Dual energy X-ray absorptiometry compared with anthropometry in relation to cardio-metabolic risk

14 Pages·2016·0.95 MB·English

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Health Sciences Papers and Journal Articles School of Health Sciences
2016
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D Demmer
L Beilin
B Hands
University of Notre Dame Australia, [email protected]
S Burrows
C Pennell
See next page for additional authors
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This article was originally published as:
Demmer, D., Beilin, L., Hands, B., Burrows, S., Pennell, C., Lye, S., Mountain, J., & Mori, T. (2016). Dual energy X-ray absorptiometry
compared with anthropometry in relation to cardio-metabolic risk factors in a young adult population: Is the ‘Gold Standard’
tarnished?. PLoS One, 11 (9).
Original article available here:
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0162164
This article is posted on ResearchOnline@ND at
https://researchonline.nd.edu.au/health_article/156. For more
information, please contact [email protected].
AAuutthhoorrss
D Demmer, L Beilin, B Hands, S Burrows, C Pennell, S Lye, J Mountain, and T Mori
This article is available at ResearchOnline@ND: https://researchonline.nd.edu.au/health_article/156
This article was originally published at:
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0162164
Demmer, D., Beilin, L., Hands, B., Burrows, S., Pennell, C., Lye, S., Mountain, J., and Mori, T. (2016)
Dual energy X-Ray absorptiometry compared with anthropometry in relation to cardio-metabolic risk
factors in a young adult population: Is the ‘Gold Standard’ tarnished? PLoS One, 11(9). doi:
10.1371/journal.pone.0162164
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RESEARCHARTICLE
Dual Energy X-Ray Absorptiometry
Compared with Anthropometry in Relation to
Cardio-Metabolic Risk Factors in a Young
Adult Population: Is the ‘Gold Standard’
Tarnished?
DeniseL.Demmer1☯*,LawrenceJ.Beilin1☯,BethHands2‡,SallyBurrows1☯,Craig
a11111
E.Pennell3,StephenJ.Lye4,JenniferA.Mountain5‡,TrevorA.Mori1☯
1 SchoolofMedicineandPharmacology,RoyalPerthHospitalUnit,TheUniversityofWesternAustralia,
Perth,Australia,2 InstituteforHealthResearch,TheUniversityofNotreDameAustralia,Perth,Australia,
3 SchoolofWomen’sandInfantsHealth,TheUniversityofWesternAustralia,Perth,Australia,4 Lunenfeld-
TanenbaumResearchInstitute,UniversityofToronto,Toronto,Canada,5 SchoolofPopulationHealth,The
UniversityofWesternAustralia,Perth,Australia
OPENACCESS ☯Theseauthorscontributedequallytothiswork.
‡Theseauthorsalsocontributedequallytothiswork.
Citation:DemmerDL,BeilinLJ,HandsB,Burrows *[email protected]
S,PennellCE,LyeSJ,etal.(2016)DualEnergyX-
RayAbsorptiometryComparedwithAnthropometryin
RelationtoCardio-MetabolicRiskFactorsinaYoung
Abstract
AdultPopulation:Isthe‘GoldStandard’Tarnished?
PLoSONE11(9):e0162164.doi:10.1371/journal.
pone.0162164
BackgroundandAims
Editor:Xiao-FengYang,TempleUniversitySchoolof
Medicine,UNITEDSTATES
Assessmentofadiposityusingdualenergyx-rayabsorptiometry(DXA)hasbeenconsid-
Received:May23,2016 eredmoreadvantageousincomparisontoanthropometryforpredictingcardio-metabolic
Accepted:July30,2016 riskintheolderpopulation,byvirtueofitsabilitytodistinguishtotalandregionalfat.None-
theless,thereisincreasinguncertaintyregardingtherelativesuperiorityofDXAandlittle
Published:September13,2016
comparativedataexistinyoungadults.Thisstudyaimedtoidentifywhichmeasureofadi-
Copyright:©2016Demmeretal.Thisisanopen
positydeterminedbyeitherDXAoranthropometryisoptimalwithinarangeofcardio-meta-
accessarticledistributedunderthetermsofthe
CreativeCommonsAttributionLicense,whichpermits bolicriskfactorsinyoungadults.
unrestricteduse,distribution,andreproductioninany
medium,providedtheoriginalauthorandsourceare
MethodsandResults
credited.
1138adultsaged20yearswereassessedbyDXAandstandardanthropometryfromthe
DataAvailabilityStatement:Thedatausedto
generatetheresultsinthispublicationareavailable WesternAustralianPregnancyCohort(Raine)Study.Cross-sectionallinearregression
uponrequestasethicalrestrictionsexistanddata analyseswereperformed.WaisttoheightratiowassuperiortoanyDXAmeasurewith
wereobtainedfromathirdparty.Readersand
HDL-C.BMIwasthesuperiormodelinrelationtobloodpressurethananyDXAmeasure.
interestedresearchersmaycontacttheExecutiveof
Midrifffatmass(DXA)andwaistcircumferencewerecomparableinrelationtoglucose.For
theWesternAustralianPregnancyCohort(Raine)
Studytorequestthedata. alltheothercardio-metabolicvariables,anthropometricandDXAmeasureswerecompara-
ble.DXAmidrifffatmasscomparedwithBMIorwaisthipratiowasthesuperiormeasurefor
Funding:DXAscanningwasfundedbyCanadian
InstitutesforHealthResearch(GrantMOP82893). triglycerides,insulinandHOMA-IR.
TheRaineStudy20yearfollowupassessmentwas
fundedbyprojectgrantsfromtheAustralianNational
PLOSONE|DOI:10.1371/journal.pone.0162164 September13,2016 1/11
DXAandAnthropometrywithCardio-MetabolicRiskFactors
HealthandMedicalResearchCouncil,theLionsEye Conclusion
InstituteinWesternAustralia,AustralianFoundation
Althoughmidrifffatmass(measuredbyDXA)wasthesuperiormeasurewithinsulinsensi-
forthePreventionofBlindnessandtheAlcon
ResearchInstitute.CoremanagementoftheRaine tivityandtriglycerides,theanthropometricmeasureswerebetterorequalwithvariousDXA
studyisprovidedbytheRaineMedicalResearch measuresformajorityofthecardio-metabolicriskfactors.Ourfindingssuggest,clinical
Foundation;TheUniversityofWesternAustralia
anthropometryisgenerallyasusefulasDXAintheevaluationoftheindividualcardio-meta-
(UWA);theTelethonKidsInstitute;theUWAFaculty
ofMedicine,DentistryandHealthSciences;the bolicriskfactorsinyoungadults.
WomenandInfantsResearchFoundationandCurtin
University.
CompetingInterests:Theauthorshavedeclared
thatnocompetinginterestsexist.
Introduction
Theprevalenceofobesityisincreasingworldwide.In2014morethan1.9billionadults,18
yearsandolder,wereoverweightandapproximately600millionadultswereobese[1].Excess
bodyfatisanestablishedriskfactorfornumerouschronicdiseasesandprematuredeath[2,3].
Moststudiesseekingtoincreasetheunderstandingofthenegativeinfluenceofobesityhave
beenbasedonbodymassindex(BMI).However,BMIdoesnotreflecttotalbodyadiposity
becauseitcannotdifferentiatebetweenleanandfatmassofanindividual.Alternativemeasures
suchaswaistcircumferenceorwaist-heightratiomaybebetterclinicalindicatorsofadiposity
[4].
Dualenergyx-rayabsorptiometry(DXA)isthegoldstandardforthediagnosisofosteopo-
rosis[5]andhasincreasinglybeenusedforthediagnosis/managementofoverweightandobe-
sityandinclinicalresearchincardiovasculardisease[6–8].Thequestionarisesastowhether
theuseofDXAinthemanagementofoverweightandobesityisjustifiedintermsofcostand/
orcomplexity,inreturnforanyclear-cutscientificorclinicalvalue.Therearecommercialdriv-
ersfortheuseofDXA;forexampleintheUSAapatientcanexpecttopayapproximately$100
USinthepublichealthsettingor$250USwithintheprivatehealthsectorforaDXAscanwith-
outrebate[9].Therehasbeena65%declineinMedicarereimbursementofDXAbonemineral
densitytestinginthenon-facilitysetting,fromapproximately$140in2006to$50in2014[10].
Atthislowerlevel,providersofDXAscansarefindingitmoredifficulttocovertheoperating
costs,duetofundingcuts[11].Therefore,therehasbeensomemotivationtofindanotheruse
forDXAthatmaybeclinicallyrelevanttohealthprofessionals,particularlytoallowforearly
identificationandinterventionofindividualsinrelationtoobesityandcardio-metabolic
health.
ThedominanceanddifferentialeffectsofDXAoveranthropometryforestimatingthepres-
enceofcardio-metabolicriskhasnotbeenclearlyestablished.Studiesinmiddleagedadults,
whichcompareanthropometricandDXAadipositymeasurements,havebeeninconsistent
withrespecttothestrengthofassociationswithcardio-metabolicriskfactors,mostlikelydue
tomethodologicalinconsistenciesindefiningadiposity[2,12].Moreover,manyofthese
reportsonlytakeintoaccountestimatesoftotalbodyfatpercentageandnotfatdistributionor
abdominalfatmass10–14.Inaddition,thereisalsoalackofreporteddatacomparingDXA
againstcardio-metabolicriskfactorssolelyinyoungadultpopulations[4,12].
Obesityinyoungadultsisanimportantpredictorforsubsequentcoronarydiseaseanddia-
betesinmiddletooldage[13,14].Australiafollowsworldwidetrendsshowingincreasinglev-
elsofobesityandType2diabetesinearlyadulthood[15].Theearlyidentificationof
individualswhoareofincreasedriskofcoronarydiseaseanddiabetesinlaterlifehasthe
potentialtoimplementlifestylemodificationsthatcouldreducethisrisk.Thisstudytherefore
aimedtoidentifywhichmeasureofadipositydeterminedbyeitherDXA(totalbodyfatper-
centage,FatDistributionIndexandmidrifffatmass)oranthropometry(abdominalskinfold,
PLOSONE|DOI:10.1371/journal.pone.0162164 September13,2016 2/11
DXAandAnthropometrywithCardio-MetabolicRiskFactors
waistcircumference,waisttoheightratio,weightandBMI),isoptimalwithinarangeofcar-
dio-metabolicriskfactorsinasampleofhealthyyoungadultsfromtheWesternAustralian
PregnancyCohort(Raine)Study.
Methods
Participants
TheWesternAustralianPregnancyCohort(Raine)Studyisaprospectivepopulationstudy
wherepregnantwomenbetween16to20weeksgestationwererecruitedfromKingEdward
MemorialHospitalandcloselylocatedpractices.Themothersgavebirthto2868liveinfants.
DetailedinformationonthemethodsoftheRaineStudyhaspreviouslybeenreported[16].
Thepresentpopulationcomprised1273adultsfromtheRaineStudywhoattendedthe20year
oldsurvey.Written informedconsentwasobtainedfromtheparticipants.Ethicsapprovalfor
the20yearassessmentwasobtainedfromtheHumanResearchEthicsCommitteeattheUni-
versityofWesternAustralia.
AdultanthropometryandDXAmeasurements
Atthe20yearfollowup,heightwasmeasuredbyawallmountedStadiometer(tothenearest
0.1cm)andweightwasmeasured(tothenearest100g)withparticipantsdressedinlight
clothes.Bodymassindex(BMI)wascalculatedasweight(kg)dividedbythesquareofheight
(m2).Waist circumferencewasevaluatedattheumbilicuslevelandhipcircumferenceatthe
levelofthemaximumposteriorextensionofthebuttockswithatapemeasure(tothenearest
0.1cm).Theabdominalskinfoldwasmeasuredfromaverticalskinfoldimmediatelytotheleft
oftheumbilicus,thesuprailiacskinfoldwasassessedfromadiagonalfoldlocated1cmabove
theanteriorsuperioriliaccrestandthetricepskinfoldwasmeasuredfromaverticalskinfold
alongthemidlineonthebackofthetricepsoftherightarmusingaskinfoldcalliper(Holtain,
Crosswell,UnitedKingdom)[17].Allskinfoldmeasurementswereassessedtwicewiththe
averageoftheskinfoldscalculated.Thesumofthreeskinfoldswascalculatedfromtheabdomi-
nal,suprailiacandtricepskinfoldsitesandthischaracterisesthesubcutaneousfatthicknessat
differentregionsofthebody[18].Waist tohipandwaisttoheightratioswerederivedfromthe
divisionofwaistcircumference(cm)bythehipcircumference(cm)andheight(m),
respectively.
ThestudyusedaNorlandXR-36densitometer(NorlandMedicalSystems,Inc.,FortAtkin-
son,WI,USA)toprovideestimatesofwholebodyfatmass(g),leanmass(g),midrifffatmass
(g)(vertebraeL1—L4).Total bodyfatpercentagewasestimatedastotalbodyfatmass(g)/
totalmassx100.TheFatDistributionIndexwascalculatedfromtheformulachestfatmass(g)
+midrifffatmass(g)/pelvisfatmass(g)+leftlegfatmass(g)+rightlegfatmass(g)[19].All
measurementswereperformedbytrainedresearchpersonnel.
Biochemistryandbloodpressuremeasurements
VenousbloodsamplestakenafteranovernightfastwereanalysedinthePathWest Laboratory
atRoyalPerthHospitalforserumglucose,insulin,totalcholesterol,triglycerides,highdensity
lipoprotein-cholesterol(HDL-C)andhighsensitivityC-reactiveprotein(hs-CRP)[20].Low
densitylipoprotein-cholesterol(LDL-C)wascalculatedusingtheFriedewaldequation[21].
Thehomeostaticmodelassessmentofinsulinresistance(HOMA-IR)wascalculatedusingthe
formula:fastinginsulin(μU/ml)xfastingglucose(mmol/L)/22.5[22].BPwasmeasured
usinganoscillometricsphygmomanometerwiththeappropriatecuffsizeforarmcircumfer-
ence(DINAMAPvitalsignsmonitor8100,DINAMAPXLvitalsignsmonitororDINAMAP
PLOSONE|DOI:10.1371/journal.pone.0162164 September13,2016 3/11
DXAandAnthropometrywithCardio-MetabolicRiskFactors
ProCare100;GEHealthcare).SixBPreadingswereobtainedevery2minuteswithina10min-
utetimeperiodinthesupineposition,aftera5minuterestingperiod.TheaverageBPvalue
wascalculatedusingthelastfivereadingstoobtainsystolicanddiastolicBPvalues[23].
Statisticalanalysis
Characteristicsofthesampleweresummarisedusingmeansandstandarddeviations(SD)sep-
aratelyformalesandfemales.Differencesbetweenthesexesweretestedusingt-tests.All
reportedpvaluesare2-tailedandsignificancewassetatα=0.05.
Linearregressionanalysesexaminedtherelationshipbetweeneachadipositymeasuresand
cardio-metabolicriskfactors.Allmodelsincludedandtestedtheinteractionbetweensexand
theadiposityindicator.Foreachoutcome,comparisonsweremadewithinthesetofmodels,
usingeachadipositymeasuretoidentifythebestDXAmeasurerelatedtoagivencardio-meta-
bolicriskfactorandsimilarlyfortheanthropometricmeasures.Themeasuresidentifiedas
bestfromeachofthesetwosets(DXAandanthropometry)werethencompared.Forexample
themodelofDXAmidrifffatmassandtriglycerideswascomparedwiththemodelofwaist/
heightandtriglycerides.Giventhatthesemodelswerenotnestedwithineachother,likelihood
ratiotestswerenotappropriatetoformallycomparemodels.ThustheAkaikeInformationCri-
terion(AIC)wasutilised[24].Asrequired,allmodelsforaparticularoutcomewerecon-
structedonastaticsampledeterminedbycompletedataonalladipositymeasures.Forany
givenoutcome,themodelwiththeminimumAICwasdeemedtobethebestmodel.Differ-
encesfromtheminimumAICprovideastrengthofevidencecomparisonandtherankingof
modelswithrespecttothebestmodel.BycalculatingdifferencesbetweenAIC,arbitraryscaling
constantsareremovedallowingtheapplicationofguidelinestointerpretthedifference.Models
wherethedifferenceinAICis(cid:20)2indicatessubstantialsupportthattheyarenotdifferent,
whilesupportforthisclaimdecreasesasthedifferenceinAICincreases(AICvaluesabove10
areconsideredtohavenosupportforequivalence).TheadjustedR2foreachmodelwasalso
reported.Inadditiontomodelswithasingleadipositymeasure,combinationsofanthropomet-
ricandDXAmeasureswereexploredinasimilarmanner.Thecombinationsincludedmidriff
fatmassandwaistcircumference,fatdistributionindexandBMI,andmidrifffatmassand
BMI.Tobit regression(forcensoreddata)wasutilisedforfastinginsulinandhs-CRPdueto
thelowerboundaryofthetest[25].Allotherregressionsperformedwereordinarylinear
regression.Variables werelogtransformedifthevalueswerenotnormallydistributed(triglyc-
erides,HDL-C,insulin,hs-CRP).DatawereanalysedusingSTATA (StataCorp,2011.Stata
StatisticalSoftware:release12.CollegeStation.TX:StataCorp,LP).
Results
Descriptivecharacteristics
GeneralcharacteristicsofbodycompositionusingDXA,anthropometryandtheindividual
cardio-metabolicriskfactorsareshownseparatelyforfemalesandmalesinTable 1.
EthnicityinthecohortwaspredominantlyCaucasian(93%).At20yearsofage,femaleshad
ahighertotalbodyfatpercentage,fatdistributionindexandmidrifffatmasscomparedwith
males(p<0.001)(Table 1).Malesweretaller,heavierandhadagreaterwaistcircumference
thanfemales(allp<0.001)buthadasignificantlylowerabdominalskinfold.Malesand
femaleswerenotstatisticallydifferentforwaisttoheightratioandBMI.Allcardio-metabolic
riskfactorswerestatisticallydifferentbetweenmalesandfemalesexceptforHOMA-IRand
diastolicBP. FemaleshadlowersystolicBP, triglyceridesandglucose,andhighertotalcholes-
terol,HDL-C,LDL-C,insulinandhs-CRP, thanmales.Thenumberofindividualsinthe
PLOSONE|DOI:10.1371/journal.pone.0162164 September13,2016 4/11
DXAandAnthropometrywithCardio-MetabolicRiskFactors
Table1. DescriptivecharacteristicsofmalesandfemalesfromtheWesternAustralianPregnancyCohortat20yearsofage.
Measure Females Males pvalue
n=532 n=606
DXA
Totalbodyfatpercentage(%) 39.3(8.9) 21.8(8.7) <0.001
Fatdistributionindex(g) 17318.2(7349.1) 11235.7(6431.0) <0.001
Midrifffatmass(g) 1401.7(932.9) 1091.0(887.6) <0.001
Anthropometry
Abdominalskinfold(mm) 25.4(8.6) 21.5(10.2) <0.001
Waistcircumference(cm) 77.2(13.0) 83.0(12.2) <0.001
Waist/heightratio 46.7(8.0) 46.4(6.6) 0.44
Height(m) 1.66(0.1) 1.78(0.1) <0.001
Weight(kg) 67.1(15.8) 78.7(16.4) <0.001
BMI(kg/m2) 24.4(5.6) 24.5(4.5) 0.71
Biochemistry
Cholesterol(mmol/L) 4.5(0.8) 4.2(0.8) <0.001
Triglycerides(mmol/L) 1.0(0.5) 1.1(0.6) 0.03
HDL-C(mmol/L) 1.4(0.3) 1.2(0.2) <0.001
LDL-C(mmol/L) 2.6(0.6) 2.4(0.7) <0.001
hsC-reactiveprotein(mg/L) 3.0(5.5) 2.1(5.8) 0.004
Glucose(mmol/L) 4.8(0.4) 5.1(0.4) <0.001
Insulin(mU/L) 1.3(0.7) 1.2(0.7) 0.002
HOMA-IR 1.1(1.2) 1.0(1.4) 0.69
BloodPressure
SystolicBP(mmHg) 111.1(10.2) 122.3(11.8) <0.001
DiastolicBP(mmHg) 65.4(7.2) 65.2(7.8) 0.62
DescriptivecharacteristicsarepresentedasmeansandSD.BMI,bodymassindex;HDL,high-densitylipoprotein;LDL,low-densitylipoprotein;hsC-
reactiveprotein;highsensitivityC-reactiveprotein;HOMA-IR,homeostaticmodelassessmentofinsulinresistance;BP, bloodpressure.
doi:10.1371/journal.pone.0162164.t001
samplewithmetabolicsyndromeaccordingtotherevisedNationalCholesterolEducationPro-
gramAdultTreatment PanelIIIguidelines[26]was26females(1.8%)and47males(3.3%).
DXAandanthropometrymeasureswiththeindividualcardio-metabolic
riskfactors
(i)ComparisonswithinDXAandanthropometricmeasures. Table 2summarisesthe
DXAandanthropometrymeasuresthatproducedthelowestAICforeachoftheindividual
cardio-metabolicriskfactors.Nostatisticallysignificantdifferencesbetweenthesexesinthe
relationshipbetweentheadipositymeasuresandthecardio-metabolicriskfactorswerefound
foranyoutcome.Theinteractiontermwasthereforeremovedandasingleadipositycoefficient
applicabletobothmalesandfemaleswasestimated.WithintheDXAmeasures,midrifffat
masswasthesuperiormodelforallriskfactorswiththeexceptionofHDL-C,hs-CRPandsys-
tolicBP. Incontrast,therewasconsiderablevariationinthebestanthropometricmeasure
acrossthecardio-metabolicriskfactors:BMIwasthebestmeasureforsystolicBP, hs-CRP,
insulinandHOMA-IR;waisttoheightratiowasbestfortriglycerides,HDL-CandLDL-C;and
abdominalskinfoldwasbestforcholesterolanddiastolicBP. Bodyadiposityindexandsumof
skinfolds,asanthropometrymeasurements,werenotevidenttohavethelowestAICwithany
oftheriskfactors.NoneofthecombinationsofDXAwithanthropometrymeasureswas
PLOSONE|DOI:10.1371/journal.pone.0162164 September13,2016 5/11
DXAandAnthropometrywithCardio-MetabolicRiskFactors
Table2. TheAkaikeInformationCriteriondifferencesbetweenthebestDXAandbestanthropometrymeasureswiththeindividualcardio-meta-
bolicriskfactorsinyoungadults.
Adiposityindices BestDXA Best AICdifference
Anthropometry (DXA–
AICR2 AICR2 Anthropometry)
Cholesterol Midrifffatmass Abdominalskinfold
N=1021 2325.30.08 2327.730.04 -2.46
Triglycerides Midrifffatmass* Waist/heightratio
N=1021 1550.20.10 1560.640.09 -10.49
HDL-C Fatdistribution Waist/heightratio*
index
N=1021 343.30.19 331.780.20 11.54
LDL-C Midrifffatmass Waist/heightratio
N=1021 1972.10.05 1971.550.06 0.52
hsC-reactiveprotein Totalbodyfat BMI
percentage
N=1021 6359.90.006 6362.340.005 -2.41
Glucose Midrifffatmass Waistcircumference
N=1021 957.80.09 958.080.09 -0.28
Insulin Midrifffatmass* BMI
N=1021 2158.30.08 2168.080.07 -9.74
HOMA-IR Midrifffatmass* BMI
N=1021 3261.10.04 3270.460.04 -9.36
SystolicBP Fatdistribution BMI*
index
N=1180 8884.80.27 8831.090.30 53.7
DiastolicBP Midrifffatmass Abdominalskinfold
N=1180 8043.40.03 8043.730.003 -0.38
*Bestadipositymeasure
AICandR2valueswerederivedfromlinearorTobitregressionmodelsadjustedforsex(S1–S3Tables).AICdifferenceswerederivedfromthebestDXA
AICvalue—thebestanthropometryAICvalue.DifferencesinAICbetweenmodelsofapproximately2wereconsideredtoindicateequivalentadiposity
measures.OtherwisethemodelwiththelowestAICwasconsideredsuperior.
AIC,Akaikeinformationcriterion;HDL-C,high-densitylipoproteincholesterol;LDL-C,low-densitylipoproteincholesterol;HOMA-IR,homeostaticmodel
assessmentofinsulinresistance;hsC-reactiveprotein;highsensitivityC-reactiveprotein;BP, bloodpressure;BMI,bodymassindex;WC,waist
circumference.
doi:10.1371/journal.pone.0162164.t002
superiortotheindividualDXAandanthropometrymeasureswithanyofthecardio-metabolic
riskfactors.
(ii)ComparisonsbetweenthebestDXAandbestanthropometrymeasure. Differences
betweenthebestDXAandanthropometrymeasureforindividualcardio-metabolicriskfactors
eitherclearlyindicatedthedominanceofonemeasure(deltaAIC>10)orsuggestedequiva-
lence(deltaAIC(cid:20)2)(Table 2).Themodelthatincorporatedmidrifffatmasswassuperiorfor
triglycerides,insulinandHOMA-IR.Incontrast,waist-to-heightratiowasthesuperiormodel
inrelationtoHDL-CandBMIwassuperiortoanyDXAmeasureinrelationtosystolicBP. For
alltheothercardio-metabolicvariables,anthropometricandDXAmeasureswerecomparable.
TheadjustedR-squaredvaluesshowmarginaldifferencesbetweenthebestDXAandanthro-
pometrymeasures.
PLOSONE|DOI:10.1371/journal.pone.0162164 September13,2016 6/11
DXAandAnthropometrywithCardio-MetabolicRiskFactors
Discussion
Inthispopulationcohortofyoungadults,clinicalanthropometrymeasureseitheroutper-
formedorwereequivalenttoDXAformajorityofthecardio-metabolicriskfactors.Theexcep-
tionwasDXAmidrifffatmassasitwassuperiorfortriglycerides,insulinandHOMA-IR.
CombinationsofvariousDXAandanthropometrymeasureswerenobetterthantheindividual
DXAoranthropometry.Whilenotreportedhere,wefoundnoadditionalvalueofleanbody
massmeasurementsorafat/leanmassratiocomparedwithclinicalanthropometry.Ourresults
showtheutilityofanthropometryasasimple,cost-effectiveprimaryscreeningtoolforthe
identificationofindividualsatrisk.Ourdata,however,donotprecludetheuseofDXAinrela-
tiontomorefocussedresearchquestionsorforafurtherdetailedclinicalassessmentof
patients.
Itisdifficulttojustifyhighequipmentandscanningcosts,burdenontheindividualand
reducedaccessibilityimposedbyDXAusedasaprimaryadiposityscreeningtoolinaclinical
settingorforpopulationlevelresearchintocardio-metabolicdisease.Inaddition,severalfac-
torsaffectingtheefficacyofDXA,includetheinterpretationofDXAscanswhichrequirespe-
cialisedtraining,technicianerroroperatingequipment,thetypeofclothingandpositioningof
thepatientonthetable.Withworldwidehealthcostsrapidlyincreasing,theuseofDXAforfat
assessmentishardtojustify,exceptinrelationtoamuchmorefocussedresearchquestionand
foramoredetailedclinicalassessmentofthepatient,asasecondaryscreeningtool.Inthislat-
tersetting,otherdirectimagingmethodssuchasmagneticresonanceimaging,computer
tomography, ultrasoundandpossiblybioelectricalimpedanceanalysistechniquescanbeuseful
forthequantificationanddifferentiationofsubcutaneousandvisceralfattissue[27–29].
Inourstudy,weshowedthatthemuchcriticisedBMIwasthebestmeasurewithsystolicBP
andwassuperiortoanyDXAderivedmeasure.Heightandweightwerealsoimportantindivid-
ualdeterminantsofBP;however,neitherwasbetterthanBMIinrelationtosystolicBP. In
otherstudies,BMIwassignificantlyrelatedtosystolicBPamongUSadolescents[4][30,31],
theNHANESstudyinchildrenfoundBMIwasmorestronglycorrelatedwithsystolicBPthan
DXAfatmasspercentage[32].Itoetal[33]reportedtheaccuracyofdetectinghypertension
anddyslipidaemiawascomparablebetweenBMI,waistcircumferenceandwaisttoheightratio
measuresandtheDXAmeasuresoftotalpercentagefatmassandmidrifffatmasspercentage
inadults.
We foundnoothercomparisonsofDXAandanthropometryinrelationtowiderangeof
cardio-metabolicriskfactorsinyoungadultsfrompopulationbasedstudies.Studiesinmiddle
agedadultsandchildrenhavealsoshownlackofdominanceofDXAoverBMI.Krachleretal
[30]foundBMIhadsimilarpredictivepowercomparedtoDXAfatmasspercentageandbio-
impedanceanalysisforhypertension,impairedfastingglucose,dyslipidaemiaandthemeta-
bolicsyndrome,inmiddleagedadults.BMIandwaistcircumferenceweresimilarlycorrelated
withDXAfatmassandfatmasspercentageinrelationtohs-CRP, BPandfastinglipids,glu-
coseandinsulininadults[31]andinyouthaged8–18yearsofage[32].Percentagebodyfat
(DXA)didnotproducestrongerassociationsinestimatingcomponentsofthemetabolicsyn-
drome[34]andcardiovascularriskfactorsthanBMIandskinfoldthicknessinyouth[35].Our
findingsonhs-CRPcontrastwiththoseofVegaetal[36]whoobservedthatDXAtotalbody
fatpercentagewasbettercorrelatedwithhs-CRPthanBMI,inmiddle-agedUSadults.
Thestrengthsofthisstudyincludedatafromalargesamplepopulationwithinanarrowage
rangeandabreadthofmeasuresevaluatingadiposityusingDXAandanthropometry.Inaddi-
tion,weexaminedanarrayofcardio-metabolicriskfactors,incontrasttomostotherstudies
whichhadanarrowerfocus.Arobuststatisticalapproachwasusedtocomparetheperfor-
manceoftheadipositymeasureswithineachoutcome.Limitationsofthisstudyincludeits
PLOSONE|DOI:10.1371/journal.pone.0162164 September13,2016 7/11