An Analytical Pipeline for Quantitative Characterization of Dietary Intake: Application To Assess Grape Intake

García-Pérez, Isabel; Posma, Joram M.; Chambers, Edward S.; Nicholson, Jeremy K.; Mathers, John C.; Beckmann, Manfred; Draper, John; Holmes, Elaine; Frost, Gary

doi:10.1021/acs.jafc.5b05878

Cited by 47 publications

(50 citation statements)

References 40 publications

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“…Where possible, use of 24 h urine samples rather than spot samples is advised for measurement of metabolic changes, particularly when excretion kinetics are not known a priori, partly because spot urine samples vary more in dilution. However, where only spot urine samples are available because of study limitations, such as in the case of the Danish cohort, we found that the time-matched cumulative sample obtained between meals predicted the spot samples with more accuracy than did the 24 h model (appendix p 14), which is consistent with previous work showing accurate quantification of grape intake based on both 24 h samples and cumulative samples matching the excretion kinetics window 22 . For this reason, we compared the spot samples, taken after the first morning void in the Danish cohort with the model derived from cumulative sample 1 (after breakfast to before lunch) (appendix p 11) rather than the 24 h model, because the sampling time was better matched.…”

Section: Discussionsupporting

confidence: 90%

“…Substantial between-person variability could be seen in concentrations of hippurate (figure 2C) and carnitine (figure 2D), but the direction of association remained the same. For tartaric acid, between-person variability was also apparent, but because it is a quantitative biomarker of grape consumption 22 there was almost no between-person variation after consumption of diet 4, which did not contain any grape-derived products (appendix p 5). …”

Section: Resultsmentioning

confidence: 99%

“…The minimum 5 day gap between dietary intervention periods ensured that any possible carryover was minimised. For example, tartaric acid, a marker of grape consumption, was shown to be cleared from the body within a few hours in an excretion kinetics study 22 . Similarly, other metabolites associated with diet, such as proline betaine, creatine, and trimethylamine N-oxide (TMAO), are cleared from the body within a few hours 23, 24…”

Section: Methodsmentioning

confidence: 99%

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Objective assessment of dietary patterns by use of metabolic phenotyping: a randomised, controlled, crossover trial

García-Pérez

Posma

Gibson

et al. 2017

The Lancet Diabetes & Endocrinology

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206

224

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SummaryBackgroundAccurate monitoring of changes in dietary patterns in response to food policy implementation is challenging. Metabolic profiling allows simultaneous measurement of hundreds of metabolites in urine, the concentrations of which can be affected by food intake. We hypothesised that metabolic profiles of urine samples developed under controlled feeding conditions reflect dietary intake and can be used to model and classify dietary patterns of free-living populations.MethodsIn this randomised, controlled, crossover trial, we recruited healthy volunteers (aged 21–65 years, BMI 20–35 kg/m2) from a database of a clinical research unit in the UK. We developed four dietary interventions with a stepwise variance in concordance with the WHO healthy eating guidelines that aim to prevent non-communicable diseases (increase fruits, vegetables, whole grains, and dietary fibre; decrease fats, sugars, and salt). Participants attended four inpatient stays (72 h each, separated by at least 5 days), during which they were given one dietary intervention. The order of diets was randomly assigned across study visits. Randomisation was done by an independent investigator, with the use of opaque, sealed, sequentially numbered envelopes that each contained one of the four dietary interventions in a random order. Participants and investigators were not masked from the dietary intervention, but investigators analysing the data were masked from the randomisation order. During each inpatient period, urine was collected daily over three timed periods: morning (0900–1300 h), afternoon (1300–1800 h), and evening and overnight (1800–0900 h); 24 h urine samples were obtained by pooling these samples. Urine samples were assessed by proton nuclear magnetic resonance (1H-NMR) spectroscopy, and diet-discriminatory metabolites were identified. We developed urinary metabolite models for each diet and identified the associated metabolic profiles, and then validated the models using data and samples from the INTERMAP UK cohort (n=225) and a healthy-eating Danish cohort (n=66). This study is registered with ISRCTN, number ISRCTN43087333.FindingsBetween Aug 13, 2013, and May 18, 2014, we contacted 300 people with a letter of invitation. 78 responded, of whom 26 were eligible and invited to attend a health screening. Of 20 eligible participants who were randomised, 19 completed all four 72 h study stays between Oct 2, 2013, and July 29, 2014, and consumed all the food provided. Analysis of 1H-NMR spectroscopy data indicated that urinary metabolic profiles of the four diets were distinct. Significant stepwise differences in metabolite concentrations were seen between diets with the lowest and highest metabolic risks. Application of the derived metabolite models to the validation datasets confirmed the association between urinary metabolic and dietary profiles in the INTERMAP UK cohort (p<0·0001) and the Danish cohort (p<0·0001).InterpretationUrinary metabolite models developed in a highly controlled environment can classify groups of free-livin...

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Section: Discussionsupporting

confidence: 90%

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Objective assessment of dietary patterns by use of metabolic phenotyping: a randomised, controlled, crossover trial

García-Pérez

Posma

Gibson

et al. 2017

The Lancet Diabetes & Endocrinology

Self Cite

206

224

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“…7−17 Dietary biomarkers are based on the concept that food intake is highly correlated with excretion levels of food-related compounds over a given period of time. These “biomarkers” can be compounds that are excreted unchanged 10,17 or that have undergone metabolic conversion, for example, by gut bacteria.…”

mentioning

confidence: 99%

“…Thus, confirmation of a candidate biomarker is best achieved by using a controlled food challenge with subsequent validation in a larger population or study cohort. 17 …”

mentioning

confidence: 99%

Integrated Analytical and Statistical Two-Dimensional Spectroscopy Strategy for Metabolite Identification: Application to Dietary Biomarkers

et al. 2017

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A major purpose of exploratory metabolic profiling is for the identification of molecular species that are statistically associated with specific biological or medical outcomes; unfortunately, the structure elucidation process of unknowns is often a major bottleneck in this process. We present here new holistic strategies that combine different statistical spectroscopic and analytical techniques to improve and simplify the process of metabolite identification. We exemplify these strategies using study data collected as part of a dietary intervention to improve health and which elicits a relatively subtle suite of changes from complex molecular profiles. We identify three new dietary biomarkers related to the consumption of peas (N-methyl nicotinic acid), apples (rhamnitol), and onions (N-acetyl-S-(1Z)-propenyl-cysteine-sulfoxide) that can be used to enhance dietary assessment and assess adherence to diet. As part of the strategy, we introduce a new probabilistic statistical spectroscopy tool, RED-STORM (Resolution EnhanceD SubseT Optimization by Reference Matching), that uses 2D J-resolved 1H NMR spectra for enhanced information recovery using the Bayesian paradigm to extract a subset of spectra with similar spectral signatures to a reference. RED-STORM provided new information for subsequent experiments (e.g., 2D-NMR spectroscopy, solid-phase extraction, liquid chromatography prefaced mass spectrometry) used to ultimately identify an unknown compound. In summary, we illustrate the benefit of acquiring J-resolved experiments alongside conventional 1D 1H NMR as part of routine metabolic profiling in large data sets and show that application of complementary statistical and analytical techniques for the identification of unknown metabolites can be used to save valuable time and resources.

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Demonstration of the utility of biomarkers for dietary intake assessment; proline betaine as an example

Gibbons

Michielsen

Rundle

et al. 2017

Molecular Nutrition Food Res

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An Analytical Pipeline for Quantitative Characterization of Dietary Intake: Application To Assess Grape Intake

Cited by 47 publications

References 40 publications

Objective assessment of dietary patterns by use of metabolic phenotyping: a randomised, controlled, crossover trial

Objective assessment of dietary patterns by use of metabolic phenotyping: a randomised, controlled, crossover trial

Integrated Analytical and Statistical Two-Dimensional Spectroscopy Strategy for Metabolite Identification: Application to Dietary Biomarkers

Demonstration of the utility of biomarkers for dietary intake assessment; proline betaine as an example

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