1H NMR-based metabolomics studies of urine reveal differences between type 1 diabetic patients with high and low HbAc1 values

Slice through the water-Exploring the fundamental challenge of water suppression for benchtop NMR systems

Benchtop NMR provides improved accessibility in terms of cost, space, and technical expertise. In turn, this encourages new users into the field of NMR spectroscopy. Unfortunately, many interesting samples in education and research, from beer to whole blood, contain significant amounts of water that require suppression in 1H NMR in order to recover sample information. However, due to the significant reduction in chemical shift dispersion in benchtop NMR systems, the sample signals are much closer to the water resonance compared to those in a corresponding high-field NMR spectrum. Therefore, simply translating solvent suppression experiments intended for high-field NMR instruments to benchtop NMR systems without careful consideration can be problematic. In this study, the effectiveness of several popular water suppression schemes was evaluated for benchtop NMR applications. Emphasis is placed on pulse sequences with no, or few, adjustable parameters making them easy to implement. These fall into two main categories: (1) those based on Pre-SAT including Pre-SAT, PURGE, NOESY-PR, and g-NOESY-PR and (2) those based on binomial inversion including JRS and W5-WATERGATE. Among these schemes, solvent suppression sequences based on Pre-SAT offer a general approach for easy solvent suppression for samples with higher analyte concentrations (sucrose standard and Redbull™). However, for human urine, binomial-like sequences were required. In summary, it is demonstrated that highly efficient water suppression approaches can be implemented on benchtop NMR systems in a simple manner, despite the limited spectral dispersion, further illustrating the potential for widespread implementation of these approaches in education and research.

Metabolite Alterations in Autoimmune Diseases: A Systematic Review of Metabolomics Studies

Autoimmune diseases, characterized by the immune system's loss of self-tolerance, lack definitive diagnostic tests, necessitating the search for reliable biomarkers. This systematic review aims to identify common metabolite changes across multiple autoimmune diseases. Following PRISMA guidelines, we conducted a systematic literature review by searching MEDLINE, ScienceDirect, Google Scholar, PubMed, and Scopus (Elsevier) using keywords "Metabolomics", "Autoimmune diseases", and "Metabolic changes". Articles published in English up to March 2023 were included without a specific start date filter. Among 257 studies searched, 88 full-text articles met the inclusion criteria. The included articles were categorized based on analyzed biological fluids: 33 on serum, 21 on plasma, 15 on feces, 7 on urine, and 12 on other biological fluids. Each study presented different metabolites with indications of up-regulation or down-regulation when available. The current study's findings suggest that amino acid metabolism may serve as a diagnostic biomarker for autoimmune diseases, particularly in systemic lupus erythematosus (SLE), multiple sclerosis (MS), and Crohn's disease (CD). While other metabolic alterations were reported, it implies that autoimmune disorders trigger multi-metabolite changes rather than singular alterations. These shifts could be consequential outcomes of autoimmune disorders, representing a more complex interplay. Further studies are needed to validate the metabolomics findings associated with autoimmune diseases.

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.

Photoacoustic Spectroscopy and Hyperglycemia in Experimental Type 1 Diabetes

According to the World Health Organization, diabetes was the seventh leading cause of death in 2016. Long-term diabetes complications are associated with hyperglycemia. It is difficult to predict the beginning and evolution of those complications. The goal of the study was to evaluate the relationship between glycemia and blood spectroscopic variables in an experimental model of type 1 diabetes (streptozotocin model). Blood samples were taken weekly (10 weeks) from the tail of male Wistar rats with or without diabetes. Blood optical absorption spectra were obtained by means of photoacoustic spectroscopy. It was possible to estimate the time-course of blood characteristic peak ratios. The area under the curve of those peaks correlated with hyperglycemia. The evolution of the optical absorption at 450 nm, related to cytochrome p450, was obtained by using the phase-resolved method. The area under the curve of p450 correlated also with hyperglycemia. It is concluded that photoacoustic spectroscopy is a reliable technology to detect the effects of hyperglycemia on blood with possible applications in the study of long-term diabetes complications.

The Effect of Blood Contained in the Samples on the Metabolomic Profile of Mouse Brain Tissue: A Study by NMR Spectroscopy

(1) Recently, metabolic profiling of the tissue in the native state or extracts of its metabolites has become increasingly important in the field of metabolomics. An important factor, in this case, is the presence of blood in a tissue sample, which can potentially lead to a change in the concentration of tissue metabolites and, as a result, distortion of experimental data and their interpretation. (2) In this paper, the metabolomic profiling based on NMR spectroscopy was performed to determine the effect of blood contained in the studied samples of brain tissue on their metabolomic profile. We used 13 male laboratory CD-1^® IGS mice for this study. The animals were divided into two groups. The first group of animals (n = 7) was subjected to the perfusion procedure, and the second group of animals (n = 6) was not perfused. The brain tissues of the animals were homogenized, and the metabolite fraction was extracted with a water/methanol/chloroform solution. Samples were studied by high-frequency ¹H-NMR spectroscopy with subsequent statistical data analysis. The group comparison was performed with the use of the Student's test. We identified 36 metabolites in the brain tissue with the use of NMR spectroscopy. (3) For the major set of studied metabolites, no significant differences were found in the brain tissue metabolite concentrations in the native state and after the blood removal procedure. (4) Thus, it was shown that the presence of blood does not have a significant effect on the metabolomic profile of the brain in animals without pathologies.

Can the FUT2 Non-secretor Phenotype Associated With Gut Microbiota Increase the Children Susceptibility for Type 1 Diabetes? A Mini Review

The global toll of type 1 diabetes (T1D) has steadily increased over the last decades. It is now widely acknowledged that T1D pathophysiology is more complex than expected. Indeed, a multifaceted interplay between genetic, metabolic, inflammatory and environmental factors exists that leads to heterogeneous clinical manifestations across individuals. Children with non-secretor phenotype and those affected by T1D share low abundance of bifidobacteria, low content of short-chain fatty acids, intestinal phosphatase alkaline and a high incidence of inflammatory bowel diseases. In this context, host-gut microbiota dyad may represent a relevant contributor to T1D development and progression due to its crucial role in shaping host immunity and susceptibility to autoimmune conditions. The FUT2 gene is responsible for the composition and functional properties of glycans in mucosal tissues and bodily secretions, including human milk. FUT2 polymorphisms may profoundly influence gut microbiota composition and host susceptibility to viral infections and chronic inflammatory disease. In this minireview, the possible interplay between mothers' phenotype, host FUT2 genetic background and gut microbiota composition will be discussed in perspective of the T1D onset. The study of FUT2-gut microbiota interaction may add a new piece on the puzzling T1D etiology and unveil novel targets of intervention to contrast T1D development and progression. Dietary interventions, including the intake of α-(1, 2)-fucosyl oligosaccharides in formula milk and the use of specific prebiotics and probiotics, could be hypothesized.

Metabolomics profiles associated with HbA1c levels in patients with type 2 diabetes

Glycated hemoglobin (HbA1c) is an indicator of the average blood glucose concentration. Failing to control HbA1c levels can accelerate the development of complications in patients with diabetes. Although metabolite profiles associated with HbA1c level in diabetes patients have been characterized using different platforms, more studies using high-throughput technology will be helpful to identify additional metabolites related to diabetes. Type 2 diabetes (T2D) patients were divided into two groups based on the HbA1c level: normal (HbA1c ≤6%) and high (HbA1c ≥9%) in both discovery and replication sets. A targeted metabolomics approach was used to quantify serum metabolites and multivariate logistic regression was used to identify significant differences between groups. The concentrations of 22 metabolites differed significantly between the two groups in the discovery set. In the replication set, the levels of 21 metabolites, including 16 metabolites identified in the discovery set, differed between groups. Among these, concentrations of eleven amino acids and one phosphatidylcholine (PC), lysoPC a C16:1, were higher and four metabolites, including three PCs (PC ae C36:1, PC aa C26:0, PC aa C34:2) and hexose, were lower in the group with normal HbA1c group than in the group with high HbA1c. Metabolites with high concentrations in the normal HbA1c group, such as glycine, valine, and PCs, may contribute to reducing HbA1c levels in patients with T2D. The metabolite signatures identified in this study provide insight into the mechanisms underlying changes in HbA1c levels in T2D.

Hair Metabolomics in Animal Studies and Clinical Settings

Metabolomics is a powerful tool used to understand comprehensive changes in the metabolic response and to study the phenotype of an organism by instrumental analysis. It most commonly involves mass spectrometry followed by data mining and metabolite assignment. For the last few decades, hair has been used as a valuable analytical sample to investigate retrospective xenobiotic exposure as it provides a wider window of detection than other biological samples such as saliva, plasma, and urine. Hair contains functional metabolomes such as amino acids and lipids. Moreover, segmental analysis of hair based on its growth rate can provide information on metabolic changes over time. Therefore, it has great potential as a metabolomics sample to monitor chronic diseases, including drug addiction or abnormal conditions. In the current review, the latest applications of hair metabolomics in animal studies and clinical settings are highlighted. For this purpose, we review and discuss the characteristics of hair as a metabolomics sample, the analytical techniques employed in hair metabolomics and the consequence of hair metabolome alterations in recent studies. Through this, the value of hair as an alternative biological sample in metabolomics is highlighted.

NMR-Based Metabolomic Profiling of Urine: Evaluation for Application in Prostate Cancer Detection

Detection of prostate cancer (PCa) and distinguishing indolent versus aggressive forms of the disease is a critical clinical challenge. The current clinical test is circulating prostate-specific antigen levels, which faces particular challenges in cancer diagnosis in the range of 4 to 10 ng/mL. Thus, a concerted effort toward building a noninvasive biomarker panel has developed. In this report, the hypothesis that nuclear magnetic resonance (NMR)-derived metabolomic profiles measured in the urine of biopsy-negative versus biopsy-positive individuals would nominate a selection of potential biomarker signals was investigated. 1H NMR spectra of urine samples from 317 individuals (111 biopsy-negative, 206 biopsy-positive) were analyzed. A double cross-validation partial least squares-discriminant analysis modeling technique was utilized to nominate signals capable of distinguishing the two classes. It was observed that after variable selection protocols were applied, a subset of 29 variables produced an area under the curve (AUC) value of 0.94 after logistic regression analysis, whereas a “master list” of 18 variables produced a receiver operating characteristic ROC) AUC of 0.80. As proof of principle, this study demonstrates the utility of NMR-based metabolomic profiling of urine biospecimens in the nomination of PCa-specific biomarker signals and suggests that further investigation is certainly warranted.

Discovering significantly different metabolites between Han and Uygur two racial groups using urinary metabolomics in Xinjiang, China

The main object of the study was to discover the associated significantly different metabolites between Han and Uygur, two main racial groups in Xinjiang, China with urinary metabolomics. Urine samples from 96 Han and 96 Uygur were analyzed using gas chromatography coupled to mass spectrometry (GCMS). Multivariate analysis was used to investigate the effect of race, age and gender on the urinary metabolomic profiles. Totally eight metabolites are identified contributed to the discrimination between Han and Uygur, including phenylacetylglutamine, myoinositol, d-galactose, ribonolactone, octadecanoic acid, galactitol, threonic acid and succinic acid. The metabolic pathways of them are mainly involved in carbohydrate, TCA cycle, fatty acid and mammalian gut microbial-related metabolism. Importantly, three metabolites, being used as biomarkers in clinic, are also differentially expressed in urine samples of two races. It suggests that the race effect should be critically considered prior to make diagnostic result in multi-race coexisted areas to decrease the false positive rate caused by above biomarkers. Moreover, the results show that the age-period and the gender also affect the urinary metabolomics profiles, but with different levels compared to race. We hope that the work can provide some help for developing novel diagnostic tests, understanding the mechanism of disease, designing clinical trials and refining precision medicine in multi-race coexisted areas.

High-Throughput Metabolomics by 1D NMR

Metabolomics deals with the whole ensemble of metabolites (the metabolome). As one of the -omic sciences, it relates to biology, physiology, pathology and medicine; but metabolites are chemical entities, small organic molecules or inorganic ions. Therefore, their proper identification and quantitation in complex biological matrices requires a solid chemical ground. With respect to for example, DNA, metabolites are much more prone to oxidation or enzymatic degradation: we can reconstruct large parts of a mammoth's genome from a small specimen, but we are unable to do the same with its metabolome, which was probably largely degraded a few hours after the animal's death. Thus, we need standard operating procedures, good chemical skills in sample preparation for storage and subsequent analysis, accurate analytical procedures, a broad knowledge of chemometrics and advanced statistical tools, and a good knowledge of at least one of the two metabolomic techniques, MS or NMR. All these skills are traditionally cultivated by chemists. Here we focus on metabolomics from the chemical standpoint and restrict ourselves to NMR. From the analytical point of view, NMR has pros and cons but does provide a peculiar holistic perspective that may speak for its future adoption as a population-wide health screening technique.

Metabolomics and Lipidomics Study of Mouse Models of Type 1 Diabetes Highlights Divergent Metabolism in Purine and Tryptophan Metabolism Prior to Disease Onset

With the increase in incidence of type 1 diabetes (T1DM), there is an urgent need to understand the early molecular and metabolic alterations that accompany the autoimmune disease. This is not least because in murine models early intervention can prevent the development of disease. We have applied a liquid chromatography (LC-) and gas chromatography (GC-) mass spectrometry (MS) metabolomics and lipidomics analysis of blood plasma and pancreas tissue to follow the progression of disease in three models related to autoimmune diabetes: the nonobese diabetic (NOD) mouse, susceptible to the development of autoimmune diabetes, and the NOD-E (transgenic NOD mice that express the I-E heterodimer of the major histocompatibility complex II) and NOD-severe combined immunodeficiency (SCID) mouse strains, two models protected from the development of diabetes. All three analyses highlighted the metabolic differences between the NOD-SCID mouse and the other two strains, regardless of diabetic status indicating that NOD-SCID mice are poor controls for metabolic changes in NOD mice. By comparing NOD and NOD-E mice, we show the development of T1DM in NOD mice is associated with changes in lipid, purine, and tryptophan metabolism, including an increase in kynurenic acid and a decrease in lysophospholipids, metabolites previously associated with inflammation.

Detection of Volatile Organic Compounds in a Drop of Urine by Ultrasonic Nebulization Extraction Proton Transfer Reaction Mass Spectrometry

Detection of volatile organic compounds (VOCs) in human urine has potential application value in screening for disease and toxin exposure. However, the current technologies are too slow to detect the concentration of VOCs in fresh urine. In this study, we developed a novel ultrasonic nebulization extraction proton transfer reaction mass spectrometry (UNE-PTR-MS) technology. The urinary VOCs can be rapidly extracted to gaseous VOCs using the UNE system and then delivered using a carrier gas to the PTR-MS instrument for rapid detection. The carrier gas flow and sample size were optimized to 100 mL/min and 100 μL, respectively. The limits of detection (LODs) and response time of the UNE-PTR-MS were evaluated by detecting three VOCs that are common in human urine: methanol, acetaldehyde, and acetone. The LODs determined for methanol (4.47 μg/L), acetaldehyde (1.98 μg/L), and acetone (3.47 μg/L) are 2-3 orders of magnitude lower than the mean concentrations of that in healthy human urine. The response time of the UNE-PTR-MS is 34 s and only 0.66 mL of urine is required for a full scan. The repeatability of this UNE-PTR-MS was evaluated, and the relative standard deviations of 5 independent determinations were between 4.62% and 5.21%. Lastly, the UNE-PTR-MS was applied for detection of methanol, acetaldehyde, and acetone in real human urine to test matrix effects, yielding relative recoveries of between 88.39% and 94.54%. These results indicate the UNE-PTR-MS can be used for the rapid detection of VOCs in a drop of urine and has practical potential for diagnosing disease or toxin exposure.

Diagnostic Applications of Nuclear Magnetic Resonance-Based Urinary Metabolomics

Metabolomics is a rapidly growing field with potential applications in various disciplines. In particular, metabolomics has received special attention in the discovery of biomarkers and diagnostics. This is largely due to the fact that metabolomics provides critical information related to the downstream products of many cellular and metabolic processes which could provide a snapshot of the health/disease status of a particular tissue or organ. Many of these cellular products eventually find their way to urine; hence, analysis of urine via metabolomics has the potential to yield useful diagnostic and prognostic information. Although there are a number of analytical platforms that can be used for this purpose, this review article will focus on nuclear magnetic resonance-based metabolomics. Furthermore, although there have been many studies addressing different diseases and metabolic disorders, the focus of this review article will be in the following specific applications: urinary tract infection, kidney transplant rejection, diabetes, some types of cancer, and inborn errors of metabolism. A number of methodological considerations that need to be taken into account for the development of a clinically useful optimal test are discussed briefly.

Recommendations and Standardization of Biomarker Quantification Using NMR-Based Metabolomics with Particular Focus on Urinary Analysis

NMR-based metabolomics has shown considerable promise in disease diagnosis and biomarker discovery because it allows one to nondestructively identify and quantify large numbers of novel metabolite biomarkers in both biofluids and tissues. Precise metabolite quantification is a prerequisite to move any chemical biomarker or biomarker panel from the lab to the clinic. Among the biofluids commonly used for disease diagnosis and prognosis, urine has several advantages. It is abundant, sterile, and easily obtained, needs little sample preparation, and does not require invasive medical procedures for collection. Furthermore, urine captures and concentrates many "unwanted" or "undesirable" compounds throughout the body, providing a rich source of potentially useful disease biomarkers; however, incredible variation in urine chemical concentrations makes analysis of urine and identification of useful urinary biomarkers by NMR challenging. We discuss a number of the most significant issues regarding NMR-based urinary metabolomics with specific emphasis on metabolite quantification for disease biomarker applications and propose data collection and instrumental recommendations regarding NMR pulse sequences, acceptable acquisition parameter ranges, relaxation effects on quantitation, proper handling of instrumental differences, sample preparation, and biomarker assessment.

The strengths and weaknesses of NMR spectroscopy and mass spectrometry with particular focus on metabolomics research

Mass spectrometry (MS) and nuclear magnetic resonance (NMR) have evolved as the most common techniques in metabolomics studies, and each brings its own advantages and limitations. Unlike MS spectrometry, NMR spectroscopy is quantitative and does not require extra steps for sample preparation, such as separation or derivatization. Although the sensitivity of NMR spectroscopy has increased enormously and improvements continue to emerge steadily, this remains a weak point for NMR compared with MS. MS-based metabolomics provides an excellent approach that can offer a combined sensitivity and selectivity platform for metabolomics research. Moreover, different MS approaches such as different ionization techniques and mass analyzer technology can be used in order to increase the number of metabolites that can be detected. In this chapter, the advantages, limitations, strengths, and weaknesses of NMR and MS as tools applicable to metabolomics research are highlighted.

Changes in urine headspace composition as an effect of strenuous walking

The present investigation uses proton transfer reaction mass spectrometry (PTR-MS) combined with multivariate and univariate statistical analyses to study potential biomarkers for altered metabolism in urine due to strenuous walking. Urine samples, in concurrence with breath and blood samples, were taken from 51 participants (23 controls, 11 type-1 diabetes, 17 type-2 diabetes) during the Dutch endurance walking event, the International Four Days Marches. Multivariate analysis allowed for discrimination of before and after exercise for all three groups (control, type-1 and type-2 diabetes) and on three out of 4 days. The analysis highlighted 12 molecular ions contributing to this discrimination. Of these, acetic acid in urine is identified as a significant marker for exercise effects induced by walking; an increase is observed as an effect of walking. Analysis of acetone concentration with univariate tools resulted in different information when compared to breath as a function of exercise, revealing an interesting effect of time over the 4 days. In breath, acetone provides an immediate snapshot of metabolism, whereas urinary acetone will result from longer term diffusion processes, providing a time averaged view of metabolism. The potential to use PTR-MS measurements of urine to monitor exercise effects is exhibited, and may be utilized to monitor subjects in mass participation exercise events.

Standardizing the experimental conditions for using urine in NMR-based metabolomic studies with a particular focus on diagnostic studies: a review

The metabolic composition of human biofluids can provide important diagnostic and prognostic information. Among the biofluids most commonly analyzed in metabolomic studies, urine appears to be particularly useful. It is abundant, readily available, easily stored and can be collected by simple, noninvasive techniques. Moreover, given its chemical complexity, urine is particularly rich in potential disease biomarkers. This makes it an ideal biofluid for detecting or monitoring disease processes. Among the metabolomic tools available for urine analysis, NMR spectroscopy has proven to be particularly well-suited, because the technique is highly reproducible and requires minimal sample handling. As it permits the identification and quantification of a wide range of compounds, independent of their chemical properties, NMR spectroscopy has been frequently used to detect or discover disease fingerprints and biomarkers in urine. Although protocols for NMR data acquisition and processing have been standardized, no consensus on protocols for urine sample selection, collection, storage and preparation in NMR-based metabolomic studies have been developed. This lack of consensus may be leading to spurious biomarkers being reported and may account for a general lack of reproducibility between laboratories. Here, we review a large number of published studies on NMR-based urine metabolic profiling with the aim of identifying key variables that may affect the results of metabolomics studies. From this survey, we identify a number of issues that require either standardization or careful accounting in experimental design and provide some recommendations for urine collection, sample preparation and data acquisition.

Clinical metabolomics and nutrition: the new frontier in neonatology and pediatrics

In the pediatric clinic, nutritional research is focusing more and more on preventing the development of long-term diseases as well as supporting the repair processes important in the therapy of already fully developed diseases. Most children who are hospitalized or affected by chronic diseases could benefit from specific and careful attention to nutrition. Indeed, the state of nutrition modulates all body functions, including the different metabolic processes which, all together, have a profound effect on the development of the health and future of all individuals. Inappropriate food, even in the first periods of life, can accelerate the development of chronic metabolic diseases, especially in the pediatric age. To gain further insights into metabolic cycles and how they are connected with diet and health, nutrition and metabolomics interact to develop and apply modern technologies for metabolic assessment. In particular, nutritionists are evaluating the metabolomic approach to establish the single nutritional phenotypes, that is, the way in which diet interacts with individuals' metabolisms. This strategy offers the possibility of providing a complete definition of the individual's nutritional and health status, predict the risk of disease, and create metabolomic databases supporting the development of “personalized nutrition,” in which diet is attuned to the nutritional needs of individual patients.

Biomarkers in Alzheimer's disease analysis by mass spectrometry-based proteomics

Alzheimer’s disease (AD) is a common chronic and destructive disease. The early diagnosis of AD is difficult, thus the need for clinically applicable biomarkers development is growing rapidly. There are many methods to biomarker discovery and identification. In this review, we aim to summarize Mass spectrometry (MS)-based proteomics studies on AD and discuss thoroughly the methods to identify candidate biomarkers in cerebrospinal fluid (CSF) and blood. This review will also discuss the potential research areas on biomarkers.