Summary of Study ST000306
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 PR000246. The data can be accessed directly via it's Project DOI: 10.21228/M8088P This work is supported by NIH grant, U2C- DK119886.
See: https://www.metabolomicsworkbench.org/about/howtocite.php
Study ID | ST000306 |
Study Title | Metabolomics Approach to Identify Molecules and Pathways Involved in the Development of Atherosclerotic Coronary Artery Disease |
Study Type | Metabolomics |
Study Summary | Genetics play major roles in the development of atherosclerotic coronary artery disease (CAD). Despite tremendous efforts worldwide invested to decipher the genetic components controlling the development of CAD, the genetic architecture of CAD remains largely unclear. As part of an on-going effort to identify molecules and pathways involved in the development of atherosclerotic CAD, we propose to use rigorous angiographic criteria to define CAD phenotype for genomics and metabolomics study. We identified two extreme groups, namely “young CAD” group, who are very young individuals (age <= 40 years) proven to have severe CAD required revascularization, and “CAD-free elderly”, who are at very advanced age (Age >= 80 years) but have no angiographically apparent CAD. Phenotypically, these two groups are in sharp contrary. Conventional risk factors account for small portion of different phenotypes. We hypothesize that there are genetically programmed pathways and molecules accelerating atherosclerotic pathogenesis, in the “young CAD” patients and preventing the development of CAD in the “CAD-free elderly” patients. We sought to combine genomics and metabolomics approaches to profile and identify these pathways and molecules. Both plasma and urine samples from patients in these two groups, and their age matched control groups, will undergo unbiased metabolomics profiling with high throughput quantitative nuclear magnetic resonance (NMR) and mass spectrometry (MS) technology in RTI metabolomics core facility. Comprehensive statistic and multi-variant analytic approaches will be used to identify pathways and molecules significance to the pathogenesis of atherosclerosis. These data will be integrated with genomics data from next generation sequencing of genetic materials from the same groups of patients to further explore the molecular mechanisms underlying atherosclerosis and CAD. |
Institute | University of North Carolina |
Department | Systems and Translational Sciences |
Laboratory | Sumner Lab |
Last Name | Sumner |
First Name | Susan |
Address | Eastern Regional Comprehensive Metabolomics Resource Core, UNC Nutrition Research Institute, 500 Laureate Way, Kannapolis, NC, 28081 |
susan_sumner @unc.edu | |
Phone | 704-250-5066 |
Submit Date | 2015-12-31 |
Total Subjects | 106 |
Raw Data Available | Yes |
Raw Data File Type(s) | fid |
Analysis Type Detail | NMR |
Release Date | 2016-12-31 |
Release Version | 1 |
Select appropriate tab below to view additional metadata details:
Project:
Project ID: | PR000246 |
Project DOI: | doi: 10.21228/M8088P |
Project Title: | Metabolomics Approach to Identify Molecules and Pathways Involved in the Development of Atherosclerotic Coronary Artery Disease |
Project Summary: | Genetics play major roles in the development of atherosclerotic coronary artery disease (CAD). Despite tremendous efforts worldwide invested to decipher the genetic components controlling the development of CAD, the genetic architecture of CAD remains largely unclear. As part of an on-going effort to identify molecules and pathways involved in the development of atherosclerotic CAD, we propose to use rigorous angiographic criteria to define CAD phenotype for genomics and metabolomics study. We identified two extreme groups, namely “young CAD” group, who are very young individuals (age <= 40 years) proven to have severe CAD required revascularization, and “CAD-free elderly”, who are at very advanced age (Age >= 80 years) but have no angiographically apparent CAD. Phenotypically, these two groups are in sharp contrary. Conventional risk factors account for small portion of different phenotypes. We hypothesize that there are genetically programmed pathways and molecules accelerating atherosclerotic pathogenesis, in the “young CAD” patients and preventing the development of CAD in the “CAD-free elderly” patients. We sought to combine genomics and metabolomics approaches to profile and identify these pathways and molecules. Both plasma and urine samples from patients in these two groups, and their age matched control groups, will undergo unbiased metabolomics profiling with high throughput quantitative nuclear magnetic resonance (NMR) and mass spectrometry (MS) technology in RTI metabolomics core facility. Comprehensive statistic and multi-variant analytic approaches will be used to identify pathways and molecules significance to the pathogenesis of atherosclerosis. These data will be integrated with genomics data from next generation sequencing of genetic materials from the same groups of patients to further explore the molecular mechanisms underlying atherosclerosis and CAD. |
Institute: | University of North Carolina at Chapel Hill |
Department: | Heart & Vascular Center |
Last Name: | Dai |
First Name: | Xuming |
Address: | BW Building, CB 7075, 160 Dental Circle, Chapel Hill, NC 27599 |
Email: | xdai@unch.unc.edu |
Phone: | 919-966-5956 |
Subject:
Subject ID: | SU000326 |
Subject Type: | Human |
Subject Species: | Homo sapiens |
Taxonomy ID: | 9606 |
Gender: | male/female |
Species Group: | Human |
Factors:
Subject type: Human; Subject species: Homo sapiens (Factor headings shown in green)
mb_sample_id | local_sample_id | CAD |
---|---|---|
SA013847 | DP2S138 | Elderly 1st Dx CAD |
SA013848 | DP2S141 | Elderly 1st Dx CAD |
SA013849 | DP2S129 | Elderly 1st Dx CAD |
SA013850 | DP2S121 | Elderly 1st Dx CAD |
SA013851 | DP2S109 | Elderly 1st Dx CAD |
SA013852 | DP2S155 | Elderly 1st Dx CAD |
SA013853 | DP2S159 | Elderly 1st Dx CAD |
SA013854 | DP1S097 | Elderly 1st Dx CAD |
SA013855 | DP2S199 | Elderly 1st Dx CAD |
SA013856 | DP2S188 | Elderly 1st Dx CAD |
SA013857 | DP2S172 | Elderly 1st Dx CAD |
SA013858 | DP2S108 | Elderly 1st Dx CAD |
SA013859 | DP2S135 | Elderly 1st Dx CAD |
SA013860 | DP2S016 | Elderly 1st Dx CAD |
SA013861 | DP2S029 | Elderly 1st Dx CAD |
SA013862 | DP2S013 | Elderly 1st Dx CAD |
SA013863 | DP2S083 | Elderly 1st Dx CAD |
SA013864 | DP2S003 | Elderly 1st Dx CAD |
SA013865 | DP2S005 | Elderly 1st Dx CAD |
SA013866 | DP2S030 | Elderly 1st Dx CAD |
SA013867 | DP2S010 | Elderly 1st Dx CAD |
SA013868 | DP2S062 | Elderly 1st Dx CAD |
SA013869 | DP2S031 | Elderly 1st Dx CAD |
SA013870 | DP2S056 | Elderly 1st Dx CAD |
SA013871 | DP2S060 | Elderly 1st Dx CAD |
SA013872 | DP2S035 | Elderly 1st Dx CAD |
SA013873 | DP2S168 | Elderly No CAD |
SA013874 | DP2S183 | Elderly No CAD |
SA013875 | DP2S164 | Elderly No CAD |
SA013876 | DP2S147 | Elderly No CAD |
SA013877 | DP2S197 | Elderly No CAD |
SA013878 | DPTotal_Pool_4 | Elderly No CAD |
SA013879 | DP2S134 | Elderly No CAD |
SA013880 | DPTotal_Pool_6 | Elderly No CAD |
SA013881 | DP3S008 | Elderly No CAD |
SA013882 | DP2S200 | Elderly No CAD |
SA013883 | DP2S001 | Elderly No CAD |
SA013884 | DP2S049 | Elderly No CAD |
SA013885 | DP2S042 | Elderly No CAD |
SA013886 | DP2S133 | Elderly No CAD |
SA013887 | DP1S081 | Elderly No CAD |
SA013888 | DP2S051 | Elderly No CAD |
SA013889 | DP2S033 | Elderly No CAD |
SA013890 | DP2S070 | Elderly No CAD |
SA013891 | DP2S102 | Elderly No CAD |
SA013892 | DP2S125 | Elderly No CAD |
SA013893 | DP2S086 | Elderly No CAD |
SA013894 | DP2S148 | Mid-Age CAD |
SA013895 | DP2S144 | Mid-Age CAD |
SA013896 | DP2S171 | Mid-Age CAD |
SA013897 | DP3S019 | Mid-Age CAD |
SA013898 | DP2S124 | Mid-Age CAD |
SA013899 | DP3S026 | Mid-Age CAD |
SA013900 | DP2S191 | Mid-Age CAD |
SA013901 | DP2S180 | Mid-Age CAD |
SA013902 | DP2S094 | Mid-Age CAD |
SA013903 | DP1S152 | Mid-Age CAD |
SA013904 | DP1S142 | Mid-Age CAD |
SA013905 | DP2S103 | Mid-Age CAD |
SA013906 | DP1S122 | Mid-Age CAD |
SA013907 | DP1S155 | Mid-Age CAD |
SA013908 | DP2S050 | Mid-Age CAD |
SA013909 | DP2S085 | Mid-Age CAD |
SA013910 | DP2S081 | Mid-Age CAD |
SA013911 | DP2S055 | Mid-Age CAD |
SA013912 | DPTotal_Pool_3 | Total Pool |
SA013913 | DPTotal_Pool_2 | Total Pool |
SA013914 | DPTotal_Pool_1 | Total Pool |
SA013915 | DP1S134 | Young CAD |
SA013916 | DP1S107 | Young CAD |
SA013917 | DP1S095 | Young CAD |
SA013918 | DP1S135 | Young CAD |
SA013919 | DP1S154 | Young CAD |
SA013920 | DP3S004 | Young CAD |
SA013921 | DP1S164 | Young CAD |
SA013922 | DP1S090 | Young CAD |
SA013923 | DP1S162 | Young CAD |
SA013924 | DP1S014 | Young CAD |
SA013925 | DP1S006 | Young CAD |
SA013926 | DP1S080 | Young CAD |
SA013927 | DP1S028 | Young CAD |
SA013928 | DP1S027 | Young CAD |
SA013929 | DP1S030 | Young CAD |
SA013930 | DP1S059 | Young CAD |
SA013931 | DP1S037 | Young CAD |
SA013932 | DP1S031 | Young CAD |
SA013933 | DP1S174 | Young No-CAD |
SA013934 | DP1S172 | Young No-CAD |
SA013935 | DP1S129 | Young No-CAD |
SA013936 | DP3S011 | Young No-CAD |
SA013937 | DP1S140 | Young No-CAD |
SA013938 | DPTotal_Pool_5 | Young No-CAD |
SA013939 | DP1S113 | Young No-CAD |
SA013940 | DP3S028 | Young No-CAD |
SA013941 | DP3S020 | Young No-CAD |
SA013942 | DP3S018 | Young No-CAD |
SA013943 | DP3S015 | Young No-CAD |
SA013944 | DP1S066 | Young No-CAD |
SA013945 | DP1S016 | Young No-CAD |
SA013946 | DP1S020 | Young No-CAD |
Collection:
Collection ID: | CO000320 |
Collection Summary: | - |
Sample Type: | plasma |
Storage Conditions: | -80C |
Treatment:
Treatment ID: | TR000340 |
Treatment Summary: | - |
Sample Preparation:
Sampleprep ID: | SP000334 |
Sampleprep Summary: | Plasma samples were transferred to labeled tubes. A total of 219 study samples were thawed on ice for sample preparation200 uL of plasma sample were thawed and transferred to labeled tubes on ice where they were mixed with 50 uL Saline master mix (5mM Formate). Analytical quality control (QC) phenotypic pooled samples were generated by transferring a 25 µL of each sample of each respective phenotypical experimental sample into different 1.5 mL tubes. Whole study (total) pools were generated by transferring 200 uL of plasma from each Pool sample into a 2.0 mL tube. The tubes were vortexed for 4 min on a multi-tube vortexer and centrifuged at 16,000 rcf for 4 min. A 200 µl aliquot of the supernatant was transferred into pre-labeled 3mm NMR tubes for data acquisition on a 700 MHz spectrometer. |
Sampleprep Protocol Filename: | CAD_Metabolomics_Procedures.docx |
Analysis:
Analysis ID: | AN000485 |
Analysis Type: | NMR |
Num Factors: | 6 |
NMR:
NMR ID: | NM000061 |
Analysis ID: | AN000485 |
Instrument Name: | Bruker |
Instrument Type: | FT-NMR |
NMR Experiment Type: | Other |
Field Frequency Lock: | Deuterium |
Standard Concentration: | 0.5 mM |
Spectrometer Frequency: | 700 MHz |
NMR Probe: | 5 mm ATMA Cryoprobe |
NMR Solvent: | D2O |
NMR Tube Size: | 5 mm |
Shimming Method: | Topshim |
Pulse Sequence: | noesypr1d |
Water Suppression: | yes |
Receiver Gain: | 4.5 |
Offset Frequency: | 3299.5 |
Chemical Shift Ref Cpd: | DSS |
Temperature: | 298.1 K |
Number Of Scans: | 128 |
Dummy Scans: | 4 |
Acquisition Time: | 3.893 |
Spectral Width: | 12.0227 ppm, 8.417 Hz |
Num Data Points Acquired: | 65536 |
Real Data Points: | 65536 |
Line Broadening: | 0.5 Hz |
Zero Filling: | yes |
Apodization: | Lorentzian |
Baseline Correction Method: | Polynomial |
Chemical Shift Ref Std: | DSS-D6 |
Binned Data Excluded Range: | water (4.60 - 5.10 ppm) EDTA (2.53 - 2.59 ppm, 2.67 -2.72 ppm, 3.06 - 3.15 ppm, 3.22 - 3.28 and 3.56 - 3.65ppm) noise regions (5.50-5.70 ppm, 5.90-6.80 ppm, 7.85-10.786 ppm) |