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Gibbons, Helena
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Gibbons, Helena
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Gibbons, Helena
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- PublicationSex matters: a focus on the impact of biological sex on metabolomic profiles and dietary interventionsThe 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.
299Scopus© Citations 20 - PublicationMetabolomic-based identification of clusters that reflect dietary patterns(Wiley, 2017-07-20)
; ; ; ; ; ; 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<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.625Scopus© Citations 21 - PublicationEstimation of chicken intake using metabolomics derived markers(Oxford University Press, 2017-10-01)
; ; ; ; ; ; ; Background: Improved assessment of meat intake using metabolomics derived markers can provide objective data and could be helpful in clarifying proposed associations between meat intake and health.Objective: The objective 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; BMI, 28.25 Kg/m2) in NutriTech Food Intake Study (NCT01684917) consumed increased amounts of chicken from 88 to 290 g/day over three weeks. Urine and blood samples were analyzed by NMR and MS, respectively. 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. Bland and Altman analysis evaluated the agreement between reported and calculated chicken intake in National Adult Nutrition Survey (NANS) cohort. Results: Multivariate data analysis of postprandial and fasting urine samples collected in NutriTech revealed good discrimination between high (290 g/day) and low (88 g/day) chicken intakes. Urinary metabolite profiles showed differences in metabolite levels between low and high chicken intakes. Examining metabolite profiles revealed guanidoacetate significantly increased from 1.47 to 3.66 mmol/L following increasing chicken intake from 88 to 290 g/day (P < 0.01). Using a calibration curve developed from NutriTech study, chicken intake was calculated in NANS, where chicken consumers had higher guanidoacetate excretion (0.70 mmol/L) than non-consumers (0.47 mmol/L) (P < 0.01). Bland and Altman analysis revealed good agreement between reported and calculated intakes with a bias of -30.2g/day. Plasma metabolite analysis demonstrated that 3-methylhistidine (3-Meth-His) was a more suitable indicator of chicken intake compared with 1-methylhistidine (1-Meth-His). Conclusions: Guanidoacetate was successfully identified and confirmed as a marker of chicken intake, and importantly its measurement in fasting urine samples could be used to determine chicken intake in a free-living population.439Scopus© Citations 20 - PublicationMetabolomics as a tool in the identification of dietary biomarkersCurrent 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.
1033Scopus© Citations 35 - PublicationExploring the Links between Diet and Health in an Irish Cohort: A Lipidomic Approach(American Chemical Society, 2017-02-01)
; ; ; ; ; ; ; 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.574Scopus© Citations 7 - PublicationMetabolomic Based Approach to Identify Biomarkers of Apple Intake(Wiley, 2020-06)
; ; ; ; ; ; ; ; ; 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 was to identify biomarkers of apple intake, examine dose-response relationships and agreement with self-reported data. METHODS AND RESULTS:Metabolomic data from three studies were examined: an acute intervention, a short-term intervention and a free-living cohort study. Fasting and postprandial urine samples were collected for analysis by 1 H-NMR and LC-MS. Calibration curves were developed to determine apple intake and classify individuals into categories of intake. Multivariate analysis of data revealed that levels of multiple metabolites increased 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 increased as apple intake increased. Urinary xylose concentrations in a free-living cohort performed poorly at an individual level but were capable of ranking individuals in categories of intake. CONCLUSION:Urinary xylose exhibited a dose-response relationship with apple intake and performed well as a ranking biomarker in the population study. Other potential biomarkers were identified and future work will combine these with xylose in a biomarker panel which may allow for a more objective determination of individual intake.322Scopus© Citations 5 - PublicationThe Relationship between Fish Intake and Urinary Trimethylamine-N-Oxide(Wiley, 2020-02)
; ; ; ; ; ; 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.183Scopus© Citations 17 - PublicationDemonstration of the utility of biomarkers for dietary intake assessment; proline betaine as an example(Wiley, 2017)
; ; ; ; ; ; ; 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<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 24h 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.663Scopus© Citations 50 - PublicationImpact of Sample Storage on the NMR Fecal Water Metabolome(ACS, 2018-12-05)
; ; ; ; The study of the fecal metabolome is an important area of research to better understand the human gut microbiome and its impact on human health and diseases. However, there is a lack of work in examining the impact of storage and processing conditions on the metabolite levels of fecal water. Furthermore, there is no universal protocol used for the storage of fecal samples and preparation of fecal water. The objective of the current study was to examine the impact of different storage conditions on fecal samples prior to metabolite extraction. Fecal samples obtained from nine healthy individuals were processed under different conditions: (1) fresh samples prepared immediately after collection, (2) fecal samples stored at 4 °C for 24 h prior to processing, and (3) fecal samples stored at −80 °C for 24 h prior to processing. All samples were analyzed using NMR spectroscopy, multivariate statistical analysis, and repeated measures ANOVA. Samples which were frozen at −80 °C prior to extraction of the metabolites exhibited an increase in the number of metabolites including branched-chain amino acids, aromatic amino acids, and tricarboxylic acid cycle intermediates. Storage of fecal samples at 4 °C ensured higher fidelity to freshly processed samples leading to the recommendation that fecal samples should not be frozen prior to extraction of fecal water. Furthermore, the work highlights the need to standardize sample storage of fecal samples to allow for the accurate study of the fecal metabolome.254Scopus© Citations 11