Application of nuclear magnetic resonance spectroscopy for the detection of metabolic disorders in patients with moderate kidney insufficiency

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

Background

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

Methods

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

Results

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

Conclusion

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

The toxicity of nanoparticles and their interaction with cells: an in vitro metabolomic perspective

Nowadays, nanomaterials (NMs) are widely present in daily life due to their significant benefits, as demonstrated by their application in many fields such as biomedicine, engineering, food, cosmetics, sensing, and energy. However, the increasing production of NMs multiplies the chances of their release into the surrounding environment, making human exposure to NMs inevitable. Currently, nanotoxicology is a crucial field, which focuses on studying the toxicity of NMs. The toxicity or effects of nanoparticles (NPs) on the environment and humans can be preliminary assessed in vitro using cell models. However, the conventional cytotoxicity assays, such as the MTT assay, have some drawbacks including the possibility of interference with the studied NPs. Therefore, it is necessary to employ more advanced techniques that provide high throughput analysis and avoid interferences. In this case, metabolomics is one of the most powerful bioanalytical strategies to assess the toxicity of different materials. By measuring the metabolic change upon the introduction of a stimulus, this technique can reveal the molecular information of the toxicity induced by NPs. This provides the opportunity to design novel and efficient nanodrugs and minimizes the risks of NPs used in industry and other fields. Initially, this review summarizes the ways that NPs and cells interact and the NP parameters that play a role in this interaction, and then the assessment of these interactions using conventional assays and the challenges encountered are discussed. Subsequently, in the main part, we introduce the recent studies employing metabolomics for the assessment of these interactions in vitro.

Identifying disease progression in chronic kidney disease using proton magnetic resonance spectroscopy

Chronic kidney disease (CKD) affects approximately 10% of the world population, higher still in some developing countries, and can cause irreversible kidney damage eventually leading to kidney failure requiring dialysis or kidney transplantation. However, not all patients with CKD will progress to this stage, and it is difficult to distinguish between progressors and non-progressors at the time of diagnosis. Current clinical practice involves monitoring estimated glomerular filtration rate and proteinuria to assess CKD trajectory over time; however, there remains a need for novel, validated methods that differentiate CKD progressors and non-progressors. Nuclear magnetic resonance techniques, including magnetic resonance spectroscopy and magnetic resonance imaging, have the potential to improve our understanding of CKD progression. Herein, we review the application of magnetic resonance spectroscopy both in preclinical and clinical settings to improve the diagnosis and surveillance of patients with CKD.

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

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

Comparative metabolomic study of high-flux hemodialysis and high volume online hemodiafiltration in the removal of uremic toxins using 1H NMR spectroscopy

Uremic toxins (UTs) accumulate in the circulation of patients with chronic kidney disease (CKD). High volume hemodiafiltration (HDF) improves clearance of low and medium molecular weight UTs compared to HD. The present study is a post-hoc analysis comparing the metabolomic profile in serum from patients under high flux HD (hf-HD) and HDF in HDFIT, a multicentric randomized controlled trial (RCTs). Per protocol, serum samples were collected pre- and post- dialysis treatments at randomization (baseline) and at the end of the follow up (6 months) and stored in a biorepository. Random (pre- and post-dialysis) samples from nine patients in study arm were selected at baseline and at the end of the follow up. To compare the samples, 26 possibly matching metabolites were identified by a t-test among the four groups using ¹H nuclear magnetic resonance (NMR). To evaluate the comparison between the modalities is a single treatment session, the clearance rates (CRs) of each metabolite were calculated based on pre-dialysis and post-dialysis samples. In addition, to evaluate to effect of UT removal during the trial follow up period, the pre-dialysis metabolite concentrations at the baseline and at 6 months were compared among the two arms of the study. There was no significant difference between in the single session CRs of metabolites when hf-HD and HDF were compared. On the other hand, the comparison between baseline and 6-month (long-term evolution) led to the identification of 16 metabolites that differentiated the hf-HD and the HDF evolutions. Most of these 16 metabolites are involved in several important metabolic pathways, such as metabolism of phenylalanine and biosynthesis of phenylalanine, tyrosine, and tryptophan, which are related to UTs and cardiovascular disease development. Although no difference was observed between hf-HD and HDF samples before and after a single session, concentrations of CKD-relevant metabolites and associated pathologies were stable in the HDF samples, but not in the hf-HD samples, over the six-month period, suggesting that HDF enhances long-term stability.

Urinary metabolic characterization with nephrotoxicity for residents under cadmium exposure

Cadmium is a well-known hazardous pollutant that mainly comes from dietary, tobacco and occupational exposure, posing threat to kidney. However, there is still a lack of systematic study on metabolic pathways and urinary biomarkers related to its nephrotoxicity under cadmium exposure for both females and males. In this study, a mass spectrometry-based metabolomics investigation of a cohort of 144 volunteers was conducted to explore sex-specific metabolic alteration and to screen biomarkers related to cadmium-induced nephrotoxicity. When the concentration of urinary cadmium increased, creatine pathway, amino acid metabolism especially the tryptophan metabolism, aminoacyl-tRNA biosynthesis, and purine metabolism were primarily influenced regardless of the gender. Also, the most specific biomarkers linked with nephrotoxicity based on the statistical analysis were detected including creatine, creatinine, l-tryptophan, adenine and uric acid. The study outcome might provide information to reflect the body burden and help improve health policy for risk assessment.

Serum-Urine Matched Metabolomics for Predicting Progression of Henoch-Schonlein Purpura Nephritis

Henoch-Schonlein purpura nephritis (HSPN) is a common glomerulonephritis secondary to Henoch-Schonlein purpura (HSP) that affects systemic metabolism. Currently, there is a rarity of biomarkers to predict the progression of HSPN. This work sought to screen metabolic markers to predict the progression of HSPN via serum-urine matched metabolomics. A total of 90 HSPN patients were enrolled, including 46 HSPN (+) patients with severe kidney damage (persistent proteinuria >0.3 g/day) and 44 HSPN (–) patients without obvious symptoms (proteinuria < 0.3 g/day). Untargeted metabolomics was determined by liquid chromatography-quadrupole time-of-flight mass spectrometry (LC-Q/TOF-MS). A total of 38 and 50 differential metabolites were, respectively, identified in serum and urine from the comparison between HSPN (+) and HSPN (–) patients. Altered metabolic pathways in HSPN (+) mainly included glycerophospholipid metabolism, pyruvate metabolism, and citrate cycle. A panel of choline and cis-vaccenic acid gave areas under the curve of 92.69% in serum and 72.43% in urine for differential diagnosis between HSPN (+) and HSPN (–). In addition, the two metabolites showed a significant association with clinical indices of HSPN. These results suggest that serum-urine matched metabolomics comprehensively characterized the metabolic differences between HSPN (+) and HSPN (–), and choline and cis-vaccenic acid could serve as biomarkers to predict HSPN progression.

Application of NMR metabolomics to search for human disease biomarkers in blood

Recently, nuclear magnetic resonance spectroscopy (NMR)-based metabolomics analysis and multivariate statistical techniques have been incorporated into a multidisciplinary approach to profile changes in small molecules associated with the onset and progression of human diseases. The purpose of these efforts is to identify unique metabolite biomarkers in a specific human disease so as to (1) accurately predict and diagnose diseases, including separating distinct disease stages; (2) provide insights into underlying pathways in the pathogenesis and progression of the malady and (3) aid in disease treatment and evaluate the efficacy of drugs. In this review we discuss recent developments in the application of NMR-based metabolomics in searching disease biomarkers in human blood samples in the last 5 years.

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.

Plasma metabolic profiling analysis of Strychnos nux-vomica Linn. and Tripterygium wilfordii Hook F-induced renal toxicity using metabolomics coupled with UPLC/Q-TOF-MS

Both Strychnos nux-vomica Linn. (SNV) and Tripterygium wilfordii Hook F (TwHF) have received extensive attention due to their excellent clinical efficacies. However, clinical applications of SNV and TwHF have been limited by their narrow therapeutic windows and severe kidney toxicities. In this paper, based on ultra-performance liquid chromatography quadrupole time-of-flight mass spectrometry (UPLC/Q-TOF-MS), endogenous metabolites after administration of SNV and TwHF extracts were detected, and biomarkers were screened successfully. Additionally, the levels of Cr and BUN in serum and pathological findings of kidneys were detected and observed. Finally, both biochemical and pathological tests of the SNV group and TwHF group indicated that kidney damage had occurred. After comparison with the normal saline group, 15 nephrotoxic biomarkers were selected from the SNV group, and 17 nephrotoxic biomarkers were selected from the TwHF group. The experimental results showed that there are some differences in the mechanisms of nephrotoxicity induced by SNV and TwHF, which are significant for revealing the mechanisms of renal injury of different medicines.

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.