Exploring blood alterations in chronic kidney disease and haemodialysis using metabolomics

A clustering approach to improve our understanding of the genetic and phenotypic complexity of chronic kidney disease

Chronic kidney disease (CKD) is a complex disorder that causes a gradual loss of kidney function, affecting approximately 9.1% of the world's population. Here, we use a soft-clustering algorithm to deconstruct its genetic heterogeneity. First, we selected 322 CKD-associated independent genetic variants from published genome-wide association studies (GWAS) and added association results for 229 traits from the GWAS catalog. We then applied nonnegative matrix factorization (NMF) to discover overlapping clusters of related traits and variants. We computed cluster-specific polygenic scores and validated each cluster with a phenome-wide association study (PheWAS) on the BioMe biobank (n = 31,701). NMF identified nine clusters that reflect different aspects of CKD, with the top-weighted traits signifying areas such as kidney function, type 2 diabetes (T2D), and body weight. For most clusters, the top-weighted traits were confirmed in the PheWAS analysis. Results were found to be more significant in the cross-ancestry analysis, although significant ancestry-specific associations were also identified. While all alleles were associated with a decreased kidney function, associations with CKD-related diseases (e.g., T2D) were found only for a smaller subset of variants and differed across genetic ancestry groups. Our findings leverage genetics to gain insights into the underlying biology of CKD and investigate population-specific associations.

A clustering approach to improve our understanding of the genetic and phenotypic complexity of chronic kidney disease

Chronic kidney disease (CKD) is a complex disorder that causes a gradual loss of kidney function, affecting approximately 9.1% of the world's population. Here, we use a soft-clustering algorithm to deconstruct its genetic heterogeneity. First, we selected 322 CKD-associated independent genetic variants from published genome-wide association studies (GWAS) and added association results for 229 traits from the GWAS catalog. We then applied nonnegative matrix factorization (NMF) to discover overlapping clusters of related traits and variants. We computed cluster-specific polygenic scores and validated each cluster with a phenome-wide association study (PheWAS) on the BioMe biobank (n=31,701). NMF identified nine clusters that reflect different aspects of CKD, with the top-weighted traits signifying areas such as kidney function, type 2 diabetes (T2D), and body weight. For most clusters, the top-weighted traits were confirmed in the PheWAS analysis. Results were found to be more significant in the cross-ancestry analysis, although significant ancestry-specific associations were also identified. While all alleles were associated with a decreased kidney function, associations with CKD-related diseases (e.g., T2D) were found only for a smaller subset of variants and differed across genetic ancestry groups. Our findings leverage genetics to gain insights into the underlying biology of CKD and investigate population-specific associations.

A clustering approach to improve our understanding of the genetic and phenotypic complexity of chronic kidney disease

Chronic kidney disease (CKD) is a complex disorder that causes a gradual loss of kidney function, affecting approximately 9.1% of the world's population. Here, we use a soft-clustering algorithm to deconstruct its genetic heterogeneity. First, we selected 322 CKD-associated independent genetic variants from published genome-wide association studies (GWAS) and added association results for 229 traits from the GWAS catalog. We then applied nonnegative matrix factorization (NMF) to discover overlapping clusters of related traits and variants. We computed cluster-specific polygenic scores and validated each cluster with a phenome-wide association study (PheWAS) on the BioMe biobank (n=31,701). NMF identified nine clusters that reflect different aspects of CKD, with the top-weighted traits signifying areas such as kidney function, type 2 diabetes (T2D), and body weight. For most clusters, the top-weighted traits were confirmed in the PheWAS analysis. Results were found to be more significant in the cross-ancestry analysis, although significant ancestry-specific associations were also identified. While all alleles were associated with a decreased kidney function, associations with CKD-related diseases (e.g., T2D) were found only for a smaller subset of variants and differed across genetic ancestry groups. Our findings leverage genetics to gain insights into the underlying biology of CKD and investigate population-specific associations.

Nutritional Strategies to Prevent Muscle Loss and Sarcopenia in Chronic Kidney Disease: What Do We Currently Know?

Loss of muscle mass is an extremely frequent complication in patients with chronic kidney disease (CKD). The etiology of muscle loss in CKD is multifactorial and may depend on kidney disease itself, dialysis, the typical chronic low-grade inflammation present in patients with chronic kidney disease, but also metabolic acidosis, insulin resistance, vitamin D deficiency, hormonal imbalances, amino acid loss during dialysis, and reduced dietary intake. All these conditions together increase protein degradation, decrease protein synthesis, and lead to negative protein balance. Aging further exacerbates sarcopenia in CKD patients. Nutritional therapy, such as protein restriction, aims to manage uremic toxins and slow down the progression of CKD. Low-protein diets (LPDs) and very low-protein diets (VLPDs) supplemented with amino acids or ketoacids are commonly prescribed. Energy intake is crucial, with a higher intake associated with maintaining a neutral or positive nitrogen balance. Adequate nutritional and dietary support are fundamental in preventing nutritional inadequacies and, consequently, muscle wasting, which can occur in CKD patients. This review explores the causes of muscle loss in CKD and how it can be influenced by nutritional strategies aimed at improving muscle mass and muscle strength.

Assessment of alteration in antiviral plasma concentration across dialysis days: computational and analytical study

Aim: Protein-bound uremic toxins (PBUTs) may displace drugs from the plasma proteins and render them more liable to clearance. This study aims to investigate the possible interplay between PBUTs and directly acting antivirals (DAAs). Methods: PBUT plasma protein binding was compared to those of paritaprevir (PRT), ombitasivir (OMB) and ritonavir (RTV) in silico to assess the possible competitive displacement. The three drugs were LC-MS/MS determined in seven patients across dialysis and non-dialysis days and results were compared. Results & conclusion: Results showed that the PBUT exhibited a lower binding than DAA reducing the liability of their competitive displacement. This was echoed by an unaltered plasma concentration across dialysis days. Results may indicate that PBUT accumulation may have limited effect on disposition of DAA.

Serum Biomarkers for Chronic Renal Failure Screening and Mechanistic Understanding: A Global LC-MS-Based Metabolomics Research

Chronic kidney disease, including renal failure (RF), is a global public health problem. The clinical diagnosis mainly depends on the change of estimated glomerular filtration rate, which usually lags behind disease progression and likely has limited clinical utility for the early detection of this health problem. Now, we employed Q-Exactive HFX Orbitrap LC-MS/MS based metabolomics to reveal the metabolic profile and potential biomarkers for RF screening. 27 RF patients and 27 healthy controls were included as the testing groups, and comparative analysis of results using different techniques, such as multivariate pattern recognition and univariate statistical analysis, was applied to screen and elucidate the differential metabolites. The dot plots and receiver operating characteristics curves of identified different metabolites were established to discover the potential biomarkers of RF. The results exhibited a clear separation between the two groups, and a total of 216 different metabolites corresponding to 13 metabolic pathways were discovered to be associated with RF; and 44 metabolites showed high levels of sensitivity and specificity under curve values of close to 1, thus might be used as serum biomarkers for RF. In summary, for the first time, our untargeted metabolomics study revealed the distinct metabolic profile of RF, and 44 metabolites with high sensitivity and specificity were discovered, 3 of which have been reported and were consistent with our observations. The other metabolites were first reported by us. Our findings might provide a feasible diagnostic tool for identifying populations at risk for RF through detection of serum metabolites.

A Fluorescence-Based Competitive Antibody Binding Assay for Kynurenine, a Potential Biomarker of Kidney Transplant Failure

Kynurenine is a tryptophan metabolite linked to several inflammatory processes including transplant failure, a significant challenge in transplant medicine. The detection of small molecules such as kynurenine, however, is often complex and time consuming. Herein, we report the successful synthesis of a fluorescently labelled kynurenine derivative, showing proper fluorescence and anti-kynurenine antibody binding behavior in a magnetic bead immunoassay (MIA). The fluorescent kynurenine–rhodamine B conjugate shows a KD-value of 5.9 µM as well as IC50 values of 4.0 µM in PBS and 10.2 µM in saliva. We thus introduce a rapid test for kynurenine as a potential biomarker for kidney transplant failure.

Kidney and plasma metabolomics provide insights into the molecular mechanisms of urate nephropathy in a mouse model of hyperuricemia

Hyperuricemia (HUA) is closely associated with kidney damage and kidney diseases in humans; however, the underlying mechanisms of HUA-induced kidney diseases remain unknown. In the present study, we examined the kidney and plasma metabolic profiles in a HUA mouse model constructed by knocking out (Ko) the urate oxidase (Uox) gene. The Uox-Ko mice were characterized by an increase in uric acid, glycine, 3'-adenosine monophosphate, citrate, N-acetyl-l-glutamate, l-kynurenine, 5-hydroxyindoleacetate, xanthurenic acid, cortisol, and (-)-prostaglandin e2 together with a decrease of inosine in the kidneys. These altered metabolites confirmed disturbances of purine metabolism, amino acid biosynthesis, tryptophan metabolism, and neuroactive ligand-receptor interaction in Uox-Ko mice. Betaine and biotin were related to kidney function and identified as the potential plasma metabolic biomarker for predicting urate nephropathy (UN). Taken together, these results revealed the underlying pathogenic mechanisms of UN. Investigating these pathways might provide novel targets for the therapeutic intervention of UN and can potentially lead to new treatment strategies.

Metabolic and Metabo-Clinical Signatures of Type 2 Diabetes, Obesity, Retinopathy, and Dyslipidemia

Macro- and microvascular complications of type 2 diabetes (T2D), obesity, and dyslipidemia share common metabolic pathways. In this study, using a total of 1,300 metabolites from 996 Qatari adults (57% with T2D) and 1,159 metabolites from an independent cohort of 2,618 individuals from the Qatar BioBank (11% with T2D), we identified 373 metabolites associated with T2D, obesity, retinopathy, dyslipidemia, and lipoprotein levels, 161 of which were novel. Novel metabolites included phospholipids, sphingolipids, lysolipids, fatty acids, dipeptides, and metabolites of the urea cycle and xanthine, steroid, and glutathione metabolism. The identified metabolites enrich pathways of oxidative stress, lipotoxicity, glucotoxicity, and proteolysis. Second, we identified 15 patterns we defined as "metabo-clinical signatures." These are clusters of patients with T2D who group together based on metabolite levels and reveal the same clustering in two or more clinical variables (obesity, LDL, HDL, triglycerides, and retinopathy). These signatures revealed metabolic pathways associated with different clinical patterns and identified patients with extreme (very high/low) clinical variables associated with extreme metabolite levels in specific pathways. Among our novel findings are the role of N-acetylmethionine in retinopathy in conjunction with dyslipidemia and the possible roles of N-acetylvaline and pyroglutamine in association with high cholesterol levels and kidney function.

Longitudinal metabolomics of human plasma reveals prognostic markers of COVID-19 disease severity

There is an urgent need to identify which COVID-19 patients will develop life-threatening illness so that medical resources can be optimally allocated and rapid treatment can be administered early in the disease course, when clinical management is most effective. To aid in the prognostic classification of disease severity, we perform untargeted metabolomics on plasma from 339 patients, with samples collected at six longitudinal time points. Using the temporal metabolic profiles and machine learning, we build a predictive model of disease severity. We discover that a panel of metabolites measured at the time of study entry successfully determines disease severity. Through analysis of longitudinal samples, we confirm that most of these markers are directly related to disease progression and that their levels return to baseline upon disease recovery. Finally, we validate that these metabolites are also altered in a hamster model of COVID-19.

Gaining Insights Into Metabolic Networks Using Chemometrics and Bioinformatics: Chronic Kidney Disease as a Clinical Model

Because of its ability to generate biological hypotheses, metabolomics offers an innovative and promising approach in many fields, including clinical research. However, collecting specimens in this setting can be difficult to standardize, especially when groups of patients with different degrees of disease severity are considered. In addition, despite major technological advances, it remains challenging to measure all the compounds defining the metabolic network of a biological system. In this context, the characterization of samples based on several analytical setups is now recognized as an efficient strategy to improve the coverage of metabolic complexity. For this purpose, chemometrics proposes efficient methods to reduce the dimensionality of these complex datasets spread over several matrices, allowing the integration of different sources or structures of metabolic information. Bioinformatics databases and query tools designed to describe and explore metabolic network models offer extremely useful solutions for the contextualization of potential biomarker subsets, enabling mechanistic hypotheses to be considered rather than simple associations. In this study, network principal component analysis was used to investigate samples collected from three cohorts of patients including multiple stages of chronic kidney disease. Metabolic profiles were measured using a combination of four analytical setups involving different separation modes in liquid chromatography coupled to high resolution mass spectrometry. Based on the chemometric model, specific patterns of metabolites, such as N-acetyl amino acids, could be associated with the different subgroups of patients. Further investigation of the metabolic signatures carried out using genome-scale network modeling confirmed both tryptophan metabolism and nucleotide interconversion as relevant pathways potentially associated with disease severity. Metabolic modules composed of chemically adjacent or close compounds of biological relevance were further investigated using carbon transfer reaction paths. Overall, the proposed integrative data analysis strategy allowed deeper insights into the metabolic routes associated with different groups of patients to be gained. Because of their complementary role in the knowledge discovery process, the association of chemometrics and bioinformatics in a common workflow is therefore shown as an efficient methodology to gain meaningful insights in a clinical context.

Longitudinal Metabolomics of Human Plasma Reveals Robust Prognostic Markers of COVID-19 Disease Severity

There is an urgent need to identify which COVID-19 patients will develop life-threatening illness so that scarce medical resources can be optimally allocated and rapid treatment can be administered early in the disease course, when clinical management is most effective. To aid in the prognostic classification of disease severity, we performed untargeted metabolomics profiling of 341 patients with plasma samples collected at six longitudinal time points. Using the temporal metabolic profiles and machine learning, we then built a predictive model of disease severity. We determined that the levels of 25 metabolites measured at the time of hospital admission successfully predict future disease severity. Through analysis of longitudinal samples, we confirmed that these prognostic markers are directly related to disease progression and that their levels are restored to baseline upon disease recovery. Finally, we validated that these metabolites are also altered in a hamster model of COVID-19. Our results indicate that metabolic changes associated with COVID-19 severity can be effectively used to stratify patients and inform resource allocation during the pandemic.

Low Protein Diets and Plant-Based Low Protein Diets: Do They Meet Protein Requirements of Patients with Chronic Kidney Disease?

A low protein diet (LPD) has historically been used to delay uremic symptoms and decrease nitrogen (N)-derived catabolic products in patients with chronic kidney disease (CKD). In recent years it has become evident that nutritional intervention is a necessary approach to prevent wasting and reduce CKD complications and disease progression. While a 0.6 g/kg, high biological value protein-based LPD has been used for years, recent observational studies suggest that plant-derived LPDs are a better approach to nutritional treatment of CKD. However, plant proteins are less anabolic than animal proteins and amino acids contained in plant proteins may be in part oxidized; thus, they may not completely be used for protein synthesis. In this review, we evaluate the role of LPDs and plant-based LPDs on maintaining skeletal muscle mass in patients with CKD and examine different nutritional approaches for improving the anabolic properties of plant proteins when used in protein-restricted diets.