Large-scale non-targeted metabolomic profiling in three human population-based studies

被引:0
作者
Andrea Ganna
Tove Fall
Samira Salihovic
Woojoo Lee
Corey D. Broeckling
Jitender Kumar
Sara Hägg
Markus Stenemo
Patrik K. E. Magnusson
Jessica E. Prenni
Lars Lind
Yudi Pawitan
Erik Ingelsson
机构
[1] Broad Institute of MIT and Harvard,Program in Medical and Population Genetics
[2] Massachusetts General Hospital and Harvard Medical School,Analytical and Translational Genetics Unit, Department of Medicine
[3] Uppsala University,Department of Medical Sciences, Molecular Epidemiology and Science for Life Laboratory
[4] Karolinska Institutet,Department of Medical Epidemiology and Biostatistics
[5] Inha University,Department of Statistics
[6] Colorado State University,Proteomics and Metabolomics Facility
[7] Colorado State University,Department of Biochemistry and Molecular Biology
[8] Uppsala University,Department of Medical Sciences
[9] Stanford University School of Medicine,Department of Medicine, Division of Cardiovascular Medicine
来源
Metabolomics | 2016年 / 12卷
关键词
Metabolomics; Epidemiology; Annotation; Cohort;
D O I
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学科分类号
摘要
Non-targeted metabolomic profiling is used to simultaneously assess a large part of the metabolome in a biological sample. Here, we describe both the analytical and computational methods used to analyze a large UPLC–Q-TOF MS-based metabolomic profiling effort using plasma and serum samples from participants in three Swedish population-based studies of middle-aged and older human subjects: TwinGene, ULSAM and PIVUS. At present, more than 200 metabolites have been manually annotated in more than 3600 participants using an in-house library of standards and publically available spectral databases. Data available at the metabolights repository include individual raw unprocessed data, processed data, basic demographic variables and spectra of annotated metabolites. Additional phenotypical and genetic data is available upon request to cohort steering committees. These studies represent a unique resource to explore and evaluate how metabolic variability across individuals affects human diseases.
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