Summary of Study ST002735

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 PR001699. The data can be accessed directly via it's Project DOI: 10.21228/M83B08 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 IDST002735
Study TitleUntargeted metabolomics revealed multiple metabolic perturbations in plasma of T2D patients in response to Liraglutide
Study SummaryDespite the global efforts put into the clinical research and studies in order to protect against Type-2 diabetes mellitus (T2DM), the incidence of T2DM remains high causing a major health problem and impacting the health and care systems. Therefore, T2DM-related treatments and therapies are continuously invented for the clinical use, including Liraglutide. The last is a GLP-1 analogue and shows its beneficial health outcomes e.g., improved glycemic control, lower body weight, and reduced cardiovascular disease risks. The intrinsic mechanisms of these beneficial effects are not fully understood; however, our research group has previously published proteomics work demonstrating the involvement of certain important proteins in part in the beneficial health outcomes of Liraglutide. Since proteomics and metabolomics are complementary to each other in the context of the biological pathways, studying the metabolic impacts of Liraglutide on T2DM patients would add further information about the beneficial health outcomes of Liraglutide. Thus, herein, we performed an untargeted metabolomics approach for identifying metabolic pathways impacted by the treatment of Liraglutide in T2DM patients. Methods: Untargeted liquid chromatography coupled with mass spectrometry was used for metabolomics analysis of plasma samples collected from T2DM patients (n=20) before and after receiving Liraglutide treatment. Metabolic profiling and related pathway and network analyses were conducted. Results: The metabolic profiling analyses identified 93 endogenous metabolites were significantly affected by the Liraglutide treatments, which 49 metabolites up-regulated and 44 metabolites down-regulated. Moreover, the metabolic pathway analyses revealed that the most pronounced metabolite and metabolic pathways that were affected by the Liraglutide treatment was Pentose and glucuronate interconversion, suggesting the last may be a potential target of the Liraglutide treatment could be involved in part in the beneficial effects seen in T2DM patients, specially, we found that glucuronate interconversion pathway which is known by its role in eliminating toxic and undesirable substances from the human body, impacted in Liraglutide treated patients. The last findings ar consistence with our previous proteomics findings. Conclusion: These findings, taken together with our previous results, provide a deeper understanding of the underlying mechanisms involved in the beneficial effects of Liraglutide at the proteomic and metabolic levels in T2DM patients.
Institute
King Faisal Specialist Hospital and Research Centre (KFSHRC)
Last NameAl Mogren
First NameMaha
AddressZahrawi Street, Al Maather, Riyadh 11211, Saudi Arabia
Emailmalmogren@alfaisal.edu
Phone966541205332
Submit Date2023-05-29
Raw Data AvailableYes
Raw Data File Type(s)raw(Waters)
Analysis Type DetailLC-MS
Release Date2023-07-02
Release Version1
Maha Al Mogren Maha Al Mogren
https://dx.doi.org/10.21228/M83B08
ftp://www.metabolomicsworkbench.org/Studies/ application/zip

Select appropriate tab below to view additional metadata details:

Project:

Project ID:PR001699
Project DOI:doi: 10.21228/M83B08
Project Title:Untargeted metabolomics revealed multiple metabolic perturbations in plasma of T2D patients in response to Liraglutide
Project Summary:Despite the global efforts put into the clinical research and studies in order to protect against Type-2 diabetes mellitus (T2DM), the incidence of T2DM remains high causing a major health problem and impacting the health and care systems. Therefore, T2DM-related treatments and therapies are continuously invented for the clinical use, including Liraglutide. The last is a GLP-1 analogue and shows its beneficial health outcomes e.g., improved glycemic control, lower body weight, and reduced cardiovascular disease risks. The intrinsic mechanisms of these beneficial effects are not fully understood; however, our research group has previously published proteomics work demonstrating the involvement of certain important proteins in part in the beneficial health outcomes of Liraglutide. Since proteomics and metabolomics are complementary to each other in the context of the biological pathways, studying the metabolic impacts of Liraglutide on T2DM patients would add further information about the beneficial health outcomes of Liraglutide. Thus, herein, we performed an untargeted metabolomics approach for identifying metabolic pathways impacted by the treatment of Liraglutide in T2DM patients. Methods: Untargeted liquid chromatography coupled with mass spectrometry was used for metabolomics analysis of plasma samples collected from T2DM patients (n=20) before and after receiving Liraglutide treatment. Metabolic profiling and related pathway and network analyses were conducted. Results: The metabolic profiling analyses identified 93 endogenous metabolites were significantly affected by the Liraglutide treatments, which 49 metabolites up-regulated and 44 metabolites down-regulated. Moreover, the metabolic pathway analyses revealed that the most pronounced metabolite and metabolic pathways that were affected by the Liraglutide treatment was Pentose and glucuronate interconversion, suggesting the last may be a potential target of the Liraglutide treatment could be involved in part in the beneficial effects seen in T2DM patients, specially, we found that glucuronate interconversion pathway which is known by its role in eliminating toxic and undesirable substances from the human body, impacted in Liraglutide treated patients. The last findings ar consistence with our previous proteomics findings. Conclusion: These findings, taken together with our previous results, provide a deeper understanding of the underlying mechanisms involved in the beneficial effects of Liraglutide at the proteomic and metabolic levels in T2DM patients.
Institute:King Faisal Specialist Hospital and Research Centre (KFSHRC)
Last Name:Al Mogren
First Name:Maha
Address:Zahrawi Street, Al Maather, Riyadh 11211, Saudi Arabia
Email:malmogren@alfaisal.edu
Phone:966541205332

Subject:

Subject ID:SU002841
Subject Type:Human
Subject Species:Homo sapiens
Taxonomy ID:9606
Gender:Male

Factors:

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

mb_sample_id local_sample_id Factor
SA288587RS_P8Post-treatment
SA288588RS_P9Post-treatment
SA288589RS_P10Post-treatment
SA288590RS_P7Post-treatment
SA288591RS_P5Post-treatment
SA288592RS_P3Post-treatment
SA288593RS_P4Post-treatment
SA288594RS_P11Post-treatment
SA288595RS_P6Post-treatment
SA288596RS_P13Post-treatment
SA288597RS_P18Post-treatment
SA288598RS_P19Post-treatment
SA288599RS_P20Post-treatment
SA288600RS_P17Post-treatment
SA288601RS_P16Post-treatment
SA288602RS_P2Post-treatment
SA288603RS_P14Post-treatment
SA288604RS_P15Post-treatment
SA288605RS_P12Post-treatment
SA288606RS_P1Post-treatment
SA288607RS_7Pre-treatment
SA288608RS_8Pre-treatment
SA288609RS_9Pre-treatment
SA288610RS_10Pre-treatment
SA288611RS_6Pre-treatment
SA288612RS_5Pre-treatment
SA288613RS_2Pre-treatment
SA288614RS_3Pre-treatment
SA288615RS_4Pre-treatment
SA288616RS_11Pre-treatment
SA288617RS_12Pre-treatment
SA288618RS_18Pre-treatment
SA288619RS_19Pre-treatment
SA288620RS_20Pre-treatment
SA288621RS_17Pre-treatment
SA288622RS_16Pre-treatment
SA288623RS_13Pre-treatment
SA288624RS_14Pre-treatment
SA288625RS_15Pre-treatment
SA288626RS_1Pre-treatment
Showing results 1 to 40 of 40

Collection:

Collection ID:CO002834
Collection Summary:The study was approved by the Institutional Review Board of the College of Medicine, King Saud University, Riyadh, Saudi Arabia (registration no. E-18-3075). Recruited patients were asked to sign a written informed consent form before enrolling. Twenty patients who were diagnosed with T2DM were referred to the King Khaled University Hospital's (KKUH), Obesity Research Center, where this study took place. Patients were treated with an appropriate dose of Liraglutide for a three months as described previously (8). Samples were taken pre-treatment and post-treatment. Note: the T2DM participants were on other medications including insulin and metformin beside the Liraglutide treatment.
Collection Protocol Filename:Liraglutide_sample_collection.docx
Sample Type:Blood (plasma)

Treatment:

Treatment ID:TR002850
Treatment Summary:Patients with indications of add-on liraglutide were started on treatment by their physician in a scaled-up dose from 0.6 mg to 1.8 mg of a once-daily subcutaneous injection over a period of three weeks. The follow-up visit was scheduled 3 months after receiving the full dose (1.8 mg) of liraglutide. Urine samples were collected at two time points: one sample before and another sample after treatment with liraglutide. Blood samples were collected by venipuncture into plain tubes (Vacutainer, BD Biosciences, San Jose, CA, USA) from each patient after a 10 h fast. The plasma was separated by centrifugation (15 min, 3000× g), divided into several aliquots, and stored at −80 °C for further analysis.
Treatment Compound:Liraglutide

Sample Preparation:

Sampleprep ID:SP002847
Sampleprep Summary:Metabolites were extracted from plasma were collected from 20 type2 diabetic patients, pre-and post-treatment with liraglutide (n=40 samples) (10). Briefly, 100 μL plasma sample were mixed with 900 μL of extraction solvent 50% acetonitrile (ACN) in methanol (MeOH). Meanwhile, QC samples were prepared with aliquots from all samples to check for system stability. The mixtures were mixed on thermomixer at 600 rpm at room temperature for one hour (Eppendorf, CITY, Germany). Afterward, the samples were centrifuged at 16000 rpm at 4ºC for 10 min. The supernatant was transferred into new Eppendrof tube, and then evaporated completely in a SpeedVac (Christ, Germany). The dried samples were reconstituted with100 μl of 50% mobile phase A: B (A: 0.1% Formic acid in dH2O, B: 0.1% Formic acid in 50% ACN: MeOH).
Sampleprep Protocol Filename:Liraglutide_Metabolites_Extraction.docx

Combined analysis:

Analysis ID AN004434 AN004435
Analysis type MS MS
Chromatography type Reversed phase Reversed phase
Chromatography system Waters Acquity UPLC Waters Acquity UPLC
Column Waters XSelect HSS C18 (100 × 2.1mm,2.5um) Waters XSelect HSS C18 (100 × 2.1mm,2.5um)
MS Type ESI ESI
MS instrument type QTOF QTOF
MS instrument name Waters Xevo-G2-S Waters Xevo-G2-S
Ion Mode POSITIVE NEGATIVE
Units Peak area Peak area

Chromatography:

Chromatography ID:CH003331
Methods Filename:LC_MS_Metabolomics_Liraglutide.docx
Instrument Name:Waters Acquity UPLC
Column Name:Waters XSelect HSS C18 (100 × 2.1mm,2.5um)
Column Temperature:55
Flow Gradient:0-16 min 95- 5% A, 16-19 min 5% A, 19-20 min 5-95% A, 20-22 min 95- 95% A
Flow Rate:300 µL/min
Solvent A:0.1% formic acid in dH2O
Solvent B:0.1% formic acid in 50% MeOH and ACN
Chromatography Type:Reversed phase

MS:

MS ID:MS004181
Analysis ID:AN004434
Instrument Name:Waters Xevo-G2-S
Instrument Type:QTOF
MS Type:ESI
MS Comments:The DIA data were collected with a Masslynx™ V4.1 workstation in continuum mode (Waters Inc., Milford, MA, USA). The raw MS data were processed following a standard pipeline using the Progenesis QI v.3.0 software.
Ion Mode:POSITIVE
Analysis Protocol File:LC_MS_Metabolomics_Liraglutide.docx
  
MS ID:MS004182
Analysis ID:AN004435
Instrument Name:Waters Xevo-G2-S
Instrument Type:QTOF
MS Type:ESI
MS Comments:The DIA data were collected with a Masslynx™ V4.1 workstation in continuum mode (Waters Inc., Milford, MA, USA). The raw MS data were processed following a standard pipeline using the Progenesis QI v.3.0 software.
Ion Mode:NEGATIVE
Analysis Protocol File:LC_MS_Metabolomics_Liraglutide.docx
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