Summary of Study ST002722
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 PR001688. The data can be accessed directly via it's Project DOI: 10.21228/M8HH7C 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.
| Study ID | ST002722 |
| Study Title | Cirrhosis-related metabolomics study |
| Study Summary | There are limited data on the diagnostic accuracy of gut microbial signatures for predicting hepatic decompensation in patients with cirrhosis. The aim of this study is to determine whether a stool genomic and metabolic signature accurately detects hepatic decompensation and mortality risk in cirrhosis secondary to nonalcoholic fatty liver disease (NAFLD). Shotgun metagenomic sequencing was performed on fecal samples collected from a prospective cohort of adults with NAFLD-related cirrhosis. The signatures were further validated with a metabolomic study on serum samples. Finally, we developed a Random Forest machine learning algorithm to make predictions on hepatic decompensation and mortality in NAFLD-related cirrhosis. Here we uploaded the metabolomics study data from LC-MS/MS. |
| Institute | University of British Columbia |
| Last Name | Zhao |
| First Name | Tingting |
| Address | 2036 Main Mall, V6T 1Z1 |
| tingzhao@chem.ubc.ca | |
| Phone | 6048221253 |
| Submit Date | 2023-05-30 |
| Raw Data Available | Yes |
| Raw Data File Type(s) | mzXML |
| Analysis Type Detail | LC-MS |
| Release Date | 2023-06-12 |
| Release Version | 1 |
Select appropriate tab below to view additional metadata details:
Project:
| Project ID: | PR001688 |
| Project DOI: | doi: 10.21228/M8HH7C |
| Project Title: | Gut genomic and metabolic signature predicts hepatic decompensation and mortality in NAFLD-related cirrhosis |
| Project Summary: | There are limited data on the diagnostic accuracy of gut microbial signatures for predicting hepatic decompensation in patients with cirrhosis. The aim of this study is to determine whether a stool genomic and metabolic signature accurately detects hepatic decompensation and mortality risk in cirrhosis secondary to nonalcoholic fatty liver disease (NAFLD). Based on the severity of cirrhosis, cirrhosis patients can be categorized as compensated or decompensated. Shotgun metagenomic sequencing was performed on fecal samples collected from a prospective cohort of adults with NAFLD-related cirrhosis. The signatures were further validated with a metabolomic study on fecal and serum samples. Finally, we developed a Random Forest machine learning algorithm to make predictions on hepatic decompensation and mortality in NAFLD-related cirrhosis. Here we uploaded the metabolomics study data of serum samples in LC-MS/MS analysis. |
| Institute: | University of British Columbia |
| Department: | Chemistry |
| Last Name: | Zhao |
| First Name: | Tingting |
| Address: | 2036 Main Mall, Vancouver, V6T 1Z1 |
| Email: | tingzhao@chem.ubc.ca |
| Phone: | 6048221253 |
| Publications: | Gut metagenome-derived signature predicts hepatic decompensation and mortality in NAFLD-related cirrhosis. Aliment Pharmacol Ther. 2022;56:1475–1485. DOI: 10.1111/apt.17236 |
Subject:
| Subject ID: | SU002828 |
| Subject Type: | Human |
| Subject Species: | Homo sapiens |
| Taxonomy ID: | 9606 |
| Gender: | Not applicable |
Factors:
Subject type: Human; Subject species: Homo sapiens (Factor headings shown in green)
| mb_sample_id | local_sample_id | Phenotype |
|---|---|---|
| SA273838 | CIR52 | compensated |
| SA273839 | CIR51 | compensated |
| SA273840 | CIR5 | compensated |
| SA273841 | CIR55 | compensated |
| SA273842 | CIR59 | compensated |
| SA273843 | CIR63 | compensated |
| SA273844 | CIR60 | compensated |
| SA273845 | CIR23 | compensated |
| SA273846 | CIR41 | compensated |
| SA273847 | CIR19 | compensated |
| SA273848 | CIR16 | compensated |
| SA273849 | CIR7 | compensated |
| SA273850 | CIR6 | compensated |
| SA273851 | CIR22 | compensated |
| SA273852 | CIR21 | compensated |
| SA273853 | CIR18 | compensated |
| SA273854 | CIR61 | decompensated |
| SA273855 | CIR57 | decompensated |
| SA273856 | CIR20 | decompensated |
| SA273857 | CIR53 | decompensated |
| SA273858 | CIR32 | decompensated |
| SA273859 | CIR58 | decompensated |
| Showing results 1 to 22 of 22 |
Collection:
| Collection ID: | CO002821 |
| Collection Summary: | Participants with non-alcohol fatty liverdisease (NAFLD)-related cirrhosis were recruited in the outpatient setting between August 2014 and November 2017 at the University of California San Diego (UCSD) NAFLD Research Center. The participant met the criteria for NAFLD related cirrhosis if they had NAFLD and biopsy-proven cirrhosis or a diagnosis of cirrhosis from a board-certified gastroenterologist based on imaging and clinical parameters. All participants underwent a baseline standardized clinical research visit that included a detailed medical history and physical examination. The severity of cirrhosis at the baseline research visit was categorized as compensated or decompensated. Decompensation was defined by any of the following: ascites requiring intervention (diuretics, paracentesis and/or transjugular intrahepatic portosystemic shunt), upper gastrointestinal haemorrhage secondary to gastroesophageal varices or hepatic encephalopathy (grade ≥ 2 based on West Haven Criteria10). Serum samples were also collected from participants at baseline. |
| Sample Type: | serum |
| Storage Conditions: | -80℃ |
Treatment:
| Treatment ID: | TR002837 |
| Treatment Summary: | N/A The severity of cirrhosis at the baseline research visit was categorised as compensated or decompensated. Thus, the participants were divided into compensated and decompensated group. |
Sample Preparation:
| Sampleprep ID: | SP002834 |
| Sampleprep Summary: | Dual extraction procedures of serum metabolome were as follows: A 50 μl serum sample was mixed with 300 μl ice-cold methanol in a 1.5 ml Eppendorf vial and vortex for 2 min. The solution was kept at −20°C for 4 h to precipitate proteins. After that, 1000 μl methyl tert-butyl ether was added to extract lipids. After 5 min shake, 350 μl H2O was added to induce the phase separation. The solution was vortex for 10 s and rested at room temperature for 10 min, followed by centrifugation at 14,000 rpm at 4°C for 15 min, for complete phase separation. The clear lower layer was separated into a new vial and dried in SpeedVac at 20°C for 4 h. The dried extract was then reconstituted in 150 μl acetonitrile and water (1:1, v:v) mixed solvent for LC–MS metabolomics analysis. The method blank was also prepared following the same protocol but without adding serum. A 5 μl aliquot from each individual sample was pooled together to make a quality control sample. |
Combined analysis:
| Analysis ID | AN004413 |
|---|---|
| Analysis type | MS |
| Chromatography type | HILIC |
| Chromatography system | 1290 Infinity II UHPLC system (Agilent Technologies) |
| Column | Merck SeQuant ZIC-pHILIC (150 x 2.1mm,5um) |
| MS Type | ESI |
| MS instrument type | QTOF |
| MS instrument name | Bruker Impact II QqTOF |
| Ion Mode | POSITIVE |
| Units | Height |
Chromatography:
| Chromatography ID: | CH003312 |
| Chromatography Summary: | SeQuant ZIC-pHILIC Column (5 μm, 2.1 mm × 150 mm) |
| Instrument Name: | 1290 Infinity II UHPLC system (Agilent Technologies) |
| Column Name: | Merck SeQuant ZIC-pHILIC (150 x 2.1mm,5um) |
| Column Temperature: | 30°C |
| Flow Gradient: | 0 min, 95% B; 20 min, 5% B; 25 min, 5% B; 25.01 min, 95% B; 30 min, 95% B; 35 min, 95% B. |
| Flow Rate: | 0.15 ml/min |
| Solvent A: | 10 mM ammonium acetate at pH 4.8 |
| Solvent B: | pure ACN |
| Chromatography Type: | HILIC |
MS:
| MS ID: | MS004160 |
| Analysis ID: | AN004413 |
| Instrument Name: | Bruker Impact II QqTOF |
| Instrument Type: | QTOF |
| MS Type: | ESI |
| MS Comments: | MS acquistion Comments: capillary voltage, 4.5 kV; nebulizer gas, 1.6 bar; dry gas, 7 L/min; dry gas temperature, 220 °C; mass scan range, 70–1000 (m/z); spectra rate, 8.00 Hz; and cycle time, 3.0 s; data-dependent acquisition (one MS1 scan is followed by multiple MS2 scan for the top-N abundant precursors) Data processing Comments: xcmsSet()function in XCMS R package. |
| Ion Mode: | POSITIVE |
| Collision Energy: | 16~30 eV |
| Collision Gas: | Nitrogen |
| Dry Gas Temp: | 220 °C |
| Fragmentation Method: | Collision induced dissociation |
| Ionization: | ESI |
| Ionization Potential: | 4.5 kV |
