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