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

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

Serum fatty acid profiles and potential biomarkers of ankylosing spondylitis determined by gas chromatography–mass spectrometry and multivariate statistical analysis

Ankylosing spondylitis (AS) is a common chronic inflammatory rheumatic disease. Early and accurate detection is essential for effective disease treatment. Recently, research has focused on genomics and proteomics. However, the associated metabolic variations, especially fatty acid profiles, have been poorly discussed. In this study, the gas chromatography–mass spectrometry (GC‐MS) approach and multivariate statistical analysis were used to investigate the metabolic profiles of serum free fatty acids (FFAs) and esterified fatty acids (EFAs) in AS patients. The results showed that significant differences in most of the FFA (C12:0, C16:0, C16:1, C18:3, C20:4, C20:5, C22:5 and C22:6) and EFA (C12:0, C16:1, C18:0, C18:1, C18:2, C18:3, C20:4 and C22:6) concentrations were found between the AS patients and healthy controls (p < 0.05). Principal component analysis and partial least squares discriminant analysis were performed to classify the AS patients and controls. Additionally, FFAs C20:4, C12:0, C18:3 and EFAs C22:6, C12:0 were confirmed as potential biomarkers to identify AS patients and healthy controls. The present study highlights that differences in the serum FFA and EFA profiles of AS patients reflect the metabolic disorder. Moreover, FFA and EFA biomarkers appear to have clinical applications for the screening and diagnosis of AS. Copyright © 2014 John Wiley & Sons, Ltd.     

基于气相色谱-质谱联用和图模型分析的脂代谢异常患者血浆代谢组学研究

利用气相色谱-质谱联用技术(GC-MS)和图模型分析方法,寻找脂代谢异常患者可能的血浆代谢标志物群。采用GC-MS技术对37例脂代谢异常患者和10例健康人的血浆样品进行分析,得到血浆代谢物的表达谱。偏最小二乘-判别分析(Partial least squares-discriminant analysis,PLS-DA)得分图可区分脂代谢异常患者与健康人,运用PLS-DA载荷图及t检验发现有9个代谢物在两组间存在显著性差异。经NIST谱库检索,它们分别为缬氨酸、甘氨酸、丙氨酸、焦谷氨酸、葡萄糖醛酸、半乳糖、甘露糖、亚油酸和甘油。在脂代谢异常患者血浆中,除甘油浓度显著高于健康人外,其余8种代谢物浓度均明显低于健康人。图模型分析结果发现这些代谢物与脂代谢异常临床常用诊断指标之间具有很好的相关性。它们可能是脂代谢异常疾病早期诊断和预后新的特异性代谢标志物群。     

GC–MS Based Plasma Metabolic Profiling of Type 2 Diabetes Mellitus

Gas chromatography–mass spectrometry based metabolic profiling was used to characterize metabolic profiles in type 2 diabetes mellitus. We found distinct differences between type 2 diabetic patients and healthy controls, type 2 diabetic patients before and after treatment of the plasma metabolic profiles. Furthermore, levels of lactate, α-hydroxyisobutyric acid, phosphate, 1-monopalmitin and 1-monostearin were closely correlated with the disease state and may be considered as potential biomarkers for type 2 diabetes mellitus. The results demonstrated that plasma GC–MS metabolic profiling combined with multi- and uni-variate statistical analysis techniques provided an efficient way of depicting metabolic perturbations of diseases, and it may also be able to assist disease and treatment surveillance.     

Identifying potential serum biomarkers of breast cancer through targeted free fatty acid profiles screening based on a GC–MS platform

Recent advances suggest that abnormal fatty acid metabolism highly correlates with breast cancer, which provide clues to discover potential biomarkers of breast cancer. This study aims to identify serum free fatty acid (FFA) metabolic profiles and screen potential biomarkers for breast cancer diagnosis. Gas chromatography–mass spectrometry and our in-house fatty acid methyl ester standard substances library were combined to accurately identify FFA profiles in serum samples of breast cancer patients and breast adenosis patients (as controls). Potential biomarkers were screened by applying statistical analysis. A total of 18 FFAs were accurately identified in serum sample. Two groups of patients were correctly discriminated by the orthogonal partial least squares–discriminant analysis model based on FFA profiles. Seven FFA levels were significantly higher in serum from breast cancer patients than that in controls, and exhibited positive correlation with malignant degrees of disease. Furthermore, five candidates (palmitic acid, oleic acid, cis-8,11,14-eicosatrienoic acid, docosanoic acid and the ratio of oleic acid to stearic acid) were selected as potential serum biomarkers for differential diagnosis of breast cancer. Our study will help to reveal the metabolic signature of FFAs in breast cancer patients, and provides valuable information for facilitating clinical noninvasive diagnosis.     

Urinary Metabonomics Study of Heart Failure Patients with HILIC and RPLC Separation Coupled to TOF–MS

In this study, urinary metabolic profiles of patients with heart failure (HF) and healthy individuals were analyzed by LC-TOF–MS. Both reversed-phase chromatography and hydrophilic interaction chromatography were used to separate the endogenous metabolites in urine. Partial least-squares to latent structure-discriminant analysis was used for discriminating HF patients from healthy persons and the selection of potential biomarkers. The results suggested that the combination of LC–MS and multivariate statistical analysis could be used for HF diagnosis. The MS/MS experiments were carried out to identify the potential biomarkers which are important for the contribution to the discrimination. As a result, 12 potential biomarkers for HF were identified and the related metabolic pathways were studied.     

慢性心功能衰竭患者尿液的代谢组学研究

采用基于液相色谱-质谱联用的方法对慢性心力衰竭(Chronic heart failure, CHF)患者和正常对照(Control)人群的尿液进行分析, 筛选慢性心力衰竭患者尿液中的差异代谢物, 研究其发病机制, 并为临床治疗提供科学依据.选择15个慢性心力衰竭患者(年龄(62.27±3.14)岁)及15个正常人(年龄(65.41±4.63)岁), 采用高分辨度快速液相色谱-四极杆-飞行时间串联质谱(RRLC-QTOF/MS)技术对尿液代谢物进行分析, 采用主成分分析(PCA)对两组代谢物进行分类, 并筛选潜在生物标记物;运用偏最小二乘判别分析法(PLS-DA)建模, 考察生物标记物对疾病筛选的预测能力.研究结果表明, CHF组和Control组尿液代谢物谱能得到很好的区分, 发现并鉴定了2种潜在生物标记物尿苷及丙氨酰色氨酸, 提示嘧啶代谢和色氨酸代谢可能在心力衰竭发生发展中有重要作用.     

基于拟靶向液相色谱-质谱联用的胃癌患者血清代谢组分析

胃癌是一种高发的恶性肿瘤,是癌症相关死亡的第二大病因。早期筛查是提高患者生存率的有效手段,但目前临床上尚缺乏实现胃癌无创筛检的可靠标志物。本研究采用了基于液相色谱-质谱联用的拟靶向代谢组学方法分析了20例胃癌患者及40例正常人血清代谢组,以期发现新的潜在代谢标志物。代谢组数据的主成分分析和偏最小二乘法数据分析结果显示,胃癌患者与健康人群的血清代谢组存在明显的差异,结合非参数检验进一步筛选并定性出57个差异代谢物。其中二氢胆固醇经验证组样本验证,具有成为胃癌代谢标志物的潜力。本研究在发现胃癌的潜在代谢标志物的同时,也为胃癌患者代谢分型提供了重要的科学依据。     

小儿肺炎患者尿液的代谢组学初步研究

利用液相色谱-质谱联用法对小儿肺炎( Childhood pneumonia, CP)患者和健康儿童( Healthy control)的尿液进行分析,发现小儿肺炎患者尿液中的潜在标记物,为其发病机制及早期筛查提供科学依据。筛选10例小儿肺炎患者(age 47.72±2.35 months)及10例健康儿童(age 46.65±1.97 months)尿液样本,采用快速高分辨液相色谱四极杆-飞行时间质谱联用( RRLC-Q TOF/MS)技术对其尿液代谢物进行分析,通过主成分分析方法( PCA)对两组代谢物进行分类,并发现潜在生物标记物。 RRLC-Q TOF/MS检测表明,CP组和Healthy Control组尿液代谢物图谱能得到很好的区分,并鉴定了5种生物标记物,提示嘌呤代谢、氨基酸代谢可能在小儿肺炎发生发展中有重要作用。     

GC/MS-based metabolomic approach to validate the role of urinary sarcosine and target biomarkers for human prostate cancer by microwave-assisted derivatization

A recent study showed that sarcosine may be potentially useful for the diagnosis and prognosis of prostate cancer (PCa). The aim of this study was to validate diagnostic value of sarcosine for PCa, to evaluate urine metabolomic profiles in patients with PCa in comparison of non-cancerous control, and to further explore the other potential metabolic biomarkers for PCa. Isotope dilution gas chromatography/mass spectrometry (ID GC/MS) metabolomic approach was applied to evaluate sarcosine using [methyl-D₃]-sarcosine as an internal standard. Microwave-assisted derivatization (MAD) together with GC/MS was utilized to obtain the urinary metabolomic information in 20 PCa patients compared with eight patients with benign prostate hypertrophy and 20 healthy men. Acquired metabolomic data were analyzed using a two-sample t test. Diagnostic models for PCa were constructed using principal component analysis and were assessed with receiver–operating characteristic curves. Results showed that the urinary sarcosine level has no statistical difference between the PCa group and the control group. In addition, nine metabolomic markers between the PCa group and the healthy male group were selected, which constructed a diagnostic model with a high area under the curve value of 0.9425. We conclude that although urinary sarcosine value has limited potential in the diagnostic algorithm of PCa, urinary metabolomic panel based on GC/MS assay following MAD may potentially become a diagnostic tool for PCa.     

Plasma metabolomic profile and potential biomarkers for missed abortion

A missed abortion (MA) is an in utero death of the embryo or fetus before the 20th week of gestation with retained products of conception, and this condition is currently common in China. In order to discover novel biomarkers for MA, ultrahigh performance liquid chromatography was applied to study plasma metabolite profiles for 33 patients with MA and 29 control subjects. Thirty‐seven differential plasma metabolites were found to discriminate between the two groups in the initial cohort (15 subjects with MA and 15 healthy controls). The feasibility of using these potential biomarkers to predict MA was further evaluated in the validation cohort (18 subjects with MA and 14 healthy controls) and 15 had an area under the receiver operating characteristic curve of >0.80, making them satisfactory. Tryptophan metabolism and sphingolipid metabolism were identified as important potential target pathways for MA using metabolic pathway impact analysis. Furthermore, three of the 15 satisfactory metabolites (glyceric acid, indole and sphingosine) were combined to establish a predictive model with 100% sensitivity and 100% specificity in the validation cohort. Taken together, these results suggest that MA results in significant disturbance of metabolism and those various novel biomarkers have satisfactory diagnostic and predictive power for MA.     

A metabonomic approach applied to predict patients with cerebral infarction

Cerebral infarction is always of sudden onset, and usually leading to serious consequence. It is of therapeutic significance to develop fast and accurate diagnosis methods for cerebral infarction so that patients can be treated timely and properly. A metabonomic approach was then proposed to investigate the potential biomarkers and metabolic pathways associated with cerebral infarction and also establish a prediction model of cerebral infarction for the fast diagnosis. Serum metabolic profiling of sixty-seven cerebral infarction patients and sixty-two controls was obtained using UPLC-TOF MS. The resulting data were then processed by multivariate statistical analysis to graphically demonstrate metabolic variations. The PLS-DA model was validated with cross validation and permutation tests to assure the model's reliability, and significant difference was obtained between the original and hypothetical models (p < 0.0001). A series of endogenous metabolites in the one-carbon cycle, such as folic acid, cysteine, S-adenosyl homocysteine and oxidized glutathione, were determined as potential biomarkers of cerebral infarction. A prediction model developed using PLS-KNN algorithm was established to differentiate cerebral infarction patients from controls, and an average accuracy of 100% was obtained. In conclusion, metabonomic approach is a powerful tool to investigate the pathogenesis of stroke and is expected to be developed as a useful method for the fast diagnosis.     

膀胱癌血清及尿液代谢组学研究

应用代谢组学研究方法,对与膀胱癌(Bladder cancer,BC)发病相关的生物标志物进行筛选,采用液相色谱-电喷雾质谱(LC-ESI/MS)联用技术对20名膀胱癌患者与24名正常人的血清和尿液进行研究.多变量统计分析结果表明,膀胱癌患者和正常人聚类明显,血清和尿液中分别发现13个潜在标志物.其中,(2E,6E,8E)-二十二碳三烯-1-醇、7-((1S,2S)-2-(庚胺)环己基)庚酸和(11E,14E,17E)-三烯-二十碳-1-醇首次在血清中发现,有潜力成为膀胱癌诊断标志物.液相色谱-质谱联用结合多变量分析的代谢组学研究技术在膀胱癌诊断中展现出巨大潜力.     

Principal component directed partial least squares analysis for combining nuclear magnetic resonance and mass spectrometry data in metabolomics: application to the detection of breast cancer

Nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS) are the two most commonly used analytical tools in metabolomics, and their complementary nature makes the combination particularly attractive. A combined analytical approach can improve the potential for providing reliable methods to detect metabolic profile alterations in biofluids or tissues caused by disease, toxicity, etc. In this paper, (1)H NMR spectroscopy and direct analysis in real time (DART)-MS were used for the metabolomics analysis of serum samples from breast cancer patients and healthy controls. Principal component analysis (PCA) of the NMR data showed that the first principal component (PC1) scores could be used to separate cancer from normal samples. However, no such obvious clustering could be observed in the PCA score plot of DART-MS data, even though DART-MS can provide a rich and informative metabolic profile. Using a modified multivariate statistical approach, the DART-MS data were then reevaluated by orthogonal signal correction (OSC) pretreated partial least squares (PLS), in which the Y matrix in the regression was set to the PC1 score values from the NMR data analysis. This approach, and a similar one using the first latent variable from PLS-DA of the NMR data resulted in a significant improvement of the separation between the disease samples and normals, and a metabolic profile related to breast cancer could be extracted from DART-MS. The new approach allows the disease classification to be expressed on a continuum as opposed to a binary scale and thus better represents the disease and healthy classifications. An improved metabolic profile obtained by combining MS and NMR by this approach may be useful to achieve more accurate disease detection and gain more insight regarding disease mechanisms and biology.     

Metabolomic profiles delineate the effect of Sanmiao wan on hyperuricemia in rats

A serum metabolomic method based on ultra‐high‐performance liquid chromatography coupled with mass spectrometry was developed to characterize hyperuricemia‐related metabolic profiles and delineate the mechanism of Sanmiao wan (SMW), a traditional Chinese medicine (TCM), in treating hyperuricemic rats. With partial least‐squares discriminant analysis for classification and selection of biomarkers, 13 potential biomarkers in mouse serum were identified in the screen, primarily involved in purine metabolism, arginine and proline metabolism, citrate cycle, phenylalanine metabolism, tryptophan metabolism and glycerophospholipid metabolism. Taking these potential biomarkers as screening indexes, SMW could reverse the pathological process of hyperuricemia through partially regulating the perturbed metabolic pathway except for glycerophospholipid metabolism. Our results showed that a metabolomic approach offers a useful tool to identify hyperuricemia‐related biomarkers and provides a new methodological cue for systematically dissecting the underlying efficacies and mechanisms of TCM in treating hyperuricemia.     

基于气相色谱-质谱联用技术的乙型肝炎血清代谢标志物的探究

探索了乙型肝炎患者和健康人血清代谢组的差异,寻找与疾病相关的潜在标志物。收集乙肝患者30例、健康对照35例,以气相色谱-质谱联用技术作为研究平台,应用主成分分析、正交偏最小二乘法-判别分析进行模式识别,然后通过变量重要性因子、非参数检验,结合数据库检索筛选鉴定有差异的代谢物。确认10个代谢物存在显著差异,其中柠檬酸、乌头酸、谷氨酰胺、N,N-二甲基甘氨酸、丙二酸与乙型肝炎患者组的相关性较好,受试者工作特征曲线下面积为0.975,具有较好的特异度和敏感度。因此这5个代谢物能够作为潜在的区分乙型肝炎患者和正常人的血清小分子标志物,有助于进一步了解病理机制,确定治疗目标。     

Using direct infusion mass spectrometry for serum metabolomics in Alzheimer’s disease

Currently, there is no cure for Alzheimer’s disease and early diagnosis is very difficult, since no biomarkers have been established with the necessary reliability and specificity. For the discovery of new biomarkers, the application of omics is emerging, especially metabolomics based on the use of mass spectrometry. In this work, an analytical approach based on direct infusion electrospray mass spectrometry was applied for the first time to blood serum samples in order to elucidate discriminant metabolites. Complementary methodologies of extraction and mass spectrometry analysis were employed for comprehensive metabolic fingerprinting. Finally, the application of multivariate statistical tools allowed us to discriminate Alzheimer patients and healthy controls, and identify some compounds as potential markers of disease. This approach provided a global vision of disease, given that some important metabolic pathways could be studied, such as membrane destabilization processes, oxidative stress, hypometabolism, or neurotransmission alterations. Most remarkable results are the high levels of phospholipids containing saturated fatty acids, respectively, polyunsaturated ones and the high concentration of whole free fatty acids in Alzheimer’s serum samples. Thus, these results represent an interesting approximation to understand the pathogenesis of disease and the identification of potential biomarkers. Graphical Abstract

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Exploratory urinary metabolic biomarkers and pathways using UPLC-Q-TOF-HDMS coupled with pattern recognition approach

Metabolomics represents an emerging and powerful discipline concerned with the comprehensive analysis of small molecules and provides a powerful approach to discover biomarkers in biological systems. Recent development of biomarkers for diagnosis and therapeutic monitoring of liver-stagnation and spleen-deficiency syndrome (LSS)-type disease remains challenging. This study was undertaken to discover novel potential biomarkers for the non-invasive early diagnosis of human LSS. Urine samples which are potentially a rich source of metabolites were collected from patients with LSS, together with healthy control samples. Metabolite profiling was performed by ultra-performance liquid-chromatography/electrospray-ionization synapt high-definition mass spectrometry (UPLC-Q-TOF-HDMS) in conjunction with multivariate data analysis and ingenuity pathway analysis that were used to select the metabolites to be used for the non-invasive diagnosis of LSS. Twelve urinary differential metabolites contributing to the complete separation of LSS patients from matched healthy controls were identified involving several key metabolic pathways such as pentose and glucuronate interconversions, ascorbate, aldarate, cysteine, methionine, tyrosine, tryptophan, amino sugar and nucleotide sugar metabolism. More importantly, of the 12 differential metabolites, 4 metabolite markers, prolylhydroxyproline, l-homocystine, 2-octenoylcarnitine and α-N-phenylacetyl-l-glutamine, were effective for the diagnosis of human LSS, with an achieved sensitivity of 93.0%. These results demonstrate that robust metabolomics has the potential as a non-invasive strategy and promising screening tool to evaluate the potential of these metabolites in the early diagnosis of LSS patients and provides new insight into pathophysiological mechanisms.     

Phospholipidomic identification of potential plasma biomarkers associated with type 2 diabetes mellitus and diabetic nephropathy

Type 2 diabetes mellitus (T2DM) and its attendant complications, such as diabetic nephropathy (DN), impose a significant societal and economic burden. The investigation of discovering potential biomarkers for T2DM and DN will facilitate the prediction and prevention of diabetes. Phospholipids (PLs) and their metabolisms are closely allied to nosogenesis and aggravation of T2DM and DN. The aim of this study is to characterize the human plasma phospholipids in T2DM and DN to identify potential biomarkers of T2DM and DN. Normal phase liquid chromatography coupled with time of flight mass spectrometry (NPLC-TOF/MS) was applied to the plasma phospholipids metabolic profiling of T2DM and DN. The plasma samples from control (n = 30), T2DM subjects (n = 30), and DN subjects (n = 52) were collected and analyzed. The significant difference in metabolic profiling was observed between healthy control group and DM group as well as between control group and DN group by the help of partial least squares discriminant analysis (PLS-DA). PLS-DA and one-way analysis of variance (ANOVA) were successfully used to screen out potential biomarkers from complex mass spectrometry data. The identification of molecular components of potential biomarkers was performed on Ion trap-MS/MS. An external standard method was applied to quantitative analysis of potential biomarkers. As a result, 18 compounds in 7 PL classes with significant regulation in patients compared with healthy controls were regarded as potential biomarkers for T2DM or DN. Among them, 3 DM-specific biomarkers, 8 DN-specific biomarkers and 7 common biomarkers to DM and DN were identified. Ultimately, 2 novel biomarkers, i.e., PI C18:0/22:6 and SM dC18:0/20:2, can be used to discriminate healthy individuals, T2DM cases and DN cases from each other group.