Summary of Study ST002949

This data is available at the NIH Common Fund's National Metabolomics Data Repository (NMDR) website, the Metabolomics Workbench, https://www.metabolomicsworkbench.org, where it has been assigned Project ID PR001834. The data can be accessed directly via it's Project DOI: 10.21228/M8NH80 This work is supported by NIH grant, U2C- DK119886.

See: https://www.metabolomicsworkbench.org/about/howtocite.php

This study contains a large results data set and is not available in the mwTab file. It is only available for download via FTP as data file(s) here.

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Study IDST002949
Study TitleSerum metabolomics reveals metabolic profile and potential biomarkers of ankylosing spondylitis
Study SummaryAnkylosing spondylitis (AS) is a chronic systemic inflammatory disease that significantly impairs physical function, quality of life, and work ability in young individuals. Nonetheless, the identification of early radiographic changes in AS is frequently delayed, and the diagnostic efficacy of biomarkers remains moderately effective, with unsatisfactory sensitivity and specificity. Hence, it is imperative to identify biomarkers that can facilitate early diagnosis, prognosis, and monitoring of AS. A total of 67 AS patients and 67 healthy controls were recruited to procure plasma samples for the purpose of screening potential biomarkers of AS via untargeted combined with targeted metabolomics approach utlizing UHPLC-QTOF-MS/MS and UHPLC-QQQ-MS/MS. Multivariate pattern recognition and univariate statistical analysis were employed to compare and elucidate the differential metabolites. The results indicated a notable divergence between the two groups, and a total of 170 different metabolites associated with the primary 6 metabolic pathways exhibiting a correlation with AS. Among those, 26 metabolites exhibited high sensitivity and specificity with area under curve (AUC) value were greater than 0.8. Subsequent targeted quantitative analysis discovered 3 metabolites, namely 3-amino-2-piperidone, hypoxanthine and octadecylamine, exhibiting excellent distinguishing ability based on the results of ROC curve and Random Forest model, thus qualifying as potential biomarkers for AS. Summarily, our non-targeted and targeted metabolomics investigations provide new insights into the metabolic profile and potential biomarker candidates of AS. These findings may provide additional diagnostic options for AS and enhance the understanding of the underlying pathophysiology of the condition.
Institute
Ningxia Medical University
Last NameMa
First NameXueqin
Address1160 Shenli Street, Yinchuan, Ningxia, 750004, China
Emailmaxueqin217@126.com
Phone+86 0951688069
Submit Date2023-09-15
Raw Data AvailableYes
Raw Data File Type(s)mzML
Analysis Type DetailLC-MS
Release Date2023-11-15
Release Version1
Xueqin Ma Xueqin Ma
https://dx.doi.org/10.21228/M8NH80
ftp://www.metabolomicsworkbench.org/Studies/ application/zip

Select appropriate tab below to view additional metadata details:

Project:

Project ID:PR001834
Project DOI:doi: 10.21228/M8NH80
Project Title:Serum metabolomics reveals metabolic profile and potential biomarkers of ankylosing spondylitis
Project Summary:Ankylosing spondylitis (AS) is a chronic systemic inflammatory disease that significantly impairs physical function, quality of life, and work ability in young individuals. Nonetheless, the identification of early radiographic changes in AS is frequently delayed, and the diagnostic efficacy of biomarkers remains moderately effective, with unsatisfactory sensitivity and specificity. Hence, it is imperative to identify biomarkers that can facilitate early diagnosis, prognosis, and monitoring of AS. A total of 67 AS patients and 67 healthy controls were recruited to procure plasma samples for the purpose of screening potential biomarkers of AS via untargeted combined with targeted metabolomics approach utlizing UHPLC-QTOF-MS/MS and UHPLC-QQQ-MS/MS. Multivariate pattern recognition and univariate statistical analysis were employed to compare and elucidate the differential metabolites. The results indicated a notable divergence between the two groups, and a total of 170 different metabolites associated with the primary 6 metabolic pathways exhibiting a correlation with AS. Among those, 26 metabolites exhibited high sensitivity and specificity with area under curve (AUC) value were greater than 0.8. Subsequent targeted quantitative analysis discovered 3 metabolites, namely 3-amino-2-piperidone, hypoxanthine and octadecylamine, exhibiting excellent distinguishing ability based on the results of ROC curve and Random Forest model, thus qualifying as potential biomarkers for AS. Summarily, our non-targeted and targeted metabolomics investigations provide new insights into the metabolic profile and potential biomarker candidates of AS. These findings may provide additional diagnostic options for AS and enhance the understanding of the underlying pathophysiology of the condition.
Institute:Ningxia Medical University
Last Name:Ma
First Name:Xueqin
Address:1160 Shenli Street, Yinchuan, Ningxia, 750004, China
Email:maxueqin217@126.com
Phone:+86 0951688069

Subject:

Subject ID:SU003062
Subject Type:Human
Subject Species:Homo sapiens
Taxonomy ID:9606
Gender:Male and female

Factors:

Subject type: Human; Subject species: Homo sapiens (Factor headings shown in green)

mb_sample_id local_sample_id Treatment
SA321066neg-AS23Ankylosing Spondylitis
SA321067neg-AS24Ankylosing Spondylitis
SA321068neg-AS25Ankylosing Spondylitis
SA321069neg-AS22Ankylosing Spondylitis
SA321070neg-AS21Ankylosing Spondylitis
SA321071neg-AS19Ankylosing Spondylitis
SA321072neg-AS20Ankylosing Spondylitis
SA321073neg-AS26Ankylosing Spondylitis
SA321074neg-AS27Ankylosing Spondylitis
SA321075neg-AS32Ankylosing Spondylitis
SA321076neg-AS33Ankylosing Spondylitis
SA321077neg-AS31Ankylosing Spondylitis
SA321078neg-AS30Ankylosing Spondylitis
SA321079neg-AS28Ankylosing Spondylitis
SA321080neg-AS29Ankylosing Spondylitis
SA321081neg-AS18Ankylosing Spondylitis
SA321082neg-AS16Ankylosing Spondylitis
SA321083neg-AS6Ankylosing Spondylitis
SA321084neg-AS7Ankylosing Spondylitis
SA321085neg-AS8Ankylosing Spondylitis
SA321086neg-AS5Ankylosing Spondylitis
SA321087neg-AS4Ankylosing Spondylitis
SA321088neg-AS2Ankylosing Spondylitis
SA321089neg-AS3Ankylosing Spondylitis
SA321090neg-AS9Ankylosing Spondylitis
SA321091neg-AS10Ankylosing Spondylitis
SA321092neg-AS15Ankylosing Spondylitis
SA321093neg-AS34Ankylosing Spondylitis
SA321094neg-AS14Ankylosing Spondylitis
SA321095neg-AS13Ankylosing Spondylitis
SA321096neg-AS11Ankylosing Spondylitis
SA321097neg-AS12Ankylosing Spondylitis
SA321098neg-AS17Ankylosing Spondylitis
SA321099neg-AS36Ankylosing Spondylitis
SA321100neg-AS57Ankylosing Spondylitis
SA321101neg-AS58Ankylosing Spondylitis
SA321102neg-AS59Ankylosing Spondylitis
SA321103neg-AS56Ankylosing Spondylitis
SA321104neg-AS55Ankylosing Spondylitis
SA321105neg-AS53Ankylosing Spondylitis
SA321106neg-AS54Ankylosing Spondylitis
SA321107neg-AS60Ankylosing Spondylitis
SA321108neg-AS61Ankylosing Spondylitis
SA321109neg-AS66Ankylosing Spondylitis
SA321110neg-AS67Ankylosing Spondylitis
SA321111neg-AS65Ankylosing Spondylitis
SA321112neg-AS64Ankylosing Spondylitis
SA321113neg-AS62Ankylosing Spondylitis
SA321114neg-AS63Ankylosing Spondylitis
SA321115neg-AS52Ankylosing Spondylitis
SA321116neg-AS51Ankylosing Spondylitis
SA321117neg-AS40Ankylosing Spondylitis
SA321118neg-AS41Ankylosing Spondylitis
SA321119neg-AS42Ankylosing Spondylitis
SA321120neg-AS39Ankylosing Spondylitis
SA321121neg-AS38Ankylosing Spondylitis
SA321122pos-AS1Ankylosing Spondylitis
SA321123neg-AS37Ankylosing Spondylitis
SA321124neg-AS43Ankylosing Spondylitis
SA321125neg-AS44Ankylosing Spondylitis
SA321126neg-AS49Ankylosing Spondylitis
SA321127neg-AS50Ankylosing Spondylitis
SA321128neg-AS48Ankylosing Spondylitis
SA321129neg-AS47Ankylosing Spondylitis
SA321130neg-AS45Ankylosing Spondylitis
SA321131neg-AS46Ankylosing Spondylitis
SA321132neg-AS35Ankylosing Spondylitis
SA321133neg-AS1Ankylosing Spondylitis
SA321134pos-AS46Ankylosing Spondylitis
SA321135pos-AS45Ankylosing Spondylitis
SA321136pos-AS44Ankylosing Spondylitis
SA321137pos-AS47Ankylosing Spondylitis
SA321138pos-AS48Ankylosing Spondylitis
SA321139pos-AS50Ankylosing Spondylitis
SA321140pos-AS49Ankylosing Spondylitis
SA321141pos-AS43Ankylosing Spondylitis
SA321142pos-AS42Ankylosing Spondylitis
SA321143pos-AS37Ankylosing Spondylitis
SA321144pos-AS36Ankylosing Spondylitis
SA321145pos-AS38Ankylosing Spondylitis
SA321146pos-AS39Ankylosing Spondylitis
SA321147pos-AS41Ankylosing Spondylitis
SA321148pos-AS40Ankylosing Spondylitis
SA321149pos-AS51Ankylosing Spondylitis
SA321150pos-AS52Ankylosing Spondylitis
SA321151pos-AS62Ankylosing Spondylitis
SA321152pos-AS61Ankylosing Spondylitis
SA321153pos-AS63Ankylosing Spondylitis
SA321154pos-AS64Ankylosing Spondylitis
SA321155pos-AS66Ankylosing Spondylitis
SA321156pos-AS65Ankylosing Spondylitis
SA321157pos-AS60Ankylosing Spondylitis
SA321158pos-AS59Ankylosing Spondylitis
SA321159pos-AS54Ankylosing Spondylitis
SA321160pos-AS53Ankylosing Spondylitis
SA321161pos-AS55Ankylosing Spondylitis
SA321162pos-AS56Ankylosing Spondylitis
SA321163pos-AS58Ankylosing Spondylitis
SA321164pos-AS57Ankylosing Spondylitis
SA321165pos-AS34Ankylosing Spondylitis
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Collection:

Collection ID:CO003055
Collection Summary:Informed consent was obtained from all subjects in this study, and all experiments were performed following the approved guidelines. 67 plasma samples were collected from individuals diagnosed with ankylosing spondylitis (AS), while 67 healthy subjects (HC) provided corresponding control samples. All samples were obtained from General Hospital of Ningxia Medical University.
Sample Type:Blood (serum)

Treatment:

Treatment ID:TR003071
Treatment Summary:Without treatment, serum from disease group and healthy control group were taken respectively.

Sample Preparation:

Sampleprep ID:SP003068
Sampleprep Summary:A volume of 50 μl of each sample was transferred to an EP tube, followed by the addition of 200 μl of extract solution (acetonitrile: methanol = 1:1, containing internal standard mixture). Then, the samples were vortexed for 30 s, sonicated for 10 min in an ice-water bath, and incubated for 1 h at -40 ℃ to precipitate proteins. Subsequently, the sample was centrifuged at 12000 rpm for 15 min at 4 ℃, and the resulting supernatant was transferred to a fresh glass vial for analysis. Quality control (QC) sample was prepared by mixing equal aliquots of the supernatants from all of the samples.

Combined analysis:

Analysis ID AN004836 AN004837
Analysis type MS MS
Chromatography type Reversed phase Reversed phase
Chromatography system Agilent 1290 Infinity II Agilent 1290 Infinity II
Column Waters XBridge BEH C18 (100 x 2.1mm,3.5um) Waters XBridge BEH C18 (100 x 2.1mm,3.5um)
MS Type ESI ESI
MS instrument type QTOF QTOF
MS instrument name Agilent 6545 QTOF Agilent 6545 QTOF
Ion Mode POSITIVE NEGATIVE
Units ppm ppm

Chromatography:

Chromatography ID:CH003655
Instrument Name:Agilent 1290 Infinity II
Column Name:Waters XBridge BEH C18 (100 x 2.1mm,3.5um)
Column Temperature:30℃
Flow Gradient:The gradient elution set as follows: 90-50% A from 0 to 1 min, 50-20% A from 1 to 2 min,20-2% A from 2 to 7 min, 2% A from 7 to 9 min, 2-20% A from 9 to 11 min, 20-50% A from 11 to 12 min, and 50-90% A from 12 to 13 min.
Flow Rate:0.2 ml/min
Solvent A:0.1% formic acid
Solvent B:acetonitrile
Chromatography Type:Reversed phase

MS:

MS ID:MS004582
Analysis ID:AN004836
Instrument Name:Agilent 6545 QTOF
Instrument Type:QTOF
MS Type:ESI
MS Comments:he LC-MS/MS raw data (.d) were initially transformed into mzXML format using ProteoWizard. Subsequently, an in-house program was employed, which was developed utilizing the R package with XCMS, for peak detection, extraction, alignment, and integration. Thirdly, metabolite annotation was performed using an in-house MS2 database (Biotree DB) with a cutoff of 0.3. To facilitate data analysis, a series of preparations and data management were conducted based on raw peaks, encompassing the following steps: (1) elimination of noise by filtering a single peak to remove noise; (2) retention of solely the peak area data with a single group of null values less than 50% or all groups of null values less than 50% by filtering a single peak; (3) utilization of the numerical simulation method to imitate the missing values in the original data and fill them in with one-half of the minimum value; (4) normalization of the peak area by the standard.
Ion Mode:POSITIVE
  
MS ID:MS004583
Analysis ID:AN004837
Instrument Name:Agilent 6545 QTOF
Instrument Type:QTOF
MS Type:ESI
MS Comments:he LC-MS/MS raw data (.d) were initially transformed into mzXML format using ProteoWizard. Subsequently, an in-house program was employed, which was developed utilizing the R package with XCMS, for peak detection, extraction, alignment, and integration. Thirdly, metabolite annotation was performed using an in-house MS2 database (Biotree DB) with a cutoff of 0.3. To facilitate data analysis, a series of preparations and data management were conducted based on raw peaks, encompassing the following steps: (1) elimination of noise by filtering a single peak to remove noise; (2) retention of solely the peak area data with a single group of null values less than 50% or all groups of null values less than 50% by filtering a single peak; (3) utilization of the numerical simulation method to imitate the missing values in the original data and fill them in with one-half of the minimum value; (4) normalization of the peak area by the standard.
Ion Mode:NEGATIVE
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