Liquid chromatography-mass spectrometry (LC-MS)-based untargeted metabolomics experiments have become increasingly popular because of the wide range of metabolites that can be analyzed and the possibility to measure novel compounds. LC-MS instrumentation and analysis conditions can differ substantially among laboratories and experiments, thus resulting in non-standardized datasets demanding customized annotation workflows. We present an ecosystem of R packages, centered around the MetaboCoreUtils, MetaboAnnotation and CompoundDb packages that together provide a modular infrastructure for the annotation of untargeted metabolomics data. Initial annotation can be performed based on MS1 properties such as m/z and retention times, followed by an MS2-based annotation in which experimental fragment spectra are compared against a reference library. Such reference databases can be created and managed with the CompoundDb package. The ecosystem supports data from a variety of formats, including, but not limited to, MSP, MGF, mzML, mzXML, netCDF as well as MassBank text files and SQL databases. Through its highly customizable functionality, the presented infrastructure allows to build reproducible annotation workflows tailored for and adapted to most untargeted LC-MS-based datasets. All core functionality, which supports base R data types, is exported, also facilitating its re-use in other R packages. Finally, all packages are thoroughly unit-tested and documented and are available on GitHub and through Bioconductor.

Metabolomics in human serum samples provide a snapshot of the current metabolic state of an individuum. Metabolite concentrations are influenced by both genetic and environmental factors. Concentrations of certain metabolites can further depend on age, sex, menopause, and diet of study participants. A better understanding of these relationships is pivotal for the planning of metabolomics studies involving human subjects and interpretation of their results. We generated one of the largest single-site targeted metabolomics data sets consisting of 175 quantified metabolites in 6872 study participants. We identified metabolites significantly associated with age, sex, body mass index, diet, and menopausal status. While most of our results agree with previous large-scale studies, we also found novel associations including serotonin as a sex and BMI-related metabolite and sarcosine and C2 carnitine showing significantly higher concentrations in post-menopausal women. Finally, we observed strong associations between higher consumption of food items and certain metabolites, mostly phosphatidylcholines and lysophosphatidylcholines. Most, and the strongest, relationships were found for habitual meat intake while no significant relationships were found for most fruits, vegetables, and grain products. Summarizing, our results reconfirm findings from previous population-based studies on an independent cohort. Together, these findings will ultimately enable the consolidation of sets of metabolites which are related to age, sex, BMI, and menopause as well as to participants’ diet.