Biogeographic Ancestry Is Associated with Higher Total Body Adiposity among African-American Females: The Boston Area Community Health Survey
Biogeographic Ancestry Is Associated with Higher Total Body Adiposity among African- American Females: The Boston Area Community Health Survey
Sunali D. Goonesekera 0 1 2 3
Shona C. Fang 0 1 2 3
Rebecca S. Piccolo 0 1 2 3
Jose C. Florez 0 1 2 3
John B. McKinlay 0 1 2 3
0 Funding: This study was funded by awards DK056842 and DK080786 from the National Institute of Diabetes and Digestive and Kidney Diseases. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript
1 Academic Editor: Farook Thameem, The University of Texas Health Science Center (UTHSCSA), UNITED STATES
2 1 Department of Epidemiology and Biostatistics, New England Research Institutes , 480 Pleasant St., Watertown, MA 02472 , United States of America, 2 Department of Environmental Health, Harvard School of Public Health , Boston, MA 02115 , United States of America, 3 Diabetes Unit/ Center for Human Genetic Research, Massachusetts General Hospital , Boston, MA 02114 , United States of America
3 Competing Interests: All authors have declared that no competing interests exist
The prevalence of obesity is disproportionately higher among African-Americans and Hispanics as compared to whites. We investigated the role of biogeographic ancestry (BGA) on adiposity and changes in adiposity in the Boston Area Community Health Survey. We evaluated associations between BGA, assessed via Ancestry Informative Markers, and adiposity (body mass index (BMI), percent body fat (PBF), and waist-to-hip ratio (WHR)) and changes in adiposity over 7 years for BMI and WHR and 2.5 years for PBF, per 10% greater proportion of BGA using multivariable linear regression. We also examined effectmodification by demographic and socio-behavioral variables. We observed positive associations between West-African ancestry and cross-sectional BMI (percent difference=0.62%; 95% CI: 0.04%, 1.20%), and PBF (=0.35; 95% CI: 0.11, 0.58). We also observed significant effect-modification of the association between West-African ancestry and BMI by gender (p-interaction: <0.002) with a substantially greater association in women. We observed no main associations between Native-American ancestry and adiposity but observed significant effect-modification of the association with BMI by diet (p-interaction: <0.003) with inverse associations among participants with higher Healthy Eating Scores. No associations were observed between BGA and changes in adiposity over time.
Findings support that West-African ancestry may contribute to high prevalence of total body
adiposity among African-Americans, particularly African-American women.
Obesity is a major public health concern which significantly increases ones risk for adverse
health including type II diabetes, cardiovascular disease, musculoskeletal disorders  and
depression . The prevalence of obesity is substantially higher among African-Americans and
Hispanics as compared to non-Hispanic whites in the United States . Moreover, a study
performed on the National Health and Nutrition Examination Survey (NHANES) data from the
last two decades has found a widening of racial disparities in regard to body mass index (BMI)
and waist circumference (WC) between non-Hispanic whites and African-Americans, and has
estimated the prevalence of central obesity and obesity among black women in 2020 to reach
90.9 and 70.7%, respectively . The racial/ethnic differences in obesity and related diseases
persist in specific populations even after adjusting for socio-economic variables 
indicating that genetics may independently contribute to racial/ethnic disparities in obesity.
The genetic basis for racial/ethnic disparities in adiposity and metabolic disease could be
explained by the thrifty gene hypothesis which was first proposed in 1960s [9, 10]. According o
this theory, genes that that promote the storage of energy as body fat, that may have been
advantageous during Paleolithic times, may lead to obesity and metabolic disease in modern
western societies where there is little need for fat storage. It has been hypothesized that thrifty
genes may be more prevalent among racial/ethnic subgroups outside of those of European
descent as many of these individuals have ancestral origins in regions where droughts and famine
commonly occur. Even though this theory has received some criticism , multiple studies
have since evaluated associations between genetic ancestry and adiposity-associated diseases in
Ancestral Informative Markers (AIMs), i.e., genetic markers unique to people of a
homogenous BGA, are often used as correlates of self-identified race in studies performed in admixed
populations [57, 1219]. Studies that examined associations between African or European
BGA using AIMs and adiposity or adiposity-related diseases have reported inconsistent
findings with some reporting positive associations with African BGA and adiposity [5, 6, 18, 19] or
inverse associations with European ancestry , while others reporting inverse associations
with African ancestry or no association [13, 14]. For example, a study conducted among
African-American and Hispanic-American postmenopausal participants in the Womens Health
Initiative (WHI) found significant positive associations between African ancestry and BMI in
the overall population, as well as in the admixed African-American and Hispanic-American
subgroups . However, when waist-to-hip ratio (WHR) was used as the measure of adiposity,
a significant positive association with African ancestry was attenuated in the overall
population, and not observed among African-Americans or Hispanic-Americans. Interestingly,
Amerindian ancestry was positively associated with WHR but not with BMI in the overall
population and among the Hispanic-American participants in this cohort . Associations
between African ancestry and type II diabetes, a disease closely tied to obesity, were also observed
among African-American participants in this study . However, a study conducted among
elderly Puerto Ricans in the U.S. found negative associations between African genetic ancestry
and type II diabetes and cardiovascular disease . Another study found significant positive
correlation between obesity/BMI and European ancestry rather than African ancestry .
Given these contradictory findings, we sought to further evaluate the relationship between
BGA and adiposity using different measures of adiposity, i.e., BMI, percent body fat (PBF), and
WHR, including changes in these measures over time, in the Boston Area Community Health
(BACH) Survey. A previous study performed on the BACH I cohort (20002002) found a
positive association between African-American race and obesity as well as significant
effect-modification of this association by gender . Building on this research, we sought to 1) to evaluate
the relationship between BGA and adiposity (as measured by BMI, WHR, and PBF) and
change in adiposity over time, and 2) to evaluate whether these associations are modified by
gender, age, diet, physical activity, income, and educational level. As previous research
indicates that body fat distribution may not be uniform across ethnic groups and BMI may not
provide accurate measures of adiposity for all individuals [15, 2123], we evaluated
associations between genetic ancestry and different measures of adiposity i.e., BMI, WHR, and PBF.
The Boston Area Community Health (BACH) Survey is a population-based prospective cohort
study that has recruited approximately equal proportions of non-Hispanic white,
non-Hispanic black, and Hispanic-American participants from Boston, MA, using multistage stratified
cluster sampling. As detailed elsewhere [24, 25], this survey was conducted in three waves that
have spanned over a period of ten years (20022012). During the first wave of the study
(BACH I) (20022005), investigators recruited 5,502 men and women aged 3079 years. The
second (BACH II) and third waves (BACH III) of the study were conducted in 200810 and
201012, respectively. Of the initial 5,502 participants, 3,155 participated in BACH III.
During all three waves, study participants were interviewed in their homes in English or
Spanish, and anthropomorphic measurements including height, weight, waist circumference,
and hip circumference were taken by trained interviewers. PBF was also assessed during BACH
II and BACH III. In addition, data on multiple socio-economic and behavioral factors,
co-morbidities, and medication use were collected. During BACH III, investigators also obtained
blood samples from participants to determine the proportion of allelic markers. Of the 3,155
participants who participated in the third follow-up survey (BACH III), we excluded
participants who reported weight loss in the absence of decreased food intake or increased exercise
(n = 409), who were pregnant (n = 1), had diagnosed or undiagnosed diabetes (n = 980), ever
had congestive heart failure (n = 142) at the BACH III survey, or reported ever having had a
diagnosis of AIDS (n = 19), cancer (n = 283), chronic lung disease (n = 235), or Alzheimers
disease (n = 12) at the BACH I survey, as these conditions could result in weight change. Even
though we were unable to exclude individuals with end-stage renal disease due to lack of
information, few, if any, such individuals would have been sufficiently healthy to have been
recruited to the BACH study. We were left with 1,726 participants for the analyses. Of these, 654
were self-identified non-Hispanic white participants, while 531 and 541 were self-identified
black and Hispanic-Americans, respectively. All participants provided written informed
consent. This study was approved by the New England Research Institutes Institutional Review
Measurement of AIMs and BGA
A total of 63 allelic markers, i.e., single nucleotide polymorphisms (SNPs) selected on their
ability to estimate percent West-African and Native-American BGA, were genotyped by the
Genetic Analysis Platform at the Broad Institute in Cambridge, MA using iPLEX (Sequenom).
Ancestry percentages were estimated for each participant using ADMIXTURE software (v.
At BACH I and III, BMI was calculated by dividing weight (kg) by height squared (m2). WHR
was calculated by dividing waist circumference (cm) by hip circumference (cm). At BACH II
and III PBF was measured via bioelectrical impedance using the Tanita scale. We calculated
the percent changes in BMI and WHR between waves I and III of the BACH Survey over a
mean duration of approximately seven years, and percent changes in PBF between waves II
and III over a mean duration of approximately 2.5 years.
Measurement of covariates
Data on socio-economic and behavioral factors for this analysis were obtained from the BACH
III assessment. For socio-economic variables, we grouped participants into categories of
annual household income (<$20 000, $20 000 - $49 999, and $50 000), occupation
(office/professional/management, service professions, manual labor, and never worked), and education
(college or higher, some college or Associates degree, high school completed, and less than
high school) using cut-offs based on prior studies .
In order to assess each participants nutritional intake, we administered the Block Food
Frequency Questionnaire (FFQ) in English or Spanish, and rated each participants dietary
intake on a scale from 0 to 7. The FFQ Score was determined by summing up points assigned to
participants based on the average daily intake of sodium (1 point if <1 500g; 0 otherwise), fiber
(1 point if between 25-30g; 0 otherwise), saturated fat (1 point if <14g; 0 otherwise), and the
average number of daily servings of vegetables (1 point if between 34; 0 otherwise), fruits (1
point if between 23; 0 otherwise), meat (1 point if between 23; 0 otherwise), and grain (1
point if between 611; 0 otherwise). The cutoff values were based on United States Department
of Agriculture  and American Heart Association guidelines for healthy eating . As
most participants food frequency scores were low, we dichotomized FFQ scores at a score of 2,
with scores above 2 indicating healthier eating.
We measured physical activity by using the Physical Activity Scale for the Elderly (PHASE)
. This 12 item questionnaire assessed each participants time spent engaging in leisure,
household, and occupational activities, and walking and sports during the prior week. The
hours spent in each activity was weighted by the estimated amount of energy spent. We
summed these values over all activities to obtain a PHASE score and categorized this score as
low (<100), medium (100249), or high ( 250).
In order to minimize potential biases and reduction in precision due to missing data, we
performed multiple imputations using Multivariate Imputation by Chained Equations (MICE)
 in R (R Foundation for Statistical Computing, Vienna, Austria) to create 15 datasets for
each combination of race and gender.
The analyses were performed using SUDAAN 11 (Research Triangle Park, NC). The data
were weighted by the inverse of the probability of being sampled at baseline (BACH I) to
account for oversampling of minority groups. We observed greater attrition among men and
participants in the lower socio-economic strata. Thus, the analyses were also adjusted for
nonresponse and loss-to-follow-up using the propensity cell adjustment approach , and
poststratified to the Boston census population in 2010.
We assessed all outcomes per 10% rather than 1% greater proportion of genetic ancestry to
avoid reporting small effect-estimates. We log-transformed cross-sectional BMI and presented
the effects as a percent difference per 10% greater proportion of BGA, as the distribution of its
residuals deviated from normality. All other outcome residuals were normally distributed. We
performed univariable and multivariable linear regressions to assess outcomes associated with
West-African and Native-American ancestry. We also evaluated for effect-modification by age,
gender, diet, physical activity, income, and education by including interaction terms between
these variables and the exposures in separate models.
Statistical significance for all analyses was initially set at p <0.05 using a two-tailed test. In
order to account for multiple comparison complexities resulting from examining six different
adiposity outcomes in the overall population and different sub-populations, we also assessed
our results applying more stringent criteria of p<0.008 for statistical significance (i.e., alpha =
Table 1 provides demographic, socio-economic, behavioral, and outcome data for the overall
population, as well as for non-Hispanic white, non-Hispanic black, and Hispanic-American
subgroups. The weighted proportions of European, West-African and Native-American BGA
in the overall population were 64.6%, 28.1%, and 7.3%, respectively. Self-identified whites had
a higher proportion of European allelic markers (86.4%) than West-African (8.5%) or
NativeAmerican (5.4%) ancestral markers. Among self-identified blacks, 78.1% of allelic markers
were West-African, while 16.6% and 5.3% were European and Native-American, respectively.
Among Hispanic-Americans, 48.7%, 29.6%, and 21.7% of the AIMs were European,
West-African, and Native-American, respectively.
At BACH I, 76.6% and 69.2% of blacks and Hispanics were overweight or obese,
respectively, compared to 61.5% of whites. A greater proportion of Hispanics compared to white or black
participants moved from normal to overweight/obese BMI categories between BACH I and
III, with 80.7%, 80.6%, and 67.1% of Hispanic, black, and white participants overweight or
obese at BACH III. In the unweighted cross-sectional data, we observed a higher median BMI
(29 kg/m2 vs. 27 kg/m2), PBF (3638% vs. 33%), and slightly higher WHR (0.90 vs. 0.89)
among African-Americans and Hispanic-Americans compared to non-Hispanic whites.
Hispanic participants had larger increases in all longitudinal measures of adiposity (median
percentage change in BMI, WHR and PBF: 3.5%, 4.5% and 2%) than did African-American or
white participants, who had approximately equal median percent increases in BMI (0.80.9%)
and WHR (3.63.7%), and no increase in PBF.
Cross-sectional measures of adiposity
Table 2 presents the results of the cross-sectional analyses. We observed 1.10% higher BMI for
each 10% greater proportion of West-African ancestry in the unadjusted analyses (p<0.0005).
The strength and significance of this association remained after adjusting for age and gender.
However, additionally adjusting for socio-economic and behavioral variables reduced the
difference to 0.62% (p = 0.04), which failed to reach the level of statistical significance required to
account for multiple comparisons (p<0.008). This reduction was mostly due to adjustment for
educational level and income. We observed no association between BMI and Native-American
ancestry. In the multivariable analyses for cross-sectional BMI, approximately 8% of the total
variance was attributable to West-African ancestry and approximately 8% to Native-American
N = 1,726a
N = 654 a
8.51 (7.44 9.58)
5.14 (4.45 5.83)
N = 531 a
N = 541 a
N = 1,726a
N = 654 a
N = 531 a
N = 541 a
28.55 (25.25, 32.40)
27.09 (23.87, 30.70)
29.24 (26.00, 33.89)
29.29 (26.37, 32.69)
3.47 (-1.86, 10.97)
35.00 (29.00, 41.00)
33.00 (26.00, 39.00)
38.00 (30.00, 43.00)
36.00 (30.00, 40.00)
0.00 (-8.16, 10.00)
2.08 (-7.32, 10.34)
aMean sample size for 15 datasets; total counts may not always add up as numbers were not the same for all 15 data sets (the number deleted was based
on imputed values for each data set); the percentages may not add up to 100% due to rounding;
bmeans and percentages are weighted;
cdata not available for 533 participants;
dbetween BACH I and III,
ebetween BACH II and III; CI = confidence interval; p25 = lower quartile; p75 = upper quartile.
We also observed positive associations between PBF and West-African ancestry in the
unadjusted and adjusted analyses (0.52 and 0.35 higher PBF, respectively, per 10% greater
proportion of West-African ancestry). The attenuation of the effect estimate in the multivariable
analysis was mostly due to adjustment for educational level which was inversely associated
with the outcome (p<0.004). No associations were observed between Native-American
ancestry and PBF in the unadjusted or adjusted analyses.
Percent difference (95% CI)
bAdjusted for age and gender only;
cAdjusted for age, gender, income, education, healthy eating score, physical activity, caloric intake, and occupation; CI = confidence interval; = effect
estimate for log BMI, or WHR or PBF;
d44% decrease in effect estimate was mostly due to adjustment for educational level and income;
e30% decrease in effect estimate was mostly due to adjustment for educational level;
*significant at p<0.005;
**significant at p<0.0005.
WHR was not associated with West-African ancestry in the univariable or multivariable
analyses. We observed statistically significant positive associations between Native-American
ancestry and WHR in the unadjusted and age and gender-adjusted analyses. However, these
associations no longer remained after adjustment for socio-economic and behavioral variables.
Among socio-economic and behavioral variables associated with higher cross-sectional
measures of adiposity, we observed significant inverse associations with higher educational
status for BMI (p<0.01) (Table 3) and PBF (p<0.004). Of note, West-African and
Native-American BGA were inversely associated with a higher educational level (OR for college or higher
education vs. less for West-African ancestry: 0.75, 95% CI: 0.71, 0.80, p <0.0001; for
NativeAmerican ancestry: 0.72, 95% CI: 0.63, 0.82, p <0.0001) and higher income (OR for income
$50,000 vs. less for West-African ancestry: 0.84, 95% CI: 0.79, 0.88, p<0.0001; for
NativeAmerican ancestry: 0.75, 95% CI: 0.65, 0.86, p<0.0001) in this study population.
Longitudinal measures of adiposity
Table 4 provides associations between BGA and longitudinal measures of adiposity. We
observed no association between genetic ancestry and percent change in BMI or WHR between
surveys performed at BACH I and BACH III, or percent change in PBF between BACH II and
BACH III. Neither income nor educational level was associated with the longitudinal measures
of adiposity in the multivariable analyses (S1 Table). However, for longitudinal BMI, we
Percent difference in BMI* (95% CI)
*Models adjusted for age, gender, income, education, healthy eating score, physical activity, caloric intake,
occupation, and ancestry.
observed a protective effect among employed individuals as compared to those who never
worked, regardless of the type of occupation.
The strengths of positive associations were somewhat diminished when analyses were
repeated in the entire cohort without applying exclusion criteria. However, the directionality and
significance of the results remained unchanged. Similarly, when analyses were repeated
including participants with type II diabetes, the effect estimates were slightly diminished but the
overall results were essentially unchanged.
Percent change in BMId
Percent change in WHRd
Percent change in PBFe
bAdjusted for age and gender only;
cAdjusted for age, gender, income, education, healthy eating score, physical activity, caloric intake, and occupation; CI = confidence interval;
dbetween BACH I and III;
ebetween BACH II and III.
Effect-modification by gender and non-genetic factors
Table 5 provides results for interactions between genetic ancestry and gender and diet. We
observed significant effect-modification by gender for the association between West-African
ancestry and BMI (p-interaction = 0.0019). Similar, albeit less significant, interactions were
observed when PBF (p-interaction = 0.04) or WHR (p-interaction = 0.02) were used as the
measure of adiposity. In the analyses stratified by gender (Table 4 and Fig 1), we observed a
positive association between West-African genetic ancestry and BMI among women which
was significant at the p<0.05 level, but not among men. Similarly, the positive association
observed between West-African ancestry and PBF among women was substantially attenuated
and no longer significant in men. West-African ancestry was not associated with WHR in the
Given the positive associations we observed between West-African BGA and
socio-economic variables, we further examined associations between West-African ancestry and BMI within
categories of gender and educational level (college or higher vs. less education) and gender and
income (annual income of $50,000 vs. less). We observed a positive association between
West-African genetic ancestry and BMI among women without a college degree (BMI percent
change = 1.29%, 95% CI: 0.20%, 2.40%), while no such association was observed among
women of a higher educational level (BMI percent change = 0.75%, 95% CI: -0.42%, 1.93%) or
among men of any educational level (BMI percent change for men with a higher
education = 0.28%, 95% CI: -0.99%, 1.56%; BMI percent change for men with less education =
-0.36%, 95% CI: -1.35%, 0.63%). Similar patterns were observed for associations between
West-African ancestry and PBF for analyses stratified by gender and educational level, and for
West-African ancestry and BMI for analyses stratified by gender and income level.
BMI % change (95% CI)
Low HE Scoreb
We also observed significant effect-modification by diet for the association between
NativeAmerican ancestry and BMI (p-interaction = 0.0023). However, interactions between
NativeAmerican ancestry and diet were significant only at the p<0.05 level when PBF
(p-interaction = 0.04) or WHR (p-interaction = 0.01) were used as the measures of adiposity (Table 5).
Among participants with higher Healthy Eating Scores, Native-American ancestry was
associated with lower BMI at the p<0.05 level of significance. The association remained negative,
albeit not statistically significant, when WHR and PBF were used as the measures of adiposity.
No negative associations were observed among participants with low Healthy Eating Scores.
We observed no effect-modification between West-African or Native-American genetic
ancestry and age, educational level, income, or physical activity for cross-sectional measures of
adiposity and no effect modification by any demographic or socio-behavioral variable for
longitudinal measures of adiposity.
In this study, we evaluated associations between genetic ancestry and cross-sectional and
longitudinal measures of adiposity. We observed positive associations between West-African
ancestry and cross-sectional BMI as well as PBF in the unadjusted analyses and after adjusting for
multiple socio-economic and behavioral variables, but not with WHR. Thus, our findings
suggest that West-African ancestry confers an increased risk for total body adiposity, rather than
central adiposity. Contrary to findings of other studies [16, 32], we did not observe significant
positive associations between Native-American ancestry and BMI in the unadjusted or
multivariable-adjusted analyses. The low prevalence of Native-American ancestral markers in the
overall population (7%) may have precluded us from detecting potential associations.
We also observed evidence suggesting possible mediation of associations between genetic
ancestry and cross-sectional BMI and PBF by education. The positive associations observed
between African ancestry and both BMI and PBF were substantially attenuated after adjusting for
Fig 1. Percent difference in BMI associated with a 10% difference in genetic ancestry by gender. A significant positive association is observed
between women of West-African ancestry and BMI (% difference = 0.01; 95% CI: 0.25, 1.84s; p<0.01) but not among men of West-African ancestry or men or
women of Native-American ancestry. WA = West-African ancestry; NA = Native-American ancestry; CI = confidence interval.
educational level. Even though standard techniques cannot be utilized to accurately evaluate
mediation in the presence of unmeasured confounding , the inverse associations we
observed between West-African and Native-American BGA and higher educational level and the
inverse associations we observed between higher education and BMI and PBF, further
strengthen this possibility.
We did not observe associations between genetic ancestry and percent changes in BMI or
WHR over a mean follow-up duration of approximately seven years (median percent change
of 1.68% and 3.98% for BMI and WHR, respectively), or with PBF over a mean follow-up
duration of approximately 2.5 years (median percent change of 0.00%). However, we are unable to
rule out the possibility of detecting such associations over longer periods of follow-up.
We observed evidence of effect modification by gender for associations between
West-African ancestry and cross-sectional measures of adiposity with greater risk among women.
Furthermore, this effect was restricted to women of a lower socio-economic status. These findings
indicate that West-African ancestry confers higher risk of adiposity among women of
West-African descent as compared to men of the same ancestral background despite a similar
prevalence of adiposity genes across both groups, perhaps due to biological differences between men
and women; access to education and other resources may counteract this effect. Given the
greater difference in the proportions of African-American women as compared to
American men in the lower vs. higher income categories (28% vs. -14% greater percentage for
women and men, respectively) and lower vs. higher educational categories (17% vs. 7% greater
percentage for women and men, respectively), it is also likely that these results reflect the
differential distribution of socio-economic variables strongly associated with adiposity among men
and women of West-African descent.
These data concur with recent patterns of obesity prevalence in the U.S. The NHANES
20112012 Survey report described a substantially greater prevalence of obesity among
African-American women compared to African-American men (56.6% vs. 37.1%) . Our results
may also partially explain contradictory results observed across studies. The association
between African BGA and adiposity may be weakly associated in a population consisting mostly
of males, while it may be apparent in a study restricted to women. Of note, the WHI study
which found strong positive associations between African ancestry and BMI consisted entirely
We also observed evidence of effect-modification of associations between Native-American
ancestry and adiposity by diet despite the relatively low proportion of Native-American
ancestral markers in this population. Individuals with a Native-American ancestry who had
relatively high Healthy Eating Scores, but not low scores, had negative associations with all
crosssectional measures of adiposity, albeit not all results achieved statistical significance. Further
research should investigate the impact of diet on associations between Native-American
ancestry and adiposity. The relatively low threshold used (FFQ Score 2) in our study to define
healthy eating, would suggest that even a small-moderate improvement in diet may be
beneficial in this subpopulation.
Our study is not without limitations. First, the BACH study population consisted of no
selfidentified Native-American participants and thus, had a low prevalence of Native-American
AIMs. This may have precluded us from detecting possible associations between
Native-American ancestry and adiposity as were observed in other study populations [16, 32]. Second,
follow-up durations between BACH I and III (mean duration of approximately seven years), and
between BACH II and III (mean duration of approximately 2.5 years) may not have been
sufficient to observe possible effects of genetic ancestry on longitudinal measures of adiposity.
Finally, due to smaller sample sizes, not all sub-cohort analyses had sufficient power to achieve
statistical significance when using the more stringent criteria accounting for multiple
The strengths of this study include evaluation of multiple measures of adiposity specifically
BMI, WHR, and PBF assessed cross-sectionally and over time. While the clinical meaning and
utility of the different measures of adiposity likely differ by race/ethnicity, consistent patterns
across different measures, as were observed in regard to BMI and PBF among participants of
West-African descent in our study population, strengthen the hypothesis that West-African
ancestry is associated with increased total body adiposity. In addition, to our knowledge, this
was the first study to report on interactions between BGA and non-genetic factors that
contribute to adiposity and which could help explain the high prevalence of adiposity in distinct
We observed elevated risk of overall adiposity among participants with West-African ancestry
which was stronger among female participants. Our results also suggested greater protective
effects of healthy eating on adiposity for participants of Native-American descent. However,
these analyses should be repeated in cohorts with a greater proportion of participants of
Native-American BGA in order to confirm our findings. These results also call for the evaluation
of longitudinal outcomes of adiposity in cohorts with longer follow-up.
S1 Table. Multivariable analysis results for longitudinal BMI. This table presents the
associations between variables included in the multivariable model and BMI.
Conceived and designed the experiments: JBM SDG SCF RSP. Analyzed the data: SDG. Wrote
the paper: SDG SCF RSP JCF JBM. Provided genotyping data, analysis of BGA based on AIMs,
and expertise in interpreting the BGA data: JCF.
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