Bile acids (BA) play an important role in lipid metabolism. They facilitate intestinal lipid absorption, and BA synthesis is the main catabolic pathway for cholesterol. The objective of this study was to investigate associations of age, sex, diet (fat intake) and parameters of lipid metabolism (triglycerides, LDL, HDL, body fat content) with fasting plasma BA concentration of healthy individuals. Fasting plasma samples from a cross-sectional study were used to determine the concentrations of 14 BA using an LC-MS stable isotope dilution assay. Triglycerides, LDL and HDL were analyzed by standard clinical chemistry methods and body fat content was measured with a DXA instrument. The dietary fat intake of the 24 h period prior to the sampling was assessed on the basis of a 24 h recall. Subsequent statistical data processing was done by means of a median regression model. Results revealed large inter-individual variations. Overall, higher median plasma concentrations of BA were observed in men than in women. Quantile regression showed significant interactions of selected BA with age and sex, affecting primarily chenodeoxycholic acid and its conjugates. No associations were found for LDL and the amount of fat intake (based on the percentage of energy intake from dietary fat as well as total fat intake). Additional associations regarding body fat content, HDL and triglycerides were found for some secondary BA plasma concentrations. We conclude that age and sex are associated with the fasting plasma concentrations. Those associations are significant and need to be considered in studies investigating the role of BA in the human metabolism.

BackgroundThe human metabolome is influenced by various intrinsic and extrinsic factors. A precondition to identify such biomarkers is the comprehensive understanding of the composition and variability of the metabolome of healthy humans. Sample handling aspects have an important impact on the composition of the metabolome; therefore, it is crucial for any metabolomics study to standardize protocols on sample collection, preanalytical sample handling, storage, and analytics to keep the nonbiological variability as low as possible.ObjectiveThe main objective of the KarMeN study is to analyze the human metabolome in blood and urine by targeted and untargeted metabolite profiling (gas chromatography-mass spectrometry [GC-MS], GC×GC-MS, liquid chromatography-mass spectrometry [LC-MS/MS], and1H nuclear magnetic resonance [NMR] spectroscopy) and to determine the impact of sex, age, body composition, diet, and physical activity on metabolite profiles of healthy women and men. Here, we report the outline of the study protocol with special regard to all aspects that should be considered in studies applying metabolomics.MethodsHealthy men and women, aged 18 years or older, were recruited. In addition to a number of anthropometric (height, weight, body mass index, waist circumference, body composition), clinical (blood pressure, electrocardiogram, blood and urine clinical chemistry) and functional parameters (lung function, arterial stiffness), resting metabolic rate, physical activity, fitness, and dietary intake were assessed, and 24-hour urine, fasting spot urine, and plasma samples were collected. Standard operating procedures were established for all steps of the study design. Using different analytical techniques (LC-MS, GC×GC-MS,1H NMR spectroscopy), metabolite profiles of urine and plasma were determined. Data will be analyzed using univariate and multivariate as well as predictive modeling methods.ResultsThe project was funded in 2011 and enrollment was carried out between March 2012 and July 2013. A total of 301 volunteers were eligible to participate in the study. Metabolite profiling of plasma and urine samples has been completed and data analysis is currently underway.ConclusionsWe established the KarMeN study applying a broad set of clinical and physiological examinations with a high degree of standardization. Our experimental approach of combining scheduled timing of examinations and sampling with the multiplatform approach (GC×GC-MS, GC-MS, LC-MS/MS, and1H NMR spectroscopy) will enable us to differentiate between current and long-term effects of diet and physical activity on metabolite profiles, while enabling us at the same time to consider confounders such as age and sex in the KarMeN study.Trial RegistrationGerman Clinical Trials Register DRKS00004890; https://drks-neu.uniklinik-freiburg.de/drks_web/navigate.do? navigationId=trial.HTML&TRIAL_ID=DRKS00004890 (Archived by WebCite at http://www.webcitation.org/6iyM8dMtx)

The aim of the study was to analyze the energy and macronutrient intake over the course of the day of selected population groups in Germany defined by sex, age, BMI, SES, and diet quality. The study was based on food consumption data from the German National Nutrition Survey II (2005-2007) assessed by two 4-day dietary weighing records of 662 women and men aged between 18 and 80 years. Energy and macronutrient intake were calculated using the German Nutrient Database 3.02 and summarized for the periods 'morning', 'midday', 'afternoon', 'evening', and 'night'. Generalized estimating equation models were used to examine differences in energy and macronutrient intake. For women and men, a three-main-meal pattern ('morning', 'midday', and 'evening') was observed, indicated as peaks in energy intake at 08:00 to 09:00, 13:00 and 19:00 o'clock. The distributions of carbohydrate, protein, and fat intake mirror the distribution of energy intake over the course of the day. The highest energy intake was found in the 'evening' period, especially in young adults, overweight persons, persons with a high SES, and men with a low diet quality. Women of the oldest age group showed a similar energy intake across the three-main-meals in contrast to young adults, who had lower peaks in the 'morning' and 'midday' periods as well as a shift to later meal times. Young adults seem to have a higher variability in energy intake and a less distinct meal pattern, while seniors have a more structured day. Because a high energy intake in the 'evening' period is associated with negative health-related factors, the distribution of energy intake should be considered by recommendations for a healthy nutritional behavior.

The aim of this article is to demonstrate the complexity of nutritional behavior and to increase understanding of this complex phenomenon. We developed a cause-effect model based on current literature, expert consultation, and instruments dealing with complexity. It presents factors from all dimensions of nutrition and their direct causal relationships with specification of direction, strength, and type. Including the interplay of all relationships, the model reveals cause-effect chains, feedback loops, multicausalities, and side effects. Analyses based on the model can further enhance understanding of nutritional behavior and help identify starting points for measures to modify food consumption.

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