Hum Genet (2009) 125:507–525

DOI 10.1007/s00439-009-0662-5

REVIEW ARTICLE

Biomarkers in nutritional epidemiology: applications, needs and new horizons

Mazda Jenab · Nadia Slimani · Magda Bictash · Pietro Ferrari · Sheila A. Bingham

Received: 16 January 2009 / Accepted: 27 March 2009 / Published online: 9 April 2009© Springer-Verlag 2009

Abstract Modern epidemiology suggests a potentialinteractive association between diet, lifestyle, genetics andthe risk of many chronic diseases. As such, many epidemio-logic studies attempt to consider assessment of dietaryintake alongside genetic measures and other variables ofinterest. However, given the multi-factorial complexities ofdietary exposures, all dietary intake assessment methodsare associated with measurement errors which aVect dietaryestimates and may obscure disease risk associations. Forthis reason, dietary biomarkers measured in biologicalspecimens are being increasingly used as additional or sub-stitute estimates of dietary intake and nutrient status.Genetic variation may inXuence dietary intake and nutrientmetabolism and may aVect the utility of a dietary biomarkerto properly reXect dietary exposures. Although there are

many functional dietary biomarkers that, if utilized appro-priately, can be very informative, a better understanding ofthe interactions between diet and genes as potentially deter-mining factors in the validity, application and interpretationof dietary biomarkers is necessary. It is the aim of thisreview to highlight how some important biomarkers arebeing applied in nutrition epidemiology and to addresssome associated questions and limitations. This review alsoemphasizes the need to identify new dietary biomarkers andhighlights the emerging Weld of nutritional metabonomicsas an analytical method to assess metabolic proWles as mea-sures of dietary exposures and indicators of dietary pat-terns, dietary changes or eVectiveness of dietaryinterventions. The review will also touch upon new statisti-cal methodologies for the combination of dietary question-naire and biomarker data for disease risk assessment. It isclear that dietary biomarkers require much further researchin order to be better applied and interpreted. Future priori-ties should be to integrate high quality dietary intake infor-mation, measurements of dietary biomarkers, metabolicproWles of speciWc dietary patterns, genetics and novel sta-tistical methodology in order to provide important newinsights into gene-diet-lifestyle-disease risk associations.

Introduction

Over the past decades, the Weld of nutritional epidemiologyhas generated a large body of evidence for a potential inter-active association between diet, lifestyle and genetics andthe risk of many chronic diseases.

Much of the evidence relating food groups, speciWcfoods and nutrients to chronic disease risk relies on infor-mation gathered using various dietary assessment instru-ments, such as dietary/food frequency questionnaires, food

M. Jenab (&)Lifestyle, Environment and Cancer Group, International Agency for Research on Cancer (IARC-WHO), Lyon, Francee-mail: jenab@iarc.fr

N. SlimaniNutritional and Database Resource Team, International Agency for Research on Cancer (IARC-WHO), Lyon, France

M. BictashDivision of Epidemiology, Public Health and Primary Care, Imperial College London, London, UK

P. FerrariData Collection and Exposure Unit, European Food Safety Authority (EFSA), Parma, Italy

S. A. BinghamMRC Centre for Nutritional Epidemiology in Cancer Prevention and Survival, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK

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diaries, food records or 24-h recalls. In most cases, thesemethodologies require a systematic estimation of the fre-quency of consumption and the portion size of the foodsconsumed as well as more or less detailed information onthe recipe ingredients, combinations of foods consumedtogether, and cooking methods, which may aVect the esti-mation of exposure to a particular dietary component. Inaddition, the estimation of nutrient intakes relies almostentirely on the existence of appropriate and reliable foodcomposition tables for the target population. When theseissues are coupled to the overwhelming complexities ofdiVerent dietary patterns, varying dietary habits, multitudesof lifestyle confounders, numerous reporting biases, dailyvariations in food intake, combinations of foods, timing ofmeals, etc., it is of no surprise that all dietary assessmentinstruments are associated, to one extent or another, withdiVerent, and sometimes considerable, random and systematicmeasurement errors.

In fact, both nutritional epidemiologists and their manycritics are acutely aware of the complexities and limitationsof various dietary assessment methods (Kaaks and Riboli2005; Michels 2005a, b). This reality of nutritional epidemio-logy is being met with intense methodological research andnot only are innovative methods (e.g. internet based assess-ment, use of digital cameras, cellular telephones and personaldigital assistants) being developed and validated (Dowell andWelch 2006; Kikunaga et al. 2007; Subar et al. 2007; Wanget al. 2006), but traditional ones are also constantly beingreWned and improved (e.g. computerized 24-h recall: EPICSoft) (Slimani et al. 1999, 2002; Slimani and Valsta 2002).

More recently, various statistical techniques have alsobeen developed to account for some of the apparent mea-surement errors (Fraser and Yan 2007; GorWne et al. 2007;Rosner et al. 2008) and to better estimate usual food intakes(Dodd et al. 2006; Tooze et al. 2006). Nevertheless, in theabsence of any ‘independent’ observation of food con-sumption, true intake cannot really be assessed. In order toobtain such ‘independent’ observations (i.e. uncorrelatedmeasurement errors), nutrition epidemiologists have uti-lized diVerent biomarkers assessed in biological samplesnot only as measures of dietary intake and nutrient status,but also as predictors of disease risk.

It is the aim of this review to highlight how some impor-tant biomarkers are being applied in the Weld of nutritionepidemiology and also to address some of the questions andshortfalls associated with their use. There is a need todevelop new dietary biomarkers, and in this respect, thereview will also highlight metabonomics as an analyticalmethod that can be utilized to assess metabolic proWles asmeasures of dietary exposures and indicators of dietary pat-terns or dietary changes. Also, new statistical methodolo-gies for the combination of dietary questionnaire andbiomarker data will be touched upon.

Applications of dietary biomarkers

A dietary biomarker can be loosely deWned as a biochemi-cal indicator of dietary intake/nutritional status (recent orlong term), or it may be an index of nutrient metabolism, ora marker of the biological consequences of dietary intake(Potischman and Freudenheim 2003). The main advantageof—or the main assumption behind—dietary biomarkers isthat they are objective measures and are independent of allthe biases and errors associated with study subjects and die-tary assessment methods (Day et al. 2001; Kaaks et al.2002; Sugar et al. 2007). An ‘ideal’ dietary biomarkerwould accurately reXect its dietary intake level and it wouldbe speciWc, sensitive and applicable to many populations.Existing dietary biomarkers are not ‘ideal’, but they arefunctional and have found wide spread applicability inmodern nutritional epidemiology. In general, dietary bio-markers can be divided into several classes (recovery, pre-dictive, concentration, replacement) which are described inmore detail below and in Fig. 1.

One of the main applications of dietary biomarkers is touse them as reference measurements to assess the validityand accuracy of dietary assessment methods (Bingham2002; Potischman and Freudenheim 2003; Tasevska et al.2005). The most important dietary biomarkers for thisapplication are the ‘recovery’ biomarkers (i.e. doublylabeled water which is utilized to measure the metabolicrate and total energy expenditure; urinary total nitrogen/potassium which are utilized to estimate total daily proteinconsumption and potassium intake, respectively) (Bingham2003; Day et al. 2001; Livingstone and Black 2003).Recovery biomarkers are based on the concept of the meta-bolic balance between intake and excretion over a Wxedperiod of time and so provide an estimate of absolute intakelevels (Kaaks et al. 1997). In other words, excretion levelsare highly correlated with intake (Bingham 2002). How-ever, before being applied to the task of questionnaire vali-dation, such biomarkers need to be tested in calibrationstudies under controlled conditions (e.g. in a metabolicsuite) in order to establish that their predictability inhumans consuming varying diets is comparable to the die-tary intake method under consideration. Unfortunately, thecost and complexity of these techniques makes themlargely inapplicable for widespread epidemiologic use andthey are best applied either in post hoc analyses of on-going investigations, or built-in to the design of newstudies, for example the use of doubly labeled water in theOPEN study (Schatzkin et al. 2003) and markers ofpotassium and nitrogen in 24 h urine collections (Bingham2002). The recently deWned class of ‘predictive’ biomark-ers can also be utilized to assess the degree of measurementerrors in dietary assessment methods. Like recoverybiomarkers, predictive biomarkers are sensitive, time

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dependent and show a dose-response relationship withintake levels but the distinction is that their overall recov-ery is lower (Tasevska et al. 2005). The only current exam-ples are 24 h urinary sucrose and fructose which are closelycorrelated with intake of sugars, despite the very smallfraction of intake which is actually present in urine collec-tions (Tasevska et al. 2005).

The class of ‘concentration’ biomarkers (e.g. serum vita-mins, blood lipids, urinary electrolytes) are also availablefor comparison with estimates of dietary intake. For exam-ple, results from a dietary intake method which agreed mostclosely with such biomarkers would be expected to yieldmore reliable estimates of intake than one which did not(Bingham et al. 2008). Concentration biomarkers cannot betranslated into absolute levels of intake but the biomarkerconcentrations do correlate with intakes of correspondingfoods or nutrients, although the strength of the correlationis often lower (<0.6) than that expected for recovery bio-markers (>0.8). ‘Replacement’ biomarkers are closelyrelated to concentration biomarkers and refer speciWcally tocompounds for which information in food composition data-bases is unsatisfactory or unavailable, for example aXatox-ins, some phytoestrogens (Grace et al. 2004; Qian et al.

1994), salt (Norat et al. 2008) or metabonomic factors(detailed in a later section). Depending on the speciWc die-tary biomarker (e.g. some fatty acids), the distinctionbetween the concentration and replacement classes may bevague.

A common application of concentration or replacementdietary biomarkers is for the estimation of diet-disease riskassociations (Potischman 2003). This use is increasinglyWnding application in population studies such as prospec-tive cohort studies, where biological samples are collectedbefore disease onset, or intervention/controlled clinicalstudies looking at the eVect of dietary treatments or nutrientsupplementation on disease risk or progress. The underly-ing concept is that the use of such biomarkers may lead to abetter ranking of subjects for exposure to a particular foodgroup or nutrient than would dietary assessment methods.In fact, the biomarker level measured in the blood or otherbiological samples takes into account any eVects of absorp-tion, inXuences of microbiota (e.g. bioconversion, releaseof bioactive dietary compounds, enterohepatic circulation),interactions between nutrients, tissue turnover, metabolismand excretion. Additional considerations are issues pertain-ing to nutrient bioaccessibility and bioavailability (Holst

Fig. 1 DiVerent classes of dietary biomarkers measured in biological samples and their application to the validation of dietary assessment meth-ods, measurement error and estimation of disease risk associations

DietObserved vs. True Intake

Random and systematic dietary measurement errorsaddressed by:Improvements in diet assessment methods•Refinement of existing methods•Enhancement of food composition databases•Innovation of new methodsDevelopment of novel statistical methodology•Integration of dietary and biomarker data for identification and correction of measurement errors

Dietary AssessmentFor estimation of dietary intakes

(e.g. questionnaire, diary, 24-hour recall)

Interactions / Confounders

Lifestyle, Environment, Genetic variability

Effect on Disease Risk• direct or indirect

Recovery Dietary Biomarkers

i.e. doubly labeled water, urinary nitrogen or potassium

Uses: •As reference measurements to assess validity / accuracy of diet assessment methods

Predictive Dietary Biomarkers

e.g. urinary sucrose or fructose

Uses: •As reference measurements to assess validity / accuracy of diet assessment methods

Concentration and Replacement Dietary Biomarkers

e.g. vitamins, carotenoids, individual fatty acids, phytoestrogens, alkylresorcinolsetc.

Uses: •Assess correlation with estimates of dietary intake•Estimation of diet-disease risk associations

-As a substitute or complimentary to dietary assessments

Identification of New Dietary Biomarkers:•Nutritional Metabonomics: Identification of metabolic profiles as biomarkers specific to different dietary/nutrient patterns and dietary changes•Others?

Enhanced Application and Interpretation of

Dietary Biomarkers

Improvements for Dietary Biomarkers in Current Use:•Enhanced laboratory methods•Better understanding of:

-nutrient metabolism-gene-diet, gene-nutrient or gene-gene interactions

Incorporation of individual SNP, whole genome or

other “omic”data

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and Williamson 2008). All of these points are very impor-tant because they highlight that food components and nutri-ents are inXuenced by a large number of host factors, bothmetabolic and genetic, that may aVect the correlation of abiomarker with the relevant dietary exposure. In addition,other factors such as the type of biological sample obtained,how the sample was collected, treated, and stored, the labo-ratory methodology used to measure the biomarker (preci-sion, accuracy and detection limits of the analyticaltechnique; variations from method to method or laboratoryto laboratory) can also aVect the measurement and utility ofdietary biomarkers (Fig. 2). The combination of all of theabove factors makes it very diYcult to compare absoluteconcentrations of certain dietary biomarkers across variousstudies which are based on diVerent populations and utilizediVerent biological samples and analytical techniques.

Currently, very little is known about how genetic variationmay inXuence dietary intake, food choices, nutrient metabo-lism, or aVect the bio-availability, absorption, transport, bio-transformation, and excretion of nutrients or bio-active dietarycomponents. Extensive information already exists on geneticvariation in taste and how that may aVect food preferencesand dietary habits (Garcia-Bailo et al. 2009). It is probablysafe to assume that genetic variability, gene-diet/nutrientinteractions and gene-gene (epistatic) interactions may resultin diVerential response to dietary factors along with changesin nutrient metabolism and dietary biomarker levels. Someexamples that have yet to be conWrmed in diVerent popula-tions are folate and the MTHFR gene, vitamin D and theVDR gene and iron and the HFE gene. This may signiWcantly

aVect the measurement and utility of a dietary biomarker toproperly reXect dietary exposures and suggests that the validityof some dietary biomarkers may well be population (oreven individual) speciWc with respect to genetic backgroundor other characteristics (Fig. 3) (Kaput 2008). Nevertheless,very few studies to date have considered possible gene-diet/nutrient or gene-gene interactions as potentially determiningfactors in the validity and application of dietary biomarkers.The interaction of genes and diet is engendered in the con-cepts of nutrigenomics (study of how diet inXuences genetranscription, protein expression and metabolism) and nutri-genetics (study of how genetic disposition aVects response todiet and its components), which are extensively reviewedelsewhere (Mutch et al. 2005; Ordovas and Mooser 2004). Akey message is that the consideration of genetics is importantfor nutritional scientists and the consideration of dietaryassessment methodology and dietary biomarker measurementis of relevance to geneticists.

The issues raised above are all pertinent to the validity,application and interpretation of dietary biomarkers. In the sec-tions below, these concepts are dealt with in the context of var-ious foods and their corresponding biomarkers, but they arerelevant and applicable to almost all other dietary biomarkers.

Biomarkers of fruits and vegetables

In their 1997 comprehensive review of the literature theWorld Cancer Research Fund (WCRF) listed the strengthof the evidence for a cancer preventive role of the fruits

Fig. 2 Factors that may aVect the measurement and utility of a dietary biomarker to properly reXect dietary exposures in indi-viduals or target populations

• Genetic Variability

• genes that may affect dietary intake patterns, taste, attraction to specific foods or food types etc.

• biological variation in nutrient absorption, metabolism, tissue turnover, excretion

• epigenetic variation, gene-gene interactions

• Lifestyle or Physiologic Factors

• smoking, alcohol consumption, exercise, gender, age, body weight / size, socioeconomic status

• influence of colonic microbiota (bioconversion, release of bioactive dietary compounds)

• enterohepatic circulation of nutrients (e.g. phytoestrogens, lignans)

• metabolic and inflammation related disorders, stress, occult / underlying disease

• Dietary Factors

• range or frequency of intake for a particular nutrient

• nutrient-nutrient interactions

• nutrient bioavailability, influence of food matrix

• Biological Sample

• type of sample collected for analysis of biomarkers (e.g. whole blood, plasma, serum, urine)

• conditions of sample collection, transport, treatment, storage conditions, length of storage

• diurnal variation, day of the week or season of sample collection

• Analytical Methodology

• precision, accuracy, detection limits of the analytical technique

• variations from method to method or laboratory to laboratory

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and/or vegetables food group as ‘convincing’ (for cancersof the mouth/pharynx, esophagus, lung, stomach, colon/rectum) or ‘probable’ (for cancers of the larynx, pancreas,breast bladder) (World Cancer Research Fund and Ameri-can Institute for Cancer Research 1997). However, thesejudgments were downgraded in the 2003 IARC Handbooksof Cancer Prevention on Fruits and Vegetables (IARCWorking Group on the Evaluation of Cancer PreventiveStrategies 2003) and in the 2007 update of the WCRF com-prehensive report (World Cancer Research Fund 2007).The revised conclusions were largely based on accumulat-ing evidence tending towards null, particularly from pro-spective studies (World Cancer Research Fund 2007).Although such null Wndings based on assessment of dietaryfruit and vegetable intake may indeed be real, it is equallypossible that the disease risk association may be attenuatedas a result of measurement errors associated with dietaryassessment instruments, the concomitant loss of statisticalpower and probable genetic variations in response to diet. Itis for these reasons that more and more studies are begin-ning to measure blood biomarkers of fruit and vegetableintake in their assessment of the disease risk associationsfor this important food group. In this section the biomarkersvitamin C and carotenoids are discussed.

Vitamin C

Blood measures of vitamin C (ascorbate and dihydroascor-bate) may be considered as a surrogate for dietary vitaminC intake and of the main fruit and vegetable sources ofvitamin C. Of the easily sampled biological Xuids, saliva

(vitamin C content very low) and urine (appreciable levelsonly in individuals in excess of adequate intake) do notappear to be appropriate for the measurement of vitamin Cstatus (Benzie 1999). There is little information on the useof tissues or individual cells for the measure of vitamin C asa dietary biomarker. Thus, most biomarker studies analyzevitamin C concentrations in plasma. However, the results ofa recent meta-analysis show that plasma vitamin C concen-tration and estimates of dietary vitamin C intake from vari-ous dietary assessment instruments are only modestlycorrelated (r = »0.4) (Dehghan et al. 2007). The magnitudeof the correlation varies between populations [e.g. range ofr values: Germany = 0.24 (Boeing et al. 1997); India = 0.12(Chiplonkar et al. 2002); Iran = 0.35 (Malekshah et al.2006); Spain = 0.53 (Schroder et al. 2001); USA = 0.25(Cooney et al. 1995), 0.39 (Jacques et al. 1993)] based onthe range of vitamin C intake, and tends to be stronger inmen than women. They may also be aVected to somedegree by other determinants such as: errors in food com-position tables from which dietary vitamin C values werederived, un-/mis-reported intake of vitamin C from dietarysupplements or food additives, diVerent food processingtechniques which may aVect vitamin C content (e.g. expo-sure to high temperatures may lead to break down of vita-min C), various lifestyle factors (e.g. smoking, exercise,chronic low-grade inXammation) which may reduce vita-min C level, biases in the assessment of dietary vitamin Cintake, or potential inXuence of genetic variability on vita-min C metabolism.

The measurement of vitamin C in biological specimenscan also be aVected by sample handling and treatment.

Fig. 3 Possible interactions of dietary intake and dietary biomarker measures with genetic variability to aVect disease risk

Diet

Use of Biomarkers to Assess Dietary Exposures

Effect on Disease Risk• direct or indirect

Risk May Be Modulated By

Variability in Genes Related to

Nutrient Metabolism

Differences in digestion, absorption, transport,

metabolism, bio-transformation, excretion etc of

nutrients or bio-active food components

Effect on Body / Tissue Exposure

Levels

Impact on Dietary Biomarker MeasuresOther factors that may affect

biomarker measurement:•Lifestyle or physiologic factors•Dietary factors•Type of biological sample•Analytical methodology(see Figure 2 for details)

Differences in the metabolic effects of nutrients

Gene-Diet/Nutrient Interactions

Gene-Gene Interactions

Genetic Influence on Dietary Choices and

Food Intake

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Vitamin C is one of the most labile vitamins. It can easilydegrade in biological samples and should ideally be stabi-lized by addition of protein precipitating agents such asmetaphosphoric acid (Bates 1994; Ching et al. 2002). But,in the case of large prospective studies biologic samples arecollected for a multitude of purposes, are not usuallytreated speciWcally for the preservation of vitamin C andare often stored for many years prior to analysis. Vitamin Cmay degrade during sample handling and processingbecause of various factors such as temperature, exposure tolight, choice of anticoagulant, time sitting on the shelf, orfreezing procedures (Ching et al. 2002; Chung et al. 2001;Key et al. 1996; Lykkesfeldt et al. 1995; Terzuoli et al.2004). Also, loss of vitamin C during long periods of stor-age has been noted (loss per year of frozen storage in thosewith low/high baseline vitamin C levels: men = 0.26/1.96,and women = 0.69/2.35 �mol/l plasma) (Jenab et al. 2005).Although some studies suggest that immediate sample sta-bilization, freezing and analysis within a short period oftime are required for best results (Karlsen et al. 2007), oth-ers show that vitamin C can be still be measured with rea-sonable reliability in un-stabilized plasma samples frozenfor long periods of time at ultra low temperatures (Jenabet al. 2005). The choice of analytical technique (e.g. chro-matographic versus Xuorometric versus spectrophotometricmethods) is also an important consideration in the analysisof vitamin C. Initially, vitamin C was often measured usingenzymatic or spectrophotometric methods, but recent Wnd-ings suggest that these methods are subject to much inter-ference, that chromatographic methods are far superior andthat high sample throughput is necessary to avoid vitamin Cdegradation (Karlsen et al. 2005; Levine et al. 1999).

Genetic variability could conceivably aVect the kineticsof vitamin C absorption (transporter saturation, down-regu-lation) and metabolism, yet very few studies have consi-dered this aspect (Wilson 2005). Indeed, geneticpolymorphisms may represent a large source of variationfor many dietary biomarkers, such as antioxidant nutrients(Nowell et al. 2004; Reszka et al. 2006). This concept hasbeen referred to as metabolic confounding (Saracci 1997).A well described example is the gene coding for humanapolipoprotein E whose three common alleles are associ-ated with diVerent levels of serum cholesterol (Burnett andHooper 2008; Ordovas 2002, 2007; Ribalta et al. 2003). Inthe case of vitamin C, the sodium dependent vitamin Ctransporter protein 1 (SVCT1) coded for by the SLC23A1gene (Eck et al. 2004; Li and Schellhorn 2007) controls theintestinal absorption of dietary vitamin C and may alsoaVect blood and/or tissue-speciWc vitamin C concentrations.Despite, intense research on the role of SVCT1 in control-ling vitamin C concentration in various cell types (Wilson2005), very little information exists on the inXuence of itsgenetic variability in terms of vitamin C pharmacokinetics

or correlations between the level of dietary vitamin Cintake and blood vitamin C concentrations. Some recentdata suggest that African Americans may be more aVectedby the functional consequences of a variation in SVCT1(Eck et al. 2004), but this remains to be conWrmed. It is pos-sible that the consideration of genetic variability in thistransporter protein may allow an improvement in the corre-lation between dietary and blood vitamin C measures indiVerent population groups and permit a better utilization ofplasma vitamin C measures as a biomarker of fruit and vege-table consumption. Perhaps an equally important aspect iswhether such a Wnding may alter previously identiWed asso-ciations between dietary and plasma vitamin C levels andrisk of various diseases.

Another issue pertaining to vitamin C is whether its useas a dietary biomarker is valid in all populations, or only insome populations depending on their range of intakes. Sim-ilar to many other nutrients, the absorption eYciency andresulting plasma concentration of vitamin C (dose-responsecurve) is not linear at all intake levels. At lower intake lev-els (approx. <100 mg/day), a linear relationship may exist,whereas it may plateau at higher intake levels (approx.>120 mg/day) such that over exposure may not at all bewell represented by plasma vitamin C measurements. Thegeneral understanding is that at lower dietary vitamin Cintake levels, vitamin C is eYciently absorbed from theintestines and renal excretion is minimized (Nelson et al.1978), whereas at higher concentrations the SVCT1 is satu-rated and down-regulated (MacDonald et al. 2002), leadingto reduced intestinal absorption and limiting blood vitaminC concentrations (Li and Schellhorn 2007). Thus, measure-ment of blood vitamin C as a marker of dietary intake maybe reasonable in populations with low to moderate or heter-ogeneous vitamin C intake levels, but it may be less infor-mative in a well fed population with a homogeneous highlevel of dietary vitamin C intake. A related issue is whetherthe measure of plasma vitamin C is a short- or long-termmarker of intake or nutritional status. Plasma vitamin Cconcentration has been shown to peak within hours ofintake in relation to the size of the dose (Benzie and Strain1997), and tends to plateau at around 100 �mol/l because ofrenal excretion and feedback regulation of intestinalabsorption (Graumlich et al. 1997). Thus, it is likely thatplasma vitamin C concentration indicates functionalreserves of vitamin C (Benzie 1999) and is a biomarker ofshort- to medium-term intake (Mayne 2003), raising con-cerns of whether a single blood sampling is suYcient forthe objectives of the study at hand or whether multiple mea-sures are necessary.

A number of studies suggest that the absorptioneYciency of vitamin C from the diet is also aVected by life-style factors such as age and smoking status (Brubacheret al. 2000). Some in vitro studies further suggest that

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vitamin C absorption or transport into certain cell typesmay also be inhibited by dietary factors such as glucose orXavonoids (Wilson 2005) or by use of drugs such as aspirin(Ioannides et al. 1982). In addition, blood vitamin C con-centration also depends in large part on the level oreYciency of renal re-absorption/excretion (Li and Schell-horn 2007), and presumably variability in their geneticdeterminants. In the setting of an observational study, manyof these factors are diYcult or impossible to control for, andit is quite probable that they may interact to modulate bloodvitamin C concentration, and aVect its correlation with die-tary vitamin C intake (Kaaks et al. 1997).

The above are all sources of variation that are at issue inthe reproducibility and validity of many biomarkers and goto the heart of the question of whether a dietary assessmentmethod can provide suYcient information or whether a bio-marker assessment is also necessary. A case in pointregarding vitamin C is a recent study based on the Euro-pean Prospective Investigation into Cancer and Nutrition(EPIC) which showed that higher concentration of plasmavitamin C concentration, but not the level of dietary vita-min C intake assessed by country speciWc questionnairesand other dietary methods, is associated with a decreasedrisk for gastric cancer (Jenab et al. 2006b).

Carotenoids

Another class of compounds that have been widely mea-sured as dietary biomarkers of fruit and vegetable intake arecarotenoids. Similar to vitamin C, and many other nutri-ents, the estimation of carotenoid intakes from dietaryassessment instruments is prone to many measurementerrors and may not reXect their actual bioavailability. Mostfood composition tables contain values for only a fewcarotenoids, primarily �-carotene and lycopene. However,the measurement of their blood concentrations as biomark-ers of their intake is subject to many of the same issuesdetailed above for vitamin C. Of the hundreds of carote-noids existing in plants, only a few (mostly: �-carotene, �-carotene, �-cryptoxanthin, canthanxanthin, lycopene,lutein, zeaxanthin) can be found in signiWcant amounts inthe blood (Barua et al. 1993; Crews et al. 2001). They areamenable to use in prospective cohort studies because theirlong term frozen storage has only a minor eVect on theirreliability (Al-Delaimy et al. 2008). Carotenoids are lipidsoluble and their intestinal absorption, and hence bioavail-ability, may be modulated by the lipid content of the diet,food matrix, competition with other carotenoids (Reboulet al. 2007), degree of colonic fermentation (Goni et al.2006), menstrual cycle and hormonal factors (Erdman2005), other host factors and, presumably, genetic variability.Their intestinal absorption can range from less than 10% toover 50% depending on the source and type (IARC

Working Group on the Evaluation of Cancer PreventiveStrategies 1998; Reboul et al. 2006) and their blood con-centrations appear to be moderately correlated with diet (rranges from »0.2 to over 0.5) (Kaaks et al. 1997), perhapsbetter in some populations than others (e.g. stronger corre-lation in normal weight than obese subjects (Vioque et al.2007)), and do not appear to be under homeostatic control(IARC Working Group on the Evaluation of Cancer Pre-ventive Strategies 1998). These points suggest that themeasurement of their circulating blood levels probably pro-vides a good estimation of their bioavailability, or overalllevel of body exposure, in diVerent populations.

The consideration of carotenoids as dietary biomarkersraises an issue that was lightly touched upon in the descrip-tion of vitamin C: does the metabolism of a nutrient aVectits validity as a dietary biomarker? The metabolism ofcarotenoids can aVect their blood concentrations anddecrease their correlation with estimates of their dietaryintake or result in a mis-interpretation of their overall expo-sure levels. The carotenoids �-carotene, �-carotene, �-cryp-toxanthin can be partially metabolized to retinol (Fraser andBramley 2004). This metabolism may be unimportant insubjects who are vitamin A replete but may be vital in sub-jects who have a lower vitamin A status (Nagao 2004), andmay lead to diVerential ranking of subjects based on bio-marker measures, even if their carotenoid intake levels aresimilar. As another example, one of the most importantroles ascribed to carotenoids is their potential as antioxi-dants (Krinsky and Johnson 2005; Rao and Rao 2007).Subjects with increased oxidative stress, such as smokers(Saintot et al. 1995) or those consuming higher levels ofalcohol (Albanes et al. 1997), have been shown to havedecreased blood concentrations of some carotenoids. How-ever, it is relatively unknown how diVerent carotenoidsmay interact with each other and other antioxidant nutrientsin vivo, or whether they have the same antioxidant poten-tial. Some biomarker studies suggest that not all carote-noids may have a negative cancer risk association (Jenabet al. 2006a), and it has been suggested that the total sum ofall blood carotenoids measured at any one time may be abetter estimate of their exposure than comparisons of indi-vidual carotenoids (Liu 2004). But there is currently littleevidence to support this notion because few studies to datehave actually measured a battery of carotenoids and mostepidemiologic data focus on a few select compounds fromthis group.

To date, very little is known about genetic variabilitypertaining to carotenoid bioavailability and metabolism. Itmay be safe to assume that such variability exists, and maycontribute in part to the applicability and validity of carote-noids as dietary biomarkers. For example, interactions havebeen noted between carotenoid intake and polymorphismsin the oxidative stress genes MPO and COMT on the risk of

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breast cancer (He et al. 2009) and between lycopene intakeand XRCC1 gene polymorphisms on the risk of prostatecancer (Goodman et al. 2006). But, it is unknown whetherthese gene-nutrient interactions may aVect carotenoidmetabolism, their antioxidant functionality or modify thevalidity of plasma carotenoids as dietary biomarkers of fruitand vegetable intake in diVerent populations. Similar tomany other nutrients, more research is required to betterunderstand the underlying genetic variability in carotenoidmetabolism. It should be noted that genetic variants associ-ated with biomarker levels are applicable to the principle ofMendelian randomization, where an observed disease riskassociation of the genetic variant may strongly suggest thatany observed cancer risk association with the relevant bio-marker is not confounded by other lifestyle or dietary vari-ables (Hunter 2006). However, so little is known aboutgenetic variants associated with dietary biomarker levelsthat the principle of Mendelian randomization is not oftenexploited in diet-biomarker-cancer studies (Schatzkin et al.2009).

Biomarkers of energy, energy yielding nutrients and dietary Wber

One of the most important aspects of diet is the measure-ment of total energy and estimation of energy yieldingnutrients. As mentioned above, satisfactory recovery bio-markers exist for total energy (doubly labeled water) andtotal protein (urinary nitrogen), but currently no recovery orpredictive biomarkers have been identiWed for total fat, orcarbohydrate. Similarly, the accurate assessment of dietaryWber would be enhanced if a recovery or predictive bio-marker existed for its intake.

Predictive biomarker of sugar intake

Sugars, in the form of monosaccharides (e.g. glucose, fruc-tose) and disaccharides (e.g. sucrose, lactose) are importantcontributors to total energy intake. Estimates suggest thatthey may supply as much as 22% of total adult energyintake in US adults about 8–20% in the European Commu-nity (Gibney et al. 1995). The food sources of sugars arediverse, and much sugar in human diets is often derivedfrom hidden sources and from processed foods. For thesereasons, the intake of total and individual sugars is verydiYcult to assess. This is complicated by the fact that obeseindividuals may underestimate their usual intakes of totalenergy and sugar and fat containing foods (Bingham et al.1995). Indeed, the reliability of dietary reports of obeseindividuals has been questioned via comparisons with sev-eral dietary biomarkers, including sugars (Bingham et al.2007). Thus, intake information from food intake surveys

should be treated with much caution, and may even be anunderlying reason for conXicting literature reports relatingsugars intake to cancer risk (Key et al. 2002).

Without an adequate biomarker of sugar intake, it isdiYcult to judge the extent of unreliability or bias of sugarintake from food records or questionnaires. Recently, a pre-dictive biomarker for sugar intake has been identiWed(Tasevska et al. 2008). In volunteers consuming their nor-mal diet, about 100 mg of sucrose and fructose in 24 hurine samples, predicts an intake of about 200 g of totalsugars intake (r = 0.77, P < 0.001) (Tasevska et al. 2008).The sucrose measured in urine is derived from dietarysucrose that has escaped enzymatic hydrolysis in the smallintestine and is excreted from the general circulation, whilethe fructose in the urine represents a small fraction of die-tary fructose and fructose derived endogenously fromhepatic metabolism of sucrose (Tasevska et al. 2008).Results from subjects housed in metabolic suites and fedcontrolled diets suggest that BMI does not aVect the valid-ity of urinary sugars as a biomarker of sugar intake (Joosenet al. 2008), which is not well estimated from dietaryassessments in obese individuals (Bingham et al. 2007).Thus, these predictive biomarkers may be utilized for thepurposes of dietary questionnaire validation or as biomark-ers of exposure in the overall population. Nevertheless,these biomarkers may be prone to some of the issues dis-cussed earlier in terms of biomarker validity and interpreta-tion, i.e. potential inXuences of sample collection, treatmentand possible deterioration with long term storage. In addi-tion, it has recently been suggested that variability in genescontrolling bitter/sweet taste reception may alter glucosehomeostasis and insulin metabolism (Dotson et al. 2008)and that sugar consumption may be modulated to somedegree by genetic variation in the glucose transporter type 2(GLUT2) gene (Eny et al. 2008). It remains to be deter-mined whether diVerences in sugar intake, absorption ormetabolism due to potential genetic control or as of yetunknown gene-diet/nutrient or gene-gene interactions mayaVect the validity of urinary sugars as predictive biomark-ers across diVerent populations with varying genetic back-grounds, dietary habits and lifestyles.

Blood lipids and fatty acid proWles

Like urinary sugars, some blood lipids may also be deemedas predictive biomarkers for the intake of dietary fats andpossibly dietary Wber (Bingham et al. 2008). In a recentpublication, plasma levels of low density cholesterol (LDL)were positively associated with the intake of saturated fats,and inverse associations were shown between plasma highdensity cholesterol (HDL) and intake of dietary carbohy-drate, and between plasma triglycerides (TG) and consump-tion levels of dietary Wber (Bingham et al. 2008). Plasma

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cholesterol and TG levels may to some extent be indicativeof level of dietary Wber intake, but the Wndings to date areconXicting (Hunninghake et al. 1994; Panlasigui et al.2003; Sonnenberg et al. 1996; Truswell 1995; Zunft et al.2003) and some Wndings suggest that some of the inter-individual variation in the plasma lipoprotein response todietary Wber may be attributable to variation in the fattyacid binding protein 2 gene (Hegele 1998). More recentobservations suggest that the correlation may also bedependent on the type and quality of the dietary assessmentinstrument used to determine dietary Wber intake (Binghamet al. 2008). In other words, consideration of genetic vari-ability is as important as the choice of the type of dietaryassessment instrument to use. For now, these measures mayWnd applicability at ecological levels for comparisons ofdiVerences in dietary patterns, but their use as dietary bio-markers in other than very controlled settings still requiresmuch validation.

Results from carefully controlled metabolic settings orcross sectional studies suggest that saturated fat intake maybe a main determinant of blood cholesterol levels (Hegstedet al. 1993). It has been recently observed that plasma LDLlevel is strongly associated with total saturated fat intake aswell as percentage energy from total saturated fat (Wu et al.2007). It is well known that LDL levels can be aVected bymany diVerent factors including diet, lifestyle and variationin a number of genes (Burnett and Hooper 2008). Someexamples from diVerent populations include a modiWedassociation of: (a) polyunsaturated fatty acid (PUFA) intakeand HDL levels by polymorphisms in the APOA1 (Ordovaset al. 2002a), hepatic lipase (LIPC) (Ordovas et al. 2002b),TNF� (Fontaine-Bisson et al. 2007) and NFKB1 (Fontaine-Bisson et al. 2009) genes, (b) PUFA intake and TG levelsby polymorphisms in the PPAR� gene (Tai et al. 2005), and(c) alcohol intake and LDL levels by polymorphisms in theAPOE locus (Corella et al. 2001). In addition, genetic vari-ation in the NPC1L1 gene has been shown to aVect sterolabsorption from the gastro-intestinal tract (Fahmi et al.2008) and may also have consequences in the induction ofsterol responsive genes in the liver, particularly the genethat codes for LDL receptor thus potentially modulatingLDL levels, mainly in terms of dietary sterol intake (Zhaoet al. 2008). Furthermore, the T-del variant of the FADS2gene (codes for the delta-6 desaturase enzyme in the elong-ase/desaturase pathway of n-3 and n-6 fatty acid metabo-lism) has been associated with higher plasma TG and lowereicosapentanoic acid concentrations (Baylin et al. 2007).The genetic modulation of such relationships is likely inter-active between several genes, as has been observed forplasma TG and polymorphisms of the LIPC and APOEgenes (Wood et al. 2008). It is clear that inconsideration ofgene-nutrient and gene-gene interactions as well as theirmetabolic consequences may aVect the validity of measures

of HDL, LDL and TG applied as dietary biomarkers indiVerent populations, but this has not been fully addressedin the literature to date.

Some studies have considered the fatty acid compositionof adipose tissues or erythrocyte/plasma phospholipid fattyacid proWles as biomarkers of dietary fats from variousfoods. This concept is reviewed in great detail elsewhere(Hodson et al. 2008; Poppitt et al. 2005). These biomarkerscan be aVected by a number of diVerent dietary and lifestylefactors as well as endogenous fatty acid synthesis and com-plex fatty acid biochemistry. But, any eVects of genetic var-iability in these pathways, direct/indirect determinants offat absorption, or gene-diet/nutrient or gene–gene inter-actions on fatty acid proWles as dietary biomarkers is largelyunexplored (Fig. 3). For example, fatty acid proWles may bealtered by variations in genes encoding for enzymes in theelongase/desaturase pathway of n-3 and n-6 fatty acidmetabolism (Baylin et al. 2007) or by interactions betweenhigh intake of dietary fat, obesity and variations in fattyacid binding protein 2 gene which may result in a modula-tion of insulin metabolism (Weiss et al. 2002). As anotherexample, a recent animal study has shown that disruption ofhepatic P450 reductase activity can cause diVerential geneexpression resulting in changes in fatty acid metabolismwith system wide eVects (Mutch et al. 2007). From a meta-bolomic proWling point of view, it has recently been shownthat genetic variants of FADS1 and LIPC induce diVer-ences in metabolites related to the long chain fatty acidmetabolism pathways in which the coded enzymes areactive (Gieger et al. 2008). Although far from being conclu-sive, these examples indicate that genetic factors that aVectfatty acid metabolism may also aVect the utility and appli-cation of these compounds as dietary biomarkers—although this remains to be validated. Nevertheless, thereare strong suggestions that levels of certain fatty acids inthe serum or plasma phospholipids may reXect medium-term intake (weeks to months) of various foods (Riboliet al. 1987) and can correlate with habitual intake of spe-ciWc dietary fats or fatty acids in diVerent populations(Kobayashi et al. 2001; Saadatian-Elahi et al. 2009; Sasakiet al. 2000; Wolk et al. 2001). In general, correlations withdiet tend to be better for fatty acids that are either Wsh oilderived or not produced endogenously.

Biomarkers of meat and meat products

Biomarkers for meat are important in cancer researchbecause meat has been linked with cancer risk at a varietyof sites (World Cancer Research Fund 2007). Meats maycontain various compounds such as 3-methyl histidine(formed in muscle breakdown and released and rapidlyexcreted in urine) or creatinine, that may serve as possible

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predictive biomarkers of total meat consumption, as wassuggested in some early research (Bingham 1987). Morerecent metabonomic studies have identiWed a variety ofmetabolites, including creatine, carnitine, and trimethyla-mine-N-oxide as biomarkers for meat based diets (Stellaet al. 2006), and have shown diVerences in metabonomicproWles of meat protein or vegetable protein based diets invarious populations (Holmes et al. 2008a). In some recentepidemiologic studies, the meat-disease risk association hasbeen more focused on the intake of red and processedmeats. This is true for diabetes and several cancers, particu-larly those of the gastro-intestinal tract (Cross et al. 2007;Gonzalez et al. 2006; Norat et al. 2005; Vang et al. 2008)where gene-diet interactions have been shown between redmeat intake and polymorphisms of cytochrome P450(CYP) genes (Kury et al. 2007) and other genes involved innucleotide excision and mismatch repair (Joshi et al. 2009).Randomized controlled diet intervention studies withhuman volunteers show that consumption of red and pro-cessed meats (but not white meat) can increase endogenousgastro-intestinal formation of N-nitroso compounds(NOCs) (Bingham et al. 2002), many of which are knowncarcinogens (SaVhill et al. 1985). In fact, red meats are richin heme which is the likely responsible agent for the endog-enous NOCs formation (Cross et al. 2003; Kuhnle et al.2007). DNA adducts speciWc for endogenous NOCs havebeen found in cells exfoliated from volunteers with a sig-niWcant increase following a high red meat diet (Lewinet al. 2006). In epidemiologic settings, the direct measure-ment of NOCs is logistically diYcult because it requiresfecal samples, which are not routinely collected. Currently,it is only possible to estimate the endogenous formation ofNOCs using data from diet intervention studies with volun-teers. Thus, data on endogenous NOC formation, as a bio-marker of red and processed meat intake, gathered fromcarefully controlled intervention trials, can be used to for-mulate an estimate of endogenous NOC exposure whichcan then be extrapolated to epidemiologic studies. One suchstudy observed an association between estimated endoge-nous NOC production and risk of non-cardia gastric cancerwhich was signiWcant, whereas exposure to exogenousNOC was not (Jakszyn et al. 2006).

It is clear from the above sections that the multi-factorialcomplexities of dietary exposures should not be—but oftenare—simplistically assessed. The science of dietary bio-markers requires much further research in order to betterunderstand, use and interpret existing biomarkers. How-ever, the development of new dietary biomarkers shouldalso be a subject of intense research. This involves develop-ing a much clearer understanding of food chemistry, inter-actions between nutrients, the role of genetic variation inmodulation of nutrient absorption and metabolism, as wellas mechanisms of action of nutrients or other bioactive food

components. Diet, which is a very complex mixture ofcompounds, and single nutrients may both aVect or modu-late biological systems diVerently and their associationswith disease risk are multi-faceted. The current understand-ing of dietary biomarkers is limited by a very incompletecomprehension of how genetics, diet and nutrients interactto aVect metabolism. In this regard, there is not only a needto better genetically characterize populations in epidemio-logic studies but also to integrate this knowledge with highquality dietary assessment methodology, established/tradi-tional dietary biomarkers (as detailed above) as well asnewer dietary biomarkers which can provide a more com-prehensive or holistic assessment of dietary exposures, die-tary/nutrient patterns and dietary changes. Thus, integrativeapproaches are required that encompass genetic and non-genetic factors and that can capture dietary and environ-mental exposures. In this respect, the emerging science ofmetabonomics is reviewed below with an emphasis on itsapplication to the study of the metabolism of dietary com-pounds and observation of population and individual diVer-ences in dietary exposures.

Metabonomics: the identiWcation of metabolic proWles as dietary biomarkers

A broad biological representation of gene function, generegulation and gene–gene and gene-diet–lifestyle–environ-mental interactions can be observed using some newlydeveloped ‘omic’ technologies such as transciptomics(study of gene expression at the RNA level), proteomics(study of protein structure and function), and metabonom-ics (study of metabolic responses). Metabonomics, which isthe focus of this section, closely reXects the visible pheno-type of interest and is deWned as ‘the quantitative measure-ment of the dynamic multi-parametric metabolic responseof living systems to patho-physiological stimuli or geneticmodiWcation (Nicholson et al. 1999).

Metabonomics is often confused with the Weld of meta-bolomics. Although they both utilize similar analyticaltools with pattern recognition techniques, metabolomicsinvolves the comprehensive analysis of all measurablemetabolite concentrations under a given set of conditions,whereas metabonomics refers more to a systems biologyapproach and aims to measure the metabolic response tobiological, genetic, environmental or dietary stimuli (Fiehn2002; Nicholson and Lindon 2008). Of particular relevanceto both Welds are technologies such as high resolutionnuclear magnetic resonance (NMR) spectroscopy, massspectrometry and a range of other hyphenated technologies,all of which are platforms that collectively support the com-prehensive analysis of metabolite proWles in biologicalXuids or tissue samples. Coupled with multivariate statistical

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analysis, these platforms enable the generation of character-istic patterns of metabolites that reXect the metabolic phe-notype or status of the organism. The metabolic phenotypecan be used either to explore molecular mechanisms ofchronic disease etiology or to determine the metabolic con-sequences of dietary changes or diVerent dietary/nutrientpatterns.

Even a simple diet contains a complex matrix of foods,which are a composite of multiple nutrients and whichundergo digestion through diverse pathways involving co-metabolism by the host and its symbiotic gut microbiota.Macro- and micro-nutrients are intimately involved in andcan potentially modulate almost all metabolic pathways.Indeed, dietary imbalances and diVerences in nutrientintake and dietary patterns may result in many metabolicchanges or disturbances. Nutrients also interact with manydiVerent genes in association with environmental factors. Inthis light, metabonomics can be a valuable tool to considerindividual or population responses to dietary exposures ordiVerences in dietary patterns in order to better understanddiet-disease associations. In other words, certain metabo-nomic proWles may potentially be considered as dietary bio-markers. In fact, this is a cornerstone of the emerging Weldof nutritional metabonomics (or nutri-metabonomics)which aims to combine the insights gained from metabo-nomic studies pertaining to health and disease with the cur-rent understanding of how diet may aVect those states. ThisWeld is still in its infancy and to date studies have largelyfocused on the experimental design and the various techni-cal challenges in the measurement of the subtle diVerencesof dietary factors and the exerted multiple, pleiotropiceVects on metabolism (Gibney et al. 2005; Rezzi et al.2007; Walsh et al. 2006). Nevertheless, examples of tar-geted approaches at nutri-metabonomic proWling exist inthe current literature and include the evaluation of metabo-nomic diVerences according to the type of fats consumed(Watson 2006), consideration of the adequacy of aminoacid intakes (Noguchi et al. 2003), green versus black teaconsumption (Van Dorsten et al. 2006), and animal studieslooking at increased longevity associated with caloricrestriction (Wang et al. 2007) or the reversal of themetabolic consequences of a high fat diet by dietary supple-mentation of catenin (Fardet et al. 2008). In other nutri-metabonomic studies, the consumption of diets withvarying phytochemical contents has been shown to altermetabolic proWles (Walsh et al. 2007) and the ingestion ofsoy has also been shown to result in an identiWablemetabolic proWle (Solanky et al. 2003) and to alter severalmetabolic pathways related to energy metabolism (Solankyet al. 2005).

This technology may also be used to validate Wndingsfrom observational studies. For example, several studiesusing data derived from dietary intake assessments have

shown that increased intake of Wsh is associated with areduction in colorectal cancer risk (Kimura et al. 2007;Norat et al. 2005), while higher consumption of somemeats is associated with an increased risk (Chao et al. 2005;Cross et al. 2007; Norat et al. 2005). It would be very inter-esting to validate these observed cancer risk associationsusing metabonomic proWling in the same populations. Thisis potentially possible, because metabonomic studies haveshown distinct proWles for higher Wsh intake in a free livingpopulation (Zuppi et al. 1997), and for a diet rich in animalproteins in a controlled cross-over feeding study of threediVerent protein source diets (Stella et al. 2006).

In fact, a key application of metabonomic methodologyis to repositories of biological samples provided by popula-tion based studies. The data can then be combined withgenetic information as well as dietary and other lifestyleexposure data. However, the typically large sample sizes ofmodern epidemiologic studies and requirements for rapidsample characterization mean that the metabonomic analyt-ical platforms need to be adapted for high throughput.Indeed, there are constant improvements in the automationand miniaturization of these analytical platforms resultingin increased capacity and the use of such platforms in pre-liminary metabonomic characterization of population stud-ies has shown great promise, despite the fact that thesestudies were not originally designed to include metabolicproWling, and that they are confounded by the presence ofextensive inter-subject metabolic variation due to greaterdiversity in genetic factors, environmental factors andhealth status compared to controlled clinical or animal stud-ies. Metabolic proWling strategies have recently beenadopted in a retrospective manner to improve informationrecovery from established large-scale populations studiessuch as INTERSALT (INTERSALT Co-operativeResearch Group 1988) and the INTERMAP study of nutri-ents and blood pressure (Stamler et al. 2003) where a cleardiVerentiation of urinary metabolic signatures was achievedwithin diVerent western (USA and UK) and East Asian(China and Japan) populations (Holmes et al. 2008a, b). Inthe INTERMAP study, NMR spectroscopy was applied tobio-banked urine samples and a map of metabolic pheno-types was established that reXected observed diVerences indiet and cardiovascular disease risk. A list was produced ofthe top twenty discriminatory metabolites for all combina-tions of country pair-wise comparisons included hippurate,trimethlamine-N-oxide, amino acids and ethanol, whichderived from a combination of endogenous, gut microbial,dietary and xenobiotic sources. The relationships betweenselected quantiWed metabolites with the recorded dietaryintakes of macro and micronutrients and also blood pres-sure values were investigated and identiWed novel candi-date urinary biomarkers that were positively (e.g. alanine)or inversely associated (e.g. formate, hippurate) with blood

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pressure. The INTERMAP metabonomic investigationsalso showed metabolic distinctions within the Asian popu-lation samples from China and Japan and even withinChina between the northern (Beijing, Shanxi) and southern(Guangxi) centers. In addition, the study metabolicallydiVerentiated between Japanese and American populationsliving in Honolulu, with similar dietary, lifestyle and envi-ronmental exposures to American populations, from Japa-nese living in Japan, whereas the Japanese–Americans(Honolulu) were not signiWcantly metabolically diVerenti-ated from other American populations (Holmes et al.2008a, b). Thus, it is possible that at an ecological level,utilization of metabonomic proWling as biomarkers of dietcan serve to diVerentiate populations with similar geneticbackgrounds but varying dietary and lifestyle habits andenvironmental exposures.

Another application of nutritional metabonomics couldbe to address the important role of the colonic microbiota innutrient metabolism and possible implications for diseaserisk associations. The diverse and active microbial popula-tions within the colonic environment are able to metabolizea wide range of dietary components and contribute toenergy production, enterohepatic circulation of nutrientsand bioactive compounds (as well as toxins) and metabolicactivation of some compounds. Historically the gutmicrobes and the host organisms were largely consideredindividually but metabonomics can enable the study of theircomplex interactions and reveal changes that will aid theunderstanding of dietary modulation of the gut microbesand the consequences for disease risk (Nicholson et al.2005). For example, in the INTERMAP population, diVer-ences were observed in several metabolites known to beproduced by gut microbes or by mammalian–microbial co-metabolism, including phenylacetylglutamine and hippu-rate, for every population investigated (Holmes et al.2008a). In animal models, metabolic proWling approacheshave shown that gut microbiota play an active role in insu-lin resistance (Dumas et al. 2006), which can be modulatedby diet (Minich and Bland 2008). Thus, it is feasible thatthe integration of information on dietary intake, lifestyleand metabonomic proWling may help to identify metabolicsignatures that are key to diet related disease mechanisms.Future studies should further attempt to utilize metabo-nomic proWles related to diVerent gut bacteria or their meta-bolic activities as biomarkers to observe eVects of dietarymodulation or diVerences in dietary regimes, intakes andpatterns. For example, the metabonomic identiWcation ofdiVerent short chain fatty acid proWles arising from diVerentdiets or dietary patterns may provide an estimate of thedegree of colonic fermentation and butyrate exposure, orconversely production of toxic metabolites (O’Keefe 2008).

Metabonomics enables simultaneous measurement oflarge numbers of metabolites across a wide range of meta-

bolite classes with both high accuracy and sensitivity. It isan integrative approach, encompassing genetic, dietary,lifestyle and environmental factors. In the foreseeablefuture, an integration of metabonomic and whole genomicplatforms can be envisioned for a detailed exploration ofgenetic variability, dietary patterns/changes and metabo-lism. However, it must be cautioned that in order to bemeaningfully applied, the identiWcation of distinct meta-bolic proWles as dietary biomarkers must be validated indiVerent populations under various experimental protocolsand using standardized methodology. Much progress isbeing made in this respect (Castle et al. 2006). If all this canbe accomplished, in the foreseeable future MetabolomeWide Association studies may highlight new dietary bio-markers and provide novel insights into chronic disease eti-ology as well as the gene-diet-lifestyle-disease connection.

Statistical considerations to integrate dietary assessments with biomarker data

The accuracy of dietary questionnaire measurements isoften evaluated in validation studies, where dietary ques-tionnaire measurements are compared to more detailed andreliable reference (R) measurements (e.g. 24-h dietaryrecalls, food diaries, etc.) (Riboli and Kaaks 2000; Stramet al. 2000; Thompson et al. 1997). These approaches arebased on two important statistical assumptions: (a) errors inquestionnaire and reference measurements are independent,(b) reference measurements follow the classical measure-ment error structure, i.e. errors are strictly random(R = T + �R, where T indicates unknown true intake and �R

models random error in R measurements). In practice, theseassumptions are likely to be violated for several importantreasons: the tendency of study subjects to consistentlyunder- or over-report speciWc food intakes, the limited per-formance of self-reported dietary assessment instruments toaccurately capture a large spectrum of dietary diversity, andthe presence of sizeable systematic measurement errors inthe reference assessment measurements. Thus, when possi-ble, biomarkers can be incorporated into the validation and/or calibration of dietary assessment methods as objectivemeasures of intake, or in other words measurements whoseerrors are assumed to be independent from self-reporteddietary measurements.

In a validation study questionnaire, reference and bio-marker measurements are linearly related to unknown trueintake levels by means of latent factor modeling (Bentlerand Weeks 1980; Bollen 1989). The error correlationbetween self-reported dietary assessments can be estimatedwhen replicate measurements of a biomarker are available,or when more than one dietary factor is being evaluated, forexample in a multivariate study (Kaaks et al. 2002). Moreover,

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recovery biomarkers make it possible to Wt models wherethe classical measurement error structure for self-reportedreference measurements can be relaxed (Day et al. 2001;Kipnis et al. 2003; Spiegelman et al. 2005). Unfortunately,as described in detail in the previous sections, only a fewrecovery biomarkers are available (Kaaks et al. 2002).

To overcome the cost and availability limitations ofrecovery biomarkers, latent factor models using two con-centration or replacement dietary biomarkers have beendeveloped, where the Wrst biomarker is assumed to provideestimates of unknown true intake, while the other bio-marker is a biologic correlate only and not directly measur-ing the variable of interest (Fraser et al. 2005). Forexample, vitamin E measured in the blood can be comple-mented by a measure of blood folic acid in order to validatequestionnaire measurements of folic acid. However, thesemodels have raised concerns about the speciWcity of addi-tional dietary biomarkers to measure a given food or nutri-ent (Kaaks and Ferrari 2006). In addition, it must beassumed that error correlations between diVerent dietarybiomarkers are close to zero. But, this assumption may notgenerally hold in the case of between-person variation inbiomarker levels among people with the same dietaryintake (Rosner et al. 2008). To overcome this latter limita-tion, two very diVerent biomarkers could be favored, forexample PUFA together with �-carotene measured in bloodto validate PUFA questionnaire measurements. This solu-tion diminishes the probability of non-zero error correla-tions between biomarkers, though it introduces issues oflarge conWdence intervals in the parameter estimates, unlessstudies of very large in size are implemented.

Novel methods have been proposed to complement self-reported measurements with objective biomarkers ofdietary intake in the evaluation of the association betweendietary exposure and risk of chronic disease (Prentice et al.2002). This statistical model allows the potential depen-dence of the systematic component of measurement errorsupon individual characteristics (e.g. body mass, age, ethni-city) to be estimated (Sugar et al. 2007). To date, thesemethods have been proposed for recovery biomarkers onlybut in theory additional types of biomarkers, for examplemetabonomic proWling, or even possibly genetic informa-tion, may also be incorporated. In the same framework,innovative research could focus on the use of recovery andconcentration biomarkers, possibly addressing departuresfrom the classical measurement error model for the refer-ence measurement, and relaxing assumptions of zero corre-lations between self-reported dietary assessments. For thispurpose, in a Bayesian framework (Richardson and Gilks1993), a measurement error model to relate observed quan-tities to unknown true intakes could integrate a risk modelwhere the association between true intake and the risk ofdisease could be quantiWed (Ferrari et al. 2008). In this

respect, genetic information accounting for the eVect ofvarious gene-diet/nutrient or gene-gene interactions on bio-marker levels or disease risk may also be incorporated,although this has not yet been attempted in practice.

Complex statistical modeling to integrate dietary assess-ments with biomarker data for the purposes of sheddinglight onto the measurement error structure of self-reporteddietary estimates is being used more readily in modernnutrition epidemiology. Calibration and validation studiesare being built a priori into the design of large cohortsallowing for statistical error correction later down the line.Nevertheless, although these techniques are themselves atbest only approximations, with improving dietary assess-ment methodology along with greater availability andselection of dietary biomarkers and more knowledge aboutgene-diet/nutrient interactions, statistical measurementerror correction will allow for the production of more accu-rate evidence on the relationship between dietary factorsand the risk of major chronic conditions.

Overall summary

This review has outlined the major advantages and short-falls of dietary biomarkers, with a particular emphasis onthe established/traditional biomarkers of fruit and vegetableintake, i.e. vitamin C and carotenoids, and a considerationof potential biomarkers for other food groups such as fats,carbohydrates and meats. Aside from the many analytical,environmental and lifestyle factors that can modulate die-tary biomarkers (Fig. 2), there is a strong potential forgene-diet/nutrient or gene–gene interactions to aVect thevalidity of dietary biomarker measures (Fig. 3). However,the contribution of these interactions to the application,measurement and interpretation of dietary biomarkersremains to be elucidated. A better understanding of theseinteractions is an urgent need that can be addressed eVec-tively by multidisciplinary collaboration and the combinedeVorts of nutritional scientists and geneticists. This can leadto better application of dietary biomarkers for exposureassessment in diVerent populations (e.g. based on geneticbackground) either for disease risk estimation in observa-tional studies or for assessing the eYcacy of dietary inter-ventions. Much of the existing information on dietarybiomarker validity and potential gene-diet/nutrient inter-actions has been derived from North American or Europeanpopulations and needs to be substantiated across manydiVerent populations and groups. The overall limitations ofthe speciWc dietary biomarkers discussed here apply tosome extent to the many other dietary biomarkers in currentuse. Careful attention to factors that can aVect dietary bio-marker measures can help to enhance their application andinterpretation. With the appropriate study design and

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methodology, the assessment of dietary intake and quantiW-cation of exposure via the use of dietary biomarkers canprovide very meaningful results.

Out of the many thousands of chemical compounds pres-ent in foods, not all bio-active dietary components havebeen identiWed or characterized and many could potentiallyaVect gene expression, modulate cellular/physiologic pro-cesses and be associated with disease risk. This review hashighlighted the need for the development of new dietary bio-markers. In this respect, the emerging Weld of nutritionalmetabonomics was outlined. SpeciWc metabolic proWles,whether in distinct populations or even individuals, may beutilized as dietary biomarkers which are speciWc to variousdietary/nutrient intake patterns or dietary changes. With theconsideration of the potential importance of gene-diet/nutri-ent interactions in the application and validity of dietary bio-markers, the inclusion of a more holistic approach to dietarybiomarker identiWcation may be very informative for assess-ing gene-diet-lifestyle-disease associations. This technologycan only get better and cheaper, and combined with mea-sures of established/traditional dietary biomarkers and stan-dardized validated dietary intake assessments it can evaluatedietary exposures and metabolic diVerences arising fromvarious dietary regimes, patterns or interventions. There is aneed to bring together high quality dietary intake informa-tion (i.e. maximal information with minimal measurementerrors), traditional dietary biomarkers and metabolic proWlesas dietary biomarkers with information about gene-diet/nutrient or gene-gene interactions. This information can alsobe integrated with data from other relevant technologies,namely other ‘omic’ platforms and whole genome scans, inthe same population groups or individuals to provide a glo-bal overview of diet-lifestyle-disease associations and themany intervening genetic and metabolic interactions. Noneof these ambitious possibilities can be achieved without thedevelopment of novel statistical methodologies and newdatabase management techniques to assess, combine, cali-brate, analyze and make sense of the generated data. Withall of these tools at its disposal in the near future, nutritionalepidemiology should provide even further insight into theassociation of dietary, lifestyle and environmental exposuresand the risk of cancer and other chronic diseases.

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