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.

Background Banana is one of the most widely consumed fruits in the world. However, information regarding its health effects is scarce. Biomarkers of banana intake would allow a more accurate assessment of its consumption in nutrition studies. Objectives Using an untargeted metabolomics approach, we aimed to identify the banana-derived metabolites present in urine after consumption, including new candidate biomarkers of banana intake. Methods A randomized controlled study with a crossover design was performed on 12 healthy subjects (6 men, 6 women, mean ± SD age: 30.0 ± 4.9 y; mean ± SD BMI: 22.5 ± 2.3 kg/m2). Subjects underwent 2 dietary interventions: 1) 250 mL control drink (Fresubin 2 kcal fiber, neutral flavor; Fresenius Kabi), and 2) 240 g banana + 150 mL control drink. Twenty-four-hour urine samples were collected and analyzed with ultra-performance liquid chromatography coupled to a quadrupole time-of-flight MS and 2-dimensional GC-MS. The discovered biomarkers were confirmed in a cross-sectional study [KarMeN (Karlsruhe Metabolomics and Nutrition study)] in which 78 subjects (mean BMI: 22.8; mean age: 47 y) were selected reflecting high intake (126–378 g/d), low intake (47.3–94.5 g/d), and nonconsumption of banana. The confirmed biomarkers were examined singly or in combinations, for established criteria of validation for biomarkers of food intake. Results We identified 33 potentially bioactive banana metabolites, of which 5 metabolites, methoxyeugenol glucuronide (MEUG-GLUC), dopamine sulfate (DOP-S), salsolinol sulfate, xanthurenic acid, and 6-hydroxy-1-methyl-1,2,3,4-tetrahydro-β-carboline sulfate, were confirmed as candidate intake biomarkers. We demonstrated that the combination of MEUG-GLUC and DOP-S performed best in predicting banana intake in high (AUCtest = 0.92) and low (AUCtest = 0.87) consumers. The new biomarkers met key criteria establishing their current applicability in nutrition and health research for assessing the occurrence of banana intake. Conclusions Our metabolomics study in healthy men and women revealed new putative bioactive metabolites of banana and a combined biomarker of intake. These findings will help to better decipher the health effects of banana in future focused studies. This study was registered at clinicaltrials.gov as NCT03581955 and with the Ethical Committee for the Protection of Human Subjects Sud-Est 6 as CPP AU 1251, IDRCB 2016-A0013–48; the KarMeN study was registered with the German Clinical Trials Register (DRKS00004890). Details about the study can be obtained from https://www.drks.de.

Non-alcoholic beverages are important sources of nutrients and bioactive compounds that may influence human health and increase or decrease the risk of chronic diseases. A wide variety of beverage constituents are absorbed in the gut, found in the systemic circulation and excreted in urine. They may be used as compliance markers in intervention studies or as biomarkers of intake to improve measurements of beverage consumption in cohort studies and reveal new associations with disease outcomes that may have been overlooked when using dietary questionnaires. Here, biomarkers of intake of some major non-alcoholic beverages—coffee, tea, sugar-sweetened beverages, and low-calorie-sweetened beverages—are reviewed. Results from dietary intervention studies and observational studies are reviewed and analyzed, and respective strengths and weaknesses of the various identified biomarkers discussed. A variety of compounds derived from phenolic acids, alkaloids, and terpenes were shown to be associated with coffee intake and trigonelline and cyclo(isoleucylprolyl) showed a particularly high specificity for coffee intake. Epigallocatechin and 4′-O-methylepigallocatechin appear to be the most sensitive and specific biomarkers for green or black tea, while 4-O-methylgallic acid may be used to assess black tea consumption. Intake of sugar-sweetened beverages has been assessed through the measurement of carbon-13 enrichment of whole blood or of blood alanine in North America where sugar from sugarcane or corn is used as a main ingredient. The most useful biomarkers for low-calorie-sweetened beverages are the low-calorie sweeteners themselves. Further studies are needed to validate these biomarkers in larger and independent populations and to further evaluate their specificity, reproducibility over time, and fields of application.Electronic supplementary materialThe online version of this article (10.1186/s12263-018-0607-5) contains supplementary material, which is available to authorized users.

Scope Data on resveratrol‐(trans‐3,5,4′‐trihydroxystilbene)‐induced caloric‐restriction‐(CR)‐mimicking effects in mice receiving a high‐fat diet (HFD) are contradictory. It is hypothesized that this can possibly stem from different bioactivities of resveratrol (RSV) microbial metabolites. Methods and results C57BL/6Rj mice are fed an ad‐libitum HFD supplemented with RSV or its metabolites, dihydroresveratrol (DHR) and lunularin (LUN) (≈28 mg (dihydro)stilbene kg−1 mouse per day). A 40% CR group was included in the study. While CR mice show robust changes in bodyweight and composition, hormone levels and mRNA expression, slight changes are found (more muscle, less adipose tissue) in body composition, leptin, and insulin levels in RSV‐supplemented mice compared to ad libitum controls. LUN hardly and DHR does not change the hormone levels measured. Metabolome analysis of serum shows changes in CR mice but only slight, if any, changes in RSV‐, DHR‐, or LUN‐supplemented mice compared to the controls. Evaluating the capability of RSV and its metabolites to inhibit carbohydrate‐hydrolyzing enzymes in vitro, it is found that RSV reduced α‐glucosidase activity to a stronger extent than DHR and LUN. Conclusion Decelerated carbohydrate breakdown by RSV may have contributed to the moderate impact of dietary RSV on mouse insulin sensitivity (lowered fasting and post‐glucose‐bolus insulin levels).

BackgroundAlthough sugars and sugar derivatives are an important class of metabolites involved in many physiologic processes, there is limited knowledge on their occurrence and pattern in biofluids.ObjectiveOur aim was to obtain a comprehensive urinary sugar profile of healthy participants and to demonstrate the wide applicability and usefulness of this sugar profiling approach for nutritional as well as clinical studies.DesignIn the cross-sectional KarMeN study, the 24-h urine samples of 301 healthy participants on an unrestricted diet, assessed via a 24-h recall, were analyzed by a newly developed semitargeted gas chromatography–mass spectrometry (GC-MS) profiling method that enables the detection of known and unknown sugar compounds. Statistical analyses were performed with respect to associations of sex and diet with the urinary sugar profile.ResultsIn total, 40 known and 15 unknown sugar compounds were detected in human urine, ranging from mono- and disaccharides, polyols, and sugar acids to currently unknown sugar-like compounds. A number of rarely analyzed sugars were found in urine samples. Maltose was found in statistically higher concentrations in the urine of women compared with men and was also associated with menopausal status. Further, a number of individual sugar compounds associated with the consumption of specific foods, such as avocado, or food groups, such as alcoholic beverages and dairy products, were identified.ConclusionsWe here provide data on the complex nature of the sugar profile in human urine, of which some compounds may have the potential to serve as dietary markers or early disease biomarkers. Thus, comprehensive urinary sugar profiling not only has the potential to increase our knowledge of host sugar metabolism, but can also reveal new dietary markers after consumption of individual food items, and may lead to the identification of early disease biomarkers in the future. The KarMeN study was registered at drks.de as DRKS00004890.