The exposome in practice: Design of the EXPOsOMICS project

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Contents lists available atScienceDirect

International

Journal

of

Hygiene

and

Environmental

Health

j o u r n a l h o m e p a g e :w w w . e l s e v i e r . c o m / l o c a t e / i j h e h

The

exposome

in

practice:

Design

of

the

EXPOsOMICS

project

P. Vineis

a,∗

_,

_M.

_Chadeau-Hyam

a

_,

_H.

_Gmuender

b

_,

_J.

_Gulliver

a

_,

_Z.

_Herceg

c

_,

_J.

_Kleinjans

d

_,

M. Kogevinas

e

_,

_S.

_Kyrtopoulos

f

_,

_M.

_{Nieuwenhuijsen}

e

_,

_D.H.

_Phillips

g

_,

_N.

_{Probst-Hensch}

h

_,

A. Scalbert

c

_,

_R.

_Vermeulen

i

_,

_C.P.

_Wild

c

_,

_The

_EXPOsOMICS

_Consortium

1 a_MRC-PHE_Centre_for_Environment_and_Health,_School_of_Public_Health,_Imperial_College_London,_UK

b_Genedata_AG,_Basel,_Switzerland

c_{International}_Agency_for_Research_on_Cancer_(IARC),_Lyon,_France

d_Maastricht_University,_Department_of_Health_Risk_Analysis_and_Toxicology,_Maastricht,_Netherlands e_Barcelona_Institute_for_Global_Health_(ISGlobal),_Barcelona,_Spain

f_National_Hellenic_Research_Foundation,_Institute_of_Biology,_Medicinal_Chemistry_and_{Biotechnology,}_Division_of_Organic_and_Medicinal_Chemistry,_Athens, Greece

g_King’s_College_London,_Analytical_and_{Environmental}_Sciences_Division,_MRC-PHE_Centre_for_Environment_and_Health,_London,_UK

h_Swiss_Tropical_School_of_Public_Health,_Department_of_Epidemiology_and_Public_Health,_Basel,_Switzerland_and_University_of_Basel,_Basel,_Switzerland i_Utrecht_University,_Institute_for_Risk_Assessment_Sciences,_{Environmental}_Epidemiology_Division,_Utrecht,_Netherlands

a

r

t

i

c

l

e

i

n

f

o

Articlehistory: Received5July2016

Receivedinrevisedform12August2016 Accepted16August2016

a

b

s

t

r

a

c

t

EXPOsOMICSisaEuropeanUnionfundedprojectthataimstodevelopanovelapproachtotheassessment ofexposuretohighpriorityenvironmentalpollutants,bycharacterizingtheexternalandtheinternal componentsoftheexposome.Itfocusesonairandwatercontaminantsduringcriticalperiodsoflife. Tothisend,theprojectcentreson1)exposureassessmentatthepersonalandpopulationlevelswithin existingEuropeanshortandlong-termpopulationstudies,exploitingavailabletoolsandmethodswhich havebeendevelopedforpersonalexposuremonitoring(PEM);and2)multiple“omic”technologiesforthe analysisofbiologicalsamples(internalmarkersofexternalexposures).Thesearchfortherelationships betweenexternalexposuresandglobalproﬁlesofmolecularfeaturesinthesameindividualsconstitutes anoveladvancementtowardsthedevelopmentof“nextgenerationexposureassessment”for environ-mentalchemicalsandtheirmixtures.Thelinkagewithdiseaserisksopensthewaytowhataredeﬁned hereas‘exposome-wideassociationstudies’(EWAS).

1. Introductionandstudydesign

Itisgenerallyacceptedthatthemajorityofimportantchronic diseasesarelikelytoresultfromthecombinationofenvironmental exposurestochemicalandphysicalstressorsandhuman genet-ics.Thereisalsoevidencethattheeffectsarelocation-speciﬁcand inﬂuencedbyclimatic,lifestyleandsocioeconomiccharacteristics. Althoughinformationonbothenvironmentalandgeneticcauses

Abbreviations:PEM,personalexposuremonitoring;GIS,geographicinformation system;EWAS,exposome-wideassociationstudies;STS,experimentalshort-term studies;MCO,mother-childcohorts;ALTS,adultlong-termstudies;LUR,land-use regression;DBP,disinfectionby-products;OP,oxidativepotential;UFP,ultraﬁne particles;PM,particulatematter.

∗Correspondingauthorat:MRC-PHECentreforEnvironmentandHealth,School ofPublicHealth,ImperialCollegeLondon,NorfolkPlace,W21PG,LondonUK.

E-mailaddress:[email protected](P.Vineis).

of disease is growingas a result oflarge-scale epidemiological research,exposuredata(including diet,lifestyle, environmental andoccupationalfactors)isoftenfragmentary(intimeanddepth), non-standardized,atcruderesolutionandoftendoesnotinclude

1_The_EXPOsOMICS_Consortium_includes_(in_alphabetic_order):_Andre_F.S. Ama-ral,TobyAthersuch,SabrinaBertinetti,MarcChadeau-Hyam,LedaChatzi,TheoDe Kok,MichaelaDijmarescu,AlmudenaEspinPerez,MarioFernandez,ClaudiaGalassi, AkramGhantous,HansGmuender,JohnGulliver,JohnHenderson,ZdenkoHerceg, GerardHoek,MedeaImboden,PoojaJain,DebbieJarvis,FrankKelly,Pekka Keski-Rahkonen,JosKleinjans,ManolisKogevinas,JulianKrauskopf,SoteriosKyrtopoulos, DavidMorley,NahidMostafaviMontazeri,AlessioNaccarati,TimNawrot,Mark Nieuwenhuijsen,GeorgiadisPanos,DavidPhillips,MichellePlusquin,George Pre-ston,NicoleProbst-Hensch,AndreaRanzi,StephenRappaport,LaiaFontRibeira, LorenzoRichiardi,SusanM.Ring,OliverRobinson,AlbertoRodriguez,Augustin Scal-bert,TerrenceSimmons,MartynT.Smith,JordiSunyer,SoniaTarallo,Veronique Terrasse,MingTsai,ErikvanNunen,KarinvanVeldhoven,RoelC.H.Vermeulen, CristinaM.Villanueva,PaoloVineis,JelleVlaanderen,MartineVrijheid,Christopher P.Wild,TimoWittenberger.

http://dx.doi.org/10.1016/j.ijheh.2016.08.001

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Box 1: A conceptual model of life-course disease risk. Population studies of chronic diseases have traditionally recruited middle-aged subjects. However, there is strong evi-dence that (a) the risk of disease is influenced by early exposures, includingin utero; (b) life-stages include critical (bearing long-term effects) and sensitive periods (susceptible to large effects) (Ben-Shlomo and Kuh, 2002). The idea of a sequence ofcritical andsensitive periods leads to the con-cept of “chain of risk”, i.e. the interplay of early exposures and late exposures. To use this concept in practice implies having access to multiple life-stages in exposure assessment and epidemiological studies, and repeated measurements of biomarkers at different time windows. This approach requires an inter-generational epidemiological study design and novel statistical analyses.

estimatesattheleveloftheindividual.Theinformationon environ-mentalfactorsisoftenincompleteorinaccurateandthesubsequent estimationofoverallrisksassociatedwiththesefactorsisseverely hindered.Asaresult,importantassociationscangoundetected. Thislimitationhasrecently beenframed within thecontext of theexposome,theenvironmentalcounterpartofthegenome.The conceptoftheexposomereferstothetotalityofenvironmental exposuresfromconceptiononwards,andhasbeendescribed in detailelsewhereincludingitsexternalandinternalcomponents (Wild,2005;RappaportandSmith,2010;Anon,2016;Vineisetal., 2009).

The context of EXPOsOMICS is the rapidly developing ﬁeld ofexposureassessment,includingtheuseofomictechnologies, accordingtotheconceptoftheexposome.Historically,akeyevent wasthepublicationofthereportToxicityTestinginthe21stCentury: AVisionandaStrategybytheUSNationalResearchCouncilin2007. Theprimarygoalsofthereportwere,amongothers:“(1)toprovide broadcoverageofchemicals,chemicalmixtures,outcomes,andlife stages,(2)toreducethecostandtimeoftesting,(3)todevelop amorerobustscientiﬁcbasisforassessinghealtheffectsof envi-ronmental agents” ( http://www.nap.edu/catalog/11970/toxicity-testing-in-the-21st-century-a-vision-and-a).EXPOsOMICSaimsat partiallycoveringpoints1(mixtures,outcomes,andlifestages)and 3.

Bycomprehensivelyaddressingtheintegrationoftheexternal andtheinternalexposomesattheindividuallevel,EXPOsOMICS providesaholisticandconsolidatedapproachtoexposurescience. BuildinguponseveralEU-fundedresearchprojectswithrichsetsof healthdata,exposuredata,biomarkermeasurementsandpublicly availabledatasources,thismultidisciplinaryproject:

− Pools and integrates information from short-term, experimental human studies and long-term epidemiological cohorts/consortia–includingadults,childrenandnewborns–to enablefocused investigationstoreﬁne environmentalexposure assessmentbasedontheconceptoflife-courseepidemiology(Box 1andFig.1).

− Characterizes the exposome, by (a) measuringthe exter-nalcomponent of the exposomeat different critical lifestages byemployingnoveltoolsand drawingonexperiencegained in existingEUinitiatives(PEM,databasescoupledwithGIS,remote sensing),withafocusonairand waterpollution;and(b) mea-suring biomarkers of the internal exposome (xenobiotics and metabolites),usingomictechnologies(adductome,metabolome, transcriptome,epigenome,proteome).

−Integratesexternalandinternalexposuremeasuresto com-prehensivelymodelandassessexposuretoairpollutionandwater contaminationinlargepopulationcohorts,throughnovelstatistical modeling.

Togethertheseapproacheswillleadtoformulatinganew con-ceptof integrated exposure assessment at theindividual level, reducinguncertainty,andassessinghowthesereﬁnements inﬂu-encediseaseriskestimatesforcombined,multipleexposuresand selecteddiseases.

Thescientiﬁcquestionstobeaddressedbytheprojectare:

(1)Isitpossibletoreﬁneexposureassessmenttoairpollutionand watercontaminantsusingacombinationofpersonalexposure monitoringandomictechnologies?

(2)Willthat reﬁnementlead tomoreaccurateestimatesofthe associationwithselecteddiseases,byreducingmeasurement error?

(3)Donewapproaches allowtheinvestigationof theeffects of mixturesinadditiontosinglecomponents?

(4)Dotheyimprovetheinvestigationofdose-response relation-ships?

(5)Is it possible to strengthen causal reasoning by using the “meet-in-the-middle” concept, i.e. investigate the temporal sequenceofexposure,biologicalpathwayperturbationand dis-easeonset?

(6)Isitpossibletousetheexposomeapproachtostudythe life-courseepidemiologyofenvironmentaldiseases?

2. Externalexposome(Fig.2)

Existingpopulation-basedstudiesonenvironmentalexposures, suchas theESCAPE project(http://www.escapeproject.eu/), are providingessentialinformationonthediseaserisksassociatedwith exposuretoairpollution,buttheyarelimitedintheirexposure assessmentmethodologiesandbecauseoftheuncertain biolog-icalplausibilityofassociationsthataredetected.Well-conceived, fullycontrolledshort-terminterventionstudies,suchastheOxford Street Randomized Trial (included in the present project, ref. (McCreanorandCullinanetal.,2007Dec6)),andTAPAShaveshown thatacutechangescanoccurinlungandheartfunctionatlowor verylowlevelsofexposuretoairpollutants.Analogousstudieshave beenconductedonwatercontaminants,wheregenotoxiceffects havebeenobserved(see below).Theidentificationoflong-term effects,however,hasbeenproblematic,duetothelackofthesame degreeofaccuracyinexposureassessmentasobtainedin short-termexperimentalstudies.ThefirstinnovationofEXPOsOMICSisin bringingtogetherbothtypesofinvestigationsandinlinkingPersonal ExposureMonitoring(PEM)(highprecision,usedmostlyin short-termpanelstudies)withup-to-datelanduseregressionmodeling andsatellite-basedexposureassessment.GPS-basedtechniques, smartphones,accelerometersandPEMtodetectpollutantscan pro-videaccurateandinstantestimatesofchangesinhumanexposure andinphysicalactivity(deNazelleetal.,2011;Donsetal.,2011; Dippold,2006).Inaddition,satellitemodeling(e.g.aerosoloptical depth)isusedforseveralpurposes,includingextensionof expo-suremodelstoareasnotcoveredbyothersourcesofinformation, and retrospectiveassessment. EXPOsOMICS utilises and further developsthesetechnologicaladvancesinacombinedmannerfor theassessmentofpersonalexposures,andtoexpandthe assess-menttoexposuresthathavenotbeengeo-spatiallyaddressedin Europetodate(e.g.ultrafineparticulates).Byintegrating technolo-giesthatprovideestimatesofexposuretomultiplechemicalsover relativelyprolongedtime-periods,attheleveloftheindividual sub-jectaswellaspopulations,andatdifferentlifestages,ourproject systematicallyexploresandfurtherdevelopstheconceptofthe exposome.

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Fig.1.TheconceptualframeworkofExposomics(life-longintegrationofcohorts).

Fig.2. Externalexposome:frompersonalmonitoringtosatelliteintegration.

3. Internalexposome

Biomarkersofexposurethatweredevelopedduringthepast coupleof decades(e.g.DNA and proteinadducts), while repre-sentingasigniﬁcantsteptowardsthedeﬁnitionofmoreaccurate measures of personal exposure, still suffer from the disadvan-tageofaddressingsinglechemicalsandcoveringthereforeonlya smallfractionoftheexposome.However,adaptationofbiomarker technologiesto provide a more global estimateof the internal component of the exposomeis emerging and is further devel-oped within EXPOsOMICS. For example, high-resolution LC/MS (LiquidChromatography/MassSpectrometry)metabolomicsnow permitsthedetection and characterisation oflarge numbersof smallmolecules,typicallywithmolecularmasslessthan2000Da,

in biological ﬂuids. This includes any metabolites that can be affectedbyenvironmentalexposures,andthatcanberelatedto inﬂammation,oxidative stressandvarious metabolicpathways. Analogousadductomictechnologieswillbepioneeredtodetect proteinadductsinanuntargetedfashion,toprovideaglobal pic-tureofbiomarkersofindividualexposureeithertoelectrophilesor chemicalsthataremetabolicallyactivatedtoelectrophiles.Because theycan directlymodify DNA and important proteins, reactive electrophilesareimportantconstituentsoftheexposome.Because oftheirgreaterabundanceandresidencetimesinhumanblood, adductsonthecirculatingproteinshemoglobinandhumanserum albumin(HSA)arepreferabletothoseofDNAandglutathionefor characterizingadductomes(Rappaportetal.,2011).

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Box 2: Basic components of the EXPOsOMICS project. Phase 1.

1. We have selected and integrated subjects, samples and data from three types of existing studies: Experimental Short-Term Studies (STS), Mother-Child Cohorts (MCO) and Adult Long-Term Studies (ALTS), between them reflecting all life stages from conception to old age.

2. We have measured the external exposome component for air and water contaminants by performing extensive, repeated Personal Exposure Monitoring (PEM). Fresh blood samples have been collected from all the individuals undergoing PEM, i.e. individuals in STS and those from a representative subsam-ples from MCO and ALTS, selected on the basis of contrasting exposures as estimated by traditional exposure assessment methods. In these samples, plus already stored samples of the recalled subjects from MCO and ALTS, EXPOsOMICS has con-ducted untargeted omic analyses. The aim is to look for new biomarkers of exposure to chemicals or mixtures and evaluate intra-individual variation of the internal exposome.

Phase 2.

3. OMIC profiles were also measured in approximately 2000 stored samples from MCO and ALTS, using untargeted ana-lytical methods, with the aim of evaluating them as predictive of risk by examining their association with health effects, and also generating new hypotheses on disease etiology. 4. We combine external and internal exposome data, Land Use Regression models (LUR) and satellite data to calibrate air pol-lution exposure estimates (e.g. PM2.5, ultra-fine particles, black carbon) obtained using traditional methods in MCO/ALTS and use these refined estimates for risk assessment and burden of disease evaluations.

In addition to global data on internal markers of expo-sure,providedbythesetechnologies,theinternalcomponentof theexposomeincludesfurtherbiological profiledatagenerated byhigh-densitybiologicalanalysistechnologies.Transcriptomic, epigenomicandproteomicprofilesofbiologicalsamplesprovidea detailedpictureoftheevolvingstateofcellsundertheinfluence ofenvironmentalchemicals,thusrevealingearlymechanisticlinks withpotentialhealtheffects(Chadeau-Hyametal.,2011;Lanetal., 2004;Smithetal.,2005).

4. Populationstudies

Thelong-termcohortsusedinEXPOsOMICShavebeenselected onthebasisofthefollowingcriteria:accesstostoredbiological samples,residentialhistories(forGISbasedmodels),information onconfoundersandabilitytorecallparticipants.Largestudieshave beenselectedthatcoverwidegeographicareasandhaveawealth ofinformation includingbiomarkers,geo-referencing,follow-up data,andextensivebehaviouralandenvironmentalvariables.Asit ishypothesizedthatmanyinternalexposomesignaturesare inﬂu-encedbyexternalexposuresbeyondairandwaterpollutants,study populationshavebeenselectedwithavailableandaccuratedataon lifestyle,anthropometry,nutritionalandenvironmentalfactors.

ThebasiccomponentsoftheprojectaredescribedinBox2. Through theintegrationof a total of 14 studiesof different types,locatedindifferentregions,theprojecthasgenerateda sub-stantialamountofnewdataofcontinent-widerelevance.Phase1 involvesreﬁnedexposureassessmentbasedontheexternal (Per-sonalExposureMonitoring)andtheinternal(omics)components oftheexposome,aswellas(forcomparison)exposureassessment usingtraditionalmethods.Thisisperformedinsubjects partici-patinginshort-termexperimentalstudies(STS)andinsub-setsof representativesubjectsfrompopulationcohorts.Thelatterinclude mother-childcohorts(MCO)andadultlong-termstudies(ALTS).

Recalledsubsetshave beenchosenattheextremesof exposure (asestimatedby traditional methods).For recalled subjectswe haveconductedomicanalysesonbothfreshlycollectedandstored samples,thusproviding repeatedmeasurementsoftheinternal exposome.InPhase2,omicsaretestedfortheirassociationwith healtheffectsusingadditionaluntargetedanalysesinsamplesfrom ALTSandMCO,accordingtoacase-cohortstudydesign.In addi-tion,thereﬁnedexposureestimatesderivedfromtheexternaland internalcomponentsof theexposome, whichtakeintoaccount individualvariation,arecomparedtoestimatesderivedfrom tradi-tional,lessaccurateexposureassessmentmethods,thusleadingto acalibrationofestimatesofexposureinlong-termstudies. Sub-sequentlytheassociationofcalibrated exposureestimates with selecteddiseasesisassessed.

Advantagesofthechosenstudydesignarethatitmakesit pos-sibleto:

(a)Linkshort-termexperimentalstudies,havingcontrolled expo-sureconditions,withlong-termstudiesinthesamegeographic areas;

(b)Address the effects of common exposures at different life stages;

(c)Addressindividualvariability(inspaceandintime)through repeated PersonalExposure Monitoring and omic measure-mentsinrepeatbloodsamples;

(d)Incorporateindividualvariabilityandbiomarkersofexposure intoexposureestimatesandriskassessmentforselected dis-eases,thusreducinguncertaintyinriskmeasures.

5. Experimentalstudies,shorttermeffects(STS)

Short-termexperimentalstudiesonairpollution(OxfordStreet RandomizedTrial,ref.(McCreanorandCullinanetal.,2007);and TAPAS)areutilized.Inthesestudiesvolunteersareexposedtohigh orlowlevelsofairpollutionwithindifferenturbancontexts.During eachsession,anumberofoutcomemeasurements(lungandheart function)havebeenmadewhilesimultaneouslymeasuring PM10-2.5,PM2.5-0.1andPM0.1.Inadditiontothesemeasurements,inthe EXPOsOMICSprojectadductomics,metabolomics,transcriptomics, andepigenomicsareappliedtoevaluatetheinternalexposomeon bloodsamplescollectedbeforeandaftertheexposureperiods,to searchforbiomarkersassociatingwithacuteexposures(PEM)and acutehealtheffects.

Exposuretowatercontaminantsandtheirshort-termeffects hasbeenevaluatedin theongoing PISCINAexperimentalstudy ofswimmersinpools,whereindividualshave highexposureto waterdisinfectionby-products(DBPs)throughskinabsorptionand inhalation.Studiesonswimmershavebeenfoundtobea good modelforhigh-exposuretoDBPsindrinkingwaterinthegeneral population.ExistingPISCINAsamples(ref.(Kogevinasetal.,2010; Richardsonetal.,2010))havebeencomplemented,inthisproject, withadditionalsamplescollectedfromswimmersinpoolswith differentlevelsofwaterDBPs.Theexternalandinternalexposome havebeenmeasuredintheseindividualsbeforeandafter swim-mingand complemented,forpurposes ofphenotypicanchoring (i.e.ofcorrelatingwithphenotypiceffects)(Kogevinasetal.,2010), withmeasurementsofmicronucleiinbloodandurine,bacterial mutagenicity(ofurinesamples),andinﬂammationandoxidative stressmarkers.

6. Observationallife-coursestudies:mother-childcohorts (MCO)andadultlong-termstudies(ALTS)

InPhase1,measurementsoftheinternalandexternal expo-somehavebeenmadeinrepresentativesubjectsof(a)ﬁvebirth

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cohorts(RHEA,ENVIRONAGE,INMA,ALSPACandPICCOLI+),(b)the EPIC,ECRHSandSAPALDIAadultcohorts,exploitingdata gener-atedwithinthelargeESCAPEconsortiumforairpollution,and(c) astudyoncoloncancer(MCC-Spain)usedfortheinvestigationof waterpollution.

Thesedataareutilizedforin-depthstudiesaimedatthe devel-opmentofimprovedexposureestimatesbasedontheexternaland internalexposomeconceptinachronicexposurecontext.Someof theabovecohortshavestoredsamplesofvenousbloodcollected atdifferenttimesoffollow-upandmostofthebirthcohortshave cordblood.Wereconstructtheinuteroexposomebasedoncord bloodsamplesintegratingdifferentpotentialcriticalexposure win-dowsofairpollutionexposure.Epidemiologicalandpublichealth signiﬁcanceoftheinuteroexposomewillbeinvestigatedbased onassociationswithchildhealthanddevelopmentincludingbirth weight,growth,obesityandotheroutcomes.

7. Exposures 7.1. Airpollution

Epidemiologicalstudieshave traditionallyassigned exposure basedonmonitoringresultsofacentralmonitoringsiteinthecity ofresidence(Jerrettetal.,2005).Inthelastdecade,methodsfor moreindividualexposureestimateshavebeenapplied,including landuseregressionmodels(LUR)anddispersionmodels(Jerrett etal.,2005;Hoeketal.,2008).Anumberofstudieshaveshown differencesbetweenlong-termaveragepersonalandambientair pollutionexposure(VanRoosbroecketal.,2005).Inthelatter stud-ies,integratedpersonalsamplerswereusedthatprovidedasingle sampleover1–2days.Hencenoassessmentofthecontributionof differentmicroenvironmentstototalexposure(anddifference)was possible.

Recentadvancesinairpollutionmonitoringtechnologymean thatpersonalmonitoringcanbeundertakenwithreal-time moni-tors,capableofloggingminute-to-minutevariabilityinexposures. Sensorsareportable and sensitive.Coupling thenewair pollu-tion monitors with GPS and accelerometry from smartphones, andinformationonpersonalactivitypatterns,thepotentialnow exists to differentiate between exposure during journeys and ﬁxed-site locations and to assess the contribution of different micro-environmentstothemagnitudeandvariabilityof environ-mentalexposures.However,noproductexistedattheoutsetof EXPOsOMICSthatintegratedthesetechnologiesintoasingle per-sonalmonitoringsystemthatcanbedeployedwithlayusers(e.g. cohorts),aproblemthathasbeenaddressedbythecurrentproject (seebelow).

Todate,themajorityofevidenceaboutthehealtheffects of airpollutionfromepidemiologyandcontrolledexposurestudiesis basedonparticlemassasthemeasureofexposure.Consequently, regulatoryagencieshaveadoptedmass-basedambientairquality standards.Yetparticulatematter(PM)isacomplexmixture,and particlesofdifferentsizeandcompositionarelikelytohave differ-enttoxiceffects.TheEU-fundedESCAPEstudyhascomprehensively characterizedsourcesofoutdoorairpollutionanddeveloped ambi-entLURmodelsforPM10,PM2.5andNO2.Modelsarecurrentlyin developmentforelementalcomposition(XRF;X-rayfluorescence), EC/OC(elementalcarbon/organiccarbon)andPAHs(polycyclic aro-matichydrocarbons).Thereis,however,aneedtodevelopmodels forultrafineparticlesforwhichthelong-termhealtheffectshave beenpoorlystudiedbecauseofdifficultiesinexposureassessment. Thisisnowpossibleusinganinnovativemobilemonitoringdesign thathasbeen showntobereliable and cost-effectivein recent studies(Klompmakeretal.,2015;Montagneetal.,2015).

Oneof theproperties of particleslikely toreﬂect toxicity is theiroxidativepotential(OP).Byanalyzingthespatialand tem-poralvariabilityoftheOPofparticulatemattercollectedonﬁlters, thedeterminantsofthatvariationarecharacterized,andnew, spa-tiallyresolvedairpollutionmodelsforOPhavebeendeveloped. Theairpollutionmodelsalone,however,onlyprovideinformation onambientoutdoorpollutantconcentrations.Recentadvancesin GIS(e.g.routemodeling)(GulliverandBriggs,2005)and micro-environmentalmodels (e.g.indoor-to-outdoor),have ledto the developmentofmoredetailed,personalexposuremodelswhich canbefedbyrichdatasourcesondetailedpopulationtime-activity patterns.

ToadvancethestateoftheartEXPOsOMICSprovidesan inno-vative framework for air pollution external exposome, via the followingsteps:

1)Thescientiﬁcpartnershaveintegratedinstrumentsfrom equip-mentmanufacturers(e.g.DiSCminiforUFPandBGIpumpfor PM2.5)todevelopanovelintegratedpersonalmonitoring sys-tem comprising UFP and PM2.5 personal air monitors with smartphone technologyaimed at characterization of micro-environments,activitypatterns,andinhalationrates.Oneofthe maintechnologicalinnovationsisthecollectionofvarious mea-surementsandtheabilitytoaccessthemviaasinglesource(i.e. smartphone).

2)Deploy the new personal monitoring system among cohort membersinasubsetofﬁvestudyareas,coveringdifferentsites (e.g.citycentre,suburban,industrial,andrural),tocollectthe largestseriesofdetailedpersonalexposuremeasurementsof UFP,PM2.5andactivitydatainEuropetodate(with simultane-ouslycollectedbloodsamplesforomics).

3)DevelopmodelsinlongitudinalstudiessuchasESCAPE(http:// www.escapeproject.eu/), enriched for all the cohorts in the study,anddevelopmethodstotransferthesemodelsofair pol-lutionconcentrationsbackintime.

4)UndertakeanUFPairpollution“mobilemonitoringcampaign” inthestudyareaswherewealsoperformPEMtodevelopand validatethenewland-useregressionmodelsforUFP.

5)ApplytheOPdepletionanalysistechniquetoextendPMmetrics tolookatOP.PM2.5ﬁlterscollectedduringﬁxed-siteoutdoor monitoringin selectedESCAPE areas have beenanalysed to detectthespatialandtemporalvariabilityinPM2.5-relatedOP andsubsequently createnewland-useregressionmodelsfor OP.Wecompareandassessthespatio-temporaldifferencesin exposureestimatesbetweenOPwiththosefromtraditional par-ticulatemetrics(e.g.PM10,PM2.5).

6)ComparePEMsin3)abovewiththosefromresidentialaddress locationstoquantifythecorrelationbetweenthetwo.We fur-therquantify the spatial and temporal micro-environmental contributionstototalexposures.

7)Assessthepotentialforexposurevariability(or misclassiﬁca-tion)inexposures,e.g.forUFPandPM2.5wherewehaveboth LURmodelsandmeasuredexposures.Comparetraditional(i.e. residentialaddress)andnewmethodstoinformtheomicstudies (seebelow)andhealthriskassessment.

8)DevelopanewEurope-wideairpollutionmodelforPM2.5and methodstoapplyexposuremodelsfortheotherpollutants(OP, UFP)toothercohortsandacrossEuropeforhealthrisk assess-ment.Inordertodevelopexposuremodelsforthepollutants beingstudiedintheexternalexposome,whichcanbeapplied tohealthriskassessment, thetransferability ofthenewLUR modelsandhybridmodelstoothercountries/regionsandtime periodsisinvestigated.Developmodelswhichincorporate satel-litedataeitherasvariablestoenhanceexistingapproachesoras

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monitoringdataaresparseordonotexist.

9)Provide model estimates for single and multiple exposures (i.e. mixtures) to feed into risk models (e.g. partial least-squareregression,ridgeregression,Bayesianmixturemethods) (Chadeau-Hyametal.,2013a;Agieretal.,2016a)tostudythe contribution of single compounds and combination of com-poundstoadversehealtheffectsinchildrenandadults. 7.2. Watercontamination

EXPOsOMICSmakesuseof existingshort-termexperimental studies(STS)andoflong-termpopulationstudies(MCOandALTS). Fortheshort-termscale(PISCINAstudy)directmeasurementsof speciﬁcchemicalsandmixturesinwaterhavemainlybeenused, whilefor thelife-course studiesa combination of modelswith measurementsforvalidationpurposes,smartphonetechnology, extensiveuseof databases, remote sensing and land usemaps areused.European-basedestimateshavebeenobtainedfurther throughsystematicuseofregionalandnationaldatabasesfor dis-infectionby-products(DBPs).

7.2.1. Personalandmicro-environmentalexposure measurements(Fig.2)

In the PISCINA swimming pool study, external exposure measurements include determination of an expanded range of disinfection by-products (DBP) (trihalomethanes, haloacetic acids,MX,chloramines,haloacetonitriles),overcomingtraditional approaches that measure only trihalomethanes. These chemi-calshave been measuredin air and water and/or in biological samplessuchasexhaledbreath(e.g.trihalomethanes)andurine (haloacetic acids) from study subjects. The external exposome iscomplementedwithomicanalysesandsupported(for pheno-typicanchoring)byinvitroassayssuchastheSalmonella(Ames) mutagenicitytest(Maronand Ames,1983)andmammaliancell chronicandacutecytotoxicityandtheSCGE(comet)assay(van Leeuwenet al.,2006).Biologicalsamples(blood,urine, exhaled breathcondensate)havebeenobtainedimmediatelybeforeand afterswimminginthepool.Repeatedbiologicalsampleshavebeen collectedafterswimmingtocoverdifferentformation/elimination kineticsofbiomarkers,thuscontributingtoanimproved quantita-tiveevaluationofinternalexposures.

7.2.2. Long-termexposureassessment

In the mother-child cohorts, external exposure measure-ment includes determinations of a range of DBP chemicals in drinkingwater(trihalomethanes, haloaceticacids,MX, haloace-tonitriles). Measurements for some of these chemicals are available from the EU-funded HiWate project (https://www. researchgate.net/publication/24037941HealthImpactsof Long-TermExposuretoDisinfectionBy-ProductsinDrinkingWater inEuropeHiwate).Inthecolorectalcancerstudy(MCC),exposure modelingofDBPsisbasedontheevaluationoflifetimeresidential historytogetherwiththecollectionofhistoricalinformationon DBPsintherelevantregionsandwatertoxicitytestingfromthe short-termstudies.

7.2.3. European-widewater-borneexposuremodelsforhealth riskassessment

ExposuretospeciﬁcDBPsisderivedforEuropeanpopulations fromroutinelycollectedinformationineachcountry.Thesedata, notcentrallyavailableintheEU,comefromfocuscontactpoints in each country (expanding workcompleted in INTARESE, and HiWate).Inaddition atheoreticalframework forfuture routine

evaluation of water contaminantinformation in Europe is pro-posed.

7.3. Internalexposome:omics

Bypermittingthesimultaneous analysisof largenumbersof potentialtargetswithoutrecoursetopriorhypotheses,omic tech-nologiesprovideuniqueopportunitiesforthediscoveryofanew generation of biomarkers of exposure and disease risk, signifi-cantlyenrichedbymechanisticinformation(Chadeau-Hyametal., 2011; Lan et al., 2004; Smith et al., 2005; Perera et al., 2009; Zhang et al., 2016a; Zhang et al., 2016b; Bictash et al., 2010; NicholsonandRantalainenetal.,2011;Wang-SattlerandYuetal., 2008; Holmes et al., 2008; Calderón-Garcidue ñaset al., 2004). TheEXPOsOMICS projectbothadvancesomic technologiesthat are related toidentification and quantification of environmen-talexposure(metabolomics,adductomics)andappliesmultiplex omictechnologiesthatmeasuredownstream effectsof environ-mentalexposures(epigenome,transcripts,miRNA,proteins).Omic technologiesaredeployedinanuntargetedmannerinboththe short-termandthelong-termstudies,toatotalofapproximately 3000 samples (Table 1). The subjects for untargeted omics in Phase1wereselectedonthebasisofextremeexposures.Publicly availabletoxicogenomicsdatabaseshavebeenexploredto iden-tifycandidateomic-basedexposurebiomarkers.Databasesarethe diXAdatainfrastructure(FP7project:diXA)andtheUS Compara-tiveToxicogenomicsDataBase(http://ctdbase.org/).Thesecontain bothinvitroandinvivotoxicogenomicdataand,toadegree,omic resultsfromexposedhumans,andhavelinkstoimportantchemical toxicitydatabasessuchastheUSNTPandtheOECDeChemPortal. WewillattempttoreplicatethesignalsofPhase1inselected case-cohortstudiesforatotalofapproximately2000subjects.Oneof themainproductsoftheconsortiumwillbeendophenotypes,i.e. intermediatephenotypescommontoseveraldiseases.

Untargetedomicanalysesingeneralareconductedinsubjects forwhomtheexternalexposomehasbeenmeasuredand,where possible,allomicplatformsareusedinthesamesubjectstoenable cross-omicanalyses.

7.3.1. Metabolomics(MStechnology)(Fig.3)

Plasmaorserumsampleshavebeenanalysedbyhigh resolu-tionmassspectrometry(MS)coupledtoUPLC.Metabolicfeatures characterizingexposedgroupsareidentiﬁedbymultivariate statis-ticswithappropriatecorrectionfortheFalseDiscoveryRate(FDR), andeffortstoidentifythediscriminatingmetabolitesaremade.A databaseofknownbiomarkersfromthemainenvironmental con-taminantsinairandwaterasmeasuredinvariouspopulationshas beencompiled fromthescientiﬁcliteratureintothe Exposome-Explorerdatabase(http://exposome-explorer.iarc.fr/).

7.3.2. Adductomics

Applicationofadductomicstotheexposomeconceptinvolves anuntargeted investigationof theinternal exposomebased on themeasurementofcompletecategoriesoffeatures(e.g.protein adducts).Theuntargetedapproachhasbeenrealisedbyfocusing onaspecificlocusofHSA,andusingMSofhydrolysisproductsto profilecovalentmodificationsoverarangeofmasses.

7.3.3. Intermediateomicbiomarkers

Additional omic technologies for the analysis of biological moleculeshavebeenusedtomeasurethemoredownstreameffects ofenvironmentalexposures.Theseincludetheeffectonthe tran-scriptome,theepigenomeandtheproteome.Measurementshave beenconductedusing:

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Table1

Characteristicsofthecohorts.

Typeofstudy Cohort StudyCharacteristics

ExperimentalStudies OxfordStreet Thestudyisarandomizedcross-overtrialontheacuteeffectsofambientexposuretotrafﬁcand exacerbationsofcardiovascularandchronicpulmonarydisease.59adultswererecruitedfortheEXPOsOMICs project:20healthyadults,20COPD(ChronicObstructivePulmonaryDisease)and19IHD(IschaemicHeart Disease).TheywereinvitedtowalkinOxfordStreet(highexposure)orinHydePark(lowexposure),London. Aseriesofclinical,physiologicalandinﬂammatoryresponsesarecomparedbetweenthetwoexposuresand tothosemeasuredinhealthycontrolsundergoingsimilarexposures,aswellasvariousomicsmeasurements (transcriptomics,metabolomics,adductomics)obtainedbefore,4hafterand24hafterwalking.

TAPASBarcelona TheTAPAS2Barcelonastudyfollowsacrossoverstudydesigninvolving30healthy,non-smokingadultsin theagerangeof18–60.Volunteerswereexposedfrom8amto10amtoeitherheavilypollutedair(busystreet inmorningrushhour,trafﬁcsite)ortolowdensetrafﬁcairpollution(controlsite)inBarcelona,Spain.Six subjectswerestudiedsimultaneouslyoneachstudyday:threeofthemperformedmoderatephysicalactivity, ridinganergometer(intermittent15mincyclingand15minbreak)whiletheotherthreevolunteersstayedat rest.Participantsthatperformedphysicalactivityhadtocycleatsuchapacethattheirheartratewas50–70% oftheirmaximumrate.

PISCINA2 TheexperimentalPISCINA2study–consistingof60healthynon-smokingvolunteers–assesseda)exposure tochlorinationbyproductsinswimmers,b)short-termrespiratoryhealtheffectsandc)geneticdamageafter swimming.Blood,urine,andexhaledbreathcondensate(EBC)werecollectedfromthevolunteersbeforeand immediatelyafterswimminginachlorinatedpoolfor40min.Transcriptomics,metabolomics,adductomics andconcentrationsofselectedinﬂammatorymarkersweremeasuredinbloodsamplesobtainedbeforeand 2haftertheexperiment.

Mother-ChildCohorts RHEA Mother-Child StudyinCrete

Themother-child“Rhea”studyinCreteisaprospectivecohortexaminingapopulationsampleofpregnant womenandtheirchildren,attheprefectureofHeraklion(n=1500).Thestudyaimsaretoevaluatea) nutritional,environmental,biologicalandpsychosocialexposuresintheprenatalperiodandinearly childhood,b)theassociationoftheseexposureswiththedevelopmentofthefoetusandthechild,c)mother’s healthduringandafterpregnancy,andd)geneticsusceptibilityandtheinteractionsbetweengeneticand environmentalfactorsaffectingchildhealth.Asetof100childrenfromtheRHEA-cohortisincludedinthe EXPOsOMICSchildrenstudies,forwhichdataoncordbloodDNA-methylation,metabolomics,proteomicsand albuminadductsareavailable.

INMA

Environmentand Childhood

TheINMA−INfanciayMedioAmbiente(EnvironmentandChildhood)projectfollowsupapopulation sampleofaround3100pregnantmothersandnewborns.Newandexistingcohortsofpregnantwomenhave beenincorporatedfromsevendifferentSpanishregions.Pregnantwomenareassessedat12,20and32weeks ofgestationtocollectinformationaboutenvironmentalexposuresandfoetalgrowth.Childrenareassessedat birth,attheageof1yearandattheageof4years.Thecohortshavebeendesignedtoevaluatetheimpactof environmentalexposuresanddietonchildren’shealth.Asetof100childrenfromtheINMAcentreSabadellis includedintheEXPOsOMICSchildrenstudies,forwhichdataoncordbloodmetabolomics,proteomicsand albuminadductsareavailable.

ALSPAC TheAvon LongitudinalStudy ofParentsand Children

ALSPACrecruitedmorethan14,000pregnantwomenwithestimateddatesofdeliverybetweenApril1991 andDecember1992.Outcomesincludeprenatalandbirthhealthevents,growth,neurodevelopment, behaviouralfunctioning,lungfunctionandcardiovascularmeasurements.NitrogenoxidelevelsandPM10 estimatesbasedonhomelocationareavailablefortheinuteroandearlylifeperiod.Asetof2300children fromtheALSPACstudyisincludedintheEXPOsOMICSchildrenstudies,forwhichdataonmetabolomicsat7 yearsofageisavailable.DNAmethylationfor650childrenisavailableincordbloodandat7and15yearsof age.Alsoinformationaboutgrowthtrajectories,asthma/lungfunctionandneurocognitivetestsareavailable. Piccoli+ Piccoli+isamulticentricItalianbirthcohortthatrecruited3000new-bornsandtheirmothersin5centres: Turin,Trieste,Florence,ViareggioandRome(www.piccolipiu.it).Participantsreceiveafollow-upinterview6, 12and24monthsafterdeliveryandamedicalexaminationwhenthechildrenturn4.Growthtrajectoriesand neurocognitivetest-resultsareavailable.Asetof99childrenfromtheTurincentreisincludedinthe EXPOsOMICSchildrenstudies,forwhichdataoncordbloodDNAmethylation,metabolomics,proteomicsand albuminadductsareavailable.

EnvironAge ENVIROnmental inﬂuenceON AGEinginearlylife

TheENVIRonAGE(ENVIRonmentalinﬂuenceONAGEinginearlylife)cohortincludes1300mother-infant pairsandfurtherrecruitmentisongoing.Dataincludemothers’lifestyleandsocio-economicstatus, gestationalhistory,measurementsincludingthenew-borns’bloodpressure(allhealthywithgestationalage 37–42weeks),biobankedplacentaltissueandcordbloodincludingRNA/DNA,toxicmetalsincordbloodand placenta,andinuteroandearlylifeexposuretoﬁneparticulatesandNO2usingaspatialtemporal

interpolationmethod.Asetof200childrenfromtheENVIRonAGE-cohortisincludedintheEXPOsOMICS childrenstudies,forwhichdataoncordbloodDNAmethylation,transcriptomics,metabolomics,proteomics andalbuminadductsareavailable.

AdultCohortsand Case-ControlStudies

SAPALDIA TheSAPALDIACohortwasrecruitedatbaselinein1991asrandomsamplesfrompopulationregistriesof8 differentcommunities.Theaimistoinvestigatethevariousdeterminants−includingenvironmentaland genetic−ofasthmainadults.Exposuremodelingincludedareaspeciﬁcoutdoormodelsofthekeypollutants (UFP,PM2.5,PM10,NO2andPM2.5chemicalconstituentsincludingsoot,tracemetalsandinorganicions). BloodandDNAsampleswerecollectedintoabiobankattheﬁrstandsecondfollow-upandthecohorthas genome-widedataonover4000participants.Sapaldiaprovides200asthmacasesandtheirmatchedcontrols toEXPOsOMICS.Matchingisbysmoking(never/timesincequitting),gender,agegroups,seasonof recruitment,yearofrecruitmentinthecohort.

European Community RespiratoryHealth Survey (Norwichand Barcelona)

TheEuropeanCommunityRespiratoryHealthSurvey(ECRHS)includedsitesinEastAngliaandin Barcelona.In1991/2randomsamplesofadultsaged20–44wereinvitedtocompleteashortquestionnaire askingaboutsymptomsofasthma(n=3000ineachcentre).Randomsubsamples(plusadditionalcaseswith symptomssuggestiveofasthma)underwentextensiveclinicalinvestigationincludingextended

questionnairesonallergicandlungsymptoms,useofhealthservicesandindoor/occupationalexposures. Spirometry(baselineand2follow-ups),andinformationonatopy(baselineandtwofollow-ups),

self-reportedrespiratorysymptoms,andallergicdiseaseswerecollected.ParticipantsinNorwich,EastAnglia participatedinPEM(seebelow).40asthmacasesand40matchedcontrolparticipatedinEXPOsOMICSfrom Barcelona,Spain.Matchingbysmoking(never/timesincequitting),gender,agegroups,seasonofrecruitment, yearofrecruitmentinthecohort.

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Typeofstudy Cohort StudyCharacteristics

EPICCVD EPICisamulticenterprospectivecohortstudy,whichrecruited518,408volunteersfrom23centresin10 countries(Sweden,Denmark,Norway,theNetherlands,UnitedKingdom,France,Germany,Spain,Italy,and Greece)between1992and2000.Thestudypopulationincludedvolunteersagedmostly25–70yearsatthe timeofrecruitment.Cancerincidence,mortalitydataandincidentCVDeventswereobtainedattheregional level.CasesofcardiovasculardiseaseandcontrolswereidentiﬁedwithinexistingcohortsofEPICItaly(Torino n=180andVaresen=208)andwitharchivedbloodsamplesavailable.InItaly,incidentcaseswereidentiﬁed viaclinicalrecordsandvalidatedbyacardiologist.Othercriteriaforselection:onlyneversmokersand ex-smokerssinceatleast1year,andanequalnumberofmenandwomen.Matchingbysmoking(never/time sincequitting),gender,agegroups,seasonofrecruitment,yearofrecruitmentinthecohort.

MultiCancer ControlStudy Spain(MCC)

MCC-Spainisamulti-case-controlstudyconductedin11Spanishprovinceswiththeaimtoevaluate environmentalandgeneticfactorsassociatedwithprevalenttumours,usingacommonsetofpopulation basedcontrols.Incidentcasesandpopulation-basedmatchedcontrols20–85yearsoldhavebeenrecruitedin Barcelona,Madrid,León,Murcia,Gipuzkoa,Navarra,Asturias,Granada,Huelva,ValenciaandCantabria provincesbetween2007and2013.Thecancersitesevaluatedarecolorectal,breast,prostate,gastricand lymphocyticchronicleukaemia.ThestudypopulationparticipatingintheEXPOsOMICSprojectincludes200 casesofcolorectalcancerand200controls.

PersonalExposure Monitoring(PEM)

PEMhavebeenperformedinsubsamplesofexistingcohortsinﬁvestudyAreas(Italy(ITn=42),the Netherlands(NL=44),Spain(SP=40),Switzerland(CH=48),andtheUnitedKingdom(UK=31)).InIT,NL,CH andUKadultswererecruited;inSPchildrenaged8–12yearsold.Allsubjectscarriedabackpackcontaining airpollutionsensorsandbatteries,andabeltcontainingasmartphone(andalsoseparateGPSand

accelerometerinsomeareas)for24htomeasureindividualexposuretoPM2.5andUFP.Thestudytookplace overayearperiodduringthreedifferentseasons(Summer,WinterandSpring/Autumn).AftereachPEM session,abloodsamplewascollectedforOMICsAnalysisinalladults.Tointerprettheairpollutionand biologicalmeasurements,asetofquestionnaireswasadministeredtotheparticipants.

Fig.3.InternalExposome–PISCINAstudy–Metabolomicanalysis(PLS-DAandtophits)–Exposuretodisinfectionby-productsandtheirshort-termeffectsinswimmers– Untargetedmetabolomicsofbloodsamples.N=3761metabolomicfeaturesdetected(courtesyKVanVeldhoven).

•Transcriptomics(Agilentplatform)(numberofsignalsper sam-ple:44k)

•Epigenomics:

GlobalDNAmethylation(Illuminaplatform)(numberofsignals persample:450k)

microRNAanalysis(Agilentplatform)(numberofsignals per sample>1000)

•Targetedproteomics(LuminexMultianalyteProﬁlingplatform forinﬂammation-relatedproteins)(numberofsignalspersample

∼30).

8. Dataanalyses

8.1. Dataprocessingandintegration:dataanalysis

This project has created a dedicated data infrastructure by buildingonexistingdatarepositoriestoavoidredundancy(atthe

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GenedataSME).Wehavedefinedquerymethods,workflowsfor qualitycontrol,dataharmonizationandnormalizationinorderto makeallthedataavailableforintegratedanalysis.Data integra-tionincludesfivetypesofdata:(a)datafromexistingdatabases (e.g.OMI,diXA,CTDB);(b)externalexposuredatafrom experimen-tal/observationalpopulationstudies;(c)untargetedomicdata;(d) healthinformationfromcohorts,indifferentagegroups;and(e) otherexposuresfromcohorts(confounders,effectmodifiers).

Thecomplexityoftheprojectdesignanddatacollectedposes new methodological and analytical data challenges. Main such challenges are: developing data analytics to address multiple exposures (both external and internal) in exposome-phenome associationstudies; identificationof statistical tools toaddress thehierarchicalstructurewithinexternaland internalexposure data(e.g.cross-omicsanalyses);andimplementinganalyticaltools toaddressthedynamicsoftheexposome-phenomeassociations overlife.Afirstefforthasbeenmadebycomparingthe perfor-mancesoflinearregressionbasedstatisticalmethodsinassessing exposome-phenomeassociations.Inanextensivesimulationstudy usingrealisticempiricalexposomedatawehaveshownthat corre-lationbetweenexposuresisindeedamajorchallengeforexposome research,andthatcurrentstatisticalmethodsarelimitedintheir abilitytoefficientlydifferentiatetruepredictorsfromcorrelated covariates.However,somecurrentlyavailablemethodslikeGUESS andDSAdoprovideamarginallybetterbalancebetweensensitivity andFDP,althoughtheydidnotoutperformtheothermultivariate methodsacrossallscenariosandpropertiesexamined.Futurework inExposomicsisfocusedonextendingthesemethodstobeable toadopttheaforementionedcomputationalcomplexityandtobe abletoincorporateexternalinformationfrompreviousresearch, networkontologies,andbiologicalpathways.Somedetailscanbe foundinmethodologicalpapersthathaveemergedfromthe col-laborationbetweenEXPOsOMICSandHELIX(Agieretal.,2016b; Chadeau-Hyametal.,2013b).

8.2. Exposurecalibration

The multidisciplinary expertise of the EXPOsOMICS group enablesthecollection,inseveralpopulations,ofmodeled expo-suresbasedonrefinedmodelsandcuttingedgetechnologies,as wellasPEM.Basedonsamplesinwhichbothtypesofdataare avail-able,wedefinecalibrationcoefficientstooptimizethepredictionof trueexposure(fromPEMstudies)fromcombinationsofvariablesin themodeledexposurematrices.Statisticalmethodsinclude clas-sicalmeasurement errormodelsand theirBayesianalternatives providingcalibratedexposureestimatesforairandwater pollu-tants.TheresultingestimatesareusedwithinEXPOsOMICSa)for thecharacterizationoftheinternalresponsetotheexternal com-ponentoftheexposomeandb)tostudytheassociationbetween thecalibratedexposuresanddiseaseendpoints.

8.3. Development,validationandinvestigationofinternal markersofexternalexposures

EXPOsOMICShasprovidedalargerangeof−omicproﬁlesin populationswherePEMandcalibratedexposuresarealsoavailable. Suchanalysesrelyontheadaptation,intheexposomeconcept,of methodsemployedinGWAS.Mainlyweuseasabenchmark uni-variateapproaches(linearorgeneralizedlinearmodels)coupled toad-hocmultipletestingcorrectionandFDRcontroltechniques. Bayesianvariable selection methods, typically seeking thebest combination of markers (omic signals) topredict the outcome (exposurelevels)areusedherefortheﬁrsttime.Inaddition,these analysesprovideanestimateofthevarianceinindividualresponses atsimilarexposurelevels.Finally,in ordertoidentifypotential commonpatterns inthe internal signatureof exposures across

platforms,thecorrelationsbetweenproﬁlesfromeachplatformare analysedwithrespecttotheexternalexposures.These‘cross-omic’ analysespotentiallyidentifyfeatures ofthebiologicalpathways linkingexposuretodiseaserisk,andrelyonwell-established meth-odssuchasnetworkandclusteringmethods.

8.4. Riskanalyses

Thecalibrationalgorithmsandestimatesofmeasurementerror andinter-individualdifferencesinbiologicalresponsesareusedto performupdatedriskanalysesintheshort-andlong-termeffect studies,usingstandardparametricandnon-parametricrisk func-tionsandadvancedriskanalysisproceduresthataccountforthe contributingeffectsofoften-correlatedexposures(e.g.partialleast squareregression,ridgeregression,andBayesianmixturemodels). Intheshort-termstudies(suchastheOxfordStreetRandomized Trial) EXPOsOMICS relatesthe external and internal exposome markersandproﬁlestoacuteoutcomessuchaspulmonaryfunction measures,otherpatho-physiologicaloutcomes,heartfunctionand asthmaepisodes.ForPISCINA,correlationsbetweenexternaland internalexposomemarkersareinvestigatedtogetherwith geno-toxicityoutcomes.Inthelong-termstudiesthecalibratedexposure estimatesandinternalexposuremarkersareappliedtostudythe associationbetweensubfractionsofairpollution(PMs,UFP,and oxidative stresspotential), watercontaminantsand health out-comesindifferentareasofEurope.Thisprojectusescase-cohort studieswithinthepooledcohorts,intwoways.InPhase2risk esti-matesforselecteddiseasesareperformed,i.e.asthma(inadults andchildren),CVDandcoloncancerinadults,and neurodevelop-mentinchildren,foratotalofapproximately1100casesand1100 controls.Forthesesubjectstheconsortiumhascompletedataon targetedanalyses,PEM-calibratedexposureassessment,and well-phenotyped disease outcomes. We have performed untargeted omicanalysesinPhase2,toallowustogeneratenew hypothe-sesontheetiologyoftheselectedhealthoutcomes,butalsoto test whethertheomic candidatesselected fromPhase1 (asso-ciatedwithexposures)predicttheriskofdisease.Subsequently, theseanalysesareextendedtoriskanalysesofgroupsof500–1000 casesformajordiseasesthathavebeenassociatedwithair pol-lutionorwatercontamination,andthecorrespondingcontrols.In thisenlargedriskestimationexercise,exposurecalibrationbased onPhases1and2isextendedtoexposureassessmentforatotal ofseveralthousandsofsubjectsinEurope.Lastly,theinclusionof adultandchildren cohortsusingthesameexternalandinternal exposomemethodologiesallowstheinvestigationofage-speciﬁc responsestoenvironmentalexposuresatcriticallifestages. 9. Conclusionsandlessonslearned

EXPOsOMICSisoneofthefirstsizableprojectsonthe expo-some.Thoughtheoriginalconceptoftheexposomeimpliesthat thestudyofalllife-courseexposuresneedstobeconsidered,this goaliscurrentlyclearlynotachievablebyasingleinvestigation.We chosethereforetofocusonairpollutionandwatercontaminants astwocommonsetsofexposures,andtoexplorethelife-course bylinkingpopulationstudiesrecruitedatdifferentages.Thereare severalchallengesinthestudyweareundertaking,whichinclude theuncertaintyin findingmeaningfulomicsignals fromlowor verylowenvironmentalexposures;theabilitytoinvestigate mix-tures(i.e.leadingtoeffectsthataredifferentfromthesumofsingle exposures);theabilitytoinvestigatedose-responserelationships withsufficientpower;thechallengeofextrapolatingPhase2 find-ings(innestedcase-controlstudies)toPhase3,i.e.inlargercohort studies,aftercorrectionformeasurementerrors.Therearealso lim-itationsinourproject,namelythelimitedsamplesizeandthelack

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10 P.Vineisetal./InternationalJournalofHygieneandEnvironmentalHealthxxx(2016)xxx–xxx ofrepeatedsamplesincohorts;thisisanincentivetoestablish

con-nectionswithsimilarprojectsandplanpooledormeta-analyses. Themainlessonslearnedare:(a)itisdifﬁculttoharmonize sev-eralsmallstudiesintheattemptoflinkingagegroups,andasingle largecohortencompassingallages,withlongfollow-upandwith repeatedsampleswouldbebettersuited.In thecurrentproject wearetakingastatisticalapproachthatlinkstogetherthe differ-entstudies,treatingthemasphasesofalife-longexperience,but thisapproachhasitsownlimitations(particularlypowerand com-parability).(b) Exposure measurement technologiesare rapidly evolvingandsuchevolutionneedstobemonitoredintheyears aheadtoallowarapidincorporationintoepidemiologicalstudies. Inspiteofcostreductionitislikelythatforalongtimeincorporation ofthenewsensorswillbepossibleonlyinhundredsorthousands ofsubjects,thusmakingtheapproachwehaveused(calibration) arealisticandconcretewayahead.(c)Thesameappliestoomic technologies,withtheadditionalneedtoevaluatetheirreliability andapplicabilitytosamplesstoredfromlargecohorts.

ThedataproducedbyEXPOsOMICSwillbemadepublicly avail-able.

Morethanadecadeaftertheoriginalpublicationofthe expo-someconcept,followedbyyearsofdiscussionsondeﬁnitionsand challengesofimplementing theconceptintoresearch,thetime hasnowcometomovetheconceptfromtheorytopractice. EXPO-sOMICSservesasaproof-of-principleoftheexposomeconceptand throughitsimplementationitwillinformfutureexposomestudies. Conﬂictofinterest

Nonedeclared Acknowledgment

ThisworkhasbeensupportedbytheExposomicsECFP7grant (Grantagreementno:308610)toPV.

References

Agier,L.,etal.,2016a.Environ.HealthPerspect.,May24Epubaheadofprint.

Agier,L.,etal.,2016b.Environ.HealthPerspect.,May24inpress.

http://exposomealliance.org.

Ben-Shlomo,Y.,Kuh,D.,2002.Int.J.Epidemiol.31(April(2)),285–293.

Bictash,M.,etal.,2010.J.Clin.Epidemiol.63(September(9)),970–979.

Calderón-Garcidue ˜nas,L.,etal.,2004.Toxicol.Pathol.32

(November–December(6)),650–658.

Chadeau-Hyam,M.,etal.,2011.Biomarkers16(February(1)),83–88.

Chadeau-Hyam,M.,etal.,2013a.Environ.Mol.Mutagen.54(August(7)),542–557,

http://dx.doi.org/10.1002/em.21797,Epub2013Aug5.Review.

Chadeau-Hyam,M.,etal.,2013b.Environ.Mol.Mutagen.54(August(7)),542–557.

deNazelle,A.,etal.,2011.ISEEProceedings,Sept.

Dippold,M.,2006.Personaldeadreckoningwithaccelerometers.Universität

BremenMobileResearchCenter.

Dons,E.,etal.,2011.Atmos.Environ.45(21),3594–3602.

Gulliver,J.,Briggs,D.J.,2005.Environ.Res.97(1),10–25.

Hoek,G.,etal.,2008.Atmos.Environ.42,7561–7578.

Holmes,E.,etal.,2008.Nature453,396–400.

Jerrett,M.,etal.,2005.J.Expo.Anal.Environ.Epidemiol.15(2),185–204.

Klompmaker,J.O.,etal.,2015.Sci.TotalEnviron.508(March(1)),266–275,http://

dx.doi.org/10.1016/j.scitotenv.2014.11.088,Epub2014Dec5.

Kogevinas,M.,etal.,2010.Environ.HealthPerspect.118(11),1531–1537.

Lan,Q.,etal.,2004.Science306(December(5702)),1774–1776.

Maron,D.M.,Ames,B.N.,1983.Mutat.Res.113,173–215.

McCreanor,J.,Cullinan,P.,etal.,2007.N.Engl.J.Med.357(December(3)), 2348–2358.

Montagne,D.R.,etal.,2015.Environ.Sci.Technol.49(14(July)),8712–8720,

http://dx.doi.org/10.1021/es505791g,Epub2015Jul8;

Plewa,M.J.,etal.,2008.Washington,D.C.:AmericanChemicalSociety,36–50. Nicholson,G.,Rantalainen,M.,etal.,2011.Mol.Syst.Biol.7,525.

Perera,F.,etal.,2009.PLoSOne4(2),e4488.

Rappaport,S.M.,Smith,M.T.,2010.Science330(6003),460–461.

Rappaport,S.M.,etal.,2011.Toxicol.lett.,Apr8.

Richardson,S.D.,etal.,2010.Environ.HealthPerspect.118(11),1523–1530.

Smith,M.T.,etal.,2005.Chem.Biol.Interact.153–154(May(30)),123–127.

VanRoosbroeck,S.,etal.,2005.Atmos.Environ.41,3381–3394.

Vineis,P.,etal.,2009.Environ.Health30(8),54.

vanLeeuwen,D.M.,etal.,2006.(August(1–2)),12–22.

Wang-Sattler,R.,Yu,Y.,etal.,2008.PLoSOne3(12).

Wild,C.P.,2005.CancerEpidemiol.BiomarkersPrev.14(August(8)),1847–1850;

Wild,C.P.,2012.Int.J.Epidemiol.(January(31)),PubMedPMID:22296988.

Zhang,Y.,etal.,2016a.Ecotoxicology18(6),722–728.