BMI and an Anthropometry-Based Estimate of Fat Mass Percentage Are Both Valid Discriminators ...

15 Pages·2015·2.75 MB·English

HindawiPublishingCorporation
JournalofObesity
Volume2013,ArticleID862514,14pages
http://dx.doi.org/10.1155/2013/862514
Research Article
BMI and an Anthropometry-Based Estimate of
Fat Mass Percentage Are Both Valid Discriminators of
Cardiometabolic Risk: A Comparison with
DXA and Bioimpedance
BennoKrachler,1,2,3EszterVölgyi,1,4KaiSavonen,2,5FrancesA.Tylavsky,4
MarkkuAlén,6andSulinCheng1,7
1DepartmentofHealthSciences,UniversityofJyva¨skyla¨,P.O.BOX35(L),40014Jyva¨skyla¨,Finland
2KuopioResearchInstituteofExerciseMedicine,Haapaniementie16,70100Kuopio,Finland
3DepartmentofPublicHealthandClinicalMedicine,OccupationalandEnvironmentalMedicine,Umea˚University,
90185Umea˚,Sweden
4DepartmentofPreventiveMedicine,UniversityofTNHealthScienceCenter,Memphis,Tennessee38163,USA
5DepartmentofClinicalPhysiologyandNuclearMedicine,KuopioUniversityHospital,70211Kuopio,Finland
6DepartmentofMedicalRehabilitation,OuluUniversityHospitalandInstituteofHealthSciences,UniversityofOulu,
90029Oulu,Finland
7SchoolofKinesiology,ShanghaiUniversityofSport,Shanghai200438,China
CorrespondenceshouldbeaddressedtoSulinCheng;[email protected]
Received19September2013;Revised3November2013;Accepted14November2013
AcademicEditor:YuichiroYano
Copyright©2013BennoKrachleretal.ThisisanopenaccessarticledistributedundertheCreativeCommonsAttributionLicense,
whichpermitsunrestricteduse,distribution,andreproductioninanymedium,providedtheoriginalworkisproperlycited.
Objective.TodeterminewhethercategoriesofobesitybasedonBMIandananthropometry-basedestimateoffatmasspercentage
(FM%equation)havesimilardiscriminativeabilityformarkersofcardiometabolicriskasmeasurementsofFM%bydual-energy
X-rayabsorptiometry(DXA)orbioimpedanceanalysis(BIA).DesignandMethods.Astudyof40–79-year-oldmale(n=205)
and female (n = 388) Finns. Weight, height, blood pressure, triacylglycerols, HDL cholesterol, and fasting blood glucose were
measured.BodycompositionwasassessedbyDXAandBIAandaFM%-equation.Results.Forgrade1hypertension,dyslipidaemia,
andimpairedfastingglucose>6.1mmol/L,thecategoriesofobesityasdefinedbyBMIandtheFM%equationhad1.9%to3.7%
(𝑃 < 0.01)higherdiscriminativepowercomparedtoDXA.Forgrade2hypertensiontheFM%equationdiscriminated1.2%(P=
0.05)lowerthanDXAand2.8%(𝑃<0.01)lowerthanBIA.ReceiveroperationcharacteristicsconfirmedBIAasbestpredictorof
grade2hypertensionandtheFM%equationasbestpredictorofgrade1hypertension.Allotherdifferencesinareaundercurvewere
small(≤0.04)and95%confidenceintervalsincluded0.Conclusions.BothBMIandFM%equationsmaypredictcardiometabolic
riskwithsimilardiscriminativeabilityasFM%measuredbyDXAorBIA.
1.Introduction percentage(FM%)[5]itgivesonlyafairestimateofFM%[6],
andindividualswithlargemusclemassmaybemisclassified
Obesity is associated with cardiometabolic risk [1, 2]. The asoverweightorobese[7].
mostcommonlyuseddefinitionofobesityisthebodymass Dual-energy X-ray absorptiometry (DXA) devices
index (BMI). Withtheadvance ofmoresophisticated mea- estimateFM%withacceptableaccuracyandhavebecomethe
surementstools,assessmentofbodycomposition,ratherthan referencemethodforestimatingbodycomposition[8].How-
body mass, is increasingly used to study obesity-associated ever,theirdrawbacksareradiationexposure,relativelyhigh
healthrisks[3,4].AlthoughBMIcorrelateswellwithfatmass cost, and limited accessibility. Compared to DXA,
2 JournalofObesity
bioimpedance analysis (BIA) has been shown to provide a within30minutesandstoredat−80∘Cuntilanalysis.Serum
good degree of accuracy in various populations of healthy glucose,totalandhigh-densitylipoprotein(HDL)cholesterol
subjects with stable hydration levels and within the normal and triacylglycerol concentrations were measured by enzy-
rangeofbodyfat[9–11].Bioimpedanceisdependentnotonly matic photometry on a Kone Pro Clinical Chemistry Ana-
onbodycomposition(watercontent)butalsoonbodysize lyzer(ThermoClinicalLabsystemsOy,Vantaa,Finland)with
(cross-sectional areas in trunk and limbs). Since the pro- commercialkits.Low-densitylipoprotein(LDL)cholesterol
portionsofbodysegmentsdependnotonlyonweight,age, wascalculatedusingtheFriedewaldequation[18].
andgenderbutalsoonrace,estimationequationsforFM%
needtobepopulationspecific.However,computationalalgo-
2.2.2.Definitions. Ingradinghypertension,wefollowedthe
rithms used to estimate FM% in commercial BIA units are
definitionsoftheEuropeanSocietyofHypertension[19]such
generally proprietary and confidential and may be changed
as: (Grade 1 = systolic BP ≥ 140 and/or diastolic BP ≥ 90,
withoutnotice.Further,populationspecificstudiestovalid-
Grade2=systolicBP≥160and/ordiastolicBP≥100,Grade
atetheseequationsareeitherlackingorunpublished.
3=systolicbp≥180and/ordiastolicBP≥110).
EquationsthatestimateFM%basedonsimpleanthropo-
Dyslipidaemia is defined as either triacylglycerol ≥
metricmeasuressuchasweight,height,andwaistcircumfer-
1.7mmol/L or HDL cholesterol < 1.0mmol/L men/1.30
ence(FM%equations)overcomesomeoftheshortcomingsof
mmol/Lwomen.ForImpairedfastingglucose,weexamined
DXA and BIA: they are simple and inexpensive and can be
both the stricter International Diabetes Federation (IDF)
applied to existing epidemiological data. Most of the FM%
sponsored 5.6mmol/L (100mg/dL) and higher level of 6.1
equationshavebeenvalidatedagainstDXAmeasurementsin
mmol/L(110mg/dL)recommendedbytheWHO[20].
differentpopulations[12,13].Consequently,theyhavealower
Fordefinitionofmetabolicsyndrome,weadoptedcutoffs
degreeofaccuracyinestimatingFM%thanDXAmeasure-
valuessuggestedbythecommontaskforcefromtheIDFand
ments.
the American Heart Association/National Heart, Lung and
Thehypothesistestedinthisstudyisthatthislossofaccu-
BloodInstitute(AHA/NHBLI)[17],requiringpresenceofat
racy in estimating FM% is irrelevant in the assessment of
least three out of the following: (1) fasting plasma glu-
obesity-related cardiometabolic risk on a group level, and
cose ≥ 5.6mmol/L (100mg/dL); (2) triacylglycerol ≥ 1.7
henceusingweightandheight-basedFM%equationstocate-
mmol/L (150mg/dL); (3) HDL Cholesterol < 1.0mmol/L
gorizeobesityyieldsasimilardiscriminativeabilityasDXA
men/1.30mmol/Lwomen(40and50mg/dL,resp.);(4)waist
orBIAmeasurements.Further,weinvestigatewhetheranyof
circumference>102cmmen/88cmwomen;and(5)systolic
theFM%-basedmeasuresofobesityimprovesonBMI.Thus,
BP≥130mmHgORdiastolicBP≥85mmHg.
weintendtotestthenull-hypothesisthatallthesemethods
have similar predictive power for hypertension, impaired
fastingglucose,dyslipidaemia,andthemetabolicsyndrome.
2.3. Anthropometric Measurements. All measurements were
If confirmed, FM% equations and/or BMI can be used to
performedafteranovernightfast.Participantswereweighed
identify individuals with elevated cardiometabolic risk
withoutshoesandwithlightclothes.Heightwasdetermined
instead of DEXA and/or BIA. This offers considerable eco-
tothenearest0.1cmusingafixedwall-scalemeasuringdev-
nomicsavings,bothinclinicalhealthcareandresearch.
ice. Weight was determined within 0.1kg for each subject
usinganelectronicscale,calibratedbeforeeachmeasurement
2
2.MaterialsandMethods session.BMIwascalculatedasweight(kg)perheight(m ).
Waistcircumferencewasmeasuredwithameasuringtapeat
2.1.StudyPopulation. Thestudypopulationconsistedof40– thelargestcircumferencelocationbetweenlandmarksofthe
79-year-old male (𝑛 = 205) and female (𝑛 = 388) healthy most proximaliliacandthemostdistalribboneasamean
volunteerswhoresidedinCentralFinlandandparticipatedin valueoftwomeasurements.
afamilystudy.Backgroundinformation,includingthehealth
status, was collected via a self-administrated questionnaire.
2.4.Anthropometry-BasedFM%EstimationEquations(FM%
Writteninformedconsentwasobtainedbeforethelaboratory
Equations). Fromaliteraturesearchwefoundfivedifferent
examinations.TheEthicalCommitteeoftheCentralFinland
equations for FM% estimation in adult men and women
HealthCareDistrictapprovedthestudy(Registrationnum-
(Table1). Four equations are based on age, gender, weight,
ber:K-Sshp:ndnro22.8.2008).Alldatawerehandledconfi-
andheight[12–14,16]andoneutilizedinadditionwaistcir-
dentially.
cumference[15].Acombinationofpredictionequationshas
beenshowntoprovideabetterestimatethanrelyingonone
2.2.CardiometabolicRiskFactors equationonly[21].Therelationshipbetweenanthropometric
measures and FM% is different, not only among the main
2.2.1.Measurements. Bloodsamplesweretakeninthemorn- racialcategories[6,15]but,evenbetweendifferentcountries
ingbetween0730and0900afterthesubjectshadfastedfor12 withinEurope[22].ThepopulationofFinlandisoneofthe
hours. Blood pressure (BP) was measured by the manual EuropeanUnion(EU)geneticallymostdistinctpopulations
oscillometricmethodafter5minrest.Ifwomenwereinthe [23].Therefore,insteadofusingall5equationsweeliminated
pre menopausal state, the blood sample was drawn on the the2withthelargestbiascomparedtoDXA.Thusthechosen
5thdayfromthestartofmenstruation.Serumwasseparated equationrelevantforaFinnishpopulationisthearithmetic
JournalofObesity 3
Table1:Anthropometry-andbioimpedance-analysis-(BIA-)basedestimatesoffatmasspercentage(FM%)andtheirrespectivebiasversus
DXAmeasurements.
Men Women Combined
Predictor EquationforestimatingFM%
𝑛 Meanbiasa SD 𝑛 Meanbiasa SD 𝑛 Meanbiasa
Women:FM%=(−24.18+1.181∗
(1)Larssonetal.[14] weight/height)/weight 205 −1.3 4.7 388 0.1 4.1 593 −0.6
Men:FM%=(−30.84+1.120∗weight/height)/weight
(2)Gallagheretal.[15] FM%=64.5–848∗(1/BMI)+0.079∗age−16.4∗ 205 −2.4 4.6 388 −1.0 4.1 593 −1.7
sexb−0.05∗sexb∗age+39.0∗sexb∗(1/BMI)
(3)Deurenbergetal. FM%=1.2∗BMI+0.23∗age−10.8∗sexb −5.4 205 1.7 4.9 388 2.3 4.8 593 2.0
[12]
(4)TanitaBC418MA bioimpedance-basedproprietaryalgorithm 82 −4.8 3.9 58 −3.6 3.2 140 −4.2
(5)InBody(720) bioimpedance-basedproprietaryalgorithm 181 −4.6 3.4 273 −4.7 3.0 454 −4.6
Women:FM%=(−2.28+1.268(weight/height)
(6)Mills[13] Men:FM%=(−7+.909.+0518.2∗86ag(ew)e/wigehitg/hhteight) 205 −11.7 5.3 388 −1.6 4.2 593 −6.7
+0.018∗age)/weight
(7)Deurenbergetal. FM%=−11.4∗sexb+0.2∗age+1.294∗BMI−8 205 5.0 6.4 388 16.5 8.9 593 10.8
[16]
(8)FM%-equation Arithmeticmeanofequations(1)–(3) 205 −0.7 4.6 388 0.5 4.2 593 −0.1
afatmasspercentage.
bmale:1,female:0.
meanofthethreeFM%equationswiththelowestbiascom- manufacturer-provided device specific software from area,
paredtoDXA: volume,length,impedance,andaconstantproportion(spe-
cificresistivity).Fatfreemass(FFM)wasestimatedbydivid-
−24.18+1.181∗(weight/height)
FM%= for women, ingTBWby0.73.ReadingsofFFMandFM%arereportedin
weight thispaper.Precisionoftherepeatedmeasurementsexpressed
(1a) ascoefficientofvariationwas,onaverage,0.6%forFM%.
−30.84+1.120∗(weight/height)
FM% = for men. 2.7.StatisticalAnalysis. Theconceptsofreclassificationindex
weight
andintegrateddiscriminationimprovementwereintroduced
(1b)
byPencinaetal.[24]anddescribedandillustratedbyCook
andRidker[25].Briefly,thenetreclassificationindex(NRI)
Forthefirstequationsee[18]
comparestwomodelsthatdivideparticipantsintodifferent
1
FM%= 64.5−848∗( ) categoriesofriskforadichotomousoutcome.Itiscalculated
BMI asthenetincreaseversusdecreaseinriskcategoriesamong
casepatientsminusthatamongnoncaseparticipants:
+0.079∗age−16.4∗sex−0.05∗sex∗age (2)
1
+39.0∗sex∗( ). NRI= [Pr(up\cases)−Pr(down\cases)]
BMI
−[Pr(up\non-cases)−Pr(down\non-cases)].
Forthesecondequationsee[15]
(4)
FM%=1.2∗BMI+0.23∗age−10.8∗sex−5.4. (3)
Forthethirdequationsee[12]wheresex=1formenand Thus,apositiveNRImeansthatthecomparisonmethodhas
0forwomen. abetterpredictivepowerthanthereferencemethod.
The integrated discrimination improvement (IDI) de-
2.5. DXA Measurements. Prodigy with software version 9.3 scribes the mean difference in predicted probabilities bet-
GELunar,Madison,WI,USAwasusedtoestimateFMand weencasepatientsandnoncaseparticipantsfortwomodels.
FM%.Precisionoftherepeatedmeasurementsexpressedas It is calculated from individual predicted probabilities for
thecoefficientofvariationwas2.2%forFM. eachparticipantintherespectivemodels:
2.6. Bioimpedance Measurements. InBody (720) (Biospace, IDI= (ave 𝑝 −ave 𝑝 )
cases controls comparisonmethod
Seoul,Korea)isamultifrequencyimpedancebodycomposi- (5)
tionanalyzer.Totalbodywater(TBW)wasestimatedwiththe − (ave 𝑝 −ave 𝑝 ) ,
cases controls referencemethod
4 JournalofObesity
Table2:Anthropometricandmetaboliccharacteristicsofthestudypopulation.
Men Women
𝑛 Mean 95%CI 𝑛 Mean 95%CI
Age(years) 205 57 (55–58) 388 56 (55–57)
Height(cm) 205 176 (175–177) 388 163 (162–164)
Weight(kg) 205 82.5 (81.0–83.9) 388 70.6 (69.3–71.9)
BMI(kg/m2) 205 26.6 (26.2–27.1) 388 26.6 (26.1–27.1)
Fatmass(kg) 205 22.7 (22–23.6) 388 26.9 (25.9–27.9)
Fatmass(%) 205 27.1 (26–27.9) 388 37.1 (36.4–37.9)
Waistcircumference(cm) 200 95 (93–96) 376 86 (85–87)
Chestcircumference(cm) 166 102 (101–103) 269 98 (96–99)
Systolicbloodpressure(mmHg) 201 146 (144–149) 377 142 (140–145)
Diastolicbloodpressure(mmHg) 201 85 (84–87) 377 83 (82–84)
Fastingglucose(mmol/L) 166 5.8 (5.6–5.9) 301 5.6 (5.5–5.7)
Fastinginsulin(𝜇IU/ml) 166 10.1 (6.5–14) 302 8.2 (7.5–9.0)
Totalcholesterol(mmol/L) 167 5.3 (5.1–5.4) 302 5.5 (5.4–5.6)
HDL(mmol/L) 167 1.5 (1.4–1.6) 302 1.8 (1.7–1.9)
LDL(mmol/L) 167 3.1 (3–3.3) 302 3.1 (3.0–3.2)
Triglycerides(mmol/L) 167 1.5 (1.3–1.6) 302 1.2 (1.2-1.3)
Freefattyacids(mmol/L) 167 496 (450–542) 302 541 (502–580)
where𝑝isthepredictedprobabilityforeachparticipant.Pre- Bland-Altmanplotswereusedtocomparethemeandif-
dictedindividualprobabilitiesarederivedfromgender-spe- ferenceofthevariousFM%equationstoDXA(Figures1and
cificlogisticregressionswithobesitycategoryasindependent 2).AcomparisonofthetwoBIAdevicesagainstDXAinour
variable,10-yearagegroupsascovariates,andthecardiomet- studypopulationhasbeenpublishedpreviously[26].
abolic risk factor in question as outcome. The difference in For models using BMI and FM% as a continuous vari-
slopesisameasureofimprovementinthemodel.Thus,apos- ables, nonnormally distributed variables were power-trans-
itiveIDImeansthatthecomparisonmethodhasabetterpre- formed.Distributionsoftheresultingvariablesfulfilledcrite-
dictivepowerthanthereferencemethod. riaforgoodnessoffitwithnormaldistribution(Kolmogorov-
Categoriesofobesityaccordingtoeachspecificobesity- Smirnov𝑃 > 0.15andAnderson-Darling𝑃 > 0.25).How-
estimation method were formed based on age and gender. ever,resultsobtainedfromcalculationsbasedontransformed
Each of the 4 subgroups (men and women aged above and variableswereidenticalwiththoseobtainedbynontransfor-
below60years,resp.)wassortedbydegreeofobesity,separ- medvariables.ThisindicatesthattheROCmodelsaremore
atelyforeachdefinition(i.e.,BMIandvariousFM%measure- robust with regards to the normal distribution assumption
mentsandestimates).PercentilesatBMI25and30wereused thansuggestedbyGoddardandHinberg[27].
toobtaincorrespondingcutoffsforobesitycategoriesaccord- Receiver operating characteristics were calculated from
ingtoeachoftheFM%-measurementandestimationmeth- sex-specificlogisticregressionswithmethodspecificobesity-
ods. Thus we obtained categories of obesity with identical categories and 10-year age categories as independent vari-
numbersofsubjectsbutpartlydifferentindividualssortedby ables. The SAS-procedure: PROC LOGISTIC/roc-contrast
degreeofobesityaccordingtotherespectivemethod.Dueto wasusedtoestimatedifferencesinareaundercurve(AUC).
smallnumbersofunderweightandseverelyobesesubjectswe The Statistical Analysis System (SAS for Windows, ver-
settled on the following categories: (1) normal BMI ≤ 24.9; sion9.2,SASInstitute,Carry,NC,USA)wasusedforallstati-
(2) overweight BMI 25–29.9, and (3) obese BMI ≥ 30 sticalevaluations.Forcalculationofnetreclassificationindex
(Table2). Based on percentiles corresponding to these cut- andintegrateddiscriminationimprovementweadaptedSAS-
offs, we categorized participants as normal, overweight, or macrosprovidedbyCookandRidkerasasupplement[25].
obese according to each of the different FM%-estimates
(DXA, BIA, FM%-prediction equations). Finally, we com- 3.Results
paredthepredictivepowerofthecategoriesofobesitywith
cardiometabolicriskfactors(hypertension,impairedfasting Anthropometric and metabolic characteristics of the study
glucose, dyslipidaemia, and metabolic syndrome) as out- population are given in Table3. The mean values showed
comes.WeusedDXA-basedcategoriesofobesityastherefer- slightoverweightandmildhypertensionbothinmalesand
enceforallothermodels.Furthermore,wemadeadditional females.Fastingglucosewasintherangeofimpairedfasting
comparisons between obesity categories based on BIA and glucose in men but not in women (according to the lower
BMI. cutoffof5.6mmol/LrecommendedbytheIDF[28]).
JournalofObesity 5
20
20
% % 15
M 15 M
diff. F 105 diff. F 105
A, 0 A, 0
DX −5 DX −5
e-−10 e-−10
mat−15 mat−15
Esti−20 Esti−20
10 15 20 25 30 35 40 45 10 15 20 25 30 35 40 45
(Estimate + DXA)/2, FM% (Estimate + DXA)/2, FM%
(1)Larsson (2)Gallagher
% 20 % 20
M 15 M 15
ff. F 10 ff. F 10
di 5 di 5
A, 0 A, 0
DX −5 DX −5
mate-−−1105 mate- −−1105
Esti−20 Esti −20
10 15 20 25 30 35 40 45 10 15 20 25 30 35 40 45
(Estimate + DXA)/2, FM% (Estimate + DXA)/2, FM%
(3) Deurenberg1 (4)TanitaBIA
% 20 % 20
M 15 M 15
ff. F 10 ff. F 10
di 5 di 5
A, 0 A, 0
DX −5 DX −5
mate-−−1105 mate-−−1105
Esti−20 Esti−20
10 15 20 25 30 35 40 45 10 15 20 25 30 35 40 45
(Estimate + DXA)/2, FM% (Estimate + DXA)/2, FM%
(5)InBodyBIA (6)Mills
20 20
% %
M 15 M 15
ff. F 10 ff. F 10
di 5 di 5
A, 0 A, 0
X X
D −5 D −5
e-−10 e-−10
at at
m−15 m−15
Esti−20 Esti−20
10 15 20 25 30 35 40 45 10 15 20 25 30 35 40 45
(Estimate + DXA)/2, FM% (Estimate + DXA)/2, FM%
Mean bias Mean bias
±2SD ±2SD
Bias, curvelinear trend Bias, curvelinear trend
95% confidence interval 95% confidence interval
(7) Deurenberg2 (8)Combinedestimate=(1)–(3)
Figure1:Bland-Altmanplotsforestimatesoffatmasspercent(FM%):DXAversusFM%-predictionequationsandbioimpedanceinmen.
Comparisonsofpredictivepowersofthevariousobesity improveddiscriminationforgrade1hypertensioncompared
measures for hypertension grades 1 and 2 [19] and dyslip- to DXA by 1.5–1.9%. BMI and FM% equation-based cate-
idaemia are given in Table4. There were too few subjects gories of obesity had 1.2% lower discrimination for grade 2
withgrade3hypertensiontobuildstablemathematicalmod- hypertension compared to DXA and 2.5–2.8% lower com-
els.BMI,FM%-equation-andBIA-basedcategoriesofobesity pared to BIA. BMI categories provided 2.5–3.7% improved
6 JournalofObesity
20 20
% 15 % 15
M M
F 10 F 10
ff. ff.
di 5 di 5
A, 0 A, 0
X X
D −5 D −5
e- e-
at −10 at −10
m m
Esti −−1250 Esti −−1250
15 20 25 30 35 40 45 50 55 60 15 20 25 30 35 40 45 50 55 60
(Estimate + DXA)/2, FM% (Estimate + DXA)/2, FM%
(1)Larsson (2)Gallagher
20 20
% 15 % 15
M M
F 10 F 10
diff. 5 diff. 5
A, 0 A, 0
X X
D −5 D −5
mate- −10 mate- −10
Esti −−1250 Esti −−1250
15 20 25 30 35 40 45 50 55 60 15 20 25 30 35 40 45 50 55 60
(Estimate + DXA)/2, FM% (Estimate + DXA)/2, FM%
(3) Deurenberg1 (4)TanitaBIA
20 20
% 15 % 15
M M
F 10 F 10
ff. ff.
di 5 di 5
A, 0 A, 0
X X
D −5 D −5
e- e-
at −10 at −10
m m
Esti −−1250 Esti −−1250
15 20 25 30 35 40 45 50 55 60 15 20 25 30 35 40 45 50 55 60
(Estimate + DXA)/2, FM% (Estimate + DXA)/2, FM%
(5)InBodyBIA (6)Mills
20 20
% 15 % 15
M M
F 10 F 10
ff. ff.
di 5 di 5
A, 0 A, 0
X X
D −5 D −5
e- e-
at −10 at −10
m m
Esti −−1250 Esti −−1250
15 20 25 30 35 40 45 50 55 60 15 20 25 30 35 40 45 50 55 60
(Estimate + DXA)/2, FM% (Estimate + DXA)/2, FM%
Mean bias Mean bias
±2SD ±2SD
Bias, curvelinear trend Bias, curvelinear trend
95% confidence interval 95% confidence interval
(7) Deurenberg2 (8)Meanof(1)+(2)+(3)=FM%-equation
Figure2:Bland-Altmanplotsforestimatesoffatmasspercent(FM%):DXAversusFM%-predictionequationsandbioimpedanceinwomen.
JournalofObesity 7
Table 3: Values in fat mass percentage (FM%) for the method-specific percentile corresponding to BMI percentiles at BMI 25 and 30,
respectively.
FM%cutoffscorrespondingtoBMI25 FM%cut-offscorrespondingtoBMI30
Men Women Men Women
Methoda/age-group <60 ≥60y <60 ≥60y <60 ≥60y <60 ≥60y
DXA 24.0 26.1 36.7 34.7 32.3 37.5 44.0 43.8
BIAInBodyb 19.3 23.0 31.5 31.9 28.7 34.0 38.7 40.8
FM%-equationc 24.0 26.3 35.3 37.4 30.1 31.7 41.6 43.8
aMethodofmeasurement,basedonwhichparticipantsareclassifiedincategoriesofobesity.
bEstimationofFM%withbioimpedancedeviceInBody(720)(Biospace,Korea).
cAnthropometry-basedestimationofFM%;arithmeticmeanofFM%estimatesaccordingtopredictionmethodsDeurenbergetal.[12],Gallagheretal.[15],
andLarssonetal.[14].
discrimination regarding dyslipidaemia compared to both Our results are consistent with findings from other
DXAandBIA.AUC,basedoncontinuousvariables,showed healthypopulationsthatusedDXAasreferencemethod.Ina
betterpredictionofgrade1hypertensionforBIAandFM% multiethnicsurveyofadults[5]BMIandwaistcircumference
equationscomparedtoDXAinwomen,butnotinmen.For werecomparabletoDXAmeasurementsoffatmassandFM%
predictionofgrade2hypertensionBIAwassuperiortoDXA, asassessedbytheircorrelationswithbloodpressure,lipids,
BMI,andFM%equations.Differencesinnetreclassification fasting glucose, C-reactive protein, and fasting insulin. In
didnotreachsignificancelevel. addition,BMIandwaistcircumferencedemonstratedsimilar
Acomparisonofthevariousobesity-measuresabilityto abilitiestodistinguishbetweenparticipantswithandwithout
indicatedifferentlevelsofimpairedfastingglucoseandmeta- metabolicsyndrome.Inasmallerstudyofelderlywomen[29]
bolic syndrome is given in Table5. Prediction of impaired BMIandDXA-measuredFM%hadsimilarcorrelationswith
fastingglucosewiththelowercutoffwassimilarforallmeth- and discriminative ability of hypertension, dyslipidaemia,
ods.BothBIA-andFM%equation-basedcategoriesoutper- and hyperglycaemia. In a study of Caucasian children [30],
formedDXAby2.6%to3.5%inpredictingimpairedfasting measuresoftotalandcentralfatmassfromDXAdidnotshow
glucoseof6.1mmol/L(110mg/dL)ormore.Allmethodshad animprovedabilityoverBMI,toidentifychildrenwithelev-
similar discrimination with regard to the metabolic syn- ated systolic BP. A recent review of studies in patients with
drome.Inthesex-specificROCanalyses,therewerenosignif- pulmonarydisease[31]foundnoevidencethatbodycompo-
icantdifferencesinAUC.Again,differencesinnetreclassifi- sitioncalculatedfromBIAandanthropometrywasbetterat
cationdidnotreachsignificancelevel. predicting clinical outcomes than body composition calcu-
Thereceiveroperatedcharacteristicsofthedifferentobes- latedbysimpleanthropometryalone.
itymeasuresaspredictorsofcardiometabolicriskfactorsare Conversely, a study combining weight-loss outpatients
showninFigures3and4.BIA-basedcategorieswerethebest andhospitalstaff[32]foundelevatedconcentrationsofcar-
predictors of hypertension in men. In women, FM% equa- diometabolicriskfactorsinnonobeseindividualsaccording
tionswerethebestpredictorofgrade1hypertensionandBIA- to BMI but obese based on FM%-categories. The reference
basedcategorieswerebestpredictorsofgrade2hypertension. methodwasairdisplacementplethysmographyand—unlike
ForallotheroutcomesdifferencesinAUCweresmall(<0.05). ourresults—BMIwasfoundtosystematicallyunderestimate
Acomparisonof95%confidenceintervalsfordifferences thedegreeofobesity.However,thisstudyutilizedadifferent
in integrated discrimination and AUC between DXA and reference method, had identical FM% cutoffs for all age
theanthropometry-basedestimateisgiveninFigure5.Inte- groups (18 to 80 years of age), and was conducted in a dif-
grated discrimination improvements varied between −3% ferentethnicgroupandahigherproportionofobesesubjects.
and+6%.DifferencesinAUCwerebetween−0.1and+0.1in A higher prevalence of diseased subjects could be another
menandbetween−0.5and0.1inwomen. potentialexplanationforthedifferentoutcomeasmeasure-
mentsofbodycompositionhavebeenreportedtohavebetter
discriminativeandprognosticabilitycomparedtoBMIboth
4.Discussion duringandafterchronicdisease[33–35].
Ethnicityinfluencedthemethodologyofourstudy. The
Inthispopulationofhealthy,middleaged,andelderlyFinns relationshipbetweenanthropometricmeasuresandFM%is
we found that anthropometry-based FM%-predictions and different,notonlyamongthemainracialcategories[6,15]but
BMIhadsimilarpredictivepowerforobesity-associatedcar- even between different countries within Europe [22]. The
diometabolic risk markers as FM% derived from DXA- or population of Finland is one of the European Union’s (EU)
BIA measurements. Some of the studied obesity measures geneticallymostdistinctpopulations[23]andwith96%ofthe
have small advantages in discriminating one single cardio- residentsbornwithinthecountry[36]alsooneofEU:smost
metabolicriskfactor.Withregardtometabolicsyndrome— homogenous. This uniqueness was further confirmed by a
which combines all of the studied risk factors—discrim- European multi centre study developing BMI-based FM%-
inativeabilityofallobesitymeasuresissimilar. predictionsbasedonBIAmeasurementsinwhichthebiasin
8 JournalofObesity
d
e
s
metry-ba men 𝑃 0.0000.0730.0000.1470.6060.000 0.0000.2550.6260.0000.0440.000 0.5100.3780.7660.1620.3900.148 y.
o o or
p W g
Table4:Prediction/discriminationofhypertensionwithdegreeofobesityasdefinedbydual-energyX-rayabsorptiometry(DXA)bioimpedanceanalysis(BIA),ananthroestimateoffatmasspercentage(FM%equation)andBMI. nROCanalysesReferenceNew𝑐𝑛fkReclassificationindex,%IDI,%Menabmethod/modelmethod/model𝑃Δ𝑃Δ𝐼𝑃egopmlijhdNon-casesNetAUCAUCCasesNon-casesCasesidiintegr.nri≥qHypertension,grade1(140/90mmHg)−rBIAInBody2691855%0.2141%6%1.7%0.0170.030.1270.06DXABMI3352586%0.2202%3%1.9%0.0060.000.9770.04sEstimate3352586%0.2082%3%1.5%0.0190.030.3830.07−−BMI2691854%0.3601%3%0.5%0.5340.030.3300.03BIAInBodyEstimate2691853%0.5020%3%0.1%0.8850.000.9790.01−BMIEstimate3352580%0.8030%0%0.4%0.1440.030.0210.04≥Hypertension,grade2(160/100mmHg)−−BIAInBody933611%0.8484%3%1.4%0.0630.020.3960.05−−−−−−DXABMI1174769%0.1288%1%1.2%0.0490.090.0640.03−−−−−Estimate1174768%0.1547%1%1.2%0.0360.010.7460.01−−−−−−BMI933619%0.1618%1%2.5%0.0030.110.0060.08BIAInBody−−−−−−Estimate9336110%0.0969%1%2.8%0.0010.040.3090.04BMIEstimate1174761%0.6821%0%0.0%0.8700.070.0010.04tDyslipidaemia−−−−−BIAInBody1113042%0.6165%3%0.1%0.9280.030.1610.01−DXABMI1243456%0.3202%4%3.5%0.0150.010.8160.02−Estimate1243454%0.4960%4%2.7%0.0400.020.6400.01BMI1113048%0.1496%2%3.1%0.0220.020.5680.03BIAInBodyEstimate1113046%0.2405%2%2.5%0.0440.010.7340.02−−−−−−BMIEstimate1243452%0.2372%1%0.8%0.1110.010.5980.01aMethodofmeasurement,basedonwhichparticipantsareclassifiedincategoriesofobesity.bDifferentmethodofestimatingobesity,thepredictivepowerofwhichiscomparedtoreferencemodel/referencemethod.cNumberofparticipants.dNumberofparticipantsthatarepositivewithregardtorespectiveoutcome.eNumberofparticipantsthatarenegativewithregardtorespectiveoutcome.f−Percentageimprovement(+)ordeterioration()inpredictivepowerofnewmodelcomparedtoreferencemodel.Categoriesofobesity/FM%asindependentvariable.gNetreclassificationofcases+netreclassificationofnoncases.Apositivenumberdenotesincreasedpredictivepowerforthenewmodel.hLikelihoodofnetreclassificationindextobe0,thatis,thenewmodelshowingnoimprovement/deteriorationoverreferencemodel.i−Netreclassificationofcases=percentageofcasesreclassifiedbythenewmodelintoahigherriskcategorypercentageofcasesreclassifiedbythenewmodelintoalowerriskcategoryj−Netreclassificationofnon-cases=percentageofnon-casesreclassifiedbythenewmodelintoalowerriskcategorypercentageofnon-casesreclassifiedbythenewmodelintoahigherriskcatek−Integrateddiscriminationimprovement(+)ordeterioration()ofnewmodelcomparedtoreferencemodel.Categoriesofobesity/FM%asindependentvariableinanage-adjustedmodel.lMeandifferenceinpredictedindividualprobabilitiesbetweencasesandnon-casesfortwomodels.Apositivenumberdenotesincreasedpredictivepowerforthenewmodel.mLikelihoodofnetreclassificationindextobe0,thatis,thenewmodelshowingnoimprovement/deteriorationoverreferencemodel.nMeasuresofobesity(BMI/FM%)ascontinuousvariableinalogisticregressionmodelpredictingrespectiveoutcomes.oDifferenceinareaundercurveofreceiveroperatingcharacteristiccomparedtoreferencemethod.pProbabilityof0-hypothesis(nodifference).q{}DefinitionsofhypertensionaccordingtoEuropeanSocietiesforHypertensionandCardiologyMancia,2007#2897.rEstimationofFM%withbioimpedancedeviceInBody(720)(Biospace,Korea).sAnthropometry-basedestimate;arithmeticmeanofFM%estimationsaccordingtopredictionmethodsDeurenbergetal.[12],Gallagheretal.[15],andLarssonetal.[14].t≥≤≤Triacylglycerols1.7mmol/LorHDLcholesterol1.29mmol/LinmenorHDL1.03mmol/Linwomen.
JournalofObesity 9
e
c
n
eda 𝑃 394394597744981386 462918796504799248 625309329429409958
mp n 0.0.0.0.0.0. 0.0.0.0.0.0. 0.0.0.0.0.0.
oi me y.
bi o or
Table5:Prediction/discriminationofimpairedfastingglucoseandthemetabolicsyndromewithdegreeofobesityasdefinedbydual-energyX-rayabsorptiometry(DXA)analysis(BIA),ananthropometry-basedestimateoffatmasspercentage(FM%-equation)andBMI. nROCanalysesReferenceNew𝑛cfkReclassificationindex,%IDI,%MenWabmethod/modelmethod/model𝑃Δ𝑃Δ𝐼𝑃megoplijdhNon-casesNetAUCAUCCasesNon-casesCasesidiintegr.nri≥Impairedfastingglucose(5.6mmol/L=100mg/dL)−−−−−qBIAInBody1642496%0.1817%1%0.5%0.5060.030.1020.01−−−−DXABMI1912762%0.7274%2%0.3%0.7230.040.2860.02−−−−rEstimate1912761%0.7714%2%0.2%0.8090.030.4040.01−−BMI1642492%0.7521%0%0.2%0.7960.010.8880.01BIAInBody−Estimate1642491%0.7691%1%0.1%0.8820.000.8900.00−BMIEstimate1912760%1.0000%0%0.1%0.7330.010.5140.01≥Impairedfastingglucose(6.1mmol/L=110mg/dL)−−−BIAInBody703431%0.9014%3%3.5%0.0090.010.5840.01DXABMI803876%0.3943%4%3.2%0.0090.000.9000.00Estimate803873%0.6160%3%2.6%0.0230.020.4380.01−−BMI703437%0.3416%1%0.7%0.6090.020.6480.02BIAInBody−−Estimate703432%0.7541%1%1.5%0.2050.040.2530.01−−−BMIEstimate803873%0.2513%-1%0.6%0.1760.020.3150.01sMetabolicsyndrome(AHA/NHBLI)−−−−BIAInBody1442684%0.4006%2%0.7%0.6910.030.1200.01−DXABMI1653014%0.4610%4%2.5%0.2570.020.6100.02−−Estimate1653013%0.5191%4%1.7%0.4070.020.4660.02BMI1442683%0.5952%1%0.9%0.6620.010.6970.01BIAInBodyEstimate1442684%0.4802%1%0.8%0.6810.010.8120.01−−−−BMIEstimate1653011%0.5771%0%0.7%0.2520.010.6220.00aMethodofmeasurement,basedonwhichparticipantsareclassifiedincategoriesofobesity.bDifferentmethodofestimatingobesity,thepredictivepowerofwhichiscomparedtoreferencemodel/referencemethod.cNumberofparticipants.dNumberofparticipantsthatarepositivewithregardtorespectiveoutcome.eNumberofparticipantsthatarenegativewithregardtorespectiveoutcome.f−Percentageimprovement(+)ordeterioration()inpredictivepowerofnewmodelcomparedtoreferencemodel.Categoriesofobesity/FM%asindependentvariable.gNetreclassificationofcases+netreclassificationofnon-cases.Apositivenumberdenotesincreasedpredictivepowerforthenewmodel.hLikelihoodofnetreclassificationindextobe0,thatis,thenewmodelshowingnoimprovement/deteriorationoverreferencemodel.i−Netreclassificationofcases=percentageofcasesreclassifiedbythenewmodelintoahigherriskcategorypercentageofcasesreclassifiedbythenewmodelintoalowerriskcategory.j−Netreclassificationofnon-cases=percentageofnon-casesreclassifiedbythenewmodelintoalowerriskcategorypercentageofnon-casesreclassifiedbythenewmodelintoahigherriskcategk−Integrateddiscriminationimprovement(+)ordeterioration()ofnewmodelcomparedtoreferencemodel.Categoriesofobesity/FM%asindependentvariableinanage-adjustedmodel.lMeandifferenceinpredictedindividualprobabilitiesbetweencasesandnon-casesfortwomodels.Apositivenumberdenotesincreasedpredictivepowerforthenewmodel.mLikelihoodofnetreclassificationindextobe0,thatis,thenewmodelshowingnoimprovement/deteriorationoverreferencemodel.nMeasuresofobesity(BMI/FM%)ascontinuousvariableinalogisticregressionmodelpredictingrespectiveoutcomes.oDifferenceinareaundercurveofreceiveroperatingcharacteristiccomparedtoreferencemethod.pProbabilityof0-hypothesis(nodifference).qEstimationofFM%withbioimpedancedeviceInBody(720)(Biospace,Korea).rAnthropometry-basedestimate;arithmeticmeanofFM%estimationsaccordingtopredictionmethodsDeurenbergetal.[12],Gallagheretal.[15],andLarssonetal.[14].sDefinitionofmetabolicsyndromesuggestedbythecommontaskforcefromtheIDFandtheAmericanHeartAssociation/NationalHeart,LungandBloodInstitute(AHA/NHBLI)[17].
10 JournalofObesity
Men Women
Hypertension grade1(>140/80) Hypertension grade1(>140/80)
1.00 1.00
0.75 0.75
y
nsitivity 0.50 ensitivit 0.50
e S
S 0.25 0.25
0.00 0.00
0.00 0.25 0.50 0.75 1.00 0.00 0.25 0.50 0.75 1.00
1−specificity 1−specificity
AUC AUC
DXA,0.614 BMI,0.613 DXA,0.673 BMI,0.708
BIA-InBody,0.647 FM% equation,0.646 BIA-InBody,0.735 FM% equation,0.744
(a) (b)
Hypertension grade2(>160/90) Hypertension grade2(>160/90)
1.00 1.00
0.75 0.75
Sensitivity 00..5205 Sensitivity 00..5205
0.00 0.00
0.00 0.25 0.50 0.75 1.00 0.00 0.25 0.50 0.75 1.00
1−specificity 1−specificity
AUC AUC
DXA,0.663 BMI,0.575 DXA,0.640 BMI,0.614
BIA-InBody,0.686 FM% equation,0.648 BIA-InBody,0.690 FM% equation,0.651
(c) (d)
Dyslipidaemia Dyslipidaemia
1.00 1.00
0.75 0.75
y y
vit vit
nsiti 0.50 nsiti 0.50
e e
S S
0.25 0.25
0.00 0.00
0.00 0.25 0.50 0.75 1.00 0.00 0.25 0.50 0.75 1.00
1−specificity 1−specificity
AUC AUC
DXA,0.692 BMI,0.683 DXA,0.678 BMI,0.697
BIA-InBody,0.660 FM% equation,0.673 BIA-InBody,0.668 FM% equation,0.685
(e) (f)
Figure3:ReceiveroperatingcharacteristicofDXA,BIA,BMI,andanthropometry-basedestimateoffatmasspercent(FM%-equation)as
predictorsofhypertensionanddyslipidaemia.(a)Indirectcomparisons,theareaundercurve(AUC)fortheanthropometry-basedestimateof
fatmasspercentage(FM%-equation)islargerthanAUCforBMI(𝑃=0.021).(b)AUCfortheBIAInBodyislargerthanforDXA(𝑃<0.001).
AUCforFM%-equationislargerthanforbothDXAandBMI(𝑃<0.001).(c)AUCforBIAInBodyislargerthanforBMI(𝑃=0.006)asis
AUCfortheFM%-equation(𝑃<0.001).(d)AUCfortheBIAInBodyislargerthanforDXAandBMI(𝑃<0.001)andalsolargerthanfor
theFM%equation(𝑃<0.044).AUCfortheFM%equationislargerthanforBMI(𝑃<0.001).(e)AUCfortheFM%-equationislargerthan
forBIAInBody(𝑃=0.013).(f)Therearenosignificantdifferencesinareasundercurve(AUC)forthedifferentmethods.