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 populationbased 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. Dementia is a growing public health burden worldwide. In 2013 an estimated 44.35 million persons had a prevalent dementia 1. By 2050 it is expected that this number triples to 135.46 million prevalent dementia cases 2,3. Because no effective causal medical therapies are available for dementia, primary prevention of dementia and of its early precursors is the most promising option currently available to cap the rising prevalence 4,5. Subjects with mild cognitive impairment (MCI) have an increased risk of progression to Alzheimer's disease (AD) and other forms of dementia. Therefore, identification of modifiable risk factors for incident MCI is important, and poor sleep is considered a potential risk factor for cognitive decline and disease progression 6-13. Sleep characteristics were suggested as modifiable risk factors for cognitive decline, for example by influencing hippocampal volume 14,15. Two up-to-date reviews on this relationship indicate an association between cognitive decline and sleep problems, such as poor sleep quality, short or long sleep duration, and sleep disturbances 12,16. However, both reviews concluded that there is still a need for long term prospective studies to ensure that sleep problems precede cognitive decline 12,16. Two recently published cohort studies on sleep characteristics and dementia were not included in these reviews 17,18. Both suggest that self-reported long sleep duration as well as self-reported sleep disturbances increase the risk of dementia. Additionally, Jackowska and Cadar 19 found an association between decreased cognitive function and self-reported long and short sleep duration in their

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.

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.

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. BackgroundDietary intakes are generally obtained by self-reports using instruments like food records, 24-h recalls, or food frequency

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.