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Using tung oil as the raw material, a new bio-based prepolymer was successfully synthesized by reacting with acrylic-modified rosin (β-acryloyl nutrient ethyl) ester (ARA)/acrylic-2-hydroxyethyl ester (HEA) followed by the use of the above composite material as the matrix and then reacting with the active diluent (2-HEMA, TPGDA) and the photoinitiator TPO and Irgacure1173 to successfully synthesize a new type of bio-based prepolymer-acrylate-epoxy tung oil polypolymer (AETP). The tung oil monomer before and after the epoxy formation was compared by proton NMR spectroscopy, and the chemical structure of AETP was analyzed by Fourier transform spectroscopy. Tung oil has an acid value of 1.5 mg KOH per g, an epoxy value of 5.38%, an iodine value of 11.28 g/100 g, and a refractive index of

Diabetic retinopathy (DR) is an important complication of diabetes, and is currently the main cause of blindness among young adults in the world. Previous studies have shown that Keluoxin (KLX) capsules have a significant effect on DR in C57BL/KsJ/db−/− mice (db/db mice), however the unclear mechanism limits its further clinical application and actual value. Further research is urgently needed for the treatment of DR disease. Discovery of key lipid biomarkers and metabolic pathways can reveal and explore the molecular mechanisms related to DR development and discover the effect of Keluoxin (KLX) capsule against DR in db/db mice. Lipidomics has been used for characterizing the pathological conditions via identification of key lipid metabolites and the metabolic pathway. In this study, the high-throughput lipidomics using UHPLC-Q-TOF/MS combined with multivariate statistical analysis, querying multiple network databases and employing ingenuity pathway analysis (IPA) method for molecular target prediction. A total of 30 lipid biomarkers were identified and 7 metabolic pathways including arachidonic acid metabolism and steroid hormone biosynthesis were found. The preventive effect of KLX intervention can regulate 22 biomarkers such as LysoPA(16:0/0:0), prostaglandin D2, cortisol and γ-linolenic acid, etc. IPA platform has predicted that PI3K/MAPK pathway are closely related to DR development. It also showed that high-throughput lipidomics combined with multivariate statistical analysis could deep excavate of the biological significance of the big data, and can provide molecular targets information about the disease treatment.

Metabolomics has been used as a promising strategy to evaluate the efficacy of and potential targets for natural products. Alcoholic liver disease (ALD) as a result of chronic ethanol consumption has high morbidity and mortality. Geniposide possesses a hepatoprotective activity against ALD, but its mechanism of action is still not clear. In this study, serum metabolomics based on ultra-performance liquid chromatography-quadrupole time of flight-tandem mass spectrometry (UPLC-Q/TOF-MS) combined with ingenuity pathway analysis was used to explore the therapeutic mechanisms of geniposide. We found that the levels of AST, ALT, MDA, TG, and γ-GT in the geniposide-treated group were significantly decreased, and the level of GSH was significantly increased, compared with the model group. Meanwhile, geniposide effectively inhibits apoptosis and caspase-3 activity in liver tissue. A total of 33 metabolites were identified and related with the model group to illuminate the pathogenesis of ALD, 21 of which are regulated by geniposide, involving the relevant metabolic pathways, such as amino acid metabolism, arachidonic acid metabolism, pyruvate metabolism, TCA cycle, etc. Furthermore, a significant change in amino acid metabolism suggested that it might be a promising mechanism-related target for geniposide against ALD. It also showed that a metabolomic strategy using UPLC-Q/TOF-MS combined with ingenuity pathway analysis is a potentially powerful tool for providing a comprehensive understanding of the therapeutic mechanisms of natural products, but it also offers a theoretical basis for the prevention or treatment of disease.

Colorectal cancer (CRC) is a highly virulent and malignant disease and always accompanied by metabolic disorders. Currently, there are no effective therapeutic drugs for the treatment of CRC. High-throughput metabolomics approaches have been used to unveil the metabolic pathways related to several diseases. In this study, ultra-performance liquid chromatography/mass spectrometry-based high-throughput metabolomics was used for deciphering the potential preventive mechanism of mirabilite on CRC via the modulation of the associated metabolic disorders; a total of 28 differential biomarkers, including indole acetaldehyde, 5-hydroxyindoleacetic acid, hypoxanthine, retinal, retinal ester, linoleic acid, stearic acid, 6-deoxocastasterone, 2-hydroxybutyric acid and LysoPC, were identified in the APCmin/+ mice. These differential biomarkers are involved in the tryptophan metabolism, glycerophospholipid metabolism and biosynthesis of unsaturated fatty acids. Note that these biomarkers and their disturbed metabolic pathways were also regulated by mirabilite. It has been found that the prevention of CRC by mirabilite is mainly associated with tryptophan metabolism; this study shows that high-throughput metabolomics can reveal the perturbed metabolic disorders targeted in the action mechanism of drug treatment.

In this review, we systematically discuss the role of traditional Chinese medicine (TCM) in rheumatoid arthritis (RA) disease treatment. TCM classifies the subtypes of RA through its own theoretical method, which is beneficial for more accurate diagnosis and treatment with Chinese herbal medicines (CHMs) that are more suitable for different syndromes. TCM mainly uses a flexible combination of CHMs to play an important role in RA treatment. The main components of these extracts can be subdivided into alkaloids, flavonoids, triterpenes, saponins and other compounds. Using a platform of transgenic and induced arthritis models, we explore the potential mechanisms of TCM against RA with the help of omics analysis techniques and methods. These mechanisms are mainly CHM and its extracts can inhibit RA patients and experimental animal models, including synovitis, vascular proliferation and bone injury; this involves many biological signal exchange targets and pathways. In conclusion, the role of TCM in RA treatment mainly involves reducing the expression and secretion of pro-inflammatory factors, thus decreasing the degree of abnormal immune response.

Yunnan Baiyao (YNBY) is a well-known traditional Chinese medicine containing Caowu (Aconiti kusnezoffii radix, CW). However, the application of YNBY is limited by the toxicity of CW. Notably, CW is not used alone in YNBY, but is combined with other herbs in a formula for clinical use. In the present study, the compatibility of the protective effects and mechanism of YNBY with the potential toxicity of CW was investigated. After combining with other compatible herbs, the serum metabolic disorder induced by CW can be regulated. Using UPLC-MS-based metabolomics, 63 endogenous serum metabolites were identified as being associated with the potential toxicity of CW, 17 of which were regulated to normal levels when CW was combined with other compatible herbs in YNBY. These regulated metabolites were closely related to glycerophospholipid metabolism, glycosylphosphatidylinositol (GPI)-anchor biosynthesis, tyrosine metabolism, and primary bile acid biosynthesis metabolic pathways. This study aims to evaluate the attenuation mechanism of CW compatibility with YNBY.

Given the highly increased incidence of human diseases, a better understanding of the related mechanisms regarding endogenous metabolism is urgently needed. Mass spectrometry-based metabolomics has been used in a variety of disease research areas. However, the deep research of metabolites remains a difficult and lengthy process. Fortunately, mass spectrometry is considered to be a universal tool with high specificity and sensitivity and is widely used around the world. Mass spectrometry technology has been applied to various basic disciplines, providing technical support for the discovery and identification of endogenous substances in living organisms. The combination of metabolomics and mass spectrometry is of great significance for the discovery and identification of metabolite biomarkers. The mass spectrometry tool could further improve and develop the exploratory research of the life sciences. This mini review discusses metabolomics biotechnology with a focus on recent applications of metabolomics as a powerful tool to elucidate metabolic disturbances and the related mechanisms of diseases.

In this study, a combination of network pharmacology and metabolomics was used to explore the mechanism by which mirabilite regulates bile acid metabolism in the treatment of colorectal cancer. The PharmMapper web server was applied to make preliminary predictions for the treatment targets of mirabilite and to predict the interaction between mirabilite and disease targets using Discovery Studio 2.5. Furthermore, the urine metabolic profile was analyzed by the UPLC-Q-TOF-MS technology. The original data were processed by Progenesis QI software and analyzed by multivariate pattern recognition, which allowed us to reveal the metabolic disturbance in colorectal cancer and explain the therapeutic effect of mirabilite. The network pharmacology results showed that mirabilite can act on the disease targets, and the sites of action include amino acid residues Arg-364 and Asp-533, as well as nucleotides TPC-11, DG-112 and DA-113. Based on metabolomics, potential biomarkers were found to lie in the relevant pathways of bile acid metabolism, such as taurine, chenodeoxycholic acid, cholic acid, and deoxycholic acid. The results showed that mirabilite could regulate the distribution of overall metabolic disturbance, and bile acid metabolism was the main targeted pathway. Additionally, we predicted the upstream targets by ingenuity pathway analysis and found that mirabilite played a significant role in regulating the bile acid-related biomarkers, which allowed comprehensive analysis of the effect of mirabilite on colorectal cancer. This study fully explained the role of mirabilite in inhibiting colorectal cancer, which mainly occurs through bile acid metabolism, via the approach of network pharmacology combined with functional metabolomics.

Yin-Chen-Hao-Tang (YCHT), a classic Chinese herbal formula, is characterized by its strong therapeutic effects of liver regulation and relief of jaundice, especially Yanghuang syndrome (YHS). YHS is a type of jaundice with damp-heat pathogenesis, and it is considered a complicated Chinese medicine syndrome (CMS). The accurate mechanism for healing YHS has not yet been completely reported. The purpose of the current research is to investigate the expression of endogenous biomarkers in YHS mice and evaluate the clinical therapeutic effect of YCHT. Serum samples were analyzed using UPLC-Q/TOF-MS techniques in order to determine differential metabolites to elucidate the functional mechanism of YCHT on YHS through metabolite profiling combined with multivariate analysis. Simultaneously, the exact diversification of YHS mice was elucidated using blood biochemistry indexes and histopathological examination, and the results indicated that YHS is markedly improved by YCHT. Unsupervised principal component analysis (PCA) patterns were constructed to dissect the variances of metabolic profiling. Overall, 22 potential biomarkers were identified using a metabolomics approach based on an accurate MS/MS approach, clustering and distinguishing analysis. The present work demonstrates that the effectiveness of YCHT against YHS prompts distinct discrepancies in metabolic profiles by adjusting biomarkers and regulating metabolic disorders. A total of 15 metabolic pathways were involved in biological disturbance. This demonstrates that metabolomic techniques are powerful means to explore the pathogenesis of CMS and the therapeutic effects of traditional Chinese formulae.

Natural products are the most representative form of conventional therapy as compared to any other traditional or alternative medicine systems. They have numerous active components, either primary or secondary metabolites, which are associated with the diverse, intricate, and distinct characteristics of natural products and result in various pharmacological effects in clinic. However, some problems are associated with research on herb quality, which is the core of the drug industry, and restrict the development of this field to a certain extent. Quality-markers (Q-markers), a novel concept for quality assessment, open up a new avenue for promotion of healthy development of traditional medicine industry and improvement of the quality standard system to enhance traditional medicine or product quality standards. In this study, we first summarized the main factors affecting the quality of traditional medicines and natural products and importance for safety and then presented the concept of background and relevant factors of Q-markers. Moreover, the modern science technology and related methods used to identify the chemical composition have been discussed. Especially, based on the systematic analysis and discussion of the basic properties and clinical features of natural products, we have discussed new trends and effective strategies for identifying relevant Q-markers from herbs and probed the future research directions and challenges.

Metabolomics is the systematic study of all the metabolites present within a biological system, which consists of a mass of molecules, having a variety of physical and chemical properties and existing over an extensive dynamic range in biological samples. Diverse analytical techniques are needed to achieve higher coverage of metabolites. The application of mass spectrometry (MS) in metabolomics has increased exponentially since the discovery and development of electrospray ionization and matrix-assisted laser desorption ionization techniques. Significant advances have also occurred in separation-based MS techniques (gas chromatography-mass spectrometry, liquid chromatography-mass spectrometry, capillary electrophoresis-mass spectrometry, and ion mobility-mass spectrometry), as well as separation-free MS techniques (direct infusion-mass spectrometry, matrix-assisted laser desorption ionization-mass spectrometry, mass spectrometry imaging, and direct analysis in real time mass spectrometry) in the past decades. This review presents a brief overview of the recent advanced MS techniques and their latest applications in metabolomics. The software/websites for MS result analyses are also reviewed.

Recent advances in the study of high-throughput metabolomics combined with high-resolution mass spectrometry have accelerated our understanding of the efficacy, mechanisms, and application of natural products. In this study, we have used chemical metabolomics to investigate and discover small molecule metabolites for the potential mechanism of Acanthopanax senticosus Harms leaf (ASL) against acute promyelocytic leukemia (APL). Based on high-throughput metabolomics, the underlying biomarker was found by combining chromatography coupled with quadrupole time-of-flight mass spectrometry with multivariate data analysis. The protective effect of ASL was dissected using biochemical indicators, pathology sections, immunohistochemistry, and multivariate analysis. Furthermore, 13 potential biomarkers associated with the pathway of sugar metabolism, amino-acid metabolism, nucleotide metabolism, and the metabolism of arachidonic acid were identified from serum samples. This study would help to understand chemical metabolomics for investigating the anti-APL effectiveness of ASL.

Disease impacts important metabolic pathways and the alteration of metabolites may serve as a potential biomarker for early-stage diagnosis. High-resolution mass spectrometry-based metabolomics have been used to discover new biomarker metabolites. Rheumatoid arthritis (RA) seriously affects the quality of life in patients, but its pathophysiology remains unclear. This study aimed to develop a high-throughput approach by screening potential biomarkers to facilitate the diagnosis using metabolomics. The alteration of the metabolic profile of RA was investigated in human urine samples based on high-resolution UPLC-QTOF/MS and multivariate statistical analysis. Furthermore, ingenuity pathway analysis (IPA) was performed for the bioinformatics analysis of the data. Variable importance for projection values was determined, and the t-test was conducted for selecting a biomarker panel for RA. Receiver operating characteristic analysis was used to evaluate diagnostic accuracy of metabolites. We found that the score plot of orthogonal partial least squares discriminant analysis showed significant discrimination between RA and healthy groups. Five metabolites were identified as potential biomarkers for RA. The values of AUC, ranging from 0.819 to 0.993, indicated the potential capacity of these metabolites to distinguish RA patients and demonstrated that the differentially expressed metabolites might be a useful tool for the effective diagnosis of RA. The most significantly altered networks included FXR/RXR activation and bile acid biosynthesis. This study demonstrates that a high-resolution mass spectrometry-based metabolomics approach could provide crucial insight into the pathogenesis mechanism of RA.

Alzheimer's disease is a progressive and irreversible neurodegenerative disease, associated with a decreased cognitive function and severe behavioral abnormalities. Due to its complex pathophysiological characteristics, complicated interactions with a large number of genes and proteins, there is still no effective drug treatment of the disease. Amyloid cascade aggregation of senile plaques and hyperphosphorylation of Tau protein to form neurofibrillary tangles are the main pathological features of Alzheimer's disease, other mechanisms, such as oxidative stress, lack of central cholinergic neurotransmitters, inflammatory reaction and toxic metal ions have also been involved. The purpose of this review is to briefly introduce the progress of the development of the therapeutic agents based on their main mechanisms of action.

Hepatocellular carcinoma (HCC), a complex public health issue that is the most common primary hepatic malignancy, remains the highest incidence in developing countries and is showing sustained growth across the developed world. HCC is asymptomatic in nature; in the clinic, the low sensitivity of tumor detection is insufficient to characterize its category, risk prediction, or morbidity progression and to direct reasonable preventive measures. Therefore, HCC is usually diagnosed at a terminal cancer stage, and interventions are not effective in time to help patients. Thus, early diagnosis before onset and better comprehension of underlying mechanisms in HCC for molecular targets are urgently needed. Currently, the emerging field of metabolomics, involving high-throughput identification and measurement of metabolites in biological fluids, is a powerful method for appraising disease mechanisms and for biomarker discovery by elucidating the framework of the general dissection of HCC. In this article, we first provided the latest understanding of HCC on risk factors, diagnosis and treatment, and then highlighted a summary of metabolomics research, which is a vital field in HCC. We also discuss the current state of HCC biomarkers and future challenges in the digital and information era of medicine.

Diabetes mellitus has imposed a huge burden on modern society and is a serious threat to human health globally. Obese people usually have a higher risk of developing T2DM, and this disease is often diagnosed in older persons, accompanied by hypertension, hyperlipidemia, arteriosclerosis and other diseases. In the early stages of T2DM, patients can have difficulties in identifying any symptoms or may only suffer from slight fatigue and thirst with no obvious hyperglycemia. The early detection of this disease may provide a means of preventing the development of this disease in a more timely way. The metabolic profiling of small molecules provides a snapshot of physiological processes. By using a metabolite profiling platform based on mass spectrometry coupled with pattern recognition analysis, outstanding biomarkers that can be used for forecasting disease may be identified. In this study, we attempted to investigate potential key metabolic pathways associated with T2DM and determine its metabolic mechanism. Using multivariate statistical analysis, 33 marker metabolites associated with the metabolism of lipids, amino acids, and carbohydrates were identified. Our experiment yielded substantial new insights into serum metabolism in type 2 diabetes mellitus patients, and these metabolites will be targets for future mechanistic research to help understand the clinical significance of these metabolic abnormalities.

Severe sepsis (SS) is a major cause of mortality and morbidity in the intensive care unit and requires rapid diagnosis and treatment. Lipids are key regulators of cellular function; hence, in this study, we hypothesized that lipid levels could be altered in patients suffering from SS. Lipidomics is the comprehensive analysis of molecular lipid species. In this study, we used an untargeted lipidomics approach coupled with multivariate data analysis and bioinformatics analysis to profile the metabolic changes of sepsis induced by cecal ligation and puncture (CLP) in rats and investigated the treatment effects of huaxian capsule (HXC), a herbal medicine with putative effects in SS treatment. Ultra-performance liquid chromatography/quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS) and pattern recognition methods were employed to evaluate the metabolic phenotypic changes. Differentially expressed lipids have been further analyzed using the ingenuity pathway analysis (IPA) software, a tool that discloses functional pathways associated with lipids of interest. As a result, ten lipids involved in multiple metabolic pathways, such as in glycerophospholipid metabolism, ether lipid metabolism, purine metabolism, and tryptophan metabolism representing characteristic phenotypes, were identified by pathway analysis. HXC administration can reverse lipid phenotype changes induced by CLP; this indicates that the effectiveness of HXC as an SS treatment depends on modulation of the lipid metabolic changes. Our study shows that lipidomics is a valuable method that can provide additional insight into the underlying mechanisms of HXC in the SS treatment.

Metabolomics is a promising approach for the identification of metabolites which serve for early diagnosis, prediction of therapeutic response and prognosis of disease. Obesity is becoming a major health concern in the world. The mechanisms underlying obesity have not been well characterized; obviously, more sensitive biomarkers for obesity diseases are urgently needed. Fortunately, metabolomics – the systematic study of metabolites, which are small molecules generated by the process of metabolism – has been important in elucidating the pathways underlying obesity-associated co-morbidities. The endogenous metabolites have key roles in biological systems and represent attractive candidates to understand obesity phenotypes. Novel metabolite markers are crucial to develop diagnostics for obesity related metabolic disorders. Metabolomics allows unparalleled opportunities to seize the molecular mechanisms of obesity and obesity-related diseases. In this review, we discuss the current state of metabolomic analysis of obesity diseases, and also highlight the potential role of key metabolites in assessing obesity disease and explore the interrelated pathways.

Traditional Chinese Medicine (TCM), an indispensable part of herbal medicine, has been used for treating many diseases and/or symptoms for thousands of years. As we know, the main active components of TCM can account for its therapeutic effects. However, rational use of TCM faces a series of obstacles due to a large diversity of species and inaccurate knowledge of the active components. In recent years, more and more applications of new technologies or methodologies for investigating the active components of TCM have provided us with much additional information on active substances. Pharmacokinetics is an effective tool which can be used to investigate the many components of TCM. A pharmacokinetics approach reveals the dynamic processes of active components in vivo, including their absorption, distribution, metabolism, and excretion which offer guidance for clinical rational uses of TCM. Therefore, the objective of this paper is to review the current status of TCM, application of pharmacokinetics in investigating TCM, and emerging trends.

Natural products are an invaluable source for drug candidates. Currently, plasma metabolome has suggested that compounds present in herbs may exert bioactivity. The present investigation employed global metabolome analysis technology to explore the key target and action mechanism of scoparone, a representative ingredient of Yinchenhao (Artemisia capillaris Thunb.). First, we applied different databases for target prediction and focused on the potential targets of scoparone by network pharmacology, which also theoretically characterizes the effectiveness of scoparone on molecular docking. Among them, we selected the top predictions as the potential and crucial target. Then, non-targeted metabolomics technology based on an advanced UPLC-MS instrument coupled with a robust data processing platform was employed to characterize the metabolic profiling of alcoholic liver disease (ALD) rats. Furthermore, the ingenuity pathway analysis platform was used for metabolic network analysis, which mainly involved multiple-pathways, including tyrosine metabolism, glutathione metabolism, and primary bile acid biosynthesis. Interestingly, as a core biomarker, dopaquinone is directly related with target prediction of tyrosinase and finally resulted in a series of disturbances. Moreover, the prediction also validated the target on a metabolic level. The present investigation demonstrated that global metabolome analysis could provide a novel strategy for deciphering the potential drug targets of natural products.

During the last few years, many metabolomics technologies have been established in biomedical research for analyzing the changes of metabolite levels. They have also become important tools in the field of life sciences and are widely applied to discover innovative drugs, providing valuable methods to explore the function and essence of formulas in traditional Chinese medicine (TCM) research. Metabolomics shows major potential to provide a unique perspective for disease diagnosis and elucidate the action mechanism of TCM by measuring endogenous metabolites in biofluids. With the gradual expansion of the application fields of metabolomics, the related number of techniques is increasing day by day. The techniques of choice for the study of various low-molecular-weight metabolic pathways, and for the analysis of metabolites in search of the changes of biomarkers in TCM treatment of disease in biological samples include nuclear magnetic resonance (NMR) spectroscopy, chromatography coupled with mass spectrometry, and so forth. These techniques have been proven to be powerful tools for the detection of metabolites and biomarkers. Here, we will introduce the newly used analytical techniques for metabolomics, and metabolomics in TCM is also summarized.

Prostate cancer (PCa) is the most frequently diagnosed cancer and the second leading cause of cancer death among men in the world. Therefore, it is important to discover new biomarkers for the diagnosis of PCa, preferably noninvasive ones. Metabolomics can be used to analyse the entire metabolic profile of a biological system and discover potential biomarkers. Advances in liquid chromatography-tandem mass spectrometry technology have led to the application of metabolomics in PCa, toward identifying metabolic alterations in PCa. In this work, the art metabolomics platform by fast ultrahigh performance liquid chromatography-tandem mass spectrometry (FPLC/MS) coupled with multivariate statistical analyses were employed to identify sensitive and economical peripheral urine biomarker(s) associated with the entire measurable metabolome from PCa patients (n = 236) and age-matched healthy controls (n = 233), with the aim of discovering alterations in the metabolic phenotype and thus discovering potential biomarkers for the diagnosis of PCa. Metabolic differences among PCa and control subjects were identified based on orthogonal signal correction-partial least squares discriminant analysis (OPLS-DA). Glycocholic acid, hippurate, 5-hydroxy-L-tryptophan, taurocholic acid, and chenodeoxycholic acid in the PCa subjects were significantly different from the control cases. Bioinformatics analysis found that these differentially-expressed metabolites had a strong correlation with the farnesoid X receptor and retinoic X receptor (FXR/RXR) activation, etc. To demonstrate the utility of urine biomarkers for the early diagnosis of PCa, three metabolites comprising glycocholic acid, hippurate and 5-hydroxy-L-tryptophan were selected as candidate biomarkers (AUC > 0.95) and validation in separate, independent patient cohorts, yielded satisfactory accuracy, sensitivity and specificity. Furthermore, these data suggest that panels of analytes may be valuable to translate metabolomic findings to clinically useful diagnostic tests. Potentially, the present study provides promising diagnosis tool for PCa.

Lipidomics, a branch of metabonomics, could provide a powerful technique for discovery of lipid molecules to reveal disease status and drug efficacy. The Shenqi pill (SQP) is a representative prescription for clinical application in the prevention and treatment of kidney-yang deficiency syndrome (KYDS). However, its effect mechanism is still not clear. This article aims to reveal the intervention effect of SQP on KYDS from the perspective of lipid metabolism. In this study, SQP was used to intervene in the rat model of KYDS, on the foundation of successfully replicating the rat model of KYDS induced by corticosterone. The MetaboAnalyst tool was used for analysis of the serum metabolic profile and pattern recognition of KYDS model, based on UPLC-SYNAPT-G2-Si-HDMS. Finally, twenty-two potential lipid biomarkers related to the KYDS model were characterized, and the effects of SQP on regulating potential lipid markers in serum of KYDS model were analyzed. There were 10 biomarkers and seven metabolic pathways closely related to SQP therapy for KYDS were found. The action mechanism and targets of SQP in treating KYDS were explored based on high-throughput lipidomics. This work could provide valuable data and scientific evidence in subsequent studies for the treatment of KYDS.

Essential hypertension (EH) is a chronic disease characterized by a variety of causes of elevated systemic arterial pressure, which often causes functional or organic damage to important organs such as the heart, brain, and kidney. Hypertension of excessive liver-fire syndrome is a type of classification for young people with essential hypertension. The disease is slower in its onset and its symptoms are more ambiguous, and thus its pathogenesis is complicated and still unclear. In this study, aconite, dried ginger and cinnamon extracts were combined with L-NAME to establish a model of excessive liver-fire hypertension. Blood pressure (systolic blood pressure), ANGII, NE and 5-HT were used as evaluation indicators to establish the model. Urinary metabolomics based on ultra-high performance liquid chromatography coupled with quadruple time-of-flight mass spectrometry was used to characterize the metabolic changes and potential biomarkers in modeled rats. Compared to the treatment group, 32 potential biomarkers were initially identified in the model using multivariate statistical analysis involving 11 metabolic pathways. After oral administration of Luozhen capsules, eight biomarkers that can be adjusted in high, medium and low doses of Luozhen capsules in urine were preliminarily determined, mainly involving two metabolic pathways of amino acid metabolism and lipid metabolism. In conclusion, this study explored the metabolomic changes in rats with hypertension of liver-fire hyperactivity syndrome and the post-dose metabolomics, determined the relevant biomarker groups, and clarified the metabonomic connotation of Luozhen capsules in the treatment of liver-fire excessive type hypertension.

This study was employed to explore the potential biomarkers of endometriosis of cold coagulation and blood stasis (ECB) model rats and the effective mechanism of action of paeoniflorin (PF). The serum metabolomics approach was carried out using the UPLC-MS technique with a pattern recognition approach to prove the possible biomarkers of the ECB model rats and the perturbed pathways. Subsequently, the mechanism of PF treatment of this disease model was elucidated. The results revealed that the serum metabolism profiles in two groups were also separated significantly. Moreover, 8 biomarkers were found in the positive mode, and 5 biomarkers were found in the negative mode. Totally, 13 biomarkers participated in the metabolism of phenylalanine, arachidonic acid, etc. After treatment with PF, 10 biomarkers were regulated. Among the 10 biomarkers, 4 were statistically significant: L-phenylalanine, L-tryptophan, LysoPC (18:4(6Z,9Z,12Z,15Z)), and LysoPC (16:1(9Z)). We initially confirmed that PF could significantly regulate the metabolic expression of multiple metabolic pathways in the ECB model rats. For the first time, this study explored the mechanism of action of PF treatment based on the metabolic pathways of the organism and demonstrated the potential of the metabolomics techniques for the study of drug action mechanisms.

Coronary heart disease (CHD) is a relatively complex disease characterized by narrowing of the arterial lumen and reduction of blood flow to the heart. There is no effective early diagnosis and prevention method. Jing Zhi Guan Xin Pian (JZGXP) is a new preparation prepared from the effective extract of Guanxin II. It is made of five components of traditional Chinese medicine and functions by promoting blood circulation and removing blood stasis and is used for the treatment of CHD and angina pectoris. In our study, a CHD rat model was prepared using a high-fat diet combined with intraperitoneal injection of vitamin D3. Clinical biochemical indexes (TG, CHO and HDL-C), histopathology (coronary and myocardial tissue), electrocardiogram and cardiac indexes were used to evaluate the efficacy of JZGXP in the treatment of CHD model rats. UPLC-HDMS-based metabolomics techniques were used to find metabolic profiles, biomarkers and related metabolic pathways in CHD models and to evaluate the effects of JZGXP on them. At the same time, the targets of JZGXP for the treatment of CHD were analyzed. Our study ultimately identified 25 biomarkers associated with CHD models. Further studies found that these 25 biomarkers involved 9 metabolic pathways in the body and found that JZGXP can recall 21 biomarkers in the urine of model rats and these biomarkers involve nine metabolic pathways. Finally, the targets of JZGXP for the treatment of CHD were β-alanine metabolism and tyrosine metabolism, i.e. amino acids metabolism. This study showed that metabolomics technology is effective for exploring potential biomarkers associated with syndromes or diseases and the therapeutic mechanisms of a traditional Chinese medicine formulation.

Severe sepsis (SS) remains among the leading causes of death in both developed and developing countries. There is an urgent need for accurate biomarkers for early diagnosis of SS. Non-targeted mass-spectrometry was used to characterize sensitive and economical peripheral biomarker(s) associated with the serum metabolome from SS patients. In this study, serum samples were obtained from healthy controls and age-matched patients with SS. Multivariate statistical analysis was performed, and the metabolites identified were studied in relation to subsequent intervention procedures by receiver operating characteristic curve analysis. Metabolic differences among SS and control subjects were identified based on orthogonal signal correction-partial least squares discriminant analysis. Sphingosine, 5-methylcytidine, 3-dehydrocarnitine, 4-acetamido-2-aminobutanoic acid and phenyllactic acid in the SS subjects were significantly different from the controls. Three metabolites comprising sphingosine, 5-methylcytidine and 3-dehydrocarnitine were selected as candidate biomarkers and validated in separate, independent patient cohorts. These metabolite markers hold great potential for further development of SS early diagnostic markers. These findings suggest mass-spectrometry serum metabolomics may possess great potential for early diagnosis of SS patients.

Sepsis-induced lung injury (SLI) with high mortality and morbidity remains a leading cause of death in intensive care. There is an urgent need for the identification of SLI biomarkers for effective diagnosis. Studies have suggested the promising use of blood lipids as functional intermediate phenotypes in medical research. Lipid metabolism is critical in disease development, and lipidomics is the comprehensive analysis of molecular lipids. Lipid profiling by mass spectrometry may improve SLI risk prediction. The aim of this study was to use lipidomics to identify lipid molecules that could predict SLI patients. We performed a comprehensive and untargeted lipidomic analysis, using ultra-performance liquid chromatography/mass spectrometry on plasma samples from SLI patients and controls, and multivariate analysis methods were used to identify the lipids associated with SLI status. The serum samples were collected from SLI patients in the ICU. Lipid metabolic profiles were evaluated and compared between SLI and healthy cases. Variable importance in projection values were obtained to identify potential lipid biomarkers. A receiver operating characteristic curve was used to evaluate the power of the diagnostic biomarkers. Multivariate analysis revealed a good predictive model to distinguish their metabolic patterns. Seven lipid signatures were identified as potential biomarkers for SLI patients. These candidate lipids were validated in an additional independent cohort. According to the ROC analysis, the values of area under the curve (AUC) range from 0.813 to 0.997, indicating their potential power to distinguish between SLI and healthy cases. These results supported the concept that mass spectrometry-based lipid phenotype profiling might be a useful tool for the effective diagnosis of SLI.

Mild cognitive impairment (MCI) confers an increased risk of developing Alzheimer's disease (AD). There is high interest in the discovery of early diagnostic biomarkers that could predict MCI to AD progression, for which saliva metabolomics exhibits a great potential. In this work, the nontargeted metabolomic approach based on fast ultra-high performance liquid chromatography coupled with time-of-flight mass spectrometry (FUPLC-MS) was developed to examine metabolic differences in saliva samples from MCI subjects and age-matched AD subjects; multivariate analyses were used to define the differences between MCI and AD groups; and receiver operating characteristic analysis was used to evaluate the diagnostic power of the candidate biomarkers. Metabolic differences among AD and MCI subjects were identified by principal component analysis. Of note, ten metabolites in the saliva of AD subjects were significantly different from MCI subjects. According to the predictive model, five metabolites were selected as the candidate biomarkers for predicting conversion of MCI to AD. The major contributors were cytidine (P = 0.0003) and sphinganine-1-phosphate (P = 0.0009). The results demonstrated that saliva metabolite profiling may contribute to making early diagnosis and understanding the pathogenic mechanism of AD and MCI.

Severe sepsis (SS) remains a worldwide threat, not only in industrialized countries but also in developing countries. Currently, the incidence of SS is rising, and there is a lack of effective drugs. Huaxian capsule (HXC) is traditional Chinese medicine with therapeutic effects on SS. The objectives of this study were to explore the underlying mechanisms of pharmacological effects of HXC on the treatment of SS. Metabolomics is a powerful tool for the characterization of metabolic phenotypes, potential metabolite biomarkers and metabolic pathways. Here, we investigated the metabolic changes in SS rats by performing metabolic profiling and metabolite biomarkers. Urinary UPLC-MS coupled with multivariate statistical analysis including principal components analysis (PCA), partial least-squares discriminant analysis (PLS-DA), and orthogonal projections to latent structures discriminant analysis (OPLS-DA) were used to assess phenotypic changes. Metabolites contributing to the discrimination the control group, SS group and HXC group were identified by using unsupervised PCA. Metabolic pathway analysis was performed using the MetPA web tool. As a result, 10 potential metabolite biomarkers in urine involved phenylalanine, tyrosine and tryptophan biosynthesis, valine, leucine and isoleucine biosynthesis, glycine, serine and threonine metabolism, tyrosine metabolism pathways, were identified in SS rats. The unsupervised PCA score plot showed that the HXC group cluster modulating dynamic metabolic changes was closer to that of the control group than the model group cluster, demonstrating that HXC has therapeutic effects on SS rats. Overall, it showed that urinary UPLC-MS metabolomics could dissect the underlying mechanisms of HXC protects against SS.

Sepsis is a commonly encountered scenario in an intensive care unit (ICU), and the kidney is one of the organs frequently affected. Sepsis-induced acute kidney injury (SIAKI) contributes to the high mortality and morbidity. A major reason for this is the lack of biomarkers for SIAKI in ICU patients. Metabolic phenotype signatures of SIAKI may enable metabolite biomarker discovery for diagnosis. With the progress of the Omics, currently, high-resolution mass spectrometry-based metabolomics is being used for biomarker discovery. Orthogonal partial least-squares discriminant analysis (OPLS-DA) afforded a good predictive tool to distinguish patients and detect the specific metabolic patterns. Variable importance for projection (VIP) was conducted to identify potential biomarkers for SIALI. Receiver operating characteristic analysis was performed for evaluating the diagnostic accuracy of potential metabolites. In this study, some potential biomarkers were successfully discovered by the commonly used variable selection method, VIP of OPLS-DA. Six metabolites were identified as the potential biomarkers for distinguishing SIAKI patients. Meanwhile, malonylcarnitine, D-glutamine and 3-methoxytyrosine were found to be the important variables to distinguish between SIAKI patients and healthy cases. According to the receiver operating characteristic analysis, we reported malonylcarnitine, with an area under concentration–time curve value of 0.995, maybe as a novel diagnostic biomarker associated with SIALI patients. Our results indicate that serum metabolic profiling by high-resolution mass spectrometry might be a helpful tool for determining the metabolic phenotype of SIAKI patients.

Hyperlipidemia (HLP) is a metabolic disorder which is characterized by a disturbance in lipid metabolism and is a primary risk factor for cardiovascular disease and atherosclerosis. Patients who were diagnosed with hyperlipidemia are usually with a serious dyslipidemia, which can be evaluated by current clinical markers such as triglyceride, total cholesterol, low density lipoprotein cholesterol, and high-density lipoprotein. Metabolic profiling has great potential to help the diagnosis and prognosis of hyperlipidemia patients in an earlier stage. To explore the metabolic profiling, we used ultraperformance liquid chromatography mass spectrometry, monitored the metabolites' alterations and impaired pathways in HLP patients. Of note, a total of 37 serum metabolites from HLP patients were tentatively identified. Furthermore, they were involved in 11 pathways such as linoleic acid metabolism, glycerophospholipid metabolism, terpenoid backbone biosynthesis, sphingolipid metabolism, glycosylphosphatidylinositol-anchor biosynthesis, steroid hormone biosynthesis, arachidonic acid metabolism, pentose and glucuronate interconversions, starch and sucrose metabolism, glycerolipid metabolism and purine metabolism. These results provide potential biomarkers for early risk assessment of HLP and offer further insights into the complex metabolic pathway changes in HLP. They also suggest the distinction of utilizing UPLC-Q-TOF-MS for exploring metabolic characterization of HLP patients.

Mild cognitive impairment (MCI) is considered to represent early AD. Currently, there is a great need for sensitive tools to monitor the progression of MCI to AD. In this work, a nontargeted metabolomics approach was developed to examine metabolic differences in serum samples from the MCI subjects and the age-matched AD subjects. Based on principal component analysis, metabolic differences among AD and MCI subjects were identified. Nine metabolites in the serum of the AD subjects were significantly different from the MCI subjects. Two metabolites were selected as the candidate biomarkers and validated in separate and independent patient cohorts. The major contributors to the predictive model were upregulated sphinganine-1-phosphate and 7-ketocholesterol, which yielded satisfactory sensitivity, and specificity, indicating potential value for predicting the conversion of MCI to probable AD. The present study may provide a diagnosis tool to monitoring the progression of MCI to AD.

Colorectal cancer (CRC) is one of the most malignant cancers resulting from abnormal metabolism alterations. As one of the essential amino acids, tryptophan has a variety of physiological functions, closely related to regulation of immune system, central nervous system, gastrointestinal nervous system and intestinal microflora. Colorectal cancer, a type of high-grade malignancy disease, stems from a variety of factors and often accompanies inflammatory reactions, dysbacteriosis, and metabolic disorders. Colorectal cancer accompanies inflammation and imbalance of intestinal microbiota and affects tryptophan metabolism. It is known that metabolites, rate-limiting enzymes, and ARH in tryptophan metabolism are associated with the development of CRC. Specifically, IDO1 may be a potential therapeutic target in colorectal cancer treatment. Furthermore, the reduction of tryptophan amount is proportional to the poor quality of life for colorectal cancer patients. This paper aims to discuss the role of tryptophan metabolism in a normal organism and investigate the relationship between this amino acid and colorectal cancer. This study is expected to provide theoretical support for research related to targeted therapy for colorectal cancer. Furthermore, strategies that modify tryptophan metabolism, effectively inhibiting tumor progression, may be more effective for CRC treatment.

The human gastrointestinal tract colonizes a large number of microbial microflora, forms a host-microbiota co-metabolism structure with the host to participate in various metabolic processes in the human body, and plays a major role in the host immune response. In addition, the dysbiosis of intestinal microbial homeostasis is closely related to many diseases. Thus, an in-depth understanding of the relationship between them is of importance for disease pathogenesis, prevention and treatment. The combined use of metagenomics, transcriptomics, proteomics and metabolomics techniques for the analysis of gut microbiota can reveal the relationship between microbiota and the host in many ways, which has become a hot topic of analysis in recent years. This review describes the mechanism of co-metabolites in host health, including short-chain fatty acids (SCFA) and bile acid metabolism. The metabolic role of gut microbiota in obesity, liver diseases, gastrointestinal diseases and other diseases is also summarized, and the research methods for multi-omics combined application on gut microbiota are summarized. According to the studies of the interaction mechanism between gut microbiota and the host, we have a better understanding of the use of intestinal microflora in the treatment of related diseases. It is hoped that the gut microbiota can be utilized to maintain human health, providing a reference for future research.

Yinchenhao Tang (YCHT), a classic traditional Chinese medicine (TCM) formulae, plays an important role in the treatment of Yang Huang syndrome (YHS). With the emergence of new biomarkers of YHS uncovered via metabonomics, the underlying functional mechanisms are still not clear. Functional metabolomics aims at converting biomarkers derived from metabonomics into disease mechanisms. Here, an integrated non-target metabolomics and IPA strategy were used to investigate the YCHT intervention on YHS. Our metabolomics study has shown that the potential protective effect of YCHT on YHS mice leads to significant changes in the metabolic profile by modulating the biomarkers and regulating the metabolic disorders. Twenty two differential metabolite biomarkers and fifteen involved metabolic pathways were correlated with the regulation of YCHT treatment on YHS. Functional metabolomics identified a core biomarker, D-glucuronic acid in pentose and glucuronate interconversion pathways, which was directly related to the target prediction of UDP-glucuronosyltransferase 1A1 and eventually leaded to a series of disturbances. In conclusion, this study shows that functional metabolomics can discover metabolic pathways as promising targets.

Altered lipid metabolism is an emerging hallmark of cancers. Mirabilite has a therapeutic effect on colorectal cancer (CRC); however, its metabolic mechanism remains unclear. This study aims to explore the potential therapeutic targets of mirabilite protection against colorectal cancer in APCmin/+ mice model. Oral administration of mirabilite was started from the ninth month, while the same dosage of distilled water was given to both the control group and the model group. Based on lipidomics, we collected serum samples of all mice at the 20th week and used a non-targeted method to identify the lipid biomarkers of CRC. Compared with C57BL/6J mice, the metabolic profile of CRC model mice was significantly disturbed, and we identified that 25 lipid-related biomarkers, including linoleic acid, 2-hydroxybutyric acid, 6-deoxocastasterone, hypoxanthine, PC(16:1), PC(18:4), and retinyl acetate, were associated with CRC. According to the abovementioned results, there were six lipid molecules with significant differences that can be used as new targets for handling of CRC through six metabolic pathways, namely, linoleic acid metabolism, retinol metabolism, propanoate metabolism, arachidonic acid metabolism, biosynthesis of unsaturated fatty acids and purine metabolism. Compared with the model group, the metabolic profiles of these disorders tend to recover after treatment. These results indicated that the lipid molecules associated with CRC were regulated by mirabilite. In addition, we identified seven key lipid molecules, of which four had statistical significance. After administration of mirabilite, all disordered metabolic pathways showed different degrees of regulation. In conclusion, high-throughput lipidomics approach revealed mirabilite regulating the altered lipid metabolism as anticancer therapeutics.

Metabolomics is an emerging and robust discipline and involves the comprehensive evaluation of small molecule endogenous metabolites and enables the exploration of the pathogenesis of diseases. For example, endometriosis – a common disease which mostly occurs in women of childbearing age. A cure for endometriosis of cold coagulation and blood stasis (ECB) is highly sought after. This study was conducted to discover the potential biomarkers of ECB and the effective mechanism undertaken by Guizhi Fuling Wan (GFW) in treating ECB in rats. Urinary metabolomics were performed by using UPLC-Q-TOF-MS with pattern recognition methods to evaluate the changes in metabolic profiles and to identify biomarkers for elucidating the mechanism of the treatment of ECB with GFW. The results showed that urinary metabolism in the two groups were distinctly separated on the 28th day, and a total of 20 differential biomarkers (16 in the positive mode, 4 in the negative mode) were confirmed involving several key metabolic pathways which included phenylalanine, tyrosine and tryptophan biosynthesis, valine, leucine and isoleucine biosynthesis, glyoxylate and dicarboxylate metabolism, tyrosine metabolism and the citrate cycle. Following the oral administration of GFW, certain pathways were affected; these included the following: phenylalanine, tyrosine and tryptophan biosynthesis, valine, leucine and isoleucine biosynthesis, glyoxylate and dicarboxylate metabolism, tyrosine metabolism, citrate cycle, steroid hormone biosynthesis, tryptophan metabolism, phenylalanine metabolism, primary bile acid biosynthesis, and aminoacyl-tRNA biosynthesis. This study also demonstrated that the administration of GFW affected the levels of urine endogenous metabolites, thereby laying a foundation for further study of the pharmacodynamical mechanism of GFW.

Metabolomics has been shown to be an effective tool for biomarker screening and pathway characterization and disease diagnosis. Metabolic characteristics of hepatocellular carcinoma (HCC) may enable the discovery of novel biomarkers for its diagnosis. In this work, metabolomics was used to investigate metabolic alterations of HCC patients. Plasma samples from HCC patients and age-matched healthy controls were investigated using high resolution ultrahigh performance liquid chromatography-mass spectrometry and metabolic differences were analyzed using pattern recognition methods. 23 distinguishable metabolites were identified. The altered metabolic pathways were associated with arginine and proline metabolism, glycine, serine and threonine metabolism, steroid hormone biosynthesis, starch and sucrose metabolism, etc. To demonstrate the utility of plasma biomarkers for the diagnosis of HCC, five metabolites comprising deoxycholic acid 3-glucuronide, 6-hydroxymelatonin glucuronide, 4-methoxycinnamic acid, 11b-hydroxyprogesterone and 4-hydroxyretinoic acid were selected as candidate biomarkers. These metabolites that contributed to the combined model could significantly increase the diagnostic performance of HCC. It has proved to be a powerful tool in the discovery of new biomarkers for disease detection and suggest that panels of metabolites may be valuable to translate our findings to clinically useful diagnostic tests.

Hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC) account for nearly all primary liver cancers. Diagnosis of ICC and HCC still remains a challenge because of the lack of sensitive diagnostic and sufficiently accurate tests. Lipidomics is a critical part of metabolomics and aims to study all the lipids within a living system. In order to have a better and noninvasive diagnostic test, we profiled the lipidomics in a total of 292 blood samples from patients and normal individuals. It revealed that four metabolites, PE(19 : 0/0 : 0), PE(18 : 2(9Z,12Z)/0 : 0), PC(14 : 0/0 : 0) and PC(18 : 0/0 : 0), were defined as “marker metabolites”, which can be used to distinguish the HCC and ICC. Discrimination analysis showed that a combination of these metabolites could be used as markers for HCC and ICC with better diagnostic parameters with high sensitivity and specificity than using a single metabolite marker. Lipidomic profiling could allow us to predict the disease in its early stages.

Traditional Chinese Medicine (TCM) has been used in clinical practice for several thousand years, with an indispensable role in prevention and treatment of disease using multiple ingredients. Kai-Xin-San (KXS) is a TCM formula consisting of four herbs, Ginseng Radix, Polygalae Radix, Poria and Acori Tatarinowii Rhizoma, which has been used to treat Alzheimer's disease, depression and Parkinson's disease. However, the constituents absorbed into blood after oral administration of KXS remain unknown. Here, a sensitive and rapid method using ultra performance liquid chromatography/quadrupole time-of-flight mass spectrometry (UPLC/Q-TOF-MS/MS) combined with a multivariate data processing approach (Mdpa) and MetaboLynx software tool was used for analysis and identification of bioactive components and their metabolites in rat plasma following oral administration of KXS. A hyphenated MS/MS analyzer was used for determination of accurate fragment ions and mass spectrometric fragmentation mechanisms, and enhanced data acquisition. Metabolite elucidation was performed using UPLC/Q-TOF-MS/MS coupled with MetaboLynx tool. With the established method, a total of 49 peaks were tentatively characterized in vivo based on MS and MS/MS data, and comparison with available databases. Of the 49 compounds tentatively characterized in rat plasma, seven metabolites were detected and identified by comparing their fragmentation patterns using MetaboLynx software tool. On the basis of the chromatographic peak area, the glucuronidated conjugates were identified as major metabolites. This work demonstrated the potential of the UPLC/Q-TOF-MS/MS with Mdpa and MetaboLynx for rapid, simple, reliable and automated identification of metabolites of herbal medicine.

Alcoholic liver disease (ALD) is associated with poor health, diseases and dysfunctions worldwide. Unfortunately, current biomarkers, including PCIII, IV-C, LN, and HA levels, are expensive and lack sensitivity in ALD detection. Because these biomarkers are invasive, time-consuming or expensive, ALD is usually diagnosed at late stages, for which there are no effective therapies. Thus, biomarkers for the early detection of ALD are urgently needed. Thankfully, metabolomics is a powerful technology that allows the assessment of global low-molecular-weight metabolites in a biological system and shows great potential in biomarker discovery. Analysis of the key metabolites in body fluids has become an important part of improving the diagnosis, prognosis, and therapy of diseases. Urine biomarkers may be a more attractive option, but none can currently detect ALD with the required accuracy. Herein, we describe our metabolomics approach for detecting ALD in a group of 206 patients. A total of six urinary differential metabolites that contributed to ALD progress were identified, and more importantly we discovered three of them with an accuracy of more than 95%. The biomarker panel may be sensitive to early diagnosis, prognosis and therapy of ALD.

Severe sepsis (SS) is a main cause of death in hospitalized patients worldwide. There is an urgent need for accurate biomarkers for early diagnosis of SS. Metabolomics technologies allow high-throughput screening of metabolite biomarkers. Non-targeted mass spectrometry (MS) was used to characterize sensitive and economical peripheral biomarker(s) associated with the urine metabolome from SS patients. In this study, urine samples were obtained from controls and age-matched patients with SS. Metabolic differences among SS and control subjects were identified based on orthogonal signal correction-partial least squares discriminant analysis. Sphingosine, 5-methylcytidine, 3-dehydrocarnitine, 4-acetamido-2-aminobutanoic acid and phenyllactic acid in the SS subjects were significantly different from the control subjects. The receiver operating characteristic curve of the differential expression of these metabolites was also performed to demonstrate the utility of these biomarkers for diagnosis of SS. Three metabolites comprising sphingosine, 5-methylcytidine and 3-dehydrocarnitine were selected as candidate biomarkers and validation in separate and independent patient cohorts. Mass-spectrometry urine profiling proved to be an efficient and convenient tool for diagnosis and screening of SS in a high-risk population.

Although many diagnostic tools have been developed for coronary heart disease (CHD), its diagnosis is still challenging. Lipids play an important role in diseases and a lipidomics approach could offer a platform to clarify the pathogenesis and pathologic changes of this disease. To the best of our knowledge, no lipidomics studies on serum have been attempted to improve the diagnosis and identify the potential biomarkers of CHD. The aim of this study was to investigate the distinctive lipid changes in serum samples of CHD patients and to identify candidate biomarkers for the reliable diagnosis of CHD using this platform. In this study, the serum lipid profiles of CHD patients were measured via ultra-performance liquid chromatography-G2-Si-high definition mass spectrometry combined with multivariate data analysis. A MetaboAnalyst tool was used for the analysis of the receiver operating-characteristic, while the IPA software was applied for the pathway analysis. The obtained results inferred that 33 lipid molecular species involving 6 fatty acids, 21 glycerophospholipids and 6 sphingolipids have significant differences in the serum of CHD patients. Simultaneously, 4 upstream regulatory proteins related to lipid metabolism disorders of CHD were predicted. Ten lipids have high clinical diagnostic significance according to the receiver operating-characteristic curves. This research shows that the in-depth study of lipids in the serum contributes to the clinical diagnosis of CHD and interprets the occurrence and development of CHD.