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Metabolomics is emerging as a significant approach to reflect the individual's response to pathophysiological conditions. Nuclear magnetic resonance (NMR) spectroscopy has evolved as a tool to identify metabolic dysregulations in critically ill patients afflicted with conditions like acute respiratory distress syndrome (ARDS), severe acute pancreatitis (SAP), acute kidney injury (AKI), and sepsis. The spectral data from the serum sample of the study and control group are recorded using an 800 MHz NMR spectrometer and processed using NMR processing and analysis tools. Furthermore, a rigorous statistical analysis, such as univariate and multivariate tests, is performed to pinpoint significant metabolites, which are then accurately identified and quantified using NMR metabolite quantification software. Additionally, pathway analysis highlights the deranged biochemical cycles that result in the severity of illness. Through this comprehensive approach, researchers aim to gain deeper insights into the metabolic alterations associated with these critical illnesses, potentially paving the way for a better understanding of the disease and improved diagnostics and treatment strategies.

Acute respiratory distress syndrome (ARDS) is associated with high mortality rates, which are further exacerbated when accompanied by acute kidney injury (AKI). Presently, there is a lack of comprehensive studies thoroughly elucidating the metabolic dysregulation in ARDS patients with AKI leading to poor outcomes. We hypothesized that metabolomics can be a potent tool to highlight the differences in the metabolic profile unraveling unidentified pathophysiological mechanisms of ARDS patients with and without AKI. 1 H nuclear magnetic resonance spectroscopy was used to identify key metabolites in the serum samples of 75 patients. Distinct clusters of both groups were obtained as the study's primary outcome using multivariate analysis. Notable alternations in the levels of nine metabolites were identified. Pathway analysis revealed the dysregulation of five significant cycles, which resulted in various complications, such as hyperammonemia, higher energy requirements, and mitochondrial dysfunction causing oxidative stress. Identified metabolites also showed a significant correlation with clinical scores, indicating severity. This study shows the alterations in the metabolite concentration highlighting the difference in the pathophysiology of both patient groups and its association with outcome, pointing in the direction of a personalized medicine approach and holding significant promise for application in critical care settings to improve clinical outcomes.

Body fluids, cells, and tissues contain a wide variety of metabolites that consist of a mixture of various low-molecular-weight compounds, including amino acids, peptides, lipids, nucleic acids, and organic acids, which makes comprehensive analysis more difficult. Quantitative nuclear magnetic resonance (NMR) spectroscopy is a well-established analytical technique for analyzing the metabolic profiles of body fluids, cells, and tissues. It enables fast and comprehensive detection, characterization, a high level of experimental reproducibility, minimal sample preparation, and quantification of various endogenous metabolites. In recent times, NMR-based metabolomics has been appreciably utilized in diverse branches of medicine, including microbiology, toxicology, pathophysiology, pharmacology, nutritional intervention, and disease diagnosis/prognosis. In this review, the utility of NMR-based metabolomics in clinical studies is discussed. The significance of in vitro NMR-based metabolomics as an effective tool for detecting metabolites and their variations in different diseases are discussed, together with the possibility of identifying specific biomarkers that can contribute to early detection and diagnosis of disease., (© 2022 John Wiley & Sons, Ltd.)

Diagnosis and management of patients with septic shock is still a significant challenge for clinicians with its high mortality amongst hospitalized patients. Septic shock is a heterogeneous condition and is usually accompanied by various underlying disease conditions. Dissecting the specific metabolic changes induced by these underlying disease conditions through metabolomics has shown the potential to improve our understanding of the disease's relevant pathophysiological mechanisms, leading to improved treatment. This study has shown the metabolic alterations caused due to co-morbid conditions like diabetes, hypertension, CAD, and CKD in septic shock. It has also shown the distinct metabolic profiles of septic shock patients with underlying respiratory illnesses and encephalopathy. Metabolic profiling of sera obtained from 50 septic shock patients and 20 healthy controls was performed using high-resolution 1D 1 H CPMG and diffusion-edited NMR spectra. Univariate and multivariate statistical analyses were performed to identify the potential molecular biomarkers. Noted dysregulations in amino acids, carbohydrates, and lipid metabolism were observed in septic shock patients. Further stratification within the septic shock patients based on co-morbid conditions and primary diagnosis has shown their role in causing metabolic alterations. Evaluation of these compounds during treatment will help design a personalized treatment protocol for the patients, improving therapeutics.

Critical illnesses contribute to the maximum morbidity and mortality of hospitalized patients. Acute respiratory distress syndrome (ARDS) and sepsis/septic shock are the two most common acute illnesses associated with intensive care unit (ICU) admission. Once triggered, both have an identical underlying mechanism, portrayed by inflammation and endothelial dysfunction. The diagnosis of ARDS is based on clinical findings, laboratory tests, and radiological imaging. Blood cultures remain the gold standard for the diagnosis of sepsis, with the limitation of time delay and low positive yield. A combination of biomarkers has been proposed to diagnose and prognosticate these acute disorders with strengths and limitations, but still, the gold standard has been elusive to clinicians. In this review article, we illustrate the potential of metabolomics to unravel biomarkers that can be clinically utilized as a rapid prognostic and diagnostic tool associated with specific patient populations (ARDS and sepsis/septic shock) based on the available scientific data., (Copyright © 2020. Published by Elsevier B.V.)

Nuclear magnetic resonance (NMR) has emerged as an effective tool in various spheres of biomedical research, amongst which metabolomics is an important method for the study of various types of disease. Metabolomics has proved its stronghold in cancer research by the development of different NMR methods over time for the study of metabolites, thus identifying key players in the aetiology of cancer. A plethora of one-dimensional and two-dimensional NMR experiments (in solids, semi-solids and solution phases) are utilized to obtain metabolic profiles of biofluids, cell extracts and tissue biopsy samples, which can further be subjected to statistical analysis. Any alteration in the assigned metabolite peaks gives an indication of changes in metabolic pathways. These defined changes demonstrate the utility of NMR in the early diagnosis of cancer and provide further measures to combat malignancy and its progression. This review provides a snapshot of the trending NMR techniques and the statistical analysis involved in the metabolomics of diseases, with emphasis on advances in NMR methodology developed for cancer research., (Copyright © 2018 John Wiley & Sons, Ltd.)

Predisposing aetiologies in Acute Respiratory Distress Syndrome (ARDS), perpetuates to heterogeneous clinical course hampering therapeutic response. Therefore, physiological variables need to be identified by stratifying ARDS subphenotypes and endotype, to target ARDS heterogeneity. The present study is stimulated by the fact that the ARDS heterogeneity arises from diverse pathophysiological changes leading to distinct ARDS endotypes characterized by perturbed biological mechanism which can be exploited in terms of metabolic profile by metabolomics. Biological endotypes using (n = 464 patients and controls), mBALF and serum samples were identified by high - resolution NMR spectroscopy from two clinically diagnosed ARDS subtypes grouped under mild, moderate and severe ARDS as subphenotype1and pulmonary and extra - pulmonary ARDS as subphenotype2. The identified mBALF endotypes (isoleucine, leucine, valine, lysine/arginine, tyrosine, threonine) and serum endotypes (proline, glutamate, phenylalanine, valine) in both subphenotypes by statistical analysis were tested for their reproducibility and robustness. By combining metabolic endotypes with clinical based mortality score (APACHE and SOFA) added to their predictive performance as ARDS mortality predictors. Thus, a comprehensive set of mBALF endotypes representing compartmentalized lung milieu and serological endotypes representing systemic markers of ARDS subtypes were validated. The interlinked biological pathway of these disease specific endotype further elucidated their role as candidate biomarker in governing ARDS heterogeneous biology.

Gallbladder cancer (GBC) is a common form of malignancy, which has high incidence rates in the northern parts of India, South America, Japan, and in Native American populations. A few metabolomic studies have revealed changes in the composition of biofluids, tissues, and gallstones by nuclear magnetic resonance (NMR), which may prove useful in understanding GBC. In this chapter, we focus on the use of NMR in unveiling the metabolomics of GBC.

Breast cancer (BC) is one of the most common malignancies among women worldwide, which is indeed associated with metabolic reprogramming. However, BC is a very complex and heterogeneous disease, which can relate with the changes in metabolic profiles during BC progression. Hence, investigating the metabolic alterations during BC stage progression may reveal the deregulated pathways and useful metabolic signatures of BC. To demonstrate the metabolic insights, we opted 1 H NMR spectroscopy based metabolomics of blood plasma of early and late stage BC (N = 72) with age and gender matched healthy subjects (N = 50). Further, the metabolic profiles were analyzed to delineate the potential signatures of BC by performing multivariate and nonparametric statistical analysis in early and late stages of BC in comparison with healthy subjects. Sixteen metabolites levels were differentially changed (p < 0.05) in the early and late stages of BC from healthy subjects. Among them, the levels of hydroxybutyrate, lysine, glutamate, glucose, N-acetyl glycoprotein, Lactate were highly distinguished in BC stages and showed a good biomarker potential using receiver-operating curves based diagnostic models. Furthermore, the significant modulation and good diagnostic performances of glutamate, N-acetyl glycoprotein and Lactate in LBC as compared to EBC give their significance in the BC progression. In general, our observations demonstrate that these panels of metabolites may act as vital component of the metabolism of early to late stage BC progression. Our results also open new avenue towards early and late stage BC diagnosis and intervention implying metabolomics approaches., (Copyright © 2018. Published by Elsevier B.V.)

Despite advancements in ventilator technologies, lung supportive and rescue therapies, the outcome and prognostication in acute respiratory distress syndrome (ARDS) remains incremental and ambiguous. Metabolomics is a potential insightful measure to the diagnostic approaches practiced in critical disease settings. In our study patients diagnosed with mild and moderate/severe ARDS clinically governed by hypoxemic P/F ratio between 100-300 but with indistinct molecular phenotype were discriminated employing nuclear magnetic resonance (NMR) based metabolomics of mini bronchoalveolar lavage fluid (mBALF). Resulting biomarker prototype comprising six metabolites was substantiated highlighting ARDS susceptibility/recovery. Both the groups (mild and moderate/severe ARDS) showed distinct biochemical profile based on 83.3% classification by discriminant function analysis and cross validated accuracy of 91% using partial least squares discriminant analysis as major classifier. The predictive performance of narrowed down six metabolites were found analogous with chemometrics. The proposed biomarker model consisting of six metabolites proline, lysine/arginine, taurine, threonine and glutamate were found characteristic of ARDS sub-stages with aberrant metabolism observed mainly in arginine, proline metabolism, lysine synthesis and so forth correlating to diseased metabotype. Thus NMR based metabolomics has provided new insight into ARDS sub-stages and conclusively a precise biomarker model proposed, reflecting underlying metabolic dysfunction aiding prior clinical decision making.

Huntington's disease (HD) is a neurodegenerative disorder induced by aggregation of the pathological form of Huntingtin protein that has expanded polyglutamine (polyQ) repeats. In the Drosophila model, for instance, expression of transgenes with polyQ repeats induces HD-like pathologies, progressively correlating with the increasing lengths of these repeats. Previous studies on both animal models and clinical samples have revealed metabolite imbalances during HD progression. To further explore the physiological processes linked to metabolite imbalances during HD, we have investigated the 1D 1 H NMR spectroscopy-based metabolomics profile of Drosophila HD model. Using multivariate analysis (PCA and PLS-DA) of metabolites obtained from methanolic extracts of fly heads displaying retinal deformations due to polyQ overexpression, we show that the metabolite imbalance during HD is likely to affect cell energetics. Six out of the 35 metabolites analyzed, namely, nicotinamide adenine dinucleotide (NAD), lactate, pyruvate, succinate, sarcosine, and acetoin, displayed segregation with progressive severity of HD. Specifically, HD progression was seen to be associated with reduction in NAD and increase in lactate-to-pyruvate ratio. Furthermore, comparative analysis of fly HD metabolome with those of mouse HD model and HD human patients revealed comparable metabolite imbalances, suggesting altered cellular energy homeostasis. These findings thus raise the possibility of therapeutic interventions for HD via modulation of cellular energetics.

1H NMR is used to detect alterations in metabolites and their linkage to metabolic processes in a number of pathological conditions including breast cancer. Inositol 1, 4, 5 trisphosphate (IP3R) receptor is an intracellular calcium channel known to regulate metabolism and cellular bioenergetics. Its expression is up regulated in a number of cancers. However, its linkage to metabolism in disease conditions has not been evaluated. This study was designed to determine the association if any, of these metabolites with altered expression of IP3R in breast cancer. We used 1H NMR to identify metabolites in the serum of breast cancer patients (n = 27) and performed Real-time Polymerase Chain Reaction analysis for quantifying the expression of IP3R type 3 and type 2 in tissues from breast cancer patients (n = 40). Principal Component Analysis (PCA) and Partial Least Square-Discriminant Analysis (PLS-DA) clearly distinguished patients with high/low IP3R expression from healthy subjects. The present study revealed high expression of IP3R type 2 and type 3 in human breast tumor tissue compared to adjacent non-tumorous tissue. Moreover, patients with ≥ 2-fold increase in IP3R (high IP3R group) had significantly higher concentration of metabolic intermediates compared to those with < 2-fold increase in IP3R (low IP3R group). We observed an increase in lipoprotein content and the levels of metabolites like lactate, lysine and alanine and a decrease in the levels of pyruvate and glucose in serum of high IP3R group patients when compared to those in healthy subjects. Receiver operating characteristic (ROC) curve analysis was performed to show the clinical utility of metabolites. In addition to the human studies, functional relevance of IP3Rs in causing metabolic disruption was observed in MCF-7 and MDA MB-231 cells. Results from our studies bring forth the importance of metabolic (or metabolomics) profiling of serum by 1H NMR in conjunction with tissue expression studies for characterizing breast cancer patients. The results from this study provide new insights into relationship of breast cancer metabolites with IP3R., Competing Interests: The authors declare that no competing interests exist.

Objective and Design: We present in this article 1 H nuclear magnetic resonance (NMR)-based metabolic approach to screen the serum metabolic alterations in human gallbladder inflammation with chronic cholecystitis (CC)., Material/methods: Total of 71 human serum samples was divided into two groups, (n = 41, CC) and (n = 30 control). 1 H NMR metabolic profiling was carried out for investigation of metabolic alterations. Multivariate statistical analysis was applied for pattern recognition and identification of metabolites playing crucial role in gallbladder inflammation. Receiver operating curve (ROC) and pathway analysis on NMR data were also carried out to validate the findings., Results: Serum metabolites such as glutamine, low-density lipoprotein (LDL), very low-density lipoprotein (VLDL), alanine, branch chained amino acids (BCAA), histidine and tyrosine were found to be depleted whereas formate, lactate, 1,2-propanediol were found to be elevated in CC. Metabolic pathways associated with metabolite alteration have also been reported., Conclusions: NMR has been established for disease diagnosis along with identification of metabolic pattern recognition in biofluids. Gallstones cause inflammation of the gallbladder in the form of CC. Inflammation plays a major role in causation of gall bladder cancer and leads the way to malignancy. Metabolic analysis of CC may lead to early diagnosis of disease and its progression to gallbladder cancer.

Aims: Proton Nuclear Magnetic Resonance (NMR) based metabolomics analysis is extensively used to explore the metabolic profiling of biofluids. This approach was used for the analysis of metabolites in serum of patients with major types of muscular dystrophy in early phase of the disease., Material and Methods: Proton NMR spectroscopy based qualitative (assignment of metabolites) and quantitative (quantification of metabolites) analysis of metabolites in native serum of patients with Duchenne muscular dystrophy (DMD) [n=88; n represent the number], Becker muscular dystrophy (BMD) [n=40], facioscapulohumeral dystrophy (FSHD) [n=22], limb girdle muscular dystrophy (LGMD)-2B [n=35] and myotonic dystrophy (DM) [n=21] as compared to normal subjects [n=50] were performed., Key Findings: Quantity of branched chain amino acids was elevated in serum of patients with DMD, BMD, FSHD and DM-1 as compared to normal subjects. Acetate level was elevated in serum of patients with DMD, BMD, FSHD, LGMD-2B and DM-1 as compared to normal subjects. Level of glutamine was reduced in serum of patients with DMD, BMD, LGMD-2B, FSHD and elevated in DM-1 patients as compared to normal subjects. Quantity of tyrosine was increased in serum of BMD patients as compared to normal subjects. There was a reduction in the level of lysine in serum of FSHD, LGMD-2B and DM-1 patients as compared to normal subjects. Citrate level was reduced in serum of FSHD patients, but elevated in LGMD-2B patients. Lactate level was reduced in serum of LGMD-2B patients and histidine was reduced in serum of patients with FSHD as compared to normal subjects., Significance: Outcome of this study may be useful as supportive information for the existing diagnostic methods of the muscular dystrophy., (Copyright © 2016 Elsevier Inc. All rights reserved.)

Two-dimensional (2D) nuclear magnetic resonance (NMR) methods have shown to be an excellent analytical tool for the identification and characterization of statistically relevant changes in low-abundance metabolites in body fluid. The advantage of 2D NMR in terms of minimized ambiguities in peak assignment, aided in metabolite identifications and comprehensive metabolic profiling comes with the cost of increased NMR data collection time; making it inconvenient choice for routine metabolic profiling. We present here a method for the reduction in NMR data collection time of 2D (1)H-(13)C NMR spectroscopy for the purpose of quantitative metabolic profiling. Our method combines three techniques; which are nonlinear sampling (NLS), forward maximum (FM) entropy reconstruction, and J-compensated quantitative heteronuclear single quantum (HSQC) (1)H-(13)C NMR spectra. We report here that approximately 22-fold reduction in 2D NMR data collection time for the body fluid samples can be achieved by this method, without any compromise in quantitative information recovery of various low abundance metabolites. The method has been demonstrated in standard mixture solution, native, and lyophilized human urine samples. Our proposed method has potential to make quantitative metabolic profiling by 2D NMR as a routine method for various metabonomic studies.

We present a method for the qualitative and quantitative study of transient metabolic flux of phage infection at the molecular level. The method is based on statistical total correlation spectroscopy (STOCSY) and partial least squares discriminant analysis (PLS-DA) applied to nuclear magnetic resonance (NMR) metabonomic data sets. An algorithm for this type of study is developed and demonstrated. The method has been implemented on (1)H NMR data sets of growth media in planktonic cultures of Pseudomonas aeruginosa infected with bacteriophage pf1. Transient metabolic flux of various important metabolites, identified by STOCSY and PLS-DA analysis applied to the NMR data set, are estimated at various stages of growth. The opportunistic and nosocomial pathogen P. aeruginosa is one of the best-studied model organism for bacterial biofilms. Complete information regarding metabolic connectivity of this system is not possible by conventional spectroscopic approach. Our study presents temporal comparative (1)H NMR metabonomic analyses of filamentous phage pf1 infection in planktonic cultures of P. aeruginosa K strain (PAK). We exemplify here the potential of STOCSY and PLS-DA tools to gain mechanistic insight into subtle changes and to determine the transient flux associated with metabolites following metabolic perturbations resulting from phage infection. Our study has given new avenues in correlating existing postgenomic data with current metabonomic results in P. aeruginosa biofilms research.