XCMS, MetDNA and Masterview software were used for identification of compounds according to retention time, accurate mass number, secondary mass spectrometry and other information. The peak area of qualitative compounds was quantified by MultiQuant software, and then the differences of various compounds between the two groups were statistically analyzed by MetaboAnalyst 5.0 online software.
Principal component analysis (PCA) was firstly performed to visualize the general grouping trend of all samples on the score plot, and the quality of data was evaluated using the tight clustering of QC samples. The processed data were further exported into the SIMCA software (version 14.1; Umetrics) for orthogonal partial least squares discriminant analysis (OPLS-DA). The variables were selected based on variable importance in the projection (VIP) values in OPLS-DA models and VIP > 1.0 was considered as a significant difference. A t-test was applied for the statistical analysis, to determine the statistical significance of differences in the metabolites (p < 0.05). False Discovery Rate (FDR) was corrected for using the Benjamini-Hochberg method. Metabolites with the statistical thresholds of VIP > 1, p < 0.05 and FDR < 0.05 were considered to be differentially expressed metabolites (DEMs). Random forest (RF) and binary logistic regression (BLR) model were applied to find the significantly altered metabolites. The clinical diagnostic efficiency of the regression analysis was evaluated by performing a receiver operating characteristic (ROC) curve analysis, and the area under the ROC curve (AUC) was utilized to identify and confirmed the significantly altered metabolites as potential diagnostic biomarkers for HF. The Pearson correlation coefficient was calculated to determine the correlation between DEMs and some clinical parameters. MetaboAnalyst 5.0 software was used to analyze the pathways of DEMs. Pathway and metabolite accumulation analyses were applied to identify pathways most related to HF.
"],"repository":["MetaboLights"],"study_status":["Public"],"ptm_modification":[""],"instrument_platform":["Liquid Chromatography MS - positive - reverse phase","Liquid Chromatography MS - negative - hilic"],"chromatography_protocol":["Analyses were performed using an Exion LC high-performance liquid chromatography system (AB SCIEX, United States).Waters HSS T3 column (100 × 2 1 mm, 1.7 μm) was used for separation of small polar metabolites in positive electrospray ionization (ESI + ) mode. The mobile phase was composed of 0.1% formic acid in water (A) and acetonitrile (B) using a gradient elution of 1% B at 0 min, 1% B at 1.5 min, 99% B at 13 min, and 99% B at 16.5 min. The flow rate, injection volume, column temperature, and sample manager were set at 0.3 mL/min, 2 μL, 40 °C, and 4 °C, respectively.
The Acquity UPLC BEH Amide column (100 x 2.1 mm, 1.7 µm) was used in the negative electrospray ionization (ESI-) mode for the analysis of polar metabolites. The column temperature was maintained at 40 °C, the temperature of the sample plate was maintained at 4 °C and the sample volume was 2 μL. The mobile phase consisting of 5 mmol/L ammonium acetate and 0.5% acetic acid in water (A) and acetonitrile (B) was delivered at a flow rate of 0.3 mL/min using a gradient program as follows: 95% (B), 0-1 min; 95-65% (B), 1-14 min; 65-40% (B), 14-16 min; 40% (B), 16-18 min; 40-95% (B), 18-19 min; and 95% (B), 19-23 min.
"],"publication":["Discovery and Validation of Potential Serum Biomarkers for Heart Failure by Untargeted Metabolomics. 10.1155/2024/7004371. PMID:39742011"],"submitter_affiliation":["Nanjing University of Chinese Medicine"],"submitter_name":["Xiaochao Hu"],"organism_part":["blood serum"],"technology_type":["mass spectrometry"],"disease":[""],"extraction_protocol":["Sample preparation was performed as follows. (1) The collected whole blood was centrifuged for 8 min at 4000 rpm to obtain serum; (2) An aliquot of serum (25 μL) was added to 100 μL of isopropyl alcohol, followed by vortex-mixing for 3 min to obtain the mixture; (3) The mixture was placed at 4 °C for 2 h and centrifuged at 12,000 rpm for 15 min in a refrigerated centrifuge (at 4 °C); (4) The supernatant was transferred into a sample bottle and stored at 4 °C for MS analysis of metabolomics. A quality control (QC) sample was created by mixing equal volumes of each test sample (10 μL) and treated as described above. During the analysis run, the pooled QC samples were injected for every 10 samples for repeatability.
"],"organism":["Homo sapiens"],"full_dataset_link":["https://www.ebi.ac.uk/metabolights/MTBLS9586"],"author":["Kunqun Shi.","Yingzhuo Wang.","Shengyan Yin.","Guisheng Zhou. Affiliated Hosptial of Nanjing University of Chinese Medicine. #155 hanzhong Road, Qinhuai District, Nanjing, China. 300219@njucm.edu.cn.","Tongtong Liu.","Hongli Guo.","Xiaochao Hu. hxc15211470639@163.com.","Huanhuan Liu.","Junzhi Zhang. Affiliated Hosptial of Nanjing University of Chinese Medicine. #155 hanzhong Road, Qinhuai District, Nanjing, China. zjz2732009841@163.com.","Chunling Liu.","Shijia Liu. liushijia2011@163.com."],"data_transformation_protocol":["Raw files were converted into mzXML format using the MSconvert module of ProteoWizard software. XCMS, MetDNA and Masterview software were used for identification of compounds according to retention time, accurate mass number, secondary mass spectrometry and other information. The peak area of qualitative compounds was quantified by MultiQuant software, and then the differences of various compounds between the two groups were statistically analyzed by MetaboAnalyst 5.0 online software. The SPSS25.0 (International Business Machines Corporation, Armonk, NY, USA) was utilized to analyze data from ROC and BLR. RF analysis was performed on Metaboanalyst 5.0. Subsequently, a heat map was generated using the R software. OPLS-DA were performed and coded using the SIMCA 13.0 software (Umetrics, Sweden).
"],"study_factor":["Disease","Age"],"submitter_email":["hxc15211470639@163.com"],"sample_collection_protocol":["Clinical serum samples were collected from the Affiliated Hospital of Nanjing University of Chinese Medicine. A total of 118 samples were included, including 83 patients with HF and 35 HCs. Detailed clinical characteristics of the subjects are shown in Table 1 in the paper associated with this study. Detection of left ventricular ejection fraction (LVEF) was usually evaluated using a Siemens ACUSON SC2000 by 2D echocardiographic testing Hospital ultrasound systems. In the hospital clinical laboratory, the levels of NT-proBNP in blood samples were measured by the VIDAS automated enzyme-linked immunosorbent assay system with fluorescent immunostaining using the kit VIDAS PBN2. Blood samples were collected and pretreated from subjects after overnight fasting were obtained by venipuncture and collected into blood separation gel tubes.
"],"omics_type":["Metabolomics"],"study_design":["heart failure","Biomarker","metabolite profile","quadrupole time-of-flight mass spectrometry"],"curator_keywords":["heart failure","Biomarker","metabolite profile","quadrupole time-of-flight mass spectrometry"],"mass_spectrometry_protocol":["UPLC-QTOF-MS analyses were performed using a TripleTOF 5600+ high-resolution mass spectrometer (AB SCIEX, USA) and an ExionLC high-performance liquid chromatography system (AB SCIEX, USA). Waters HSS T3 column (100 x 2.1 mm, 1.7 µm) was used for separation of small polar metabolites in positive electrospray ionization (ESI+) mode. The mobile phase was composed of 0.1% formic acid in water (A) and acetonitrile (B) using a gradient elution of 1% B at 0 min, 1% B at 1.5 min, 99% B at 13 min, 99% B at 16.5 min. The flow rate, injection volume, column temperature and sample manager were set at 0.3 mL/min, 2 μL, 40 °C and 4 °C, respectively.
"],"pubmed_abstract":["Detection of biomarkers was extremely important for the early diagnosis, prognosis, and therapy optimization of diseases. The purpose of this study was to investigate the differences in serum metabolites between patients with heart failure (HF) and healthy control (HC) and to diagnose HF qualitatively. In this study, serum samples from 83 patients with HF and 35 HCs were used as the research subjects for untargeted metabolomic analysis using ultraperformance liquid chromatography combined with quadrupole-time of flight mass spectrometry (UPLC-QTOF/MS) technology. Potential biomarkers were screened and validated using the orthogonal partial least squares discriminant analysis (OPLS-DA), random forest (RF), binary logistic regression (BLR), and receiver operating characteristic (ROC) analysis. The results indicated that a total of 43 metabolites were considered as differentially expressed metabolites (DEMs). Among these DEMs, glycodeoxycholate was identified as a specific biomarker of HF. A ROC curve analysis for HC versus HF discrimination showed an area under the ROC curve (AUC) of 0.9853 (95% CI: 0.9859-1.0000), a sensitivity of 95%, and a specificity of 100%. Hence, glycodeoxycholate might serve as a potential biomarker for HF. Furthermore, the amino acid metabolism was screened as the most significantly altered pathway in patients with HF. By identifying serum biomarkers and analyzing metabolic pathways, our study provided opportunities to enhance the understanding of the pathogenesis and early diagnosis of HF."],"pubmed_title":["Discovery and Validation of Potential Serum Biomarkers for Heart Failure by Untargeted Metabolomics."],"pubmed_authors":["Zhou Guisheng G, Zhang Junzhi J, Guo Hongli H, Hu Xiaochao X, Wang Yingzhuo Y, Shi Kunqun K, Liu Tongtong T, Yin Shengyan S, Liu Huanhuan H, Liu Chunling C, Liu Shijia S"],"additional_accession":[]},"is_claimable":false,"name":"Discovery and validation of potential serum biomarkers for heart failure by untargeted metabolomics","description":"Detection of biomarkers was extremely important for the early diagnosis, prognosis and therapy optimization of diseases. The purpose of this study was to investigate the differences in serum metabolites between patients with heart failure (HF) and healthy controls (HC), and to diagnose HF qualitatively. In this study, serum samples from 83 patients with HF and 35 HC were used as the research subjects for untargeted metabolomic analysis using ultra-performance liquid chromatography combined with quadrupole-time of flight mass spectrometry (UPLC-QTOF/MS) technology. Potential biomarkers were screened and validated using the orthogonal partial least squares-discriminant analysis (OPLS-DA), random forest (RF), binary logistic regression (BLR) and receiver operating characteristic (ROC) analysis. The results indicated that a total of 43 metabolites were considered as differentially expressed metabolites (DEMs). Among these DEMs, glycodeoxycholate was identified as a specific biomarker for differential diagnosis of HF. A ROC curve analysis for HC vs HF discrimination, showed an area under the ROC curve (AUC) of 0.9853 (95% CI: 0.9859-1.0000), a sensitivity of 95% and a specificity of 100%. Hence, glycodeoxycholate might serve as a potential biomarker for HF. Furthermore, the amino acid metabolism was screened as the most significantly altered pathway in patients with HF. By identifying serum biomarkers and analyzing metabolic pathways, our study provided opportunities to enhance the understanding of the pathogenesis and early diagnosis of HF.
","dates":{"publication":"2025-07-18","submission":"2024-05-17"},"accession":"MTBLS9586","cross_references":{"pubmed":["39742011"]}}