Biogeographic Ancestry Is Associated with Higher Total Body Adiposity among African-American

15 Pages·2015·0.43 MB·English

RESEARCHARTICLE
Biogeographic Ancestry Is Associated with
Higher Total Body Adiposity among African-
American Females: The Boston Area
Community Health Survey
SunaliD.Goonesekera1*,ShonaC.Fang1,2,RebeccaS.Piccolo1,JoseC.Florez3,John
B.McKinlay1
1 DepartmentofEpidemiologyandBiostatistics,NewEnglandResearchInstitutes,480PleasantSt.,
Watertown,MA02472,UnitedStatesofAmerica,2 DepartmentofEnvironmentalHealth,HarvardSchoolof
PublicHealth,Boston,MA02115,UnitedStatesofAmerica,3 DiabetesUnit/CenterforHumanGenetic
Research,MassachusettsGeneralHospital,Boston,MA02114,UnitedStatesofAmerica
* [email protected]
Abstract
OPENACCESS
Citation:GoonesekeraSD,FangSC,PiccoloRS,
FlorezJC,McKinlayJB(2015)Biogeographic
AncestryIsAssociatedwithHigherTotalBody Objectives
AdiposityamongAfrican-AmericanFemales:The
BostonAreaCommunityHealthSurvey.PLoSONE TheprevalenceofobesityisdisproportionatelyhigheramongAfrican-AmericansandHis-
10(4):e0122808.doi:10.1371/journal.pone.0122808 panicsascomparedtowhites.Weinvestigatedtheroleofbiogeographicancestry(BGA)
AcademicEditor:FarookThameem,TheUniversity onadiposityandchangesinadiposityintheBostonAreaCommunityHealthSurvey.
ofTexasHealthScienceCenter(UTHSCSA),
UNITEDSTATES
Methods
Received:November14,2014
WeevaluatedassociationsbetweenBGA,assessedviaAncestryInformativeMarkers,and
Accepted:February13,2015
adiposity(bodymassindex(BMI),percentbodyfat(PBF),andwaist-to-hipratio(WHR))
Published:April13,2015
andchangesinadiposityover7yearsforBMIandWHRand2.5yearsforPBF,per10%
Copyright:©2015Goonesekeraetal.Thisisan
greaterproportionofBGAusingmultivariablelinearregression.Wealsoexaminedeffect-
openaccessarticledistributedunderthetermsofthe
modificationbydemographicandsocio-behavioralvariables.
CreativeCommonsAttributionLicense,whichpermits
unrestricteduse,distribution,andreproductioninany
medium,providedtheoriginalauthorandsourceare
Results
credited.
DataAvailabilityStatement:Allrelevantdataare WeobservedpositiveassociationsbetweenWest-Africanancestryandcross-sectional
withinthepaperanditsSupportingInformationfiles. BMI(percentdifference=0.62%;95%CI:0.04%,1.20%),andPBF(β=0.35;95%CI:0.11,
Funding:Thisstudywasfundedbyawards 0.58).Wealsoobservedsignificanteffect-modificationoftheassociationbetweenWest-Af-
DK056842andDK080786fromtheNationalInstitute ricanancestryandBMIbygender(p-interaction:<0.002)withasubstantiallygreaterassoci-
ofDiabetesandDigestiveandKidneyDiseases.The
ationinwomen.WeobservednomainassociationsbetweenNative-Americanancestry
fundershadnoroleinstudydesign,datacollection
andadipositybutobservedsignificanteffect-modificationoftheassociationwithBMIbydiet
andanalysis,decisiontopublish,orpreparationof
themanuscript. (p-interaction:<0.003)withinverseassociationsamongparticipantswithhigherHealthy
EatingScores.NoassociationswereobservedbetweenBGAandchangesinadiposity
CompetingInterests:Allauthorshavedeclaredthat
nocompetinginterestsexist. overtime.
PLOSONE|DOI:10.1371/journal.pone.0122808 April13,2015 1/15
BiogeographicAncestryandAdiposity
Conclusion
FindingssupportthatWest-Africanancestrymaycontributetohighprevalenceoftotalbody
adiposityamongAfrican-Americans,particularlyAfrican-Americanwomen.
Introduction
Obesityisamajorpublichealthconcernwhichsignificantlyincreasesone’sriskforadverse
healthincludingtypeIIdiabetes,cardiovasculardisease,musculoskeletaldisorders[1]andde-
pression[2].TheprevalenceofobesityissubstantiallyhigheramongAfrican-Americansand
Hispanicsascomparedtonon-HispanicwhitesintheUnitedStates[3].Moreover,astudyper-
formedontheNationalHealthandNutritionExaminationSurvey(NHANES)datafromthe
lasttwodecadeshasfoundawideningofracialdisparitiesinregardtobodymassindex(BMI)
andwaistcircumference(WC)betweennon-HispanicwhitesandAfrican-Americans,andhas
estimatedtheprevalenceofcentralobesityandobesityamongblackwomenin2020toreach
90.9and70.7%,respectively[4].Theracial/ethnicdifferencesinobesityandrelateddiseases
persistinspecificpopulationsevenafteradjustingforsocio-economicvariables[5–8]indicat-
ingthatgeneticsmayindependentlycontributetoracial/ethnicdisparitiesinobesity.
Thegeneticbasisforracial/ethnicdisparitiesinadiposityandmetabolicdiseasecouldbeex-
plainedbythethriftygenehypothesiswhichwasfirstproposedin1960’s[9,10].Accordingo
thistheory,genesthatthatpromotethestorageofenergyasbodyfat,thatmayhavebeenad-
vantageousduringPaleolithictimes,mayleadtoobesityandmetabolicdiseaseinmodern
westernsocietieswherethereislittleneedforfatstorage.Ithasbeenhypothesizedthat“thrifty
genes”maybemoreprevalentamongracial/ethnicsubgroupsoutsideofthoseofEuropeande-
scentasmanyoftheseindividualshaveancestraloriginsinregionswheredroughtsandfamine
commonlyoccur.Eventhoughthistheoryhasreceivedsomecriticism[11],multiplestudies
havesinceevaluatedassociationsbetweengeneticancestryandadiposity-associateddiseasesin
westernsocieties.
AncestralInformativeMarkers(AIMs),i.e.,geneticmarkersuniquetopeopleofahomoge-
nousBGA,areoftenusedascorrelatesofself-identifiedraceinstudiesperformedinadmixed
populations[5–7,12–19].StudiesthatexaminedassociationsbetweenAfricanorEuropean
BGAusingAIMsandadiposityoradiposity-relateddiseaseshavereportedinconsistentfind-
ingswithsomereportingpositiveassociationswithAfricanBGAandadiposity[5,6,18,19]or
inverseassociationswithEuropeanancestry[7],whileothersreportinginverseassociations
withAfricanancestryornoassociation[13,14].Forexample,astudyconductedamongAfri-
can-AmericanandHispanic-AmericanpostmenopausalparticipantsintheWomen’sHealth
Initiative(WHI)foundsignificantpositiveassociationsbetweenAfricanancestryandBMIin
theoverallpopulation,aswellasintheadmixedAfrican-AmericanandHispanic-American
subgroups[6].However,whenwaist-to-hipratio(WHR)wasusedasthemeasureofadiposity,
asignificantpositiveassociationwithAfricanancestrywasattenuatedintheoverallpopula-
tion,andnotobservedamongAfrican-AmericansorHispanic-Americans.Interestingly,Am-
erindianancestrywaspositivelyassociatedwithWHRbutnotwithBMIintheoverall
populationandamongtheHispanic-Americanparticipantsinthiscohort[6].Associationsbe-
tweenAfricanancestryandtypeIIdiabetes,adiseasecloselytiedtoobesity,werealsoobserved
amongAfrican-Americanparticipantsinthisstudy[5].However,astudyconductedamongel-
derlyPuertoRicansintheU.S.foundnegativeassociationsbetweenAfricangeneticancestry
PLOSONE|DOI:10.1371/journal.pone.0122808 April13,2015 2/15
BiogeographicAncestryandAdiposity
andtypeIIdiabetesandcardiovasculardisease[13].Anotherstudyfoundsignificantpositive
correlationbetweenobesity/BMIandEuropeanancestryratherthanAfricanancestry[14].
Giventhesecontradictoryfindings,wesoughttofurtherevaluatetherelationshipbetween
BGAandadiposityusingdifferentmeasuresofadiposity,i.e.,BMI,percentbodyfat(PBF),and
WHR,includingchangesinthesemeasuresovertime,intheBostonAreaCommunityHealth
(BACH)Survey.ApreviousstudyperformedontheBACHIcohort(2000–2002)foundaposi-
tiveassociationbetweenAfrican-Americanraceandobesityaswellassignificanteffect-modifi-
cationofthisassociationbygender[20].Buildingonthisresearch,wesoughtto1)toevaluate
therelationshipbetweenBGAandadiposity(asmeasuredbyBMI,WHR,andPBF)and
changeinadiposityovertime,and2)toevaluatewhethertheseassociationsaremodifiedby
gender,age,diet,physicalactivity,income,andeducationallevel.Aspreviousresearchindi-
catesthatbodyfatdistributionmaynotbeuniformacrossethnicgroupsandBMImaynot
provideaccuratemeasuresofadiposityforallindividuals[15,21–23],weevaluatedassocia-
tionsbetweengeneticancestryanddifferentmeasuresofadiposityi.e.,BMI,WHR,andPBF.
Methods
Studydesign,participantsanddatacollection
TheBostonAreaCommunityHealth(BACH)Surveyisapopulation-basedprospectivecohort
studythathasrecruitedapproximatelyequalproportionsofnon-Hispanicwhite,non-Hispan-
icblack,andHispanic-AmericanparticipantsfromBoston,MA,usingmultistagestratified
clustersampling.Asdetailedelsewhere[24,25],thissurveywasconductedinthreewavesthat
havespannedoveraperiodoftenyears(2002–2012).Duringthefirstwaveofthestudy
(BACHI)(2002–2005),investigatorsrecruited5,502menandwomenaged30–79years.The
second(BACHII)andthirdwaves(BACHIII)ofthestudywereconductedin2008–10and
2010–12,respectively.Oftheinitial5,502participants,3,155participatedinBACHIII.
Duringallthreewaves,studyparticipantswereinterviewedintheirhomesinEnglishor
Spanish,andanthropomorphicmeasurementsincludingheight,weight,waistcircumference,
andhipcircumferenceweretakenbytrainedinterviewers.PBFwasalsoassessedduringBACH
IIandBACHIII.Inaddition,dataonmultiplesocio-economicandbehavioralfactors,co-mor-
bidities,andmedicationusewerecollected.DuringBACHIII,investigatorsalsoobtained
bloodsamplesfromparticipantstodeterminetheproportionofallelicmarkers.Ofthe3,155
participantswhoparticipatedinthethirdfollow-upsurvey(BACHIII),weexcludedpartici-
pantswhoreportedweightlossintheabsenceofdecreasedfoodintakeorincreasedexercise
(n=409),whowerepregnant(n=1),haddiagnosedorundiagnoseddiabetes(n=980),ever
hadcongestiveheartfailure(n=142)attheBACHIIIsurvey,orreportedeverhavinghadadi-
agnosisofAIDS(n=19),cancer(n=283),chroniclungdisease(n=235),orAlzheimer’sdis-
ease(n=12)attheBACHIsurvey,astheseconditionscouldresultinweightchange.Even
thoughwewereunabletoexcludeindividualswithend-stagerenaldiseaseduetolackofinfor-
mation,few,ifany,suchindividualswouldhavebeensufficientlyhealthytohavebeenre-
cruitedtotheBACHstudy.Wewereleftwith1,726participantsfortheanalyses.Ofthese,654
wereself-identifiednon-Hispanicwhiteparticipants,while531and541wereself-identified
blackandHispanic-Americans,respectively.Allparticipantsprovidedwritteninformedcon-
sent.ThisstudywasapprovedbytheNewEnglandResearchInstitutes’InstitutionalReview
Board.
MeasurementofAIMsandBGA
Atotalof63allelicmarkers,i.e.,singlenucleotidepolymorphisms(SNPs)selectedontheir
abilitytoestimatepercentWest-AfricanandNative-AmericanBGA,weregenotypedbythe
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BiogeographicAncestryandAdiposity
GeneticAnalysisPlatformattheBroadInstituteinCambridge,MAusingiPLEX(Sequenom).
AncestrypercentageswereestimatedforeachparticipantusingADMIXTUREsoftware(v.
1.12http://www.genetics.ucla.edu/software/admixture/).
Outcomemeasurement
AtBACHIandIII,BMIwascalculatedbydividingweight(kg)byheightsquared(m2).WHR
wascalculatedbydividingwaistcircumference(cm)byhipcircumference(cm).AtBACHII
andIIIPBFwasmeasuredviabioelectricalimpedanceusingtheTanitascale.Wecalculated
thepercentchangesinBMIandWHRbetweenwavesIandIIIoftheBACHSurveyovera
meandurationofapproximatelysevenyears,andpercentchangesinPBFbetweenwavesII
andIIIoverameandurationofapproximately2.5years.
Measurementofcovariates
Dataonsocio-economicandbehavioralfactorsforthisanalysiswereobtainedfromtheBACH
IIIassessment.Forsocio-economicvariables,wegroupedparticipantsintocategoriesofannu-
alhouseholdincome(<$20000,$20000-$49999,and(cid:1)$50000),occupation(office/profes-
sional/management,serviceprofessions,manuallabor,andneverworked),andeducation
(collegeorhigher,somecollegeorAssociate’sdegree,highschoolcompleted,andlessthan
highschool)usingcut-offsbasedonpriorstudies[26].
Inordertoassesseachparticipant’snutritionalintake,weadministeredtheBlockFood
FrequencyQuestionnaire(FFQ)inEnglishorSpanish,andratedeachparticipant’sdietaryin-
takeonascalefrom0to7.TheFFQScorewasdeterminedbysumminguppointsassignedto
participantsbasedontheaveragedailyintakeofsodium(1pointif<1500g;0otherwise),fiber
(1pointifbetween25-30g;0otherwise),saturatedfat(1pointif<14g;0otherwise),andthe
averagenumberofdailyservingsofvegetables(1pointifbetween3–4;0otherwise),fruits(1
pointifbetween2–3;0otherwise),meat(1pointifbetween2–3;0otherwise),andgrain(1
pointifbetween6–11;0otherwise).ThecutoffvalueswerebasedonUnitedStatesDepartment
ofAgriculture[27]andAmericanHeartAssociationguidelinesforhealthyeating[28].As
mostparticipants’foodfrequencyscoreswerelow,wedichotomizedFFQscoresatascoreof2,
withscoresabove2indicatinghealthiereating.
WemeasuredphysicalactivitybyusingthePhysicalActivityScalefortheElderly(PHASE)
[29].This12itemquestionnaireassessedeachparticipant’stimespentengaginginleisure,
household,andoccupationalactivities,andwalkingandsportsduringthepriorweek.The
hoursspentineachactivitywasweightedbytheestimatedamountofenergyspent.We
summedthesevaluesoverallactivitiestoobtainaPHASEscoreandcategorizedthisscoreas
low(<100),medium(100–249),orhigh((cid:1)250).
StatisticalAnalyses
Inordertominimizepotentialbiasesandreductioninprecisionduetomissingdata,weper-
formedmultipleimputationsusingMultivariateImputationbyChainedEquations(MICE)
[30]inR(RFoundationforStatisticalComputing,Vienna,Austria)tocreate15datasetsfor
eachcombinationofraceandgender.
TheanalyseswereperformedusingSUDAAN11(ResearchTrianglePark,NC).Thedata
wereweightedbytheinverseoftheprobabilityofbeingsampledatbaseline(BACHI)toac-
countforoversamplingofminoritygroups.Weobservedgreaterattritionamongmenandpar-
ticipantsinthelowersocio-economicstrata.Thus,theanalyseswerealsoadjustedfornon-
responseandloss-to-follow-upusingthepropensitycelladjustmentapproach[31],andpost-
stratifiedtotheBostoncensuspopulationin2010.
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BiogeographicAncestryandAdiposity
Weassessedalloutcomesper10%ratherthan1%greaterproportionofgeneticancestryto
avoidreportingsmalleffect-estimates.Welog-transformedcross-sectionalBMIandpresented
theeffectsasapercentdifferenceper10%greaterproportionofBGA,asthedistributionofits
residualsdeviatedfromnormality.Allotheroutcomeresidualswerenormallydistributed.We
performedunivariableandmultivariablelinearregressionstoassessoutcomesassociatedwith
West-AfricanandNative-Americanancestry.Wealsoevaluatedforeffect-modificationbyage,
gender,diet,physicalactivity,income,andeducationbyincludinginteractiontermsbetween
thesevariablesandtheexposuresinseparatemodels.
Statisticalsignificanceforallanalyseswasinitiallysetatp<0.05usingatwo-tailedtest.In
ordertoaccountformultiplecomparisoncomplexitiesresultingfromexaminingsixdifferent
adiposityoutcomesintheoverallpopulationanddifferentsub-populations,wealsoassessed
ourresultsapplyingmorestringentcriteriaofp<0.008forstatisticalsignificance(i.e.,alpha’=
0.05/6).
Results
Table1providesdemographic,socio-economic,behavioral,andoutcomedatafortheoverall
population,aswellasfornon-Hispanicwhite,non-Hispanicblack,andHispanic-American
subgroups.TheweightedproportionsofEuropean,West-AfricanandNative-AmericanBGA
intheoverallpopulationwere64.6%,28.1%,and7.3%,respectively.Self-identifiedwhiteshad
ahigherproportionofEuropeanallelicmarkers(86.4%)thanWest-African(8.5%)orNative-
American(5.4%)ancestralmarkers.Amongself-identifiedblacks,78.1%ofallelicmarkers
wereWest-African,while16.6%and5.3%wereEuropeanandNative-American,respectively.
AmongHispanic-Americans,48.7%,29.6%,and21.7%oftheAIMswereEuropean,West-Afri-
can,andNative-American,respectively.
AtBACHI,76.6%and69.2%ofblacksandHispanicswereoverweightorobese,respective-
ly,comparedto61.5%ofwhites.AgreaterproportionofHispanicscomparedtowhiteorblack
participantsmovedfromnormaltooverweight/obeseBMIcategoriesbetweenBACHIand
III,with80.7%,80.6%,and67.1%ofHispanic,black,andwhiteparticipantsoverweightor
obeseatBACHIII.Intheunweightedcross-sectionaldata,weobservedahighermedianBMI
(29kg/m2vs.27kg/m2),PBF(36–38%vs.33%),andslightlyhigherWHR(0.90vs.0.89)
amongAfrican-AmericansandHispanic-Americanscomparedtonon-Hispanicwhites.His-
panicparticipantshadlargerincreasesinalllongitudinalmeasuresofadiposity(medianper-
centagechangeinBMI,WHRandPBF:3.5%,4.5%and2%)thandidAfrican-Americanor
whiteparticipants,whohadapproximatelyequalmedianpercentincreasesinBMI(0.8–0.9%)
andWHR(3.6–3.7%),andnoincreaseinPBF.
Cross-sectionalmeasuresofadiposity
Table2presentstheresultsofthecross-sectionalanalyses.Weobserved1.10%higherBMIfor
each10%greaterproportionofWest-Africanancestryintheunadjustedanalyses(p<0.0005).
Thestrengthandsignificanceofthisassociationremainedafteradjustingforageandgender.
However,additionallyadjustingforsocio-economicandbehavioralvariablesreducedthedif-
ferenceto0.62%(p=0.04),whichfailedtoreachthelevelofstatisticalsignificancerequiredto
accountformultiplecomparisons(p<0.008).Thisreductionwasmostlyduetoadjustmentfor
educationallevelandincome.WeobservednoassociationbetweenBMIandNative-American
ancestry.Inthemultivariableanalysesforcross-sectionalBMI,approximately8%ofthetotal
variancewasattributabletoWest-Africanancestryandapproximately8%toNative-American
ancestry.
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Table1. Characteristicsoftheoverallpopulationandbyself-identifiedrace/ethnicity.
Overallpopulation White Black Hispanic
N=1,726a N=654a N=531a N=541a
PercentageBGA
Meanb(95%CI)
West-African 28.10 8.51(7.44–9.58) 78.08 29.64
(25.11–31.10) (75.29–80.88) (25.41–33.88)
Native-American 7.30(6.55–8.04) 5.14(4.45–5.83) 5.27(4.29–6.25) 21.69
(17.68–25.70)
European 64.60 86.35 16.64 48.66
(61.55–67.64) (85.05–87.65) (14.00–19.29) (43.64–53.69)
Agecategory
N(%)b
34–44yrs. 403(43.69) 136(40.33) 115(45.05) 151(57.50)
45–54yrs. 564(27.09) 165(25.35) 200(30.77) 199(28.66)
55–64yrs. 437(14.76) 185(16.15) 122(14.00) 129(6.91)
65–74yrs. 223(8.67) 106(10.49) 70(6.91) 47(3.07)
75+yrs. 100(5.79) 62(7.68) 24(3.26) 15(1.34)
Gender
N(%)b
Male 643(46.19) 278(47.81) 186(41.94) 179(46.27)
Female 1083(53.81) 376(52.19) 345(58.06) 362(53.73)
Incomecategory
N(%)b
<20,000 561(20.07) 123(14.40) 173(27.24) 265(34.11)
20,000–54,000 542(25.19) 144(17.36) 200(37.93) 198(39.49)
55,000+ 623(54.73) 387(68.24) 158(34.73) 78(26.40)
Occupation
N(%)b
Professional,Managerial,SalesandOfficework 941(68.74) 489(78.49) 288(58.11) 164(41.06)
Service 453(17.48) 83(11.99) 142(24.72) 227(30.66)
Manuallabor 238(10.60) 63(7.41) 87(15.74) 88(16.50)
Neverworked 94(3.18) 19(2.11) 13(1.42) 61(11.78)
Education
N(%)b
LessthanHighschool 239(5.50) 24(1.71) 60(5.98) 156(23.21)
Highschoolorequivalent 490(22.21) 111(14.11) 187(35.92) 191(35.99)
SomecollegeorAssociatesdegree 372(19.16) 103(12.89) 156(34.91) 113(20.11)
Collegeoradvanceddegree 625(53.12) 416(71.29) 128(23.19) 81(20.70)
HealthyEatingScore
N(%)b
(N=1193c)
Low 932(75.77) 395(71.61) 263(83.56) 274(85.48)
RelativelyHigh 261(24.45) 146(28.39) 63(16.44) 52(14.52)
Meancaloricintake(95%CI)(logtransformed 7.37(7.33,7.41) 7.42(7.37,7.46) 7.33(7.24,7.42) 7.19(7.10,7.29)
kilocalories)b
PhysicalActivity
N(%)b
Low 516(25.57) 194(26.03) 168(25.58) 154(23.34)
(Continued)
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Table1. (Continued)
Overallpopulation White Black Hispanic
N=1,726a N=654a N=531a N=541a
Medium 911(54.73) 351(55.30) 268(52.51) 292(56.11)
High 298(19.70) 109(18.67) 95(21.91) 94(20.55)
BaselineBMI(BACHI)
N(%)b
Normal(<25kg/m2) 478(27.85) 241(38.53) 116(23.38) 121(30.79)
Overweight(25-<30kg/m2) 665(39.02) 244(39.73) 173(30.49) 247(40.06)
(cid:1)30kg/m2) 583(33.13) 169(21.74) 242(46.13) 172(29.15)
CurrentBMI(BACHIII)
N(%)b
Normal(<25kg/m2) 403(33.86) 213(32.86) 99(19.39) 91(19.31)
Overweight(25-<30kg/m2) 660(37.53) 248(40.00) 192(33.94) 219(43.80)
Obese((cid:1)30kg/m2) 663(28.61) 194(27.14) 239(46.67) 230(36.89)
BMI(kg/m2)
Median(p25,p75) 28.55(25.25,32.40) 27.09(23.87,30.70) 29.24(26.00,33.89) 29.29(26.37,32.69)
PercentchangeinBMId
Median(p25,p75) 1.68(-3.68,8.21) 0.78(-3.90,7.08) 0.85(-4.78,7.26) 3.47(-1.86,10.97)
AbsolutechangeinBMId
Median(p25,p75) 0.46(-1.00.2.20) 0.22(-1.04,1.83) 0.25(-1.37,2.14) 1.03(-0.54,3.02)
WHR
Median(p25,p75) 0.90(0.83,0.95) 0.89(0.82,0.95) 0.90(0.84,0.95) 0.90(0.84,0.96)
PercentchangeinWHRd
Median(p25,p75) 3.98(-0.46,9.29) 3.73(-0.25,8.68) 3.55(-0.88,8.67) 4.45(0.02,10.54)
AbsolutechangeinWHRd
Median(p25,p75) 0.03(-0.00,0.08) 0.03(-0.00,0.07) 0.03(-0.01,0.07) 0.04(0.00,0.09)
PBF
Median(p25,p75) 35.00(29.00,41.00) 33.00(26.00,39.00) 38.00(30.00,43.00) 36.00(30.00,40.00)
PercentchangeinPBFe
Median(p25,p75) 0.00(-8.33,9.76) 0.00(-9.38,8.82) 0.00(-8.16,10.00) 2.08(-7.32,10.34)
AbsolutechangeinPBFe
Median(p25,p75) 0.00(-3.00,3.00) 0.00(-3.00,3.00) 0.00(-3.00,3.00) 1.00(-3.00,3.00)
aMeansamplesizefor15datasets;totalcountsmaynotalwaysaddupasnumberswerenotthesameforall15datasets(thenumberdeletedwasbased
onimputedvaluesforeachdataset);thepercentagesmaynotaddupto100%duetorounding;
bmeansandpercentagesareweighted;
cdatanotavailablefor533participants;
dbetweenBACHIandIII,
ebetweenBACHIIandIII;CI=confidenceinterval;p25=lowerquartile;p75=upperquartile.
doi:10.1371/journal.pone.0122808.t001
WealsoobservedpositiveassociationsbetweenPBFandWest-Africanancestryintheun-
adjustedandadjustedanalyses(0.52and0.35higherPBF,respectively,per10%greaterpropor-
tionofWest-Africanancestry).Theattenuationoftheeffectestimateinthemultivariable
analysiswasmostlyduetoadjustmentforeducationallevelwhichwasinverselyassociated
withtheoutcome(p<0.004).NoassociationswereobservedbetweenNative-Americanances-
tryandPBFintheunadjustedoradjustedanalyses.
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BiogeographicAncestryandAdiposity
Table2. Associationsbetweengeneticancestry(per10%greaterproportionofBGA)andBMI,WHRandPBF(N=1726).
BMI p-value WHR p-value PBF p-value
Percentdifference(95%CI) β(95%CI) β(95%CI)
Model1a
European 0.00 0.00 0.00
West-African 1.10(0.61,1.58) <0.001** 0.0001(-0.0018,0.0020) 0.89 0.52(0.29,0.75) <0.001**
Native-American 0.84(-0.16,1.85) 0.1 0.0057(0.0012,0.0102) 0.01 0.08(-0.34,0.49) 0.72
Model2b
European 0.00 0.00 0.00
West-African 1.10(0.60,1.58) <0.001** 0.0011(-0.0007,0.0029) 0.23 0.50(0.30,0.70) <0.001**
Native-American 0.82(-0.19,1.84) 0.11 0.0054(0.0016,0.0091) <0.01 0.27(-0.13,0.66) 0.19
Model3c
European 0.00 0.00 0.00
West-African 0.62(0.04,1.20)d 0.04 -0.0004(-0.0026,0.0018) 0.73 0.35(0.11,0.58)e <0.01
Native-American -0.63(-2.01,0.77) 0.38 0.0006(-0.0046,0.0057) 0.83 -0.15(-0.72,0.42) 0.61
aUnivariateanalysis;
bAdjustedforageandgenderonly;
cAdjustedforage,gender,income,education,healthyeatingscore,physicalactivity,caloricintake,andoccupation;CI=confidenceinterval;β=effect
estimateforlogBMI,orWHRorPBF;
d44%decreaseineffectestimatewasmostlyduetoadjustmentforeducationallevelandincome;
e30%decreaseineffectestimatewasmostlyduetoadjustmentforeducationallevel;
*significantatp<0.005;
**significantatp<0.0005.
doi:10.1371/journal.pone.0122808.t002
WHRwasnotassociatedwithWest-Africanancestryintheunivariableormultivariable
analyses.WeobservedstatisticallysignificantpositiveassociationsbetweenNative-American
ancestryandWHRintheunadjustedandageandgender-adjustedanalyses.However,theseas-
sociationsnolongerremainedafteradjustmentforsocio-economicandbehavioralvariables.
Amongsocio-economicandbehavioralvariablesassociatedwithhighercross-sectional
measuresofadiposity,weobservedsignificantinverseassociationswithhighereducationalsta-
tusforBMI(p<0.01)(Table3)andPBF(p<0.004).Ofnote,West-AfricanandNative-Ameri-
canBGAwereinverselyassociatedwithahighereducationallevel(ORforcollegeorhigher
educationvs.lessforWest-Africanancestry:0.75,95%CI:0.71,0.80,p<0.0001;forNative-
Americanancestry:0.72,95%CI:0.63,0.82,p<0.0001)andhigherincome(ORforincome(cid:1)
$50,000vs.lessforWest-Africanancestry:0.84,95%CI:0.79,0.88,p<0.0001;forNative-
Americanancestry:0.75,95%CI:0.65,0.86,p<0.0001)inthisstudypopulation.
Longitudinalmeasuresofadiposity
Table4providesassociationsbetweenBGAandlongitudinalmeasuresofadiposity.Weob-
servednoassociationbetweengeneticancestryandpercentchangeinBMIorWHRbetween
surveysperformedatBACHIandBACHIII,orpercentchangeinPBFbetweenBACHIIand
BACHIII.Neitherincomenoreducationallevelwasassociatedwiththelongitudinalmeasures
ofadiposityinthemultivariableanalyses(S1Table).However,forlongitudinalBMI,we
PLOSONE|DOI:10.1371/journal.pone.0122808 April13,2015 8/15
BiogeographicAncestryandAdiposity
Table3. Multivariableresultsforcross-sectionalBMI(N=1726).
PercentdifferenceinBMI*(95%CI) p-value
BGA
West-African 0.62(0.04,1.20) 0.04
Native-American -0.63(-2.01,0.77) 0.38
European 0.00
Agecategory
34–44yrs. 1.97(-4.11,8.44) 0.53
45–54yrs. 3.83(-2.10,10.13) 0.21
55–64yrs. 4.83(-0.88,10.86) 0.10
65–74yrs. 2.02(-3.78,8.16) 0.50
75+yrs. 0.00
Gender
Male 1.01(-1.98,4.08) 0.56
Female 0.00
Incomecategory
<20,000 4.32(-0.69,9.58) 0.09
20,000–54,000 2.09(-2.06,6.43) 0.33
55,000+ 0.00
Occupation
Professional,Managerial,SalesandOfficework -4.24(-10.38,2.34) 0.20
Service -3.54(-10.24,3.67) 0.33
Manuallabor -4.89(-11.74,2.50) 0.19
Neverworked 0.00
Education
Lessthanhighschool 4.54(-2.59,12.19) 0.22
Highschoolorequivalent 6.80(1.43,12.47) 0.01
SomecollegeorAssociatesdegree 7.04(2.41,11.91) 0.00
Collegeoradvanceddegree 0.00
HealthyEatingScore
Low 0.06(-3.45,3.69) 0.98
RelativelyHigh 0.00
PhysicalActivity
Low 2.26(-2.42,7.14) 0.35
Medium 1.53(-2.25,5.46) 0.43
High 0.00
*Modelsadjustedforage,gender,income,education,healthyeatingscore,physicalactivity,caloricintake,
occupation,andancestry.
doi:10.1371/journal.pone.0122808.t003
observedaprotectiveeffectamongemployedindividualsascomparedtothosewhonever
worked,regardlessofthetypeofoccupation.
Thestrengthsofpositiveassociationsweresomewhatdiminishedwhenanalyseswerere-
peatedintheentirecohortwithoutapplyingexclusioncriteria.However,thedirectionalityand
significanceoftheresultsremainedunchanged.Similarly,whenanalyseswererepeatedinclud-
ingparticipantswithtypeIIdiabetes,theeffectestimateswereslightlydiminishedbuttheover-
allresultswereessentiallyunchanged.
PLOSONE|DOI:10.1371/journal.pone.0122808 April13,2015 9/15
BiogeographicAncestryandAdiposity
Table4. Associationsbetweengeneticancestry(per10%greaterproportionofBGA)andpercentchangeinadiposity(N=1726).
PercentchangeinBMId p-value PercentchangeinWHRd p-value PercentchangeinPBFe p-value
β(95%CI) β(95%CI) β(95%CI)
Model1a
European 0.00 0.00 0.00
West-African 0.07(-0.19,0.33) 0.61 -0.18(-0.36,0.01) 0.06 0.48(-0.27,1.23) 0.21
Native-American 0.49(-0.26,1.24) 0.2 0.01(-0.42,0.44) 0.97 0.30(-1.47,2.08) 0.74
Model2b
European 0.00 0.00 0.00
West-African -0.00(-0.26,0.26) 0.98 -0.19(-0.37,-0.01) 0.04 0.48(-0.28,1.23) 0.21
Native-American 0.36(-0.37,1.09) 0.33 0.04(-0.37,0.45) 0.85 0.13(-1.65,1.91) 0.88
Model3c
European 0.00 0.00 0.00
West-African -0.06(-0.37,0.25) 0.72 -0.13(-0.34,0.08) 0.22 0.43(-0.51,1.37) 0.37
Native-American -0.13(-0.99,0.73) 0.77 0.04(-0.52,0.61) 0.88 -0.07(-2.57,2.42) 0.95
aUnivariateanalysis;
bAdjustedforageandgenderonly;
cAdjustedforage,gender,income,education,healthyeatingscore,physicalactivity,caloricintake,andoccupation;CI=confidenceinterval;
dbetweenBACHIandIII;
ebetweenBACHIIandIII.
doi:10.1371/journal.pone.0122808.t004
Effect-modificationbygenderandnon-geneticfactors
Table5providesresultsforinteractionsbetweengeneticancestryandgenderanddiet.Weob-
servedsignificanteffect-modificationbygenderfortheassociationbetweenWest-Africanan-
cestryandBMI(p-interaction=0.0019).Similar,albeitlesssignificant,interactionswere
observedwhenPBF(p-interaction=0.04)orWHR(p-interaction=0.02)wereusedasthe
measureofadiposity.Intheanalysesstratifiedbygender(Table4andFig1),weobserveda
positiveassociationbetweenWest-AfricangeneticancestryandBMIamongwomenwhich
wassignificantatthep<0.05level,butnotamongmen.Similarly,thepositiveassociationob-
servedbetweenWest-AfricanancestryandPBFamongwomenwassubstantiallyattenuated
andnolongersignificantinmen.West-AfricanancestrywasnotassociatedwithWHRinthe
stratifiedanalyses.
GiventhepositiveassociationsweobservedbetweenWest-AfricanBGAandsocio-econom-
icvariables,wefurtherexaminedassociationsbetweenWest-AfricanancestryandBMIwithin
categoriesofgenderandeducationallevel(collegeorhighervs.lesseducation)andgenderand
income(annualincomeof(cid:1)$50,000vs.less).Weobservedapositiveassociationbetween
West-AfricangeneticancestryandBMIamongwomenwithoutacollegedegree(BMIpercent
change=1.29%,95%CI:0.20%,2.40%),whilenosuchassociationwasobservedamong
womenofahighereducationallevel(BMIpercentchange=0.75%,95%CI:-0.42%,1.93%)or
amongmenofanyeducationallevel(BMIpercentchangeformenwithahighereduca-
tion=0.28%,95%CI:-0.99%,1.56%;BMIpercentchangeformenwithlesseducation=
-0.36%,95%CI:-1.35%,0.63%).Similarpatternswereobservedforassociationsbetween
West-AfricanancestryandPBFforanalysesstratifiedbygenderandeducationallevel,andfor
West-AfricanancestryandBMIforanalysesstratifiedbygenderandincomelevel.
PLOSONE|DOI:10.1371/journal.pone.0122808 April13,2015 10/15