Data for: The salivary metabolome of children and parental caregivers in a large-scale family environment study
Data files
Apr 19, 2024 version files 62.42 MB
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90mo_metabolome_table_for_dryad.xlsx
62.42 MB
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README.md
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Abstract
Human metabolism is complex and dynamic and is impacted by genetics, diet, health, and countless inputs from the environment. Beyond the genetics shared by family members, cohabitation leads to shared microbial and environmental exposures. Furthermore, metabolism is affected by factors such as inflammation, antibiotic potential, environmental tobacco smoke (ETS) exposure, metabolic regulation, and environmental exposure to heavy metals within the home. Metabolomics represents a useful analytical method to assay the metabolism of individuals to find potential biomarkers for metabolic conditions that may not be phenotypically obvious or represent unknown physiological processes. As such, we applied untargeted LC-MS metabolomics to archived saliva samples from a racially diverse group of elementary school-aged children and their caregivers collected during the “90-month” assessment of the Family Life Project. We assayed a total of 1,425 saliva samples of which 1,344 were paired into 672 caregiver/child dyads. We compared the metabolomes of children (N = 719) and caregivers (N = 706) within and between homes, performed population-wide “metabotype” analyses, and measured associations between metabolites and salivary biomeasures of inflammation, antioxidant potential, ETS exposure, metabolic regulation, and heavy metals. Dyadic analyses revealed that children and their caregivers have largely similar salivary metabolomes. Although there were differences between the dyads at the individual levels of analysis, dyads explained most (62%) of the metabolome variation. At a population level of analysis, our data clustered into two large groups, indicating that people likely share most of their metabolomes, but that there are distinct “metabotypes” across large sample sets. Lastly, individual differences in several metabolites – which were putative oxidative damage-associated or pathological markers – were significantly correlated with salivary measures indexing inflammation, antioxidant potential, ETS exposure, metabolic regulation, and heavy metals. Implications of the effects of family environment on metabolomic variation at the population, dyadic, and individual levels of analyses for health and human development are discussed.
This file contains the full unedited mass spectrometry data for each sample, quality control pool samples, and blank samples.
Each column header is as follows:
- “Sample_ID”: The sample name. Samples have numerical identifiers, “Blank” is a method blank sample, and “QCPool” are quality control sample.
- “Identifier”: A unique identifier corresponding to the mass/charge ratio and retention time.
- “Annotation”: The annotated name of each compound.
- “Species”: The ionization species of the molecule detected.
- “MSI Level”: The confidence of classification accuracy for each annotation:
- MSI level 1 is MSMS + accurate precursor mz + rt match
- MSI level 2 is MSMS + accurate precursor mz but no rt match
- MSI level 3 is an accurate precursor mz + rt match
- MSI level 4 is Sugar Class-specific, exact Sugar not confirmed with rt
- MSI level 5 is unknown
- “m/z”: The mass/charge ratio for each molecule.
- “ESI mode”: Electrospray Ionization mode for each molecule.
- “RT”: The retention time in seconds for each molecule.
Note that the West Coast Metabolomics Center adds internal standards for mass calibration. Those samples are marked “iSTD” and colored RED in the data. Peak heights are unitless. Empty cells represent no data present, not a true “zero.”
More information about the West Coast Metabolomics Center and their methods can be found here: https://metabolomics.ucdavis.edu/