Metabolites related to eGFR: Evaluation of candidate molecules for GFR estimation using untargeted metabolomics

Evidence of a causal and modifiable relationship between kidney function and circulating trimethylamine N-oxide

The host-microbiota co-metabolite trimethylamine N-oxide (TMAO) is linked to increased cardiovascular risk but how its circulating levels are regulated remains unclear. We applied "explainable" machine learning, univariate, multivariate and mediation analyses of fasting plasma TMAO concentration and a multitude of phenotypes in 1,741 adult Europeans of the MetaCardis study. Here we show that next to age, kidney function is the primary variable predicting circulating TMAO, with microbiota composition and diet playing minor, albeit significant, roles. Mediation analysis suggests a causal relationship between TMAO and kidney function that we corroborate in preclinical models where TMAO exposure increases kidney scarring. Consistent with our findings, patients receiving glucose-lowering drugs with reno-protective properties have significantly lower circulating TMAO when compared to propensity-score matched control individuals. Our analyses uncover a bidirectional relationship between kidney function and TMAO that can potentially be modified by reno-protective anti-diabetic drugs and suggest a clinically actionable intervention for decreasing TMAO-associated excess cardiovascular risk.

Novel Metabolites to Improve Glomerular Filtration Rate Estimation

INTRODUCTION

The glomerular filtration rate (GFR) is crucial for chronic kidney disease (CKD) diagnosis and therapy. Various studies have sought to recognize ideal endogenous markers to improve the estimated GFR for clinical practice. To screen out potential novel metabolites related to GFR (mGFR) measurement in CKD patients from the Chinese population, we identified more biomarkers for improving GFR estimation.

METHODS

Fifty-three CKD participants were recruited from the Third Affiliated Hospital of Sun Yat-sen University in 2020. For each participant, mGFR was evaluated by utilizing the plasma clearance of iohexol and collecting serum samples for untargeted metabolomics analyses by ultrahigh-performance liquid chromatography-tandem mass spectroscopy. All participants were divided into four groups according to mGFR. The metabolite peak area data were uploaded to MetaboAnalyst 5.0 for one-way analysis of variance, principal component analysis, and partial least squares-discriminant analysis and confirmed the metabolites whose levels increased or decreased with mGFR and variable importance in projection (VIP) values >1. Metabolites were ranked by correlation with the original values of mGFR, and metabolites with a correlation coefficient >0.8 and VIP >2 were identified.

RESULTS

We screened out 198 metabolites that increased or decreased with mGFR decline. After ranking by correlation with mGFR, the top 50 metabolites were confirmed. Further studies confirmed the 10 most highly correlated metabolites.

CONCLUSION

We screened out the metabolites that increased or decreased with mGFR decline in CKD patients from the Chinese population, and 10 of them were highly correlated. They are potential novel metabolites to improve GFR estimation.

Changes in glomerular filtration rate and metabolomic differences in severely ill coronavirus disease survivors 3 months after discharge

To explore the recovery of renal function in severely ill coronavirus disease (COVID-19) survivors and determine the plasma metabolomic profile of patients with different renal outcomes 3 months after discharge, we included 89 severe COVID-19 survivors who had been discharged from Wuhan Union Hospital for 3 months. All patients had no underlying kidney disease before admission. At patient recruitment, renal function assessment, laboratory examination, chest computed tomography (CT) were performed. Liquid chromatography-mass spectrometry was used to detect metabolites in the plasma. We analyzed the longitudinally change in the estimated glomerular filtration rate (eGFR) based on serum creatinine and cystatin-c levels using the CKD-EPI equation and explored the metabolomic differences in patients with different eGFR change patterns from hospitalization to 3 months after discharge. Lung CT showed good recovery; however, the median eGFR significantly decreased at the 3-month follow-up. Among the 89 severely ill COVID-19 patients, 69 (77.5%) showed abnormal eGFR (<90 mL/min per 1.73 m²) at 3 months after discharge. Age (odds ratio [OR] = 1.26, 95% confidence interval [CI] = 1.08-1.47, p = 0.003), body mass index (OR = 1.97, 95% CI = 1.20-3.22, p = 0.007), and cystatin-c level (OR = 1.22, 95% CI = 1.07-1.39, p = 0.003) at discharge were independent risk factors for post-discharge abnormal eGFR. Plasma metabolomics at the 3-months follow-up revealed that β-pseudouridine, uridine, and 2-(dimethylamino) guanosine levels gradually increased with an abnormal degree of eGFR. Moreover, the kynurenine pathway in tryptophan metabolism, vitamin B6 metabolism, cysteine and methionine metabolism, and arginine biosynthesis were also perturbed in survivors with abnormal eGFR.

Metabolome-wide association study of estimated glomerular filtration rates in Hispanics

Circulating metabolites are by-products of endogenous metabolism or exogenous sources and may inform disease states. Our study aimed to identify the source of variability in the association of metabolites with estimated glomerular filtration rate (eGFR) in Hispanics/Latinos with low chronic kidney disease prevalence by testing the association of 640 metabolites in 3,906 participants of the Hispanic Community Health Study/Study of Latinos. Metabolites were quantified in fasting serum through non-targeted mass spectrometry analysis. eGFR was regressed on inverse normally transformed metabolites in models accounting for study design and covariates. To identify the source of variation on eGFR associations, we tested the interaction of metabolites with lifestyle and clinical risk factors, and results were integrated with genotypes to identify metabolite genetic regulation. The mean age was 46 years, 43% were men, 22% were current smokers, 47% had a Caribbean Hispanic background, 19% had diabetes and the mean cohort eGFR was 96.4 ml/min/1.73 m2. We identified 404 eGFR-metabolite associations (False Discovery Rate under 0.05). Of these, 69 were previously reported, and 79 were novel associations with eGFR replicated in one or more published studies. There were significant interactions with lifestyle and clinical risk factors, with larger differences in eGFR-metabolite associations within strata of age, urine albumin to creatinine ratio, diabetes and Hispanic/Latino background. Several newly identified metabolites were genetically regulated, and variants were located at genomic regions previously associated with eGFR. Thus, our results suggest complex mechanisms contribute to the association of eGFR with metabolites and provide new insights into these associations.

Non-invasive metabolic biomarkers for early diagnosis of diabetic nephropathy: Meta-analysis of profiling metabolomics studies

AIM

Diabetic nephropathy (DN) is one of the worst complications of diabetes. Despite a growing number of DN metabolite profiling studies, most studies are suffering from inconsistency in their findings. The main goal of this meta-analysis was to reach to a consensus panel of significantly dysregulated metabolites as potential biomarkers in DN.

DATA SYNTHESIS

To identify the significant dysregulated metabolites, meta-analysis was performed by "vote-counting rank" and "robust rank aggregation" strategies. Bioinformatics analyses were performed to identify the most affected genes and pathways. Among 44 selected studies consisting of 98 metabolite profiles, 17 metabolites (9 up-regulated and 8 down-regulated metabolites), were identified as significant ones by both the meta-analysis strategies (p-value<0.05 and OR>2 or <0.5) and selected as DN metabolite meta-signature. Furthermore, enrichment analyses confirmed the involvement of various effective biological pathways in DN pathogenesis, such as urea cycle, TCA cycle, glycolysis, and amino acid metabolisms. Finally, by performing a meta-analysis over existing time-course studies in DN, the results indicated that lactic acid, hippuric acid, allantoin (in urine), and glutamine (in blood), are the topmost non-invasive early diagnostic biomarkers.

CONCLUSION

The identified metabolites are potentially involved in diabetic nephropathy pathogenesis and could be considered as biomarkers or drug targets in the disease.

PROSPERO REGISTRATION NUMBER

CRD42020197697.

Quantification of serum C-mannosyl tryptophan by novel assay to evaluate renal function and vascular complications in patients with type 2 diabetes

C-Mannosyl tryptophan (CMW) is a unique glycosylated amino acid, and a candidate novel biomarker of renal function. In type 2 diabetes (T2D), a combination of metabolites including CMW has recently been the focus of novel biomarkers for the evaluation of renal function and prediction of its decline. However, previous quantification methods for serum CMW have several limitations. We recently established a novel assay for quantifying serum CMW. Serum CMW from 99 Japanese patients with T2D was quantified by this assay using hydrophilic interaction liquid chromatography. The serum CMW levels were cross-sectionally characterized in relation to clinical features, including renal function and vascular complications. Serum CMW level was more strongly correlated with serum creatinine and cystatin C levels and with eGFR than with albumin urea level. The ROC curve to detect eGFR < 60 ml/min/1.73 m2 revealed that the cutoff serum CMW level was 337.5 nM (AUC 0.883). Serum CMW levels were higher in patients with a history of macroangiopathy than in those without history. They correlated with ankle-brachial pressure index, whereas cystatin C did not. Serum CMW levels quantified by the novel assay could be useful in evaluation of glomerular filtration of renal function and peripheral arterial disease in T2D.

Differential metabolomic signatures of declining renal function in Types 1 and 2 diabetes

BACKGROUND

Chronic kidney disease (CKD) shows different clinical features in Types1 (T1D) and 2 diabetes (T2D). Metabolomics have recently provided useful contribution to the identification of biomarkers of CKD progression in either form of the disease. However, no studies have so far compared plasma metabolomics between T1D and T2D in order to identify differential signatures of progression of estimated glomerular filtration rate (eGFR) decline.

METHODS

We used two large cohorts of T1D (from Finland) and T2D (from Italy) patients followed up to 7 and 3 years, respectively. In both groups, progression was defined as the top quartile of yearly decline in eGFR. Pooled data from the two groups were analysed by univariate and bivariate random forest (RF), and confirmed by bivariate partial least squares (PLS) analysis, the response variables being type of diabetes and eGFR progression.

RESULTS

In progressors, yearly eGFR loss was significantly larger in T2D [-5.3 (3.0), median (interquartile range)mL/min/1.73 m2/year] than T1D [-3.7 (3.1) mL/min/1.73 m2/year ; P = 0.018]. Out of several hundreds, bivariate RF extracted 22 metabolites associated with diabetes type (all higher in T1D than T2D except for 5-methylthioadenosine, pyruvate and β-hydroxypyruvate) and 13 molecules associated with eGFR progression (all higher in progressors than non-progressors except for sphyngomyelin). Three of the selected metabolites (histidylphenylalanine, leucylphenylalanine, tryptophylasparagine) showed a significant interaction between disease type and progression. Only eight metabolites were common to both bivariate RF and PLS.

CONCLUSIONS

Identification of metabolomic signatures of CKD progression is partially dependent on the statistical model. Dual analysis identified molecules specifically associated with progressive renal impairment in both T1D and T2D.

Urinary proteome and metabolome in dogs (Canis lupus familiaris): The effect of chronic kidney disease

Chronic kidney disease (CKD) is a progressive and irreversible disease. Although urine is an ideal biological sample for proteomics and metabolomics studies, sensitive and specific biomarkers are currently lacking in dogs. This study characterised dog urine proteome and metabolome aiming to identify and possibly quantify putative biomarkers of CKD in dogs. Twenty-two healthy dogs and 28 dogs with spontaneous CKD were selected and urine samples were collected. Urinary proteome was separated by SDS-PAGE and analysed by mass spectrometry, while urinary metabolome was analysed in protein-depleted samples by 1D ¹H NMR spectra. The most abundant proteins in urine samples from healthy dogs were uromodulin, albumin and, in entire male dogs, arginine esterase. In urine samples from CKD dogs, the concentrations of uromodulin and albumin were significantly lower and higher, respectively, than in healthy dogs. In addition, these samples were characterised by a more complex protein pattern indicating mixed glomerular (protein bands ≥65 kDa) and tubular (protein bands <65 kDa) proteinuria. Urine spectra acquired by NMR allowed the identification of 86 metabolites in healthy dogs, belonging to 49 different pathways mainly involved in amino acid metabolism, purine and aminoacyl-tRNA biosynthesis or tricarboxylic acid cycle. Seventeen metabolites showed significantly different concentrations when comparing healthy and CKD dogs. In particular, carnosine, trigonelline, and cis-aconitate, might be suggested as putative biomarkers of CKD in dogs. SIGNIFICANCE: Urine is an ideal biological sample, however few proteomics and metabolomics studies investigated this fluid in dogs and in the context of CKD (chronic kidney disease). In this research, applying a multi-omics approach, new insights were gained regarding the molecular changes triggered by this disease in canine urinary proteome and metabolome. In particular, the involvement of the tubular component was highlighted, suggesting uromodulin, trigonelline and carnosine as possible biomarkers of CKD in dogs.

Application of Metabolomics to Renal and Cardiometabolic Diseases

Metabolomics has been increasingly applied to study renal and related cardiometabolic diseases, including diabetes and cardiovascular diseases. These studies span cross-sectional studies correlating metabolites with specific phenotypes, longitudinal studies to identify metabolite predictors of future disease, and physiologic/interventional studies to probe underlying causal relationships. This chapter provides a description of how metabolomic profiling is being used in these contexts, with an emphasis on study design considerations as a practical guide for investigators who are new to this area. Research in kidney diseases is underlined to illustrate key principles. The chapter concludes by discussing the future potential of metabolomics in the study of renal and cardiometabolic diseases.

An update of urine and blood metabolomics in chronic kidney disease

Chronic kidney disease is considered as a serious obstacle in global health, with increasing incidence and prevalence. In spite of numerous attempts by using recent omics technologies, specially metabolomics, for understanding pathophysiology, molecular mechanism and identification reliable consensus biomarkers for diagnosis and prognosis of this complex disease, the current biomarkers are still insensitive and many questions about its pathomechanism are still to be unanswered. This review is focused on recent findings about urine and serum/plasma metabolite biomarkers and changes in the pathways that occurs in the disease conditions. The urine and blood metabolome content in the normal and disease state is investigated based on the current metabolomics studies and well known metabolite candidate biomarkers for chronic kidney disease are discussed.

Metabolomics biomarkers and the risk of overall mortality and ESRD in CKD: Results from the Progredir Cohort

INTRODUCTION

Studies on metabolomics and CKD have primarily addressed CKD incidence defined as a decline on eGFR or appearance of albuminuria in the general population, with very few evaluating hard outcomes. In the present study, we investigated the association between metabolites and mortality and ESRD in a CKD cohort.

SETTING AND METHODS

Data on 454 participants of the Progredir Cohort Study, Sao Paulo, Brazil were used. Metabolomics was performed by GC-MS (Agilent MassHunter) and metabolites were identified using Agilent Fiehn GC/MS and NIST libraries. After excluding metabolites present in <50% of participants, 293 metabolites were analyzed. An FDR q value <0.05 criteria was applied in Cox models on the composite outcome (mortality or incident renal replacement therapy) adjusted for batch effect, resulting in 34 metabolites associated with the outcome. Multivariable-adjusted Cox models were then built for the composite outcome, death, and ESRD incident events. Competing risk analysis was also performed for ESRD.

RESULTS

Mean age was 68±12y, mean eGFR-CKDEPI was 38.4±14.6 ml/min/1.73m2 and 57% were diabetic. After adjustments (GC-MS batch, sex, age, DM and eGFR), 18 metabolites remained significantly associated with the composite outcome. Nine metabolites were independently associated with death: D-malic acid (HR 1.84, 95%CI 1.32-2.56, p = 0.0003), acetohydroxamic acid (HR 1.90, 95%CI 1.30-2.78, p = 0.0008), butanoic acid (HR 1.59, 95%CI 1.17-2.15, p = 0.003), and docosahexaenoic acid (HR 0.58, 95%CI 0.39-0.88, p = 0.009), among the top associations. Lactose (SHR 1.49, 95%CI 1.04-2.12, p = 0.03), 2-O-glycerol-α-D-galactopyranoside (SHR 1.76, 95%CI 1.06-2.92, p = 0.03), and tyrosine (SHR 0.52, 95%CI 0.31-0.88, p = 0.02) were associated to ESRD risk, while D-threitol, mannitol and myo-inositol presented strong borderline associations.

CONCLUSION

Our results identify specific metabolites related to hard outcomes in a CKD population. These findings point to the need of further exploration of these metabolites as biomarkers in CKD and the understanding of the underlying biological mechanisms related to the observed associations.