Serum metabolomics study of patients with allergic rhinitis

Allergic rhinitis (AR) negatively affects the healthy lives of many individuals. Most previous studies on AR focused on the expression of cytokines, with only a few analyzing cytokine expression from a metabolomics viewpoint. Therefore, it is worthwhile to study AR at the metabolic level. Consequently, we aimed to identify differential serum biomarkers by metabolomics. In this study, the orthogonal partial least squares discriminant analysis (OPLS-DA) model was applied to characterize the differences in serum samples collected from patients with AR and healthy volunteers. Ten metabolites (except hexadecanoic acid) were found to be altered significantly (p < .05) in the former group, according to results of principal component analysis and OPLS-DA, indicating that these metabolites could be potential biomarkers. MetaboAnalyst 4.0 and pathway enrichment analysis showed that these changes in metabolites mainly involved three pathways, namely, porphyrin and chlorophyll metabolism, arachidonic acid metabolism, and purine metabolism. Our findings may contribute to a better understanding of the potential pathogenesis mechanisms and provide a metabolic evidence for in-depth studies of AR.     

1 H NMR‐based metabolomics study for identifying urinary biomarkers and perturbed metabolic pathways associated with severity of IgA nephropathy: a pilot study

The severity of IgA nephropathy (IgAN), the most common primary glomerulonephritis, is judged on the basis of histologic and clinical features. A limited number of studies have considered molecular signature of IgAN for this issue, and no reliable biomarkers have been presented non‐invasively for use in patient evaluations. This study aims to identify metabolite markers excreted in the urine and impaired pathways that are associated with a known marker of severity (proteinuria) to predict mild and severe stages of IgAN. Urine samples were analysed using nuclear magnetic resonance from biopsy‐proven IgAN patients at mild and severe stages. Multivariate statistical analysis and pathway analysis were performed. The most changed metabolites were acetoacetate, hypotaurine, homocysteine, L‐kynurenine and phenylalanine. Nine metabolites were positively correlated with proteinuria, including mesaconic acid, trans‐cinnamic acid, fumaric acid, 5‐thymidylic acid, anthranilic acid, indole, deoxyguanosine triphosphate, 13‐cis‐retinoic acid and nicotinamide riboside, while three metabolites were negatively correlated with proteinuria including acetoacetate, hypotaurine and hexanal. ‘Phenylalanine metabolism’ was the most significant pathway which was impaired in severe stage in comparison to mild stage of IgAN. This study indicates that nuclear magnetic resonance is a versatile technique that is capable of detecting metabolite biomarkers in combination with advanced multivariate statistical analysis. Copyright © 2017 John Wiley & Sons, Ltd.     

Feature selection for OPLS discriminant analysis of cancer tissue lipidomics data

The mass spectrometry‐based molecular profiling can be used for better differentiation between normal and cancer tissues and for the detection of neoplastic transformation, which is of great importance for diagnostics of a pathology, prognosis of its evolution trend, and development of a treatment strategy. The aim of the present study is the evaluation of tissue classification approaches based on various data sets derived from the molecular profile of the organic solvent extracts of a tissue. A set of possibilities are considered for the orthogonal projections to latent structures discriminant analysis: all mass spectrometric peaks over 300 counts threshold, subset of peaks selected by ranking with support vector machine algorithm, peaks selected by random forest algorithm, peaks with the statistically significant difference of the intensity determined by the Mann‐Whitney U test, peaks identified as lipids, and both identified and significantly different peaks. The best predictive potential is obtained for OPLS‐DA model built on nonpolar glycerolipids (Q² = 0.64, area under curve [AUC] = 0.95); the second one is OPLS‐DA model with lipid peaks selected by random forest algorithm (Q² = 0.58, AUC = 0.87). Moreover, models based on particular molecular classes are more preferable from biological point of view, resulting in new explanatory mechanisms of pathophysiology and providing a pathway analysis. Another promising features for OPLS‐DA modeling are phosphatidylethanolamines (Q² = 0.48, AUC = 0.86).     

Urinary metabolite profiling provides potential differentiation to explore the mechanisms of adjuvant‐induced arthritis in rats

To explore the pathogenesis of rheumatoid arthritis (RA) from the perspective of metabolomics, gas chromatography time‐of‐flight mass spectrometry (GC‐TOF/MS) technology was used to observe changes in the metabolic profiles of urine output from rats with adjuvant‐induced arthritis (AA). Sprague–Dawley rats were randomly divided into a control group and an experimental group, with eight in each. Rats in the experimental group were induced by intracutaneous innoculation of 0.1 mL Freund's complete adjuvant to right paws. On day 20 after immunization, the metabolic profiles between rat control and experimental groups were compared by combining GC‐TOF/MS technology with multivariate statistical approaches, including principal component analysis, partial least squares discriminant analysis and orthogonal projections to latent structures–discriminant analysis. Nine potential biomarkers were identified, including 2,2‐dimethylsuccinic acid, tartronic acid, dehydroshikimic acid, hippuric acid, adenine, phenaceturic acid, l ‐dopa, 1,4‐dihydroxy‐2‐naphthoic acid and melibiose. The findings indicate that the rats with AA are disturbed in metabolism of purine, amino acid, fat and energy. This study also demonstrates that the dysfunction in a range of biosynthetic and catabolic pathways, which leads to increased oxygen free radicals and inflammation, could cause underlying pathogenesis of RA. Copyright © 2016 John Wiley & Sons, Ltd.     

Metabolomic comparison between wild Ophiocordyceps sinensis and artificial cultured Cordyceps militaris

A systematic study on the metabolome differences between wild Ophiocordyceps sinensis and artificial cultured Cordyceps militaris was conducted using liquid chromatography−mass spectrometry. Principal component analysis and orthogonal projection on latent structure‐discriminant analysis results showed that C. militaris grown on solid rice medium (R‐CM) and C. militaris grown on tussah pupa (T‐CM) evidently separated and individually separated from wild O. sinensis, indicating metabolome difference among wild O. sinensis, R‐CM and T‐CM. The metabolome differences between R‐CM and T‐CM indicated that C. militaris could accommodate to culture medium by differential metabolic regulation. Hierarchical clustering analysis was further performed to cluster the differential metabolites and samples based on their metabolic similarity. The higher content of amino acids (pyroglutamic acid, glutamic acid, histidine, phenylalanine and arginine), unsaturated fatty acid (linolenic acid and linoleic acid), peptides, mannitol, adenosine and succinoadenosine in O. sinensis make it as an excellent choice as a traditional Chinese medicine for invigoration or nutritional supplementation. Similar compositions with O. sinensis and easy cultivation make artificially cultured C. militaris a possible alternative to O. sinensis.     

Plasma metabonomics study on toxicity biomarker in rats treated with Euphorbia fischeriana based on LC–MS

Lang‐du (LD) has been traditionally used to treat human diseases in China. Plasma metabolic profiling was applied in this study based on LC–MS to elucidate the toxicity in rats induced by injected ethanol extract of LD. LD injection was given by intraperitoneal injection at doses of 0.1, 0.05, 0.025 and 0 g kg^?1 body weight per day to rats. The blood biochemical levels of alanine aminotransferase, direct bilirubin, creatinine, serum β2‐microglobulin and low‐density lipoprotein increased in LD‐injected rats, and the levels of total protein and albumin decreased in these groups. The metabolic profiles of the samples were analyzed by multivariate statistics analysis, including principal component analysis, partial least squares discriminant analysis and orthogonal projection to latent structures discriminate analysis (OPLS‐DA). The metabolic characters in rats injected with LD were perturbed in a dose‐dependent manner. By OPLS‐DA, 18 metabolites were served as the potential toxicity biomarkers. Moreover, LD treatment resulted in an increase in the p‐cresol, p‐cresol sulfate, lysophosphatidylethanolamine (LPE) (18:0), LPE (16:0), lysophosphatidylcholine (16:0) and 12‐HETE concentrations, and a decrease in hippuric acid, cholic acid and N‐acetyl‐l ‐phenylalanine. These results suggested that chronic exposure to LD could cause a disturbance in lipids metabolism and amino acids metabolism, etc. Therefore, an analysis of the metabolic profiles can contribute to a better understanding of the adverse effects of LD. Copyright © 2016 John Wiley & Sons, Ltd.     

Expressing Forest Origins in the Chemical Composition of Cooperage Oak Woods and Corresponding Wines by Using FTICR‐MS

A non‐targeted, ultra‐high‐resolution mass spectrometric, direct analysis of oak‐wood extracts from two species (Quercus robur L. and Quercus petraea Liebl.) from three French forests, and of a wine aged in barrels derived therefrom has been performed to identify families of metabolites that could discriminate both the species and the geographical origin of woods. From 12 T ultra‐high‐resolution Fourier transform ion cyclotron resonance mass spectra of wood extracts, hundreds of mass signals were identified as possible significant biomarkers of the two species, with phenolic and carbohydrate moieties leading the differentiation between Q. robur and Q. petraea, respectively, as corroborated by both FTMS and NMR data. For the first time, it is shown that oak woods can also be discriminated on the basis of hundreds of forest‐related compounds, and particular emphasis is put on sessile oaks from the Tronçais forest, for which sugars are significantly discriminant. Despite the higher complexity and diversity of wine metabolites, forest‐related compounds can also be detected in wines aged in related barrels. It is only by using these non‐targeted analyses that such innovative results, which reveal specific chemodiversities of natural materials, can be obtained.     

LC Coupled with TOFMS for Metabonomics Study of Mini-pigs with Atherosclerosis

In this study, serum metabolic profiles of mini-pigs with atherosclerosis (AS) were analyzed by LC–TOFMS. Partial least-squares to latent structure-discriminant analysis and orthogonal projection to latent structure-discriminant analysis were used for group differentiation and selection of potential biomarkers. The mini-pig disease models were constructed by feeding a high-fat diet and inducing coronary injury, in accordance with the mechanism of AS pathogenesis. To characterize the development of AS, serum samples were collected and analyzed at two time points (two and ten weeks). Separate distinct clustering of results from normal and model mini-pigs could be observed for both the two and ten-week samples. With the development of AS, the metabolism of the model mini-pigs was more substantially disturbed. Major metabolites contributing to the discrimination were fatty acids, lysophosphatidylcholines, and bile acids. These potential biomarkers are related with inflammation, oxidative stress, and abnormal lipid and energy metabolism.

     

Orthogonal projection to latent structures solution properties for chemometrics and systems biology data

Partial least squares (PLS) is a widely used algorithm in the field of chemometrics. In calibration studies, a PLS variant called orthogonal projection to latent structures (O‐PLS) has been shown to successfully reduce the number of model components while maintaining good prediction accuracy, although no theoretical analysis exists demonstrating its applicability in this context. Using a discrete formulation of the linear mixture model known as Beer's law, we explicitly analyze O‐PLS solution properties for calibration data. We find that, in the absence of noise and for large n, O‐PLS solutions are simpler but just as accurate as PLS solutions for systems in which analyte and background concentrations are uncorrelated. However, the same is not true for the most general chemometric data in which correlations between the analyte and background concentrations are nonzero and pure profiles overlap. On the contrary, forcing the removal of orthogonal components may actually degrade interpretability of the model. This situation can also arise when the data are noisy and n is small, because O‐PLS may identify and model the noise as orthogonal when it is statistically uncorrelated with the analytes. For the types of data arising from systems biology studies, in which the number of response variables may be much greater than the number of observations, we show that O‐PLS is unlikely to discover orthogonal variation whether or not it exists. In this case, O‐PLS and PLS solutions are the same. Copyright © 2011 John Wiley & Sons, Ltd.     

Fatty Acids Profiling and Biomarker Identification in Snow Alga Chlamydomonas Nivalis by NaCl Stress Using GC/MS and Multivariate Statistical Analysis

The snow alga Chlamydomonas nivalis is a model species of microalgae for the investigation of cell response mechanism and adaptation ability in natural habitats in polar regions and similar extreme environments. The alteration of fatty acids in cellular lipids is known to play a vital role for cell survival and reproduction under various stress conditions. In the present work, an integrated approach of gas chromatography/mass spectrometry (GC/MS) coupled with multivariate statistical analysis was developed to investigate the fatty acid profiles and identify the biomarkers in response to NaCl stress. The data of fatty acid profiles between the control and NaCl-stress group was classified by orthogonal projection on latent structure discriminant analysis (OPLS-DA) and hierarchical cluster analysis (HCA); six of fatty acids (C16:0, C16:3, C18:0, C18:1, C18:2, and C18:3) were identified as biomarkers. These biomarkers showed a regulatory role by decreasing the degree of lipid unsaturation (DLU), providing an expected function in reducing membrane fluidity and permeability for enhancing the tolerance to higher salinity. This is the first report to demonstrate the fatty acid biomarkers in microalgae as the physiological regulators corresponding to the response and adaptation to NaCl stress based on an integrated approach at the lipidomic level.     

Chemometrics comparison of gas chromatography with mass spectrometry and comprehensive two‐dimensional gas chromatography with time‐of‐flight mass spectrometry Daphnia magna metabolic profiles exposed to salinity

The performances of gas chromatography with mass spectrometry and of comprehensive two‐dimensional gas chromatography with time‐of‐flight mass spectrometry are examined through the comparison of Daphnia magna metabolic profiles. Gas chromatography with mass spectrometry and comprehensive two‐dimensional gas chromatography with mass spectrometry were used to compare the concentration changes of metabolites under saline conditions. In this regard, a chemometric strategy based on wavelet compression and multivariate curve resolution–alternating least squares is used to compare the performances of gas chromatography with mass spectrometry and comprehensive two‐dimensional gas chromatography with time‐of‐flight mass spectrometry for the untargeted metabolic profiling of Daphnia magna in control and salinity‐exposed samples. Examination of the results confirmed the outperformance of comprehensive two‐dimensional gas chromatography with time‐of‐flight mass spectrometry over gas chromatography with mass spectrometry for the detection of metabolites in D. magna samples. The peak areas of multivariate curve resolution–alternating least squares resolved elution profiles in every sample analyzed by comprehensive two‐dimensional gas chromatography with time‐of‐flight mass spectrometry were arranged in a new data matrix that was then modeled by partial least squares discriminant analysis. The control and salt‐exposed daphnids samples were discriminated and the most relevant metabolites were estimated using variable importance in projection and selectivity ratio values. Salinity de‐regulated 18 metabolites from metabolic pathways involved in protein translation, transmembrane cell transport, carbon metabolism, secondary metabolism, glycolysis, and osmoregulation.     

Metabolic profiling of serum in patients with cartilage tumours using 1H-NMR spectroscopy: A pilot study

Cartilage-forming lesions include tumours that can vary in severity from benign enchondromas to high-grade malignant chondrosarcomas. Chondrosarcoma is the second most frequent malignant bone tumour, accounting for 20–30% of all malignant bone neoplasms. Surgery is the standard treatment for cartilage tumours (CTs); however, their incidental diagnosis and the difficult differentiation of low-grade lesions like chondrosarcoma grade I from benign entities like enchondroma are challenges for clinical management. In this sense, the search for circulating biomarkers for early detection and prognosis is an ongoing interest. Targeted metabolomics is a powerful tool that can propose potential biomarkers in biological fluids as well as help to discover disturbed metabolic pathways to reveal tumour pathogenesis. In this context, the aim of this study was to investigate the ¹H nuclear magnetic resonance metabolomic serum profile of patients with CTs contrasted with healthy controls. Forty-one metabolites were identified and quantified; the multivariate statistical methods principal component analysis and partial least squares discriminant analysis reveal a clear separation of the CT group, that is, the differential metabolites that were involved in two main metabolic pathways: the taurine and hypotaurine metabolism and synthesis and degradation of ketone bodies. Our results represent preliminary work for emergent serum-based diagnostics or prognostic methods for patients with chondrogenic tumours.     

Assessment of Polygonum capitatum Buch.‐Ham. ex D.Don by metabolomics based on gas chromatography with mass spectrometry

Polygonum capitatum is widely used in southwest China. It has considerable therapeutic efficacy for urinary tract infections. P. capitatum contains multiple components and quality assessment can be achieved by means of metabolic fingerprinting. In this paper, a new strategy for P. capitatum quality determination was developed. Eleven batches of P. capitatum were collected from five geographical areas in China including a standard batch regulated by Good Agriculture Practice. Gas chromatography with mass spectrometry was used to generate fingerprints from triplicate extractions to each batch (n = 33). Hierarchical clustering analysis was applied to assess similarities among the ten batches to the standard batches. Orthogonal projection to latent structures discriminate analysis, cross‐validated with permutation tests, was performed to investigate discriminating metabolites. Results demonstrated that the overall evaluation hierarchical clustering analysis clustered two batches with distance > 3. Orthogonal projection to latent structures discriminate analysis (R²Y (cum) = 0.997, Q² (cum) = 0.97, CV‐ANOVA = 8.48 × 10^?11) indicated that several sugars contributed to batch classification. This method is a rational approach that can classify against a regulated plant standard and distinguishes samples from different origins or processing time in a holistic manner and metabolites driving any differences can be easily identified.     

Different fingerprinting strategies to differentiate Porana sinensis and plants of Erycibe by high‐performance liquid chromatography with diode array detection,ultra high performance liquid chromatography with tandem quadrupole mass spectrometry,and chemometrics

Plants of Erycibe are widely used in traditional Chinese medicine for the treatment of joint pain and rheumatoid arthritis. With the reduction of Erycibe resources in the wild, Porana sinensis has been widely used as a substitute. However, it is important to understand the chemical distinctions between the two kinds of plants and identify their individual chemical markers. In this study, multiwavelength chromatographic fingerprint and precursor ion fingerprint techniques were used in conjunction with chemometric tools to fingerprint and thus differentiate between plant samples. The similar results obtained from different fingerprints prove the reliability of the two fingerprints. Results obtained from principal component analysis and orthogonal projection to latent structures discriminant analysis identified similarities between the chemical components of P. sinensis and plants of Erycibe. However, concentrations of 4‐caffeoylquinic acid, 3,5‐dicaffeoylquinic acid, 3,4‐dicaffeoylquinic acid, and 4,5‐dicaffeoylquinic acid were higher in P. sinensis than in plants of Erycibe, suggesting that P. sinensis may be more effective in medical treatments of some diseases than Erycibe.     

Metabolomic variation in wild and cultured cordyceps and mycelia of Isaria cicadae

Isaria cicadae is one of the fungi used in traditional Chinese medicine with the longest tradition. It is used not only as a herbal medicine but also as a health food in Asia, together with cultured cordyceps and mycelia of the fungus used as substitute. However, the differences in their metabolite are unknown. Using a high‐performance liquid chromatography–mass spectrometry (HPLC–MS)‐based metabolomic method, we found that the fungus varies in its metabolism during growth on wild insects, artificially raised insects and artificial medium. There were 109 discriminatory metabolites detected in the samples by orthogonal projection to latent structure discriminant analysis and one‐way ANOVA. High level of nonribosomal peptides (NRPs) only existed in the insect portions of the wild cordyceps (WI) and cultured cordyceps (CI), revealing that immunostimulation of the host insects enhanced the synthesis of NRPs in the fungus. The finding of a significantly higher level of sphingolipids in both the insect portions (WI, CI) and the coremia of the wild cordyceps (WC) and cultured cordyceps (CC) but not in cultured mycelia (CM) of I. cicadae implies that the immunostimulation of the live insects can induce the fungus to produce more sphingolipids, and this enhanced ability is probably heritable. Apart from NRPs and sphingolipids, the insect portions also contained higher levels of bioactive compounds such as lateritin, anisomycin, streptimidone and ustiloxins. In contrast, the coremium groups (WC, CC) and CM contained 10‐fold less NRP but much higher levels of sanative metabolites such as tocotrienol, 3′‐deoxy‐hanasanagin, γ‐aminobutyric acid and phospholipids than the insect portions. The significantly higher content of antioxidants in WC, CC and CM than in WI and CI suggests that environmental oxygen has a significant effect on the metabolites. The temperature stress which the wild cordyceps encounters during growth is responsible for the relatively high content of trehalose. These findings indicate that the immunity of the host insect and growth environment have a strong impact on the metabolomic variation in Isaria cicadae. The variation in metabolites suggests differential utilization value for the insect portions, coremia and mycelia of the fungus.     

Metabolomics analysis of the therapeutic mechanism of Semen Descurainiae Oil on hyperlipidemia rats using 1H‐NMR and LC–MS

Semen descurainiae oil (SDO) is an important traditional Chinese medicine that was recently discovered to have the function of reducing blood lipids. Metabolomics analyses of plasma, liver and kidney in rats were performed using ¹H‐NMR and LC–MS to illuminate the lower blood lipid concentration effect of SDO, and niacin was considered as the active control. The measure of total cholesterol (TC) and low‐density‐lipoprotein cholesterol (LDL‐C) in plasma showed that SDO treatment decreased significantly the content of TC and LDL‐C. An orthogonal partial least squares–discriminant analysis approach was applied to identify the different metabolic profiles of plasma, liver and kidney in rats and to detect related potential biomarkers. The results suggested that the metabolic profiles of the control group and hyperlipidemia group showed significant difference and the SDO and niacin group had effective anti‐hyperlipidemia function. The biomarkers primarily concern lipid metabolism, amino acid metabolism and glycometabolism, and the change in biomarkers indicated that hyperlipidemia could cause the unbalance of these metabolic pathways in vivo. SDO reduced blood lipids by repairing amino acid and lipid metabolism.     

Metabonomic profiling of liver metabolites by gas chromatography–mass spectrometry and its application to characterizing hyperlipidemia

The measurement of metabolites in tissues is of great importance in metabonomic research in the biomedical sciences, providing more relevant information than is available from systemic biofluids. The liver is the most important organ/tissue for most biochemical reactions, and the metabolites in the liver are of great interest to scientists. To develop an optimized extraction method and comprehensive profiling technique for liver metabolites, organic solvents of various compositions were designed using design of experiments to extract metabolites from the liver, and the metabolites were profiled by gas chromatography/time‐of‐flight mass spectrometry (GC/TOF‐MS). The resolved peak areas were processed by principle components analysis, partial least‐squares projections to latent structures, and discriminant analysis. The results suggest the highest extraction efficiency was for methanol–water, which maximized the majority of GC/TOF‐MS responses. The optimal solvent was applied to extract metabolites in liver of hyperlipidemia hamster and the control. The GC/TOF‐MS profiles of liver metabolites showed obvious differences between hyperlipidemic hamsters and controls. A comparison of liver and serum data from the same animals identified common biomarkers and presented complementary information. Our results suggest that liver metabonomics is a valuable technique and that the combined analysis of systematic biofluids and local tissues is meaningful and complementary, recovering more comprehensive metabonomic data than either analysis alone. Copyright © 2009 John Wiley & Sons, Ltd.     

Ultra high performance liquid chromatography with synapt high‐definition mass spectrometry and a pattern recognition approach to characterize chemical constituents and rat metabolites after the oral administration of Phellinus igniarius

Phellinus igniarius has antibacterial, antiviral, antioxidative, antitumor, and antimutagenic effects. In this study, an integrative pattern recognition approach using principal component analysis and orthogonal partial least squares discriminant analysis was successfully applied for the rapid analysis of natural compounds in traditional Chinese medicine. An ultra high performance liquid chromatography with synapt high‐definition mass spectrometry method and MassLynx software was used. This method employed gradient elution to rapidly analyze and characterize chemical constituents and metabolites after the oral administration of a P. igniarius ethanol extract. There were 24 peaks within 10 min of the analysis time and 20 of these were identified or tentatively characterized on the basis of their fragmentation behaviors. In the S‐plot of the orthogonal partial least squares discriminant analysis, 27 ions were extracted to make the serum. Among them, nine absorbed the prototype components and 18 metabolites were identified in vivo. Glucuronidation, oxidation, and methylation were the major metabolic reactions. This study is the first systematic analysis and characterization of the chemical constituents and metabolites in an ethanol extract of P. igniarius. This method can be applied to the rapid analysis and characterization of constituents in rat serum after the oral administration of other compounds used in traditional Chinese medicines.     

Exploring metabolic syndrome serum profiling based on gas chromatography mass spectrometry and random forest models

Metabolic syndrome (MetS) is a constellation of the most dangerous heart attack risk factors: diabetes and raised fasting plasma glucose, abdominal obesity, high cholesterol and high blood pressure. Analysis and representation of the variances of metabolic profiles is urgently needed for early diagnosis and treatment of MetS. In current study, we proposed a metabolomics approach for analyzing MetS based on GC–MS profiling and random forest models. The serum samples from healthy controls and MetS patients were characterized by GC–MS. Then, random forest (RF) models were used to visually discriminate the serum changes in MetS based on these GC–MS profiles. Simultaneously, some informative metabolites or potential biomarkers were successfully discovered by means of variable importance ranking in random forest models. The metabolites such as 2-hydroxybutyric acid, inositol and d-glucose, were defined as potential biomarkers to diagnose the MetS. These results obtained by proposed method showed that the combining GC–MS profiling with random forest models was a useful approach to analyze metabolites variances and further screen the potential biomarkers for MetS diagnosis.