Clinical Metabolomics Research Articles

Clinical metabolomics characteristics of diabetic kidney disease: A meta-analysis of 1875 cases with diabetic kidney disease and 4503 controls.

Diabetic Kidney Disease (DKD), one of the major complications of diabetes, is also a major cause of end-stage renal disease. Metabolomics can provide a unique metabolic profile of the disease and thus predict or diagnose the development of the disease. Therefore, this study summarises a more comprehensive set of clinical biomarkers related to DKD to identify functional metabolites significantly associated with the development of DKD and reveal their driving mechanisms for DKD. We searched PubMed, Embase, the Cochrane Library and Web of Science databases through October 2022. A meta-analysis was conducted on untargeted or targeted metabolomics research data based on the strategy of standardized mean differences and the process of ratio of means as the effect size, respectively. We compared the changes in metabolite levels between the DKD patients and the controls and explored the source of heterogeneity through subgroup analyses, sensitivity analysis and meta-regression analysis. The 34 clinical-based metabolomics studies clarified the differential metabolites between DKD and controls, containing 4503 control subjects and 1875 patients with DKD. The results showed that a total of 60 common differential metabolites were found in both meta-analyses, of which 5 metabolites (p<0.05) were identified as essential metabolites. Compared with the control group, metabolites glycine, aconitic acid, glycolic acid and uracil decreased significantly in DKD patients; cysteine was significantly higher. This indicates that amino acid metabolism, lipid metabolism and pyrimidine metabolism in DKD patients are disordered. We have identified 5 metabolites and metabolic pathways related to DKD which can serve as biomarkers or targets for disease prevention and drug therapy.

Antibiotic-induced intestinal microbiota depletion can attenuate the acute kidney injury to chronic kidney disease transition via NADPH oxidase 2 and trimethylamine-N-oxide inhibition

Intestinal microbiota and their metabolites affect systemic inflammation and kidney disease outcomes. Here, we investigated the key metabolites associated with the acute kidney injury (AKI)-to chronic kidney disease (CKD) transition and the effect of antibiotic-induced microbiota depletion (AIMD) on this transition. In 61 patients with AKI, 59 plasma metabolites were assessed to determine the risk of AKI-to-CKD transition. An AKI-to-CKD transition murine model was established four weeks after unilateral ischemia-reperfusion injury (IRI) to determine the effects of AIMD on the gut microbiome, metabolites, and pathological responses related to CKD transition. Human proximal tubular epithelial cells were challenged with CKD transition-related metabolites and inhibitory effects of NADPH oxidase 2 (NOX2) signals were tested. Based on clinical metabolomics, plasma trimethylamine N-oxide (TMAO) was associated with a significant increased risk for AKI-to-CKD transition [adjusted odds ratio 4.389 (95% confidence interval 1.106–17.416)]. In vivo, AIMD inhibited a unilateral IRI-induced increase in TMAO, along with a decreased in apoptosis, inflammation, and fibrosis. The expression of NOX2 and oxidative stress decreased after AIMD. In vitro, TMAO induced fibrosis with NOX2 activation and oxidative stress. NOX2 inhibition successfully attenuated apoptosis, inflammation, and fibrosis with suppression of G2/M arrest. NOX2 inhibition (in vivo) showed improvement in pathological changes with a decrease in oxidative stress without changes in TMAO levels. Thus, TMAO is a key metabolite associated with the AKI-to-CKD transition and NOX2 activation was identified as a key regulator of TMAO-related AKI-to-CKD transition both in vivo and in vitro.

MetDIT: Transforming and Analyzing Clinical Metabolomics Data with Convolutional Neural Networks.

Clinical metabolomics is growing as an essential tool for precision medicine. However, classical machine learning algorithms struggle to comprehensively encode and analyze the metabolomics data due to their high dimensionality and complex intercorrelations. This article introduces a new method called MetDIT, designed to analyze intricate metabolomics data effectively using deep convolutional neural networks (CNN). MetDIT comprises two components: TransOmics and NetOmics. Since CNN models have difficulty in processing one-dimensional (1D) sequence data efficiently, we developed TransOmics, a framework that transforms sequence data into two-dimensional (2D) images while maintaining a one-to-one correspondence between the sequences and images. NetOmics, the second component, leverages a CNN architecture to extract more discriminative representations from the transformed samples. To overcome the overfitting due to the small sample size and class imbalance, we introduced a feature augmentation module (FAM) and a loss function to improve the model performance. Furthermore, we systematically optimized the model backbone and image resolution to balance the model parameters and computational costs. To demonstrate the performance of the proposed MetDIT, we conducted extensive experiments using three different clinical metabolomics data sets and achieved better classification performance than classical machine learning methods used in metabolomics, including Random Forest, SVM, XGBoost, and LightGBM. The source code is available at the GitHub repository at https://github.com/Li-OmicsLab/MetDIT, and the WebApp can be found at http://metdit.bioinformatics.vip/.

Critical Factors in Sample Collection and Preparation for Clinical Metabolomics of Underexplored Biological Specimens.

This review article compiles critical pre-analytical factors for sample collection and extraction of eight uncommon or underexplored biological specimens (human breast milk, ocular fluids, sebum, seminal plasma, sweat, hair, saliva, and cerebrospinal fluid) under the perspective of clinical metabolomics. These samples are interesting for metabolomics studies as they reflect the status of living organisms and can be applied for diagnostic purposes and biomarker discovery. Pre-collection and collection procedures are critical, requiring protocols to be standardized to avoid contamination and bias. Such procedures must consider cleaning the collection area, sample stimulation, diet, and food and drug intake, among other factors that impact the lack of homogeneity of the sample group. Precipitation of proteins and removal of salts and cell debris are the most used sample preparation procedures. This review intends to provide a global view of the practical aspects that most impact results, serving as a starting point for the designing of metabolomic experiments.

Data processing solutions to render metabolomics more quantitative: case studies in food and clinical metabolomics using Metabox 2.0.

In classic semiquantitative metabolomics, metabolite intensities are affected by biological factors and other unwanted variations. A systematic evaluation of the data processing methods is crucial to identify adequate processing procedures for a given experimental setup. Current comparative studies are mostly focused on peak area data but not on absolute concentrations. In this study, we evaluated data processing methods to produce outputs that were most similar to the corresponding absolute quantified data. We examined the data distribution characteristics, fold difference patterns between 2 metabolites, and sample variance. We used 2 metabolomic datasets from a retail milk study and a lupus nephritis cohort as test cases. When studying the impact of data normalization, transformation, scaling, and combinations of these methods, we found that the cross-contribution compensating multiple standard normalization (ccmn) method, followed by square root data transformation, was most appropriate for a well-controlled study such as the milk study dataset. Regarding the lupus nephritis cohort study, only ccmn normalization could slightly improve the data quality of the noisy cohort. Since the assessment accounted for the resemblance between processed data and the corresponding absolute quantified data, our results denote a helpful guideline for processing metabolomic datasets within a similar context (food and clinical metabolomics). Finally, we introduce Metabox 2.0, which enables thorough analysis of metabolomic data, including data processing, biomarker analysis, integrative analysis, and data interpretation. It was successfully used to process and analyze the data in this study. An online web version is available at http://metsysbio.com/metabox.

Evaluating protocols for reproducible targeted metabolomics by NMR.

Metabolomics aims to study the downstream effects of variables like diet, environment, or disease on a given biological system. However, inconsistencies in sample preparation, data acquisition/processing protocols lead to reproducibility and accuracy concerns. A systematic study was conducted to assess how sample preparation methods and data analysis platforms affect metabolite susceptibility. A targeted panel of 25 metabolites was evaluated in 69 clinical metabolomics samples prepared following three different protocols: intact, ultrafiltration, and protein precipitation. The resulting metabolic profiles were characterized by 1D 1H nuclear magnetic resonance (NMR) spectroscopy and analyzed with Chenomx v8.3 and SMolESY software packages. Greater than 90% of the metabolites were extracted more efficiently using protein precipitation than filtration, which aligns with previously reported results. Additionally, analysis of data processing software suggests that metabolite concentrations were overestimated by Chenomx batch-fitting, which only appears reliable for determining relative fold changes rather than absolute quantification. However, an assisted-fit method provided sufficient guidance to achieve accurate results while avoiding a time-consuming fully manual-fitting approach. By combining our results with previous studies, we can now provide a list of 5 common metabolites [2-hydroxybutyrate (2-HB), choline, dimethylamine (DMA), glutamate, lactate] with a high degree of variability in reported fold changes and standard deviations that need careful consideration before being annotated as potential biomarkers. Our results show that sample preparation and data processing package critically impact clinical metabolomics study success. There is a clear need for an increased degree of standardization and harmonization of methods across the metabolomics community to ensure reliable outcomes.

Hierarchical superstructure aerogels for in situ biofluid metabolomics.

High-throughput biofluid metabolomics analysis for screening life-threatening diseases is urgently needed. However, the high salt content of biofluid samples, which introduces severe interference, can greatly limit the analysis throughput. Here, a new 3-D interconnected hierarchical superstructure, namely a "plasmonic gold-on-silica (Au/SiO2) double-layered aerogel", integrating distinctive features of an upper plasmonic gold aerogel with a lower inert silica aerogel was successfully developed to achieve in situ separation and storage of inorganic salts in the silica aerogel, parallel enrichment of metabolites on the surface of the functionalized gold aerogel, and direct desorption/ionization of enriched metabolites by the photo-excited gold aerogel for rapid, sensitive, and comprehensive metabolomics analysis of human serum/urine samples. By integrating all these unique advantages into the hierarchical aerogel, multifunctional properties were introduced in the SALDI substrate to enable its effective utilization in clinical metabolomics for the discovery of reliable metabolic biomarkers to achieve unambiguous differentiation of early and advanced-stage lung cancer patients from healthy individuals. This study provides insight into the design and application of superstructured nanomaterials for in situ separation, storage, and photoexcitation of multi-components in complex biofluid samples for sensitive analysis.

Clinical metabolomics of Zicuiyin in treatment of diabetic kidney disease

This study aimed to analyze the therapeutic effect of Zicuiyin on diabetic kidney disease(DKD) and explore the possible targets of this formula. Eighteen DKD patients treated in the endocrine department or nephrology department of Second Affilia-ted Hospital of Tianjin University of Traditional Chinese Medicine from January to December in 2019 were enrolled and assigned into a test group(n=10) and a control group(n=8). Both groups received routine chemical medicine treatment. In addition, the test group was treated with Zicuiyin and the control group with Huangkui Capsules for 8 weeks. The clinical trial was approved by the Ethics Committee of Second Affiliated Hospital of Tianjin University of Traditional Chinese Medicine, with the ethical approval No. 2017-023-01, and all the patients signed the informed consent form. The results showed that the 8-week treatment with Zicuiyin lowered the level of glycosylated hemoglobin(HbA1c) and recovered the 24 h urinary protein(24hUP), 24 h urinary microalbumin(24hmAlb), urine albumin-to-creatinine ratio(UACR), and estimated glomerular filtration rate(eGFR) of the patients with 24hUP&lt;3.5 g. According to the different levels in 24hUP, all the patients were divided into two subgroups(subgroup A with 24hUP&lt;3.5 g and subgroup B with 24hUP≥3.5 g). The ultra-high performance liquid chromatography-quadrupole-time-of-flight tandem mass spectrometry(UPLC-Q-TOF-MS/MS)-based non-targeted metabolomics analysis was conducted on the baseline serum samples from diffe-rent subgroups of patients. Nineteen biomarker candidates were identified to distinguish the metabolic differences between the two subgroups, and their correlations with clinical indicators were analyzed. Zicuiyin lowered the levels of phenylalanine, pseudouridine, and adenosine [fold change(FC)&lt;0.5, P&lt;0.05] in subgroup A. The results indicated that Zicuiyin was more effective on the DKD patients with low urinary protein levels, and its targets were involved in phenylalanine metabolism and nucleoside metabolism.

High-abundance peaks and peak clusters associate with pharmaceutical polymers and excipients in urinary untargeted clinical metabolomics data: exploration of their origin and possible impact on label-free quantification.

Pharmaceutical polymers and excipients represent interesting but often overlooked chemical classes in clinical exposure and bioanalytical research. These chemicals may cause hypersensitivity reactions, they can be useful to confirm exposure to pharmaceuticals, and they may pose bioanalytical challenges, including ion suppression in liquid chromatography-mass spectrometry (LC-MS-)based workflows. In this work, we assessed these chemicals in light of a rather surprising finding presented in two previously published studies, namely that usage of cyclosporine A, an immunosuppressive drug which is known to be cleared through excretion in the bile, explained the largest amount of variance in principal component analysis of urinary LC-SWATH/MS small-molecule profiling data. Specifically, we examined the freely-accessible 24-hour urine metabolomics data of 570 kidney transplant recipients included in the TransplantLines Biobank and Cohort Study (NCT03272841). These data unveiled thousands of high-abundance polymer peaks in some samples, which were associated with the use of the macrogol (i.e., polyethylene glycol) 3350 oral laxative agent. In addition, we found multiple clusters of high-abundance peaks which were linked to the exposure to two pharmaceutical excipients, namely short-chain polyethylene glycol (molecular weight <1000 Da) and polyethoxylated castor oil (also known as Kolliphor® EL or Cremophor® EL). Respectively, these excipients are used in temazepam capsules and cyclosporine A capsules, and the latter provides a plausible explanation for the rather surprising finding that instigated our work. Moreover, such explanation and our findings in general put emphasis on taking into consideration these and other pharmaceutical polymers and excipients when exploring, processing, and interpreting clinical small-molecule profiling data.

Clinical metabolomics by NMR revealed serum metabolic signatures for differentiating sarcoidosis from tuberculosis.

Pulmonary sarcoidosis (SAR) and tuberculosis (TB) are two granulomatous lung-diseases and often pose a diagnostic challenge to a treating physicians. The present study aims to explore the diagnostic potential of NMR based serum metabolomics approach to differentiate SAR from TB. The blood samples were obtained from three study groups: SAR (N = 35), TB (N = 28) and healthy normal subjects (NC, N = 56) and their serum metabolic profiles were measured using 1D 1H CPMG (Carr-Purcell-Meiboom-Gill) NMR spectra recorded at 800 MHz NMR spectrometer. The quantitative metabolic profiles were compared employing a combination of univariate and multivariate statistical analysis methods and evaluated for their diagnostic potential using receiver operating characteristic (ROC) curve analysis. Compared to SAR, the sera of TB patients were characterized by (a) elevated levels of lactate, acetate, 3-hydroxybutyrate (3HB), glutamate and succinate (b) decreased levels of glucose, citrate, pyruvate, glutamine, and several lipid and membrane metabolites (such as very-low/low density lipoproteins (VLDL/LDL), polyunsaturated fatty acids, etc.). The metabolic disturbances not only found to be well in concordance with various previous reports, these further demonstrated very high sensitivity and specificity to distinguish SAR from TB patients suggesting serum metabolomics analysis can serve as surrogate method in the diagnosis and clinical management of SAR.

Metabolomics of nasal lavage fluid in patients with allergic rhinitis treated by Xiaoqinglong Decoction

This study used nasal lavage fluid for metabolomics to explore its feasibility, and applied it to the clinical metabolomics study of Xiaoqinglong Decoction in the treatment of allergic rhinitis(AR), aiming to investigate the molecular mechanism of Xiaoqing-long Decoction in the treatment of AR through differential changes in local nasal metabolism. AR patients were selected as the research subjects, and nasal lavage fluid was collected as the sample. Metabolomics analysis using liquid chromatography-mass spectrometry was performed on normal group, AR group, and Xiaoqinglong Decoction group. The differences in metabolic profiles among the groups were compared using principal component analysis and partial least squares discriminant analysis, and differential metabolites were identified and subjected to corresponding metabolic pathway analysis. The results showed that Xiaoqinglong Decoction significantly improved the symptoms of AR patients. The metabolomics analysis revealed 20 differential metabolites between AR group and Xiaoqinglong Decoction group. The core metabolite with a trending return in comparison to normal group was trimethyladipic acid. The metabolites were involved in multiple pathways, including β-alanine metabolism, glutathione metabolism, and phenylalanine, tyrosine, and tryptophan biosynthesis. The feasibility of applying nasal lavage fluid in nasal metabolomics was preliminarily demonstrated. Differential metabolites and enriched pathways in the treatment of AR patients with Xiaoqinglong Decoction were identified, indicating that it may improve rhinitis symptoms through the regulation of various metabolites, including antioxidant effects and correction of Th1/Th2 imbalance.

Research into the anti-pulmonary fibrosis mechanism of Renshen Pingfei formula based on network pharmacology, metabolomics, and verification of AMPK/PPAR-γ pathway of active ingredients

Ethnopharmacological relevanceIdiopathic pulmonary fibrosis (IPF) is a chronic and progressive disease with limited therapy. Renshen Pingfei Formula (RPFF), a classic Chinese medicine derivative formula, has been shown to exert therapeutic effects on IPF. Aim of the studyThe study aimed to explore the anti-pulmonary fibrosis mechanism of RPFF through network pharmacology, clinical plasma metabolomics, and in vitro experiment. MethodsNetwork pharmacology was used to study the holistic pharmacological mechanism of RPFF in the treatment of IPF. The differential plasma metabolites for RPFF in the treatment of IPF were identified by untargeted metabolomics analysis. By integrated analysis of metabolomics and network pharmacology, the therapeutic target of RPFF for IPF and the corresponding herbal ingredients were identified. In addition, the effects of the main components of the formula, kaempferol and luteolin, which regulate the adenosine monophosphate (AMP)-activated protein kinase (AMPK)/peroxisome proliferator-activated receptor γ (PPAR-γ) pathway were observed in vitro according to the orthogonal design. ResultsA total of 92 potential targets for RPFF in the treatment of IPF were obtained. The Drug–Ingredients–Disease Target network showed that PTGS2, ESR1, SCN5A, PPAR-γ, and PRSS1 were associated with more herbal ingredients. The protein–protein interaction (PPI) network identified the key targets of RPFF in IPF treatment, including IL6, VEGFA, PTGS2, PPAR-γ, and STAT3. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis acquired the main enriched pathways, and PPAR-γ involved in multiple signaling pathways, including the AMPK signaling pathway. Untargeted clinical metabolomics analysis revealed plasma metabolite variations in patients with IPF versus controls and before versus after RPFF treatment for patients with IPF. Six differential metabolites were explored as differential plasma metabolites for RPFF in IPF treatment. Combined with network pharmacology, a therapeutic target PPAR-γ of RPFF in IPF treatment and the corresponding herbal components were identified. Based on the orthogonal experimental design, the experiments showed that kaempferol and luteolin can decrease the mRNA and protein expression of α-smooth muscle actin (α-SMA), and the combination of lower dose can inhibit α-SMA mRNA and protein expression by promoting the AMPK/PPAR-γ pathway in transforming growth factor beta 1 (TGF-β1)-treated MRC-5 cells. ConclusionsThis study revealed that the therapeutic effects of RPFF are due to multiple ingredients and have multiple targets and pathways, and PPAR-γ is one of therapeutic targets for RPPF in IPF and involved in the AMPK signaling pathway. Two ingredients of RPFF, kaempferol and luteolin, can inhibit fibroblast proliferation and the myofibroblast differentiation of TGF-β1, and exert a synergistic effect through AMPK/PPAR-γ pathway activation.

Possibilities of personalized pharmacotherapy and medical rehabilitation of patients with long COVID from the perspective of clinical metabolomics

Introduction. The material presents the results of the study of the effectiveness of the drug Ethylmethylhydroxypyridine malate in patients with long COVID, based on the dynamics of clinical manifestations and metabolomic parameters responsible for the level of oxidative stress. Aim. Evaluation of efficacy and registration of adverse reactions of ethylmethylhydroxypyridine malate administration to decrease oxidative stress level and reduce symptoms of asthenia and cognitive disorders in patients with long COVID. Materials and methods. 24 patients diagnosed with U09.9 “Condition after COVID-19 unspecified” were included in a non-randomized controlled prospective study, Patients were divided into 2 groups of 12 each. The experimental group received Ethylmethylhydroxypyridine malate tablets chewable 100 mg in a dosage of 400 mg/day. Clinical assessment by scales (mMRC dyspnea scale; 6-minute walk test (6MWT); MFI-20 subjective asthenia assessment scale; Montreal Cognitive Assessment Scale (MoCA); Barthel Baseline Functional Activity Rating Scale), and determination of metabolomic parameters were performed on days 1 and 14–18 of the study. Results. Use of the drug ethylmethylhydroxypyridine malate in patients with long COVID, led to a decrease in the level of oxidative stress, and normalization of mitochondrial function, as well as more pronounced improvements of the clinical picture. According to the scale of evaluation of asthenia (MFI-20) in the group of patients who received ethylmethylhydroxypyridine malate improvement of 21% was noted, in the control group improvement of 13% was noted. According to cognitive impairment assessment scale (MoCA), the drug group showed 20% improvement, while the control group showed 12,5% improvement. Conclusion. The drug ethylmethylhydroxypyridine malate tablets chewable 100 mg in a dosage of 400 mg/day demonstrates clinical efficacy characterized by, among other things, a decrease in oxidative stress, as well as clinical safety due to the absence of development of adverse reactions in patients with long COVID.

Interpretable machine learning with tree-based shapley additive explanations: Application to metabolomics datasets for binary classification.

Machine learning (ML) models are used in clinical metabolomics studies most notably for biomarker discoveries, to identify metabolites that discriminate between a case and control group. To improve understanding of the underlying biomedical problem and to bolster confidence in these discoveries, model interpretability is germane. In metabolomics, partial least square discriminant analysis (PLS-DA) and its variants are widely used, partly due to the model's interpretability with the Variable Influence in Projection (VIP) scores, a global interpretable method. Herein, Tree-based Shapley Additive explanations (SHAP), an interpretable ML method grounded in game theory, was used to explain ML models with local explanation properties. In this study, ML experiments (binary classification) were conducted for three published metabolomics datasets using PLS-DA, random forests, gradient boosting, and extreme gradient boosting (XGBoost). Using one of the datasets, PLS-DA model was explained using VIP scores, while one of the best-performing models, a random forest model, was interpreted using Tree SHAP. The results show that SHAP has a more explanation depth than PLS-DA's VIP, making it a powerful method for rationalizing machine learning predictions from metabolomics studies.

Performance comparison of narrow-bore and capillary liquid-chromatography for non-targeted metabolomics profiling by HILIC-QTOF-MS

Clinical metabolomics studies often have to cope with limited sample amounts, thus miniaturized liquid chromatography (LC) systems are a promising alternative. Their applicability has already been demonstrated in various fields, including a few metabolomics studies that mainly used reversed-phase chromatography. However, hydrophilic interaction chromatography (HILIC), which is widely used in metabolomics due to its particular suitability for the analysis of polar molecules, has rarely been tested for miniaturized LC-MS analysis of small molecules.In the present work, the suitability of a capillary HILIC (CapHILIC)-QTOF-MS system for non-targeted metabolomics was evaluated based on extracts of porcine formalin-fixed, paraffin-embedded (FFPE) tissue samples. The performance was assessed with respect to the number and retention time span of metabolic features as well as the analytical repeatability, the signal-to-noise ratio and the signal intensity of 16 annotated metabolites from different compound classes. The results were compared with a well established narrow-bore HILIC-QTOF-MS system. Both platforms have detected a similar number of features and performed excellent with respect to retention time stability (median RT span <0.05 min) and analytical repeatability (>75% of features with CV < 20%). The signal areas of all metabolites assessed were increased up to 18-fold by the use of CapHILIC, although the signal-to-noise ratio was only improved for 50% of the metabolites. An even better reproducibility (median CV = 5.2%) and up to 80-fold increase in signal intensity were observed after optimization of CapHILIC conditions for analysis of bile acid standard solutions. Even though the observed improvement for specific bile acids (e.g. taurocholic acid) in biological matrix needs to be evaluated, the platform comparison indicates, that the tested CapHILIC system is particularly suitable for analyses of a less broad metabolite spectrum, and specifically optimized chromatography.

Modulation of the biological network of lumbar spinal stenosis by Tongdu Huoxue Decoction based on clinical metabolomics.

Objective: To explore the clinical efficacy and metabolic mechanism of Tongdu Huoxue Decoction (THD) in treating lumbar spinal stenosis (LSS). Methods: A total of 40 LSS patients and 20 healthy participants were recruited from January 2022 to June 2022. The patients' pre- and post-treatment visual analogue scale (VAS) and Japanese Orthopaedic Association (JOA) scores were recorded. ELISA kits were used to assess pre- and post-treatment levels of serum Interleukin-1beta (IL-1β), Alpha tumour necrosis factor (TNF-α) and prostaglandin E2 (PGE2). Finally, the patients' pre- and post-treatment and healthy human sera were subjected to extensively targeted metabolomics using Ultra Performance Liquid Chromatography (UPLC) to identify potential differential metabolites and metabolic pathways using multivariate statistical analysis. Results: Compared to the pre-treatment (group A), the patients' VAS scores decreased significantly (p < 0.05), while JOA scores increased significantly (p < 0.05) post-treatment (group B), indicating that THD could effectively improve the pain and lumbar spine function of LSS patients. Moreover, THD could effectively inhibit the expression of IL-1β, TNF-α and PGE2-associated inflammatory factors in serum. Regarding metabolomics, the levels of 41 differential metabolites were significantly different in the normal group (group NC) compared to group A, and those were significantly restored after treatment with THD, including chenodeoxycholic acid 3-sulfate, taurohyodeoxycholic acid, 3,5-Dihydroxy-4-methoxybenzoic acid, pinocembrin. These biomarkers are mainly involved in purine metabolism, steroid hormone biosynthesis and amino acid metabolism. Conclusion: This clinical trial demonstrated that THD is effective in improving pain, lumbar spine function and serum levels of inflammation in patients with LSS. Moreover, its mechanism of action is related to the regulation of purine metabolism, steroid hormone biosynthesis and the expression of key biomarkers in the metabolic pathway of amino acid metabolism.

MAW: the reproducible Metabolome Annotation Workflow for untargeted tandem mass spectrometry

Mapping the chemical space of compounds to chemical structures remains a challenge in metabolomics. Despite the advancements in untargeted liquid chromatography-mass spectrometry (LC–MS) to achieve a high-throughput profile of metabolites from complex biological resources, only a small fraction of these metabolites can be annotated with confidence. Many novel computational methods and tools have been developed to enable chemical structure annotation to known and unknown compounds such as in silico generated spectra and molecular networking. Here, we present an automated and reproducible Metabolome Annotation Workflow (MAW) for untargeted metabolomics data to further facilitate and automate the complex annotation by combining tandem mass spectrometry (MS2) input data pre-processing, spectral and compound database matching with computational classification, and in silico annotation. MAW takes the LC-MS2 spectra as input and generates a list of putative candidates from spectral and compound databases. The databases are integrated via the R package Spectra and the metabolite annotation tool SIRIUS as part of the R segment of the workflow (MAW-R). The final candidate selection is performed using the cheminformatics tool RDKit in the Python segment (MAW-Py). Furthermore, each feature is assigned a chemical structure and can be imported to a chemical structure similarity network. MAW is following the FAIR (Findable, Accessible, Interoperable, Reusable) principles and has been made available as the docker images, maw-r and maw-py. The source code and documentation are available on GitHub (https://github.com/zmahnoor14/MAW). The performance of MAW is evaluated on two case studies. MAW can improve candidate ranking by integrating spectral databases with annotation tools like SIRIUS which contributes to an efficient candidate selection procedure. The results from MAW are also reproducible and traceable, compliant with the FAIR guidelines. Taken together, MAW could greatly facilitate automated metabolite characterization in diverse fields such as clinical metabolomics and natural product discovery.

What clinical metabolomics will bring to the medicine of tomorrow

The purpose of this review is to explore how clinical metabolomics could help physicians in the future. The recent advent of medical genomics brings new and interesting technological tools to measure genetic predispositions to a disease. But metabolomics will allow us to go even further by linking the patient’s pathological phenotype with gene expression defects and metabolic disorders. It is in this context that the clinical chemist must adapt and be a force of proposal to meet these health challenges. He must help the clinician by mastering these new innovative tools, in order to participate in the implementation of clinical studies for the discovery of biomarkers, but also to propose the assays of biomarkers called “signatures,” which can be composite biomarkers or fingerprints, which will ultimately guide the clinician. He will have to propose them as clinical chemistry tests. In the first part, we will look at some concrete examples of the use of clinical metabolomics in clinical research projects that have led to the identification of a new biomarker. We will use the example of trimethylamine N-oxide (or TMAO) and review the clinical studies that have proposed TMAO as a biomarker for cardiovascular diseases. In a second part, we will see through bibliographic studies, how the metabolomic fingerprint can be useful to build a supervised model for patient stratification. In conclusion, we will discuss the limitations currently under debate.

EASY-FIA: A Readably Usable Standalone Tool for High-Resolution Mass Spectrometry Metabolomics Data Pre-Processing.

Flow injection analysis coupled with high-resolution mass spectrometry (FIA-HRMS) is a fair trade-off between resolution and speed. However, free software available for data pre-processing is few, web-based, and often requires advanced user specialization. These tools rarely embedded blank and noise evaluation strategies, and direct feature annotation. We developed EASY-FIA, a free standalone application that can be employed for FIA-HRMS metabolomic data pre-processing by users with no bioinformatics/programming skills. We validated the tool's performance and applicability in two clinical metabolomics case studies. The main functions of our application are blank subtraction, alignment of the metabolites, and direct feature annotation by means of the Human Metabolome Database (HMDB) using a minimum number of mass spectrometry parameters. In a scenario where FIA-HRMS is increasingly recognized as a reliable strategy for fast metabolomics analysis, EASY-FIA could become a standardized and feasible tool easily usable by all scientists dealing with MS-based metabolomics. EASY-FIA was implemented in MATLAB with the App Designer tool and it is freely available for download.

A Comprehensive Mass Spectrometry-Based Workflow for Clinical Metabolomics Cohort Studies.

As a comprehensive analysis of all metabolites in a biological system, metabolomics is being widely applied in various clinical/health areas for disease prediction, diagnosis, and prognosis. However, challenges remain in dealing with the metabolomic complexity, massive data, metabolite identification, intra- and inter-individual variation, and reproducibility, which largely limit its widespread implementation. This study provided a comprehensive workflow for clinical metabolomics, including sample collection and preparation, mass spectrometry (MS) data acquisition, and data processing and analysis. Sample collection from multiple clinical sites was strictly carried out with standardized operation procedures (SOP). During data acquisition, three types of quality control (QC) samples were set for respective MS platforms (GC-MS, LC-MS polar, and LC-MS lipid) to assess the MS performance, facilitate metabolite identification, and eliminate contamination. Compounds annotation and identification were implemented with commercial software and in-house-developed PAppLineTM and UlibMS library. The batch effects were removed using a deep learning model method (NormAE). Potential biomarkers identification was performed with tree-based modeling algorithms including random forest, AdaBoost, and XGBoost. The modeling performance was evaluated using the F1 score based on a 10-times repeated trial for each. Finally, a sub-cohort case study validated the reliability of the entire workflow.