Physiological and functional parameters, such as body composition, or physical fitness are known to differ between men and women and to change with age. The goal of this study was to investigate how sex and age-related physiological conditions are reflected in the metabolome of healthy humans and whether sex and age can be predicted based on the plasma and urine metabolite profiles. In the cross-sectional KarMeN (Karlsruhe Metabolomics and Nutrition) study 301 healthy men and women aged 18–80 years were recruited. Participants were characterized in detail applying standard operating procedures for all measurements including anthropometric, clinical, and functional parameters. Fasting blood and 24 h urine samples were analyzed by targeted and untargeted metabolomics approaches, namely by mass spectrometry coupled to one- or comprehensive two-dimensional gas chromatography or liquid chromatography, and by nuclear magnetic resonance spectroscopy. This yielded in total more than 400 analytes in plasma and over 500 analytes in urine. Predictive modelling was applied on the metabolomics data set using different machine learning algorithms. Based on metabolite profiles from urine and plasma, it was possible to identify metabolite patterns which classify participants according to sex with > 90% accuracy. Plasma metabolites important for the correct classification included creatinine, branched-chain amino acids, and sarcosine. Prediction of age was also possible based on metabolite profiles for men and women, separately. Several metabolites important for this prediction could be identified including choline in plasma and sedoheptulose in urine. For women, classification according to their menopausal status was possible from metabolome data with > 80% accuracy. The metabolite profile of human urine and plasma allows the prediction of sex and age with high accuracy, which means that sex and age are associated with a discriminatory metabolite signature in healthy humans and therefore should always be considered in metabolomics studies.

Superparamagnetic iron-oxide particles are used frequently for cellular magnetic resonance imaging and in vivo cell tracking. The purpose of this study was to compare the labeling characteristics and efficiency as well as toxicity of superparamagnetic iron oxide (SPIO) and ultrasmall superparamagnetic iron oxide (USPIO) for 3 cell lines. Using human fibroblasts, immortalized rat progenitor cells and HEP-G2-hepatoma cells, dose- and time-dependence of SPIO and USPIO uptake were evaluated. The amount of intracellular (U)SPIO was monitored over 2 weeks after incubation by T2-magnetic resonance relaxometry, ICP-mass-spectrometry, and histology. Transmission-electronmicroscopy was used to specify the intracellular localization of the endocytosed iron particles. Cell death-rate and proliferation-index were assessed as indicators of cell-toxicity. For all cell lines, SPIO showed better uptake than USPIO, which was highest in HEP-G2 cells (110 +/- 2 pg Fe/cell). Cellular iron concentrations in progenitor cells and fibroblasts were 13 +/- 1pg Fe/cell and 7.2 +/- 0.3pg Fe/cell, respectively. For all cell lines T2-relaxation times in cell pellets were below detection threshold (<3 milliseconds) after 5 hours of incubation with SPIO (3.0 micromol Fe/mL growth medium) and continued to be near the detection for the next 6 days. For both particle types and all cell lines cellular iron oxide contents decreased after recultivation and surprisingly were found lower than in unlabeled control cells after 15 days. Viability and proliferation of (U)SPIO-labeled and unlabeled cells were not significantly different. The hematopoetic progenitor, mesenchymal fibroblast and epithelial HEP-G2 cell lines accumulated SPIO more efficiently than USPIO indicating SPIO to be better suited for cell labeling. However, the results indicate that there may be an induction of forced cellular iron elimination after incubation with (U)SPIO.