Novel Anthropometry Based on 3D-Bodyscans Applied to a Large Population Based Cohort

20 Pages·2016·3.78 MB·English

RESEARCHARTICLE
Novel Anthropometry Based on 3D-
Bodyscans Applied to a Large Population
Based Cohort
HenryLöffler-Wirth1,2*,EdithWillscher1,2,PeterAhnert2,3,KerstinWirkner2,
ChristophEngel2,3,MarkusLoeffler1,2,3‡,HansBinder1,2‡
1 InterdisciplinaryCentreforBioinformatics,LeipzigUniversity,Härtelstraße16–18,04107Leipzig,
Germany,2 LIFE,LeipzigResearchCenterforCivilizationDiseases;LeipzigUniversity,Philipp-Rosenthal-
Straße27,04103Leipzig,Germany,3 InstituteforMedicalInformatics,StatisticsandEpidemiology,Leipzig
a11111 University,Härtelstraße16–18,04107Leipzig,Germany
‡Theseauthorsarejointseniorauthorsonthiswork.
*[email protected]
Abstract
OPENACCESS
Three-dimensional(3D)wholebodyscannersareincreasinglyusedasprecisemeasuring
Citation:Löffler-WirthH,WillscherE,AhnertP,
WirknerK,EngelC,LoefflerM,etal.(2016)Novel toolsfortherapidquantificationofanthropometricmeasuresinepidemiologicalstudies.
AnthropometryBasedon3D-BodyscansAppliedtoa Weanalyzed3Dwholebodyscanningdataofnearly10,000participantsofacohortcol-
LargePopulationBasedCohort.PLoSONE11(7):
lectedfromtheadultpopulationofLeipzig,oneofthelargestcitiesinEasternGermany.
e0159887.doi:10.1371/journal.pone.0159887
Wepresentanovelapproachforthesystematicanalysisofthisdatawhichaimsatidentify-
Editor:DongHoonShin,SeoulNationalUniversity
ingdistinguishableclustersofbodyshapescalledbodytypes.Inthefirststep,ourmethod
CollegeofMedicine,REPUBLICOFKOREA
aggregatesbodymeasuresprovidedbythescannerintometa-measures,eachrepresent-
Received:February11,2016
ingonerelevantdimensionofthebodyshape.Inanextstep,westratifiedthecohortinto
Accepted:July8,2016 bodytypesandassessedtheirstabilityanddependenceonthesizeoftheunderlying
Published:July28,2016 cohort.Usingself-organizingmaps(SOM)weidentifiedthirteenrobustmeta-measures
andfifteenbodytypescomprisingbetween1and18percentofthetotalcohortsize.Thir-
Copyright:©2016Löffler-Wirthetal.Thisisanopen
accessarticledistributedunderthetermsofthe teenofthemarevirtuallygenderspecific(sixforwomenandsevenformen)andthus
CreativeCommonsAttributionLicense,whichpermits reflectmostabundantbodyshapesofwomenandmen.Twobodytypesincludeboth
unrestricteduse,distribution,andreproductioninany
womenandmen,anddescribeandrogynousbodyshapesthatlacktypicalgenderspecific
medium,providedtheoriginalauthorandsourceare
features.Thebodytypesdisentanglealargevariabilityofbodyshapesenablingdistinc-
credited.
tionswhichgobeyondthetraditionalindicessuchasbodymassindex,thewaist-to-height
DataAvailabilityStatement:Processedbody
ratio,thewaist-to-hipratioandthemortality-hazardABSI-index.Inanextstep,wewilllink
scannerdataareprovidedalongwiththemanuscript
(meta-measuresofallparticipants),whichwillenable theidentifiedbodytypeswithdiseasepredispositionstostudyhowsizeandshapeofthe
interestedresearcherstoperformtheirownclass humanbodyimpacthealthanddisease.
discovery.Rawdataarenotyetpubliclyavailabledue
toconsortialanddatasafetyrestrictions,butthese
datacanberequestedfromtheLIFEConsortium
(www.life.uni-leipzig.de/en/).
Funding:ThispublicationissupportedbyLIFE–
LeipzigResearchCenterforCivilizationDiseases, Introduction
LeipzigUniversity.LIFEisfundedbymeansofthe
Anthropometricmeasuresareimportanttoassessdevelopmentalnormalityandpredisposi-
EuropeanUnion,bytheEuropeanRegional
DevelopmentFund(ERDF),bytheEuropeanSocial tionstodiseasesandtocalculatedrugandchemotherapydosages.Therelationshipbetween
PLOSONE|DOI:10.1371/journal.pone.0159887 July28,2016 1/20
NovelAnthropometryBasedon3D-Bodyscans
Fund(ESF)andbymeansoftheFreeStateof thefatdistribution,theassociatedhumanbodyshapeandhealthrisk,e.g.forcardiovascular
Saxonywithintheframeworkoftheexcellence diseases,metabolicsyndromeorcancer,areamajorissueinmanypopulationstudies[1–6]
initiative.WeacknowledgesupportfromtheGerman
wheresizeandshapeofthehumanbodyhavetraditionallymeasuredintermsofonlyafew
ResearchFoundation(DFG)andUniversitätLeipzig
anthropometricmeasures.Simplecombinationsofbasalmeasuressuchasheight,waistcir-
withintheprogramofOpenAccessPublishing.
cumference,andweightwerecombinedinto‘healthindices’tojudgethehealthstatusof
CompetingInterests:Theauthorshavedeclared
humanindividuals.TheimpactandsuitabilityofhealthindicessuchastheBMI(body-mass
thatnocompetinginterestsexist.
index[7]),WHtR(waistcircumferencetoheightratio[1]),WHR(waisttohipcircumference
Abbreviations:Bodysurfaceimage,Imageshowing ratio[8,9])andABSI(abodysizeindex[3])wereunderdiscussioninthecontextofthe“obe-
the3dbodysurfacescan;Bodyscannermeasure, sity-mortalityparadox”[10],showingthatmoderateoverweightdoesnotimplyshorter
Bodymeasurederivedfrom3dsurfacescan;Body
lifetime.Theseresultscallforarethinkingofhowmetabolichealthisassessedintermsofalter-
measure,Singleanthropometricmeasure;
Normalizedbodymeasure,Bodymeasuredividedby nativeanthropometricmeasureswhichbettercharacterizetherelationshipbetweenthedimen-
thebodyheight.Weusetheterms‘bodymeasure’ sionsofthehumanbodyandhealth.
and‘normalizedbodymeasure’assynonymsinthe Currently,wholebodyscannersusingtriangulationarethemosteffectivemeasuringtools
paper;Bodyscannerdata,Datasetcomprisingall
fortherapid,accurate,preciseandreproduciblequantificationofthedimensionsofthehuman
bodymeasuresobtainedbymeansofthe3dbody body[11–16].Thesedevicescapture3Dbodymodelsinafewsecondsofmeasurement.There-
scanner;Featuremap,Self-organizingmapofbody
fore,theparticipantisilluminatedbyfourlaserswhichprojecthorizontallinesaroundthe
measures;Meta-measure,Representativemeasure
characterizingaclusterofbodymeasuresdefinedin body.Thoselinesarecapturedbyeightcamerasondifferentheightsandutilizedtotriangulate
thefeaturemap;Bodyshape,Anthropometric thebodysurface,whichisthentransformedintoaboutonehundredlengthandcircumference
proportionsofthebody.Inourstudyitisdefinedby measuresbyappropriatesoftwaretoolsinafullyautomatedwaywithhighreproducibility,pre-
thesetofmeta-measuresperparticipant;Bodymap, cisionandaccuracy[16–18].Thesenewandamendeddataareexpectedtoimprovethediag-
Secondlevelself-organizingmapthatclusters
nosticsofmanydiseases,replacingthecurrentrelianceonsimplebodyindices[15,16].
participantsaccordingtosimilarbodyshapes;Body
type,Meanbodyshapeaveragedoverthebody Bodyscanningproducesnewtypesofdatawhich,inturn,neednewalgorithmsand
shapesineachcluster;Bodygram,polardiagramof approachesfor3Dshapeanalysisincludingdimensionreductionandnormalization[19].They
themeta-measurescharacterizingthebodyshapeor alsochallengenewconceptsforanthropometricphenotypingtogetfinermorphologicaldis-
bodytype;Relevantdimensions,Independent
tinctionsforwhole-bodycharacteristics[20].Thefirstand,toourbestknowledge,so-far
coordinatesrequiredtocharacterizevariabilityof uniquestudyof‘body-‘typing(i.e.thequantificationandclusteringofhumanbodyshapes)
bodyshapes.
withinputsfrom3Danthropometrywaspublishedonlyrecently[21].Thisfirstattemptto
clusterbodyscannerdataishoweverbasedonarelativelysmallcohortofabout300adultpeo-
ple.Itprovidedasimpleclassificationintoendomorphic(highfatness),ectomorphic(highlin-
earity),andendo-mesomorphic(amixtureoffatnessandmuscularity)bodytypes.Other
studiesbasedon3Dbodyscanningusedonlyafewsinglemeasurestoderivecombinedindices
suchasBMIorWTHwithoutconsideringtheincreasedsetofbodymeasurespotentiallyavail-
able[13,14].
3Dbodyscanningisidealforscreeninglargepopulationsofsubjectsinlarge-scaleepidemi-
ologicalsurveysduetodetailedacquisitionofbodydimensions,andeasyandefficientuse[13].
Itisappliedtogenerateadatabaseofhumanphysicaldimensionsformenandwomenofvari-
ousweights,betweentheagesof18and65yearsofatotalof2,500peopleintheUnitedStates
and2,500inEurope(TheNetherlandsandItaly)intheframeoftheCAESAR(CivilianAmeri-
canandEuropeanSurfaceAnthropometryResourceProject)project[22],whichpromotedto
tackleaseriesofmethodicalissuesof3Dscanningtechnology[17–19].
3DbodyscanswereappliedintheLeipzigResearchCenterforCivilizationDiseases
(LIFE).LIFE-ADULT(seereference[23]foradescriptionofthestudydesign)sofarcon-
ductedthelargestpopulationbasedstudywithanextensivephenotypingofurbanindividuals
inGermany.Ithasrecentlycompletedthebaselineexaminationof10,000randomlyselected
adultparticipantsfromLeipzig,acityineasternGermanywith550,000inhabitants.Thegen-
eralobjectiveistoinvestigateprevalences,earlyonsetmarkers,andtheroleoflifestylefactors
ofmajorcivilizationdiseases.Allparticipantsunderwentanextensivecoreassessmentpro-
gramincludingbesidesanthropometrystructuredinterviews,questionnaires,physicalexami-
nationsandbiospecimencollection.Thestudycoversamainagerangefrom40–79yearsand
PLOSONE|DOI:10.1371/journal.pone.0159887 July28,2016 2/20
NovelAnthropometryBasedon3D-Bodyscans
predominantlycollectspeopleofmiddleEuropeanethnicity.Afollow-upinvestigationis
plannedtostartfrom2016,re-assessingthesameparticipantsandallowinglongitudinal
comparisons.
Inthispublicationwesystematicallyanalyzedthecompletesetofbodymeasuresprovided
by3Dbodyscanningof8,499adultparticipantsoftheLIFEstudytodefinenovelanthropo-
metricphenotypeswithpossiblerelevanceforlifestylefactorsandcivilizationdiseases.This
dataconstitutes,toourbestknowledge,oneofthelargestsetsofsuchdatapresentlyavailable.
Wepursueseveralobjectives:Howmanyandparticularlywhatkindsofdimensionsofbody
shapearerelevant?Aretherecharacteristic‘bodytypes’?Howtodetermine,todefineandto
describesuchbodytypesinarobustfashion?Forthiswepresentamethodicalframeworkof
dataanalysisusingmachinelearningasthebasalclusteringtechnique.Wedemonstratethat
ourapproachallowsforrepresentationofhumanbodyshapeswithhigh-resolution.Itprovides
robustbodytypesasanimportantprerequisiteforidentifyingnewanthropometricfactorsof
healthriskinsubsequent,presentlyongoinganalyses.
MaterialandMethods
Ethicsapprovalandconsenttoparticipate
Asaprerequisitetoenrolment,writteninformedconsentwasobtainedfromallparticipants.
ThestudywasapprovedbytheresponsibleinstitutionalethicsboardoftheMedicalFacultyof
theUniversityofLeipzig.Thedataprivacyandsafetyconceptofthestudywasapprovedbythe
responsibledataprotectionofficer.
3DbodyscanningoftheLIFEadultcohort
Inthispublicationweanalyzedanthropometric3DbodyscannerdatacollectedintheLIFE-A-
DULTcohortbetween8/2011and11/2014.Acomprehensivedescriptionofthestudydesignis
givenin[23].Thestudycomprisedinteraliainterviews,questionnaires,classicalanthropome-
try,laboratoryinvestigations,biobanking,endocrinological,cardiovascularandcognitive
assessments,MRIofthebrain,and3Dbodyscanning.Basicanthropometriccharacteristicsof
thecohortaresummarizedinTable1.
3Dbodyscanningwasperformedbymeansofacommercial‘VitusSmartXXL’3Dlaser
scanner(HumanSolutionsGmbH,Kaiserslautern,Germany)whichprovidesanimageofthe
bodysurfaceofeachparticipant.BodymeasureswereextractedfromthisimageusingAnthro-
Scan2.9.9software(HumanSolutionsGmbH)andstoredintheLIFEresearchdatabase.Mea-
surementanddatagenerationareinagreementwithISO20685,theinternationalstandardfor
3-Dscanningmethodologiesforinternationallycompatibleanthropometricdatabases.About
Table1. BasiccharacteristicsoftheLIFE-ADULTcohort(meanvalues±standarddeviation). Distribu-
tionfunctionscanbefoundinS1File.
Gender Male Female
Numberofparticipants 4,117 4,363
Age(y) 57±13 56±12
Height(cm) 176±7 165±7
Weight(kg) 86±14 71±14
Waistcircumference(cm) 101±12 91±13
BMI(kg/m2) 28±4 26±5
WHR 0.96±0.08 0.84±0.08
WHtR 0.57±0.07 0.55±0.09
doi:10.1371/journal.pone.0159887.t001
PLOSONE|DOI:10.1371/journal.pone.0159887 July28,2016 3/20
NovelAnthropometryBasedon3D-Bodyscans
20–30participantspassedthestudyprogramofLIFEperdayincluding3Dbodyscanning.The
scannerwascalibratedeverymorningbeforedatacollectionusingastandardcalibrationbody.
Measurementsofallparticipants(standingposition)wererealizedinastandardizedwayby
applyingin-housestandardoperatingprocedures(SOP)basedonmanufacturer’sinstructions.
SOPdefinethepositioningoftheparticipants,scannersettings,andtheprotocolthroughout
thescanningprocess.Bodymeasureswereextractedautomaticallybythescannersoftware
usingdefaultparametersettingsasimplementedbythemanufacturerwhichincludesLand-
markidentificationandgapfillingalgorithms.Thedataofallparticipantsweregenerated
usingthesamescannerdeviceandthesamesoftwareasdescribedabove.Wevalidatedthe
resultsofdifferentsoftwareversions(v2.9.9–3.0.7),showingidenticalresults(unpublisheddata
generatedintheNaKolevelIIIstudy).Surfacecalculationwasvalidatedusinganin-house
MathCadprogramconfirmingoutcomeofthebodyscannersoftware.
Foreachparticipant140measureswerecollectedandstoredintheLIFEresearchdatabase.
Thesemeasuresinclude97(linear)lengthsanddistances,36(curved)girths,2angles,weight,
andthefouraggregatedcharacteristics‘bodymassindex’(BMI[7]),‘waisttohipcircumfer-
enceratio’(WHR[24]),‘waistcircumferencetoheightratio’(WHtR[1])and‘abodyshape
index’(ABSI[3]).
Preprocessing
TheP=140bodymeasuresofN=9,892participantconstitutethePxNmatrixofrawdata
(seeFig1).Theirpreprocessingincludesthreesteps:i)removalofmissingvalues;ii)normali-
zationwithrespecttobodyheight;andiii)Z-normalizationofeachbodymeasure:
1. Thematrixcontained17,721(≙1.4%)missingvaluesfor1,868participantswhichcould
notbeestimatedbythescannersoftware.Inafirststep,46participantswereremoved
whichshowmissingvaluesformorethan50%ofthebodymeasures.Inthesecondstep,6
bodymeasureswereremovedwhichweremissedinmorethan5%oftheparticipants(fora
listseeS1Table).Inthelaststep,theremaining1,347participants(≙13.6%),whichstill
havemissingvalues,wereremoved.Theresultingdatamatrixwithoutmissingvaluescom-
prisesP=134bodymeasuresofN=8,499participants.
2. Thebodymeasuresofeachparticipantwerethendividedbythebodyheight.Thisnormali-
zationstepadjustsmeasuresforbodyheightandassumesthatbodyshapelinearlyscales
withbodyheight.
3. Finally,eachmeasurewasZ-normalized,i.e.centralizedwithrespecttoitsmeanvalueaver-
agedoverallparticipantsanddividedbyitsstandarddeviation.Z-normalizationmakesthe
differentmeasurescomparablebyprovidingacommonscaleinunitsofthestandarddevia-
tionofeachmeasureinthecohort.
SOMclusteringofbodymeasures—‘featuremap’andbodygrams
Weappliedself-organizingmap(SOM)machinelearningforboth,clusteringofthe134body
measuresinto13meta-measuresaswellasforclusteringofparticipantsintobodytypesusing
thesemeta-measures.SOMclusteringintobodytypesisdescribedinthenextsubsection.
SOMmachinelearningprojectsthemultidimensionalfeatureslandscape,givenbythebody
measures,intoatwo-dimensionalmapspace[25].Wedevelopedacomprehensiveanalysis
workflowbasedontheSOMmethod.Thissocalled‘high-dimensionaldataportraying’was
previouslyappliedtodifferentdatatypesinmolecularproteome,genomeand,firstofall,tran-
scriptomestudies[26–30].WeappliedSOMlearningtothepreprocessedbodyscannerdata
PLOSONE|DOI:10.1371/journal.pone.0159887 July28,2016 4/20
NovelAnthropometryBasedon3D-Bodyscans
Fig1.WorkflowofSOM-basedanalysisof3Dbodyscannerdata.
doi:10.1371/journal.pone.0159887.g001
usingstandardparametersettingsoftheR-package‘som’[31].ASOMsizeof50x50unitswas
chosenafterthoroughadjustmentofmapresolutiontooptimallyresolvetheclusterstructure
inherentinthedata(seeS1File).AlargerSOMsizedoesnotchangeclusteringresults.Clusters
ofbodymeasuresweredeterminedinthissocalled‘featuremap’basedonthepatternsofthe
distancemapwhichvisualizesthemeanEuclideandistanceofeachSOMunittoitsadjacent
neighbors(fordetailssee[32]).Aclusteristhendefinedasanareasurroundedbyunitsof
(local)maximumdistances(seeS1File).ThisSOM-basedclusteringoutperformsalternative
approachessuchasprincipalcomponentanalysisandhierarchicalclusteringwhenappliedto
bodyscannerdataasshowninS1File.Intotalwedetected13clustersofbodymeasurestermed
‘meta-measures’.Thesemeta-measurescollectbetween2and27singlemeasures(seeS1
Table).Theirvalueswerecalculatedasmeanvaluesaveragedovertherespectivesinglemea-
suresineachoftheclusters.Elevensingletonmeasuresarenotincludedintheclustersand
excludedfromdownstreamanalyses.Theresultingsetofthirteenmeta-measuresanthropome-
tricallycharacterizeseachparticipantofthestudy.WeZ-normalizedthemeta-measuresof
eachparticipanttoremoveadditive‘offsets’.
SOMclusteringofparticipants—‘bodymap’
ThesecondSOMcalled‘bodymap’wasgeneratedtoclusterparticipantswithsimilarbody
measures.ForinputdataweutilizedthethirteenZ-normalizedmeta-measuresofall8,499par-
ticipantsofthestudy.WedeterminedtheminimumSOMsizerequiredtoseparatedistinct
bodytypesbyprogressivelyincreasingSOMsizeuntiltheasymptoticrangewasreachedand
PLOSONE|DOI:10.1371/journal.pone.0159887 July28,2016 5/20
NovelAnthropometryBasedon3D-Bodyscans
nonewbodytypesemerged(seeS1File).Asizeof130x130unitswasfinallychosen.Each
unitinthebodytypemapcollectsoneormoreparticipantswithsimilarbodyshape,orit
remainsemptyotherwise.Bodytypesweredefinedbyseparatedclustersinthebodymap
whichcontainatleast85participants(≙1%ofthecohort)tofocusonrobustandrelevant
clusters.
Avisualdescriptionofbodytypesandtheirmutualrelationsisachievedbystainingthe
bodymapaccordingtomeancharacteristicsoftheparticipantsineachofthetilessuchasage,
gender,BMI,WHtR,WHR,andABSI.
Availabilityofdata
Preprocessedmeta-measuresofallparticipantsusedinthisstudytogetherwiththeassignment
ofgender,age,BMIandbodytypeareprovidedasS2Table.Rawdatacanberequestedfrom
theLIFEConsortium(www.life.uni-leipzig.de/en/).
Results
SOM-basedanalysisworkflowforbodyscannerdata
Wedevelopedacomprehensiveworkflowtoprocessbodyscannerdatainlargeepidemiologi-
calcohorts(seemethodssectionandFig1).Inthefirststep,rawdataofbodymeasureswere
preprocessedwhichincludestheremovalofmissingvalues,bodyheight-andZ-normaliza-
tions.Heightnormalizationissupposedtominimizebodysize-scalingeffectstofocusouranal-
ysisonthevarietyofbodyshapes.Z-normalizationisappliedtomakethedifferentmeasures
comparableintermsofacommonscale.
Afterpreprocessing,ourdatasetcomprises134bodymeasurescollectedwithintheadult
cohortoftheLIFEstudyfrom8,499participants.Weadditionallyincludedfourclassicalbody
indicesintothedataset,namely‘bodymassindex’(BMI[7]),‘waisttohipcircumferenceratio’
(WHR[24]),‘waistcircumferencetoheightratio’(WHtR[1])and‘abodyshapeindex’(ABSI
[3]).InthenextstepsweappliedSOMmachinelearningeithertoclusterthebodymeasuresor
theparticipants,respectively.Detailsaredescribedbelowandinthemethodssection.
134bodyscannermeasuresaggregateinto13meta-measures
AfeaturemapofpreprocessedbodyscannerdataisgeneratedusingSOMmachinelearning
[25].Agridsizeof50x50waschosentoachievestableclusteringofthe134bodyscannermea-
suresintothirteenclusters(seeS1File).TheSOMalgorithmorganizescontinuousfeatures,
herebodymeasures,withinatwo-dimensionalgridsuchthatfeatureswithsimilarprofiles
acrossthecohortlocateatthesameoratclosepositions,whereasfeatureswithdissimilarpro-
filesarefoundindifferentregionsofthemap.Fig2ashowsthelocalizationofeachbodymea-
sureinthefeaturemap.Eachdotrepresentsatleastoneindividualmeasure.Ifmultiple
measuresarelocatedatthesameposition,theirnumberisindicatedbythecolorscalegiven
withinthefigure:bluedotsrepresentpositionswith1singlemeasure,darkredrepresentsa
positionwith16measures.Importantly,thedistancesbetweenthebodymeasuresinSOM
spacearescalednon-linearly,i.e.theyexpandinregionshighlypopulatedwithfeaturesand
decreaseinsparselypopulatedregions.Wemakeuseofthispropertyfortheunsupervised
determinationofclustersoffeatureswithsimilarprofiles.Weobtainedthirteenclusterstermed
meta-measureswhichcontainfrom2to27individualbodymeasures(Fig2b).Themeta-mea-
sureswerelabeledwithcapitallettersfollowingclockwiseorderinthefeaturemap.Thisway
themeta-measuresweresortedaccordingtothemutualsimilarityoftheirprofileswhichonly
partlyagreeswiththeiranatomicalassignment.Meta-measuresinthetopleftregionofthe
PLOSONE|DOI:10.1371/journal.pone.0159887 July28,2016 6/20
NovelAnthropometryBasedon3D-Bodyscans
Fig2.SOMclusteranalysisofbodymeasures.(a)Thepopulationmapshowsthelocalizationofall134
bodymeasuresinSOMspace.Thecolorcodeassignsthenumberofmeasuresineachofthe50x50SOM
units,emptyunitsarewhite.Singletonbodymeasuresnotincludedinthemeta-measureclustersare
highlighted.(b)Thesamemapasinpanel(a),wherethirteenclustersweredetectedandassignedwitha
‘nickname’characterizingthemeasuresinthecluster.Theirnumberperclusterisgiveninparenthesis.(c)
Consensusclustermapofthefeatures.Lighttodeepbluecoloringindicatestheincreasingfrequencyof
pairwiseappearanceofdifferentfeaturesinthesameclustersasdeterminedin100-foldbootstrappedSOM
trainingandclustering.(d)Meanintra-clusterconsensusasafunctionofthenumberofparticipantsusedin
consensusclustering.
doi:10.1371/journal.pone.0159887.g002
featuremapmainlyrefertogirthmeasures,whereasthoseinthebottomrightregiontolength
measures.Forexample,cluster‘I’,calledmeta-measure‘armlength’,containssixrelatedmea-
sures,namely‘armlengthsleft&right’,‘armlengthstoneckleft&right’,and‘uparmlengths
left&right’(seeS1Tableforthecompletelistofmeta-measureassignments).Thelocalization
oflengthandgirthmeasuresinoppositeareasofthemapreflectsanti-correlatedbehavior,i.e.
largergirthsonaverageassociatewithsmallerlengthsandviceversa.Anothermeta-measure
‘G’,whichwastermed‘Inseamandlowerbodylengths’,isaclusterof18featuresmainly
relatedtoheightsdrawingonthelowerbodysuchas‘sideseamlengthsleft&right’,‘buttock
height’,‘kneeheight’and‘distancenecktoknee’.Pleaseremindthatthemeta-measuresrefer
toscaledbodymeasuresinrelationtobodyheight.
Wejudgedthestabilityofourclusterassignmentswithregardtovariationofthecohort
compositionusingconsensusclusteringonrandomlysampledsub-cohortsof4,000partici-
pants((cid:1)50%ofthecohort)asdescribedinS1File.Theconsensusmaprevealsdeepblue
squaresalongthediagonalwhichindicatestableclustersofbodymeasureswhichgrouped
togetherinmostbootstrappingiterations(Fig2c).Lightblueoff-diagonalregionsindicatea
residuallevelofuncertaintyofclusterassignment,e.g.betweenclustersGandH.Theconsen-
susmapindicatesaclearandrobustclusterstructureinherentinthebodyscannerdata.Thisis
PLOSONE|DOI:10.1371/journal.pone.0159887 July28,2016 7/20
NovelAnthropometryBasedon3D-Bodyscans
furthersupportedbythemeaninter-clusterconsensusvalueofhm(k)i=0.91,meaningthat
featurepairsfromthesamecluster(asclassifiedusingthefullcohort)attaincommoncluster
assignmentsin91%ofthebootstrappingiterations.
Finallywestudiedclusterstabilityasafunctionofcohortsizeusingpartialinter-clustercon-
nectivityforconsensusclusteringofrandomlysampledsub-cohortsofsizesrangingfrom100
to8,000participants(Fig2d).Itturnedoutthatthemeaninter-clusterconsensusvalueasymp-
toticallylevelsoffatabout0.9forcohortsgreaterthan4,000,indicatingrobustclustering.For
cohortssmallerthan1,000,clusteringoffeaturesbecomesratherunstable.
Bodygrams
Wevisualizethevaluesofthethirteenmeta-measuresusingapolardiagramrepresentation
calledbodygramwheretheorderingofpolaraxeswaschosenaccordingtotheorderingof
meta-measuresintheSOM(Fig3a).TheblackpolygonreferstoZ=0andthustothemean
valueofeachmeasureaveragedoverthecohort.Inthefollowingtheterms‘big’,‘small’,‘long’
and‘short’relatetotheseaveragevalues.
Fig3bshowsfourbodygramsofselectedparticipantstogetherwiththecorrespondingbody
surfaceimages.Theyrevealthatrelativelytallandthickpeopleandalsoparticipantswithshort
andlongextremitiescanbeeasilyidentifiedbysectionsofthebodygramlargelydeviatingfrom
theZ=0baseline.Thebodygramenablesasimpleshape-basedperceptionofanthropometric
phenotypes.
Inthenextstepwegeneratedmeanbodygramsofsub-cohortsspecifiedbygender,ageand
weight(Fig3c).Comparisonofthefemaleandmalebodygramsrevealstypicalgender-specific
differencesbetweenbodyshapes:Menshowhighervaluesoftorsoandextremitieslengths,
whereaswomenshowlargersideseamlengthsandthighgirths.Ingeneral,thesegenderspecif-
icsaremorepronouncedforolderpeople(especiallywomen)whoseupperbodygirthsand
shoulderanglemarkedlyincrease.Womenalsoshowlarger(relative)‘headcircumference’val-
uesthanmen.Largerbodyheightsofmenthusdonotassociatewithaproportionalincreaseof
theirheadcircumferences.Wefurtherstratifiedyoungerandolderwomenandmenwith
respecttotheirbodyweight:Foryoungmenahighweightassociateswithlongerlengthmea-
suresandgirths(upperbody,thigh),whereasforoldermenastrongincreaseofupperbody
girthandshoulderwidthisobserved.Higherweightsaretriviallyfoundforobeseaswellas
‘strong’men.Interestingly,youngeroverweightwomenshowextraordinarilylargethighgirths,
whereasolderwomenalsoshowarmandneckgirthsbeyondtheiraveragevalues.
Thissimpleapproachclearlyshowsthatgender,age,weightandBMIreflectmultiplechar-
acteristicsofthebodyshapeasseenbythemeta-measures,whereastheformer,virtuallyone-
dimensionalparametersarenotabletocomprehensivelydescribethemultidimensionaldiver-
sityofthebodyshape.
TheLIFE-ADULTcohortsplitsintodistinctbodytypes
AfterclusteringofbodymeasuresweclusteredtheparticipantsintobodytypesusingSOM
machinelearning.WetrainedSOMsofincreasingsizeswiththemeta-measuredataofthe
LIFEcohort.Wefoundthatthenumberofresolvedbody(type)clustersincreasesuntilaSOM
sizeof130x130,andthenlevelsoffatanumberoffifteenclusters(seeS1File).Hence,aSOM
sizeof130x130wasrequiredtoachievesufficientresolutionoftheclustersinherentinthe
LIFEcohortofnearly8,500adultparticipants.Theseclusterseithercontainmainlywomen
(labelledasF1...F6),men(M1...M7)orparticipantsofbothgenders(B1&B2)(seeFig4a).A
fewparticipantscouldnotbeassignedtoanybodytypecluster(n=96,≙1.1%ofthecohort).
PLOSONE|DOI:10.1371/journal.pone.0159887 July28,2016 8/20
NovelAnthropometryBasedon3D-Bodyscans
Fig3.Visualizationofbodyshapesusingbodygrams.(a)Bodygramsarepolardiagramswiththemeta-
measuresinZ-unitsasaxes.TheblackpolygonreferstoZ=0.(b)Examplebodygramsandthe
correspondingbodysurfacescanimagesofselectedparticipants.Thedotted‘cobweb’linesindicate
deviationsfromthemeaninunitsof+/-1,2etc.standarddeviations.(c)Bodygramsaveragedoverallmen
(leftbranch)andwomen(rightbranch).Thegender-specificgroupswerefurtherstratifiedintoyoungandold
PLOSONE|DOI:10.1371/journal.pone.0159887 July28,2016 9/20
NovelAnthropometryBasedon3D-Bodyscans
men/womenandalsolightandheavypersons.Thenumbersindicatethesizeoftherespectivesub-cohort.
Notethatdatawasnotcompleteforallparticipants.
doi:10.1371/journal.pone.0159887.g003
Theywerelocatedinisolatedunitsofthebodymapandwillbeaddressedinfollow-up
analyses.
Wegeneratedaconsensusmatrixbyapplying100-foldbootstrappedclusteringusingsub-
cohortsof8000participants.Thestabilityofbodytypeclusterswasthenevaluatedusingintra-
andinter-clusterconsensusvalues:Bodytypeswithhighintra-clusterconsensus(F1-F4,M1)
canberegardedascompactandstable,whileclusterswithlowervalues(B1,F5,M4,M7)are
moreuncertain(Fig4b).Notethatintra-clusterconsensusandnumberofparticipantsinthe
bodytypesarecorrelatedwithPearsoncorrelationcoefficientofr=0.5indicatingadirectrela-
tionbetweenthesizeoftheclustersandtheirstability:Highlypopulatedbodytypescollecting
prevalentbodyshapestendtobemorestablethanrareones.
Fig4.SOMclusteranalysisoftheparticipants.(a)Thebodymapprojectsthe8,499participantsintoatwo-
dimensionalgrid.Itreveals15distinctgroupsofparticipantswithsimilarbodyshapesdefinedbyseparatedcluster
regions.Eachdotinthemaprepresentsoneparticipantandiscoloredaccordingtoitsgenderandagestratification
asgiveninthelegendwithinthemap.(b)Intra-clusterconsensusvaluesforeachofthebodytypeclustersreflectthe
degreeofreliabilityandstability.(c)Hierarchicalclusterdendrogramofbodytypeclusters.Inter-clusterconsensus
valueswereusedassimilaritymeasuretodefinethebranchingheightsofthedendrogram.
doi:10.1371/journal.pone.0159887.g004
PLOSONE|DOI:10.1371/journal.pone.0159887 July28,2016 10/20