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Background: The capacity of an individual to respond to changes in food intake so that postprandial metabolic perturbations are resolved, and metabolism returns to its pre-prandial state, is called phenotypic flexibility. This ability may be a more important indicator of current health status than metabolic markers in a fasting state., Aim: In this parallel randomized controlled trial study, an energy-restricted healthy diet and 2 dietary challenges were used to assess the effect of weight loss on phenotypic flexibility., Methods: Seventy-two volunteers with overweight and obesity underwent a 12-wk dietary intervention. The participants were randomized to a weight loss group (WLG) with 20% less energy intake or a weight-maintenance group (WMG). At weeks 1 and 12, participants were assessed for body composition by MRI. Concurrently, markers of metabolism and insulin sensitivity were obtained from the analysis of plasma metabolome during 2 different dietary challenges-an oral glucose tolerance test (OGTT) and a mixed-meal tolerance test., Results: Intended weight loss was achieved in the WLG (-5.6 kg, P < 0.0001) and induced a significant reduction in total and regional adipose tissue as well as ectopic fat in the liver. Amino acid-based markers of insulin action and resistance such as leucine and glutamate were reduced in the postprandial phase of the OGTT in the WLG by 11.5% and 28%, respectively, after body weight reduction. Weight loss correlated with the magnitude of changes in metabolic responses to dietary challenges. Large interindividual variation in metabolic responses to weight loss was observed., Conclusion: Application of dietary challenges increased sensitivity to detect metabolic response to weight loss intervention. Large interindividual variation was observed across a wide range of measurements allowing the identification of distinct responses to the weight loss intervention and mechanistic insight into the metabolic response to weight loss., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

Diet-related diseases are the leading cause of death globally and strategies to tailor effective nutrition advice are required. Personalised nutrition advice is increasingly recognised as more effective than population-level advice to improve dietary intake and health outcomes. A potential tool to deliver personalised nutrition advice is metabotyping which groups individuals into homogeneous subgroups (metabotypes) using metabolic profiles. In summary, metabotyping has been successfully employed in human nutrition research to identify subgroups of individuals with differential responses to dietary challenges and interventions and diet–disease associations. The suitability of metabotyping to identify clinically relevant subgroups is corroborated by other fields such as diabetes research where metabolic profiling has been intensely used to identify subgroups of patients that display patterns of disease progression and complications. However, there is a paucity of studies examining the efficacy of the approach to improve dietary intake and health parameters. While the application of metabotypes to tailor and deliver nutrition advice is very promising, further evidence from randomised controlled trials is necessary for further development and acceptance of the approach.

Scope: The objectives are to develop a metabolomic-based model capable of classifying individuals into dietary patterns and to investigate the reproducibility of the model., Methods and Results: K-means cluster analysis is employed to derive dietary patterns using metabolomic data. Differences across the dietary patterns are examined using nutrient biomarkers. The model is used to assign individuals to a dietary pattern in an independent cohort, A-DIET Confirm (n = 175) at four time points. The stability of participants to a dietary pattern is assessed. Four dietary patterns are derived: moderately unhealthy, convenience, moderately healthy, and prudent. The moderately unhealthy and convenience patterns has lower adherence to the alternative healthy eating index (AHEI) and the alternative mediterranean diet score (AMDS) compared to the moderately healthy and prudent patterns (AHEI = 24.5 and 22.9 vs 26.7 and 28.4, p < 0.001). The dietary patterns are replicated in A-DIET Confirm, with good reproducibility across four time points. The stability of participants' dietary pattern membership ranged from 25.0% to 61.5%., Conclusion: The multivariate model classifies individuals into dietary patterns based on metabolomic data. In an independent cohort, the model classifies individuals into dietary patterns at multiple time points furthering the potential of such an approach for nutrition research., (© 2021 The Authors. Molecular Nutrition & Food Research published by Wiley-VCH GmbH.)

Personalised nutrition is at its simplest form the delivery of dietary advice at an individual level. Incorporating response to different diets has resulted in the concept of precision nutrition. Harnessing the metabolic phenotype to identify subgroups of individuals that respond differentially to dietary interventions is becoming a reality. More specifically, the classification of individuals in subgroups according to their metabolic profile is defined as metabotyping and this approach has been employed to successfully identify differential response to dietary interventions. Furthermore, the approach has been expanded to develop a framework for the delivery of targeted nutrition. The present review examines the application of the metabotype approach in nutrition research with a focus on developing personalised nutrition. Application of metabotyping in longitudinal studies demonstrates that metabotypes can be associated with cardiometabolic risk factors and diet-related diseases while application in interventions can identify metabotypes with differential responses. In general, there is strong evidence that metabolic phenotyping is a promising strategy to identify groups at risk and to potentially improve health promotion at a population level. Future work should verify if targeted nutrition can change behaviours and have an impact on health outcomes.

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.

Classical approaches to assessing dietary intake are associated with measurement error. In an effort to address inherent measurement error in dietary self-reported data there is increased interest in the use of dietary biomarkers as objective measures of intake. Furthermore, there is a growing consensus of the need to combine dietary biomarker data with self-reported data. A review of state of the art techniques employed when combining biomarker and self-reported data is conducted. Two predominant methods, the calibration method and the method of triads, emerge as relevant techniques used when combining biomarker and self-reported data to account for measurement errors in dietary intake assessment. Both methods crucially assume measurement error independence. To expose and understand the performance of these methods in a range of realistic settings, their underpinning statistical concepts are unified and delineated, and thorough simulation studies are conducted. Results show that violation of the methods' assumptions negatively impacts resulting inference but that this impact is mitigated when the variation of the biomarker around the true intake is small. Thus there is much scope for the further development of biomarkers and models in tandem to achieve the ultimate goal of accurately assessing dietary intake.

Background: Measurement error associated with self-reported dietary intake is a well-documented issue. Combining biomarkers of food intake and dietary intake data is a high priority., Objectives: The aim of this study was to develop calibration equations for food intake, illustrated with an application for citrus intake. Further, a simulation-based framework was developed to determine the portion of biomarker data needed for stable calibration equation estimation in large population studies., Methods: Calibration equations were developed using mean daily self-reported citrus intake (4-d semiweighed food diaries) and biomarker-derived intake (urinary proline betaine biomarker) data from participants (n = 565) as part of a cross-sectional study. Different functional specifications and biomarker transformations were tested to derive the optimal calibration equation specifications. The simulation study was developed using linear regression for the calibration equations. Stability in the calibration equation estimations was investigated for varying portions of biomarker and intake data "qualities.", Results: With citrus intake, linear regression on nontransformed biomarker data resulted in the optimal calibration equation specifications and produced good-quality predicted intakes. The lowest mean squared error (14,354) corresponded to a linear regression model, defined with biomarker-derived estimates of intakes on the original scale. Using this model in a subpopulation without biomarker data resulted in an average mean ± SD citrus intake of 81 ± 66 g/d. The simulation study suggested that in large population studies, biomarker data on 20-30% of the subjects are required to guarantee stable estimation of calibration equations. This article is accompanied by a web application ("Bio-Intake"), which was developed to facilitate measurement error correction in self-reported mean daily citrus intake data., Conclusions: Calibration equations proved to be a useful instrument to correct measurement error in self-reported food intake data. The simulation study demonstrated that the use of food intake biomarkers may be feasible and beneficial in the context of large population studies., (Copyright © American Society for Nutrition 2019.)

The application of metabolomics in nutrition epidemiology holds great promise and there is a high expectation that it will play a leading role in deciphering the interactions between diet and health. However, while significant progress has been made in the identification of putative biomarkers, more work is needed to address the use of the biomarkers in dietary assessment. The aim of this review is to critically evaluate progress in these areas and to identify challenges that need to be addressed going forward. The notable applications of dietary biomarkers in nutritional epidemiology include 1) determination of food intake based on biomarkers levels and calibration equations from feeding studies, 2) classification of individuals into dietary patterns based on the urinary metabolic profile, and 3) application of metabolome wide-association studies. Further work is needed to address some specific challenges to enable biomarkers to reach their full potential., (© 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

A significant body of evidence demonstrates that isoflavone metabolites are good markers of soy intake, while research is lacking on specific markers of other leguminous sources such as peas. In this context, the objective of our current study was to identify biomarkers of pea intake using an untargeted metabolomics approach. A randomized cross-over acute intervention study was conducted on eleven participants who consumed peas and couscous (control food) in random order. The urine samples were collected in fasting state and postprandially at regular intervals and were further analysed by ultra-performance liquid chromatography coupled to quadrupole time of flight mass spectrometry (UPLC-QTOF-MS). Multivariate statistical analysis resulted in robust Partial least squares Discriminant Analysis (PLS-DA) models obtained for comparison of fasting against the postprandial time points (0 h vs. 4 h, (R²X = 0.41, Q² = 0.4); 0 h vs. 6 h, ((R²X = 0.517, Q² = 0.495)). Variables with variable importance of projection (VIP) scores ≥1.5 obtained from the PLS-DA plot were considered discriminant between the two time points. Repeated measures analysis of variance (ANOVA) was performed to identify features with a significant time effect. Assessment of the time course profile revealed that ten features displayed a differential time course following peas consumption compared to the control food. The interesting features were tentatively identified using accurate mass data and confirmed by tandem mass spectrometry (MS using commercial spectral databases and authentic standards. 2-Isopropylmalic acid, asparaginyl valine and N-carbamoyl-2-amino-2-(4-hydroxyphenyl) acetic acid were identified as markers reflecting pea intake. The three markers also increased in a dose-dependent manner in a randomized intervention study and were further confirmed in an independent intervention study. Overall, key validation criteria were met for the successfully identified pea biomarkers. Future work will examine their use in nutritional epidemiology studies.

Background/objectives: To identify predictors of obesity in adults and investigate to what extent these predictors are independent of other major confounding factors., Subjects/methods: Data collected at baseline from 1441 participants from the Food4Me study conducted in seven European countries were included in this study. A food frequency questionnaire was used to measure dietary intake. Accelerometers were used to assess physical activity levels (PA), whereas participants self-reported their body weight, height and waist circumference via the internet., Results: The main factors associated (p < 0.05) with higher BMI per 1-SD increase in the exposure were age (β:1.11 kg/m 2 ), intakes of processed meat (β:1.04 kg/m 2 ), red meat (β:1.02 kg/m 2 ), saturated fat (β:0.84 kg/m 2 ), monounsaturated fat (β:0.80 kg/m 2 ), protein (β:0.74 kg/m 2 ), total energy intake (β:0.50 kg/m 2 ), olive oil (β:0.36 kg/m 2 ), sugar sweetened carbonated drinks (β:0.33 kg/m 2 ) and sedentary time (β:0.73 kg/m 2 ). In contrast, the main factors associated with lower BMI per 1-SD increase in the exposure were PA (β:-1.36 kg/m 2 ), intakes of wholegrains (β:-1.05 kg/m 2 ), fibre (β:-1.02 kg/m 2 ), fruits and vegetables (β:-0.52 kg/m 2 ), nuts (β:-0.52 kg/m 2 ), polyunsaturated fat (β:-0.50 kg/m 2 ), Healthy Eating Index (β:-0.42 kg/m 2 ), Mediterranean diet score (β:-0.40 kg/m 2 ), oily fish (β:-0.31 kg/m 2 ), dairy (β:-0.31 kg/m 2 ) and fruit juice (β:-0.25 kg/m 2 )., Conclusions: These findings are important for public health and suggest that promotion of increased PA, reducing sedentary behaviours and improving the overall quality of dietary patterns are important strategies for addressing the existing obesity epidemic and associated disease burden.

Scope: Previous work highlighted the potential of odd-chain length saturated fatty acids as potential markers of dairy intake. The aim of this study was to assess the reproducibility of these biomarkers and their sensitivity to changes in dairy intake., Methods and Results: Fatty acid profiles and dietary intakes from food frequency questionnaires (FFQs) were measured three times over six months in the Food4Me Study. Reproducibility was explored through intra-class correlation coefficients (ICCs) and within-subject coefficients of variation (WCV). Sensitivity to changes in diet was examined using regression analysis. C15:0 blood levels showed high correlation over time (ICC: 0.62, 95% CI: 0.57, 0.68), however, the ICC for C17:0 was much lower (ICC: 0.32, 95% CI: 0.28, 0.46). The WCV for C15:0 was 16.6% and that for C17:0 was 14.6%. There were significant associations between changes in intakes of total dairy, high-fat dairy, cheese and butter and C15:0; and change in intakes of high-fat dairy and cream and C17:0., Conclusion: Results provide evidence of reproducibility of C15:0 levels over time and sensitivity to change in intake of high-fat dairy products with results comparable to the well-established biomarker of fish intake (EPA+DHA)., (© 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

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 24 h and fasting samples (p < 0.001). In a test set, predicted orange juice intakes displayed excellent agreement with true intake. There were significant associations between predicted intake measured in 24 h and fasting samples and true intake (r = 0.710-0.919). Citrus intakes predicted for the NANS cohort demonstrated good agreement with self-reported intake and this agreement improved following normalization to osmolality., Conclusion: The developed calibration curves successfully predicted citrus intakes in an independent cohort. Expansion of this approach to other foods will be important for the development of objective intake measurements., (© 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

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, fish and fish dishes (p < 0.05) representing a "healthy" cluster. Cluster 2 had significantly higher intakes of chips/processed potatoes, meat products, savory snacks and high-energy beverages (p < 0.05) representing an "unhealthy cluster". Classification was supported by significant differences in nutrient status (p < 0.05). Validation in an independent group revealed that 94% of subjects were correctly classified., Conclusion: The model developed was capable of classifying individuals into dietary patterns based on metabolomics data. Future applications of this approach could be developed for rapid and objective assignment of subjects into dietary patterns., (© 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Diet is a key modifiable risk factor for non-communicable diseases. However, we currently are not benefitting from the full potential of its protective effects. This is due to a number of reasons, including high individual variability in response to certain diets. It is now well acknowledged that in order to gain the full benefit of dietary regimes it is essential to take into account individual responses. With this in mind, the present review examines the concept of precision nutrition and the performance of n-of-1 studies, and discusses the development of certain approaches that will be critical for development of the concepts., Competing Interests: The authors declare no conflict of interest.

Evidence suggests that processed red meat consumption is a risk factor for CVD and type 2 diabetes (T2D). This analysis investigates the association between dietary patterns, their processed red meat contributions, and association with blood biomarkers of CVD and T2D, in 786 Irish adults (18-90 years) using cross-sectional data from a 2011 national food consumption survey. All meat-containing foods consumed were assigned to four food groups (n 502) on the basis of whether they contained red or white meat and whether they were processed or unprocessed. The remaining foods (n 2050) were assigned to twenty-nine food groups. Two-step and k-means cluster analyses were applied to derive dietary patterns. Nutrient intakes, plasma fatty acids and biomarkers of CVD and T2D were assessed. A total of four dietary patterns were derived. In comparison with the pattern with lower contributions from processed red meat, the dietary pattern with greater processed red meat intakes presented a poorer Alternate Healthy Eating Index (21·2 (sd 7·7)), a greater proportion of smokers (29 %) and lower plasma EPA (1·34 (sd 0·72) %) and DHA (2·21 (sd 0·84) %) levels (P<0·001). There were no differences in classical biomarkers of CVD and T2D, including serum cholesterol and insulin, across dietary patterns. This suggests that the consideration of processed red meat consumption as a risk factor for CVD and T2D may need to be re-assessed.

Background: Optimal nutritional choices are linked with better health, but many current interventions to improve diet have limited effect. We tested the hypothesis that providing personalized nutrition (PN) advice based on information on individual diet and lifestyle, phenotype and/or genotype would promote larger, more appropriate, and sustained changes in dietary behaviour., Methods: : Adults from seven European countries were recruited to an internet-delivered intervention (Food4Me) and randomized to: (i) conventional dietary advice (control) or to PN advice based on: (ii) individual baseline diet; (iii) individual baseline diet plus phenotype (anthropometry and blood biomarkers); or (iv) individual baseline diet plus phenotype plus genotype (five diet-responsive genetic variants). Outcomes were dietary intake, anthropometry and blood biomarkers measured at baseline and after 3 and 6 months' intervention., Results: At baseline, mean age of participants was 39.8 years (range 18-79), 59% of participants were female and mean body mass index (BMI) was 25.5 kg/m 2 . From the enrolled participants, 1269 completed the study. Following a 6-month intervention, participants randomized to PN consumed less red meat [-5.48 g, (95% confidence interval:-10.8,-0.09), P  = 0.046], salt [-0.65 g, (-1.1,-0.25), P  = 0.002] and saturated fat [-1.14 % of energy, (-1.6,-0.67), P  < 0.0001], increased folate [29.6 µg, (0.21,59.0), P  = 0.048] intake and had higher Healthy Eating Index scores [1.27, (0.30, 2.25), P  = 0.010) than those randomized to the control arm. There was no evidence that including phenotypic and phenotypic plus genotypic information enhanced the effectiveness of the PN advice., Conclusions: Among European adults, PN advice via internet-delivered intervention produced larger and more appropriate changes in dietary behaviour than a conventional approach., (© The Author 2016; all rights reserved. Published by Oxford University Press on behalf of the International Epidemiological Association)

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.

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., (© 2017 American Society for Nutrition.)

Current dietary assessment methods including FFQ, 24-h recalls and weighed food diaries are associated with many measurement errors. In an attempt to overcome some of these errors, dietary biomarkers have emerged as a complementary approach to these traditional methods. Metabolomics has developed as a key technology for the identification of new dietary biomarkers and to date, metabolomic-based approaches have led to the identification of a number of putative biomarkers. The three approaches generally employed when using metabolomics in dietary biomarker discovery are: (i) acute interventions where participants consume specific amounts of a test food, (ii) cohort studies where metabolic profiles are compared between consumers and non-consumers of a specific food and (iii) the analysis of dietary patterns and metabolic profiles to identify nutritypes and biomarkers. The present review critiques the current literature in terms of the approaches used for dietary biomarker discovery and gives a detailed overview of the currently proposed biomarkers, highlighting steps needed for their full validation. Furthermore, the present review also evaluates areas such as current databases and software tools, which are needed to advance the interpretation of results and therefore enhance the utility of dietary biomarkers in nutrition research.

Scope: Using pattern analysis, we investigated the relationship between plasma fatty acid patterns, dietary intake, and biomarkers of metabolic health using data from the Irish National Adult Nutrition Survey., Methods and Results: Plasma fatty acid patterns were derived from 26 plasma fatty acids using k-means cluster analysis. Four clusters were identified, each with a distinct fatty acid profile. Cluster 1 included high proportions of linoleic acid (LA) and low proportions of stearic acid (SA); cluster 2 was higher in n-3 polyunsaturated fatty acids and SA; the profile of cluster 3 was higher in very-long-chain saturated fatty acid (VLCSFA) and lower in α-linolenic acid (ALA) (cluster 3); while cluster 4 was higher in fatty acids related to de novo lipogenesis and 20:3n-6 and lower in LA (cluster 4). In general, cluster 4 was associated with adverse metabolic profile and higher metabolic risk (p < 0.033). Clusters 2 and 3 were associated with healthier and protective phenotypes (p < 0.033)., Conclusion: Distinct fatty acid patterns were identified which were related to demographics, dietary habits, and metabolic profile. A pattern higher in VLCSFA and lower in ALA was associated with healthier metabolic outcome., (© 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Background: Despite numerous healthy eating campaigns, the prevalence of diets high in saturated fatty acids, sugar, and salt and low in fiber, fruit, and vegetables remains high. With more people than ever accessing the Internet, Web-based dietary assessment instruments have the potential to promote healthier dietary behaviors via personalized dietary advice., Objective: The objectives of this study were to develop a dietary feedback system for the delivery of consistent personalized dietary advice in a multicenter study and to examine the impact of automating the advice system., Methods: The development of the dietary feedback system included 4 components: (1) designing a system for categorizing nutritional intakes; (2) creating a method for prioritizing 3 nutrient-related goals for subsequent targeted dietary advice; (3) constructing decision tree algorithms linking data on nutritional intake to feedback messages; and (4) developing personal feedback reports. The system was used manually by researchers to provide personalized nutrition advice based on dietary assessment to 369 participants during the Food4Me randomized controlled trial, with an automated version developed on completion of the study., Results: Saturated fatty acid, salt, and dietary fiber were most frequently selected as nutrient-related goals across the 7 centers. Average agreement between the manual and automated systems, in selecting 3 nutrient-related goals for personalized dietary advice across the centers, was highest for nutrient-related goals 1 and 2 and lower for goal 3, averaging at 92%, 87%, and 63%, respectively. Complete agreement between the 2 systems for feedback advice message selection averaged at 87% across the centers., Conclusions: The dietary feedback system was used to deliver personalized dietary advice within a multi-country study. Overall, there was good agreement between the manual and automated feedback systems, giving promise to the use of automated systems for personalizing dietary advice., Trial Registration: Clinicaltrials.gov NCT01530139; https://clinicaltrials.gov/ct2/show/NCT01530139 (Archived by WebCite at http://www.webcitation.org/6ht5Dgj8I).

Background: Accurate dietary assessment is key to understanding nutrition-related outcomes and is essential for estimating dietary change in nutrition-based interventions., Objective: The objective of this study was to assess the pan-European reproducibility of the Food4Me food-frequency questionnaire (FFQ) in assessing the habitual diet of adults., Methods: Participants from the Food4Me study, a 6-mo, Internet-based, randomized controlled trial of personalized nutrition conducted in the United Kingdom, Ireland, Spain, Netherlands, Germany, Greece, and Poland, were included. Screening and baseline data (both collected before commencement of the intervention) were used in the present analyses, and participants were included only if they completed FFQs at screening and at baseline within a 1-mo timeframe before the commencement of the intervention. Sociodemographic (e.g., sex and country) and lifestyle [e.g., body mass index (BMI, in kg/m(2)) and physical activity] characteristics were collected. Linear regression, correlation coefficients, concordance (percentage) in quartile classification, and Bland-Altman plots for daily intakes were used to assess reproducibility., Results: In total, 567 participants (59% female), with a mean ± SD age of 38.7 ± 13.4 y and BMI of 25.4 ± 4.8, completed both FFQs within 1 mo (mean ± SD: 19.2 ± 6.2 d). Exact plus adjacent classification of total energy intake in participants was highest in Ireland (94%) and lowest in Poland (81%). Spearman correlation coefficients (ρ) in total energy intake between FFQs ranged from 0.50 for obese participants to 0.68 and 0.60 in normal-weight and overweight participants, respectively. Bland-Altman plots showed a mean difference between FFQs of 210 kcal/d, with the agreement deteriorating as energy intakes increased. There was little variation in reproducibility of total energy intakes between sex and age groups., Conclusions: The online Food4Me FFQ was shown to be reproducible across 7 European countries when administered within a 1-mo period to a large number of participants. The results support the utility of the online Food4Me FFQ as a reproducible tool across multiple European populations. This trial was registered at clinicaltrials.gov as NCT01530139., (© 2016 American Society for Nutrition.)

Purpose: Personalised interventions may have greater potential for reducing the global burden of non-communicable diseases and for promoting better health and well-being across the lifespan than the conventional "one size fits all" approach. However, the characteristics of individuals interested in personalised nutrition (PN) are unclear. Therefore, the aim of this study was to describe the characteristics of European adults interested in taking part in an internet-based PN study., Methods: Individuals from seven European countries (UK, Ireland, Germany, The Netherlands, Spain, Greece and Poland) were invited to participate in the study via the Food4Me website ( http://www.food4me.org ). Two screening questionnaires were used to collect data on socio-demographic, anthropometric and health-related characteristics as well as dietary intakes., Results: A total of 5662 individuals expressed an interest in the study (mean age 40 ± 12.7; range 15-87 years). Of these, 65 % were female and 97 % were Caucasian. Overall, 13 % were smokers and 47 % reported the presence of a clinically diagnosed disease. Furthermore, 47 % were overweight or obese and 35 % were sedentary during leisure time. Assessment of dietary intakes showed that 54 % of individuals reported consuming at least 5 portions of fruit and vegetables per day, 46 % consumed more than 3 servings of wholegrains and 37 % limited their salt intake to <5.75 g per day., Conclusions: Our data indicate that individuals volunteering to participate in an internet-based PN study are broadly representative of the European adult population, most of whom had adequate nutrient intakes but could benefit from improved dietary choices and greater physical activity. Future use of internet-based PN approaches is thus relevant to a wide target audience.

Wheat bran, and especially wheat aleurone fraction, are concentrated sources of a wide range of components which may contribute to the health benefits associated with higher consumption of whole-grain foods. This study used NMR metabolomics to evaluate urine samples from baseline at one and two hours postprandially, following the consumption of minimally processed bran, aleurone or control by 14 participants (7 Females; 7 Males) in a randomized crossover trial. The methodology discriminated between the urinary responses of control, and bran and aleurone, but not between the two fractions. Compared to control, consumption of aleurone or bran led to significantly and substantially higher urinary concentrations of lactate, alanine, N-acetylaspartate acid and N-acetylaspartylglutamate and significantly and substantially lower urinary betaine concentrations at one and two hours postprandially. There were sex related differences in urinary metabolite profiles with generally higher hippurate and citrate and lower betaine in females compared to males. Overall, this postprandial study suggests that acute consumption of bran or aleurone is associated with a number of physiological effects that may impact on energy metabolism and which are consistent with longer term human and animal metabolomic studies that used whole-grain wheat diets or wheat fractions.

Obesity prevalence is increasing. The management of this condition requires a detailed analysis of the global risk factors in order to develop personalised advice. This study is aimed to identify current dietary patterns and habits in Spanish population interested in personalised nutrition and investigate associations with weight status. Self-reported dietary and anthropometrical data from the Spanish participants in the Food4Me study, were used in a multidimensional exploratory analysis to define specific dietary profiles. Two opposing factors were obtained according to food groups' intake: Factor 1 characterised by a more frequent consumption of traditionally considered unhealthy foods; and Factor 2, where the consumption of "Mediterranean diet" foods was prevalent. Factor 1 showed a direct relationship with BMI (β = 0.226; r² = 0.259; p < 0.001), while the association with Factor 2 was inverse (β = -0.037; r² = 0.230; p = 0.348). A total of four categories were defined (Prudent, Healthy, Western, and Compensatory) through classification of the sample in higher or lower adherence to each factor and combining the possibilities. Western and Compensatory dietary patterns, which were characterized by high-density foods consumption, showed positive associations with overweight prevalence. Further analysis showed that prevention of overweight must focus on limiting the intake of known deleterious foods rather than exclusively enhance healthy products.

Scope: Acute metabolic challenges provide an opportunity to identify mechanisms of metabolic and nutritional health. In this study, we assessed the transcriptomic response to oral glucose and lipid challenges in a cohort of individuals ranging in age and BMI. The main goal is to identify whether BMI can mediate the metabolic and transcriptional response to dietary challenges, and the differences between lipid and glucose tests., Methods and Results: Two hundred fourteen healthy adults were assigned to the challenges and twenty-three individuals were selected for further transcriptomic proofing, using microarray analysis of peripheral blood mononuclear cells. Through linear-mixed models and network analysis, different sets of transcripts and pathways were identified that responded to the challenges depending on BMI. Different transcripts that responded to the lipid and glucose tests, independently of BMI, were also identified. In the network analysis, inflammatory and adhesion processes were strongly represented for both challenges., Conclusion: Our results indicate that BMI is strongly linked to the transcriptomic and metabolic response to acute challenges. The emerging biological processes are mainly inflammation-related pathways, highlighting an interconnection between obesity, inflammation/adhesion, and response to nutritional challenge. The comparison between lipid and glucose challenges shows how these trigger a substantially different molecular response., (© 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Background: Studies examining vitamin E intake and the percentage of the population meeting dietary guidelines do not distinguish between natural (RRR-α-tocopherol) and synthetic (all-rac-α-tocopherol) intake, even though these different isomeric forms differ in bioactivity., Objective: This study aimed to determine the effect of RRR-α-tocopherol vs. all-rac-α-tocopherol intake on the percentage of the population meeting the vitamin E recommendation and on plasma α-tocopherol stereoisomer distribution., Methods: With the use of data from the Irish National Adult Nutrition Survey (NANS), this study examined the percentage of the Irish population meeting the European Union (EU) RDA for vitamin E of 12 mg/d, correcting for a bioactivity difference in all-rac- vs. RRR-α-tocopherol, where 1 mg of all-rac-α-tocopherol is considered to be equivalent to 1:1.36 (0.74) mg in the EU RDA. In a subcohort of supplement users and nonusers, plasma α- and γ-tocopherol concentrations and α-tocopherol stereoisomer distribution were measured. Receiver operating characteristic (ROC) curve analysis was conducted to determine ability to discriminate supplement user types., Results: Analysis of the NANS showed that 100% of participants still met the recommended intake of 12 mg/d, after all-rac-α-tocopherol intake was corrected for α-tocopherol equivalent bioactivity. In the subcohort analysis, the percentage of plasma RRR-α-tocopherol was significantly lower in high all-rac-α-tocopherol supplement (>11 mg/d) users (82%) compared with nonusers and with high RRR-α-tocopherol supplement (>35 mg/d) users (91% and 93% respectively, P < 0.01). High RRR-α-tocopherol supplement users had a significantly higher plasma α-tocopherol than low all-rac-α-tocopherol supplement (<2.5 mg/d) users (34 vs. 25 μmol/L, P = 0.01). ROC analysis demonstrated an ability to distinguish between RRR- and all-rac-α-tocopherol consumers, which may be useful in investigating the potential effect of RRR- and all-rac-α-tocopherol intake on health., Conclusions: This study demonstrated that the percentage of the population meeting the vitamin E recommendation was unaffected when all-rac-α-tocopherol intake was corrected for α-tocopherol equivalent bioactivity. all-rac-α-Tocopherol intake led to a decrease in the percentage of plasma RRR-α-tocopherol relative to RRR-α-tocopherol intake., (© 2015 American Society for Nutrition.)

Metabolomics focuses on the global study of metabolites in cells, tissues and biofluids. Analytical technologies such as nuclear magnetic resonance (NMR) spectroscopy and hyphenated mass spectrometry (MS) combined with advanced multivariate statistical methods allow us to study perturbations in metabolism. The close link between metabolism and nutrition has seen the application of metabolomics in nutritional research increase in recent times. Such applications can be divided into three main categories, namely (1) the area of dietary biomarker identification, (2) diet-related diseases and (3) nutritional interventions. The present perspective gives an overview of these applications and an outlook to the future., (© 2015 Society of Chemical Industry.)

Background: Meal pattern analysis can be complex because of the large variability in meal consumption. The use of aggregated, generic meal data may address some of these issues., Objective: The objective was to develop a meal coding system and use it to explore meal patterns., Design: Dietary data were used from the National Adult Nutrition Survey (2008-2010), which collected 4-d food diary information from 1500 healthy adults. Self-recorded meal types were listed for each food item. Common food group combinations were identified to generate a number of generic meals for each meal type: breakfast, light meals, main meals, snacks, and beverages. Mean nutritional compositions of the generic meals were determined and substituted into the data set to produce a generic meal data set. Statistical comparisons were performed against the original National Adult Nutrition Survey data. Principal component analysis was carried out by using these generic meals to identify meal patterns., Results: A total of 21,948 individual meals were reduced to 63 generic meals. Good agreement was seen for nutritional comparisons (original compared with generic data sets mean ± SD), such as fat (75.7 ± 29.4 and 71.7 ± 12.9 g, respectively, P = 0.243) and protein (83.3 ± 26.9 and 80.1 ± 13.4 g, respectively, P = 0.525). Similarly, Bland-Altman plots demonstrated good agreement (<5% outside limits of agreement) for many nutrients, including protein, saturated fat, and polyunsaturated fat. Twelve meal types were identified from the principal component analysis ranging in meal-type inclusion/exclusion, varying in energy-dense meals, and differing in the constituents of the meals., Conclusions: A novel meal coding system was developed; dietary intake data were recoded by using generic meal consumption data. Analysis revealed that the generic meal coding system may be appropriate when examining nutrient intakes in the population. Furthermore, such a coding system was shown to be suitable for use in determining meal-based dietary patterns., (© 2015 American Society for Nutrition.)

Obese pregnant women are the focus of numerous dietary and lifestyle intervention studies, however there is a paucity of literature examining the habitual dietary and lifestyle habits of this population. This paper aims to assess maternal dietary and lifestyle habits in an obese cohort, in order to identify priority areas to be addressed in future studies and in clinical practice. This prospective observational study recruited 100 pregnant women with a body mass index 30.0-39.9 kg/m(2) from routine antenatal clinics. Dietary intakes were assessed using a 3-day food diary and a structured lifestyle questionnaire assessed physical activity levels, smoking and alcohol habits and wellbeing. Macronutrient intakes as a percentage of total energy were not compliant to healthy eating guidelines with an inadequate intake of carbohydrate and excess intake of saturated fat. Compliance to recommended intakes of calcium, iron, folate and vitamin D was poor from diet alone. The consumption of energy dense food groups high in fat and sugar was greater than for published pregnant populations and the general female non-pregnant population. One-third of women reported engaging in weekly physical activity that would comply with recommendations for pregnant women while 25 % reported low mood status indicating potential depression. High intakes of energy-dense processed foods and poor compliance to micronutrient recommendations are critical dietary issues of concern among obese pregnant women. Low mood is a barrier to motivation for changing behaviour which would also need to be addressed in future lifestyle intervention studies.

Background: Advances in nutritional assessment are continuing to embrace developments in computer technology. The online Food4Me food frequency questionnaire (FFQ) was created as an electronic system for the collection of nutrient intake data. To ensure its accuracy in assessing both nutrient and food group intake, further validation against data obtained using a reliable, but independent, instrument and assessment of its reproducibility are required., Objective: The aim was to assess the reproducibility and validity of the Food4Me FFQ against a 4-day weighed food record (WFR)., Methods: Reproducibility of the Food4Me FFQ was assessed using test-retest methodology by asking participants to complete the FFQ on 2 occasions 4 weeks apart. To assess the validity of the Food4Me FFQ against the 4-day WFR, half the participants were also asked to complete a 4-day WFR 1 week after the first administration of the Food4Me FFQ. Level of agreement between nutrient and food group intakes estimated by the repeated Food4Me FFQ and the Food4Me FFQ and 4-day WFR were evaluated using Bland-Altman methodology and classification into quartiles of daily intake. Crude unadjusted correlation coefficients were also calculated for nutrient and food group intakes., Results: In total, 100 people participated in the assessment of reproducibility (mean age 32, SD 12 years), and 49 of these (mean age 27, SD 8 years) also took part in the assessment of validity. Crude unadjusted correlations for repeated Food4Me FFQ ranged from .65 (vitamin D) to .90 (alcohol). The mean cross-classification into "exact agreement plus adjacent" was 92% for both nutrient and food group intakes, and Bland-Altman plots showed good agreement for energy-adjusted macronutrient intakes. Agreement between the Food4Me FFQ and 4-day WFR varied, with crude unadjusted correlations ranging from .23 (vitamin D) to .65 (protein, % total energy) for nutrient intakes and .11 (soups, sauces and miscellaneous foods) to .73 (yogurts) for food group intake. The mean cross-classification into "exact agreement plus adjacent" was 80% and 78% for nutrient and food group intake, respectively. There were no significant differences between energy intakes estimated using the Food4Me FFQ and 4-day WFR, and Bland-Altman plots showed good agreement for both energy and energy-controlled nutrient intakes., Conclusions: The results demonstrate that the online Food4Me FFQ is reproducible for assessing nutrient and food group intake and has moderate agreement with the 4-day WFR for assessing energy and energy-adjusted nutrient intakes. The Food4Me FFQ is a suitable online tool for assessing dietary intake in healthy adults.

Background: Dietary assessment methods are important tools for nutrition research. Online dietary assessment tools have the potential to become invaluable methods of assessing dietary intake because, compared with traditional methods, they have many advantages including the automatic storage of input data and the immediate generation of nutritional outputs., Objective: The aim of this study was to develop an online food frequency questionnaire (FFQ) for dietary data collection in the "Food4Me" study and to compare this with the validated European Prospective Investigation of Cancer (EPIC) Norfolk printed FFQ., Methods: The Food4Me FFQ used in this analysis was developed to consist of 157 food items. Standardized color photographs were incorporated in the development of the Food4Me FFQ to facilitate accurate quantification of the portion size of each food item. Participants were recruited in two centers (Dublin, Ireland and Reading, United Kingdom) and each received the online Food4Me FFQ and the printed EPIC-Norfolk FFQ in random order. Participants completed the Food4Me FFQ online and, for most food items, participants were requested to choose their usual serving size among seven possibilities from a range of portion size pictures. The level of agreement between the two methods was evaluated for both nutrient and food group intakes using the Bland and Altman method and classification into quartiles of daily intake. Correlations were calculated for nutrient and food group intakes., Results: A total of 113 participants were recruited with a mean age of 30 (SD 10) years (40.7% male, 46/113; 59.3%, 67/113 female). Cross-classification into exact plus adjacent quartiles ranged from 77% to 97% at the nutrient level and 77% to 99% at the food group level. Agreement at the nutrient level was highest for alcohol (97%) and lowest for percent energy from polyunsaturated fatty acids (77%). Crude unadjusted correlations for nutrients ranged between .43 and .86. Agreement at the food group level was highest for "other fruits" (eg, apples, pears, oranges) and lowest for "cakes, pastries, and buns". For food groups, correlations ranged between .41 and .90., Conclusions: The results demonstrate that the online Food4Me FFQ has good agreement with the validated printed EPIC-Norfolk FFQ for assessing both nutrient and food group intakes, rendering it a useful tool for ranking individuals based on nutrient and food group intakes.

The food metabolome is defined as the part of the human metabolome directly derived from the digestion and biotransformation of foods and their constituents. With >25,000 compounds known in various foods, the food metabolome is extremely complex, with a composition varying widely according to the diet. By its very nature it represents a considerable and still largely unexploited source of novel dietary biomarkers that could be used to measure dietary exposures with a high level of detail and precision. Most dietary biomarkers currently have been identified on the basis of our knowledge of food compositions by using hypothesis-driven approaches. However, the rapid development of metabolomics resulting from the development of highly sensitive modern analytic instruments, the availability of metabolite databases, and progress in (bio)informatics has made agnostic approaches more attractive as shown by the recent identification of novel biomarkers of intakes for fruit, vegetables, beverages, meats, or complex diets. Moreover, examples also show how the scrutiny of the food metabolome can lead to the discovery of bioactive molecules and dietary factors associated with diseases. However, researchers still face hurdles, which slow progress and need to be resolved to bring this emerging field of research to maturity. These limits were discussed during the First International Workshop on the Food Metabolome held in Glasgow. Key recommendations made during the workshop included more coordination of efforts; development of new databases, software tools, and chemical libraries for the food metabolome; and shared repositories of metabolomic data. Once achieved, major progress can be expected toward a better understanding of the complex interactions between diet and human health., (© 2014 American Society for Nutrition.)

The application of metabolomics in multi-centre studies is increasing. The aim of the present study was to assess the effects of geographical location on the metabolic profiles of individuals with the metabolic syndrome. Blood and urine samples were collected from 219 adults from seven European centres participating in the LIPGENE project (Diet, genomics and the metabolic syndrome: an integrated nutrition, agro-food, social and economic analysis). Nutrient intakes, BMI, waist:hip ratio, blood pressure, and plasma glucose, insulin and blood lipid levels were assessed. Plasma fatty acid levels and urine were assessed using a metabolomic technique. The separation of three European geographical groups (NW, northwest; NE, northeast; SW, southwest) was identified using partial least-squares discriminant analysis models for urine (R² X: 0·33, Q²: 0·39) and plasma fatty acid (R² X: 0·32, Q²: 0·60) data. The NW group was characterised by higher levels of urinary hippurate and N-methylnicotinate. The NE group was characterised by higher levels of urinary creatine and citrate and plasma EPA (20 : 5 n-3). The SW group was characterised by higher levels of urinary trimethylamine oxide and lower levels of plasma EPA. The indicators of metabolic health appeared to be consistent across the groups. The SW group had higher intakes of total fat and MUFA compared with both the NW and NE groups (P≤ 0·001). The NE group had higher intakes of fibre and n-3 and n-6 fatty acids compared with both the NW and SW groups (all P< 0·001). It is likely that differences in dietary intakes contributed to the separation of the three groups. Evaluation of geographical factors including diet should be considered in the interpretation of metabolomic data from multi-centre studies.

Alterations in intestinal microbiota composition are associated with several chronic conditions, including obesity and inflammatory diseases. The microbiota of older people displays greater inter-individual variation than that of younger adults. Here we show that the faecal microbiota composition from 178 elderly subjects formed groups, correlating with residence location in the community, day-hospital, rehabilitation or in long-term residential care. However, clustering of subjects by diet separated them by the same residence location and microbiota groupings. The separation of microbiota composition significantly correlated with measures of frailty, co-morbidity, nutritional status, markers of inflammation and with metabolites in faecal water. The individual microbiota of people in long-stay care was significantly less diverse than that of community dwellers. Loss of community-associated microbiota correlated with increased frailty. Collectively, the data support a relationship between diet, microbiota and health status, and indicate a role for diet-driven microbiota alterations in varying rates of health decline upon ageing.

Low vitamin D intake and status have been reported worldwide and many studies have suggested that this low status may be involved in the development of several chronic diseases. There are a limited number of natural dietary sources of vitamin D leading to a real need for alternatives to improve dietary intake. Enhancement of foods with vitamin D is a possible mode for ensuring increased consumption and thus improved vitamin D status. The present review examines studies investigating effects of vitamin D enhanced foods in humans and the feasibility of the approach is discussed.

Background: It has been suggested that metabolomics could play a role in dietary assessment and identification of novel biomarkers of dietary intake., Objective: This study examined the link between habitual dietary patterns and metabolomic profiles., Design: A total of 160 volunteers participated in a double-blind, randomized, placebo-controlled dietary intervention. We collected biofluids and recorded 3-d food diaries. Food data were reduced to 33 food groups, and a k-means cluster analysis was performed to identify dietary patterns. (1)H Nuclear magnetic resonance (NMR) spectra were acquired for plasma and urine samples, and gas chromatography was used for plasma fatty acid profiling., Results: Cluster analysis identified 3 distinct dietary patterns on the basis of the energy contribution of different food groups. Dietary clusters were reflected in plasma fatty acid profiles and in metabolomic data. (1)H NMR spectra of urine allowed the identification of metabolites associated with different dietary patterns. Several of the metabolites identified were linked to the intake of specific food groups; in particular, there was a positive association between O-acetylcarnitine and phenylacetylglutamine and red-meat and vegetable intakes, respectively., Conclusions: Habitual dietary patterns are shown in metabolomic data. This approach successfully identified potential biomarkers of red-meat and vegetable intakes.

Background: Diversity in dietary intake contributes to variation in human metabolomic profiles and artifacts from acute dietary intake can affect metabolomics data., Objective: We investigated the role of dietary phytochemicals on shaping human urinary metabolomic profiles., Design: First void urine samples were collected from 21 healthy volunteers (12 women, 9 men) following their normal diet (ND), a 2-d low-phytochemical diet (LPD), or a 2-d standard phytochemical diet (SPD). Nutrient intake was assessed during the study. Urine samples were analyzed by using (1)H nuclear magnetic resonance spectroscopy ((1)H NMR) and mass spectrometry (MS), which was followed by multivariate data analysis., Results: Macronutrient intake did not change throughout the study. Partial least-squares-discriminant analysis indicated a clear distinction between the LPD samples and the ND and SPD samples, relating to creatinine and methylhistidine excretion after the LPD and hippurate excretion after the ND and SPD. The predictive power of the LPD versus the ND model was 74 +/- 3% and 82 +/- 6% with the (1)H NMR and MS data sets, respectively. The predictive power of the LPD versus the SPD model was 83 +/- 8% and 69 +/- 4% for the (1)H NMR and MS data sets respectively. A cross platform comparison of both data sets by co-inertia analysis showed a similar distinction between the LPD and SPD., Conclusions: Acute changes in urinary metabolomic profiles occur after the consumption of dietary phytochemicals. Dietary restrictions in the 24 h before sample collection may reduce diversity in phytochemical intakes and therefore reduce variation and improve data interpretation in metabolomics studies using urine.

Background: Metabolomics in human nutrition research is faced with the challenge that changes in metabolic profiles resulting from diet may be difficult to differentiate from normal physiologic variation., Objective: We assessed the extent of intra- and interindividual variation in normal human metabolic profiles and investigated the effect of standardizing diet on reducing variation., Design: Urine, plasma, and saliva were collected from 30 healthy volunteers (23 females, 7 males) on 4 separate mornings. For visits 1 and 2, free food choice was permitted on the day before biofluid collection. Food choice on the day before visit 3 was intended to mimic that for visit 2, and all foods were standardized on the day before visit 4. Samples were analyzed by using 1H nuclear magnetic resonance spectroscopy followed by multivariate data analysis., Results: Intra- and interindividual variations were considerable for each biofluid. Visual inspection of the principal components analysis scores plots indicated a reduction in interindividual variation in urine, but not in plasma or saliva, after the standard diet. Partial least-squares discriminant analysis indicated time-dependent changes in urinary and salivary samples, mainly resulting from creatinine in urine and acetate in saliva. The predictive power of each model to classify the samples as either night or morning was 85% for urine and 75% for saliva., Conclusions: Urine represented a sensitive metabolic profile that reflected acute dietary intake, whereas plasma and saliva did not. Future metabolomics studies should consider recent dietary intake and time of sample collection as a means of reducing normal physiologic variation.

Background Early applications of metabolomics in nutrition and health research identified associations between dietary patterns and metabolomic profiles. Twin studies show that diet-related phenotypes and diet-associated metabolites are influenced by genes. However, studies have not examined whether diet–metabolite associations are explained by genetic or environmental factors and whether these associations are reproducible over multiple time points. Objective This research aims to examine the genetic and environmental factors influencing covariation in diet–metabolite associations that are reproducible over time in healthy twins. Methods The UCD Twin Study is a semi-longitudinal classic twin study that collected repeated dietary, anthropometric, and urinary data over 2 months. Correlation analysis identified associations between diet quality measured using the Healthy Eating Index (HEI) and urinary metabolomic profiles at 3 time points. Diet-associated metabolites were examined using linear regression to identify those significantly influenced by familial factors between twins and those significantly influenced by unique factors. Cholesky decomposition modeling quantified the genetic and environmental path coefficients through associated dietary components onto the metabolites. Results The HEI was associated with 14 urinary metabolites across 3 metabolomic profiles (r: ±0.15–0.49). For 8 diet–metabolite associations, genetic or shared environmental factors influencing HEI component scores significantly influenced variation in metabolites (β: 0.40–0.52). A significant relation was observed between dietary intakes of whole grain and acetoacetate (β: −0.50, P  < 0.001) and β-hydroxybutyrate (β: −0.46, P  < 0.001), as well as intakes of saturated fat and acetoacetate (β: 0.47, P  < 0.001) and β-hydroxybutyrate (β: 0.52, P  < 0.001). For these diet–metabolite associations a common shared environmental factor explained 66–69% of variance in the metabolites. Conclusions This study shows that diet–metabolite associations are reproducible in 3 urinary metabolomic profiles. Components of the HEI covary with metabolites, and covariation is largely due to the shared environment. [ABSTRACT FROM AUTHOR]

In recent years there has been general agreement that dietary advice needs to be tailored to the individual and that we need to move from a one size fits all approach. Evidence has emerged that personalising dietary advice results in improved dietary behaviours. Concomitant with this there has been an increase in the application of developing technologies such as metabolomics to nutrition studies. The concept of the metabotype has emerged and set to play a key role in the development and delivery or personalised nutrition. The term metabotype refers to a group of individuals with similar metabolic profiles. This review gives an overview of the potential role of this approach in delivering optimal nutrition advice. [ABSTRACT FROM AUTHOR]

Food records or diaries, dietary recalls and FFQ are methods traditionally used to measure dietary intake; however, advancing technologies and growing awareness in personalised health have heightened interest in the application of new technologies to assess dietary intake. Dietary intake data can be used in epidemiology, dietary interventions and in the delivery of personalised nutrition advice. Compared with traditional dietary assessment methods, new technologies have many advantages, including their ability to automatically process data and provide personalised dietary feedback advice. This review examines the new technologies presently under development for the assessment of dietary intakes, and their utilisation and efficacy for personalising dietary advice. New technology-based methods of dietary assessment can broadly be categorised into three key areas: online (web-based) methods, mobile methods and sensor technologies. Several studies have demonstrated that utilising new technologies to provide tailored advice can result in positive dietary changes and have a significant impact on selected nutrient and food group intakes. However, comparison across studies indicates that the magnitude of change is variable and may be influenced by several factors, including the frequency and type of feedback provided. Future work should establish the most effective combinations of these factors in facilitating dietary changes across different population groups. [ABSTRACT FROM PUBLISHER]

A review is presented of the article "Untargeted metabolomics identifies novel potential biomarkers of habitual food intake in a cross-sectional study of postmenopausal women" by Y. Wang, T.J. Hartman, M.L. McCullough and others which was published in the same issue concerning dietary intake biomarkers using metabolomics.

Twin studies are a valuable resource for studying phenotypes and their underlying biology. Heritability estimates based on the classic twin model show that genes influence human traits including health, diet, and food choice. Metabolomics is a promising tool in nutrition and health research where complex metabolite profiles reflect the metabolic effects of foods and diets as well as the biological pathways associated with diet-related diseases. In recent years, publications arising from twin research have incorporated metabolomic analysis, providing insights into genetic and environmental influences on metabolomic profiles. This article reviews the application of metabolomics in twin research with a particular focus on nutrition and diet-related diseases. The review begins by describing the classic twin study design, followed by a look at its application in nutrition research. Indeed, there is clear evidence for a genetic influence on dietary intake, regardless of the outcome measure: energy, macronutrients, dietary patterns, or food choice. The latter part of the review introduces metabolomic research showing how twin studies can separate aspects of the metabolome that are strongly influenced by genetics vs those that are more influenced by environment. The combination of metabolomics and twin research brings the promise of untangling gene-environment effects on complex phenotypes such as the metabolome, obesity, and diet-related diseases. For example, metabolomics is used in nutrition research to identify metabolites associated with particular dietary patterns. When combined within a twin study design, heritability of metabolite–dietary pattern associations can be established allowing further insight into complex gene-environment interactions that shape individual metabolomes. [ABSTRACT FROM AUTHOR]

Background: Acute inflammation impairs reverse cholesterol transport (RCT) and reduces high-density lipoprotein (HDL) function in vivo. This study hypothesized that obesity-induced inflammation impedes RCT and alters HDL composition, and investigated if dietary replacement of saturated (SFA) for monounsaturated (MUFA) fatty acids modulates RCT.Methods and Results: Macrophage-to-feces RCT, HDL efflux capacity, and HDL proteomic profiling was determined in C57BL/6j mice following 24 weeks on SFA- or MUFA-enriched high-fat diets (HFDs) or low-fat diet. The impact of dietary SFA consumption and insulin resistance on HDL efflux function was also assessed in humans. Both HFDs increased plasma (3)H-cholesterol counts during RCT in vivo and ATP-binding cassette, subfamily A, member 1-independent efflux to plasma ex vivo, effects that were attributable to elevated HDL cholesterol. By contrast, ATP-binding cassette, subfamily A, member 1-dependent efflux was reduced after both HFDs, an effect that was also observed with insulin resistance and high SFA consumption in humans. SFA-HFD impaired liver-to-feces RCT, increased hepatic inflammation, and reduced ABC subfamily G member 5/8 and ABC subfamily B member 11 transporter expression in comparison with low-fat diet, whereas liver-to-feces RCT was preserved after MUFA-HFD. HDL particles were enriched with acute-phase proteins (serum amyloid A, haptoglobin, and hemopexin) and depleted of paraoxonase-1 after SFA-HFD in comparison with MUFA-HFD.Conclusions: Ex vivo efflux assays validated increased macrophage-to-plasma RCT in vivo after both HFDs but failed to capture differential modulation of hepatic cholesterol trafficking. By contrast, proteomics revealed the association of hepatic-derived inflammatory proteins on HDL after SFA-HFD in comparison with MUFA-HFD, which reflected differential hepatic cholesterol trafficking between groups. Acute-phase protein levels on HDL may serve as novel biomarkers of impaired liver-to-feces RCT in vivo. [ABSTRACT FROM AUTHOR]