{
"METABOLOMICS WORKBENCH":{"STUDY_ID":"ST002735","ANALYSIS_ID":"AN004434","VERSION":"1","CREATED_ON":"June 12, 2023, 10:41 am"},

"PROJECT":{"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"},

"STUDY":{"STUDY_TITLE":"Untargeted metabolomics revealed multiple metabolic perturbations in plasma of T2D patients in response to Liraglutide","STUDY_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_TYPE":"Human","SUBJECT_SPECIES":"Homo sapiens","TAXONOMY_ID":"9606","GENDER":"Male"},
"SUBJECT_SAMPLE_FACTORS":[
{
"Subject ID":"-",
"Sample ID":"RS_1",
"Factors":{"Factor":"Pre-treatment"},
"Additional sample data":{"RAW_FILE_NAME":"RS_1"}
},
{
"Subject ID":"-",
"Sample ID":"RS_2",
"Factors":{"Factor":"Pre-treatment"},
"Additional sample data":{"RAW_FILE_NAME":"RS_2"}
},
{
"Subject ID":"-",
"Sample ID":"RS_3",
"Factors":{"Factor":"Pre-treatment"},
"Additional sample data":{"RAW_FILE_NAME":"RS_3"}
},
{
"Subject ID":"-",
"Sample ID":"RS_4",
"Factors":{"Factor":"Pre-treatment"},
"Additional sample data":{"RAW_FILE_NAME":"RS_4"}
},
{
"Subject ID":"-",
"Sample ID":"RS_5",
"Factors":{"Factor":"Pre-treatment"},
"Additional sample data":{"RAW_FILE_NAME":"RS_5"}
},
{
"Subject ID":"-",
"Sample ID":"RS_6",
"Factors":{"Factor":"Pre-treatment"},
"Additional sample data":{"RAW_FILE_NAME":"RS_6"}
},
{
"Subject ID":"-",
"Sample ID":"RS_7",
"Factors":{"Factor":"Pre-treatment"},
"Additional sample data":{"RAW_FILE_NAME":"RS_7"}
},
{
"Subject ID":"-",
"Sample ID":"RS_8",
"Factors":{"Factor":"Pre-treatment"},
"Additional sample data":{"RAW_FILE_NAME":"RS_8"}
},
{
"Subject ID":"-",
"Sample ID":"RS_9",
"Factors":{"Factor":"Pre-treatment"},
"Additional sample data":{"RAW_FILE_NAME":"RS_9"}
},
{
"Subject ID":"-",
"Sample ID":"RS_10",
"Factors":{"Factor":"Pre-treatment"},
"Additional sample data":{"RAW_FILE_NAME":"RS_10"}
},
{
"Subject ID":"-",
"Sample ID":"RS_11",
"Factors":{"Factor":"Pre-treatment"},
"Additional sample data":{"RAW_FILE_NAME":"RS_11"}
},
{
"Subject ID":"-",
"Sample ID":"RS_12",
"Factors":{"Factor":"Pre-treatment"},
"Additional sample data":{"RAW_FILE_NAME":"RS_12"}
},
{
"Subject ID":"-",
"Sample ID":"RS_13",
"Factors":{"Factor":"Pre-treatment"},
"Additional sample data":{"RAW_FILE_NAME":"RS_13"}
},
{
"Subject ID":"-",
"Sample ID":"RS_14",
"Factors":{"Factor":"Pre-treatment"},
"Additional sample data":{"RAW_FILE_NAME":"RS_14"}
},
{
"Subject ID":"-",
"Sample ID":"RS_15",
"Factors":{"Factor":"Pre-treatment"},
"Additional sample data":{"RAW_FILE_NAME":"RS_15"}
},
{
"Subject ID":"-",
"Sample ID":"RS_16",
"Factors":{"Factor":"Pre-treatment"},
"Additional sample data":{"RAW_FILE_NAME":"RS_16"}
},
{
"Subject ID":"-",
"Sample ID":"RS_17",
"Factors":{"Factor":"Pre-treatment"},
"Additional sample data":{"RAW_FILE_NAME":"RS_17"}
},
{
"Subject ID":"-",
"Sample ID":"RS_18",
"Factors":{"Factor":"Pre-treatment"},
"Additional sample data":{"RAW_FILE_NAME":"RS_18"}
},
{
"Subject ID":"-",
"Sample ID":"RS_19",
"Factors":{"Factor":"Pre-treatment"},
"Additional sample data":{"RAW_FILE_NAME":"RS_19"}
},
{
"Subject ID":"-",
"Sample ID":"RS_20",
"Factors":{"Factor":"Pre-treatment"},
"Additional sample data":{"RAW_FILE_NAME":"RS_20"}
},
{
"Subject ID":"-",
"Sample ID":"RS_P1",
"Factors":{"Factor":"Post-treatment"},
"Additional sample data":{"RAW_FILE_NAME":"RS_P1"}
},
{
"Subject ID":"-",
"Sample ID":"RS_P2",
"Factors":{"Factor":"Post-treatment"},
"Additional sample data":{"RAW_FILE_NAME":"RS_P2"}
},
{
"Subject ID":"-",
"Sample ID":"RS_P3",
"Factors":{"Factor":"Post-treatment"},
"Additional sample data":{"RAW_FILE_NAME":"RS_P3"}
},
{
"Subject ID":"-",
"Sample ID":"RS_P4",
"Factors":{"Factor":"Post-treatment"},
"Additional sample data":{"RAW_FILE_NAME":"RS_P4"}
},
{
"Subject ID":"-",
"Sample ID":"RS_P5",
"Factors":{"Factor":"Post-treatment"},
"Additional sample data":{"RAW_FILE_NAME":"RS_P5"}
},
{
"Subject ID":"-",
"Sample ID":"RS_P6",
"Factors":{"Factor":"Post-treatment"},
"Additional sample data":{"RAW_FILE_NAME":"RS_P6"}
},
{
"Subject ID":"-",
"Sample ID":"RS_P7",
"Factors":{"Factor":"Post-treatment"},
"Additional sample data":{"RAW_FILE_NAME":"RS_P7"}
},
{
"Subject ID":"-",
"Sample ID":"RS_P8",
"Factors":{"Factor":"Post-treatment"},
"Additional sample data":{"RAW_FILE_NAME":"RS_P8"}
},
{
"Subject ID":"-",
"Sample ID":"RS_P9",
"Factors":{"Factor":"Post-treatment"},
"Additional sample data":{"RAW_FILE_NAME":"RS_P9"}
},
{
"Subject ID":"-",
"Sample ID":"RS_P10",
"Factors":{"Factor":"Post-treatment"},
"Additional sample data":{"RAW_FILE_NAME":"RS_P10"}
},
{
"Subject ID":"-",
"Sample ID":"RS_P11",
"Factors":{"Factor":"Post-treatment"},
"Additional sample data":{"RAW_FILE_NAME":"RS_P11"}
},
{
"Subject ID":"-",
"Sample ID":"RS_P12",
"Factors":{"Factor":"Post-treatment"},
"Additional sample data":{"RAW_FILE_NAME":"RS_P12"}
},
{
"Subject ID":"-",
"Sample ID":"RS_P13",
"Factors":{"Factor":"Post-treatment"},
"Additional sample data":{"RAW_FILE_NAME":"RS_P13"}
},
{
"Subject ID":"-",
"Sample ID":"RS_P14",
"Factors":{"Factor":"Post-treatment"},
"Additional sample data":{"RAW_FILE_NAME":"RS_P14"}
},
{
"Subject ID":"-",
"Sample ID":"RS_P15",
"Factors":{"Factor":"Post-treatment"},
"Additional sample data":{"RAW_FILE_NAME":"RS_P15"}
},
{
"Subject ID":"-",
"Sample ID":"RS_P16",
"Factors":{"Factor":"Post-treatment"},
"Additional sample data":{"RAW_FILE_NAME":"RS_P16"}
},
{
"Subject ID":"-",
"Sample ID":"RS_P17",
"Factors":{"Factor":"Post-treatment"},
"Additional sample data":{"RAW_FILE_NAME":"RS_P17"}
},
{
"Subject ID":"-",
"Sample ID":"RS_P18",
"Factors":{"Factor":"Post-treatment"},
"Additional sample data":{"RAW_FILE_NAME":"RS_P18"}
},
{
"Subject ID":"-",
"Sample ID":"RS_P19",
"Factors":{"Factor":"Post-treatment"},
"Additional sample data":{"RAW_FILE_NAME":"RS_P19"}
},
{
"Subject ID":"-",
"Sample ID":"RS_P20",
"Factors":{"Factor":"Post-treatment"},
"Additional sample data":{"RAW_FILE_NAME":"RS_P20"}
}
],
"COLLECTION":{"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.","SAMPLE_TYPE":"Blood (plasma)"},

"TREATMENT":{"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"},

"SAMPLEPREP":{"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)."},

"CHROMATOGRAPHY":{"CHROMATOGRAPHY_TYPE":"Reversed phase","INSTRUMENT_NAME":"Waters Acquity UPLC","COLUMN_NAME":"Waters XSelect HSS C18 (100 × 2.1mm,2.5um)","SOLVENT_A":"0.1% formic acid in dH2O","SOLVENT_B":"0.1% formic acid in 50% MeOH and ACN","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","COLUMN_TEMPERATURE":"55"},

"ANALYSIS":{"ANALYSIS_TYPE":"MS"},

"MS":{"INSTRUMENT_NAME":"Waters Xevo-G2-S","INSTRUMENT_TYPE":"QTOF","MS_TYPE":"ESI","ION_MODE":"POSITIVE","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.","MS_RESULTS_FILE":"ST002735_AN004434_Results.txt UNITS:Peak area Has m/z:Yes Has RT:Yes RT units:Minutes"}

}