Precise Lipidomics Decipher Circulating Ceramide and Sphingomyelin Cycle Associated with the Progression of Rheumatoid Arthritis.

Rheumatoid arthritis (RA) is a long-term autoimmune condition that causes joint and surrounding tissue inflammation. Lipid mediators are involved in inflammation and deterioration of the joints. Despite attempts to discover effective drug targets to intervene with lipid metabolism in the disease, progress has been limited. In this study, precise lipidomic technology was employed to quantify a broad range of serum ceramides and sphingomyelin (SM) in a large cohort, revealing an association between the accumulation of circulating ceramides and disturbed ceramide/SM cycles during the progression of RA. In our investigation, we discovered that eight ceramides exhibited a positive correlation with the activity of RA, thereby enhancing the accuracy of RA diagnosis, particularly in patients with serum antibody-negative RA. Furthermore, the enzyme SM phosphodiesterase 3 (SMPD3) was found to disrupt the circulating SM cycle and accelerate the progression of RA. The activity of SMPD3 can be inhibited by methotrexate, resulting in decreased metabolic conversion of SM to ceramide. These findings suggest that targeting the SM cycle may provide a new therapeutic option for RA.

Rapid, sensitive, and high-throughput quantification of broad serological ceramides by using isotope dilution liquid chromatography-negative ion electrospray tandem mass spectrometry.

Ceramides are important intermediates in the metabolism of sphingolipids. High-throughput liquid chromatography-mass spectrometry has been used extensively for monitoring the levels of serological ceramides, but is still limited by inadequate coverage or lack of sensitivity. Herein, a rapid, sensitive, and high-throughput isotope dilution liquid chromatography-negative ion electrospray tandem mass spectrometry (IDLC-nESI-MS/MS) method was developed and verified for accurate quantification of 41 ceramides, involving ceramides with C16-20 sphingosine, dihydro-ceramide, and dehydro-ceramide. This method was validated with excellent linearity (R2 > 0.99) and good recovery in the range of 90-110%. Intra- and inter-day imprecision were below 5.57% and 7.83% respectively. The improved high-throughput quantitative method developed in this study may aid in the accurate characterization of ceramides for understanding ceramide biology and application in disease diagnosis.

The adenosine analog prodrug ATV006 is orally bioavailable and has preclinical efficacy against parental SARS-CoV-2 and variants.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus driving the ongoing coronavirus disease 2019 (COVID-19) pandemic, continues to rapidly evolve. Because of the limited efficacy of vaccination in prevention of SARS-CoV-2 transmission and continuous emergence of variants of concern (VOCs), orally bioavailable and broadly efficacious antiviral drugs are urgently needed. Previously, we showed that the parent nucleoside of remdesivir, GS-441524, has potent anti-SARS-CoV-2 activity. Here, we report that esterification of the 5'-hydroxyl moieties of GS-441524 markedly improved antiviral potency. This 5'-hydroxyl-isobutyryl prodrug, ATV006, demonstrated excellent oral bioavailability in rats and cynomolgus monkeys and exhibited potent antiviral efficacy against different SARS-CoV-2 VOCs in vitro and in three mouse models. Oral administration of ATV006 reduced viral loads and alleviated lung damage when administered prophylactically and therapeutically to K18-hACE2 mice challenged with the Delta variant of SARS-CoV-2. These data indicate that ATV006 represents a promising oral antiviral drug candidate for SARS-CoV-2.

Serum metabolomic and lipidomic profiling identifies diagnostic biomarkers for seropositive and seronegative rheumatoid arthritis patients.

BACKGROUND: Diagnosing seronegative rheumatoid arthritis (RA) can be challenging due to complex diagnostic criteria. We sought to discover diagnostic biomarkers for seronegative RA cases by studying metabolomic and lipidomic changes in RA patient serum. METHODS: We performed comprehensive metabolomic and lipidomic profiling in serum of 225 RA patients and 100 normal controls. These samples were divided into a discovery set (n = 243) and a validation set (n = 82). A machine-learning-based multivariate classification model was constructed using distinctive metabolites and lipids signals. RESULTS: Twenty-six metabolites and lipids were identified from the discovery cohort to construct a RA diagnosis model. The model was subsequently tested on a validation set and achieved accuracy of 90.2%, with sensitivity of 89.7% and specificity of 90.6%. Both seropositive and seronegative patients were identified using this model. A co-occurrence network using serum omics profiles was built and parsed into six modules, showing significant association between the inflammation and immune activity markers and aberrant metabolism of energy metabolism, lipids metabolism and amino acid metabolism. Acyl carnitines (20:3), aspartyl-phenylalanine, pipecolic acid, phosphatidylethanolamine PE (18:1) and lysophosphatidylethanolamine LPE (20:3) were positively correlated with the RA disease activity, while histidine and phosphatidic acid PA (28:0) were negatively correlated with the RA disease activity. CONCLUSIONS: A panel of 26 serum markers were selected from omics profiles to build a machine-learning-based prediction model that could aid in diagnosing seronegative RA patients. Potential markers were also identified in stratifying RA cases based on disease activity.

Machine Learning for Investigation on Endocrine-Disrupting Chemicals with Gestational Age and Delivery Time in a Longitudinal Cohort.

Endocrine-disrupting chemicals (EDCs) are widespread environmental chemicals that are often considered as risk factors with weak activity on the hormone-dependent process of pregnancy. However, the adverse effects of EDCs in the body of pregnant women were underestimated. The interaction between dynamic concentration of EDCs and endogenous hormones (EHs) on gestational age and delivery time remains unclear. To define a temporal interaction between the EDCs and EHs during pregnancy, comprehensive, unbiased, and quantitative analyses of 33 EDCs and 14 EHs were performed for a longitudinal cohort with 2317 pregnant women. We developed a machine learning model with the dynamic concentration information of EDCs and EHs to predict gestational age with high accuracy in the longitudinal cohort of pregnant women. The optimal combination of EHs and EDCs can identify when labor occurs (time to delivery within two and four weeks, AUROC of 0.82). Our results revealed that the bisphenols and phthalates are more potent than partial EHs for gestational age or delivery time. This study represents the use of machine learning methods for quantitative analysis of pregnancy-related EDCs and EHs for understanding the EDCs' mixture effect on pregnancy with potential clinical utilities.

Combinatory Data-Independent Acquisition and Parallel Reaction Monitoring Method for Deep Profiling of Gangliosides.

Ganglioside is an important class of lipid species involved in intercellular signaling and various diseases, especially for neurodegenerative diseases. Systematic ganglioside profiling is challenging because of their naturally low abundance and highly diverse species. Herein, a new data-independent acquisition and parallel reaction monitoring (DIA/PRM) method with superior sensitivity was developed. The untargeted DIA acquisition consecutively records all the precursor ion and fragment ions at the same time, while the targeted PRM analysis with versatile higher collisional dissociation generates full MS/MS spectra for structure elucidation and verification. As compared with traditional data-dependent acquisition (DDA), the DIA/PRM method unbiasedly detected the majority of abundant ganglioside species and as low as 50 pg of ganglioside in an untargeted manner. Gangliosides in four kinds of biological samples including the mouse brain, mouse plasma, HeLa cell, and human colon cancer tissue were systematically identified, and low-abundance ganglioside species were further extended on the basis of linear chromatography retention rules of the most frequently detected ganglioside species. A total of 383 ganglioside features were defined with 329 of them derived from 32 ganglioside species. Taking advantage of the high-resolution MS analysis, rare ganglioside species were further elucidated according to their characteristic fragment ions and neutral losses. In total, 18 gangliosides with a ceramide carbon number from 20 to 25 and modified gangliosides, including 18 acetylated, 8 diacetylated, 1 phosphorylated, 36 N-glycolyneuraminic acid (NeuGc)-containing, and 7 di-NeuGc-containing gangliosides, were newly identified. The developed DIA/PRM method therefore generated a rich ganglioside resource for further functional exploration and is a unique alternative for DDA analysis for global ganglioside profiling in various biological systems.

CPVA: a web-based metabolomic tool for chromatographic peak visualization and annotation.

MOTIVATION: Liquid chromatography-mass spectrometry-based non-targeted metabolomics is routinely performed to qualitatively and quantitatively analyze a tremendous amount of metabolite signals in complex biological samples. However, false-positive peaks in the datasets are commonly detected as metabolite signals by using many popular software, resulting in non-reliable measurement. RESULTS: To reduce false-positive calling, we developed an interactive web tool, termed CPVA, for visualization and accurate annotation of the detected peaks in non-targeted metabolomics data. We used a chromatogram-centric strategy to unfold the characteristics of chromatographic peaks through visualization of peak morphology metrics, with additional functions to annotate adducts, isotopes and contaminants. CPVA is a free, user-friendly tool to help users to identify peak background noises and contaminants, resulting in decrease of false-positive or redundant peak calling, thereby improving the data quality of non-targeted metabolomics studies. AVAILABILITY AND IMPLEMENTATION: The CPVA is freely available at http://cpva.eastus.cloudapp.azure.com. Source code and installation instructions are available on GitHub: https://github.com/13479776/cpva. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The health concern of polychlorinated biphenyls (PCBs) in a notorious e-waste recycling site.

Polychlorinated biphenyls (PCBs) remain a relatively high level in e-waste recycling regions 3 decades after ban on use. Illegal recycling activities cunningly moved under the environmental law enforcement. Here, we analyzed PCBs in soils and plants from Guiyu, China (one of the world's largest recycling areas) to understand the relationship between PCBs pollution and the transition of recycling activities (locations and techniques). High concentrations of PCBs were found in soil and plant samples from emerging recycling sites, up to 234 ng g-1 and 236 ng g-1 (dry weight), respectively. The recycling activities, specifically the open burning process, would obviously aggravate the PCB pollution levels in its environment. The calculated values of estimated daily intake and hazard ratios of PCBs in dietary routes showed that health risks should be taken seriously.

Study of metabolic disorders associated with BDE-47 exposure in Drosophila model by MS-based metabolomics.

Epidemiological and animal studies have revealed a possible linkage between 2,2',4,4'-tetrabromodiphenyl ether (BDE-47) exposure and neurodegenerative disease such as Parkinson's disease (PD). However, whether or how BDE-47 would affect the PD progression remains unclear. Here, we carried out a metabolomics study based on liquid chromatography-mass spectrometry (LC-MS) and gas chromatography-mass spectrometry (GC-MS) to investigate the possible contribution of BDE-47 exposure to PD progression in Drosophila (fly) model. Transgenic PD flies were exposed to BDE-47 through diet for 30 days. Global metabolomic analysis identified 48 altered metabolites after the exposure. These metabolites were mainly involved in tryptophan metabolism, phenylalanine metabolism, purine metabolism, and alanine, aspartate and glutamate metabolism. Further, by quantifying metabolites of interest using LC-MS/MS, we confirmed that the formation of neuro-protector kynurenic acid was slowed down while the formation of neurotoxin 3-hydroxy-kynurenine was speeded up on the 20th exposure day. Moreover, the levels of SAM/SAH (an index of methylation potential) and GSH/GSSG (an indicator of oxidative stress) were found to decrease on the 30th exposure day. Our results suggest that BDE-47 could induce imbalance of kynurenine metabolism and methylation potential, and oxidative stress, which might further accelerate PD progression.

Study of BDE-47 induced Parkinson's disease-like metabolic changes in C57BL/6 mice by integrated metabolomic, lipidomic and proteomic analysis.

As the predominant congener of polybrominated diphenyl ethers (PBDEs) detected in human serum, 2,2',4,4'-tetrabromodiphenyl ether (BDE-47) has been reported to induce neurotoxicity. However, the possible linkage between BDE-47 and typical neurodegenerative diseases such as Parkinson's disease (PD) is still unclear. Here we carried out omics studies using liquid chromatography-orbitrap mass spectrometry (LC-orbitrap MS) to depict the BDE-47 induced metabolic changes in C57BJ/L mice to explore the possible contribution of BDE-47 exposure to PD pathology. BDE-47 dissolved in corn oil was orally administered to mice for 30 consecutive days. Results of metabolomics and lipidomics studies of PD-related brain regions revealed significant metabolite changes in pathways involved in oxidative stress and neurotransmitter production. Moreover, isobaric tags for relative and absolute quantitation (iTRAQ) proteomics study of the striatum, which is the part of brain that is most intensively studied in PD pathogenesis, revealed that BDE-47 could induce neurotransmitter system disturbance, abnormal phosphorylation, mitochondrial dysfunction and oxidative stress. Overall, this study depicts the possible contribution of BDE-47 exposure to PD pathology and highlights the powerfulness of omics platforms to deepen the mechanistic understanding of environmental pollutant-caused toxicity.

Variations of phthalate exposure and metabolism over three trimesters.

Maternal exposure to phthalates may cause some adverse health effects on both mother and fetus, but variations of phthalate exposure and metabolism during pregnancy have not been thoroughly characterized. A total of 946 participants were selected from a cohort study conducted in Wuhan between 2014 and 2015 through which they had provided a complete set of urine samples at three trimesters. Eight phthalate metabolites were analyzed in 2838 urine samples. Based on urinary concentrations, various parameters (i.e. phthalate metabolite concentrations, ratios of metabolites of bis(2-ethylhexyl) phthalate (DEHP) in DEHP, and percentages of individual metabolites in total phthalates) were compared over three visits. We observed that levels of phthalate metabolites showed a U-shaped trend across three trimesters. The significant variations in the ratios of DEHP metabolites indicated that the efficiency in metabolizing DEHP declined during pregnancy and less recent exposure occurred in mid-pregnancy. The changes of percentages of individual compound in total phthalates suggested the inconsistent pattern over trimesters. This longitudinal study found that the exposure pattern, exposure timing and metabolic susceptibility varied by trimesters, which suggests that urine samples should be collected at multiple time points and mothers should be especially careful in the early pregnancy.

Mass spectrometry-based metabolomics: Targeting the crosstalk between gut microbiota and brain in neurodegenerative disorders.

Metabolomics seeks to take a "snapshot" in a time of the levels, activities, regulation and interactions of all small molecule metabolites in response to a biological system with genetic or environmental changes. The emerging development in mass spectrometry technologies has shown promise in the discovery and quantitation of neuroactive small molecule metabolites associated with gut microbiota and brain. Significant progress has been made recently in the characterization of intermediate role of small molecule metabolites linked to neural development and neurodegenerative disorder, showing its potential in understanding the crosstalk between gut microbiota and the host brain. More evidence reveals that small molecule metabolites may play a critical role in mediating microbial effects on neurotransmission and disease development. Mass spectrometry-based metabolomics is uniquely suitable for obtaining the metabolic signals in bidirectional communication between gut microbiota and brain. In this review, we summarized major mass spectrometry technologies including liquid chromatography-mass spectrometry, gas chromatography-mass spectrometry, and imaging mass spectrometry for metabolomics studies of neurodegenerative disorders. We also reviewed the recent advances in the identification of new metabolites by mass spectrometry and metabolic pathways involved in the connection of intestinal microbiota and brain. These metabolic pathways allowed the microbiota to impact the regular function of the brain, which can in turn affect the composition of microbiota via the neurotransmitter substances. The dysfunctional interaction of this crosstalk connects neurodegenerative diseases, including Parkinson's disease, Alzheimer's disease and Huntington's disease. The mass spectrometry-based metabolomics analysis provides information for targeting dysfunctional pathways of small molecule metabolites in the development of the neurodegenerative diseases, which may be valuable for the investigation of underlying mechanism of therapeutic strategies.

Large-Scale Longitudinal Metabolomics Study Reveals Different Trimester-Specific Alterations of Metabolites in Relation to Gestational Diabetes Mellitus.

Despite the increasing research attention paid to gestational diabetes mellitus (GDM) due to its high prevalence, limited knowledge is available about its pathogenesis. In this study, 428 serum samples were collected from 107 pregnant women suffering from GDM and 107 matched healthy controls. The nontargeted metabolomics data of maternal serum samples from the first (T1, n = 214) and second trimesters (T2, n = 214) were acquired by using ultrahigh performance liquid chromatography coupled with Orbitrap mass spectrometry (MS). A total of 93 differential metabolites were identified on the basis of the accurate mass and MS/MS fragmentation. After false discovery rate correction, the levels of 31 metabolites in GDM group were significantly altered in the first trimester. The differential metabolites were mainly attributed to purine metabolism, fatty acid ß-oxidation, urea cycle, and tricarboxylic acid cycle pathways. The fold changes across pregnancy (T2/T1) of six amino acids (serine, proline, leucine/isoleucine, glutamic acid, tyrosine, and ornithine), a lysophosphatidylcholine (LysoPC(20:4)), and uric acid in GDM group were significantly different from those in the control groups, suggesting that these 8 metabolites might have contributed to the occurrence and progression of GDM. The findings revealed that the amino acid metabolism, lipid metabolism, and other pathways might be disturbed prior to GDM onset and during the period from the first to the second trimester of pregnancy.

Metabolomics studies on db/db diabetic mice in skeletal muscle reveal effective clearance of overloaded intermediates by exercise.

Type 2 diabetes mellitus (T2DM) is characterized by hyperinsulinemia, hyperglycemia and insulin resistance, which correlated with high mortality worldwide. Exercise is one of the effective lifestyle interventions in maintaining blood glucose level in the normal range and lowering risk factors. Metabolomics approaches are powerful tools in systematic study of overall metabolic changes in response to disease or interventions. In this study, mass spectrometry-based metabolomics studies were performed to investigate the regulatory effect of moderate intensity of exercise on db/db diabetic mice in skeletal muscle. Both liquid chromatography-mass spectrometry (LC-MS) and gas chromatography-mass spectrometry (GC-MS) have been carried out to monitor a wide range of regulated metabolites. Ninety-five metabolites were identified which contributing to the discrimination of db/m  + control and db/db diabetic mice. The regulatory effects of exercise on these metabolites were mainly focusing on attenuating the levels of long-chain fatty acids (C14 to C18) and medium-to long-chain acylcarnitines (C12 to C18), indicated that exercise might play a positive role in inhibiting the accumulation of excessive lipids, which is positively related to insulin resistance. In addition, uric acid, which is a risk factor for inflammation, cardiovascular complications, and fatty liver in diabetic patients, together with its intermediates (such as inosinic acid, hypoxanthine, etc.) in purine metabolism pathway, were also substantially down regulated after exercise, indicating exercise might also be protective against hyperuricemia related risks in T2DM. These findings reveal that moderate intensity of exercise might play a positive role in improving the efficiency of lipid metabolism in skeletal muscle and meanwhile enhancing uric acid clearance to prevent lipid accumulation, which might contribute to improved body fitness and body muscle composition.

statTarget: A streamlined tool for signal drift correction and interpretations of quantitative mass spectrometry-based omics data.

Large-scale quantitative mass spectrometry-based metabolomics and proteomics study requires the long-term analysis of multiple batches of biological samples, which often accompanied with significant signal drift and various inter- and intra-batch variations. The unwanted variations can lead to poor inter- and intra-day reproducibility, which is a hindrance to discover real significance. The use of quality control samples and data treatment strategies in the quality assurance procedure provides a mechanism to evaluate the quality and remove the analytical variance of the data. The statTarget we developed is a streamlined tool with an easy-to-use graphical user interface and an integrated suite of algorithms specifically developed for the evaluation of data quality and removal of unwanted variations for quantitative mass spectrometry-based omics data. A novel quality control-based random forest signal correction algorithm, which can remove inter- and intra-batch unwanted variations at feature-level was implanted in the statTarget. Our evaluation based on real samples showed the developed algorithm could improve the data precision and statistical accuracy for mass spectrometry-based metabolomics and proteomics data. Additionally, the statTarget offers the streamlined procedures for data imputation, data normalization, univariate analysis, multivariate analysis, and feature selection. To conclude, the statTarget allows user-friendly the improvement of the data precision for uncovering the biologically differences, which largely facilitates quantitative mass spectrometry-based omics data processing and statistical analysis.

PCI-GC-MS-MS approach for identification of non-amino organic acid and amino acid profiles.

Alkyl chloroformate have been wildly used for the fast derivatization of metabolites with amino and/or carboxyl groups, coupling of powerful separation and detection systems, such as GC-MS, which allows the comprehensive analysis of non-amino organic acids and amino acids. The reagents involving n-alkyl chloroformate and n-alcohol are generally employed for providing symmetric labeling terminal alkyl chain with the same length. Here, we developed an asymmetric labeling strategy and positive chemical ionization gas chromatography-tandem mass spectrometry (PCI-GC-MS-MS) approach for determination of non-amino organic acids and amino acids, as well as the short chain fatty acids. Carboxylic and amino groups could be selectively labelled by propyl and ethyl groups, respectively. The specific neutral loss of C3H8O (60Da), C3H5O2 (74Da) and C4H8O2 (88Da) were useful in the selective identification for qualitative analysis of organic acids and amino acid derivatives. PCI-GC-MS-MS using multiple reaction monitoring (MRM) was applied for semi-quantification of typical non-amino organic acids and amino acids. This method exhibited a wide range of linear range, good regression coefficient (R2) and repeatability. The relative standard deviation (RSD) of targeted metabolites showed excellent intra- and inter-day precision (<5%). Our method provided a qualitative and semi-quantitative PCI-GC-MS-MS, coupled with alkyl chloroformate derivatization.

Placental Transfer of Perfluoroalkyl Substances and Associations with Thyroid Hormones: Beijing Prenatal Exposure Study.

Perfluoroalkyl substances (PFASs) have been detected in wildlife and human samples worldwide. Toxicology research showed that PFASs could interfere with thyroid hormone homeostasis. In this study, eight PFASs, fifteen PFAS precursors and five thyroid hormones were analyzed in 157 paired maternal and cord serum samples collected in Beijing around delivery. Seven PFASs and two precursors were detected in both maternal and cord sera with significant maternal-fetal correlations (r = 0.336 to 0.806, all P < 0.001). The median ratios of major PFASs concentrations in fetal versus maternal serum were from 0.25:1 (perfluorodecanoic acid, PFDA) to 0.65:1 (perfluorooctanoic acid, PFOA). Spearman partial correlation test showed that maternal thyroid stimulating hormone (TSH) was negatively correlated with most maternal PFASs (r = -0.261 to -0.170, all P < 0.05). Maternal triiodothyronin (T3) and free T3 (FT3) showed negative correlations with most fetal PFASs (r = -0.229 to -0.165 for T3; r = -0.293 to -0.169 for FT3, all P < 0.05). Our results suggest prenatal exposure of fetus to PFASs and potential associations between PFASs and thyroid hormone homeostasis in humans.

Matrix-assisted laser desorption/ionization mass spectrometry imaging of cell cultures for the lipidomic analysis of potential lipid markers in human breast cancer invasion.

RATIONALE: Breast cancer is the leading cause of cancer death among women worldwide. Identification of lipid targets that play a role in breast cancer invasion may advance our understanding of the rapid progression of cancer and may lead to the development of new biomarkers for the disease. METHODS: Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI) was applied for the lipidomic profiling of two poorly invasive and two highly invasive breast cancer cell lines to identify the differentially accumulated lipids related to the invasive phenotype. The four cell lines were individually grown on indium tin oxide (ITO)-coated glass slides, analyzed as cell cultures. The raster width and matrix for detection were optimized to improve detection sensitivity. RESULTS: Optimized MSI measurements were performed directly on the cell culture with 9-aminoacridine as matrix, resulting in 215 endogenous compounds detected in positive ion mode and 267 endogenous compounds in negative ion mode in all the four cell lines, representing the largest group of analytes that have been analyzed from cells by a single MSI study. In highly invasive cell lines, 31 lipids including phosphatidylglycerol (PG) and phosphatidic acids were found upregulated and eight lipids including sphingomyelin (SM) downregulated in negative ion mode. The products of de novo fatty acid synthesis incorporated into membrane phospholipids, like oleic-acid-containing PG, may be involved in mitochondrial dysfunction and thus affect the invasion of breast cancer cells. The deficiency of SM may be related to the disruption of apoptosis in highly invasive cancer cells. CONCLUSIONS: This work uncovered more analytes in cells by MSI than previous reports, providing a better visualization and novel insights to advance our understanding of the relationship between rapid progression of breast cancer and lipid metabolism. The most altered lipids may aid the discovery of diagnostic markers and therapeutic targets of breast cancer.

Comprehensive urinary metabolomic profiling and identification of potential noninvasive marker for idiopathic Parkinson's disease.

Urine metabolic phenotyping has been associated with the development of Parkinson's disease (PD). However, few studies using a comprehensive metabolomics approach have investigated the correlation between changes in the urinary markers and the progression of clinical symptoms in PD. A comprehensive metabolomic study with robust quality control procedures was performed using gas chromatography - mass spectrometry (GC - MS) and liquid chromatography - mass spectrometry (LC - MS) to characterize the urinary metabolic phenotypes of idiopathic PD patients at three stages (early, middle and advanced) and normal control subjects, with the aim of discovering potential urinary metabolite markers for the diagnosis of idiopathic PD. Both GC-MS and LC-MS metabolic profiles of idiopathic PD patients differed significantly from those of normal control subjects. 18 differentially expressed metabolites were identified as constituting a unique metabolic marker associated with the progression of idiopathic PD. Related metabolic pathway variations were observed in branched chain amino acid metabolism, glycine derivation, steroid hormone biosynthesis, tryptophan metabolism, and phenylalanine metabolism. Comprehensive, successive metabolomic profiling revealed changes in the urinary markers associated with progression of idiopathic PD. This profiling relies on noninvasive sampling, and is complementary to existing clinical modalities.