Serum metabolite concentrations and decreased GFR in the general population

Population-Level Analysis to Determine Parameters That Drive Variation in the Plasma Metabolite Profiles

The plasma metabolome is associated with multiple phenotypes and diseases. However, a systematic study investigating clinical determinants that control the metabolome has not yet been conducted. In the present study, therefore, we aimed to identify the major determinants of the plasma metabolite profile. We used ultra-high performance liquid chromatography (UHPLC) coupled to quadrupole time of flight mass spectrometry (QTOF-MS) to determine 106 metabolites in plasma samples from 2503 subjects in a cross-sectional study. We investigated the correlation structure of the metabolite profiles and generated uncorrelated metabolite factors using principal component analysis (PCA) and varimax rotation. Finally, we investigated associations between these factors and 34 clinical covariates. Our results suggest that liver function, followed by kidney function and insulin resistance show the strongest associations with the plasma metabolite profile. The association of specific phenotypes with several components may suggest multiple independent metabolic mechanisms, which is further supported by the composition of the associated factors.

Optimization of kidney dysfunction prediction in diabetic kidney disease using targeted metabolomics

AIMS

Metabolomics have been used to evaluate the role of small molecules in human disease. However, the cost and complexity of the methodology and interpretation of findings have limited the transference of knowledge to clinical practice. Here, we apply a targeted metabolomics approach using samples blotted in filter paper to develop clinical-metabolomics models to detect kidney dysfunction in diabetic kidney disease (DKD).

METHODS

We included healthy controls and subjects with type 2 diabetes (T2D) with and without DKD and investigated the association between metabolite concentrations in blood and urine with eGFR and albuminuria. We also evaluated performance of clinical, biochemical and metabolomic models to improve kidney dysfunction prediction in DKD.

RESULTS

Using clinical-metabolomics models, we identified associations of decreased eGFR with body mass index (BMI), uric acid and C10:2 levels; albuminuria was associated to years of T2D duration, A1C, uric acid, creatinine, protein intake and serum C0, C10:2 and urinary C12:1 levels. DKD was associated with age, A1C, uric acid, BMI, serum C0, C10:2, C8:1 and urinary C12:1. Inclusion of metabolomics increased the predictive and informative capacity of models composed of clinical variables by decreasing Akaike's information criterion, and was replicated both in training and validation datasets.

CONCLUSIONS

Targeted metabolomics using blotted samples in filter paper is a simple, low-cost approach to identify outcomes associated with DKD; the inclusion of metabolomics improves predictive capacity of clinical models to identify kidney dysfunction and DKD-related outcomes.

Metabolomics in chronic kidney disease: Strategies for extended metabolome coverage

Chronic kidney disease (CKD) is becoming a major public health issue as prevalence is increasing worldwide. It also represents a major challenge for the identification of new early biomarkers, understanding of biochemical mechanisms, patient monitoring and prognosis. Each metabolite contained in a biofluid or tissue may play a role as a signal or as a driver in the development or progression of the pathology. Therefore, metabolomics is a highly valuable approach in this clinical context. It aims to provide a representative picture of a biological system, making exhaustive metabolite coverage crucial. Two aspects can be considered: analytical and biological coverage. From an analytical point of view, monitoring all metabolites within one run is currently impossible. Multiple analytical techniques providing orthogonal information should be carried out in parallel for coverage improvement. The biological aspect of metabolome coverage can be enhanced by using multiple biofluids or tissues for in-depth biological investigation, as the analysis of a single sample type is generally insufficient for whole organism extrapolation. Hence, recording of signals from multiple sample types and different analytical platforms generates massive and complex datasets so that chemometric tools, including data fusion approaches and multi-block analysis, are key tools for extracting biological information and for discovery of relevant biomarkers. This review presents the recent developments in the field of metabolomic analysis, from sampling and analytical strategies to chemometric tools, dedicated to the generation and handling of multiple complementary metabolomic datasets enabling extended metabolite coverage to improve our biological knowledge of CKD.

Association Between Newborn Metabolic Profiles and Pediatric Kidney Disease

INTRODUCTION

Metabolomics offers considerable promise in early disease detection. We set out to test the hypothesis that routine newborn metabolic profiles at birth, obtained through screening for inborn errors of metabolism, would be associated with kidney disease and add incremental information to known clinical risk factors.

METHODS

We conducted a population-level cohort study in Ontario, Canada, using metabolic profiles from 1,288,905 newborns from 2006 to 2015. The primary outcome was chronic kidney disease (CKD) or dialysis. Individual metabolites and their ratio combinations were examined by logistic regression after adjustment for established risk factors for kidney disease and incremental risk prediction measured.

RESULTS

CKD occurred in 2086 (0.16%, median time 612 days) and dialysis in 641 (0.05%, median time 99 days) infants and children. Individual metabolites consisted of amino acids, acylcarnitines, markers of fatty acid oxidation, and others. Base models incorporating clinical risk factors only provided c-statistics of 0.61 for CKD and 0.70 for dialysis. The addition of identified metabolites to risk prediciton models resulted in significant incremental improvement in the performance of both models (CKD model: c-statistic 0.66 NRI 0.36 IDI 0.04, dialysis model: c-statistic 0.77 NRI 0.57 IDI 0.09). This was consistent after internal validation using bootstrapping and a sensitivity analysis excluding outcomes within the first 30 days.

CONCLUSION

Routinely collected screening metabolites at birth are associated with CKD and the need for dialytic therapies in infants and children, and add incremental information to traditional clinical risk factors.

MicroRNA, Proteins, and Metabolites as Novel Biomarkers for Prediabetes, Diabetes, and Related Complications

Type 2 diabetes mellitus (T2DM) is no more a lifestyle disease of developed countries. It has emerged as a major health problem worldwide including developing countries. However, how diabetes could be detected at an early stage (prediabetes) to prevent the progression of disease is still unclear. Currently used biomarkers like glycated hemoglobin and assessment of blood glucose level have their own limitations. These classical markers can be detected when the disease is already established. Prognosis of disease at early stages and prediction of population at a higher risk require identification of specific markers that are sensitive enough to be detected at early stages of disease. Biomarkers which could predict the risk of disease in people will be useful for developing preventive/proactive therapies to those individuals who are at a higher risk of developing the disease. Recent studies suggested that the expression of biomolecules including microRNAs, proteins, and metabolites specifically change during the progression of T2DM and related complications, suggestive of disease pathology. Owing to their omnipresence in body fluids and their association with onset, progression, and pathogenesis of T2DM, these biomolecules can be potential biomarker for prognosis, diagnosis, and management of disease. In this article, we summarize biomolecules that could be potential biomarkers and their signature changes associated with T2DM and related complications during disease pathogenesis.

Associations of Plasma Amino Acid and Acylcarnitine Profiles with Incident Reduced Glomerular Filtration Rate

BACKGROUND AND OBJECTIVES

Metabolomics is instrumental in identifying novel biomarkers of kidney function to aid in the prevention and management of CKD. However, data linking the metabolome to incident eGFR are sparse, particularly in Asian populations with different genetic backgrounds and environmental exposures. Therefore, we aimed to investigate the associations of amino acid and acylcarnitine profiles with change in eGFR in a Chinese cohort.

DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS

This study included 1765 community-living Chinese adults aged 50-70 years with baseline eGFR≥60 ml/min per 1.73 m2. At baseline, 22 amino acids and 34 acylcarnitines in plasma were quantified by gas or liquid chromatography coupled with mass spectrometry. Annual rate of change in eGFR was calculated, and incident eGFR decline was defined as eGFR<60 ml/min per 1.73 m2 by the end of 6 years of follow-up.

RESULTS

The mean (SD) unadjusted annual change in eGFR was 2.2±2.0 ml/min per 1.73 m2 and the incidence of reduced eGFR was 16%. After Bonferroni correction, 13 of 56 metabolites were significantly associated with annual eGFR change. After multivariable adjustment of baseline covariates, including baseline eGFR, seven of the 13 metabolites, including cysteine, long-chain acylcarnitines (C14:1OH, C18, C18:2, and C20:4), and other acylcarnitines (C3DC and C10), were significantly associated with incident reduced eGFR (relative risks ranged from 1.16 to 1.25 per SD increment of metabolites; P<3.8E-03 after Bonferroni correction of multiple testing of the 13 metabolites). Moreover, principal component analysis identified two factors, consisting of cysteine and long-chain acylcarnitines, respectively, that were associated with incident reduced eGFR.

CONCLUSIONS

Elevated plasma levels of cysteine and a panel of acylcarnitines were associated with a higher incidence of reduced eGFR in Chinese adults, independent of baseline eGFR and other conventional risk factors.

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.

Renal insufficiency mimicking glutaric acidemia type 1 on newborn screening

BACKGROUND

Glutaryl carnitine (C5DC) in dried blood spots is used as a biomarker for glutaric aciduria type 1 (GA-1) screening. C5DC, however, is the only screening marker for this condition, and various pathological conditions may interfere with C5DC metabolism. Recently, C5DC elevation has been reported in cases of renal insufficiency.

METHOD

Five patients who were positive for GA-1 on newborn screening with tandem mass spectrometry between September 2012 and March 2015 at Kobe University Hospital were enrolled in this study.

RESULTS

GA-1 was not confirmed on urinary organic acids analysis in any of the patients. C5DC decreased immediately in four patients, but one patient, who had high C5DC for at least 4 months, was diagnosed with bilateral renal hypoplasia.

CONCLUSION

In the case of persistently elevated C5DC, renal insufficiency should be considered as a differential diagnosis.

From Discovery to Translation: Characterization of C-Mannosyltryptophan and Pseudouridine as Markers of Kidney Function

Using a non-targeted metabolomics platform, we recently identified C-mannosyltryptophan and pseudouridine as non-traditional kidney function markers. The aims of this study were to obtain absolute concentrations of both metabolites in blood and urine from individuals with and without CKD to provide reference ranges and to assess their fractional excretions (FE), and to assess the agreement with their non-targeted counterparts. In individuals without/with CKD, mean plasma and urine concentrations for C-mannosyltryptophan were 0.26/0.72 µmol/L and 3.39/4.30 µmol/mmol creatinine, respectively. The respective concentrations for pseudouridine were 2.89/5.67 µmol/L and 39.7/33.9 µmol/mmol creatinine. Median (25^th, 75^th percentiles) FEs were 70.8% (65.6%, 77.8%) for C-mannosyltryptophan and 76.0% (68.6%, 82.4%) for pseudouridine, indicating partial net reabsorption. Association analyses validated reported associations between single metabolites and eGFR. Targeted measurements of both metabolites agreed well with the non-targeted measurements, especially in urine. Agreement for composite nephrological measures FE and urinary metabolite-to-creatinine ratio was lower, but could be improved by replacing non-targeted creatinine measurements with a standard clinical creatinine test. In summary, targeted quantification and additional characterization in relevant populations are necessary steps in the translation of non-traditional biomarkers in nephrology from non-targeted discovery to clinical application.

Circulating acylcarnitine profile in human heart failure: a surrogate of fatty acid metabolic dysregulation in mitochondria and beyond

Heart failure (HF) is associated with metabolic perturbations, particularly of fatty acids (FAs), which remain to be better understood in humans. This study aimed at testing the hypothesis that HF patients with reduced ejection fraction display systemic perturbations in levels of energy-related metabolites, especially those reflecting dysregulation of FA metabolism, namely, acylcarnitines (ACs). Circulating metabolites were assessed using mass spectrometry (MS)-based methods in two cohorts. The main cohort consisted of 72 control subjects and 68 HF patients exhibiting depressed left ventricular ejection fraction (25.9 ± 6.9%) and mostly of ischemic etiology with ≥2 comorbidities. HF patients displayed marginal changes in plasma levels of tricarboxylic acid cycle-related metabolites or indexes of mitochondrial or cytosolic redox status. They had, however, 22-79% higher circulating ACs, irrespective of chain length (P < 0.0001, adjusted for sex, age, renal function, and insulin resistance, determined by shotgun MS/MS), which reflects defective mitochondrial β-oxidation, and were significantly associated with levels of NH₂-terminal pro-B-type natriuretic peptide levels, a disease severity marker. Subsequent extended liquid chromatography-tandem MS analysis of 53 plasma ACs in a subset group from the primary cohort confirmed and further substantiated with a comprehensive lipidomic analysis in a validation cohort revealed in HF patients a more complex circulating AC profile. The latter included dicarboxylic-ACs and dihydroxy-ACs as well as very long chain (VLC) ACs or sphingolipids with VLCFAs (>20 carbons), which are proxies of dysregulated FA metabolism in peroxisomes. Our study identified alterations in circulating ACs in HF patients that are independent of biological traits and associated with disease severity markers. These alterations reflect dysfunctional FA metabolism in mitochondria but also beyond, namely, in peroxisomes, suggesting a novel mechanism contributing to global lipid perturbations in human HF.NEW & NOTEWORTHY Mass spectrometry-based profiling of circulating energy metabolites, including acylcarnitines, in two cohorts of heart failure versus control subjects revealed multiple alterations in fatty acid metabolism in peroxisomes in addition to mitochondria, thereby highlighting a novel mechanism contributing to global lipid perturbations in heart failure.Listen to this article's corresponding podcast at http://ajpheart.podbean.com/e/acylcarnitines-in-human-heart-failure/.

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.

Profiling of Plasma Metabolites Suggests Altered Mitochondrial Fuel Usage and Remodeling of Sphingolipid Metabolism in Individuals With Type 2 Diabetes and Kidney Disease

Introduction

Pathophysiology of diabetic kidney disease (DKD) is incompletely understood. We aim to elucidate metabolic abnormalities associated with DKD in type 2 diabetes mellitus (T2DM) by targeted plasma metabolomics.

Methods

A total of 126 T2DM participants with early DKD (urinary albumin-to-creatinine ratio [ACR] 30−299 mg/g and eGFR ≥ 60 ml/min/1.73 m²), 154 overt DKD (ACR ≥ 300 mg/g or eGFR < 60 ml/min/1.73 m²), and 129 non-DKD T2DM controls (ACR < 30 mg/g and eGFR ≥ 60 ml/min/1.73 m²) were included in discovery study. Findings were subsequently validated in 149 T2DM with macroalbuminuria (ACR ≥ 300 mg/g) and 149 matched non-DKD T2DM controls. Plasma amino acid, acylcarnitine, Krebs cycle organic acid, and sphingolipids/ceramide levels were quantified by liquid chromatography−mass spectrometry and gas chromatography−mass spectrometry.

Results

Of 123 metabolites included in the data analysis, 24 differed significantly between DKD and controls in the same direction in both discovery and validation subpopulations. A number of short acylcarnitines including their dicarboxylic derivatives (C2−C6) were elevated in DKD, suggesting abnormalities in fatty acids and amino acids metabolic pathways. Five phosphatidylcholines were lower whereas 4 metabolites in the sphingomyelin−ceramide subfamily were higher in DKD. Principal component regression revealed that long-chain ceramides were independently associated with ACR but not eGFR. Conversely, essential amino acids catabolism and short dicarboxylacylcarnitine accumulation were associated with eGFR but not ACR.

Discussion

DKD is associated with altered fuel substrate use and remodeling of sphingolipid metabolism in T2DM with DKD. Associations of albuminuria and impaired filtration function with distinct metabolomic signatures suggest different pathophysiology underlying these 2 manifestations of DKD.

Identification of biomarkers for development of end-stage kidney disease in chronic kidney disease by metabolomic profiling

A critical issue in the management of chronic kidney disease (CKD) is to prevent patients from the progression to end-stage kidney disease (ESKD), however, there is only limited number of biomarkers for the discrimination of the high-risk CKD patients. We aimed to identify the metabolites which possess the ability to predict the earlier kidney deterioration. We performed capillary electrophoresis and liquid chromatography mass spectrometry (CE-MS)-based metabolic profiling in a prospective cohort, which consisted of referred 112 CKD patients with median follow-up period of 4.4 years. The association between the levels of candidate metabolites and the outcomes (progression to ESKD alone or in combination with death before ESKD) were assessed by multivariate Cox proportional hazard models after adjusting for the baseline covariates. A total of 218 metabolites were detected in the plasma of CKD patients. We identified 16 metabolites which have predictive values for the composite outcome: The risk for composite outcome was elevated from 2.0- to 8.0-fold in those with higher levels of 16 plasma metabolites. Our results suggest that the measurement of these metabolites may facilitate CKD management by predicting the risk of progression to ESKD.

Metabolomics of renal venous plasma from individuals with unilateral renal artery stenosis and essential hypertension

Objective:

To compare the metabolite profiles of venous effluent from both kidneys of individuals with unilateral atherosclerotic renal artery stenosis (ARAS) in order to directly examine how impaired renal blood flow impacts small-molecule handling in humans.

Methods:

We applied liquid chromatography–mass spectrometry based metabolite profiling to venous plasma obtained from the stenotic (STK) and contralateral (CLK) kidneys of ARAS patients (n = 16), and both the kidneys of essential hypertensive controls (n = 11). Study samples were acquired during a 3-day protocol that included iothalamate clearance measurements, radiographic kidney phenotyping (Duplex ultrasound, multidetector computed tomography, and blood-oxygen-level-dependent MRI), and controlled sodium and caloric intake and antihypertensive treatment.

Results:

Partial least squares-discriminant analysis demonstrated clear separation of essential hypertensive kidney metabolite profiles versus STK and CLK metabolite profiles, but no separation between metabolite profiles of STK and CLK samples. All of the discriminating metabolites were similarly elevated in the STK and CLK samples, likely reflecting the lower glomerular filtration rate in the ARAS versus essential hypertensive individuals (mean 66.1 versus 89.2 ml/min per 1.73 m²). In a paired analysis within the ARAS group, no metabolite was significantly altered in STK compared with CLK samples; notably, creatinine was the same in STK and CLK samples (STK/CLK ratio = 1.0, P = 0.9). Results were unchanged in an examination of ARAS patients in the bottom half of renal tissue perfusion or oxygenation.

Conclusion:

Metabolite profiling does not differentiate venous effluent from STKs or CLKs in individuals with unilateral ARAS, despite the measurable loss of kidney volume and blood flow on the affected side. These findings are consistent with the kidney's ability to adapt to ARAS to maintain a range of metabolic functions.

Metabolomics and renal disease

PURPOSE OF REVIEW

This review summarizes recent metabolomics studies of renal disease, outlining some of the limitations of the literature to date.

RECENT FINDINGS

The application of metabolomics in nephrology research has expanded from the initial analyses of uremia to include both cross-sectional and longitudinal studies of earlier stages of kidney disease. Although these studies have nominated several potential markers of incident chronic kidney disease (CKD) and CKD progression, a lack of overlap in metabolite coverage has limited the ability to synthesize results across groups. Furthermore, direct examination of renal metabolite handling has underscored the substantial impact kidney function has on these potential markers (and many other circulating metabolites). In experimental studies, metabolomics has been used to identify a signature of decreased mitochondrial function in diabetic nephropathy and a preference for aerobic glucose metabolism in polycystic kidney disease. In each case, these studies have outlined novel therapeutic opportunities. Finally, as a complement to the longstanding interest in renal metabolite clearance, the microbiome has been increasingly recognized as the source of many plasma metabolites, including some with potential functional relevance to CKD and its complications.

SUMMARY

The high-throughput, high-resolution phenotyping enabled by metabolomics technologies has begun to provide insight on renal disease in clinical, physiologic, and experimental contexts.

Metabonomics and diagnostics

Metabonomic techniques have considerable potential in the field of clinical diagnostics, typifying the application of a translational research paradigm. Care must be taken at all stages to apply appropriate methodology with accurate patient selection and profiling, and rigorous data acquisition and handling, to ensure clinical validity.An ever-increasing number of publications in a wide range of diseases and diverse patient groups suggest a variety of potential clinical uses; prospective studies in large validation cohorts are required to bring metabonomics into routine clinical practice. In this chapter, the utility of metabonomics as a diagnostic tool will be discussed.

Blood, urine and faecal metabolite profiles in the study of adult renal disease

Chronic kidney disease (CKD) is a major public health burden and to date traditional biomarkers of renal function (such as serum creatinine and cystatin C) are unable to identify at-risk individuals before the disease process is well under way. To help preventive strategies and maximize the potential for effective interventions, it is important to characterise the molecular changes that take place in the development of renal damage. Metabolomics is a promising tool to identify markers of renal disease since the kidneys are involved in the handling of major biochemical classes of metabolites. These metabolite levels capture a snap-shot of the metabolic profile of the individual, allowing for the potential identification of early biomarkers, and the monitoring of real-time kidney function. In this review, we describe the current status of the identification of blood/urine/faecal metabolic biomarkers in different entities of kidney diseases including: acute kidney injury, chronic kidney disease, renal transplant, diabetic nephropathy and other disorders.

A Metabolome-Wide Association Study of Kidney Function and Disease in the General Population

Small molecules are extensively metabolized and cleared by the kidney. Changes in serum metabolite concentrations may result from impaired kidney function and can be used to estimate filtration (e.g., the established marker creatinine) or may precede and potentially contribute to CKD development. Here, we applied a nontargeted metabolomics approach using gas and liquid chromatography coupled to mass spectrometry to quantify 493 small molecules in human serum. The associations of these molecules with GFR estimated on the basis of creatinine (eGFRcr) and cystatin C levels were assessed in ≤1735 participants in the KORA F4 study, followed by replication in 1164 individuals in the TwinsUK registry. After correction for multiple testing, 54 replicated metabolites significantly associated with eGFRcr, and six of these showed pairwise correlation (r≥0.50) with established kidney function measures: C-mannosyltryptophan, pseudouridine, N-acetylalanine, erythronate, myo-inositol, and N-acetylcarnosine. Higher C-mannosyltryptophan, pseudouridine, and O-sulfo-L-tyrosine concentrations associated with incident CKD (eGFRcr <60 ml/min per 1.73 m(2)) in the KORA F4 study. In contrast with serum creatinine, C-mannosyltryptophan and pseudouridine concentrations showed little dependence on sex. Furthermore, correlation with measured GFR in 200 participants in the AASK study was 0.78 for both C-mannosyltryptophan and pseudouridine concentration, and highly significant associations of both metabolites with incident ESRD disappeared upon adjustment for measured GFR. Thus, these molecules may be alternative or complementary markers of kidney function. In conclusion, our study provides a comprehensive list of kidney function-associated metabolites and highlights potential novel filtration markers that may help to improve the estimation of GFR.

Metabolic biomarkers for chronic kidney disease

Chronic kidney disease (CKD) is an increasingly recognized burden for patients and health care systems with high (and growing) global incidence and prevalence, significant mortality, and disproportionately high treatment costs. Yet, the available diagnostic tools are either impractical in clinical routine or have serious shortcomings impeding a well-informed disease management although optimized treatment strategies with proven benefits for the patients have become available. Advances in bioanalytical technologies have facilitated studies that identified genomic, proteomic, and metabolic biomarker candidates, and confirmed some of them in independent cohorts. This review summarizes the CKD-related markers discovered so far, and focuses on compounds and pathways, for which there is quantitative data, substantiating evidence from translational research, and a mechanistic understanding of the processes involved. Also, multiparametric marker panels have been suggested that showed promising diagnostic and prognostic performance in initial analyses although the data basis from prospective trials is very limited. Large-scale studies, however, are underway and will provide the information for validating a set of parameters and discarding others. Finally, the path from clinical research to a routine application is discussed, focusing on potential obstacles such as the use of mass spectrometry, and the feasibility of obtaining regulatory approval for targeted metabolomics assays.

Biomarkers of rapid chronic kidney disease progression in type 2 diabetes

Here we evaluated the performance of a large set of serum biomarkers for the prediction of rapid progression of chronic kidney disease (CKD) in patients with type 2 diabetes. We used a case-control design nested within a prospective cohort of patients with baseline eGFR 30-60 ml/min per 1.73 m(2). Within a 3.5-year period of Go-DARTS study patients, 154 had over a 40% eGFR decline and 153 controls maintained over 95% of baseline eGFR. A total of 207 serum biomarkers were measured and logistic regression was used with forward selection to choose a subset that were maximized on top of clinical variables including age, gender, hemoglobin A1c, eGFR, and albuminuria. Nested cross-validation determined the best number of biomarkers to retain and evaluate for predictive performance. Ultimately, 30 biomarkers showed significant associations with rapid progression and adjusted for clinical characteristics. A panel of 14 biomarkers increased the area under the ROC curve from 0.706 (clinical data alone) to 0.868. Biomarkers selected included fibroblast growth factor-21, the symmetric to asymmetric dimethylarginine ratio, β2-microglobulin, C16-acylcarnitine, and kidney injury molecule-1. Use of more extensive clinical data including prebaseline eGFR slope improved prediction but to a lesser extent than biomarkers (area under the ROC curve of 0.793). Thus we identified several novel associations of biomarkers with CKD progression and the utility of a small panel of biomarkers to improve prediction.

A Metabolomic Approach to Understanding the Metabolic Link between Obesity and Diabetes

Obesity and diabetes arise from an intricate interplay between both genetic and environmental factors. It is well recognized that obesity plays an important role in the development of insulin resistance and diabetes. Yet, the exact mechanism of the connection between obesity and diabetes is still not completely understood. Metabolomics is an analytical approach that aims to detect and quantify small metabolites. Recently, there has been an increased interest in the application of metabolomics to the identification of disease biomarkers, with a number of well-known biomarkers identified. Metabolomics is a potent approach to unravel the intricate relationships between metabolism, obesity and progression to diabetes and, at the same time, has potential as a clinical tool for risk evaluation and monitoring of disease. Moreover, metabolomics applications have revealed alterations in the levels of metabolites related to obesity-associated diabetes. This review focuses on the part that metabolomics has played in elucidating the roles of metabolites in the regulation of systemic metabolism relevant to obesity and diabetes. It also explains the possible metabolic relation and association between the two diseases. The metabolites with altered profiles in individual disorders and those that are specifically and similarly altered in both disorders are classified, categorized and summarized.

Genome-wide association study identifies novel genetic variants contributing to variation in blood metabolite levels

Metabolites are small molecules involved in cellular metabolism, which can be detected in biological samples using metabolomic techniques. Here we present the results of genome-wide association and meta-analyses for variation in the blood serum levels of 129 metabolites as measured by the Biocrates metabolomic platform. In a discovery sample of 7,478 individuals of European descent, we find 4,068 genome- and metabolome-wide significant (Z-test, P < 1.09 × 10(-9)) associations between single-nucleotide polymorphisms (SNPs) and metabolites, involving 59 independent SNPs and 85 metabolites. Five of the fifty-nine independent SNPs are new for serum metabolite levels, and were followed-up for replication in an independent sample (N = 1,182). The novel SNPs are located in or near genes encoding metabolite transporter proteins or enzymes (SLC22A16, ARG1, AGPS and ACSL1) that have demonstrated biomedical or pharmaceutical importance. The further characterization of genetic influences on metabolic phenotypes is important for progress in biological and medical research.

Urine Metabolite Profiles Predictive of Human Kidney Allograft Status

Noninvasive diagnosis and prognostication of acute cellular rejection in the kidney allograft may help realize the full benefits of kidney transplantation. To investigate whether urine metabolites predict kidney allograft status, we determined levels of 749 metabolites in 1516 urine samples from 241 kidney graft recipients enrolled in the prospective multicenter Clinical Trials in Organ Transplantation-04 study. A metabolite signature of the ratio of 3-sialyllactose to xanthosine in biopsy specimen-matched urine supernatants best discriminated acute cellular rejection biopsy specimens from specimens without rejection. For clinical application, we developed a high-throughput mass spectrometry-based assay that enabled absolute and rapid quantification of the 3-sialyllactose-to-xanthosine ratio in urine samples. A composite signature of ratios of 3-sialyllactose to xanthosine and quinolinate to X-16397 and our previously reported urinary cell mRNA signature of 18S ribosomal RNA, CD3ε mRNA, and interferon-inducible protein-10 mRNA outperformed the metabolite signatures and the mRNA signature. The area under the receiver operating characteristics curve for the composite metabolite-mRNA signature was 0.93, and the signature was diagnostic of acute cellular rejection with a specificity of 84% and a sensitivity of 90%. The composite signature, developed using solely biopsy specimen-matched urine samples, predicted future acute cellular rejection when applied to pristine samples taken days to weeks before biopsy. We conclude that metabolite profiling of urine offers a noninvasive means of diagnosing and prognosticating acute cellular rejection in the human kidney allograft, and that the combined metabolite and mRNA signature is diagnostic and prognostic of acute cellular rejection with very high accuracy.

Estimated GFR is biased by non-traditional cardiovascular risk factors

BACKGROUND

Estimated glomerular filtration rate (eGFR) based on either cystatin C or creatinine performs similarly in estimating measured GFR, but associate differently with cardiovascular disease (CVD) and mortality. This could be due to confounding by non-GFR-related traits associated with cystatin C and creatinine levels. We investigated non-GFR-related associations between eGFR and two types of nontraditional risk factors for CVD and death: L-arginine/dimethylarginine metabolism and insulin resistance.

METHODS

GFR was measured via iohexol clearance in a cross-sectional study of 1,624 middle-aged persons from the general population without CVD, diabetes or chronic kidney disease. The dimethylarginines were measured using liquid chromatography tandem mass spectrometry (LC-MSMS). Insulin resistance was determined by the homeostasis model assessment (HOMA-IR).

RESULTS

Asymmetric dimethylarginine (ADMA), symmetric dimethylarginine (SDMA), the L-arginine/ADMA ratio and insulin resistance were associated with creatinine-based eGFR after accounting for measured GFR in multivariable adjusted analyses. The cystatin C-based eGFR showed a similar residual association with SDMA; an oppositely directed, borderline significant association with ADMA; and a stronger residual association with insulin resistance compared with eGFR based on creatinine.

CONCLUSION

Both creatinine- and cystatin C-based eGFR are influenced by nontraditional risk factors, which may bias risk prediction by eGFR in longitudinal studies.

Genome-wide association study reveals a polymorphism in the podocyte receptor RANK for the decline of renal function in coronary patients

Impaired kidney function is a significant health problem and a major concern in clinical routine and is routinely determined by decreased glomerular filtration rate (GFR). In contrast to single assessment of a patients' kidney function providing only limited information on patients' health, serial measurements of GFR clearly improves the validity of diagnosis. The decline of kidney function has recently been reported to be predictive for mortality and vascular events in coronary patients. However, it has not been investigated for genetic association in GWA studies. This study investigates for the first time the association of cardiometabolic polymorphisms with the decline of estimated GFR during a 4 year follow up in 583 coronary patients, using the Cardio-Metabo Chip. We revealed a suggestive association with 3 polymorphisms, surpassing genome-wide significance (p = 4.0 e-7). The top hit rs17069906 (p = 5.6 e-10) is located within the genomic region of RANK, recently demonstrated to be an important player in the adaptive recovery response in podocytes and suggested as a promising therapeutic target in glomerular diseases.

Urine and plasma metabolites predict the development of diabetic nephropathy in individuals with Type 2 diabetes mellitus

AIMS

Early detection of individuals with Type 2 diabetes mellitus or hypertension at risk for micro- or macroalbuminuria may facilitate prevention and treatment of renal disease. We aimed to discover plasma and urine metabolites that predict the development of micro- or macroalbuminuria.

METHODS

Patients with Type 2 diabetes (n = 90) and hypertension (n = 150) were selected from the community-cohort 'Prevention of REnal and Vascular End-stage Disease' (PREVEND) and the Steno Diabetes Center for this case-control study. Cases transitioned in albuminuria stage (from normo- to microalbuminuria or micro- to macroalbuminuria). Controls, matched for age, gender, and baseline albuminuria stage, remained in normo- or microalbuminuria stage during follow-up. Median follow-up was 2.9 years. Metabolomics were performed on plasma and urine. The predictive performance of a metabolite for albuminuria transition was assessed by the integrated discrimination index.

RESULTS

In patients with Type 2 diabetes with normoalbuminuria, no metabolites discriminated cases from controls. In patients with Type 2 diabetes with microalbuminuria, plasma histidine was lower (fold change = 0.87, P = 0.02) and butenoylcarnitine was higher (fold change = 1.17, P = 0.007) in cases vs. controls. In urine, hexose, glutamine and tyrosine were lower in cases vs. controls (fold change = 0.20, P < 0.001; 0.32, P < 0.001; 0.51, P = 0.006, respectively). Adding the metabolites to a model of baseline albuminuria and estimated glomerular filtration rate metabolites improved risk prediction for macroalbuminuria transition (plasma integrated discrimination index = 0.28, P < 0.001; urine integrated discrimination index = 0.43, P < 0.001). These metabolites did not differ between hypertensive cases and controls without Type 2 diabetes.

CONCLUSIONS

Type 2 diabetes-specific plasma and urine metabolites were discovered that predict the development of macroalbuminuria beyond established renal risk markers. These results should be confirmed in a large, prospective cohort.

Serum metabolomic profiling and incident CKD among African Americans

BACKGROUND AND OBJECTIVES

Novel biomarkers that more accurately reflect kidney function and predict future CKD are needed. The human metabolome is the product of multiple physiologic or pathophysiologic processes and may provide novel insight into disease etiology and progression. This study investigated whether estimated kidney function would be associated with multiple metabolites and whether selected metabolomic factors would be independent risk factors for incident CKD.

DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS

In total, 1921 African Americans free of CKD with a median of 19.6 years follow-up among the Atherosclerosis Risk in Communities Study were included. A total of 204 serum metabolites quantified by untargeted gas chromatography-mass spectrometry and liquid chromatography-mass spectrometry was analyzed by both linear regression for the cross-sectional associations with eGFR (specified by the Chronic Kidney Disease Epidemiology Collaboration equation) and Cox proportional hazards model for the longitudinal associations with incident CKD.

RESULTS

Forty named and 34 unnamed metabolites were found to be associated with eGFR specified by the Chronic Kidney Disease Epidemiology Collaboration equation with creatine and 3-indoxyl sulfate showing the strongest positive (2.8 ml/min per 1.73 m(2) per +1 SD; 95% confidence interval, 2.1 to 3.5) and negative association (-14.2 ml/min per 1.73 m(2) per +1 SD; 95% confidence interval, -17.0 to -11.3), respectively. Two hundred four incident CKD events with a median follow-up time of 19.6 years were included in the survival analyses. Higher levels of 5-oxoproline (hazard ratio, 0.70; 95% confidence interval, 0.60 to 0.82) and 1,5-anhydroglucitol (hazard ratio, 0.68; 95% confidence interval, 0.58 to 0.80) were significantly related to lower risk of incident CKD, and the associations did not appreciably change when mutually adjusted.

CONCLUSIONS

These data identify a large number of metabolites associated with kidney function as well as two metabolites that are candidate risk factors for CKD and may provide new insights into CKD biomarker identification.

Analysis of purine metabolites in maternal serum for evaluating the risk of gestosis

Metabolome analysis of the serum from pregnant patients aimed at detection of low-molecular-weight biomarkers of gestation process disorders indicated a relationship between the metabolic profile of maternal serum and risk of gestosis. In women with pre-eclampsia or preterm delivery, analysis of serum purine metabolites revealed changes in the metabolite concentrations, associated with pregnancy complications.

Analysis of serum metabolites for the discovery of amino acid biomarkers and the effect of galangin on cerebral ischemia

Ischemic stroke, a devastating disease with a complex pathophysiology, is a leading cause of death and disability worldwide. In our previous study, we reported that galangin provided direct protection against ischemic injury and acted as a potential neuroprotective agent. However, its associated neuroprotective mechanism has not yet been clarified. In this paper, we explored the potential AA biomarkers in the acute phase of cerebral ischemia and the effect of galangin on those potential biomarkers. In our study, 12 AAs were quantified in rat serum and found to be impaired by middle cerebral artery occlusion (MCAO)-induced focal cerebral ischemia. Using partial least squares discriminate analysis (PLS-DA), we identified the following amino acids as potential biomarkers of cerebral ischemia: glutamic acid (Glu), homocysteine (Hcy), methionine (Met), tryptophan (Trp), aspartic acid (Asp), alanine (Ala) and tyrosine (Tyr). Moreover, four amino acids (Hcy, Met, Glu and Trp) showed significant change in galangin-treated (100 and 50 mg kg(-1)) groups compared to vehicle groups. Furthermore, we identified three pathway-related enzymes tyrosine aminotransferase (TAT), glutamine synthetase (GLUL) and monocarboxylate transporter (SLC16A10) by multiplex interactions with Glu and Hcy, which have been previously reported to be closely related to cerebral ischemia. Through an analysis of the metabolite-protein network analysis, we identified 16 proteins that were associated with two amino acids by multiple interactions with three enzymes; five of them may become potential biomarkers of galangin for acute ischemic stroke as the result of molecule docking. Our results may help develop novel strategies to explore the mechanism of cerebral ischemia, discover potential targets for drug candidates and elucidate the related regulatory signal network.

Uremic solutes and risk of end-stage renal disease in type 2 diabetes: metabolomic study

Here we studied plasma metabolomic profiles as determinants of progression to ESRD in patients with Type 2 diabetes (T2D). This nested case-control study evaluated 40 cases who progressed to ESRD during 8-12 years of follow-up and 40 controls who remained alive without ESRD from the Joslin Kidney Study cohort. Controls were matched with cases for baseline clinical characteristics; although controls had slightly higher eGFR and lower levels of urinary albumin excretion than T2D cases. Plasma metabolites at baseline were measured by mass spectrometry-based global metabolomic profiling. Of the named metabolites in the library, 262 were detected in at least 80% of the study patients. The metabolomic platform recognized 78 metabolites previously reported to be elevated in ESRD (uremic solutes). Sixteen were already elevated in the baseline plasma of our cases years before ESRD developed. Other uremic solutes were either not different or not commonly detectable. Essential amino acids and their derivatives were significantly depleted in the cases, whereas certain amino acid-derived acylcarnitines were increased. All findings remained statistically significant after adjustment for differences between study groups in albumin excretion rate, eGFR or HbA1c. Uremic solute differences were confirmed by quantitative measurements. Thus, abnormal plasma concentrations of putative uremic solutes and essential amino acids either contribute to progression to ESRD or are a manifestation of an early stage(s) of the disease process that leads to ESRD in T2D.

Metabolomics insights into pathophysiological mechanisms of nephrology

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 high-throughput 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. 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.

Familial resemblance for serum metabolite concentrations

Metabolomics is the comprehensive study of metabolites, which are the substrates, intermediate, and end products of cellular metabolism. The heritability of the concentrations of circulating metabolites bears relevance for evaluating their suitability as biomarkers for disease. We report aspects of familial resemblance for the concentrations in human serum of more than 100 metabolites, measured using a targeted metabolomics platform. Age- and sex-corrected monozygotic twin correlations, midparent-offspring regression coefficients, and spouse correlations in subjects from two independent cohorts (Netherlands Twin Register and Leiden Longevity Study) were estimated for each metabolite. In the Netherlands Twin Register subjects, who were largely fasting, we found significant monozygotic twin correlations for 121 out of 123 metabolites. Heritability was confirmed by midparent-offspring regression. For most detected metabolites, the correlations between spouses were considerably lower than those between twins, indicating a contribution of genetic effects to familial resemblance. Remarkably high heritability was observed for free carnitine (monozygotic twin correlation 0.66), for the amino acids serine (monozygotic twin correlation 0.77) and threonine (monozygotic twin correlation 0.64), and for phosphatidylcholine acyl-alkyl C40:3 (monozygotic twin correlation 0.77). For octenoylcarnitine, a consistent point estimate of approximately 0.50 was found for the spouse correlations in the two cohorts as well as for the monozygotic twin correlation, suggesting that familiality for this metabolite is explained by shared environment. We conclude that for the majority of metabolites targeted by the used metabolomics platform, the familial resemblance of serum concentrations is largely genetic. Our results contribute to the knowledge of the heritability of fasting serum metabolite concentrations, which is relevant for biomarker research.

Associations between thyroid hormones and serum metabolite profiles in an euthyroid population

The aim was to characterise associations between circulating thyroid hormones-free thyroxine (FT4) and thyrotropin (TSH)-and the metabolite profiles in serum samples from participants of the German population-based KORA F4 study. Analyses were based on the metabolite profile of 1463 euthyroid subjects. In serum samples, obtained after overnight fasting (≥8), 151 different metabolites were quantified in a targeted approach including amino acids, acylcarnitines (ACs), and phosphatidylcholines (PCs). Associations between metabolites and thyroid hormone concentrations were analysed using adjusted linear regression models. To draw conclusions on thyroid hormone related pathways, intra-class metabolite ratios were additionally explored. We discovered 154 significant associations (Bonferroni p < 1.75 × 10-04) between FT4 and various metabolites and metabolite ratios belonging to AC and PC groups. Significant associations with TSH were lacking. High FT4 levels were associated with increased concentrations of many ACs and various sums of ACs of different chain length, and the ratio of C2 by C0. The inverse associations observed between FT4 and many serum PCs reflected the general decrease in PC concentrations. Similar results were found in subgroup analyses, e.g., in weight-stable subjects or in obese subjects. Further, results were independent of different parameters for liver or kidney function, or inflammation, which supports the notion of an independent FT4 effect. In fasting euthyroid adults, higher serum FT4 levels are associated with increased serum AC concentrations and an increased ratio of C2 by C0 which is indicative of an overall enhanced fatty acyl mitochondrial transport and β-oxidation of fatty acids.

A combined epidemiologic and metabolomic approach improves CKD prediction

Metabolomic approaches have begun to catalog the metabolic disturbances that accompany CKD, but whether metabolite alterations can predict future CKD is unknown. We performed liquid chromatography/mass spectrometry-based metabolite profiling on plasma from 1434 participants in the Framingham Heart Study (FHS) who did not have CKD at baseline. During the following 8 years, 123 individuals developed CKD, defined by an estimated GFR of <60 ml/min per 1.73 m(2). Numerous metabolites were associated with incident CKD, including 16 that achieved the Bonferroni-adjusted significance threshold of P≤0.00023. To explore how the human kidney modulates these metabolites, we profiled arterial and renal venous plasma from nine individuals. Nine metabolites that predicted CKD in the FHS cohort decreased more than creatinine across the renal circulation, suggesting that they may reflect non-GFR-dependent functions, such as renal metabolism and secretion. Urine isotope dilution studies identified citrulline and choline as markers of renal metabolism and kynurenic acid as a marker of renal secretion. In turn, these analytes remained associated with incident CKD in the FHS cohort, even after adjustment for eGFR, age, sex, diabetes, hypertension, and proteinuria at baseline. Addition of a multimarker metabolite panel to clinical variables significantly increased the c-statistic (0.77-0.83, P<0.0001); net reclassification improvement was 0.78 (95% confidence interval, 0.60 to 0.95; P<0.0001). Thus, the addition of metabolite profiling to clinical data may significantly improve the ability to predict whether an individual will develop CKD by identifying predictors of renal risk that are independent of estimated GFR.

Metabolomics in chronic kidney disease

Chronic kidney disease (CKD) represents a major challenge to public healthcare. Traditional clinical biomarkers of renal function (blood urea nitrogen and serum creatinine) are not sensitive or specific enough and only increase significantly after the presence of substantial CKD. Therefore, more sensitive biomarkers of CKD are needed. CKD-specific biomarkers at an early disease stage and early diagnosis of specific renal diseases would enable improved therapeutic treatment and reduced the personal and financial burdens. The goal of metabolomics is to identify non-targeted, global small-molecule metabolite profiles of complex samples, such as biofluids and tissues. This method offers the potential for a holistic approach to clinical medicine, as well as improvements in disease diagnoses and the understanding of pathological mechanisms. This review article presents an overview of the recent developments in the field of metabolomics, followed by an in-depth discussion of its application to the study of CKD (primary, chronic glomerulonephritis such as IgA nephropathy; secondary, chronic renal injury such as diabetic nephropathy; chronic renal failure including end-stage kidney disease with and without undergoing replacement therapies, etc), including metabolomic analytical technologies, chemometrics, and metabolomics in experimental and clinical research. We describe the current status of the identification of metabolic biomarkers in CKD. Several markers have been confirmed across multiple studies to detect CKD earlier than traditional clinical chemical and histopathological methods. The application of metabolomics in CKD studies provides researchers the opportunity to gain new insights into metabolic profiling and pathophysiological mechanisms. Particular challenges in the field are presented and placed within the context of future applications of metabolomic approaches to the studies of CKD.

Metabolites associate with kidney function decline and incident chronic kidney disease in the general population

BACKGROUND

Serum metabolites are associated cross-sectionally with kidney function in population-based studies.

METHODS

Using flow injection and liquid chromatography tandem mass spectrometry methods, we examined longitudinal associations of baseline concentrations of 140 metabolites and their 19 460 ratios with kidney function decline and chronic kidney disease (CKD) incidence over 7 years in 1104 participants of the Cooperative Health Research in the Region of Augsburg S4/F4 study.

RESULTS

Corrected for multiple testing, a significant association with annual change in the estimated glomerular filtration rate was observed for spermidine (P = 5.8 × 10(-7)) and two metabolite ratios, the phosphatidylcholine diacyl C42:5-to-phosphatidylcholine acyl-alkyl C36:0 ratio (P = 1.5 × 10(-6)) and the kynurenine-to-tryptophan ratio (P = 1.9 × 10(-6)). The kynurenine-to-tryptophan ratio was also associated with significantly higher incidence of CKD at the follow-up visit with an odds ratio of 1.36 per standard deviation increase; 95% confidence interval 1.11-1.66, P = 2.7 × 10(-3)). In separate analyses, the predictive ability of the metabolites was assessed: both the three significantly associated metabolite (ratios) as well as a panel of 35 metabolites selected from all metabolites in an unbiased fashion provided as much but not significantly more prognostic information than selected clinical predictors as judged by the area under the curve.

CONCLUSIONS

Baseline serum concentrations of spermidine and two metabolite ratios were associated with kidney function change over subsequent years in the general population. In separate analyses, baseline serum metabolites were able to predict incident CKD to a similar but not better extent than selected clinical parameters. Our longitudinal findings provide a basis for targeted studies of certain metabolic pathways, e.g. tryptophan metabolism, and their relation to kidney function decline.

Novel biomarkers for pre-diabetes identified by metabolomics

A targeted metabolomics approach was used to identify candidate biomarkers of pre-diabetes. The relevance of the identified metabolites is further corroborated with a protein-metabolite interaction network and gene expression data.

Three metabolites (glycine, lysophosphatidylcholine (LPC) (18:2) and acetylcarnitine C2) were found with significantly altered levels in pre-diabetic individuals compared with normal controls.

Lower levels of glycine and LPC (18:2) were found to predict risks for pre-diabetes and type 2 diabetes (T2D).

Seven T2D-related genes (PPARG, TCF7L2, HNF1A, GCK, IGF1, IRS1 and IDE) are functionally associated with the three identified metabolites.

The unique combination of methodologies, including prospective population-based and nested case–control, as well as cross-sectional studies, was essential for the identification of the reported biomarkers.

Type 2 diabetes (T2D) can be prevented in pre-diabetic individuals with impaired glucose tolerance (IGT). Here, we have used a metabolomics approach to identify candidate biomarkers of pre-diabetes. We quantified 140 metabolites for 4297 fasting serum samples in the population-based Cooperative Health Research in the Region of Augsburg (KORA) cohort. Our study revealed significant metabolic variation in pre-diabetic individuals that are distinct from known diabetes risk indicators, such as glycosylated hemoglobin levels, fasting glucose and insulin. We identified three metabolites (glycine, lysophosphatidylcholine (LPC) (18:2) and acetylcarnitine) that had significantly altered levels in IGT individuals as compared to those with normal glucose tolerance, with P-values ranging from 2.4 × 10⁻⁴ to 2.1 × 10⁻¹³. Lower levels of glycine and LPC were found to be predictors not only for IGT but also for T2D, and were independently confirmed in the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam cohort. Using metabolite–protein network analysis, we identified seven T2D-related genes that are associated with these three IGT-specific metabolites by multiple interactions with four enzymes. The expression levels of these enzymes correlate with changes in the metabolite concentrations linked to diabetes. Our results may help developing novel strategies to prevent T2D.