Background: Meat and fish intakes have been associated with various chronic diseases. The use of specific biomarkers may help to assess meat and fish intake and improve subject classification according to the amount and type of meat or fish consumed. Objective: A metabolomic approach was applied to search for biomarkers of meat and fish intake in a dietary intervention study and in free-living subjects from the European Prospective Investigation into Cancer and Nutrition (EPIC) study. Design: In the dietary intervention study, 4 groups of 10 subjects consumed increasing quantities of chicken, red meat, processed meat, and fish over 3 successive weeks. Twenty-four-hour urine samples were collected during each period and analyzed by high-resolution liquid chromatography–mass spectrometry. Signals characteristic of meat or fish intake were replicated in 50 EPIC subjects for whom a 24-h urine sample and 24-h dietary recall were available and who were selected for their exclusive intake or no intake of any of the 4 same foods. Results: A total of 249 mass spectrometric features showed a positive dose-dependent response to meat or fish intake in the intervention study. Eighteen of these features best predicted intake of the 4 food groups in the EPIC urine samples on the basis of partial receiver operator curve analyses with permutation testing (areas under the curve ranging between 0.61 and 1.0). Of these signals, 8 metabolites were identified. Anserine was found to be specific for chicken intake, whereas trimethylamine-N-oxide showed good specificity for fish. Carnosine and 3 acylcarnitines (acetylcarnitine, propionylcarnitine, and 2-methylbutyrylcarnitine) appeared to be more generic indicators of meat and meat and fish intake, respectively. Conclusion: The meat and fish biomarkers identified in this work may be used to study associations between meat and fish intake and disease risk in epidemiologic studies. This trial was registered at clinicaltrials.gov as NCT01684917

Health has been defined as the capability of the organism to adapt to challenges. In this study, we tested to what extent comprehensively phenotyped individuals reveal differences in metabolic responses to a standardized mixed meal tolerance test (MMTT) and how these responses change when individuals experience moderate weight loss. Metabolome analysis was used in 70 healthy individuals. with profiling of ∼300 plasma metabolites during an MMTT over 8 h. Multivariate analysis of plasma markers of fatty acid catabolism identified 2 distinct metabotype clusters (A and B). Individuals from metabotype B showed slower glucose clearance, had increased intra-abdominal adipose tissue mass and higher hepatic lipid levels when compared with individuals from metabotype A. An NMR-based urine analysis revealed that these individuals also to have a less healthy dietary pattern. After a weight loss of ∼5.6 kg over 12 wk, only the subjects from metabotype B showed positive changes in the glycemic response during the MMTT and in markers of metabolic diseases. Our study in healthy individuals demonstrates that more comprehensive phenotyping can reveal discrete metabotypes with different outcomes in a dietary intervention and that markers of lipid catabolism in plasma could allow early detection of the metabolic syndrome.-Fiamoncini, J., Rundle, M., Gibbons, H., Thomas, E. L., Geillinger-Kästle, K., Bunzel, D., Trezzi, J.-P., Kiselova-Kaneva, Y., Wopereis, S., Wahrheit, J., Kulling, S. E., Hiller, K., Sonntag, D., Ivanova, D., van Ommen, B., Frost, G., Brennan, L., Bell, J. Daniel, H. Plasma metabolome analysis identifies distinct human metabotypes in the postprandial state with different susceptibility to weight loss-mediated metabolic improvements.

Publication informationproline betaine was quantified and normalized to osmolality. Calibration curves were 8 developed and used to predict citrus intake in an independent cohort; the Irish National Adult 9

Bile acids (BA) are signaling molecules with a wide range of biological effects, also identified among the most responsive plasma metabolites in the postprandial state. We here describe this response to different dietary challenges and report on key determinants linked to its interindividual variability. Healthy men and women ( = 72, 62 ± 8 yr, mean ± SE) were enrolled into a 12-wk weight loss intervention. All subjects underwent an oral glucose tolerance test and a mixed-meal tolerance test before and after the intervention. BA were quantified in plasma by liquid chromatography-tandem mass spectrometry combined with whole genome exome sequencing and fecal microbiota profiling. Considering the average response of all 72 subjects, no effect of the successful weight loss intervention was found on plasma BA profiles. Fasting and postprandial BA profiles revealed high interindividual variability, and three main patterns in postprandial BA response were identified using multivariate analysis. Although the women enrolled were postmenopausal, effects of sex difference in BA response were evident. Exome data revealed the contribution of preselected genes to the observed interindividual variability. In particular, a variant in the gene, encoding the small intestinal BA transporter organic anion-transporting polypeptide-1A2 (OATP1A2), was associated with delayed postprandial BA increases. Fecal microbiota analysis did not reveal evidence for a significant influence of bacterial diversity and/or composition on plasma BA profiles. The analysis of plasma BA profiles in response to two different dietary challenges revealed a high interindividual variability, which was mainly determined by genetics and sex difference of host with minimal effects of the microbiota. Considering the average response of all 72 subjects, no effect of the successful weight loss intervention was found on plasma bile acid (BA) profiles. Despite high interindividual variability, three main patterns in postprandial BA response were identified using multivariate analysis. A variant in the gene, encoding the small intestinal BA transporter organic anion-transporting polypeptide-1A2 (OATP1A2), was associated with delayed postprandial BA increases in response to both the oral glucose tolerance test and the mixed-meal tolerance test.

Improved assessment of meat intake with the use of metabolomics-derived markers can provide objective data and could be helpful in clarifying proposed associations between meat intake and health. The objective of this study was to identify novel markers of chicken intake using a metabolomics approach and use markers to determine intake in an independent cohort. Ten participants [age: 62 y; body mass index (in kg/m): 28.25] in the NutriTech food intake study consumed increasing amounts of chicken, from 88 to 290 g/d, in a 3-wk span. Urine and blood samples were analyzed by nuclear magnetic resonance and mass spectrometry, respectively. A multivariate data analysis was performed to identify markers associated with chicken intake. A calibration curve was built based on dose-response association using NutriTech data. A Bland-Altman analysis evaluated the agreement between reported and calculated chicken intake in a National Adult Nutrition Survey cohort. Multivariate data analysis of postprandial and fasting urine samples collected in participants in the NutriTech study revealed good discrimination between high (290 g/d) and low (88 g/d) chicken intakes. Urinary metabolite profiles showed differences in metabolite levels between low and high chicken intakes. Examining metabolite profiles revealed that guanidoacetate increased from 1.47 to 3.66 mmol/L following increasing chicken intakes from 88 to 290 g/d ( < 0.01). Using a calibration curve developed from the NutriTech study, chicken intake was calculated through the use of data from the National Adult Nutrition Survey, in which consumers of chicken had a higher guanidoacetate excretion (0.70 mmol/L) than did nonconsumers (0.47 mmol/L; < 0.01). A Bland-Altman analysis revealed good agreement between reported and calculated intakes, with a bias of -30.2 g/d. Plasma metabolite analysis demonstrated that 3-methylhistidine was a more suitable indicator of chicken intake than 1-methylhistidine. Guanidoacetate was successfully identified and confirmed as a marker of chicken intake, and its measurement in fasting urine samples could be used to determine chicken intake in a free-living population. This trial was registered at clinicaltrials.gov as NCT01684917.

Scope: Fish intake is reported to be associated with certain health benefits; however, accurate assessment of fish intake is still problematic. The objective of this study is to identify fish intake biomarkers and examine relationships with health parameters in a free-living population. Methods and results: In the NutriTech study, ten participants randomized into the fish group consume increasing quantities of fish for 3 days per week for 3 weeks. Urine is analyzed by NMR spectroscopy. Trimethylamine-N-oxide (TMAO), dimethylamine, and dimethyl sulfone are identified and display significant dose-response with intake (p < 0.05). Fish consumption yields a greater increase in urinary TMAO compared to red meat. Biomarker-derived fish intake is calculated in the National Adult Nutrition Survey cross-sectional study. However, the correlation between fish intake and TMAO (r = 0.148, p < 0.01) and that between fish intake and calculated fish intake (r = 0.142, p < 0.01) are poor. In addition, TMAO shows significantly positive correlation with serum insulin and insulin resistance in males and the relationship is more pronounced for males with high dietary fat intake. Conclusion: Urinary TMAO displays a strong dose-response relationship with fish intake; however, use of TMAO alone is insufficient to determine fish intake in a free-living population.

Metabolic challenge tests may be a valuable tool to magnify the effects of diet on health. The use of transcriptomics enables a more extensive characterization of the effects of diet. The question remains whether transcriptome time‐course analyses during challenge tests will deliver more information on the effect of diet than a static fasting measurement. A dietary intervention known to improve health is energy restriction (ER). Seventy‐two healthy, overweight men and women aged 50–65 were subjected to an oral glucose tolerance test (OGTT) and a mixed‐meal test (MMT) before and after 12 wk of a 20% ER diet or control diet. Whole‐genome gene expression of peripheral blood mononuclear cells was performed before and after the intervention. This was done during fasting, during the OGTT at 30, 60, and 120 min, and during the MMT at 60, 120, 240, and 360 min. Upon ER, the OGTT resulted in a faster and more pronounced down‐regulation in gene expression of oxidative phosphorylation, cell adhesion, and DNA replication compared with the control. The MMT showed less‐consistent effects. The OGTT combined with transcriptomics can be used to measure dynamic cellular adaptation upon an intervention that cannot be determined with a static fasting measurement.—Van Bussel, I. P. G., Fazelzadeh, P., Frost, G. S., Rundle, M., Afman, L. A. Measuring phenotypic flexibility by transcriptome time‐course analyses during challenge tests before and after energy restriction. FASEB J. 33, 10280–10290 (2019). http://www.fasebj.org

Insulin secretion following ingestion of a carbohydrate load affects a multitude of metabolic pathways that simultaneously change direction and quantity of interorgan fluxes of sugars, lipids and amino acids. In the present study, we aimed at identifying markers associated with differential responses to an OGTT a population of healthy adults. By use of three metabolite profiling platforms, we assessed these postprandial responses of a total of 202 metabolites in plasma of 72 healthy volunteers undergoing comprehensive phenotyping and of which half enrolled into a weight-loss program over a three-month period. A standard oral glucose tolerance test (OGTT) served as dietary challenge test to identify changes in postprandial metabolite profiles. Despite classified as healthy according to WHO criteria, two discrete clusters (A and B) were identified based on the postprandial glucose profiles with a balanced distribution of volunteers based on gender and other measures. Cluster A individuals displayed 26% higher postprandial glucose levels, delayed glucose clearance and increased fasting plasma concentrations of more than 20 known biomarkers of insulin resistance and diabetes previously identified in large cohort studies. The volunteers identified by canonical postprandial responses that form cluster A may be called pre-pre-diabetics and defined as “at risk” for development of insulin resistance. Moreover, postprandial changes in selected fatty acids and complex lipids, bile acids, amino acids, acylcarnitines and sugars like mannose revealed marked differences in the responses seen in cluster A and cluster B individuals that sustained over the entire challenge test period of 240 min. Almost all metabolites, including glucose and insulin, returned to baseline values at the end of the test (at 240 min), except a variety of amino acids and here those that have been linked to diabetes development. Analysis of the corresponding metabolite profile in a fasting blood sample may therefore allow for early identification of these subjects at risk for insulin resistance without the need to undergo an OGTT.