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

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Abstract

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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Acknowledgments

We thank Dr. Alexandra Jauhiainen for helpful insights and comments. Further, we want to extend our thanks to all participants of the TwinGene, ULSAM and PIVUS studies for the kind contribution to science. The computations were performed on resources provided by SNIC through Uppsala Multidisciplinary Center for Advanced Computational Science (UPPMAX) under project b2011036.

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Author notes

    Authors and Affiliations

    1. Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA

      Andrea Ganna

    2. Analytical and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA

      Andrea Ganna

    3. Department of Medical Sciences, Molecular Epidemiology and Science for Life Laboratory, Uppsala University, Box 1115, SE-751 41, Uppsala, Sweden

      Andrea Ganna, Tove Fall, Samira Salihovic, Jitender Kumar, Markus Stenemo & Erik Ingelsson

    4. Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden

      Andrea Ganna, Sara Hägg, Patrik K. E. Magnusson & Yudi Pawitan

    5. Department of Statistics, Inha University, Incheon, South Korea

      Woojoo Lee

    6. Proteomics and Metabolomics Facility, Colorado State University, Fort Collins, CO, USA

      Corey D. Broeckling & Jessica E. Prenni

    7. Department of Biochemistry and Molecular Biology, Colorado State University, Fort Collins, CO, USA

      Jessica E. Prenni

    8. Department of Medical Sciences, Uppsala University, Uppsala, Sweden

      Lars Lind

    9. Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, 94305, CA, USA

      Erik Ingelsson

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    1. Andrea Ganna
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    2. Tove Fall
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    Corresponding author

    Correspondence to Tove Fall.

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    Conflict of interest

    All authors declared no conflict of interest.

    Ethical statement

    All participants gave informed written consent and the Ethics Committees of Karolinska Institutet or Uppsala University approved the respective study protocol.

    Funding

    This study was supported by grants from Knut och Alice Wallenberg Foundation (Wallenberg Academy Fellow), European Research Council (ERC-2013-StG; Grant No. 335395), Swedish Diabetes Foundation (Grant No. 2013-024), Swedish Heart–Lung Foundation (Grant No. 20120197), the Family Ernfors Fund, the Swedish Government’s strategic research area EXODIAB (Excellence of Diabetes Research in Sweden), and Swedish Research Council (Grant No. 2012-1397). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

    Samples, subjects, and data outputs

    We uploaded this information as ISA-Tab metadata format to the Metabolights repository.

    Additional information

    Andrea Ganna and Tove Fall have equal contribution to this work.

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    Ganna, A., Fall, T., Salihovic, S. et al. Large-scale non-targeted metabolomic profiling in three human population-based studies. Metabolomics 12, 4 (2016). https://doi.org/10.1007/s11306-015-0893-5

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