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Epidemiology and clinical studies provide clear evidence of the complex links between diet and health. To understand these links, reliable dietary assessment methods are pivotal. Biomarkers have emerged as more objective measures of intake compared with traditional dietary assessment methods. However, there are only a limited number of putative biomarkers of intake successfully identified and validated. The use of biomarkers that reflect food intake to examine diet related diseases represents the next step in biomarker research. Therefore, the aim of this study was to (1) identify and confirm biomarkers associated with dietary fat intake and (2) examine the relationship between those biomarkers with health parameters. Heatmap analysis identified a panel of 22 lipid biomarkers associated with total dietary fat intake in the Metabolic Challenge (MECHE) Study. Confirmation of four of these biomarkers demonstrated responsiveness to different levels of fat intake in a separate intervention study (NutriTech study). Linear regression identified a significant relationship between the panel of dietary fat biomarkers and HOMA-IR, with three lipid biomarkers (C16, PCaaC36:2, and PCae36:4) demonstrating significant associations. Identifying such links allows us to explore the relationship between diet and health to determine whether these biomarkers can be modulated through diet to improve health outcomes. Department of Agriculture, Food and the Marine

Scope: There is an increased interest in developing biomarkers of food intake to address some of the limitations associated with self-reported data. The objective is to identify biomarkers of apple intake, examine dose–response relationships, and agreement with self-reported data. Methods and results: Metabolomic data from three studies are examined: an acute intervention, a short-term intervention, and a free-living cohort study. Fasting and postprandial urine samples are collected for analysis by 1H-NMR and liquid chromatography–mass spectrometry (LC–MS). Calibration curves are developed to determine apple intake and classify individuals into categories of intake. Multivariate analysis of data reveals that levels of multiple metabolites increase significantly post-apple consumption, compared to the control food—broccoli. In the dose-response study, urinary xylose, epicatechin sulfate, and 2,6-dimethyl-2-(2-hydroxyethyl)-3,4-dihydro-2H-1-benzopyran increase as apple intake increases. Urinary xylose concentrations in a free-living cohort perform poorly at an individual level but are capable of ranking individuals in categories of intake. Conclusion: Urinary xylose exhibits a dose–response relationship with apple intake and performs well as a ranking biomarker in the population study. Other potential biomarkers are identified and future work will combine these with xylose in a biomarker panel which may allow for a more objective determination of individual intake.

The Nutrition Society Spring Meeting, Abertay University, Scotland, 1-2 April 2019 The metabolomic profile of a biofluid can be altered by dietary intake, exercise and disease processes and, thus provides an important tool for the study of many physiological processes. However, in addition to perturbation due to disease, the metabolomic profile of urine and plasma has also been shown to vary due to many intrinsic physiological factors such as age, sex, hormonal status and diurnal variation. Characterisation of this normal degree of variation in the metabolomic profiles of human biofluids is a necessary and important step in the development of metabolomics for use in nutrition-related research. The current review focuses on the impact of sex on the metabolomic profile. A number of studies have reported that sex impacts metabolites such as amino acids, lipids, sugars and keto acids. Furthermore, we examine the effect of the menstrual cycle on the metabolomic profile. Responses to dietary interventions can also differ between the sexes and highlighting this is important for the development of the field of precision nutrition. European Commission Horizon 2020 Update citation details during checkdate report - AC 2019-12-13 Embargo reduced to 6m for H2020 funding

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-Kastle, 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.

Scope: Classification of subjects into dietary patterns generally relies on self-reporting dietary data which are prone to error. The aim of the present study was to develop a model for objective classification of people into dietary patterns based on metabolomic data. Methods and results: Dietary and urinary metabolomic data from the National Adult Nutrition Survey (NANS) was used in the analysis (n=567). Two-step cluster analysis was applied to the urinary data to identify clusters. The subsequent model was used in an independent cohort to classify people into dietary patterns. Two distinct dietary patterns were identified. Cluster 1 was characterized by significantly higher intakes of breakfast cereals, low fat and skimmed milks, potatoes, fruit and fish, fish dishes (P

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. Department of Agriculture, Food and the Marine European Commission Horizon 2020 European Commission - Seventh Framework Programme (FP7) European Research Council NutriTech

Scope: There is a dearth of studies demonstrating the use of dietary biomarkers for determination of food intake. The objective of this study was to develop calibration curves for use in quantifying citrus intakes in an independent cohort. Methods and results: Participants (n=50) from the NutriTech food-intake study consumed standardized breakfasts for three consecutive days over three consecutive weeks. Orange juice intake decreased over the weeks. Urine samples were analyzed by NMR-spectroscopy and proline betaine was quantified and normalized to osmolality. Calibration curves were developed and used to predict citrus intake in an independent cohort; the Irish National Adult Nutrition Survey (NANS) (n=565). Proline betaine displayed a dose-response relationship to orange juice intake in 24h and fasting samples (p

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.

Background: The association between sugar-sweetened beverages (SSBs) and health risks remains controversial. To clarify proposed links, reliable and accurate dietary assessment methods of food intakes are essential. Objective: The aim of this present work was to use a metabolomics approach to identify a panel of urinary biomarkers indicative of SSB consumption from a national food consumption survey and subsequently validate this panel in an acute intervention study. Design: Heat map analysis was performed to identify correlations between 1 H nuclear magnetic resonance (NMR) spectral regions and SSB intakes in participants of the National Adult Nutrition Survey (n = 565). Metabolites were identified and receiver operating characteristic (ROC) analysis was performed to assess sensitivity and specificity of biomarkers. The panel of biomarkers was validated in an acute study (n = 10). A fasting first-void urine sample and postprandial samples (2, 4, 6 h) were collected after SSB consumption. After NMR spectroscopic profiling of the urine samples, multivariate data analysis was applied. Results: A panel of 4 biomarkers—formate, citrulline, taurine, and isocitrate—were identified as markers of SSB intake. This panel of biomarkers had an area under the curve of 0.8 for ROC analysis and a sensitivity and specificity of 0.7 and 0.8, respectively. All 4 biomarkers were identified in the SSB sample. After acute consumption of an SSB drink, all 4 metabolites increased in the urine. Conclusions: The present metabolomics-based strategy proved to be successful in the identification of SSB biomarkers. Future work will ascertain how to translate this panel of markers for use in nutrition epidemiology. Am J Clin Nutr doi: 10.3945/ajcn.114.095604.