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Clinical Usefulness of a New Equationfor Estimating Body Fat

JAVIER GOacuteMEZ-AMBROSI PHD12

CAMILO SILVA MD23

VICTORIA CATALAacuteN PHD12

AMAIA R ODRIacuteGUEZ PHD12

JUAN CARLOS GALOFREacute MD PHD3

JAVIER ESCALADA MD PHD23

VICTOR VALENTIacute MD PHD2

FERNANDO R OTELLAR MD PHD2

SONIA R OMERO MSC23

BEATRIZ R AMIacuteREZ MSC12

JAVIER SALVADOR MD PHD23

GEMA FRUumlHBECK MD PHD123

OBJECTIVEdTo assess the predictive capacity of a recently described equation that we havetermed CUN-BAE (Cliacutenica Universidad de Navarra-Body Adiposity Estimator) based on BMIsex and age for estimating body fat percentage (BF) and to study its clinical usefulness

RESEARCH DESIGN AND METHODSd We conducted a comparison study of the de-veloped equation with many other anthropometric indices regarding its correlation with actualBF in a large cohort of 6510 white subjects from both sexes (67 female) representing a widerange of ages (18ndash80 years) and adiposity Additionally a validation study in a separate cohort(n = 1149) and a further analysis of the clinical usefulness of this prediction equation regarding

its association with cardiometabolic risk factors (n = 634) was carried out

RESULTSdThe mean BF in the cohort of 6510 subjects determined by air displacementplethysmography was 3996 101 and the mean BFestimated by theCUN-BAEwas 393689 (SE of the estimate 466) In this group BF calculated with the CUN-BAE showed thehighest correlation with actual BF (r = 089 P 0000001) compared with other anthropo-metric measures or BF estimators Similar agreement was found in the validation sampleMoreover BF estimated by the CUN-BAE exhibits in general better correlations with cardi-ometabolic risk factors than BMI as well as waist circumference in the subset of 634 subjects

CONCLUSIONSdCUN-BAE is an easy-to-apply predictive equation that may be used as a1047297rst screening tool in clinical practice Furthermore our equation may be a good tool for iden-tifying patients at cardiovascular and type 2 diabetes risk

Diabetes Care 35383ndash388 2012

The prevalence of obesity has increaseddramatically worldwide (1) Obesity is de1047297ned as a state of increased

adipose tissue of enough magnitude toproduce adverse health consequencesbeingassociated with increased morbidity andmortality (2) In this sense excess adiposity increases the risk among other diseases of type 2 diabetes cardiovascular diseasefatty liver sleep-breathing disordersand certain forms of cancer (1) reducinglife expectancy (23)

Although excess adiposity but notexcess body weight is the real culprit of

obesity-associated complications the stud-iesexamining theeffectof obesity-associatedhealth risks in which adiposity is actually measured are less frequent than desired (4)Body fat percentage (BF) can be mea-sured by different techniques encompass-ing skin-fold measurements to magneticresonance imaging (5) Other frequently used methodsfor determiningBFincludebioelectrical impedance analysis (BIA) anddual-energy X-ray absorptiometry (DEXA)More accurate and reproducible methods

include underwater weighing and air dis-placement plethysmography (ADP) (5ndash7)

When BF determinatio n is notavailable BMI is the most frequently

used surrogate measure of adiposityHowever BMI although easyto calculateexhibits notable inaccuracies not pre-cisely re1047298ecting body fat changes inbody composition that take place in thedifferent periods of life or the sexualdimorphism characteristics of body adi-posity (8ndash11) Several prediction equa-tions that account for sex andor age inconverting weight and height to body fathave been published and are reasonably effective in overcoming the aforemen-tioned problem but they have been de-

rived fromsmall samples or fromimprecisemethods of measurement of body compo-sition (1012ndash14)

Because it is crucial to have available anaccurate estimator of BF not only tobetter analyze the effect of adiposity onobesity-associated cardiometabolic risk butalso to perform studies involving body composition in which body fat may notbe actually measuredtheaim of thecurrentstudy was to assess the predictive capacity of a recently described equation by ourgroup for estimating body adiposity and tostudy its clinical usefulness Therefore we

conducted a comparison study of thisequation with many other anthropometricindices in a largecohort of adults from bothsexes representing a wide range of ages andadiposity accompanied by a validationstudy in a separate large cohort and afurther analysis of the clinical usefulnessof this prediction equation

RESEARCH DESIGN ANDMETHODS

Study design We studied a sample of 6510 whitesubjects (2154 men 4356 women)aged 18ndash80 years including patients vis-iting our department The study was per-formed to evaluate the usefulness of a new equationBF= ndash44988+ (05033 age) +(106893 sex) + (31723BMI) ndash (00263BMI2) + (0181 3 BMI 3 sex) ndash (002 3BMI 3 age) ndash (0005 3BMI2 3 sex) +(000021 3 BMI2 3 age) where male =0 and female = 1 for sex and age in yearsdeveloped by multiple regression to predictBF with a SE of the estimate (SEE) of 474 (15) Our equation which may be

c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c

From the 1Metabolic Research Laboratory Cliacutenica Universidad de Navarra Pamplona Spain the 2Centro deInvestigacioacuten Biomeacutedica en Red-Fisiopatologiacutea de la Obesidad y Nutricioacuten (CIBERobn) Instituto de SaludCarlos III Pamplona Spain and the 3Department of Endocrinology and Nutrition Cliacutenica Universidad deNavarra Pamplona Spain

Corresponding author Javier Goacutemez-Ambrosi jagomezunavesReceived 15 July 2011 and accepted 24 October 2011DOI 102337dc11-1334 Clinical trial reg no NCT01055626 clinicaltrialsgovThis article contains Supplementary Data online at httpcarediabetesjournalsorglookupsuppldoi10

2337dc11-1334-DC1copy 2012 by the American Diabetes Association Readers may use this article as long as the work is properly

citedtheuse iseducationaland notforpro1047297t andthe work is notaltered See httpcreativecommonsorg licensesby-nc-nd30 for details

carediabetesjournalsorg DIABETES CARE VOLUME 35 FEBRUARY 2012 383

C a r d i o v a s c u l a r a n d M e t a b o l i c R i s k

O R I G I N A L A R T I C L E



used as an accurate body adiposity estima-tor (BAE) was compared with commonextensively used anthropometric measure-ments includingBMI waistcircumferencewaist-to-hip ratio and waist-to-height aswell as with other measurements less fre-quently used to estimate adiposity such aswaist-to-height2 waist-to-height3 and

weight-to-height ratios the Rohrer indexand the recently described body adiposity index (BAI) (16) To further validate thepredictability of the equation we assessedit in a separate cohort of 1149 white sub-

jects (366 men 783 women) aged 18ndash76years enrolled in another study for analyz-ing the adiposity-associated type 2 diabetesrisk (17) Furthermore we studied the as-sociation of BF with cardiometabolic riskfactors of 634 patients comparing it withBMI and waist circumference Patients withanorexia nervosa or bulimia nervosa wereexcluded The experimental design was ap-proved from an ethics and scienti1047297c stand-point by the hospital ethics committeeInformed consent was obtained

Anthropometric measurementsThe anthropometric and body composi-tion determinations as well as the bloodextraction were performed on a single day

as previously described (1517) BMI wascalculated as weight in kilograms dividedby the square of height in meters TheRohrer index was calculated as weight inkilograms divided by height in meters cu-bed Blood pressure was measured as pre-viously described (1517)

Body compositionBody density was estimated by ADP (Bod-Pod Life Measurements Concord CA)Data for calculation of BF by this ple-thysmographic method has been reportedto agree closely with the traditional goldstandard of hydrodensitometry underwa-ter weighing (6) ADP uses the pressure-volume relationship to estimate volumeand density and has been shown to pre-dict fat mass and fat-free mass more accu-rately than DEXA and BIA (67) BF wasestimated from body density using theSiri equation

Laboratory proceduresBlood samples were collected after anovernight fast Plasma biochemistry wasanalyzed as previously described (17ndash

19) Indirect measures of insulin resis-tanceand insulin sensitivitywere calculatedby using homeostasis model assessment

(HOMA) and quantitative insulin sensitiv-ity check index (QUICKI) respectively

Statistical analysisData are mean6 SD A Bland-Altman plotwas used to graphically assess the agree-ment between BF determined by ADPand BF calculated by the CUN-BAE

(20) HOMA values were logarithmically transformed because of their non-normaldistribution Correlations between twovariables were computed by Pearson cor-relation coef 1047297cient Differences betweencorrelations were assessed by the two-tailed Steiger Z test for comparing two de-pendent correlations within a populationThe accuracy of the predictions was as-sessed by the SEE A helpful Excel (Micro-soft Corp Redmond WA) spreadsheet forthe use of the equation can be found in theSupplementary Data The calculationswereperformed using SPSS 1501 software(SPSS Chicago IL) A P value 005was considered statistically signi1047297cant

RESULTSdDemographic characteris-tics of the subjects included in the com-parison and validation studies arepresented in Supplementary Table 1Both cohorts consisted mainly of women

Table 1dCorrelation matrix of BF with different BAEs and anthropometric variables

Variable All (n = 6510) Men (n = 2154) Women (n = 4356)

BF BMI CUN-BAE BF BMI CUN-BAE BF BMI CUN-BAE

BMI 070 d d 077 d d 084 d d

0001 d d 0001 d d 0001 d d

CUN-BAE 089 080 d 081 097 d 089 094 d

0001 0001 d 0001 0001 d 0001 0001 d

BAI 064 061 068 035 041 040 081 090 086

0001 0001 0001 0001 0001 0001 0001 0001 0001

Waist-to-height 070 091 075 082 092 094 084 091 091

0001 0001 0001 0001 0001 0001 0001 0001 0001

Waist-to-height2 076 085 081 077 085 088 081 087 087

0001 0001 0001 0001 0001 0001 0001 0001 0001

Waist-to-height3 075 073 080 069 074 078 075 080 081

0001 0001 0001 0001 0001 0001 0001 0001 0001

Weight-to-height 060 097 068 074 098 094 082 099 0920001 0001 0001 0001 0001 0001 0001 0001 0001

Rohrer index 076 096 086 076 098 095 083 098 093

0001 0001 0001 0001 0001 0001 0001 0001 0001

Weight 047 089 054 068 092 087 077 094 088

0001 0001 0001 0001 0001 0001 0001 0001 0001

Waist 057 089 061 081 092 093 082 091 090

0001 0001 0001 0001 0001 0001 0001 0001 0001

Hip 051 065 054 034 041 039 080 091 085

0001 0001 0001 0001 0001 0001 0001 0001 0001

Waist-to-hip ratio 015 044 016 049 046 054 047 046 053

0001 0001 0001 0001 0001 0001 0001 0001 0001

Data are Pearson correlation coef 1047297cients (upper) and associated P values (lower)

384 DIABETES CARE VOLUME 35 FEBRUARY 2012 carediabetesjournalsorg

Usefulness of a new body fat estimator



(67ndash68) Individuals from the valida-tion cohort were younger (426 6 131vs 451 6 131 years P 0001) andexhibited higher weight (952 6 261vs 861 6 222 kg P 0001) BMI(349 6 87 vs 315 6 72 kgm2 P 0001) and BF (4286 105 vs 3996101 P 0001) than subjects from the

comparison cohort The proportion of lean overweight and obese subjects inboth cohorts was similar except for theproportion of overweight individualswhich was slightly higher in the valida-tion cohort (P = 0003) Therefore bothcohorts include a wide range of age BMIand BF representing a broad spectrumof the population

The mean BF in thewhole sample of the comparison cohort determined by

ADP was 399 6 101 (men 344 687 women 427 6 96) whereasthe mean BF estimated by the CUN-BAE was 393 6 89 (men 338 671 women 420 6 85) Both varia-bles showed a high correlation (wholesample r = 089 SEE = 466 men r =081 SEE = 520 women r = 089SEE = 436 P 00001 for all Table 1)The Bland-Altman method for compari-son of agreement between BF measuredby ADP and calculated by the CUN-BAE

prediction equation showed a mean biasof 2064 6 922 (2 SD 2986 6858 Fig 1) A total of 6215 subjects(955) fell within the 95 CI Linear re-gression analysis showed a signi1047297cantdependence (P 0001) between the dif-ference of CUN-BAE and ADP and themean of both methods being attributable

to the high sample size which is not con-sidered clinically relevant (R 2 = 007)

We next examined which anthropo-metric measurements best correlated withBF measured by ADP In the wholegroup of 6510 subjects BF calculatedwith the CUN-BAE showed the highestcorrelation with actual BF (r = 089) fol-lowed by waist-to-height2 ratio and theRohrer index (r = 076 for both) (Table 1)In the sample of 2154 men waist-to-height ratio showed the highest correla-tion (r = 082) followed by CUN-BAEand waist circumference (r = 081 forboth) When only women were includedin the analysis (n = 4356) the CUN-BAEwas the best estimator (r = 089) followedby BMI and waist-to-height ratio (r = 084for both) The correlation of CUN-BAEwith BF was signi1047297cantly higher thanthat of BMI with BF for the whole sam-ple and strati1047297ed by sex (P 00001 forthe three comparisons by Steiger Z tests)

The new equation was validated in aseparate cohort of 1149 individuals Ascan be observed in Fig 2 BF estimatedby CUN-BAE showed a higher correlationwith BF measured by ADP for men (r =085 P 00001 SEE = 553) orwomen (r = 090 P 00001 SEE =413) than BMI (men r = 083 P

00001 women r = 084 P 00001)The correlation was also very strongwhen the whole sample was analyzedglobally (r = 090 P 00001 SEE =462) The correlations of CUN-BAEwith BF were again signi1047297cantly higherthan that of BMI with BF (P 00001for the whole sample and women P =00003 for men) A further advantage of estimating BF by CUN-BAE is that thewell-known sex differences in BMI aredispelled because sex is included in theequation as evidenced in Fig 2B

To evaluate the degree of associationof BF estimated with the CUN-BAEwithdifferent cardiometabolic risk factorsand to compare it with BMI and waistcircumference a bivariate correlationanalysis was done This study was per-formed in a subgroup of 634 individualswhere blood pressure glucose level lip-id pro1047297le and several in1047298ammatory prothrombotic markers were available forall of the subjects In men body adiposity estimated with the CUN-BAE was bettercorrelated with systolic blood pressure(P = 0017) logHOMA (P = 0004)

QUICKI (P = 00004) and total choles-terol (P = 00005) than BMI MoreoverCUN-BAE was better correlated withQUICKI (P = 0029) and marginally with logHOMA (P = 0056) than waistcircumference (Table 2) In women BF calculated with the CUN-BAE was bet-ter correlated with systolic blood pressure(P = 0002) triglycerides (P = 0012) andtotal (P 00001) and LDL cholesterol(P 00001) exhibiting weaker correla-tion with insulin levels (P = 00001) thanBMI Furthermore CUN-BAE was bettercorrelated with logHOMA (P = 0018)QUICKI (P = 0003) total (P = 0009)and LDL cholesterol (P = 0023) andC-reactive protein (P = 0001) than waistcircumference (Table 2) Of the 634 sub-

jects of this sample 224 exhibited a fast-ing plasma glucose level$100 mgdL Of the 224 34 individuals had a BMI 30kgm2 with 32 showing a BF estimatedwith the CUN-BAE well within the obe-sity range (25 for men and 35 forwomen) Therefore 5 of the subjectsfrom the sample would bene1047297t fromhaving a blood test after being diagnosed

Figure 1dBland-Altman plot shows the limits of agreement between BF estimated using CUN-BAE and BF measured by ADP in the comparison sample of 6510 subjects The middle red linerepresents the mean difference between the estimated and the measured BF The dotted linesindicate 6 2 SDs from the mean


Goacutemez-Ambrosi and Associates



as obese according to the CUN-BAE todiscard an impaired glucose tolerance

CONCLUSIONSdThe mainobjective

of the current study was to analyze theaccuracy and utility of a new equationbased on BF measured by ADP anddeveloped for the prediction of BFusing BMI age and sex (15) We hereinshow that CUN-BAE may estimate BFwith a good accuracy providing a usefultool in epidemiologic and clinical stud-ies without access to specialized body composition measurements to analyzeadiposity-related cardiometabolic risks

BMI is frequently used as an indicatorof BF However although it is useful inepidemiologic studies it is highly impre-cise at estimating body fat at an individuallevel (915) We herein have validated a re-cently described prediction equation thatcan estimate BF in adults with low errorrate and acceptable accuracy The BF cal-culated with the CUN-BAE correlated bet-ter with the actual BF measured by ADPthan any other anthropometric variable orBF estimator in a sample of 6510 indi-viduals from both sexes with a wide rangeof BMI BF and age

Several prediction equations havebeen developed to predict body adiposity

In general these prediction models arederived from small samples and are fre-quently based on not very precise body composition techniques such as skinfolds

or BIA or are focused on speci1047297c age ranges(1012ndash14) To our knowledge only fourstudies have provided prediction equa-tions developed in samples1000 adultsfrom a wide spectrum of age ranges andfrom data obtained with body compositiontechniques such as underwater weighingDEXA or four-compartment model(1621ndash23) Our equation was developedfrom data obtained from 6123 subjectsaged 18ndash80 years and encompassingBMIs between 124 and 728 kgm2 andBF between 21 and 696 In thissense previous equations have some lim-itations including having been derivedfrom individuals with a maximum BMIof 409 kgm2 (24) and 350 kgm2 (22)or were obtained from adults aged 30ndash61years and with body weights 110 kg(23) which may not be representative toapply to the whole population affectingits accuracy

Very recently another index for body adiposity named BAI was developedbased on hip circumference data of 1733Mexican American adults In our handsthis index exhibited a lower correlation

with BF measured by ADP and a markedsexual dimorphism being better corre-lated in women than in men with a similartendency than that observed for hip cir-

cumference Although hip circumferencedoes not seem to be a good estimator of BF and is known to be associated withlower cardiometabolic risk this novel in-dex may be useful in Mexican American or

African American populations (16)One important aspect of our study is

that our equation takes into account theeffect of age The relation between BMIand BF has been shown to be dependenton age (9) Older adults irrespective of sex have on average more body adiposity than younger adults at any given BMI (8)Therefore prediction equations devel-oped to estimate BF only from BMIeven if they are derived from a sample in-cluding subjects from all ages will gener-ally tend to underestimate the amount of body fat in the elderly and to overestimateit in the young (911) Including age in theprediction equation and the interactionsof age with the linear and quadratic BMIcomponents consistently reduces the er-ror due to age in the BF estimations

Another advantage of CUN-BAE re-lies on better correlations with cardio-metabolic risk factors than BMI and even

Figure 2dCorrelation strati 1047297ed by sex between BF measured by ADP and BMI ( A) and BF estimated using CUN-BAE ( B) in the validationsample of 1149 subjects (366 men and 783 women) Pearson correlation coef 1047297cients and associated P values are shown for the whole sample andstrati 1047297ed by sex Tendency lines are shown for men and women in panel A and for the whole sample in panel B





than waist circumference for both menand womenin a subset of 634 subjects Toour knowledge this is the 1047297rst study val-idating a prediction equation for BFgoing a step further and studying its clin-ical usefulness analyzing how predictedBF might help to explain the changesobserved in these risk factors in relation tobody composition This aspect is extremely relevant because BF has been shown tobetter correlate with cardiometabolic riskfactor than BMI (15) Furthermore be-cause actual adiposity is a major risk factorfor the development of prediabetes andtype 2 diabetes (17) our equation may also represent a helpful tool to detectpatients at risk for these conditions

Our study has several strengths Firstour prediction equation has been devel-oped from a large sample of 6123 subjectsfrom both sexes with a wide range of body

adiposity from constitutional thinness toextreme obesity and from all adult ages(18ndash80 years) and has been validated intwo large cohorts representing all ages andponderal groups Second actual BF datahave been measured by a highly precisetechnique such as the ADP This techniquehas been shown to predict fat mass moreaccurately than DEXA and BIA using hy-drodensitometry as the reference method(5ndash725) Third as mentioned before BFestimated with our equation may be usefulwhen studying cardiometabolic risk factors

However our study has also one po-tential limitation that pertains to the gen-eralizability to other populations Thepresent work was conducted in white sub-

jects and needs to be extended to otherpopulations to determine its applicability

In summary because the possibility of measuring BF is not always available

and the relation between BMI and BF ishighly dependent on sex and age we havedeveloped and validated an easy-to-apply predictive equation that may be used as a1047297rst screening tool in medical practiceFurthermore our equation may be a use-ful clinical tool for identifying patientswith increased cardiovascular and type 2

diabetes risk

AcknowledgmentsdThisstudy wassupportedby grants from the ISCIII (FIS PI061458 PS09 02330 and PI0991029) and the Departments of Health (202005 and 312009) and Educationof the Gobierno de Navarra CIBER de Fisiopa-tologiacutea de la Obesidad y Nutricioacuten (CIBERobn)is an initiative of the ISCIII Spain

No potential con1047298icts of interest relevant tothis article were reported

JG-A designed the study enrolled pa-tients collected and analyzed data wrote the1047297rst draft of the manuscript contributed to

discussion and reviewed the manuscript CSenrolled patients collected data contributedto discussion and reviewed the manuscript VC and AR enrolled patients collected andanalyzed data contributed to discussion andreviewed the manuscript JCG JE VVFR SR BR and JS enrolled patientscollected data contributed to discussion andreviewed the manuscript GF designed thestudy enrolled patients collected and ana-lyzed data wrote the 1047297rst draft of the manu-script contributed to discussion and reviewedthe manuscript JG-A and GF are guarantorsfor the contents of the article

The authors thank all of the members of theNutrition Unit of the Department of Endocri-nology amp Nutrition (Cliacutenica Universidad deNavarra Pamplona Spain) for their technicalsupport in body composition analysis

References1 Haslam DW James WP Obesity Lancet

20053661197ndash12092 Berrington de Gonzalez A Hartge P Cerhan

JR et al Body-mass index and mortality among 146 million white adults N Engl J Med 20103632211ndash2219

3 Heitmann BL Erikson H Ellsinger BM

Mikkelsen KL Larsson B Mortality asso-ciated with body fat fat-free mass andbody mass index among 60-year-old swed-ish men-a 22-year follow-up The study of men born in 1913 Int J Obes Relat MetabDisord 20002433ndash37

4 Fruumlhbeck G Screening and interventionsfor obesity in adults Ann Intern Med2004141245ndash246 author reply 246

5 Das SK Body composition measurementin severe obesity Curr Opin Clin NutrMetab Care 20058602ndash606

6 Fields DA Goran MI McCrory MA Body-compositionassessmentvia air-displacementplethysmography in adults and children

Table 2dCorrelation of BMI waist circumference and estimated BF with different

cardiometabolic variables

Variable

Men (n = 225) Women (n = 409)

BMI CUN-BAE Waist BMI CUN-BAE Waist

Blood pressure

Systolic 035 038 035 051 054 052

0001 0001 0001 0001 0001 0001Diastolic 043 045 042 055 057 055

0001 0001 0001 0001 0001 0001

Glucose 021 023 023 035 035 037

0002 0001 0001 0001 0001 0001

Insulin 045 045 043 060 055 054

0001 0001 0001 0001 0001 0001

logHOMA 052 056 052 066 064 061

0001 0001 0001 0001 0001 0001

QUICKI 2052 2056 2051 2064 2064 2059

0001 0001 0001 0001 0001 0001

Triglycerides 010 012 011 034 037 037

0132 0064 0111 0001 0001 0001

CholesterolTotal 010 015 012 035 042 037

0134 0029 0063 0001 0001 0001

LDL 007 012 010 043 048 044

0276 0076 0129 0001 0001 0001

HDL 2006 2008 2007 2029 2027 2030

0361 0238 0303 0001 0001 0001

Fibrinogen 016 018 021 021 019 023

0018 0009 0002 0001 0001 0001

Homocysteine 2008 2007 2001 017 018 017

0242 0278 0829 0001 0001 0001

LogCRP 040 042 041 067 067 062

0001 0001 0001 0001 0001 0001

vWF 010 013 011 018 016 019

0132 0053 0115 0001 0001 0001

Data are Pearson correlation coef 1047297cients (upper) and associated P values (lower) HOMA values were loga-rithmically transformed because of their non-normal distribution CRP C-reactive protein vWF von Wil-lebrand factor





a review Am J Clin Nutr 200275453ndash

4677 Ginde SR Geliebter A Rubiano F et al

Air displacement plethysmography vali-dation in overweight and obese subjectsObes Res 2005131232ndash1237

8 Gallagher D Visser M Sepuacutelveda DPierson RN Harris T Heyms1047297eld SBHow useful is body mass index for com-

parison of body fatness across age sexand ethnic groups Am J Epidemiol 1996143228ndash239

9 Prentice AM Jebb SA Beyond body massindex Obes Rev 20012141ndash147

10 Jackson AS Stanforth PR Gagnon J et alThe effect of sex age and race on esti-mating percentage body fat from body mass index The Heritage Family StudyInt J Obes Relat Metab Disord 200226789ndash796

11 Snijder MB van Dam RM Visser MSeidell JC What aspects of body fat areparticularly hazardous and how do wemeasure them Int J Epidemiol 200635

83ndash

9212 Visser M van den Heuvel E Deurenberg

P Prediction equations for the estimationof body composition in the elderly usinganthropometric data Br J Nutr 199471823ndash833

13 Dupler TL Tolson H Body compositionprediction equations for elderly men

J Gerontol A Biol Sci Med Sci 200055M180ndashM184

14 Martarelli D Martarelli B Pompei P Body composition obtained from the bodymassindex an Italian study Eur J Nutr 200847409ndash416

15 Goacutemez-Ambrosi J Silva C Galofreacute JCet alBody mass index classi1047297cation misses subjects with increased cardiometabolic risk

factors related to elevated adiposity Int JObes 17 May 2011 [Epub ahead of print]

16 Bergman RN Stefanovski D BuchananTA et alA better index of body adiposityObesity (Silver Spring) 2011191083ndash1089

17 Goacutemez-Ambrosi J Silva C Galofreacute JCet al Body adiposity and type 2 diabetesincreased risk with a high body fat per-centage even having a normal BMI Obe-sity (Silver Spring) 2011191439ndash1444

18 Goacutemez-Ambrosi J Salvador J Rotellar Fet al Increased serum amyloid A con-centrations in morbid obesity decreaseafter gastric bypass Obes Surg 200616

262ndash

26919 Goacutemez-Ambrosi J Catalaacuten V Ramiacuterez B

et al Plasma osteopontin levels and ex-pression in adipose tissue are increased inobesityJ ClinEndocrinol Metab 2007923719ndash3727

20 Bland JM Altman DG Statistical methodsfor assessing agreement between two

methods of clinical measurement Lancet19861307ndash310

21 Deurenberg P Weststrate JA Seidell JCBody mass index as a measure of body fatness age- and sex-speci1047297c predictionformulas Br J Nutr 199165105ndash114

22 Gallagher D Heyms1047297eld SB Heo M JebbSA Murgatroyd PR Sakamoto Y Healthy percentage body fat ranges an approach

for developing guidelines based on body mass index Am J Clin Nutr 200072694ndash

70123 Larsson I Henning B Lindroos AK

Naumlslund I Sjoumlstroumlm CD Sjoumlstroumlm LOptimized predictions of absolute andrelative amounts of body fat from weightheight other anthropometric predictorsand age 1 Am J Clin Nutr 200683252ndash

25924 Deurenberg P van der Kooy K Leenen R

Weststrate JA Seidell JC Sex and agespeci1047297c prediction formulas for estimat-ing body composition from bioelectricalimpedance a cross-validation study Int J

Obes 19911517ndash

2525 Biaggi RR Vollman MW Nies MA et al

Comparison of air-displacement plethys-mography with hydrostatic weighing andbioelectrical impedance analysis for the as-sessment of body composition in healthy adults Am J Clin Nutr 199969898ndash

903





used as an accurate body adiposity estima-tor (BAE) was compared with commonextensively used anthropometric measure-ments includingBMI waistcircumferencewaist-to-hip ratio and waist-to-height aswell as with other measurements less fre-quently used to estimate adiposity such aswaist-to-height2 waist-to-height3 and

weight-to-height ratios the Rohrer indexand the recently described body adiposity index (BAI) (16) To further validate thepredictability of the equation we assessedit in a separate cohort of 1149 white sub-

jects (366 men 783 women) aged 18ndash76years enrolled in another study for analyz-ing the adiposity-associated type 2 diabetesrisk (17) Furthermore we studied the as-sociation of BF with cardiometabolic riskfactors of 634 patients comparing it withBMI and waist circumference Patients withanorexia nervosa or bulimia nervosa wereexcluded The experimental design was ap-proved from an ethics and scienti1047297c stand-point by the hospital ethics committeeInformed consent was obtained

Anthropometric measurementsThe anthropometric and body composi-tion determinations as well as the bloodextraction were performed on a single day

as previously described (1517) BMI wascalculated as weight in kilograms dividedby the square of height in meters TheRohrer index was calculated as weight inkilograms divided by height in meters cu-bed Blood pressure was measured as pre-viously described (1517)

Body compositionBody density was estimated by ADP (Bod-Pod Life Measurements Concord CA)Data for calculation of BF by this ple-thysmographic method has been reportedto agree closely with the traditional goldstandard of hydrodensitometry underwa-ter weighing (6) ADP uses the pressure-volume relationship to estimate volumeand density and has been shown to pre-dict fat mass and fat-free mass more accu-rately than DEXA and BIA (67) BF wasestimated from body density using theSiri equation

Laboratory proceduresBlood samples were collected after anovernight fast Plasma biochemistry wasanalyzed as previously described (17ndash

19) Indirect measures of insulin resis-tanceand insulin sensitivitywere calculatedby using homeostasis model assessment

(HOMA) and quantitative insulin sensitiv-ity check index (QUICKI) respectively

Statistical analysisData are mean6 SD A Bland-Altman plotwas used to graphically assess the agree-ment between BF determined by ADPand BF calculated by the CUN-BAE

(20) HOMA values were logarithmically transformed because of their non-normaldistribution Correlations between twovariables were computed by Pearson cor-relation coef 1047297cient Differences betweencorrelations were assessed by the two-tailed Steiger Z test for comparing two de-pendent correlations within a populationThe accuracy of the predictions was as-sessed by the SEE A helpful Excel (Micro-soft Corp Redmond WA) spreadsheet forthe use of the equation can be found in theSupplementary Data The calculationswereperformed using SPSS 1501 software(SPSS Chicago IL) A P value 005was considered statistically signi1047297cant

RESULTSdDemographic characteris-tics of the subjects included in the com-parison and validation studies arepresented in Supplementary Table 1Both cohorts consisted mainly of women

Table 1dCorrelation matrix of BF with different BAEs and anthropometric variables

Variable All (n = 6510) Men (n = 2154) Women (n = 4356)

BF BMI CUN-BAE BF BMI CUN-BAE BF BMI CUN-BAE

BMI 070 d d 077 d d 084 d d

0001 d d 0001 d d 0001 d d

CUN-BAE 089 080 d 081 097 d 089 094 d

0001 0001 d 0001 0001 d 0001 0001 d

BAI 064 061 068 035 041 040 081 090 086

0001 0001 0001 0001 0001 0001 0001 0001 0001

Waist-to-height 070 091 075 082 092 094 084 091 091

0001 0001 0001 0001 0001 0001 0001 0001 0001

Waist-to-height2 076 085 081 077 085 088 081 087 087

0001 0001 0001 0001 0001 0001 0001 0001 0001

Waist-to-height3 075 073 080 069 074 078 075 080 081

0001 0001 0001 0001 0001 0001 0001 0001 0001

Weight-to-height 060 097 068 074 098 094 082 099 0920001 0001 0001 0001 0001 0001 0001 0001 0001

Rohrer index 076 096 086 076 098 095 083 098 093

0001 0001 0001 0001 0001 0001 0001 0001 0001

Weight 047 089 054 068 092 087 077 094 088

0001 0001 0001 0001 0001 0001 0001 0001 0001

Waist 057 089 061 081 092 093 082 091 090

0001 0001 0001 0001 0001 0001 0001 0001 0001

Hip 051 065 054 034 041 039 080 091 085

0001 0001 0001 0001 0001 0001 0001 0001 0001

Waist-to-hip ratio 015 044 016 049 046 054 047 046 053

0001 0001 0001 0001 0001 0001 0001 0001 0001

Data are Pearson correlation coef 1047297cients (upper) and associated P values (lower)















































diabetes risk
















Variable

Men (n = 225) Women (n = 409)


Blood pressure

Systolic 035 038 035 051 054 052

0001 0001 0001 0001 0001 0001Diastolic 043 045 042 055 057 055

0001 0001 0001 0001 0001 0001

Glucose 021 023 023 035 035 037

0002 0001 0001 0001 0001 0001

Insulin 045 045 043 060 055 054

0001 0001 0001 0001 0001 0001

logHOMA 052 056 052 066 064 061

0001 0001 0001 0001 0001 0001

QUICKI 2052 2056 2051 2064 2064 2059

0001 0001 0001 0001 0001 0001

Triglycerides 010 012 011 034 037 037

0132 0064 0111 0001 0001 0001


0134 0029 0063 0001 0001 0001

LDL 007 012 010 043 048 044

0276 0076 0129 0001 0001 0001

HDL 2006 2008 2007 2029 2027 2030

0361 0238 0303 0001 0001 0001

Fibrinogen 016 018 021 021 019 023

0018 0009 0002 0001 0001 0001

Homocysteine 2008 2007 2001 017 018 017

0242 0278 0829 0001 0001 0001

LogCRP 040 042 041 067 067 062

0001 0001 0001 0001 0001 0001

vWF 010 013 011 018 016 019

0132 0053 0115 0001 0001 0001














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diabetes risk
















Variable

Men (n = 225) Women (n = 409)


Blood pressure

Systolic 035 038 035 051 054 052

0001 0001 0001 0001 0001 0001Diastolic 043 045 042 055 057 055

0001 0001 0001 0001 0001 0001

Glucose 021 023 023 035 035 037

0002 0001 0001 0001 0001 0001

Insulin 045 045 043 060 055 054

0001 0001 0001 0001 0001 0001

logHOMA 052 056 052 066 064 061

0001 0001 0001 0001 0001 0001

QUICKI 2052 2056 2051 2064 2064 2059

0001 0001 0001 0001 0001 0001

Triglycerides 010 012 011 034 037 037

0132 0064 0111 0001 0001 0001


0134 0029 0063 0001 0001 0001

LDL 007 012 010 043 048 044

0276 0076 0129 0001 0001 0001

HDL 2006 2008 2007 2029 2027 2030

0361 0238 0303 0001 0001 0001

Fibrinogen 016 018 021 021 019 023

0018 0009 0002 0001 0001 0001

Homocysteine 2008 2007 2001 017 018 017

0242 0278 0829 0001 0001 0001

LogCRP 040 042 041 067 067 062

0001 0001 0001 0001 0001 0001

vWF 010 013 011 018 016 019

0132 0053 0115 0001 0001 0001














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