metabolomics as a tool in the identiﬁcation of dietary

Proceedings

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The Nutrition Society Irish Section 25th Annual Postgraduate Meeting, hosted by the University of Cork and held at the RadissonBlu Hotel on 11–12 February 2016

Irish Section Postgraduate Meeting

Metabolomics as a tool in the identification of dietary biomarkers

Helena Gibbons1,2 and Lorraine Brennan1,2*1Institute of Food and Health, UCD School of Agriculture and Food Science, University College Dublin (UCD),

Belfield, Dublin, Ireland2UCD Conway Institute of Biomolecular Research, UCD, Belfield, Dublin, Ireland

Current dietary assessment methods including FFQ, 24-h recalls and weighed food diariesare associated with many measurement errors. In an attempt to overcome some of theseerrors, dietary biomarkers have emerged as a complementary approach to these traditionalmethods. Metabolomics has developed as a key technology for the identification of new diet-ary biomarkers and to date, metabolomic-based approaches have led to the identification ofa number of putative biomarkers. The three approaches generally employed when usingmetabolomics in dietary biomarker discovery are: (i) acute interventions where participantsconsume specific amounts of a test food, (ii) cohort studies where metabolic profiles are com-pared between consumers and non-consumers of a specific food and (iii) the analysis of diet-ary patterns and metabolic profiles to identify nutritypes and biomarkers. The present reviewcritiques the current literature in terms of the approaches used for dietary biomarker discov-ery and gives a detailed overview of the currently proposed biomarkers, highlighting stepsneeded for their full validation. Furthermore, the present review also evaluates areas suchas current databases and software tools, which are needed to advance the interpretationof results and therefore enhance the utility of dietary biomarkers in nutrition research.

Metabolomics: Dietary biomarkers: Diet and nutrition: Dietary assessment

Dietary biomarkers and the concept of metabolomics

The contribution of diet to the increasing burdens ofCVD, diabetes, obesity and cancers has been recognisedsince the 1970s(1). Selected foods and nutrients as well asdietary patterns are now known to interact with variousmetabolic processes contributing to a reduction or an in-crease in the risk of disease(2). For example, it is wellestablished that high salt consumption raises blood pres-sure(3) and high consumption of red meat has been asso-ciated with increased incidence of type 2 diabetes(4,5),CVD(6) and cancers(7). In contrast, dietary patternssuch as the dietary approaches to stop hypertensiondiet, which emphasises consumption of fruit and vegeta-bles, low-fat dairy foods and whole grains and reducedintake of red meats and sugars has been shown to de-crease blood pressure and CVD risk(8,9). Similarly, theMediterranean diet which emphasises high fruit, vege-table and olive oil consumption has been shown to

reduce CVD and type-2 diabetes risk(10,11). As diet is akey environmental risk factor, the identification and tar-geting of dietary factors with the greatest prospective forreducing or increasing disease risk is of major scientificand public health importance(12). It is therefore essentialthat dietary assessment methods are reliable and accuratefor the advancement of our understanding of the linksbetween diet and health.

Diet is traditionally measured via self-reporting meth-ods such as FFQ, 24-h recalls and weighed food diaries.There is however a number of methodological issuesassociated with each of these assessment methods, in-cluding energy underreporting, recall errors and diffi-culty in assessment of portion sizes(2,13,14). Such errorscan lead to reduced power, underestimated associationsand false findings which may contribute to inconsisten-cies in the field of nutritional epidemiology(14,15). In aneffort to address some of these measurement issues, theuse of dietary biomarkers, which are found in biological

*Corresponding author: Professor L. Brennan, email [email protected]: SSB, sugar-sweetened beverage.

Proceedings of the Nutrition Society (2017), 76, 42–53 doi:10.1017/S002966511600032X© The Authors 2016 First published online 25 May 2016

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samples and are related to ingestion of a specific food orfood group, have emerged(16). Currently dietary biomar-kers exist for salt, protein, sucrose/fructose intake (so-dium/nitrogen/sucrose and fructose measured in 24 hurine samples) and energy expenditure (the doubly labelledwater technique)(2,17). These dietary biomarkers can beused in conjunction with traditional dietary assessmentmethods to improve the accuracy of dietary intake meas-urement and can also be used to more accurately associatedietary intake with disease risk and nutritional status(18).

In recent years, metabolomics has developed as a keytechnology for the identification of new dietary biomarkers.Metabolomics provides a powerful approach for the com-prehensive description of all low molecular weight mole-cules present in biological samples(16). In metabolomicsresearch, the analytical platforms predominantly used areNMR spectroscopy and MS coupled with a chromato-graphic step, for example, GC or liquid chromatography.Each of these techniques is associated with a number ofadvantages and disadvantages, for example MS-basedtechniques have high sensitivity and therefore may detectmetabolites below the detection limit of NMR spectros-copy; however, sample treatment is necessary beforeMS-based analysis, while little or no pre-treatment isrequired for NMR(19).While in the past many articlesdetailed the advantages and disadvantages of differentapproaches there has now been a realisation that usingone platform alone will not give complete coverage of themetabolite profile; therefore, a combination of technologiesand approaches is usually recommended for optimal cover-age. Analysis of metabolomic data is commonly performedusing multivariate statistics and there are an increasing se-lection of databases and tools available to assist in the in-terpretation of these multivariate results(20).

Examination of the literature reveals that there are threeapproaches generally employed for dietary biomarker dis-covery. These can be summarised as: (i) acute or mediuminterventionswhere participants consume specific amountsof a test food and biological samples are collected post con-sumption, (ii) cohort studies where metabolic profiles arecompared between consumers and non-consumers of aspecific food and (iii) the analysis of dietary patterns andmetabolic profiles to identify nutritypes and biomarkers.Although these study designs have led to the identificationof a number of biomarkers in the literature in recent years,each of these approaches has a number of limitationsassociated with it. Awareness of these is important in theinterpretation and potential use of such biomarkers.Therefore the objective of the present review is to give anoverview of currently proposed biomarkers and secondlythe present review aims to critique the current literaturein terms of approaches for dietary biomarker discovery,highlighting steps needed for their full validation.

Dietary biomarker discovery using intervention studies

Dietary intervention studies involve participants consum-ing specific amounts of a test food in a single meal (acuteintervention) or for a short-to-medium term interventionthe test food is consumed in repeated meals. In this

approach, baseline and postprandial biofluids are col-lected and following analysis, potential biomarkers areidentified. This approach has led to the identification ofa number of putative biomarkers of specific foods andbeverages as summarised in Table 1. An excellent ex-ample of a biomarker successfully identified using thisapproach is proline betaine, a robust biomarker of citrusfruit intake. Proline betaine was originally identified byAtkinson et al.(21) and following this Heinzmann et al.performed an acute intervention study with a mixed-fruitmeal, which consisted of apples, grapes, oranges andgrapefruit(22). Eight participants consumed standardisedmeals over 3 d and on the second day the mixed-fruitmeal was consumed(22). Urine samples were collectedand analysed using NMR spectroscopy. Followingmultivariate analysis proline betaine was identified as apotential biomarker. To assign the origin of urinary pro-line betaine excretion after the mixed-fruit meal, concen-trations of proline betaine in fruits and fruit juices weremeasured. Concentrations of proline betaine were higherin citrus fruit compared with other commonly availablefruit and fruit juices tested. The urinary excretionprofile of proline betaine was then measured in six indi-viduals after consumption of orange juice. This biomark-er was confirmed using data from participants in theINTERMAP UK cohort and demonstrated a high sensi-tivity and specificity for citrus fruit consumption (90·6and 86·3 %, respectively)(22). Lloyd et al. also identifiedproline betaine and a number of biotransformed pro-ducts in postprandial urine samples after consumptionof 200 ml orange juice as part of a standardised testbreakfast(23). Subsequent biomarker validation demon-strated sensitivities and specificities of 80·8–92·2 and74·2–94·1 %, respectively, for elevated proline betainein high reporters of citrus fruit consumption(23).Following on from these acute studies, a medium-termintervention study used MS to profile the urinary meta-bolomes of twelve volunteers that consumed orangejuice regularly for 1 month as part of their habitualdiet. Proline betaine was again identified as a potentialmarker of citrus fruit(24). Considering the range of studiesthat consistently report proline betaine as a marker of cit-rus fruit intake the evidence base is strong to support itsuse.

A number of research groups have also used dietaryinterventions to investigate biomarkers of cruciferous vege-tables(25–27). Andersen et al. performed a controlled cross-over meal study with nine brassica-containing New NordicDiet meals in seventeen subjects(26). The 24 h urine sampleswere collected and analysed by ultra-performance liquidchromatography–quadruple time-of-flight–MS. To investi-gate the food sources of the biomarkers found in the mealstudy, a range of small single food studies were performedwith three to four participants in each. Using a sensitivityand specificity analyses to select the most promising bio-markers, a range of conjugated isothiocyanates were iden-tified as biomarkers of brassica intake(26). Furtherbiomarkers of other foods, including fish were also iden-tified(26). To validate the biomarkers from this study,Andersen et al.(27) carried out a 6-month parallel dietaryintervention study where 107 participants were randomised

Metabolomics in dietary biomarker discovery 43


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Table

1.Sum

maryof

putativebiomarke

rsiden

tified

usingametab

olom

icsap

proac

hin

interven

tionstud

ies

Dietary

factor

Study

duration

No.

ofsu

bjects

Sam

ple

Metab

olom

ictech

nique

Biomarke

rReferen

ce

Citrus

fruit

Acu

teinterven

tion

8Fa

stingan

dpos

tprand

ial

urine

NMR

Prolinebetaine

Heinz

man

net

al.(2

2)

Citrus

fruit

Acu

teinterven

tion

424

hurine

LC–ESI–qTO

F,LT

Q-O

rbitrap

Prolinebetaine

,hyd

roxy

prolinebetaine

,hes

peretin

3′-O

-glucu

ronide,

narin

genin7-O-glucu

ronide,

limon

ene8,9-diolg

lucu

ronide,

nootka

tone

13,14-diolg

lucu

ronide,

N-M

ethy

ltyraminesu

lpha

te

Pujos

-Guillo

tet

al.(2

4)

Citrus

fruit

4-wee

kinterven

tion

1224

hurine

LC–ESI–qTO

F,LT

Q-O

rbitrap

Prolinebetaine

,hyd

roxy

prolinebetaine

,hes

peretin

3′-O

-glucu

ronide,

narin

genin7-O-glucu

ronide,

limon

ene8,9-diolg

lucu

ronide,

nootka

tone

13,14-diolg

lucu

ronide,

N-M

ethy

ltyraminesu

lpha

te

Pujos

-Guillo

tet

al.(2

4)

Citrus

fruit

Acu

teinterven

tion

12Fa

stingan

dpos

tprand

ial

urine

FIE–FT

ICR–MS

Prolinebetaine

,hyd

roxy

prolinebetaine

Lloy

det

al.(2

3)

Citrus

fruit

6-mon

thinterven

tion

107

24hurine

LC–qTO

FProlinebetaine

,hes

peretin-3-glucu

ronide

And

erse

net

al.(2

7)

Red

cabbag

e6-mon

thinterven

tion

107

24hurine

LC–qTO

F3-Hyd

roxy

-3-(methy

lsulfiny

l)propa

noic

acid,3

-hyd

roxy

hippuric

acid-sulpha

te,3

-hyd

roxy

hippuric

acid,ibe

rinN-ace

tyl-cy

steine

,N-ace

tyl-S-(N-3-m

ethy

lthiopropyl)cysteine,

N-ace

tyl-S-(N-

lylth

ioca

rbam

oyl)c

ysteine,

sulfo

rapha

neN-ace

tylcysteine

And

erse

net

al.(2

7)

Bee

troo

t6-mon

thinterven

tion

107

24hurine

LC–qTO

F4-Ethyl-5-aminop

yroc

atec

hols

ulpha

te,

4-ethy

l-5-methy

laminop

yroc

atec

hols

ulpha

te,

4-ethy

lpyridine-2-ca

rbox

ylic

acid

glyc

ineco

njug

ate

And

erse

net

al.(2

7)

Walnu

ts6-mon

thinterven

tion

107

24hurine

LC–qTO

F5-Hyd

roxy

indole-3-ac

etic

acid

And

erse

net

al.(2

7)

Straw

berrie

s6-mon

thinterven

tion

107

24hurine

LC–qTO

F2,5-Dim

ethy

l-4-metho

xy-3(2H)-furano

ne-sulpha

teAnd

erse

net

al.(

27)

Cho

colate

6-mon

thinterven

tion

107

24hurine

LC–qTO

F6-Amino-5-(N-m

ethy

lform

ylam

ino)-1-m

ethy

luracil,theo

brom

ine,

7-methy

luric

acid

And

erse

net

al.(2

7)

Ras

pberrie

sAcu

teinterven

tion

24Fa

stingan

dpos

tprand

ial

urine

FIE–FT

ICR–MS,

GC–TO

F–MS

Caffeic

acid-sulpha

te,m

ethy

lepicatec

hin-su

lpha

teLloy

det

al.(2

8)

Cruciferous

vege

tables

2-wee

kinterven

tion

20Fa

stingan

dpos

tprand

ial

urine

NMR

S-m

ethy

l-L-cy

steine

sulfo

xide

Edman

dset

al.(2

5)

Cruciferous

vege

tables

Acu

teinterven

tion

17Fa

stingan

dpos

tprand

ial

urine

UPLC

–qTO

F–MS

N-ace

tyl-S-(N-3-m

ethy

lthiopropy

l)cy

steine

,N-ace

tyl-S-(Nallylth

ioca

rbam

oyl)cy

steine

,Iberin

N-ace

tyl-cy

steine

,N-ace

tyl-cy

steine

conjug

ate,

4-im

inop

entylisothioc

yana

te,S

ulforapha

neN-ace

tyl-cy

steine

,ErucinN-ace

tyl-cy

steine

,N-A

cetyl-(N

′ -ben

zylth

ioca

rbam

oyl)-cy

steine

,Sulforapha

neN-cysteine

And

erse

net

al.(2

6)

Brocc

olli

Acu

teinterven

tion

24Fa

stingan

dpos

tprand

ial

urine

FIE–FT

ICR–MS

Tetron

icac

id,x

ylon

ate/lyxo

nate,threitol/e

rythritol

Lloy

det

al.(2

8)

Coffee

Acu

teinterven

tion

5Fa

stingan

dpos

tprand

ial

urine

NMR

2-furoylglyc

ine

Heinz

man

net

al.(4

1)

H. Gibbons et al.44


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Coffee

Acu

teinterven

tion

9Fa

sting,

morning

spot,

24hurine

HILIC–MS/M

SN-M

ethy

lpyridinium,trig

onelline

Lang

etal.(4

6)

Black

tea

Acu

teinterven

tion

20Fa

stingan

dpos

tprand

ial

urine

NMR

Hippuric

acid,4

-hyd

roxy

hippuric

acid,1

,3-dihyd

rophe

nyl-2-O-sulph

ate,

allic

acid,4

-O-m

ethy

lgallic

acid

Van

Velze

net

al.(4

7)

Black

tea

Acu

teinterven

tion

324

hurine

NMR

Hippuric

acid,g

allic

acid,1

,3-dihyd

roxy

phe

nyl-2-O-sulph

ate

Day

kinet

al.(4

8)

Black

andgree

ntea

2d interven

tion

1724

hurine

NMR

Hippuric

acid,1

,3-dihyd

roph

enyl-2-O

-sulph

ate

vanDorsten

etal.(4

9)

Cha

mom

iletea

2-wee

kinterven

tion

14Spot

urine

NMR

Hippuric

acid

Wan

get

al.(5

0)

Mixed

nuts

12-w

eek

interven

tion

4224

hurine

LC–qTO

F,LT

Q-O

rbitrap

10-H

ydroxy

dec

ene-4,6-diyno

icac

id-sulpha

te,trid

ecad

ieno

ic/trid

ecyn

oic

acidgluc

uron

ide,

dod

ecan

edioic

acid,1

,3-dihyd

roxy

phe

nyl-2-O-

sulpha

te,p

-cou

maroy

lalcoh

ol-glucu

ronidean

d-sulph

ate,

N-ace

tylseroton

ine-su

lpha

te,5

-hyd

roxy

indolea

cetic

acid,u

rolitin

A-glucu

ronide,

sulpha

te,s

ulpha

tegluc

uron

ide

Tulip

anie

tal.(5

1)

Bee

fAcu

teinterven

tion

17Pos

tprand

ial

plasm

aGC–MS

2-am

inoa

dipic

acid,β

-alanine

,4-hyd

roxy

proline

Ros

set

al.(3

0)

Herrin

gAcu

teinterven

tion

17Pos

tprand

ial

plasm

aGC–MS

Cetoleicac

id,d

ocos

ahex

aeno

icac

idRos

set

al.(3

0)

Salmon

Acu

teinterven

tion

24Fa

stingan

dpos

tprand

ial

urine

FIE–FT

ICR–MS

Ans

erine,

methy

lhistid

ine,

TMAO

Lloy

det

al.(2

8)

Red

mea

t15

dinterven

tion

1724

hurine

Ionex

chan

gech

romatog

raph

y1an

d3methy

lhistid

ine

Cross

etal.(5

2)

Red

mea

t15

dinterven

tion

1224

hurine

NMR

Carnitin

e,crea

tinine,

TMAO,a

cetyl-ca

rnitine

,tau

rine,

1an

d3

methy

lhistid

ine

Stella

etal.(2

9)

Cruciferous

vege

tables

,citrus

andso

ya

2-wee

kinterven

tion

10Fa

stingurine

LTQ–FT

LC–MS/

MS

Prolinebetaine

,sulforapha

ne,h

ippuric

acid,g

enistein,d

aidze

in,e

quol,

glyc

itein,O

-des

methy

lang

olen

sin,

trigon

elline,

(iso)va

lerlg

lycine

,hy

droxy

phe

nylace

tyl-glyc

ine,

nico

tinuric

acid

May

etal.(4

0)

Ling

onberrie

sAcu

teinterven

tion

14Pos

tprand

ial

urine

NMR

Hippuric

acid,4

-hyd

roxy

hippuric

acid

Lehton

enet

al.(5

3)

Wine

28d

interven

tion

6124

hurine

NMR

Tartrate,4

-hyd

roxy

phe

nylace

tate,m

annitol,etha

nol

Vaz

eque

z-Fres

noet

al.(5

4)

Mixed

redwine/

grap

ejuiceex

trac

ts4-wee

kinterven

tion

5824

hurine

NMR,G

C–TO

F–MS

Syringicac

id,3

-hyd

roxy

hippuric

acid,4

-hyd

roxy

hippuric

acid,

3-hy

droxy

phe

nylace

ticac

id,4

-hyd

roxy

man

delic

acid,v

anilm

andelic

acid,h

ippuric

acid,3

-hyd

roxy

phe

nylpropionicac

id,

1,2,3-trihyd

roxy

ben

zene

,4-hyd

roxy

ben

zoic

acid,h

omov

anillic

acid,

dihyd

roferulic

acid,p

heny

lace

tylglutamine

vanDorsten

etal.(5

5)

Mixed

redwine/

grap

ejuiceex

trac

ts4d interven

tion

3524

hurine

GC–MS,L

C–MS

Syringicac

id,3

-hyd

roxy

hippuric

acid,p

yrog

allol,3-hy

droxy

phe

nylace

ticac

id,3

-hyd

roxy

phe

nylpropionicac

id,3

,4-dihyd

roxy

phe

nylpropion

icac

id,

indo

le-3-lac

ticac

id,h

ippuric

acid,c

atec

hol,4-hy

droxy

hippuric

acid,

3,4-dihyd

roxy

phe

nylace

ticac

id,v

anillic

acid

Jaco

bset

al.(5

6)

Dietary

fibres(oat

bran,

ryebran,

and

suga

rbee

tfibres)

5-wee

kinterven

tion

25Fa

stingplasm

aLC

–qTO

F–MS

2-am

inop

heno

lsulpha

te,2

,6-dihyd

roxy

ben

zoic

acid,h

ydroxy

latedan

dgluc

uron

idated

nuatigen

inJo

hans

son-Persson

etal.(5

7)

Dietary

fibre

6-mon

thinterven

tion

7724

hurine

NMR

Hippuric

acid

Ras

mus

senet

al.(5

8)



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into two distinct dietary patterns. Combining liquid chro-matography–MS data from 24 h urine samples and datafrom 3-d weighed dietary data the present study againidentified conjugates of isothiocyanates as brassica biomar-kers. However, using this approach it was only possible toverify 23 % of potential biomarkers observed in theprevious-meal studies(27). As this was a less controlledintervention that included a wider selection of foods withvaried amounts of intake and different preparation meth-ods, it highlights the need for the validation of biomarkersin different subjects and study settings(27).

A number of red meat and fish biomarkers have beenidentified using this intervention approach(7,28,29). Mostrecently, metabolomics has been applied to comparethe different effects of meat and fish on the plasma meta-bolome(30). Ross et al.(30) carried out an interventionstudy analysing the differences in the postprandialplasma metabolic response to meals containing bakedbeef, baked herring and pickled herring. Seventeenmales consumed three test meals in a crossover designwith 1 week washout between the meals. Postprandialblood plasma samples were taken over 7 h and analysedby GC–MS. Concentrations of 2-aminoadipic acid,β-alanine and 4-hydroxyproline were significantly higherfollowing the beef meal compared with the baked herringmeal. Herring intake led to a greater plasma postprandialresponse from DHA and cetoleic acid compared withbeef(30). However, further studies are needed to confirmthese dietary biomarkers and decipher their specificity.

Dietary biomarker discovery using cohort studies

Searching for new dietary biomarkers in cohort studiesrequires the use of self-reported dietary data to identifylow and high consumers of a specific food. Followingthis, the metabolomic profiles are compared betweenlow and high consumers and potential biomarkers areidentified. Putative biomarkers of foods, identifiedusing this approach, are presented in Table 2. Work inour laboratory combined this approach with an acuteintervention to identify and confirm a panel of biomar-kers indicative of sugar-sweetened beverage (SSB) in-take(31). Heat map analysis was performed to identifycorrelations between NMR spectral regions and SSBintakes in the cohort study. A panel of four biomarkers:formate, citrulline, taurine and isocitrate were identifiedas markers of SSB intake. Following the acute consump-tion of the SSB all four metabolites were shown to in-crease in the urine and the panel of biomarkers weresuccessfully identified in the SSB(31). Another studyusing this cohort study approach, analysed the correla-tions between serum profiles and dietary data collectedusing FFQ in participants from the Prostate, Lung,Colorectal and Ovarian Cancer Screening Trial(32). Theapplication of untargeted metabolomics to this epidemio-logic data set detected thirty-nine metabolites of knownidentity that were correlated with a total of thirteen diet-ary groups, for example citrus intake was associated withstachydrine, chiro-inositol, scyllo-inositol and N-methylproline, fish with 3-carboxy-4-methyl-5-propyl-2-Ta

ble1.

(Con

t.)

Dietary

factor

Study

duration

No.

ofsu

bjects

Sam

ple

Metab

olom

ictech

nique

Biomarke

rReferen

ce

Who

le-grain

rye

bread

4-wee

kinterven

tion

2024

hurine

LC–qTO

F3-(3,5-D

ihyd

roxy

phe

nyl)-1-prop

anoicac

id-sulph

atean

d-glucu

ronide,

enterolacton

e-gluc

uron

ide,

azelaicac

id,2

-aminop

heno

l-su

lpha

te,

2,4-dihyd

roxy

-1,4-ben

zoxa

zin-3-on

e,2-am

inop

heno

l-su

lpha

te,2

–

4-dihyd

roxy

-1,4-ben

zoxa

zin-3-on

e-su

lpha

te,ind

olylac

ryloylglyc

ine,

ferulic

acid-sulpha

te,3

,5-dihyd

roxy

phe

nylethan

ol-sulpha

te,

3,5-dihyd

roxy

cinn

amic

acid-sulpha

te

Bon

dia-P

onset

al.(5

9)

Who

le-grain

sourdou

ghrye

bread

8-wee

kinterven

tion

2824

hurine

FIE–FT

ICR–MS

HHPAAgluc

uron

ide,HPAAsu

lpha

te,H

BOAgluc

uron

ide,

N-feruloy

lglycine

sulpha

te,H

HPAAsu

lpha

teBec

kman

net

al.(6

0)

Che

ese

6-wee

kinterven

tion

2324

hurine

UPLC

–ESI–qTO

FTy

ramine,

sulpha

te,iso

butyryl

glyc

ine(and

othe

rac

ylglyc

ines

),xa

nthu

renicac

id,4

-hyd

roxy

phe

nylace

ticac

idHjerpsted

etal.(6

1)

Milk

andch

eese

14d

interven

tion

15Fa

eces

,24h

urine

NMR

Milk;c

itrate,

crea

tine,

crea

tinine,

urea

,che

ese;

prolinebetaine

,tyros

ine,

hipp

urate

Zhe

nget

al.(6

2)

LC,liquidch

romatog

raphy

;ESI,elec

tros

prayionisa

tion;

qTO

F,qua

drupoletim

e-of-flight;LT

Q,linea

rtrap

qua

drupole;

FIE,flow

infusion

elec

tros

prayionisa

tion;

FTICR,Fo

uriertran

sform-ion

cyclotronreso

nanc

e;qTO

F,qua

drupoletim

e-of-flight;U

PLC

,ultra-perform

ance

liquidch

romatog

raphy

;HILIC,h

ydrophilic

liquidinteractionch

romatog

raphy

;TMAO,trim

ethy

lamine-N-oxide;

LTQ-FT.

linea

riontrap

-Fou

riertran

sform

mas

ssp

ectrom

eter;HHPAA,2

-hyd

roxy

-N-(2-hy

droxy

phe

nyl)a

cetamide;

HPAA,N

-(2-hy

droxy

phe

nyl)a

cetamide;

HBOA,2

-hyd

roxy

-1,4-ben

zoxa

zin-3-one

;HHPAA,2

-hyd

roxy

-N-(2-hy

droxy

phe

nyl)a

cetamide.

H. Gibbons et al.46


https://doi.org/10.1017/S002966511600032X



Proceedings

oftheNutritionSo

ciety

Table

2.Sum

maryof

putativebiomarke

rsiden

tified

usingametab

olom

icsap

proac

hin

coho

rtstud

ies

Dietary

factor

Dietary

asse

ssmen

ttool

No.

ofsu

bjects

Sam

ple

Metab

olom

ictech

nique

Biomarke

rsReferen

ce

Oily

fish

FFQ

68Fa

sting,

morning

spot,2

4hurine

FIE–FT

ICR–MS

Methy

lhistid

ine

Lloy

det

al.(6

3)

Citrus

fruit

24-h

dietary

reco

rdEighty

Fastingurine

LC–ESI–qTO

F,LT

Q-O

rbitrap

Prolinebetaine

,hyd

roxy

prolinebetaine

,hes

peretin

3′-O

-glucu

ronide,

narin

genin7-O-glucu

ronide,

limon

ene8,9-diol

gluc

uron

ide,

nootka

tone

13,14-diolg

lucu

ronide,

N-M

ethy

ltyramine

sulpha

te

Pujos

-Guillo

tet

al.(2

4)

Sug

ar-swee

tene

dbev

erag

es4-dfood

diary

565

Fastingurine

NMR

Form

ate,

isoc

itrate,

citrulline,

taurine

Gibbon

set

al.(3

1)

Citrus

,green

vege

tables

,redmea

t,sh

ellfish

,fish

,pea

nuts,c

offee,

etc.

FFQ

502

Fastingse

rum

UHPLC

–MS/M

S,

GC–MS

Citrus

;Scy

llo-&ch

iro-ino

sitol,Green

s;CMPF,

Red

mea

t;indo

lepropiona

te,S

hellfish

;CMPF,

Pea

nuts;T

ryptopha

nbetaine

,4-Vinylphe

nols

ulpha

te,C

offee;

trigon

elline-N-m

ethy

lnicotinate

andquina

te

Gue

rtin

etal.(3

2)

Coffee

24-h

dietary

reco

rd,F

FQ39

Morning

spot

urine

UPLC

–qTO

F–MS

Atrac

tylig

enin

gluc

uron

ide,

Cyc

lo(isoleu

cyl-prolyl),

1-Methy

lxan

thine,

1,7-dim

ethy

luric

acid,k

ahweo

loxide

gluc

uron

ide,

1-methy

luric

acid,trig

onelline,

dim

ethy

lxan

thine

gluc

uron

ide,

5-ac

etylam

ino-6-form

ylam

ino-3-methy

luracil(AMFU

),hipp

uric

acid,trim

ethy

luric

acid,p

arax

anthine,

3-hy

droxy

hippuric

acid,1

,3or

3,7-dim

ethy

luric

acid,c

affeine

Rothw

ell

etal.(6

4)

Coffee

FFQ

68Fa

sting,

morning

spot,2

4hurine

FIE–FT

ICR–MS

Dihyd

roca

ffeicac

idLloy

det

al.(6

3)

Red

mea

t24

-hdietary

reco

rd,F

FQ12

6Fa

stingurine

Ionex

chan

gech

romatog

raph

y1-Methy

lhistid

ine

Myint

etal.(6

5)

Red

mea

tFF

Q20

47Serum

FIA–MS/M

SPCaa

36:0

,PCaa

36:4

,PCaa

38:0

,PCaa

38:4

,PCae

34:2

,PC

ae34

:3,P

Cae

36:3

,PCae

36:4

,PCae

36:5

,PCae

38:4

,PCae

38:5

,PCae

38:6

,PCae

40:4

,Lyso-PC20

:4,S

M24

:1,F

erritin

Witten

bech

eret

al.(3

3)

White

bread

and

who

le-grain

bread

FFQ

155

Fastingsp

oturine

HPLC

–qTO

F–MS

2-Aminop

heno

lsulpha

te,H

PAAgluc

uron

ide,

HHPAA,H

MBOA

gluc

uron

ide,

HBOAglyc

oside,

HPPA,H

MBOA,D

HPPA

gluc

uron

ide,

3,5-dihyd

roxy

phe

nylethan

olsu

lpha

te,D

HPPTA

sulpha

te,h

ydroxy

ben

zoic

acid

gluc

uron

ide,

dihyd

roferulic

acid

sulpha

te,e

nterolac

tone

gluc

uron

ide,

pyrraline,

3-indolec

arbox

ylic

acid

gluc

uron

ide,

ribofl

avin,2

,8-dihyd

roxy

quino

linegluc

uron

ide

Garcia-Aloy

etal.(3

4)

Cruciferous

vege

tables,

citrus

andso

ya3-dfood

reco

rds,

FFQ

93Fa

stingurine

LTQ–FT

LC–MS/

MS

Prolinebetaine

May

etal.(4

0)

Polyp

heno

l-ric

hfood

s24

-hdietary

reco

rd,F

FQ48

124

hurine

UHPLC

–qTO

F–MS

Coffee;

dihyd

roferulic

acid

sulpha

te.R

edwine;

gallicac

idethy

les

ter.Citrus

fruit;na

ringe

ningluc

uron

ide.

Tea;

4-O-m

ethy

lgallic

acid.A

pplesan

dpea

rs;p

hloretingluc

uron

ide.

Cho

colate

produ

cts;

methy

l(epi)catec

hinsu

lpha

te

Edman

ds

etal.(6

6)

Walnu

tsFF

Q38

1Fa

stingsp

oturine

HPLC

–qTo

F–MS

3-indolec

arbox

ylic

acid

gluc

uron

ide,

hydroxy

indolea

cetic

acid

sulpha

te,N

-ace

tylseroton

insu

lpha

te,

10-hyd

roxy

-dec

ene-4,6-diyno

icac

idsu

lpha

te,trid

ecad

ieno

ic/

tridec

ynoicac

idgluc

uron

ide,

enterolacton

egluc

uron

ide,

urolith

ins

Garcia-Aloy

etal.(6

7)

FIE,fl

owinfusion

elec

tros

prayionisa

tion;

FTICR,Fo

uriertran

sform-ion

cyclotronreso

nanc

e;LC

,liquidch

romatog

raphy

;ESI,elec

tros

prayionisa

tion;

qTO

F,qua

drupoletim

e-of-flight;L

TQ,linea

rtrap

qua

drupole;

UHPLC

,ultra-HPLC

;CMPF,

3-ca

rbox

y-4-methy

l-5-propyl-2-furan

propan

oicac

id;F

IA,fl

owinjectionan

alysis;P

Caa

,diacy

lpho

spha

tidylch

olines

;PC

ae,a

cylalkyl

pho

spha

tidylch

olines

;Ly

so-P

C,

lyso

pho

spha

tidylch

olines

;SM,s

phing

omye

lin;HPAA,N-(2-hy

droxy

phe

nyl)ac

etam

ide;

HHPAA,2-hy

droxy

-N-(2-hy

droxy

phe

nyl)ac

etam

ide;

HMBOA,2

-hyd

roxy

-7-m

etho

xy-2H-1,4

-ben

zoxa

zin-3-on

e;HBOA,

2-hy

droxy

-1,4-ben

zoxa

zin-3-on

e;HPPA,2

-hyd

roxy

-N-(2-hy

droxy

phe

nyl)ac

etam

ide;

DHPPA,3-(3,5-dihyd

roxy

phe

nyl)propan

oicac

id;D

HPPTA

,5-(3,5-dihyd

roxy

phe

nyl)pen

tano

icac

id.



https://doi.org/10.1017/S002966511600032X



Proceedings

oftheNutritionSo

ciety

furanpropanoic acid, DHA and EPA, peanut intake withtryptophan betaine and 4-vinylphenol sulphate and coffeeintake was associated with trigonelline-N-methylnicotinateand quinate(32). To complicate interpretation further, the in-take of foods is highly correlated making identification ofspecific biomarkers difficult and this highlights the needfor the validation of biomarkers. The majority of biomar-kers identified using cohort studies have been predominantlyidentified in urine, this study demonstrates the potential useof serum samples in dietary biomarker discovery. However,the proposed biomarkers identified are only based on asso-ciations and some biomarkers were not food specific, for ex-ample DHA was correlated with fish and rice intake.Further validation in intervention studies is therefore neces-sary to demonstrate responsiveness to intake.

Wittenbecher et al. also demonstrated the use of serumsamples when identifying biomarkers of red meat intakein a subset of participants from the European ProspectiveInvestigation into Cancer and Nutrition-Potsdam cohort(n 2047)(33). Total red meat consumption was assessedusing FFQ and serum samples were analysed using a tar-geted metabolomics approach. Ferritin, glycine, four diacylphosphatidylcholines, eleven acylalkyl phosphatidylcho-lines, two lysophosphatidylcholines and two sphingomye-lins were associated with total red meat consumption andsix of these biomarkers were also found to be associatedwith type-2 diabetes risk(33). This is the first study evaluat-ing a large set of metabolites as potential mediators of theassociation between red meat intake and diabetes risk,however, dietary information relied on estimates of habit-ual consumption over the past year by FFQ and metabo-lites were measured at a single time point. Furthermore,total red meat was defined as processed and unprocessedmeat and therefore did not identify biomarkers of specifictypes of meat. Additional study is essential to validatethe biomarkers identified and to further dissect such rela-tionships with disease risk.

Biomarkers of bread intake have also been investi-gated in 155 subjects from the PERIMED study(34). A137-item FFQ was used to stratify subjects into threegroups: non-consumers of bread (n 56), white-bread con-sumers (n 48) and whole-grain bread consumers (n 51).Fasting urine samples, analysed by untargeted HPLC–quadruple time-of-flight–MS, identified higher concen-trations of compounds, including benzoxazinoids andalkylresorcinol metabolites and compounds producedby gut microbiota (enterolactones, hydroxybenzoic anddihydroferulic acid metabolites) in bread consumers. 2,8-dihydroxyquinoline glucuronide was also found to bemore abundant in whole-grain bread consumers(34).The biomarkers identified are based on a FFQ; thereforefurther validation is essential to demonstrate a direct re-lationship with bread consumption.

Dietary biomarker discovery using dietary patterns

The third approach, analysing dietary patterns and meta-bolomic profiles to identify nutritypes (i.e. metabolicprofiles that reflect dietary intake) and biomarkers havebeen demonstrated by a number of research groups (see

Table 3). One of the first examples emerged from our la-boratory when a k-means cluster analysis was performedon self-reporting dietary data and three distinct dietary pat-terns, which were associated with unique food intakes wereidentified(35). Dietary clusters were reflected in the urinarymetabolomic profiles of the 125 participants and a numberof metabolites were identified and linked to the intake ofspecific food groups(35). These nutritypes have the potentialto aid dietary assessment by unobjectively classifying peo-ple into certain dietary patterns. Further work within ourresearch group, applying the concept of using biomarkersto reflect dietary patterns, has focused on lipidomics, asubfield of metabolomics that concentrates on the globalstudy of lipids(36). Dietary data, measured by FFQ andlipid profiles measured from serum samples, in thirty-fourMetabolic Challenge Study participants were used for thisanalysis. Principal component analysis reduced lipidprofiles into lipid patterns and these were regressed againstdietary data to identify biomarkers related to the intake ofcertain foods and nutrients. Six lipid patterns were iden-tified including lipid pattern 1 which was found to be high-ly predictive of dietary fat intake (AUC= 0·82), lipidpattern 4 which was highly predictive of alcohol intake(AUC= 0·81) and lipid pattern 6 which had a reasonablygood ability to predict dietary fish intake (AUC= 0·76).Lysophosphatidylcholine alkyl C18 : 0 (LPCeC18 : 0)was identified as a potential biomarker of alcohol con-sumption and lysophosphatidylethanolamine acyl C18 : 2(LPEaC18 : 2) and phoshatidylethanolamine diacyl C38 :4 (PEaaC38 : 4) were identified as potential biomarkersof fish intake(36). This approach demonstrates the utilityof serum in the identification of key dietary factors thatinfluence the lipidomic profile. However, again validationof the biomarkers through use of intervention studies isneeded.

Most recently, Andersen et al. used an untargeted meta-bolomics approach to distinguish between two dietary pat-terns with the purpose of developing a compliancemeasure(37). In a parallel intervention study, 181 partici-pants were randomly assigned to follow a New NordicDiet or an Average Danish Diet. The 24 h urine sampleswere collected, analysed by ultra-performance liquid chro-matography–quadruple time-of-flight–MS and partialleast-squares discriminant analysis was applied to developa compliance model for Average Danish Diet and NewNordic Diet based on the most discriminative featuresdetected in urine. This resulted in a robust model with amisclassification rate of 19 %(37). Metabolites characteris-ing the Average Danish Diet and the New Nordic Dietare listed in Table 3. The present study demonstrates thepotential of metabolomics in discovering biomarkers indi-cative of dietary patterns, but furthermore it highlights apromising approach that may be used to develop compli-ance measures that cover the most important discriminantmetabolites of complex diets.

Limitations of current approaches/study designs

In general, metabolomic-based approaches have pro-duced reasonably robust models for dietary biomarker

H. Gibbons et al.48


https://doi.org/10.1017/S002966511600032X



Proceedings

oftheNutritionSo

ciety

Table

3.Sum

maryof

putativebiomarke

rsiden

tified

usingdietary

patternsan

dmetab

olom

icprofiles

Dietary

patterns

Dietary

pattern

approac

hSam

ple

Metab

olom

ictech

nique

Biomarke

rsReferen

ce

Pruden

tan

dWes

tern

dietary

patterns

PCA

Fastingplasm

aESI–MS/M

SWes

tern

dietary

pattern;inc

reas

edam

inoac

idsan

dsh

ort-ch

ainac

ylca

rnitine

sBou

chard-M

ercier

etal.(6

8)

Hea

lthy,

unhe

althy,

trad

ition

alIrish

dietary

pattern

k-mea

nsclus

ter

analysis

Fastingurine

NMR

Hea

lthy;

glyc

ine,

phe

nylace

tylglutaminean

dac

tetoac

etateTrad

ition

alIrish

;TMAO,O

-ace

tylcarnitin

ean

dnn

dimethy

lglycine

O’Sullivan

etal.(3

5)

Sev

endietary

patterns(e.g.h

ealth

ydiet,trad

ition

alBav

arian)

PCA

Fastingplasm

aESI–MS/M

SHea

lthydiet;dec

reas

ein

thedeg

reeof

saturatio

nof

the

fattyac

idmoietiesof

differen

tglyc

erol-

pho

spha

tidylch

olines

Altm

aier

etal.(6

9)

Sev

endietary

patterns(e.g.d

ietary

fat

lipid

pattern,a

lcoh

ollip

idpattern)

PCA

Fastingse

rum

ESI–MS/M

SAlcoh

olco

nsum

ptio

n;LP

CeC

18:0

Fish

cons

umptio

n;LP

EaC

18:2

,PEaa

C38

:4O’G

orman

etal.(3

6)

Five

dietary

patterns(e.g.e

nergy

intake

,plant

v.an

imal-bas

eddiet)

PCA

Fastingplasm

aNMR

Ene

rgyintake

;greater

conc

entrations

oflip

id-related

high

-ene

rgyintake

,highe

rcirculating

pho

spha

tidyc

holinerelatedto

lower

energy

intake

.Animal-bas

eddiet;high

erco

ncen

trations

oflysine

,arginine

,glutamine/glutam

ate,

threon

ine,

aspartate/

asparag

ine,

citratean

dpolyo

lcom

poun

ds

Peré-Trep

atet

al.(7

0)

NNDan

dan

ADD

24hurine

UPLC

–qTO

F–MS

NNDdiet;TM

AO,h

ippuric

acid,h

ydroquino

ne-

gluc

uron

ide,

(2-oxo

-2,3-dihyd

ro-1H-ind

ol-3-yl)a

cetic

acid

and3,4,5,6-tetrah

ydrohippu

rate.A

DDdiet;

pyrraline,

gluc

uron

ideco

njug

ated

produc

ts,

theo

brom

ine,

7-methy

luric

acid,3

,7-dim

ethy

luric

acid,

7-methy

lxan

thine,

6-am

ino-5-[N-m

ethy

lform

ylam

ino]-1-

methy

luracil,prolinebetaine

andgluc

uron

ides

ofperillic

acid

And

erse

net

al.(3

7)

Dietary

patternse.g.

high

intake

ofbutter/low

intake

ofmarga

rine,

high

intake

ofredmea

tan

dfish

/low

intake

ofwho

le-grain

bread

,tea

andco

ffee

RRR

Fastingse

rum

FIA–MS/M

SHighintake

ofbutteran

dlow

intake

ofmarga

rine;

acylca

rnitine

s,ac

yl-alkyl-pho

spha

tidylch

olines

,lyso-

pho

spha

tidylch

olines

andhy

droxy

-sphing

omye

lins.

Highintake

ofredmea

tand

fish

andlowintake

ofwho

le-

grainbread

andtea;

hexo

sean

dpho

spha

tidylch

olines

Floe

gale

tal.(7

1)

ADD,A

verage

Dan

ishDiet;PCA,prin

cipal

compon

entan

alysis;E

SI,elec

tros

prayionisa

tion;

LPCeC

18:0

,lysop

hosp

hatid

ylch

olinealky

lC18

:0.L

PEaC

18:2

,lysop

hosp

hatid

yletha

nolamineac

ylC18

:2.N

ND,N

ewNordic

Diet;PEaa

C38

:4,p

hosh

atidyletha

nolaminediaclyl

C38

:4.T

MAO,trim

ethy

lamine-N-oxide;

UPLC

,ultra-perform

ance

liquidch

romatog

raphy

;qTO

F,qua

drupoletim

e-of-flight;R

RR,red

uced

rank

regres

sion

;FIA,flow

injectionan

alysis.



https://doi.org/10.1017/S002966511600032X



Proceedings

oftheNutritionSo

ciety

identification. However, following the discovery of anybiomarker, validation in an independent study is criticalto enable the generalisability of the results. This valid-ation step is essential because factors which may not bepresent in traditional dietary assessment methods, includ-ing genetic factors, lifestyle and physiological factors,dietary factors, the biological sample or the analyticmethodology could skew biomarker measures of dietaryintake(38). For many of the study designs discussed, val-idation of the biomarker is often absent, making it diffi-cult for the translation of these biomarkers into practice.

It has been proposed that the confirmation of dietarybiomarkers should occur in two stages, firstly the dose–response effect should be included in intervention studiesand secondly the suitability of the candidate biomarkerin a free-living population should be investigated usinga (controlled) habitual diet(39). Evaluation of the dose–re-sponse relationship is critical as it allows for the assess-ment of the suitability of the biomarker over a range ofintakes(20). Unfortunately, in many studies, this import-ant step is often absent. Biomarkers identified using sam-ples from cohort studies do not assess the directrelationships of food amounts consumed and levels ofbiomarkers and do not demonstrate responsiveness tointakes, therefore the relationship is only an associ-ation(16). Such studies should ideally be combined withintervention studies to demonstrate direct relationshipsand dose–response relationships. Conversely, dietarybiomarkers identified within acute intervention studiesadvantageously allow for the examination of dose–response relationships; however, to date few studieshave incorporated such designs.

When using self-reporting dietary data from cohortstudies in the biomarker discovery process, one shouldbe aware of reporting errors and the potential for missingimportant correlations and attenuation of results. Mayet al. investigated the metabolomic profiles of partici-pants consuming a high-phytochemical diet comparedwith a diet without fruit and vegetables in a randomisedcontrolled trial and also investigated the metabolomicprofiles of participants in a cross-sectional study, wherehigh and low fruit and vegetable diets were identifiedbased on 3-d food records and FFQ. The interventionstudy found forty-six putatively annotated ions, withMS/MS fragment ion support that were differentiallyabundant between the two intervention diets; however,within the cross-sectional study only one compound anno-tated with MS/MS support was identified using the 3-dfood records and there were no metabolites that signifi-cantly separated groups based on FFQ data(40). This there-fore demonstrates the drawbacks of using self-reporteddata in dietary biomarker discovery. Furthermore, whenusing cohort studies to identify or confirm biomarkers itis imperative that it is acknowledged that many of thefoods consumed are highly correlated and therefore bio-markers identified may not be specific to the particularfood of interest(20). Following identification of putativebiomarkers from cohort studies we recommend that the re-lationship is confirmed using an intervention study in adose–response manner where the sensitivity and specificityof the biomarkers can also be assessed. The importance of

such a step is key to the validation of the biomarkers andimportant to support their use.

Use of acute and medium-term interventions is notwithout limitations in terms of dietary biomarker iden-tification: many of the biomarkers identified using thisapproach are markers of acute intake. For example, pro-line betaine is excreted rapidly in urine and excretion isalmost complete ≤24 h(22). These acute biomarkers maytherefore only be valid for people that regularly and fre-quently consume the particular foods. The identificationof dietary biomarkers that reflect habitual intake requireslonger-term studies. Furthermore, it must also be notedthat the majority of the acute and medium-term interven-tion study designs involve only a small number of partici-pants(22,24,41). The proposed dietary biomarkers identifiedusing these approaches therefore cannot always be extra-polated to population studies in free-living individuals.However, this can be in part be dealt with by confirmationin cohort studies with a diverse range of characteristics.

While considering the earlier described limitations instudy designs, there is also the need for development ofdatabases and software tools to advance the interpret-ation of metabolomics results and therefore enhancethe utility of dietary biomarkers in nutrition research.Current databases such as the Human MetabolomeDatabase provides access to an online database containingdetailed information about small molecule metabolites(>40 000) found in the human body(42). Since it was firstdescribed in 2007, it is constantly being expanded andupdated and has become a valuable resource that containsspectroscopic, quantitative, analytic and physiological in-formation about human metabolites(42). The FoodMetabolome Database is another database of >28 000food constituents that contains information about foodsources and food concentrations(43). This resource pro-vides an aid for the identification of new metabolitesthat are reflective of food intake. While this resource isvaluable, the identification of metabolites originatingfrom food remains difficult and there is a need for sharingof databases to aid identification. Most recently, a com-prehensive and electronically accessible human urinemetabolome database, which includes quantitative con-centrations of metabolites in urine samples was estab-lished(44). This database also represents a significantdevelopment and resource for biomarker identificationand quantification. Other new software tools includeBAYESIL; this system provides fully automated andfully quantitative NMR-based metabolomics of complexmixtures(45). This will have a significant impact onNMR spectroscopy and NMR-based metabolomics.

Conclusion

The use of dietary biomarkers in nutrition research holdsgreat promise. However, prior to having a suite of reli-able dietary biomarkers that could be used in nutritionresearch a number of validation steps need to considered.Furthermore, the challenges identified in this reviewneed to be acknowledged and addressed. Appropriatevalidation steps are essential, otherwise the robustness

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of biomarkers will remain uncertain and the translationof these biomarkers into practice will be challenging.Longer-term studies are also needed for the identificationof dietary biomarkers reflective of habitual dietary in-take. Until well-validated biomarkers are identified it isunlikely we will see uptake by the research communityof the emerging biomarkers. The challenge for theresearchers working in this field, in the coming years,will be to develop a suite of well-validated biomarkers.To this end the Joint Programming Initiatives fundedprogramme FoodBall will address some of these issuesand pave the way forward (http://foodmetabolome.org/).They may also have the potential for the assessment ofcompliance to dietary interventions in both a clinicaland a research setting. Ultimately these dietary biomar-kers will be used to further elucidate the proposed linksbetween certain foods and disease.

Financial Support

The authors would like to acknowledge the followingfunding: FP7 Project NutriTech (289511), SFI (14/JP-HDHL/B3075) and ERC (647783).

Conflict of Interest

None.

Authorship

H. G. drafted the outline of the manuscript, conductedthe literature search and drafted the manuscript. L. B.was responsible for critically reviewing the manuscript.Both authors read and approved the final manuscript be-fore submission.

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