MARK-AGE biomarkers of ageing

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MechanismsofAgeingandDevelopment151(2015)2–12

ContentslistsavailableatScienceDirect

Mechanisms of Ageing and Development

j o ur na l h o me p a g e:w w w . e l s e v i e r . c o m / l o c a te / m e c h a g e d e v

MARK-AGE biomarkers of ageing

Alexander Bürkle

^a,∗

, María Moreno-Villanueva

^a

, Jürgen Bernhard

^b

, María Blasco

^c

,

Gerben Zondag

^d

, Jan H.J. Hoeijmakers

^e

, Olivier Toussaint

^f

, Beatrix Grubeck-Loebenstein

^g

, Eugenio Mocchegiani

^h

, Sebastiano Collino

ⁱ

, Efstathios S. Gonos

^j

, Ewa Sikora

^k

,

Daniela Gradinaru

^l

, Martijn Dollé

^m

, Michel Salmon

ⁿ

, Peter Kristensen

^o

,

Helen R. Grifﬁths

^p

, Claude Libert

^q

, Tilman Grune

^r,s

, Nicolle Breusing

^r

, Andreas Simm

^t

, Claudio Franceschi

^u

, Miriam Capri

^u

, Duncan Talbot

^v

, Paola Caiafa

^w

, Bertrand Friguet

^x

, P. Eline Slagboom

^y

, Antti Hervonen

^z

, Mikko Hurme

^z

, Richard Aspinall

^A

aMolecularToxicologyGroup,DepartmentofBiology,Box628,UniversityofKonstanz,78457Konstanz,Germany

bBioTeSysGmbH,73728Esslingen,Germany

cSpanishNationalCancerResearchCentre(CNIO),3MelchorFernandezAlmagro,28029Madrid,Spain

dDNageBV¹,Leiden,TheNetherlands

eDepartmentofGenetics,ErasmusUniversityMedicalCenter,P.O.Box1738,3000DRRotterdam,TheNetherlands

fUniversityofNamur,ResearchUnitonCellularBiology,RuedeBruxelles,61,NamurB-5000,Belgium

gResearchInstituteforBiomedicalAgingResearch,UniversityofInnsbruck,Rennweg,10,6020Innsbruck,Austria

hTranslationalResearchCenterofNutritionandAgeing,IRCCS-INRCA,ViaBirarelli8,60121Ancona,Italy

iNestléInstituteofHealthSciencesSA,MolecularBiomarkers,EPFLInnovationPark,1015Lausanne,Switzerland

jNationalHellenicResearchFoundation,InstituteofBiology,MedicinalChemistryandBiotechnology,Athens,Greece

kLaboratoryoftheMolecularBasesofAgeing,NenckiInstituteofExperimentalBiology,PolishAcademyofSciences,3Pasteurstreet,02-093Warsaw,Poland

lAnaAslan–NationalInstituteofGerontologyandGeriatrics,Bucharest,Romania

mNationalInstituteforPublicHealthandtheEnvironment(RIVM),CentreforPreventionandHealthServicesResearch,P.O.Box1,3720BABilthoven,The Netherlands

nStraticell,ScienceParkCrealys,RueJeanSonet10,5032LesIsnes,Belgium

oDepartmentofEngineering–BCEProteinEngineering,GustavWiedsvej10,8000Aarhus,Denmark

pLifeandHealthSciences,AstonResearchCentreforHealthyAgeing,AstonUniversity,Birmingham,UK

qDepartmentforMolecularBiomedicalResearch,VIB,Ghent,Belgium

rInstituteofNutritionalMedicine,UniversityofHohenheim,70593Stuttgart,Germany

sDepartmentofNutritionalToxicology,FriedrichSchillerUniversityJena,DornburgerStr.24,07743Jena,Germany

tDepartmentofCardiothoracicSurgery,UniversityHospitalHalle,Ernst-GrubeStr.40,06120Halle(Saale),Germany

uCIG-InterdepartmentalCenter“L.Galvani”,AlmaMaterStudiorum,UniversityofBologna,40126Bologna,Italy

vUnileverCorporateResearch,Sharnbrook,UK

wDepartmentofCellularBiotechnologiesandHematology,FacultyofPharmacyandMedicine,“Sapienza”UniversityRome,V.leReginaElena324,00161 Rome,Italy

xSorbonneUniversités,UPMCUnivParis06,UMRUPMCCNRS8256,Biologicaladaptationandageing–IBPS,INSERMU1164,F-75005Paris,France

yDepartmentofMolecularEpidemiology,LeidenUniversityMedicalCentre,Leiden,TheNetherlands

zMedicalSchool,UniversityofTampere,33014Tampere,Finland

ARegenerativeMedicineGroup,CranﬁeldHealth,Cranﬁeld,UK

a r t i c l e i n f o

Articlehistory:

Received2December2014

Receivedinrevisedform19March2015 Accepted21March2015

Availableonline24March2015

Keywords:

Ageingbiomarkers Humanstudies MARK-AGE

a b s t r a c t

Manycandidatebiomarkersofhumanageinghavebeenproposedinthescientiﬁcliteraturebutinall casestheirvariabilityincross-sectionalstudiesisconsiderable,andthereforenosinglemeasurementhas proventoserveausefulmarkertodetermine,onitsown,biologicalage.Aplausiblereasonforthisisthe intrinsicmulti-causalandmulti-systemnatureoftheageingprocess.TherecentlycompletedMARK-AGE studywasalarge-scaleintegratedprojectsupportedbytheEuropeanCommission.Themajoraimofthis projectwastoconductapopulationstudycomprisingabout3200subjectsinordertoidentifyasetof biomarkersofageingwhich,asacombinationofparameterswithappropriateweighting,wouldmeasure biologicalagebetterthananymarkerinisolation.

∗Correspondingauthor.Tel.:+497531884035;fax:+497531884033.

E-mailaddress:alexander.buerkle@uni-konstanz.de(A.Bürkle).

http://dx.doi.org/10.1016/j.mad.2015.03.006

licenses/by-nc-nd/4.0/).

Konstanzer Online-Publikations-System (KOPS) URL: http://nbn-resolving.de/urn:nbn:de:bsz:352-0-295751 Erschienen in: Mechanisms of Ageing and Development ; 151 (2015). - S. 2-12

https://dx.doi.org/10.1016/j.mad.2015.03.006

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1. Introduction

Ageinghasbeendefinedasthetime-dependentdeclineoffunc- tional capacity and stress resistance, associated withincreased riskofmorbidity andmortality.Ageingis aprocessthat affects mostifnotalltissuesandorgansofthebody.Moreover,cross-talk canoccurbetweenmultiplephysiologicalsystems,e.g.metabolic systems may influence the ageingof theimmune system.The mechanismsunderlyingtheageingprocessare beginningtobe unravelledatthemolecularlevel(López-Otínetal.,2013),yetthere isclearevidencethattherateofageingdifferssignificantlybetween membersofthesameanimalspecies,includinghumans.Inother words,the“biologicalage”maydifferfromthechronologicalage.

Theclassical quantitativeassessment of “therateof ageing”

reliesontheanalysisofmortalitycurves(Gompertzfunction)of populations.Inotherwords,individuals havetobefollowedup untiltheendoftheirlives inordertodeterminetheir“biologicalage”at anytime point duringlife.Therefore,at thelevelof alivingindividual,areliableofassessmentofthestateofageing,i.e.

thestateoftheabove-mentionedfunctionaldecline,andapredic- tionoftheriskoftheonsetofmorbidityandtheresidualindividual lifeexpectancyarenotpossiblewiththismethod.

Onestrategytosolvethisproblemistheidentiﬁcationof(an) age-relatedchange(s)inbodyfunctionorcompositionthatcould serveasameasureof“biological”ageandpredictthefutureonsetof age-relateddiseasesand/orresiduallifetimemoreaccuratelythan chronologicalage.Suchparametersaretermed“biomarkersofageing”(BakerandSprott,1988).Thistermhasbeencoinedinanalogy tobiomarkersofspeciﬁcchronicdiseases,suchasHIVinfection,or biomarkersofexposuretotoxins.

TheAmericanFederationforAgingResearchhasproposedthe followingcriteriaforabiomarkerofageing:

1.Itmustpredicttherateofageing.Inotherwords,itwouldtell exactlywhereapersonisintheirtotallifespan.Itmustbea betterpredictoroflifespanthanchronologicalage.

2.Itmustmonitorabasicprocessthatunderliestheageingprocess, nottheeffectsofdisease.

3.It mustbeabletobetestedrepeatedly withoutharmingthe person,forexample,abloodtestoranimagingtechnique.

4.Itmustbesomethingthatworksinhumansandinlaboratory animals,suchasmice.Thisissothatitcanbetestedinlabanimals beforebeingvalidatedinhumans.

Thefourthoftheabovecriteriamay,however,bequestioned astherearecertainlysomeparameterswhoseimportanceforthe

ageingprocessmaydifferbetweenmammalianspecies.Oneexam- plewouldbetelomereshorteningin humansandin laboratory mice:While in humansomatictissues telomereshorteningcan readilybedetected,this isnot thecasein wild-typelaboratory mousestrainsowingtotheirmuchgreateroveralllengthoftelom- eres.Thereforeeliminatingsomecandidateparametersjustbased ontheirlack of relevancein somemodel systems maylead to anexclusionofparametersthatarepotentiallyinterestingforthe humansystem.

Itshouldbenotedthatmanycandidatebiomarkersofhuman ageinghavebeenproposedinthescientiﬁc literaturebutin all cases theirvariability incross-sectional studies is considerable, andthereforenosinglemeasurementhasproventoserveauseful markertodetermine,onitsown,biologicalage.Aplausiblereason forthis istheintrinsic multi-causal(Holliday,2006)andmulti- systemnatureoftheageingprocess.MARK-AGEwasalarge-scale integrated projectsupportedby theEuropeanCommission.The majoraimofthisprojectwastoconductapopulationstudycom- prisingabout3200subjectsinordertoidentifyasetofbiomarkers ofageingwhich,asacombinationofparameterswithappropriate weighting,wouldmeasurebiologicalagebetterthananymarker inisolation.

2. MARK-AGEconsortium

Inordertotacklethescientificproblemofestablishingpower- fulbiomarkersofhumanageing,theMARK-AGEconsortium,which consistedof26 Partnerscomprising 21non-profitorganisations (universitiesandpublicresearchinstitutes),3smallandmedium sizedenterprises(SMEs),and2largecompanies,wasformed.The scientificgroupsinvolvedareattheforefrontinthefieldofageing research,andsomePartnersareinternationalleaderseveninwider fieldssuchasGenetics.TheMARK-AGEconsortiumwascharac- terisedbyahighdegreeofinterdisciplinarity:Thearrayofexpertise rangedfromGeriatrics,EpidemiologyandHumanGeneticstoClin- icalChemistry,Biochemistry,CellBiology,Immunology,Molecular Genetics,BioinformaticsandMathematicalModelling.Suchalevel ofinterdisciplinarityisessentialforthesuccessofaprojectofthis largescale.Theleadresearchersaretheauthorsonthisdocument.

3. TheMARK-AGEstrategy

IntheLarge-ScaleIntegratingProjectMARK-AGE,thePartners proposed to perform a comprehensive and coherent Europe- wide populationstudy aimingat theidentiﬁcationof powerful biomarkers of human ageing across a range of physiological

Fig.1.SchematicrepresentationofthemanagementstructureoftheMARK-AGEproject.

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Table1

MARK-AGEworkpackages.

Workpackagenumber Workpackagetitle

1 Recruitmentofprobandsandphysiological markers

2 DNA-basedmarkers

3 Markersbasedonproteinsandtheir

modiﬁcations

4 Immunologicalmarkers

5 Clinicalchemistry,hormonesandmarkersof metabolism

6 Oxidativestressmarkers

7 Emergentbiomarkersofageingfrommodel

systemsandnovelmethodologicalapproaches

8 Dataanalysisandbioinformatics

9 Disseminationandtraining

10 Projectmanagementandethicalissues

systems.Thestudypopulationcomprisedofabout3200subjects andrepresentedseveraldifferentgeographicalregionsofEurope.

Thestudypopulationcoveredtheagerangeof35–74years,asthis isthetimespanduringwhichprophylaxis/interventiontocounter age-relateddiseasesmaybepossibleandpromising.Awiderange ofcandidatebiomarkerswastested,including(1)“classical”ones forwhichdatafromseveralsmallerstudieshavebeenpublished;

(2)“new”ones,basedonpreliminarydataobtainedinsmall-scale studies,aswellas(3)“novel”ones,basedonrecentresearchon mechanisticaspectsofageing,conductedbyprojectPartners.

It is reasonable to assume that a combination of several biomarkerswillprovideamuch bettertool tomeasure biologi- calagethananysinglebiomarkerinisolation.Ithastobetaken intoaccount,though,thatnotallbiomarkersareofequalweight.

Therefore averaging allpossible candidatebiomarkers may not appropriate.Amajortaskofthisprojectwas,therefore,toopti- misetheweightingofthedifferentmarkers,byusingmulti-variate analysis,withtheaimofreducingvarianceandtoderiveamathe- maticalformulathatwillyielda“biologicalagescore”.Itshouldbe mentionedthatworkperformedinthecontextofthe“MacArthur studiesofsuccessfulaging”onacohortof171adultsaged70–79 hadalreadyprovidedproof-of-conceptbyshowingthatan“allo- static load score”, incorporating 10 biological markers, can be predictiveofmortalityrisk(Seemanetal.,1995).

TheMARK-AGEprojectprovideda systematicapproach:The Consortium established Standard Operating Procedures for the recruitmentofsubjectsandprocessingofsamples(seeMoreno- VillanuevaandCaprietal.,thisissue),aswellasqualitycontrol measures(Jansen et al.,this issue). It was deemedessential to recruitanewpopulationofsubjects,sincepreviousrecruitment effortsperformedinmanyEuropeancountriesneitherhavecov- eredtheagerangeofinterestnorhavetheyprovidedthebiological materialstobestudied,includingcryopreservedbloodcells.

TheactivitieswithintheMARK-AGEprojectweredistributedin WorkPackages(Table1)

3.1. WP1:recruitmentofsubjectsandassessmentofphysiological markers

Twolargegroups ofsubjectswererecruited. Afterexclusion of 138hepatitis positive subjects, theﬁrst group comprised of 2262randomlyrecruitedage-stratiﬁedindividualsfromthegen- eralpopulation(RASIG)fromseveraldifferentgeographicalregions ofEurope.Equalnumbersofmenandwomenwereenrolled,com- prisingsimilarnumbersofindividualsinthefollowingageclasses:

35–39yrs,40–44yrs,45–49yrs,50–54yrs,55–59yrs,60–64yrs, 65–69yrs, 70–74yrs.Thisgroupwasthoughttobedisplaythe

“averagepopulationageingrate”.

Thesecondgroupcomprisedofsubjectsbornfromalong-living parentbelongingtoafamilywithlong-livingsibling(s),suchasthe

“90+sibpairs”recruitedwithintheframeworkoftheEUIntegrated ProjectGEHA,andhenceforthdesignatedGEHAoffspring(GO)(528 subjects).GOcovertheagerangeof55–74years.Accordingtodata fromtherecentliterature,indicatingthatoffspringoflong-living parentsageina“better”waythancontrolsbornfromnonlong- livingparents,GOarepredictedtoageataslowerratethanthe averagepopulation.

WithintheMARK-AGEproject,theGOsubjectsweretherefore comparedwiththeirspouses,henceforthdesignatedspousesof GEHAoffspring(SGO)(305subjects).SystematiccomparisonofGO andSGOcohortsshouldprovideauniqueopportunityforaﬁrstval- idationofthebiomarkersidentiﬁedinthecross-sectionalstudyof theRASIGsubjects.ItisexpectedthatGOdisplayalowerbiological agethanSGO.

Theprojectalsotookadvantageofthefactthatsomerelatively rare‘segmental progeroid syndromes’present characteristicsof dramaticallyacceleratedageingandprematuredeathfromtypi- calageing-associateddiseases.Thisisthecaseforsubjectsaffected byDown’ssyndrome(DS)orWerner’ssyndrome(WS).Duetothe (extreme)rarityofthesesyndromes,onlyasmallnumberofDS subjectswererecruited,whilebiologicalmaterial(serum,plasma, urineandblood)fromWSpatientswasstoredintheMARK-AGE biobank.TheageingprocessofDSandWSsubjectsisbeingcom- paredwithRASIGandGO/SGOsubjects.Itisexpectedthattheir biomarkersindicateahigherbiologicalage,andsothiscomparison isexpectedtoprovideanadditionalvalidationforthebiomarkers identiﬁedinthecross-sectionalanalysisofRASIG.

Inordertoascertainthebiologicalandanalyticalrobustnessof themeasurementsofcandidatebiomarkers,97 donorsfromthe wholestudypopulationhavebeenre-sampledwithin3–6months.

Insuchashorttimeperiod,nosigniﬁcantchangeinthebiological agestatusofthesubjectsisexpected;thereforeanidealbiomarker essentiallyshouldyieldthesameresults.

Finally,alimitedrandomsampleofsubjectswasfollowed-upin asmalllongitudinalstudy,compatiblewiththetimeandbudgetary constraintsoftheproject.Were-tested12%oftherecruitedsub- jectsRASIG,GO,SGO(389subjectsintotal)after3years.Itwas expectedthatthose subjectswhosebiomarkerproﬁleindicated anadvancedbiologicalageatbaselineshoulddisplayasimilaror acceleratedpatternatthe3-yearfollow-up,andviceversaforthe biologicallyyoungerindividuals.

Fromallsubjectsenrolled,anthropometric,clinicalanddemo- graphicdatahavebeencollectedinastandardisedfashion.Upon writteninformedconsent,thefollowing setof information was obtainedbyusingastandardisedquestionnaire:

•Demographicinformation: familycomposition, maritalstatus, education,occupation,andhousingconditions.

•Lifestyle:useoftobaccoandalcohol,dailyactivities.

•Functional status:Activities ofDaily Living(ADL)and Norton Scale.

•Cognitivestatus:STROOPtest,15-picturelearningtest.

•Healthstatus:presentandpastdiseases,self-perceivedhealth, numberandtypeofprescribeddrugs.

•Mood:ZUNGdepressionscale.

Aphysicalexaminationofallsubjectscomprisedmeasurement ofthefollowing“classical”candidatebiomarkers:

•Bodymassindex.

•Waistandhipcircumference.

•Bloodpressureatrest.

•Heartrateatrest.

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•Lungfunction–forcedexpiratoryvolumein1s(FEV1).

•Lungfunction–forcedvitalcapacity(FVC).

•Five-timeschairstanding.

•Handgripstrength.

Allsubjectswereaskedtodonateblood(55ml)byvenipuncture afterovernightfasting.Thebloodsamplewasprocessedtoobtain plasma,serum andperipheralblood mononuclearcells(PBMC).

PBMC werecryopreserved and all the othercomponents were frozendown.Buccalmucosalcellswerealsocollected(usingakit) aswellasspoturinesamples(seeMoreno-VillanuevaandCapri etal.,thisissue).

3.2. WP2:DNA-basedmarkers

Theintegrityofthenucleargenomeandtheepigenomeisof vitalimportancefortheproperfunctionofcells,tissuesandorgans.

Thereis,however,aconstantattackbyexogenousandendogenous compoundsandagents(includingreactiveoxygenspecies[ROS]) thatcandamageDNAand/ordisturbepigeneticregulation.Possible consequencesaremutationanddysregulationofgeneexpression, whicheither canleadtocelldeathorcellularsenescenceorto malignanttransformationofthecellsultimatelyresultingincan- cer.Protectionandmaintenancesystemshaveevolvedthathelp maintainasustainablesteady-statelevelofmoleculardamage,and theseincludeDNArepairsystemsandDNAmethyltransferases.

FurthermoretelomericDNAundergoesattritionwitheachreplica- tioncycleandalsoasaconsequenceofDNAdamage.Acriticalloss oftelomererepeatsequenceshasbeenshowntopreventfurther cellproliferationandinsomecelltypesinducescellularsenescence.

MitochondrialDNAisanespeciallyvulnerabletargetformuta- genesis, in viewof thehigh locallevels of endogenous ROS in mitochondria. Damage and mutation of mitochondrial DNA is viewedasamajormechanismdrivingtheageingprocess(Niemi et al., 2003; Wong et al., 2009; Altilia et al., 2012).Neverthe- lessD-loopregioncontainslevelofheteroplasmyassociatedwith longevity,aspreviouslyidentiﬁed(Roseetal.,2007),suggesting alsomtDNAvariants-basedmechanismsofprotection(Rauleetal., 2014).Further,APOEgenotype,whichisconsideredagoldstan- dardforthegeneticsoflongevityandwasrecentlyre-conﬁrmed (Deelenetal.,2014),wastakenintoaccountwiththebasicideato identifypossiblesubgroupsofindividualswithbestorworsthealth conditions(extremephenotypes).

Our overarching hypothesis was that thepresence of proﬁ- cientsystemstoprevent/repairdamageandmutation(Benekeand Burkle,2007)tothenucleargenome(Realeetal.,2005;Caiafaand Zampieri,2005;Zardoetal.,2002),includingtelomereshortening (Canelaetal.,2007;Benettietal.,2007;Floresetal.,2005;Gonzalo etal.,2006),andtothemitochondrialgenome(Bellizzietal.,2006;

DeBenedictisetal.,1999)shouldhelpretardtheageingprocess inmanyifnotalltissues.Thereforethesecellularfunctionshavea potentialtoserveasbiomarkersofageing.

Thefollowingresearchtaskshavespeciﬁcallybeenaddressed:

•Wehavestudiedthemaintenanceoftheepigenomebyanalysing geneexpressionpatternsinPBMCandcytosinemethylationsta- tus.DNA methylation wastobe correlated withthepossible age-dependentexpressionlevelofsomegeneswhoseexpression hasbeenassociatedwithageingorlongevity.

•Theageing-dependentdeclineofDNArepairwasevaluatedby functionalanalysisoftherepairofDNAstrandbreaksinducedby X-raysandinstudiesoncellularpoly(ADP-ribosyl)ationcapacity andPARP-1expressionlevels.

•Attentionwasalsodirectedtowardstelomerelength,whichis beingcorrelatedwithmodiﬁcationsofsubtelomericDNAmethy- lationpattern.

•The questionof an age-related accumulation of mutationsin mtDNAwastobeaddressedbyquantifyingthelevelofhetero- plasmy.SpecialattentionwaspaidtoheteroplasmyofthemtDNA controlregion.

•DonorswerestratiﬁedfortheirAPOEgenotypetocorrelatethis withthetypeofageing,i.e.successfulorunsuccessfulageing.

3.3. WP3:markersbasedonproteinsandtheirmodiﬁcations Oneimportantphysiologicalposttranslationalmodiﬁcationof secreted proteins is addition of N-linked oligosaccharides (N- glycans).SincemostN-glycansareontheoutersurfaceofcellular andsecretedmacromolecules,theycanmodulateormediateawide varietyofeventsincell-cellandcell-matrixinteractionscrucialfor thedevelopmentandfunctionofcomplexmulticellularorganisms.

Becausethebiosynthesisofglycansisnotcontrolledbyinterac- tionwithatemplatebutdependsonthecomplicatedconcerted actionofglycosyltransferases,thestructuresofglycansaremuch morevariablethanthoseofproteinsandnucleicacids,andthey canbeeasilyalteredbythephysiologicalconditionsofthecells.

Accordingly,studyingage-relatedalterationsoftheglycanscould berelevanttounderstandingthecomplexphysiologicalchanges inageingindividuals(Vanhoorenetal.,2010).Theobjectiveofthis sub-taskwastodeterminethechangesinthebloodN-glycomedur- inghumanageingofhealthyhumansandtodevelopmethodology forproﬁlingurineN-glycans(Vanhoorenetal.,2007).

TheapolipoproteinJ/Clusterin(ApoJ/CLU)isahighlyconserved multifunctionalglycoprotein.Amongstitsmultiplephysiological functions,thisproteinisachaperonethatstabilizesstressedpro- teinsinafolding-competentstate.Previousworkhadshownthat ApoJ/CLU is associated with humanageing and with ageingof humancellsinvitro,andthatitslevelisincreasedinpatientswith typeIIdiabetes,coronaryheartdisease,andmyocardialinfarction.

ThereforeApoJ/CLUmayrepresentavaluableageingbiomarker.

Non-enzymaticproteinglycationisacommonposttranslational modificationofproteinsinvivo,resultingfromreactionsbetween glucoseandaminogroupsonproteins;thisprocessistermed“Mail- lardreaction”andleadstotheformationofAdvancedGlycation Endproducts(AGEs).Duringnormalageing,thereisaccumulation ofAGEsoflong-livedproteinssuchascollagensandcartilage.AGEs, either directlyor throughinteractions withtheirreceptors,are involvedinthepathophysiologyofnumerousage-relateddiseases (Simmetal.,2014),suchascardiovascularandrenaldiseasesand neurodegeneration.However,inacohortstudyonhumanageing, thecorrelationofAGEswithhumanageremaineddebatable.By analysingoverallparametersofAGEsaswellasspecificAGEs,itwas tobedeterminedifthesemodificationscorrelatewithageindepen- dentlyofdiseaseandiftherearegenderdifferences(Scheubeletal., 2006;Simmetal.,2004).

Itiswellknownthatlevelsofoxidisedproteinsincreasewith age,duetoincreasedproteindamageinducedbyROS,decreased eliminationofoxidisedprotein(i.e.repairanddegradation),ora combinationofthetwo.Sincetheproteasomeisinchargeofboth generalproteinturnoverandtheselectiveremovalofoxidizedpro- tein, itsfate duringageinghasreceivedconsiderable attention, andevidencehasbeenprovidedforanimpairmentofproteasome functionwithageindifferentcellularsystems(Chondrogianniand Gonos,2010;BaraibarandFriguet,2013),includinghumanPBMC (Chondrogianniet al.,2003,2005; Carrardet al.,2003; Friguet, 2006).Apartfromtobeingdegraded,certainoxidisedproteinscan berepaired.However,repairislimitedtothereversionofafew oxidativemodiﬁcationsofsulphur-containingaminoacids,suchas thereductionofmethioninesulfoxidebythemethioninesulfoxide reductase(Msr)system.Msractivityisknowntobeimpaireddur- ingageingandreplicativesenescence.Therefore,thestatusofboth proteasomeandMsrsAandBinhumanPBMCfromtherecruited

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donorsofdifferentageswastobeassessed.Theseparametershad previouslybeenshowntobekeyplayersinoxidisedproteindegra- dationandrepairandtoexhibitadecliningactivitywithage(Picot etal.,2004,2007).Theseproteinmaintenancesystems,viewedas potentialbiomarkersofageing,weretobemonitoredatthelevels ofenzymaticactivity,proteinexpression,andRNAexpression.

•AnalysisoftheN-glycomicchangesinglycoproteinsfromblood ofalldonors.Urineglycoproteinchangesweretobestudiedina subsetofsubjects.

•ApoJ/CLUlevelsinserumfromalldonors.

•AGEsinplasmabyﬂuorescencespectroscopyandbyimmuno- logical analysis of carboxy-methyllysine, pentosidine, arg- pyrimidineandimidazolone.

•Proteindamageinbloodatdifferentlevels,i.e.activitiesofpro- teasomeandmethioninesulfoxidereductasesinPBMClysates;

RNAlevelsofrepresentativeproteasomesubunits(catalyticand regulatory)and methioninesulfoxidereductasesAandB;and proteinlevelsofrepresentativeproteasomesubunitsandmethi- oninesulfoxidereductasesAandBinPBMClysates.

3.4. WP4:immunologicalmarkers

Thymicoutputisknowntodeclinewithage;furthermorethe rateofdeclineisdependentongender,withgreaterthymicout- putforlongerin femalescompared withmales(Aspinallet al., 2007).Femalesoftendevelopbetterimmuneresponsesthanmales, whichmayrelatetotheirlongerlifespan.WithinMARK-AGE,signal jointT-cellreceptorrearrangementexcisioncircles(sjTREC)were assessedasacandidatebiomarkerofageingandthymicinvolution, byanalysingsjTREClevelsinindividualsatvariousages(Aspinall etal.,2007).

Arobustimmunologicalmemoryisknowntobeaguarantorof healthinadultsandinparticularinelderlypersons,whilechronic latentinfections,suchasCMVinfection,havebeenshowntobe associatedwithshorterlifeexpectancy.Auto-immuneresponses mayalsorestrictthediversityofimmuneresponsivenesstofor- eignantigens.Wethereforeevaluatedlong-termandshort-term immunologicalmemoryandautoimmuneresponsesaspotential biomarkersofageing(Almanzaretal.,2005;Kovaiouetal.,2007;

Weinbergeretal.,2007;Herndler-Brandstetteretal.,2005).

In vitro, two types of senescence have been described. One istelomere-dependentreplicativesenescenceand thesecondis stress-inducedprematuresenescence(SIPS)(Sikoraetal.,2014).

In view of previous results we hypothesised that during ageing, chronic antigenic load as well as oxidative stress may causedecreasedlymphocytesusceptibilitytoDamage-InducedCell Death(DICD)and,ontheotherhand,increasedsusceptibilityto Activation-Induced Cell Death (AICD).As an intact equilibrium betweensurvivalandeliminationofimmunecellsmaybedeci- siveforintactimmunefunctionandhealthwestudiedDICDand AICDinTcellsfromdonorsamples(seeSikoraetal.,thisissue).

•Analysisoftotal IgG,IgE,IgMand IgA;serum/plasmaconcen- trationsof 14 cytokines; blood counts and differential blood counts(performedbytherecruiterslocally);andphenotyping ofTcells,BcellsNKcellsandmonocytesbyimmunoﬂuorescence inprobandsamples.

•AnalysisofthenumberofsjTRECs;itwasofparticularinterestto analysewhethersjTRECconcentrationsdifferinpersonswithand withoutlatentviralinfectionssuchasCMV,HHV-6andHHV-7.

•Analysisofantibodiesandcellularimmunity(IFNgammapro- ductionbyElispot)againstmeaslesandmumpsvirus(typically

childhoodexposure)inordertoassesslong-termimmunological memory.

•Analysis of antibodies and cellular immunity to highly con- servedproteinsof theinﬂuenzavirus(NPandMproteins)as wellastetanus(anagentagainstwhichmostadultpersonsare vaccinated at regular intervals)in order toassess short-term immunologicalmemory.

•AnalysisofimmuneresponsesagainstCMV,inordertoassessthe effectoflatentviralinfection.

•Analysisof autoantibodiesagainstthyroglobulin (asanexam- pleofa tissue-speciﬁcantigen)andantinuclearantibodies(as exampleforasystemicimmuneresponse).

•AnalysisofsusceptibilitytoDamage-InducedCellDeath(DICD) andActivation-InducedCellDeath(AICD),respectively,byusing apoptosismarkers.

3.5. WP5:clinicalchemistry,hormonesandmarkersof metabolism

Intheliteratureaplethoraofclassicalclinicalchemistryparam- eters have been proposed as potential biomarkers of ageing.

Prominent examples are parameters of carbohydrate and lipid metabolismorhormones.Wehaveselectedthemostpromising onesforinclusionintheMARK-AGEprojectandwehaveaddedsev- eralnewpotentialbiomarkersrelatedwithmetabolismthathave emergedintherecentworkofsomePartners(Al-Delaimyetal., 2006;Rezzietal.,2007a,b;Kochharetal.,2006;Heijmansetal., 2006;Mooijaartetal.,2006;Hurmeetal.,2005,2007;Rontuetal., 2006;Lehtimakietal.,2007).

The following candidate biomarkers have speciﬁcally been addressed:

Systemicmetabolismandtoxicityparameters

•Bloodureanitrogenandcreatinine,usedfortheevaluationof renalfunction.

•Metal binding proteins including transferrin, ferritin, ␣2- macroglobulinandceruloplasmin,inordertocomplementmetal iondeterminations(seebelow).

•Fasting glucose and fasting insulin as a measure for glucose homeostasis,insulinresistanceanddiabeticconditions.

•Glycosylatedhemoglobin(A1C)asameasureforthelong-term systemicglucoseload,inordertodetect(pre)diabeticconditions.

•Somebasic/referenceparameters,includingalbuminandserum proteinconcentration.

Fattyacidandcholesterolmetabolismparameters

•Fastingtriglyceridesandfreefattyacidsweremeasuredtodetect metabolicdisordersinlipidmetabolism.

•Total cholesterol, HDL and LDL-cholesterol were measured (togetherwithtriglycerides)forriskassessmentofcardiovascular diseases.

•ConcentrationsoflipoproteinparticlesizeclassesbyNMR.

Systemicinﬂammationparameters

•C-reactiveprotein(CRP),homocystein,uricacidandﬁbrinogen areinﬂammatorymarkersassociatedcardiovasculardiseaseand hypertension.

•SerumamyloidAandP,andpentraxin3wereamongsttheacute phaseproteinsstudied.

•Adiponectiniscorrelated withananti-inﬂammatorystateand suppressesmetabolicderangementsthatmayresultintypeII diabetes,obesity, atherosclerosis andnon-alcoholic fattyliver disease.

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Additionalcandidatebiomarkers

•Testosterone,theprincipalmalesexhormonewhoselevelsare knowntodeclinegraduallywithageinmales.

•Prostatespeciﬁcantigen(PSA)wasmeasuredforthedetectionof (pre)neoplasticprocessesintheprostateandprostatecancerin particular.

•VitaminD,arecentlyidentiﬁedpromisingcandidaterelatedto ageingandseveralchronicdiseases,wasalsostudied.

•Dehydroepiandrosteronesulfate (DHEAS)is known todecline withageandisaclassicalcandidatebiomarkerofhumanageing (Laneetal.,1997).

Novelbiomarkerstobederivedfrommetabonomicsanalysis

•NuclearMagnetic Resonance(NMR)-basedmetabolicproﬁling of serum/plasma samples and urine samples from probands.

NMRproﬁlesdisplayasetofresonancesarisingfrommajorlow- molecularweightmolecules,suchasketonebodies,organicacids, aminoacids,andaromaticmetabolites(Oostendorpetal.,2006;

Rezzietal.,2007a,b;Ramadanetal.,2007)

3.6. WP6:oxidativestressmarkers

Ithasbeenpostulatedthatoxidativestressiscausalfortheage- ingprocess.OxidativestressreferstoanimbalancebetweenROS formationandantioxidantdefence.Inhumanbeingslargeamounts ofoxidantsareformedinvariousphysiologicalmetabolicreactions andevenawidervarietyofpathophysiologicalconditions.Thebody isabletorespondtosuchenhancedoxidantformationwithcom- pensatoryantioxidantreactions.Thesereactionsarealsocomplex andalargevarietyofdifferentenzymesareinvolved.Ifantioxi- dantprotectionisinsufﬁcient,oxidativestresswithanenhanced formationofoxidativestressmarkersoccurs.

Oxidativedamageaccumulationinmacromoleculeshasbeen consideredcausativeforcellulardamageandpathology.Suchdam- ageseemstobecloselyrelatedtotheageingprocess.Although therelationshipbetweenoxidativedamageandtheageingprocess hasbeenestablishedinvariousmodelsystems,onlyfewstudies reportedasystematicanalysisofoxidative stressparametersin healthyhumansrelatedtoageofindividuals(PandeyandRizvi, 2010;Giletal.,2006).

Thepurposeofthisworkpackagewastoanalyseasetofparam- eters of oxidative stress parameters (Gil et al., 2006), vitamins andtraceelements(Mazzattietal.,2007;Malavoltaetal.,2006;

Mocchegianietal.,2006)inhumanblood,serum,urineandbuc- calmucosacells.Preferencewasgiventonewtechnologiesforthe assessmentof oxidationmarkers andtomarkersalready estab- lishedandsuitableforadaptationtohigh-throughputformats.

The following candidate biomarkers have speciﬁcally been addressed:

•Malondialdehyde.

•Carbonylatedandnitratedproteins.

•OxidationofLDL.

•NOmetabolic-pathwayproducts(NOx)_x.

•Isoprostanes.

•Cellularglutathione.

•Vitamin content (␣-tocopherol, ␣-carotene and ascorbate) of serumandbuccalmucosalcells.

•Traceelements(Zn,Cu,SeandFe)inblood/serum.

3.7. WP7:emergentbiomarkersofageingfrommodelsystems andnovelmethodologicalapproaches

Whilst conventional biomarkersof diseasehave beenestab- lishedbyhypothesis-drivenapproachesbasedonanunderlying knowledge ofthediseaseprocess orserendipity, studieswhich focusonidentificationofbiomarkersofhealthyageingarecon- strainedintheirabilitytofollowindividualsoverprolongedperiods oftimeuntiltheirchronologicalagedeviatesfromtheirbiological age.Toovercomethis problem,weadoptedparallel,systematic approachestoinvestigateputativebiomarkersinspecificageing cohorts(asdefinedinWP1)alongsidethestudyofmodelsofaccel- eratedageing,suchasprogeroidsyndromes(inhumansandmice) andinducedsenescenceinleukocytesfromsubjectsofdifferent ages.Weusedbothestablishedandnovelapproachestosearchfor biomarkersofageinginaniterativeprocess,wheremarkersderived frommodelswouldinforminvivobiomarkersearches.

(a)ModelSystems

Progeroidmousemodels

Progeroidsyndromesareraredisorderswithprematureageing andashortenedlifeexpectancy.Theseconditionsarecharacter- izedby extremelyaccelerated ageing,showingmanyhallmarks ofnormalageingincludingcessationofgrowth,liver,kidneyand bone abnormalities, retinopathy, hearing loss, sarcopenia, neurodegeneration, sensitivity to UV light, and a premature aged appearancedue tokyphosis, baldness,lossofsubcutaneousfat, and dry wrinkled skin. Cockayne syndrome (CS), Hutchinson- Gilfordsyndrome,Werner’s syndrome(WS),Bloom’ssyndrome, and trichothiodystrophy are all autosomal recessive disorders withprogeroidsymptoms.Althoughsomedifferencesexistinthe pathology of these conditionsit is strikingthat thecausal fac- torofallthesesyndromesliesinimpairedgenomemaintenance duetoDNArepairdeficienciesorgenomeinstability.Oneaimof theprojectwastheidentificationofbiomarkersofageinginCS, a rare human disorder, in which patientssuffer from segmental but bonafide accelerated ageing.The mean ageat deathof CSpatientsis12.5years. ThereiscurrentlynotreatmentforCS and related disorders availableand theclinicalmanagement of patientsispurelysupportive.CSisanautosomalrecessivedisorder causedbymutationsintheCSAorCSBgenes,whichareinvolvedin transcription-couplednucleotideandbaseexcisionrepair(TCER).

TheTCERsub-pathwayselectivelyremoveslesionsfromthetran- scribedstrandthatactuallyblocktranscription.Assuchthisprocess isimportantforpromotingrecoveryofthevitalprocessoftran- scription and thus cellular survival from transcription-blocking DNAlesions.Oneoftheobjectivesoftheprojectwastoapplythe biomarkersstudiedin WorkPackages2through6toprogeroid patients,therebydeterminingtowhichextentprematureandnor- malageingresembleeach otherandtheirsuitabilitytoidentify ageingfeaturesindependentofchronologicalage.

TounderstandtheaetiologyofCSand otherDNArepair dis- orderssuchas trichothiodystrophy (TTD)and thecancer-prone conditionxerodermapigmentosum(XP),anextensivecollectionof DNArepair-deﬁcienttransgenicmicehadpreviouslybeengener- ated,manyof whichmimic thehallmarksofthecorresponding humanrepairsyndrome.Thesemicedisplayeitherastrongcan- cerpredisposition(XP-like)ormanyfeaturesofprematureageing, or a combination. Although the Csb^m/m mouse model reliably reﬂectstherepairdefectandUV-sensitivephenotypeofthepatient, animalsshowrelativelymildgrowthretardationandneurological abnormalities,accompaniedbyage-relatedretinaldegeneration.

Interestingly, when TCER-defective Csb^m/m mice were crossed withcompletely NER-deﬁcient Xpa^−/− animalsdouble mutants phenocopyhumanCSsurprisinglywell,includingitsage-related

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pathology.AlthoughCsb^m/m/Xpa^−/−pupsaredevoidofanyovert embryonicdevelopmental phenotype,theydisplay severepost- natal growth retardation, impaired psychomotor development, ataxia,progressive cachexia, and kyphosis. Loss of retinal pho- toreceptorsis also furtheraccelerated, as compared to Csb^m/m animals.Moreover,mostCsb^m/m/Xpa^−/− newbornsdieduringor shortlyafterbirth,whereasanimalsthat survivebirth stressdo notsurvivebeyondthreeweeks.Exploitingthegeneticandenvi- ronmentallyfullydefinedmousesystemtheseandothermouse mutants for XP, XP/CS and TTD provided a convenient tool to deduce specific biomarkers in various organs/tissues including serum.WithinMARK-AGEa setofmousebiomarkersforageing was developed and evaluated, using transcriptomics, immuno- histochemistryandserum/urine markersin prematurelyageing mousemodelswithdifferentlifespanandageing-relatedpathol- ogytodeliveruniversalmarkersofageing.Biologicalmaterialfrom geneticallyandenvironmentallycontrolled,(histo)pathologically well-defined cohort studies withNER-deficient mouse models, servedtoidentifyparametersthatwereexpectedreportonthe biologicalageoftheanimalsand/ortheonsetandprogressionof ageing-relatedpathologyinvarioustissues(e.g.liver,brain)(van derPluijm etal., 2007;de Boeret al.,2002; Rossietal., 2007;

Niedernhoferetal.,2006).

Stressinducedprematuresenescence

Thereareseveralpathwaysactivatingcellularsenescence;these includetelomere uncapping,DNA damage, oxidativestress and oncogene,amongstothers(Ben-PorathandWeinberg,2005).Nor- malhumandiploidfibroblastsculturedinvitroirreversiblystop dividingafteracertainnumberofcumulativepopulationdoublings inaprocessknownasreplicativesenescence(Hayflick,1965).This limitedproliferativelifespanhasbeenobservedin manyother eukaryoticcelltypesandhasbeeninterpretedasamanifestation ofcellularageing.Randommetabolicmodificationsappearwithin thesecellsovertime,leadingtorandomdamageofthecellularcom- ponents.Thesedamagedcellularcomponentsarenotcompletely eliminatedor repairedandthereforeaccumulate withtime and progressivelyimpaircellularfunctions.Cellularsenescencecanbe alsoregardedasapermanentlymaintainedDNAdamageresponse state(vonZglinickietal.,2005).ROSareimportantcontributorsto theageingprocessandwehaveconfirmedthesimilaritiesbetween replicativesenescenceandstressinducedprematuresenescence (SIPS) (Dumont et al., 2000; Dierick et al., 2002; Pascal et al., 2005).In MARK-AGE,weusedSIPSto“age” Tand Bcells from thesubjectsrecruitedinWP1andsearchedfornovelbiomarkers usinggenomicarrayandproteomicapproachesdescribedbelow (Debacq-Chainiauxetal.,2005;Frippiatetal.,2001).

•Novelmethodologicalapproaches

AnalysisofmiRNAs

MicroRNAs (miRNAs) are small, abundant non-coding RNA moleculesofabout21–23nucleotidesthathavebeenshownto affectabroadspectrumofbiologicalactivities.Interestingly,there isevidence thata remarkablylargeproportion of genes(>30%) are subject tomicroRNA-mediated regulation. In general, miR- NAsfunctionpost-transcriptionallybyinhibitingtranslationfrom specifictargetmRNAs.Uptonow,about600miRNAshavebeen characterizedinhumans.ThesesmallRNAmoleculeswerethought tocontributetoageingofC.elegans.Ithasbeenpreviouslyshown thatmiRNAcauseageneralreductionofmessage-specifictransla- tionalinhibitionduringageing.Reducingtheactivityofaspecific miRNA lin-4 shortened life span and accelerated tissue aging, whereas overexpressinglin-4 or reducing theactivity oflin-14 extendedlifespanofC.elegans,frequentlyusedasamodelsys- temformammalianageing(BoehmandSlack,2005).Studieson

miRNAexpressionlevelsintissuesofyoungandoldmiceshowed thedifferentialandclearlytissuespecificexpressionofsomemiR- NAs(Smith-Vikosand Slack,2012).Onthelevelof cells,it was alsoshown that such differentialexpressioncan directly influ- encecellularageing.miR-21wasfoundup-regulatedbyreplicative andstress-inducedsenescencein humanendothelialcells.miR- 21over-expressionreducesthereplicativelifespan,whilestable knock-downextendsthereplicativelifespanoftheseendothelial cells(Dellagoetal.,2013).Ontheorganlevel,itisclearthatnot allmiRNAsthatareup-ordown-regulatedduringageingneces- sarilyplay crucialrolesduringageing.Asno“keyregulator” on tissueageingwasidentifiedyetinmammalians,onehastoclarify whichmiRNAsareactivatedorrepressedespeciallyindegenera- tivediseasecontextsandwhicharereallyassociatedwithaging perse.ThereforeweareevaluatingmiRNAexpressionasa“novel”

biomarkerofageingusingtheleukocytesofsubjectsrecruitedin WP1.

PhageantibodiesagainstnovelmarkersofendothelialandTcell ageing

Thephagedisplayantibodylibrarytechnologyhasbeenfound tobeauseful methodtoisolateantigen-specific antibodyfrag- ments,sincetherepertoireofantibodyspecificitiesisbroadand sinceit bypassestheneedofimmunization.Whenappliedasa discoverytool, thephagedisplaytechnologycanbeconsidered acomplementationtotraditionalproteomicapproachesusing2- Dgelelectrophoresisandmassspectrometry,whichquiteoften haveproblemsinidentifyingproteinswhichareveryhydrophobic (Gonzalez-Dosaletal.,2006;Jensenetal.,2003).During ageing, both in vivo and invitro, changes in theproteomic profileare observed.Byperformingsubtractiveselectionofrecombinantanti- bodiesbindingtoe.g.endothelialcellsallowedtoageinvitro,where youngculturedendothelialcellsisappliedascompetitor,antibod- iesbindingpotentialbiomarkersofageingcanbeobtained(Boisen etal.,2010;BoisenandKristensen,2010)

Suchapproacheshaveenabledthedevelopmentofapanelof antibodiesrecognizinginvitroageinghumanendothelialcellsdur- ingthepreviousEUprojectProteomage,inparticularthesecretome ofendothelialcells.Withagethereisadecreaseintheabilityto formnewbloodvessels,whichisabiomarkerofageing.Thepur- poseof theworkintheMARK-AGEprojectwastoevaluatethe invivosignificance ofendothelialsecretome biomarkers identi- fiedfrominvitromodelsfortheirinvivorelevancebyscreening plasmafromsubjectsrecruitedinWP1forthesemarkers.More specificallyit wasfoundthattheintermediatefilamentprotein, vimentin,isfoundintheserum.AsstudieswithintheEUfunded project,Proteomage,establishedthatextracellularvimentincan exertfunctionalchangestotheabilitytoformnewbloodvessels,it hasbeenofparticularinteresttoseeifthereisanagespecificcor- relationwiththeamountofvimentininserum.This,inpart,might explainwhyingeneralolderpeopleexhibitdecreased abilityto formnewbloodvessels.Usingabatteryofantibodiesraisedagainst endothelialprogenitorcells,itwasfurtherproposedtoevaluate whetherthenumberofendothelialprogenitorcellsinserumqual- ifiesasabiomarkerofageing(Bertelsenetal.,2014;Williamson etal.,2012).Thisagaincouldhaveimplicationsforthegenerally decreasedabilityofoldersubjectstoformnewbloodvessels.

Microarrayandproteomics

Genomicsandproteomicsoffertheopportunityforanunbiased systematicdiscoveryroutefornovelbiomarkersandarebecom- ingincreasinglypopular(Grifﬁthsetal.,2002;Grifﬁthsetal.,2006;

Aldredetal.,2006;Grantetal.,2007).Nevertheless,onlyfewgroups haveundertakenproteomicstudiesofeitherplasmaproteinsof mononuclearcells in healthy humanageing.The ﬁrst ofthese, publishedbyThambiettyet al.in 2010,described a differential plasmaproteinpatternbetween57olderadultswithandwithout amyloiddepositioninthebrain(Thambisettyetal.,2010).Subse-

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quently,differentialexpressionofApoEandantioxidantproteins wasobservedintheplasmaof10Japanesesupercentenarianscom- paredwith10youngpeople(Miuraetal.,2011).In2012,someof usdescribedalterationsintransferringlycosylationduringhealthy ageing(Dunstonetal.,2012).Theadvantagesofapplyingsuchan approachintheMARKAGEpopulationisthegreaterpowertoevalu- atethevalidityofnovelbiomarkersdiscoveredthroughproteomics whencompared withsmallsamplesizediscoveryprogrammes.

AfterthecompletionofMARKAGE,oneotherstudyhasanalysed byELISAthelevelsofproteinbiomarkersthatwerenotdiscovered usingproteomicsinplasmaduringhealthyageingincomparison witholderadultsthat developfrailtysyndromes.Theseauthors showedthathigherlevelsoftransferrinﬁbrinogenandinterleukin- 6wereassociatedwithfrailtystatusandfrailtyscore(Darvinetal., 2014).

Ever since the concept of MARK-AGE has been developed microarray has been used extensively to characterise ageing- relatedchangesingeneexpression.Indeed,thesystematicanalysis of miR, single nucleotidepolymorphisms and deep sequencing approacheshaveledtofurtherinsightintoexpressionchangesin specificcelltypes (Laurie etal., 2012;Smigielska-Czepiel etal., 2014; van der Brug et al., 2010). The latter are by necessity cross-sectionalstudies.TheapproachtakenbyMARK-AGEwasto investigateexpressionchanges withinuniqueaccelerated mod- elsofageingtheconsortiumhadaccessto.Systematicmicroarray analysisofprogeroidmutantshasalreadyyieldednewpotential biomarkers:usingfull-genomemicroarrayanalysissomeofushave recentlyidentifieda ‘survival’ responsein theprogeroidmouse modelsdirected bydown-regulationof theIGF1somatotrophic axisthatboostsantioxidantdefence,down-regulatesmetabolism andredirectsenergyresourcesfromgrowthanddevelopmentto protection,maintenanceandrepair.Thisadaptiveswitchaimsto slowdownageing-relatedpathologyandpostponesdeath,thereby promotingsuccessfulageing.Itisconstitutivelyturnedoninthe repair-compromisedmousemutantsasafutileattempttoextend lifespanandexplainstheirdwarfphenotype.Innormalmicethe sameprincipallybeneficialresponsecanbetransientlytriggered bychronicexposuretoDNA-damagingagentsandROS-producing compounds.We hypothesisethat normal ageingis also associ- atedwithasimilarresponseduetoage-dependentaccumulation ofdamage.Theexistenceof sucha responseallows predictions for shiftsin levelsof specific proteins, activities,pathwaysand metabolitesthatcouldserveasbiomarkers.Astheproteomeisfar moreextensivethanthetranscriptome,itoffersarichersourceof potentialbiomarkersbutalsoposesincreasedproblemsintermsof dynamicrange,particularlyinplasma.Thisisbeingaddressedusing quality-assuredsubfractionationstepsandrestrictedIPGrangein thefirstdimension.InMARK-AGE,proteomicswastobeadopted inaccordancewithHUPOguidelines,inthesearchforbiomark- ersinplasmafromsubjectsrecruitedinWP1,inCS/progeriaand inTand Bcellssubjected toSIPSinsubjectsrecruited inWP1.

The identificationof putative markers wasto beconfirmed by sequenceanalysisandtheirvalidationasbiomarkersofageingcon- firmedbyalternative approachessuchasELISAwherepossible.

Inviewofthestrongparallelsbetweenthemousemutantsand thehumansyndromes,down-regulationoftheIGF1somatotrophic axisbiomarkersislikelytobeinstrumentalforidentiﬁcationofcor- respondingmarkersinhumanpatientsandevennormalageingand willbeevaluatedinWP1subjects.

3.8. WP8:dataanalysisandbioinformatics

Inviewofthelargeamountsofclinicalandbiochemicaldata, which havebeencollectedintheframework of theMARK-AGE projectanappropriateandcoherentstrategyofdataanalysisand modelbuildingismandatory.

Inordertoextractarobustsetofbiomarkersofhumanageing andtoderiveamodelforhealthyageing,thefollowingtaskswere performed:

•Datapropertyanalysis.Partialknowledgeaboutsuspectedcor- relations between measurements is available and beingused tojudge thenoise ratiowithin someof thesemeasurements usingclassicalstatisticaltechniques.Wealsoappliedcorrelation measurestoidentifyadditionalrelationshipsbetweenmeasure- ments.Repeatedsampling,i.e.obtainingsamplesafter6months from97subjects,wasdonetofurtherinvestigatebiologicaland analyticalvariabilityinthemeasurements.

•Modelling. Both,statistical models as wellas machine learn- ing/dataminingmethodsweretobeusedinordertobuildmodels aiming topredict biological age from theavailable measurements.Weusedclassicaltechniques,suchasregressionanalysis butalsoaimtointroduceadditionalknowledge(monotonicity)to improvethosemodels.NeuralNetworksandDecision/Regression Treesweretobeusedtoﬁndmorelocalrelationshipswithinthe data,forinstancerevealingpropertiesthatarerelevantonlyfor asubsetofthechosenpopulation.

•Variance reduction. Through dimensionality reduction tech- niques (principal component analysis and others) we aim at reducing thenumberof requiredmeasurementswhile,at the sametime,reducingthevarianceinthepredictionsgenerated.

MachineLearningoffersensemblesofmodelsforthispurpose, whichallowscombiningdifferent,diversepredictorstogenerate modelswithlowervariance.

•Clustering/visualisation. We expect to discover previously unknownor unexpectedrelationshipsin thedata that deﬁne successfulageing.Datavisualisationtechniquesandinteractive methodssuchasvisualclusteringmodelsmayhelpuncoversome oftheserelationships.Itisexpectedthatsomemeasurements willhavehighercorrelationswiththebiologicalagethanothers inpartsofthepopulation.Findingsuchclustersisfurtherhelping reducevarianceinthegeneratedmodelssincewewillbeableto bettermodelcharacteristicsofsubgroups.

3.9. WP9andWP10

WP9 andWP 10were dedicatedtodissemination,training, projectmanagement(Fig.1)andethicalissues.

4. Discussion

Biomarkers of human ageing are urgently needed for vari- etyofreasons,includingtheidentiﬁcationofindividualsathigh riskofdevelopingage-associateddiseaseordisability.Thiswould prompt targeted follow-up examinations and, if available, pro- phylactic intervention (e.g. changes in lifestyle) or early-stage treatmentofage-relateddisease.Furthermore,theavailabilityof powerfulbiomarkerswouldallowtheassessmentoftheefﬁcacy offorthcomingpharmacologicalandotherinterventions(including optimisationofmicronutrientintakeandotherdietarycomponents or physicalactivity) currentlybeingdeveloped withtheaimto lowertheriskofage-associateddiseaseeveninindividualswithout acceleratedageing.

In view of therapidly increasingaverage life expectancyof humanbeingsworld-wide,theprevalenceofage-relateddiseasesis likelytoincreaseaswell.Thisnecessitateseffectivenewstrategies forpreventionandearlydiagnosisofsuchconditions.

Itshouldbenotedthatdifferenttypesofbiomarkershavebeen envisaged:(1) “neutral” markersof age(also called markersof

“chronologicalageing”)possiblylackingthepowerofdirectlypre- dictingdiseaserisk,astheunderlyingphysiologicalchangemayper