Helena Gibbons

Metabolomics in the identification of biomarkers of dietary intake

Traditional methods for assessing dietary exposure can be unreliable, with under reporting one of the main problems. In an attempt to overcome such problems there is increasing interest in identifying biomarkers of dietary intake to provide a more accurate measurement. Metabolomics is an analytical technique that aims to identify and quantify small metabolites. Recently, there has been an increased interest in the application of metabolomics coupled with statistical analysis for the identification of dietary biomarkers, with a number of putative biomarkers identified. This minireview focuses on metabolomics based approaches and highlights some of the key successes.

Metabolomics as a tool in nutritional research.

Purpose of review Metabolomics is emerging as a powerful tool for studying metabolic processes and in recent years, the applications in the area of nutrition have risen rapidly. The present review gives an overview of the current applications in the field of nutrition and identifies areas in need of advancement. Recent findings Applications in nutrition research can in general be divided into three main areas: identification of dietary biomarkers, study of diet-related diseases and identification of biomarkers of disease and application to dietary intervention studies as a tool to identify molecular mechanisms. Summary Metabolomics has made a significant impact on all the areas identified above and is set to have a major impact on the study of diet–health relationships.

A metabolomic study of biomarkers of meat and fish intake

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.

A metabolomics approach to the identification of biomarkers of sugar-sweetened beverage intake

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 ¹H 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.

Metabolomics as a tool in the identification of dietary biomarkers.

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.

Estimation of Chicken Intake by Adults Using Metabolomics-Derived Markers

Background: 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.Objective: 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.Methods: Ten participants [age: 62 y; body mass index (in kg/m2): 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.Results: 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 (P < 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; P < 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.Conclusions: 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.

Exploring the Links between Diet and Health in an Irish Cohort: A Lipidomic Approach

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.

Development and validation testing of a short nutrition questionnaire to identify dietary risk factors in preschoolers aged 12 36 months

Background Although imbalances in dietary intakes can have short and longer term influences on the health of preschool children, few tools exist to quickly and easily identify nutritional risk in otherwise healthy young children. Objectives To develop and test the validity of a parent-administered questionnaire (NutricheQ) as a means of evaluating dietary risk in young children (12–36 months). Design Following a comprehensive development process and internal reliability assessment, the NutricheQ questionnaire was validated in a cohort of 371 Irish preschool children as part of the National Preschool Nutrition Survey. Dietary risk was rated on a scale ranging from 0 to 22 from 11 questions, with a higher score indicating higher risk. Results Children with higher NutricheQ scores had significantly (p<0.05) lower mean daily intakes of key nutrients such as iron, zinc, vitamin D, riboflavin, niacin, folate, phosphorous, potassium, carotene, retinol, and dietary fibre. They also had lower (p<0.05) intakes of vegetables, fish and fish dishes, meat and infant/toddler milks and higher intakes of processed foods and non-milk beverages, confectionery, sugars and savoury snack foods indicative of poorer dietary quality. Areas under the curve values of 84.7 and 75.6% were achieved for ‘medium’ and ‘high’ dietary risk when compared with expert risk ratings indicating good consistency between the two methods. Conclusion NutricheQ is a valid method of quickly assessing dietary quality in preschoolers and in identifying those at increased nutritional risk. In Context Analysis of data from national food and nutrition surveys typically identifies shortfalls in dietary intakes or quality of young children. This can relate to intakes of micronutrients such as iron or vitamin D as well as to the balance of macronutrients they consume (e.g. fat or sugar). Alongside this lie concerns regarding overweight and obesity and physical inactivity. This combination of risk factors has potential negative effects for both short and longer term health. Hence, screening tools, such as NutricheQ described here, offer an opportunity for early identification and subsequent appropriate timely intervention from 12 months of age. This paper describes the development and validation of NutricheQ, a short user-friendly questionnaire. Designed to be administered by parents or carers, it aims to help healthcare professionals identify children at risk based on known, evidence-based nutritional risk factors. It is hoped in the longer term that this tool can be adapted for use globally and improve child health through early identification, which can be followed up by targeted, cost-effective interventions.