Postprandial urine composition reflects recent dietary exposure

Table 1 lists several recent studies in which non-targeted metabolite fingerprinting or metabolite profiling has been used to discover dietary biomarkers in both acute and short-term food intervention studies⁽ Reference Bertram, Hoppe and Petersen ^{17 ,} Reference Edmands, Beckonert and Stella ^{19 –} Reference Lloyd, Favé and Beckmann ^{25 ,} Reference Martin, Rezzi and Peré-Trepat ^{28 ,} Reference Stalmach, Mullen and Barron ^{32 ,} Reference Stella, Beckwith-Hall and Cloarec ^{33 ,} Reference Walsh, Brennan and Pujos-Guillot ^{35 )}. The basic design principles are illustrated in Fig. 1 with reference to the MEDE study in which fasted participants consumed specific foods (Fig. 1(a)) as part of a standardised breakfast. Metabolome fingerprints representing postprandial urines⁽ Reference Lloyd, Favé and Beckmann ^{25 )} were then generated using non-targeted, nominal mass flow injection electrospray–ionisation MS⁽ Reference Beckmann, Parker and Enot ^{15 ,} Reference Favé, Beckmann and Lloyd ^{20 ,} Reference Lloyd, Favé and Beckmann ^{25 )}. Figure 1(b) illustrates a typical flow injection electrospray-ionisation MS urine fingerprint of a mass range from m/z 100 to 800, which shows that urine fingerprints are both complex and information-rich. The question of whether a postprandial urine sample contains chemicals distinctive of exposure to specific foods can be evaluated by subjecting the urine fingerprint data to powerful supervised multivariate analysis including principal component-linear discriminant analysis. Figure 1(c) shows a scores plot from a typical principal component-linear discriminant analysis⁽ Reference Enot, Lin and Beckmann ^{16 )} of flow injection electrospray–ionisation MS fingerprints representing urine samples from volunteers exposed to a standard breakfast or to a breakfast in which the cereal component of the standard breakfast was replaced by smoked salmon, broccoli or raspberries⁽ Reference Lloyd, Favé and Beckmann ^{25 )}. With an eigenvalue (Tw) of 2.55 in the dimension of maximal discrimination (Fig. 1(c)), it is evident that the ‘test’ foods (particularly smoked salmon) are adequately discriminated from the standard breakfast. A range of feature selection methods⁽ Reference Enot, Lin and Beckmann ^{16 )} can then be employed to determine the flow injection electrospray–ionisation MS signals responsible for the discrimination of each food and, under most circumstances, it has been found that agglomerative decision trees such as random forest perform consistently well⁽ Reference Favé, Beckmann and Lloyd ^{20 ,} Reference Lloyd, Favé and Beckmann ^{25 ,} Reference Lloyd, Beckmann and Favé ^{26 ,} Reference Beckmann, Enot and Overy ^{38 )}. The detailed analysis of highlighted nominal masses can be undertaken by targeting them for further investigation on MS instruments capable of ultra-high mass resolution⁽ Reference Lloyd, Favé and Beckmann ^{25 –} Reference Lloyd, Beckmann and Favé ^{26 )}. The Fourier transform-ion cyclotron resonance ultra–MS plot shown in Fig. 1(d) represents the analysis of the nominal mass ‘bin’ m/z 241, a signal linked with the consumption of oily fish, indicating that a signal with a mass of 241·12958 is a likely biomarker candidate for oily fish exposure⁽ Reference Lloyd, Favé and Beckmann ^{25 )}. The accurate mass information can be used to generate an elemental formula and to predict candidate metabolites that could yield the measured ion using annotation tools such as MZedDB⁽ Reference Draper, Enot and Parker ^{39 )}, which take into account anticipated ionisation behaviour. Further experiments in which the selected ion is fragmented and the resulting spectrum compared with that of a chemical standard are required to assign a putative structure. For example, in the MEDE study these subsequent targeted analyses suggested that anserine (nominal mass m/z 241) is a candidate biomarker for oily fish consumption and proline betaine for citrus foods (Fig. 1(e))⁽ Reference Lloyd, Favé and Beckmann ^{25 ,} Reference Lloyd, Beckmann and Favé ^{26 )}.

Fig. 1.

(colour online) Typical analytical strategy for food biomarker discovery based on an acute food intervention. All elements of this example workflow are derived from data from experiments previously reported⁽ Reference Lloyd, Favé and Beckmann ^{25 )}. (a) The standardised breakfast consisted of orange juice, a cup of tea with milk and sugar, a butter croissant and cornflakes with milk, while in the test breakfasts the cornflakes and milk were replaced with smoked salmon, steamed broccoli or raspberries. Postprandial urine was collected. (b) Typical flow infusion electrospray-ionisation MS (FIE-MS) fingerprints of postprandial urine. (c) Scores plot from a typical principal component-linear discriminant analysis (PC-LDA) of the metabolite fingerprinting data (m/z 100–800) derived from analysis postprandial urine. (d) Fourier transform-ion cyclotron resonance ultra-mass-spectrometry (FT-ICR-MS) plot of the nominal mass ‘bin’ m/z 241. (e) Potential biomarkers from the MEtabolomics to characterise Dietary Exposure (MEDE) study. PCA, principal component analysis; RF, random forest; MS/MS, flow infusion electrospray-ionisation tandem MS.

Exposure to citrus foods has provided a paradigm for discovery of dietary biomarkers by analysis of dietary data

Citrus fruits and citrus fruit juices are a distinctive and frequently consumed (i.e. often once per d) component of the UK diet and thus represent a paradigm for validation of biomarkers of habitual dietary exposure. Studies using either NMR⁽ Reference Heinzmann, Merrifield and Rezzi ^{21 )} or ESI–MS fingerprinting⁽ Reference Favé, Beckmann and Lloyd ^{20 )} of postprandial urines demonstrated initially that proline betaine was a potential biomarker of acute exposure to citrus foods. There was thus an opportunity to determine whether the urinary concentration of this metabolite reflected habitual exposure to the same foods. Recent publications described a multivariate modelling strategy for habitual dietary biomarker discovery dependent on the comparison of urine metabolome fingerprints from groups of individuals reporting differential exposure to specific dietary components⁽ Reference Lloyd, Beckmann and Favé ^{26 ,} Reference Lloyd, Beckmann and Haldar ^{27 ,} Reference O'Sullivan, Gibney and Brennan ^{29 ,} Reference Pujos-Guillot, Hubert and Martin ^{30 )}. An initial analysis⁽ Reference Lloyd, Beckmann and Favé ^{26 )} of habitual citrus exposure data in the MEDE study (n 24) indicated that there were sufficient individuals reporting citrus consumption to allow the assignment of volunteers into three broad exposure levels (High (two–three citrus portions per d), Medium (about one citrus portion per d) and Low (less than two citrus portions per week)). With the larger number of participants in the GrainMark study it was possible to assign individuals to a more quantitative scale of habitual dietary exposure (i.e. never, 1 per week, 2–4 per week, 5–6 per week, 1 per d, 2–3 per d)⁽ Reference Lloyd, Beckmann and Haldar ^{27 )}. Overnight void urine samples available from both studies were subjected to metabolite fingerprinting and the m/z signals responsible for discriminating higher v. lower habitual consumption levels were identified using random forest feature selection⁽ Reference Enot, Lin and Beckmann ^{16 )}. In both studies the majority of the top twenty highest ranked signals responsible for the classification of High v. Low habitual citrus consumption were identified as ionisation products of proline betaine⁽ Reference Lloyd, Beckmann and Favé ^{26 ,} Reference Lloyd, Beckmann and Haldar ^{27 )}. Although not strictly quantitative, the relative intensity of one of the major ions (m/z 144; [M+H]⁺) reflected the level of habitual citrus exposure reported by individual participants in both MEDE and GrainMark studies. A similar result has been reported for NMR signals associated with recent exposure to citrus foods that were found to be derived from the presence of proline betaine in 24 h urine samples associated with recent exposure to citrus foods⁽ Reference Heinzmann, Merrifield and Rezzi ^{21 )}.

Habitual consumption frequency for individual foods impacts on the ability to detect differential dietary exposure using overnight urine samples

Following the success with citrus foods there have been several recent reports in which dietary data have been analysed from studies where, in most cases, participants have eaten freely-chosen diets (Table 1)⁽ Reference Lloyd, Beckmann and Favé ^{26 ,} Reference Lloyd, Beckmann and Haldar ^{27 ,} Reference O'Sullivan, Gibney and Brennan ^{29 ,} Reference Pujos-Guillot, Hubert and Martin ^{30 )}. These meta-data allowed the assignment of individuals into different food consumption frequency classes and could be used to study habitual exposure for a range of other foods. In the GrainMark study, as expected, habitual consumption frequency differed greatly between individual foods, and these food-specific consumption patterns were generally consistent with the MEDE study⁽ Reference Lloyd, Beckmann and Haldar ^{27 )}. Overall, the patterns of intake of individual foods could be summarised in four general exposure categories (Fig. 2) ranging from foods consumed very infrequently (e.g. liver or kidneys) to those consumed, on average, more than once per d (e.g. coffee). Although habitual citrus exposure could be modelled adequately using data from the MEDE study⁽ Reference Lloyd, Beckmann and Favé ^{26 )}, with only twenty-four participants the present strategy was not suitable for biomarker discovery for foods which are consumed very infrequently by most people. Assignment to habitual consumption frequency ranges for the remaining foods (grouped into High skewed, Normal distribution or Low skewed consumption patterns) identified sufficient numbers of individuals within the GrainMark study to develop higher v. lower frequency exposure groups for multivariate classification⁽ Reference Lloyd, Beckmann and Haldar ^{27 )}. The likelihood of discovering potential biomarkers for each food was assessed by determining the ‘goodness’ of class discrimination using random forest margin values and area under the receiver operating characteristic curve values as robust classification statistics⁽ Reference Enot, Lin and Beckmann ^{16 )}. Figure 2 illustrates a clear trend that classification efficiency (as assessed by random forest margins and area under the receiver operating characteristic curve values) is generally higher in foods that are consumed more frequently. We suggest that such foods will form likely candidates for monitoring using a biomarker strategy if it can be proven that the presence of potential biomarker signals in urine can be linked to the ingestion and chemical composition of specific dietary components. An example ‘data-driven’ strategy for the discovery of potential dietary biomarkers based on the analysis of GrainMark FFQ structure and data is summarised in Fig. 3.

Fig. 2.

Habitual food consumption patterns and overnight urine fingerprint modelling outcomes. Consumption patterns in both the MEtabolomics to characterise Dietary Exposure (MEDE) and GrainMark studies⁽ Reference Lloyd, Beckmann and Favé ^{26 –} Reference Lloyd, Beckmann and Haldar ^{27 )}. Statistic ranges from the GrainMark study in positive ionisation mode. RF margin, random forest margin values; AUC, area under the receiver operating characteristic curve. Figures are derived from the data from experiments reported previously⁽ Reference Lloyd, Beckmann and Favé ^{26 ,} Reference Lloyd, Beckmann and Haldar ^{27 )}.

Fig. 3.

Example strategy for ‘data-driven’ biomarker discovery based on the GrainMark study using overnight urine samples. RF, random forest; FDR, false discovery rate; dark grey arrows denote fail; light grey arrows denote pass. Figure adapted from that presented previously⁽ Reference Lloyd, Beckmann and Haldar ^{27 )}.

Structural analysis of potential biomarkers representing three selected foods shows that their presence in overnight void urine reflects original food chemical composition

Metabolome models (based on overnight void urine samples) of three distinctive foods representing examples of Low skewed, Normal distribution and High skewed habitual dietary exposure groups have been investigated in more detail to determine whether the selected metabolites could be correlated with known food chemistry⁽ Reference Mennen, Sapinho and Ito ^{10 ,} Reference Lloyd, Beckmann and Favé ^{26 ,} Reference Stalmach, Mullen and Barron ^{32 ,} Reference Stella, Beckwith-Hall and Cloarec ^{33 ,} Reference Abe ^{40 –} Reference Visciano, Perugini and Manera ^{51 )}. Groups of participants were identified for each dietary component to represent High, Medium and Low exposure classes within the consumption ranges described for each food in the GrainMark study. Thus for oily fish (Low skewed) the Low consumption category represents less than one portion per week, Medium is approximately one per week and High is greater than or equal to two to four portions week. For tomato (Normal distribution), the Low consumers ate less than or equal to one portion per week, Medium consumers ate greater than two to four portions per week, while High consumers ate up to five to six portions per week. High consumers of coffee (High skewed) drank more than one cup per d while Medium consumers had greater than or equal to two to four cups per week and Low consumers more than or equal to one per week. Despite the wide range of consumption frequencies each of these distinctive foods generated adequate classification models (Fig. 4(a)).

Fig. 4.

Habitual consumption of distinctive foods eaten in a range of consumption frequencies is detectable in overnight urine samples by flow infusion electrospray–ionisation MS (FIE-MS) fingerprinting. The data illustrated in this figure are derived from experiments previously reported⁽ Reference Lloyd, Beckmann and Haldar ^{27 )}. (a) Principal component-linear discriminant analysis (PC-LDA) of the metabolite fingerprinting data (m/z 100–550; oily fish, positive ionisation mode; tomato and coffee, negative mode) derived from the analysis of overnight ‘PRE’ urine from participants classified as representative of low/medium/high exposure ranges (ten to twelve per exposure group) based on the average FFQ score for exposure to these different foods. Oily fish: low, less than one per week; medium, one per week; high, greater than or equal to two to four per week; tomato: low, less than or equal to one per week; medium, greater than two to four per week to less than five to six per week; high, greater than or equal to five to six per week; coffee: low, less than or equal to one per week; medium, greater than or equal to two to four per week to less than five to six per week; high, greater than one per d; eigenvalues (Tw values) are given in parentheses. Markers confirmed using Fourier transform-ion cyclotron resonance ultra-mass-spectrometry (FT-ICR-MS), flow infusion electrospray-ionisation tandem MS and MZedDB. (b) Signals highly ranked in habitual coffee exposure models. (c) Signals highly ranked in habitual oily fish exposure models. (d) Signals highly ranked in habitual tomato exposure models.

In the GrainMark study, dihydrocaffeic acid-3-O-glucuronide was highly ranked as a potential biomarker of habitual coffee exposure (Fig. 4(b)). Previous studies on acute exposure to coffee reported the presence in urine and/or plasma of at least ten phenolic compounds which represented metabolic endpoints of the biotransformation of chlorogenic acid and caffeic acid which is present at high levels in this beverage⁽ Reference Mennen, Sapinho and Ito ^{10 ,} Reference Stalmach, Mullen and Barron ^{32 ,} Reference Ito, Gonthier and Manach ^{43 ,} Reference Rechner, Spencer and Kuhnle ^{49 )}. Although chlorogenic acid and caffeic acid are found in many fruits and vegetables and it is unlikely that dihydrocaffeic acids will prove to be unique biomarkers of coffee consumption, it is interesting that other phenolic metabolites identified in acute exposure studies were not highlighted by this analysis. Investigation of top ranked signals for oily fish exposure showed that methyl-histidine (probably derived from anserine, which was also highly ranked, by the action of carnosinase)⁽ Reference Abe ^{40 –} Reference Dragsted ^{42 )}, was an excellent biomarker candidate (Fig. 4(c)). Thus, the identity of both of these potential biomarkers could be linked directly to the previous chemical analysis of each food. Although hippuric acids (presumably derived from colonic fermentation of the hydroxycinnamic acid content of tomato fruits⁽ Reference Sánchez-Rodríguez, Ruiz and Ferreres ^{48 )}) were indicative of tomato exposure, many highly ranked signals proved to be dihydroxyphenylvalerolactone conjugates that commonly result from the colonic fermentation of flavonoids, such as flavanols⁽ Reference Urpi-Sarda, Monagas and Khan ^{52 )} (Fig. 4(d)). As tomato contains insignificant amounts of such polyphenols it is most likely that these compounds are associated with foods strongly co-consumed in meals containing tomatoes. This observation highlights the requirement for careful validation of any potential food biomarkers in the context of the whole diet and emphasises the utility of the test meal approach (as used in the MEDE study) for the identification of putative biomarkers which are causally linked with food exposure. In addition, such data-driven strategies for putative biomarker discovery are constrained by the robustness of the original dietary intake data which may contain inherent biases. Further, both conceptual and practical challenges are likely in cases where there are strong correlations between intakes of foods with related chemistries.