McNulty, Breige A.
McNulty, Breige A.
McNulty, Breige A.
Now showing 1 - 9 of 9
- 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. 171Scopus© Citations 16
- PublicationThe Potential of Multi-Biomarker Panels in Nutrition Research: Total Fruit Intake as an Example(Frontiers Media, 2021-01-14)
; ; ; ; ;Dietary and food intake biomarkers offer the potential of improving the accuracy of dietary assessment. An extensive range of putative intake biomarkers of commonly consumed foods have been identified to date. As the field of food intake biomarkers progresses toward solving the complexities of dietary habits, combining biomarkers associated with single foods or food groups may be required. The objective of this work was to examine the ability of a multi-biomarker panel to classify individuals into categories of fruit intake. Biomarker data was measured using H NMR spectroscopy in two studies: (1) An intervention study where varying amounts of fruit was consumed and (2) the National Adult Nutrition Survey (NANS). Using data from an intervention study a biomarker panel (Proline betaine, Hippurate, and Xylose) was constructed from three urinary biomarker concentrations. Biomarker cut-off values for three categories of fruit intake were developed. The biomarker sum cut-offs were ≤ 4.766, 4.766–5.976, >5.976 μM/mOsm/kg for <100, 101–160, and >160 g fruit intake. The ability of the biomarker sum to classify individuals into categories of fruit intake was examined in the cross-sectional study (NANS) (N = 565). Examination of results in the cross-sectional study revealed excellent agreement with self-reported intake: a similar number of participants were ranked into each category of fruit intake. The work illustrates the potential of multi-biomarker panels and paves the way forward for further development in the field. The use of such panels may be key to distinguishing foods and adding specificity to the predictions of food intake. 1 32Scopus© Citations 5
- 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
- PublicationPatterns of dairy food intake, body composition and markers of metabolic health in Ireland: Results from the National Adult Nutrition Survey(Springer, 2017-02-20)
; ; ; ;Background: Studies examining the association between dairy consumption and metabolic health have shown mixed results. This may be due, in part, to the use of different definitions of dairy, and to single types of dairy foods examined in isolation. Objective: The objective of the study was to examine associations between dairy food intake and metabolic health, identify patterns of dairy food consumption and determine whether dairy dietary patterns are associated with outcomes of metabolic health, in a cross-sectional survey. Design:A 4-day food diary was used to assess food and beverage consumption, including dairy (defined as milk, cheese, yogurt, cream and butter) in free-living, healthy Irish adults aged 18-90 years (n=1500). Fasting blood samples (n=897) were collected, and anthropometric measurements taken. Differences in metabolic health markers across patterns and tertiles of dairy consumption were tested via analysis of covariance. Patterns of dairy food consumption, of different fat contents, were identified using cluster analysis. Results: Higher (total) dairy was associated with lower body mass index, %body fat, waist circumference and waist-to-hip ratio (P<0.001), and lower systolic (P=0.02) and diastolic (P<0.001) blood pressure. Similar trends were observed when milk and yogurt intakes were considered separately. Higher cheese consumption was associated with higher C-peptide (P<0.001). Dietary pattern analysis identified three patterns (clusters) of dairy consumption; 'Whole milk', 'Reduced fat milks and yogurt' and 'Butter and cream'. The 'Reduced fat milks and yogurt' cluster had the highest scores on a Healthy Eating Index, and lower-fat and saturated fat intakes, but greater triglyceride levels (P=0.028) and total cholesterol (P=0.015). conclusion: Overall, these results suggest that while milk and yogurt consumption is associated with a favourable body phenotype, the blood lipid profiles are less favourable when eaten as part of a low-fat high-carbohydrate dietary pattern. More research is needed to better understand this association. Conclusion: Overall, these results suggest that although milk and yogurt consumption is associated with a favourable body phenotype, the blood lipid profiles are less favourable when eaten as part of a low-fat high-carbohydrate dietary pattern. More research is needed to better understand this association. 220
- 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. 658Scopus© Citations 50
- 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. 432Scopus© Citations 20
- PublicationCombining biomarker and food intake data: calibration equations for citrus intake(Oxford University Press, 2019-10)
; ; ; ;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 objective 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. 279Scopus© 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. 318Scopus© Citations 5
- PublicationClassifying Individuals Into a Dietary Pattern Based on Metabolomic Data(Wiley, 2021-06)
; ; ; ; ;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. 23Scopus© Citations 7