discrepancies between CKD and CAM-CKD patients and further advocate the use of different treatment strategies for CKD and CAM-CKD patients.

Association between serum uric acid/HDL-cholesterol ratio and chronic kidney disease: a cross-sectional study based on a health check-up population

OBJECTIVE

Evidence suggests that both serum uric acid (SUA) and high-density lipoprotein cholesterol (HDL-C) are risk factors for chronic kidney disease (CKD). The SUA-to-HDL-C ratio (UHR) has recently attracted attention as a new biomarker to evaluate the role between inflammatory and anti-inflammatory substances. Thus, we explored the association between UHR and CKD in a large Chinese population.

DESIGN

A cross-sectional study.

SETTING

Annual health check-up population in Nanjing.

PARTICIPANTS

19 458 individuals who underwent an annual health check-up in 2019 were included in our study.

MAIN OUTCOME MEASURE

CKD was diagnosed according to an estimated glomerular filtration rate (eGFR) <60 mL/min/1.73 m².

RESULTS

Correlation analysis showed that UHR was negatively associated with eGFR after adjusting for confounding factors (r=-0.34). In addition, participants in the highest quartile of UHR had a higher risk of CKD than those in the lowest quartiles (OR=9.28, p<0.001).

CONCLUSION

We found that high UHR values were positively associated with CKD risk in health check-up population. An increased UHR may be a useful measure by which to assess CKD risk in the preclinical stage.

Trans- and Multigenerational Maternal Social Isolation Stress Programs the Blood Plasma Metabolome in the F3 Generation

Metabolic risk factors are among the most common causes of noncommunicable diseases, and stress critically contributes to metabolic risk. In particular, social isolation during pregnancy may represent a salient stressor that affects offspring metabolic health, with potentially adverse consequences for future generations. Here, we used proton nuclear magnetic resonance (¹H NMR) spectroscopy to analyze the blood plasma metabolomes of the third filial (F3) generation of rats born to lineages that experienced either transgenerational or multigenerational maternal social isolation stress. We show that maternal social isolation induces distinct and robust metabolic profiles in the blood plasma of adult F3 offspring, which are characterized by critical switches in energy metabolism, such as upregulated formate and creatine phosphate metabolisms and downregulated glucose metabolism. Both trans- and multigenerational stress altered plasma metabolomic profiles in adult offspring when compared to controls. Social isolation stress increasingly affected pathways involved in energy metabolism and protein biosynthesis, particularly in branched-chain amino acid synthesis, the tricarboxylic acid cycle (lactate, citrate), muscle performance (alanine, creatine phosphate), and immunoregulation (serine, threonine). Levels of creatine phosphate, leucine, and isoleucine were associated with changes in anxiety-like behaviours in open field exploration. The findings reveal the metabolic underpinnings of epigenetically heritable diseases and suggest that even remote maternal social stress may become a risk factor for metabolic diseases, such as diabetes, and adverse mental health outcomes. Metabolomic signatures of transgenerational stress may aid in the risk prediction and early diagnosis of non-communicable diseases in precision medicine approaches.

Metabolomic Profiling of Amino Acids in Human Plasma Distinguishes Diabetic Kidney Disease From Type 2 Diabetes Mellitus

Background: Diabetic kidney disease (DKD) is a highly prevalent complication in patients with type 2 diabetes mellitus (T2DM). Patients with DKD exhibit changes in plasma levels of amino acids (AAs) due to insulin resistance, reduced protein intake, and impaired renal transport of AAs. The role of AAs in distinguishing DKD from T2DM and healthy controls has yet to be elucidated. This study aimed to investigate the metabolomic profiling of AAs in the plasma of patients with DKD.

Methods: We established an ultra-performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS) method to detect the plasma levels of the 20 AAs in healthy controls (n = 112), patients with T2DM (n = 101), and patients with DKD (n = 101). The key AAs associated with DKD were identified by orthogonal partial least-squares discriminant analysis (OPLS-DA) models with loading plots, shared and unique structures (SUS) plots, and variable importance in projection (VIP) values. The discrimination accuracies of these key AAs were then determined by analyses of receiver-operating characteristic (ROC) curves.

Results: Metabolomic profiling of plasma revealed significant alterations in levels of the 20 AAs in patients with DKD when compared to those in either patients with T2DM or healthy controls. Metabolomic profiling of the 20 AAs showed a visual separation of patients with DKD from patients with T2DM and healthy controls in OPLS-DA models. Based on loading plots, SUS plots, and VIP values in the OPLS-DA models, we identified valine and cysteine as potential contributors to the progression of DKD from patients with T2DM. Histidine was identified as a key mediator that could distinguish patients with DKD from healthy controls. Plasma levels of histidine and valine were decreased significantly in patients with DKD with a decline in kidney function, and had excellent performance in distinguishing patients with DKD from patients with T2DM and healthy controls according to ROC curves.

Conclusion: Plasma levels of histidine and valine were identified as the main AAs that can distinguish patients with DKD. Our findings provide new options for the prevention, treatment, and management of DKD.

NMR-based metabolomics associated with chronic kidney disease in humans and animals: a one health perspective

Chronic kidney disease (CKD) is a renal dysfunction that can lead to high rates of mortality and morbidity, particularly when coupled with late diagnosis. CKD has become a major health problem due to its challenging detection at early stages when clear symptoms are yet to be presented. Thus, CKD is likely to be identified when the substantive conditions of the disease are manifest. In order to address the development of the disease and provide necessary treatments at the initial stage, the investigation of new biomarkers and metabolites associated with early detection of CKD are needed. Identified metabolites could be used to confirm the presence of the disease, obtain information on its mechanism and facilitate the development of novel pharmaceutical treatments. Such metabolites may be detected from biofluids and tissues using a range of analytical techniques. There are a number of metabolites that have been identified by mass spectrometry at high sensitivities, whilst the detection of metabolites directly from biofluids using NMR could present a more rapid way to expand our understanding of this disease. This review is focused on NMR-based metabolomics associated with CKD in humans and animals.

Involvement of Tricarboxylic Acid Cycle Metabolites in Kidney Diseases

Mitochondria are complex organelles that orchestrate several functions in the cell. The primary function recognized is energy production; however, other functions involve the communication with the rest of the cell through reactive oxygen species (ROS), calcium influx, mitochondrial DNA (mtDNA), adenosine triphosphate (ATP) levels, cytochrome c release, and also through tricarboxylic acid (TCA) metabolites. Kidney function highly depends on mitochondria; hence mitochondrial dysfunction is associated with kidney diseases. In addition to oxidative phosphorylation impairment, other mitochondrial abnormalities have been described in kidney diseases, such as induction of mitophagy, intrinsic pathway of apoptosis, and releasing molecules to communicate to the rest of the cell. The TCA cycle is a metabolic pathway whose primary function is to generate electrons to feed the electron transport system (ETS) to drives energy production. However, TCA cycle metabolites can also release from mitochondria or produced in the cytosol to exert different functions and modify cell behavior. Here we review the involvement of some of the functions of TCA metabolites in kidney diseases.

New Potential Biomarkers for Chronic Kidney Disease Management-A Review of the Literature

The prevalence of chronic kidney disease (CKD) is increasing worldwide, and the mortality rate continues to be unacceptably high. The biomarkers currently used in clinical practice are considered relevant when there is already significant renal impairment compromising the early use of potentially successful therapeutic interventions. More sensitive and specific biomarkers to detect CKD earlier on and improve patients' prognoses are an important unmet medical need. The aim of this review is to summarize the recent literature on new promising early CKD biomarkers of renal function, tubular lesions, endothelial dysfunction and inflammation, and on the auspicious findings from metabolomic studies in this field. Most of the studied biomarkers require further validation in large studies and in a broad range of populations in order to be implemented into routine CKD management. A panel of biomarkers, including earlier biomarkers of renal damage, seems to be a reasonable approach to be applied in clinical practice to allow earlier diagnosis and better disease characterization based on the underlying etiologic process.

Metabolomics of the diabetic nephropathy: behind the fingerprint of development and progression indicators

Current diagnostic methods are not very sensitive to detect the initial stages diabetic nephropathy of type 2. In this work, a review of metabolomic approximation studies for the identification of biomarkers of this disease with potential to differentiate between early stages, evaluate and direct treatment and help slow kidney damage. Using public (Pubmed and Google Scholar) and private (Scopus and Web of Knowledge) databases, a systematic search of the information published related to metabolomics of diabetic nephropathy in different biospecimens (urine, serum, plasma and blood) was made. Later, the MetaboAnalyst 4.0 software was used to identify the metabolic pathways associated with these metabolites. Groups of potential metabolites were identified for monitoring diabetic nephropathy with the available literature data. In the urine, oxide-3-hydroxyisovalerate, TMAO, aconite and citrate and hydroxypropionate derivatives are highlighted; meanwhile, in the serum: citrate, creatinine, arginine and its derivatives; and in the plasma: amino acids such as histidine, methionine and arginine has a potential contribution. Using MetaboAnalyst 4.0 the metabolic pathways related to these metabolites were related. The search for biomarkers to measure the progression of diabetic nephropathy, together with analytical strategies for their detection and quantification, are the starting point for designing new methods of clinical chemistry analysis. The association between the metabolic pathway dysfunction could be useful for the overall assessment of the treatment and clinical follow-up of this disease.

Potential for Novel Biomarkers in Diabetes-Associated Chronic Kidney Disease: Epigenome, Metabolome, and Gut Microbiome

Diabetes-associated chronic kidney disease is a pandemic issue. Despite the global increase in the number of individuals with this chronic condition together with increasing morbidity and mortality, there are currently only limited therapeutic options to slow disease progression. One of the reasons for this is that the current-day "gold standard" biomarkers lack adequate sensitivity and specificity to detect early diabetic chronic kidney disease (CKD). This review focuses on the rapidly evolving areas of epigenetics, metabolomics, and the gut microbiome as potential sources of novel biomarkers in diabetes-associated CKD and discusses their relevance to clinical practice. However, it also highlights the problems associated with many studies within these three areas-namely, the lack of adequately powered longitudinal studies, and the lack of reproducibility of results which impede biomarker development and clinical validation in this complex and susceptible population.

Amino Acid Metabolites Associated with Chronic Kidney Disease: An Eight-Year Follow-Up Korean Epidemiology Study

The discovery of metabolomics-based biomarkers has been a focus of recent kidney dysfunction research. In the present study, we aimed to identify metabolites associated with chronic kidney disease (CKD) in the general population using a cross-sectional study design. At baseline, 6.5% of subjects had CKD. Pearson correlation analysis showed that 28 metabolites were significantly associated with estimated glomerular filtration rate (eGFR) after Bonferroni correction. Among these metabolites, 4 acylcarnitines, 12 amino acids, 4 biogenic amines, 1 phosphatidylcholine, and 1 sphingolipid were associated with CKD (p < 0.05). After eight years, 13.5% of subjects had CKD. Three amino acid metabolites were positively associated with new-onset CKD: citrulline [odds ratio (OR): 2.41, 95% confidence interval (CI): 1.26–4.59], kynurenine (OR: 1.98, 95% CI: 1.05–3.73), and phenylalanine (OR: 2.68, 95% CI: 1.00–7.16). The kynurenine:tryptophan ratio was also associated with CKD (OR: 3.20; 95% CI: 1.57–6.51). The addition of multiple metabolites significantly improved the CKD prediction by C statistics (0.756–0.85, p < 0.0001), and the net reclassification improvement was 0.84 (95% CI: 0.72–0.96). Elevated hs-C reactive protein (CRP) was associated with new-onset CKD (OR: 1.045, 95% CI: 1.005–1.086); however, this association disappeared following adjustment with the kynurenine:tryptophan ratio. The levels of citrulline and kynurenine and their ratio to tryptophan in CKD patients with proteinuria were worse than those with one or neither characteristic. Together, the results of this study demonstrate that amino acid metabolites are associated with CKD eight years after initial metabolite assessment. These results could improve the identification of subjects at high risk of CKD who have modified amino acid metabolism.

Urinary metabolites associate with the rate of kidney function decline in patients with autosomal dominant polycystic kidney disease

Background

The variable course of autosomal dominant polycystic kidney disease (ADPKD), and the advent of renoprotective treatment require early risk stratification. We applied urinary metabolomics to explore differences associated with estimated glomerular filtration rate (eGFR; CKD-EPI equation) and future eGFR decline.

Methods

Targeted, quantitative metabolic profiling (¹H NMR-spectroscopy) was performed on baseline spot urine samples obtained from 501 patients with ADPKD. The discovery cohort consisted of 338 patients (56% female, median values for age 46 [IQR 38 to 52] years, eGFR 62 [IQR 45 to 85] ml/min/1.73m², follow-up time 2.5 [range 1 to 3] years, and annual eGFR slope –3.3 [IQR –5.3 to –1.3] ml/min/1.73m²/year). An independent cohort (n = 163) was used for validation. Multivariate modelling and linear regression were used to analyze the associations between urinary metabolites and eGFR, and eGFR decline over time.

Results

Twenty-nine known urinary metabolites were quantified from the spectra using a semi-automatic quantification routine. The model optimization routine resulted in four metabolites that most strongly associated with actual eGFR in the discovery cohort (F = 128.9, P = 7×10⁻⁵⁴, R² = 0.724). A model using the ratio of two other metabolites, urinary alanine/citrate, showed the best association with future annual change in eGFR (F = 51.07, P = 7.26×10⁻¹², R² = 0.150). This association remained significant after adjustment for clinical risk markers including height-adjusted total kidney volume (htTKV). Results were confirmed in the validation cohort.

Conclusions

Quantitative NMR profiling identified urinary metabolic markers that associated with actual eGFR and future rate of eGFR decline. The urinary alanine/citrate ratio showed additional value beyond conventional risk markers.

Potential Role of Nutrient Intake and Malnutrition as Predictors of Uremic Oxidative Toxicity in Patients with End-Stage Renal Disease

Oxidative stress is an important risk factor for cardiovascular disease and death in hemodialysis (HD) patients. However, whether biochemical and nutritional markers might be useful to stratify HD patients according to the risk of oxidative damage remains unclear. We investigated whether low-cost and easily available parameters such as the profile of nutrients intake, nutritional status, and antioxidant defenses can predict lipid and protein oxidation in HD patients. Forty-nine HD patients (women = 20, men = 29), ranging from 18 to 65 years of age (73.5%) were submitted to biochemical and nutritional analysis. At least 93.9% of HD patients had malnutrition. A patient's stratification according to nutritional risk was highly coherent with anthropometric parameters and nutrients intake, which were complementarily used as markers of malnutrition. Nutritional stratification was unable to reveal differences in the oxidative status. On the other hand, carbohydrate and zinc intake, serum zinc (Zn), glutathione peroxidase (GPx) activity, total antioxidant capacity (TAC), and nonprotein antioxidants (npAC) in serum were predictive markers of lipid (R ² = 0.588, P < 0.001) and protein (R ² = 0.581, P < 0.001) oxidation. Interestingly, GPx activity, TAC, and npAC exhibited good (>80% < 90%) or excellent (>90%) accuracy to estimate lipid oxidation (P ≤ 0.01). Regarding the prediction of protein oxidation, GPx activity and TAC presented regular accuracy (>70% < 80%), and Zn serum levels exhibited good sensitivity (P ≤ 0.01). Herein, we provided evidence that clinical characteristics relevant to predict different levels of lipid and protein oxidation in HD patients can be easily obtained, during routine hospital visits by means of the combined analyses of biochemical and nutritional parameters.

Chronic kidney disease: Biomarker diagnosis to therapeutic targets

Chronic kidney disease (CKD), characterized as renal dysfunction, is recognized as a major public health problem with high morbidity and mortality worldwide. Unfortunately, there are no obvious clinical symptoms in early stage disease until severe damage has occurred. Further complicating early diagnosis and treatment is the lack of sensitive and specific biomarkers. As such, novel biomarkers are urgently needed. Metabolomics has shown an increasing potential for identifying underlying disease mechanisms, facilitating clinical diagnosis and developing pharmaceutical treatments for CKD. Recent advances in metabolomics revealed that CKD was closely associated with the dysregulation of numerous metabolites, such as amino acids, lipids, nucleotides and glycoses, that might be exploited as potential biomarkers. In this review, we summarize recent metabolomic applications based on animal model studies and in patients with CKD and highlight several biomarkers that may play important roles in diagnosis, intervention and development of new therapeutic strategies.

Metabolomic and biochemical characterization of a new model of the transition of acute kidney injury to chronic kidney disease induced by folic acid

Background

Renal diseases represent a major public health problem. The demonstration that maladaptive repair of acute kidney injury (AKI) can lead to the development of chronic kidney disease (CKD) and end-stage renal disease has generated interest in studying the pathophysiological pathways involved. Animal models of AKI–CKD transition represent important tools to study this pathology. We hypothesized that the administration of multiple doses of folic acid (FA) would lead to a progressive loss of renal function that could be characterized through biochemical parameters, histological classification and nuclear magnetic resonance (NMR) profiling.

Methods

Wistar rats were divided into groups: the control group received a daily intraperitoneal (I.P.) injection of double-distilled water, the experimental group received a daily I.P. injection of FA (250 mg kg body weight⁻¹). Disease was classified according to blood urea nitrogen level: mild (40–80 mg dL⁻¹), moderate (100–200 mg dL⁻¹) and severe (>200 mg dL⁻¹). We analyzed through biochemical parameters, histological classification and NMR profiling.

Results

Biochemical markers, pro-inflammatory cytokines and kidney injury biomarkers differed significantly (P < 0.05) between control and experimental groups. Histology revealed that as damage progressed, the degree of tubular injury increased, and the inflammatory infiltrate was more evident. NMR metabolomics and chemometrics revealed differences in urinary metabolites associated with CKD progression. The main physiological pathways affected were those involved in energy production and amino-acid metabolism, together with organic osmolytes. These data suggest that multiple administrations of FA induce a reproducible model of the induction of CKD. This model could help to evaluate new strategies for nephroprotection that could be applied in the clinic.

Proteomic and metabolomic approaches in the search for biomarkers in chronic kidney disease

Chronic kidney disease (CKD) is an aging-related disorder that represents a major global public health burden. Current biochemical biomarkers, such as serum creatinine and urinary albumin, have important limitations when used to identify the earliest indication of CKD or in tracking the progression to more advanced CKD. These issues underline the importance of finding and testing new molecular biomarkers that are capable of successfully meeting this clinical need. The measurement of changes in nature and/or levels of proteins and metabolites in biological samples from patients provide insights into pathophysiological processes. Proteomic and metabolomic techniques provide opportunities to record dynamic chemical signatures in patients over time. This review article presents an overview of the recent developments in the fields of metabolomics and proteomics in relation to CKD. Among the many different proteomic biomarkers proposed, there is particular interest in the CKD273 classifier, a urinary proteome biomarker reported to predict CKD progression and with implementation potential. Other individual non-invasive peptidomic biomarkers that are potentially relevant for CKD detection include type 1 collagen, uromodulin and mucin-1. Despite the limited sample sizes and variability of the metabolomics studies, some metabolites such as trimethylamine N-oxide, kynurenine and citrulline stand out as potential biomarkers in CKD.

The metabolomic quest for a biomarker in chronic kidney disease

Chronic kidney disease (CKD) is a growing burden on people and on healthcare for which the diagnostics are niether disease-specific nor indicative of progression. Biomarkers are sought to enable clinicians to offer more appropriate patient-centred treatments, which could come to fruition by using a metabolomics approach. This mini-review highlights the current literature of metabolomics and CKD, and suggests additional factors that need to be considered in this quest for a biomarker, namely the diet and the gut microbiome, for more meaningful advances to be made.

1,5-Anhydroglucitol predicts CKD progression in macroalbuminuric diabetic kidney disease: results from non-targeted metabolomics

INTRODUCTION

Metabolomics allows exploration of novel biomarkers and provides insights on metabolic pathways associated with disease. To date, metabolomics studies on CKD have been largely limited to Caucasian populations and have mostly examined surrogate end points.

OBJECTIVE

In this study, we evaluated the role of metabolites in predicting a primary outcome defined as dialysis need, doubling of serum creatinine or death in Brazilian macroalbuminuric DKD patients.

METHODS

Non-targeted metabolomics was performed on plasma from 56 DKD patients. Technical triplicates were done. Metabolites were identified using Agilent Fiehn GC/MS Metabolomics and NIST libraries (Agilent MassHunter Work-station Quantitative Analysis, version B.06.00). After data cleaning, 186 metabolites were left for analyses.

RESULTS

During a median follow-up time of 2.5 years, the PO occurred in 17 patients (30.3%). In non-parametric testing, 13 metabolites were associated with the PO. In univariate Cox regression, only 1,5-anhydroglucitol (HR 0.10; 95% CI 0.01-0.63, p = .01), norvaline and L-aspartic acid were associated with the PO. After adjustment for baseline renal function, 1,5-anhydroglucitol (HR 0.10; 95% CI 0.02-0.63, p = .01), norvaline (HR 0.01; 95% CI 0.001-0.4, p = .01) and aspartic acid (HR 0.12; 95% CI 0.02-0.64, p = .01) remained significantly and inversely associated with the PO.

CONCLUSION

Our results show that lower levels of 1,5-anhydroglucitol, norvaline and L-aspartic acid are associated with progression of macroalbuminuric DKD. While norvaline and L-aspartic acid point to interesting metabolic pathways, 1,5-anhydroglucitol is of particular interest since it has been previously shown to be associated with incident CKD. This inverse biomarker of hyperglycemia should be further explored as a new tool in DKD.

Feline urine metabolomic signature: characterization of low-molecular-weight substances in urine from domestic cats

Objectives This aim of this study was to characterize the composition and content of the feline urine metabolome. Methods Eight healthy domestic cats were acclimated at least 10 days before starting the study. Urine samples (~2 ml) were collected by ultrasound-guided cystocentesis. Samples were centrifuged at 1000 × g for 8 mins, and the supernatant was analyzed by gas chromatography/time-of-flight mass spectrometery. The urine metabolome was characterized using an untargeted metabolomics approach. Results Three hundred and eighteen metabolites were detected in the urine of the eight cats. These molecules are key components of at least 100 metabolic pathways. Feline urine appears to be dominated by carbohydrates, carbohydrate conjugates, organic acid and derivatives, and amino acids and analogs. The five most abundant molecules were phenaceturic acid, hippuric acid, pseudouridine phosphate and 3-(4-hydroxyphenyl) propionic acid. Conclusions and relevance This study is the first to characterize the feline urine metabolome. The results of this study revealed the presence of multiple low-molecular-weight substances that were not known to be present in feline urine. As expected, the origin of the metabolites detected in urine was diverse, including endogenous compounds and molecules biosynthesized by microbes. Also, the diet seemed to have had a relevant role on the urine metabolome. Further exploration of the urine metabolic phenotype will open a window for discovering unknown, or poorly understood, metabolic pathways. In turn, this will advance our understanding of feline biology and lead to new insights in feline physiology, nutrition and medicine.

Metabolomics for clinical use and research in chronic kidney disease

Chronic kidney disease (CKD) has a high prevalence in the general population and is associated with high mortality; a need therefore exists for better biomarkers for diagnosis, monitoring of disease progression and therapy stratification. Moreover, very sensitive biomarkers are needed in drug development and clinical research to increase understanding of the efficacy and safety of potential and existing therapies. Metabolomics analyses can identify and quantify all metabolites present in a given sample, covering hundreds to thousands of metabolites. Sample preparation for metabolomics requires a very fast arrest of biochemical processes. Present key technologies for metabolomics are mass spectrometry and proton nuclear magnetic resonance spectroscopy, which require sophisticated biostatistic and bioinformatic data analyses. The use of metabolomics has been instrumental in identifying new biomarkers of CKD such as acylcarnitines, glycerolipids, dimethylarginines and metabolites of tryptophan, the citric acid cycle and the urea cycle. Biomarkers such as c-mannosyl tryptophan and pseudouridine have better performance in CKD stratification than does creatinine. Future challenges in metabolomics analyses are prospective studies and deconvolution of CKD biomarkers from those of other diseases such as metabolic syndrome, diabetes mellitus, inflammatory conditions, stress and cancer.