MassIVE MSV000086876

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GNPS - Metabolomics data complemented drug use information in epidemiological databases: pilot study of potential kidney donors

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Description

Import from https://yareta.unige.ch/#/home/detail/1671012c-e04b-46cd-9628-45e264b49759 Objective : The objective of this study was to investigate whether clinical metabolomics, which is increasingly applied in population-based and epidemiological studies, can be used to provide analytical evidence of exposures, and whether such information can be useful to strengthen and/or complement corresponding clinical database entries, taking drug use as an example. Study design and setting : Liquid chromatography-mass spectrometry (LC-MS) metabolomics analyses were performed on urine from 100 randomly-selected control subjects (50% females) from the TransplantLines Food and Nutrition Biobank and Cohort Study (NCT identifier NCT02811835), and drugs were identified through spectral library searching and targeted signal extraction. Results : In 83 subjects for whom drug use information was available, 22 expected and 26 unexpected prescription-only drugs were identified, while 28 expected prescription-only drugs remained undetected. In addition, 7 prescription-only drugs were found in 17 subjects for whom drug use information was unavailable, and 58 over-the-counter drugs were identified in all 100 subjects. Conclusion : Molecular evidence for many drugs could be retrieved from LC-MS metabolomics data, which could be useful to complement and strengthen epidemiological databases given that considerable discrepancies were found between analytically-identified drugs and drugs listed in the available clinical database. [doi:10.25345/C51J57] [dataset license: CC0 1.0 Universal (CC0 1.0)]

Keywords: Database ; Drugs ; Epidemiology ; Metabolomics

Contact

Principal Investigators:
(in alphabetical order) 		Gerard Hopfgartner, University of Geneva, Switzerland
Submitting User: mwang87
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GNPS content goes here (MSV000086876 [task=5fbe45054a6349d3b47af7bf6f19bf08])
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Distinct condition labels are counted across all files submitted in the "Metadata" category having a "Condition" column in this dataset.

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Number of distinct biological replicates across all analyses (original submission and reanalyses) associated with this dataset.

Distinct replicate labels are counted across all files submitted in the "Metadata" category having a "BioReplicate" or "Replicate" column in this dataset.

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Number of distinct technical replicates across all analyses (original submission and reanalyses) associated with this dataset.

The technical replicate count is defined as the maximum number of times any one distinct combination of condition and biological replicate was analyzed across all files submitted in the "Metadata" category. In the case of fractionated experiments, only the first fraction is considered.

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Originally identified proteins that were automatically remapped by MassIVE to proteins in the SwissProt human reference database.

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Number of distinct protein accessions reported across all analyses (original submission and reanalyses) associated with this dataset.

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Number of distinct unmodified peptide sequences reported across all analyses (original submission and reanalyses) associated with this dataset.

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Number of distinct peptide sequences (including modified variants or peptidoforms) reported across all analyses (original submission and reanalyses) associated with this dataset.

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Total number of peptide-spectrum matches (i.e. spectrum identifications) reported across all analyses (original submission and reanalyses) associated with this dataset.

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Number of distinct proteins quantified across all analyses (original submission and reanalyses) associated with this dataset.

Distinct protein accessions are counted across all files submitted in the "Statistical Analysis of Quantified Analytes" category having a "Protein" column in this dataset.

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Number of distinct proteins found to be differentially abundant in at least one comparison across all analyses (original submission and reanalyses) associated with this dataset.

A protein is differentially abundant if its change in abundance across conditions is found to be statistically significant with an adjusted p-value <= 0.05 and lists no issues associated with statistical tests for differential abundance.

Distinct protein accessions are counted across all files submitted in the "Statistical Analysis of Quantified Analytes" category having a "Protein" column in this dataset.

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This dataset may not contain all raw spectra data as originally deposited in PRIDE. It has been imported to MassIVE for reanalysis purposes, so its spectra data here may consist solely of processed peak lists suitable for reanalysis with most software.