Exploring the association of physical activity with the plasma and urine metabolome in adolescents and young adults.

BACKGROUND: Regular physical activity elicits many health benefits. However, the underlying molecular mechanisms through which physical activity influences overall health are less understood. Untargeted metabolomics enables system-wide mapping of molecular perturbations which may lend insights into physiological responses to regular physical activity. In this study, we investigated the associations of habitual physical activity with plasma and urine metabolome in adolescents and young adults. METHODS: This cross-sectional study included participants from the DONALD (DOrtmund Nutritional and Anthropometric Longitudinally Designed) study with plasma samples n = 365 (median age: 18.4 (18.1, 25.0) years, 58% females) and 24 h urine samples n = 215 (median age: 18.1 (17.1, 18.2) years, 51% females). Habitual physical activity was assessed using a validated Adolescent Physical Activity Recall Questionnaire. Plasma and urine metabolite concentrations were determined using ultra-high-performance liquid chromatography-tandem mass spectroscopy (UPLC-MS/MS) methods. In a sex-stratified analysis, we conducted principal component analysis (PCA) to reduce the dimensionality of metabolite data and to create metabolite patterns. Multivariable linear regression models were then applied to assess the associations between self-reported physical activity (metabolic equivalent of task (MET)-hours per week) with single metabolites and metabolite patterns, adjusted for potential confounders and controlling the false discovery rate (FDR) at 5% for each set of regressions. RESULTS: Habitual physical activity was positively associated with the "lipid, amino acids and xenometabolite" pattern in the plasma samples of male participants only (ß = 1.02; 95% CI: 1.01, 1.04, p = 0.001, adjusted p = 0.042). In both sexes, no association of physical activity with single metabolites in plasma and urine and metabolite patterns in urine was found (all adjusted p > 0.05). CONCLUSIONS: Our explorative study suggests that habitual physical activity is associated with alterations of a group of metabolites reflected in the plasma metabolite pattern in males. These perturbations may lend insights into some of underlying mechanisms that modulate effects of physical activity.

Replication and mediation of the association between the metabolome and clinical markers of metabolic health in an adolescent cohort study.

Metabolomics-derived metabolites (henceforth metabolites) may mediate the relationship between modifiable risk factors and clinical biomarkers of metabolic health (henceforth clinical biomarkers). We set out to study the associations of metabolites with clinical biomarkers and a potential mediation effect in a population of young adults. First, we conducted a systematic literature review searching for metabolites associated with 11 clinical biomarkers (inflammation markers, glucose, blood pressure or blood lipids). Second, we replicated the identified associations in a study population of n = 218 (88 males and 130 females, average age of 18 years) participants of the DONALD Study. Sex-stratified linear regression models adjusted for age and BMI and corrected for multiple testing were calculated. Third, we investigated our previously reported metabolites associated with anthropometric and dietary factors mediators in sex-stratified causal mediation analysis. For all steps, both urine and blood metabolites were considered. We found 41 metabolites in the literature associated with clinical biomarkers meeting our inclusion criteria. We were able to replicate an inverse association of betaine with CRP in women, between body mass index and C-reactive protein (CRP) and between body fat and leptin. There was no evidence of mediation by lifestyle-related metabolites after correction for multiple testing. We were only able to partially replicate previous findings in our age group and did not find evidence of mediation. The complex interactions between lifestyle factors, the metabolome, and clinical biomarkers warrant further investigation.

Exploring the Association between Habitual Food Intake and the Urine and Blood Metabolome in Adolescents and Young Adults: A Cohort Study.

SCOPE: Habitual diet may be reflected in metabolite profiles that can improve accurate assessment of dietary exposure and further enhance our understanding of their link to health conditions. The study aims to explore the relationship of habitual food intake with blood and urine metabolites in adolescents and young adults. METHODS: The study population comprises 228 participants (94 males and 134 females) of the DONALD study. Dietary intake is assessed by yearly repeated 3d-food records. Habitual diet is estimated as the average consumption of 23 food groups in adolescence. Using an untargeted metabolomics approach, the study quantifies 2638 metabolites in plasma and 1407 metabolites in urine. In each sex, unique diet-metabolite associations using orthogonal projection to latent structures (oPLS) and random forests (RF) is determined. RESULTS: Six metabolites in agreement between oPLS and RF in urine, one in female (vanillylmandelate to processed/other meat) and five in males (indole-3-acetamide, and N6-methyladenosine to eggs; hippurate, citraconate/glutaconate, and X - 12111 to vegetables) are observed. No association in blood in agreement is observed. CONCLUSION: A limited reflection of habitual food group intake by single metabolites in urine and not in blood is observed. The explored biomarkers should be confirmed in additional studies.

Blood Metabolomic Profiling Confirms and Identifies Biomarkers of Food Intake.

Metabolomics can be a tool to identify dietary biomarkers. However, reported food-metabolite associations have been inconsistent, and there is a need to explore further associations. Our aims were to confirm previously reported food-metabolite associations and to identify novel food-metabolite associations. We conducted a cross-sectional analysis of data from 849 participants (57% men) of the PopGen cohort. Dietary intake was obtained using FFQ and serum metabolites were profiled by an untargeted metabolomics approach. We conducted a systematic literature search to identify previously reported food-metabolite associations and analyzed these associations using linear regression. To identify potential novel food-metabolite associations, datasets were split into training and test datasets and linear regression models were fitted to the training datasets. Significant food-metabolite associations were evaluated in the test datasets. Models were adjusted for covariates. In the literature, we identified 82 food-metabolite associations. Of these, 44 associations were testable in our data and confirmed associations of coffee with 12 metabolites, of fish with five, of chocolate with two, of alcohol with four, and of butter, poultry and wine with one metabolite each. We did not identify novel food-metabolite associations; however, some associations were sex-specific. Potential use of some metabolites as biomarkers should consider sex differences in metabolism.

Associations of BMI and Body Fat with Urine Metabolome in Adolescents Are Sex-Specific: A Cross-Sectional Study.

Epidemiologic studies examining the relationship between body composition and the urine metabolome may improve our understanding of the role of metabolic dysregulation in body composition-related health conditions. Previous studies, mostly in adult populations, have focused on a single measure of body composition, body mass index (BMI), and sex-specific associations are rarely explored. We investigate sex-specific associations of two measures of body composition-BMI and body fat (BF)-with the urine metabolome in adolescents. In 369 participants (age 16-18, 49% female) of the Dortmund Nutritional and Anthropometric Longitudinally Designed (DONALD) study, we examined sex-specific associations of these two measures of body composition, BMI and BF, and 1407 (467 unknown) 24 h urine metabolites analyzed by untargeted metabolomics cross-sectionally. Missing metabolites were imputed. We related metabolites (dependent variable) to BMI and BF (independent variable) separately using linear regression. The models were additionally adjusted for covariates. We found 10 metabolites associated with both BMI and BF. We additionally found 11 metabolites associated with only BF, and nine with only BMI. None of these associations was in females. We observed a strong sexual dimorphism in the relationship between body composition and the urine metabolome.

Associations between self-reported sleep characteristics and incident mild cognitive impairment: The Heinz Nixdorf Recall Cohort Study.

Associations of sleep characteristics with mild cognitive impairment (MCI) have been examined in cross-sectional, but rarely in longitudinal studies. Incident MCI and sleep characteristics were assessed in 1,890 participants of the first and second follow-up of the Heinz Nixdorf Recall study, a population-based cohort study in Germany (age at first follow-up 50-80 years, mean follow-up 5.2 years). MCI was assessed with extensive cognitive tests. Sleep questionnaires including PSQI (Pittsburgh Sleep Quality Index) were used to assess sleep quality, sleep disturbances, time asleep, and time in bed. Relative risks (RR) of developing MCI when exposed to sleep characteristics were assessed in regression models adjusted for sociodemographic and cardiovascular risk factors. Poor sleep quality (PSQI > 5) (RR = 1.43, 95% CI: 1.12-1.82, fully adjusted, reference: PSQI ≤ 5) and difficulties initiating sleep (almost nightly versus never) (RR = 1.40, 0.94-2.08) were associated with incident MCI. For time in bed, the risk of MCI was increased for ≤ 5 hours (RR = 2.86, 1.24─6.60, reference:7 to <8 hours). In this longitudinal study with older participants, MCI risk was increased in persons with poor sleep quality, difficulties initiating sleep, and short time in bed.