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J Hum Hypertens. Author manuscript; available in PMC Feb 1, 2012.
Published in final edited form as:
PMCID: PMC3172395
NIHMSID: NIHMS295844
Genetic admixture, social-behavioral factors, and body composition are associated with blood pressure differently by racial-ethnic group among children.
Yann C. Klimentidis, PhD,a Akilah Dulin-Keita, PhD,b Krista Casazza, PhD,b Amanda L. Willig, PhD,b David B. Allison, PhD,a and Jose R. Fernandez, PhDab
aSection on Statistical Genetics, Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL, USA
bDepartment of Nutrition Sciences, University of Alabama at Birmingham, Birmingham, AL, USA
Corresponding Author: Yann C. Klimentidis, Section on Statistical Genetics, Department of Biostatistics, RPHB 327, University of Alabama at Birmingham, Birmingham, AL 35294, USA; Phone: (205) 975-9273; Fax: (205) 975-2540; yann/at/uab.edu
Cardiovascular disease has a progressively earlier age of onset, and disproportionately affects African Americans in the US. It has been difficult to establish the extent to which group differences are due to physiological, genetic, social, or behavioral factors. In this study, we examined the association between blood pressure and these factors among a sample of 294 children, identified as African-, European-, or Hispanic-American. We use body composition, behavioral (diet and physical activity), and survey-based measures (socio-economic status and perceived racial discrimination), as well as genetic admixture based on 142 ancestry informative markers (AIM) to examine associations with systolic and diastolic blood pressure. We find that associations differ by ethnic/racial group. Notably, among African Americans, physical activity and perceived racial discrimination, but not African genetic admixture, are associated with blood pressure, while the association between blood pressure and body fat is nearly absent. We find an association between blood pressure and an AIM near a marker identified by a recent genome-wide association study. Our findings shed light on the differences in risk factors for elevated blood pressure among ethnic/racial groups, and the importance of including social and behavioral measures to grasp the full genetic/environmental etiology of disparities in blood pressure.
Keywords: blood pressure, racial/ethnic disparities, children, genetic admixture, social and behavioral risk factors
Hypertension is one of the leading causes of morbidity in the United States. The rate of hypertension is increasing among children [1], is known to differ among racial/ethnic groups among adults [2] and children [1], and has been found to be higher in the southern states of the USA [3]. In addition, the risk factors for hypertension appear to be different by racial/ethnic group [4]. The etiology of hypertension consists of an elevated blood pressure due to a combination of genetic and environmental risk factors. It has been difficult to tease these factors apart, and to identify the racial/ethnic-specific risk factors.
The heritability of high blood pressure has been estimated to be approximately 50% [5,6], and some progress is being made at identifying the genetic factors that contribute to high blood pressure [7]. It has been hypothesized that disparate hypertension risk across racial/ethnic groups also has an underlying genetic component. However, several studies have examined the relationship between elevated blood pressure and genetic admixture with equivocal results. While some researchers find a negative relationship between Amerindian admixture and blood pressure [8], and a positive relationship between African admixture and blood pressure [9], others fail to find any relationship between African admixture and blood pressure [8,10,11]. Genetic mapping by admixture linkage disequilibrium has been used to identify population-specific genetic risk factors, with little success in finding common variants that contribute to population differences, prompting the authors of one study to state that “This increases the weight of evidence that non-genetic causes explain most of the differences in rates across populations” [12]. The inconsistency of these results suggests that the cause of higher blood pressure among African-Americans is not due mainly to genetic factors that differ among groups. Instead, it may be factors that are correlated or interact with genetic admixture or behavioral, social, or other environmental factors that are responsible for these disparities [13,14].
Behavioral and social factors play a vital and somewhat well characterized role in the etiology of elevated blood pressure. Physical activity and sodium consumption are two commonly cited risk factors for elevated blood pressure, and both may be associated with blood pressure in children [15-18]. Social stressors such as socioeconomic status, racial discrimination, and perceived skin color bias have been identified as significant factors associated with hypertension outcomes and increased health risks among adults [19-26]. Research exploring the role of social correlates in hypertension outcomes among children have also identified many similar associations [27-30]. One of the few studies that integrates both genetic and social factors is a recent study by Gravlee et al. [31] among an admixed population in Puerto Rico. They find that social classification in terms of a socially ascribed notion of “color” is a better predictor of blood pressure than genetic ancestry. There is therefore a need for more studies that integrate both genetic and environmental factors in order to determine the relative importance of each of these, and to provide a more complete understanding of disparities in hypertension risk [32,33].
In this study, we use social, physiological, and genetic data from a cohort of multi-racial/ethnic children to examine the relative explanatory power of the various potential predictors of systolic (SBP) and diastolic blood pressure (DBP). We also examine the association between each of 142 ancestry informative markers (AIMs) and blood pressure. We hypothesize that a combination of genetic, social, economic, and behavioral factors are associated with blood pressure, and that these relationships differ across racial/ethnic groups, possibly due to underlying pathophysiological differences between groups and/or different distributions of risk factors.
Study Participants
A total of 294 children, age 7 to 12 years (53% male), were recruited as part of a cross-sectional cohort study examining population differences in metabolic phenotypes among healthy children (no major illnesses or medical diagnoses). Race/ethnicity was determined by the parents of the subjects who could classify their children into either of these categories: African American (AA; n=96), Hispanic American (HA; n=78), European American (EA; n=114), or Bi-racial (n=6). The Hispanic children were mostly generation 1.5 immigrants who had recently migrated from Latin American countries before the age of 12 (~ 86%) and were mainly of Mexican origin (~ 80%). All children were pubertal stage ≤3 as assessed by a pediatrician according to the criteria of Marshall and Tanner [34]. Informed assent and consent were obtained from children and parents respectively, as approved by the University of Alabama at Birmingham Institutional Review Board. All measurements were taken between 2004 and 2008 at the University of Alabama at Birmingham General Clinical Research Center (GCRC) and Department of Nutrition Sciences.
Anthropometric Measurements
In the first of two sessions completed by participants, pubertal status, anthropometric measurements, and body composition were assessed. Pubertal status was determined by a physical exam assessing reproductive maturity by assignment of a stage (I to V) for breast/genitalia development and pubic hair. The higher of the two numbers serves to designate pubertal status (Tanner stage). Height was measured without shoes to the nearest centimeter using a stadiometer (Heightronic 235; Quick Medical, Issaquah, WA). Body composition was assessed by dual-energy x-ray absorptiometry (DXA) using a GE Lunar Prodigy densitometer (GE LUNAR Radiation Corp., Madison, WI) as previously described [35]. Participants were measured while lying flat on their backs with arms at their sides, and wore light clothing. Analysis of DXA scans was done using pediatric software (enCore 2002 version 6.10.029).
Blood Pressure Measurements
Blood pressure was measured using an automated cuff (Dinamap Pro 200, GE Medical Systems) during the overnight visit at the GCRC. Trained nurses conducted the measurements using appropriate child-sized cuffs based on the participant’s arm size. Two blood pressure measurements were taken during the first night’s stay and another set was taken immediately upon awakening the following morning. Children were seated with feet flat on the floor, and measurements were taken after a minimum of 10min of seated rest, and a five minute rest period was allowed between measurements. Values were recorded for SBP and DBP, and the average of the four measurements was used for each individual.
Survey and behavioral variables
Socio-economic status (SES) was estimated using the Hollingshead 4-factor index of social class, which combines information on the education and occupational prestige of parents [36]. Scores range from 8 to 66, with higher scores representing higher status. Perceived discrimination was measured by the Williams’ Every-Day-Discrimination Scale [37]. The scale was administered during a face-to-face interview conducted by trained staff. In order to make the measure appropriate for children, we used the everyday discrimination component of the scale to assess discriminatory encounters over a thirty day period, since the first component assesses institutionalized discrimination which is not appropriate for children. The measure was pretested using the behavioral coding method with in-depth qualitative follow-up probes. We have previously addressed the issue of the suitability of this scale for children [38], and it has previously been found to be both reliable and valid for adolescents [39]. Each statement was qualified by, ‘because of your race’. Explaining the term “ethnicity/national origin” (which should be used when discussing Hispanic ethnicity) poses difficulties for children aged 7 to 12. Therefore, the classification was worded as simplistically as possible. To gauge how well children understood the questions, they were also prompted with the phrase, “can you tell me what happened?” to give more detailed examples of their experiences. They were not required to go into more detail if they were not comfortable recalling events (n=4 Hispanic children). This part of the interview underscored the fact that children were able to accurately describe events, and were not simply relaying incidents experienced by their family members. The scale was pretested among a subset of 30 African, European, and Hispanic American children using the behavioral coding method to determine cognitive understanding of the scale items. Demographics were similar for the pretested and full study sample. The use of this discrimination measure was added shortly after the study began. We were able to re-contact most (65%), but not all of the study participants who had already participated. We have no reason to believe that the missing values for this measure are not missing at random, such that it would bias our results.
Diet was assessed by two 24-hour recalls. Intake was entered by trained dietitians into the Nutrient Data Systems for Research dietary analysis program for evaluation of diet (version 2006 developed by the Nutrition Coordinating Center (NCC), University of Minnesota, Minneapolis, MN). For our objective, only the reported sodium intake variable was used in the analyses.
Physical activity was objectively determined by an accelerometer (Actigraph, Pensacola, FL) worn for approximately one week. The children wore the monitors in accordance with the protocol of Lopez-Alarcon [40]. Data were excluded if the children did not have at least four days of accelerometer data. Daily and total counts per minute were summed and averaged as minutes spent in light, moderate, hard or very hard activity. We used the sum of light, moderate, hard and very hard activity in these analyses. While study coordinators made every attempt to encourage compliance with wearing accelerometers, we were unable to obtain full physical activity data on all participants (missing rate – 27%).
Genetic Admixture Analysis
DNA was obtained from all study participants and was typed at 142 AIMs, a subset of AIMs described elsewhere [41], by Prevention Genetics (Marshfield, WI). These markers were chosen because they exhibit large frequency differences among geographically separated human groups. Individual West African, Amerindian, and European genetic admixture estimates were obtained by maximum likelihood estimation [42],using the genotypes at each AIM and information on the allele frequencies of these AIMs in un-admixed parental populations (see Supplementary Material). Given ancestral allele frequencies at a locus, the probability of observing a marker genotype is computed for each locus. The logs of the individual locus probabilities at all loci are then summed. For every possible admixture proportion from 0 to 100, the probability of the observed genotype is computed. The admixture proportion that corresponds to the maximum combined probability across all loci is the one that is the maximum likelihood estimate of ancestry for that individual. These estimates were subsequently used as covariates in all models to evaluate the genetic basis of population differences, and as a way to control for population stratification.
Statistical Analyses
Differences among racial/ethnic groups in mean values for phenotypes were examined using ANOVA with post-hoc Fisher’s LSD tests. To avoid the potential reduction in statistical power that might arise from having missing values in some of the variables (physical activity and perceived racial discrimination, as shown in Table 1), a multiple imputation procedure was used. For this procedure, we used a Markov chain Monte Carlo (MCMC) method with a single chain, 200 burn-in iterations before the first imputation, and 100 iterations between imputations, as described elsewhere [43]. In the initial model, we included age, Tanner stage, sex, total body fat, height, African admixture, Amerindian admixture, SES, sodium consumption, physical activity, and perceived racial discrimination as covariates. Controlling for two out of three admixture estimates prevents overspecification of the statistical models, since the three admixture estimates add up to 1. We used the “dredge” function in the Multimodel Inference program in R which runs models with all possible combinations of the explanatory variables in the supplied model and ranks all possible models according to Akaike’s Information Criteria (AIC), while correcting for small sample size (AICC). AIC is a particularly useful method in our study because of the large number of potential predictors, and because we are interested in examining whether there is a different etiology (i.e. model) of blood pressure among different racial/ethnic groups. The models with the lowest AICC score were then analyzed using multiple linear regression. To conform to the assumptions of regression, all models were evaluated for residual normality. Given the number of tests performed (i.e. models tested), resulting p-values should be interpreted with caution and only as heuristic guides. Finally, we performed a sensitivity analysis to determine our results differ if we do not impute missing values by analyzing the datasets with all variables included and with perceived racial discrimination removed. We calculated AICc on the dataset with no imputed values, and then performed multiple linear regression using the AICc-identified model.
Table 1
Table 1
Sample Characteristics
All AIMs were tested for association with SBP and DBP using linear regression under additive, dominant, recessive and genotypic models in the entire sample and each of the ethnic groups. First, we tested single marker associations without physical activity, racial discrimination and SES as covariates so as to have as complete a sample size as possible, given the missing data for these particular covariates. We then tested the association with the imputed dataset containing all covariates. Since we are testing association with 142 markers and four different models, we apply a conservative Bonferroni multiple testing correction, such that our p-value cutoff is 8.8 × 10−5. All analyses were carried out with PLINK [44] and SAS 9.1 software (SAS Institute, Cary, NC).
Descriptive statistics
Descriptive statistics are presented in Table 1. African American (AA) subjects had a higher SBP than European Americans (EA) and Hispanic Americans (HA) (p<0.05). There was no significant difference in SBP between EA and HA. A similar pattern was found for DBP. Total body fat was significantly greater among HA (p<0.0001). There were no statistically significant differences between AA and EA in total body fat. There were no differences in sodium consumption among the three racial/ethnic groups (p=0.14). Levels of perceived discrimination were highest among HA followed by AA (p <0.0001). SES was significantly different between groups (p<0.0001) and for all pair-wise comparisons (p<0.05), being lowest in HA, followed by AA and EA, in that order. There were no significant differences in physical activity levels among groups (p=0.79).
Association between blood pressure and genetic, social, behavioral, and body composition traits
For SBP, in the entire sample, the model including physical activity, African and Amerindian admixture and total fat is the one with the lowest AICC. Results of multiple regression are shown in Table 2a. Among all subjects, physical activity and Amerindian admixture are negatively associated with SBP (p=0.018 and 0.025, respectively), whereas W. African admixture and total body fat are positively associated with SBP (p=0.003 and <0.0001, respectively). Among HA and EA, higher total body fat is strongly associated with higher SBP (p=0.0003). Among AA, however, total body fat is not in the best fitting model. Instead, physical activity is negatively associated with SBP (p=0.0001), and perceived racial discrimination is positively associated with SBP (p=0.041) (see Table 2a).
Table 2
Table 2
Results of multiple regression for a) SBP and b) DBP for dataset with missing values imputed. Shown are standardized coefficients and p-value in parentheses for each variable in best fitting model according to AICC
For DBP, among all subjects, Amerindian admixture, and SES are negatively associated with DBP (p=0.005 and 0.049, respectively), while W. African admixture and total fat are negatively associated with DBP (p=0.0005 and 0.025, respectively). Among HA, no variables are significantly associated with DBP. Among EA, total body fat is positively associated with DBP (p=0.015), while SES and perceived racial discrimination are negatively associated with DBP (p=0.004 and 0.015, respectively). Among AA, only Amerindian admixture is negatively associated with DBP (p=0.039).
Association analysis without imputation of missing values
Using only individuals with non-missing values for all potential predictors (n=132), and the models identified by AICC on this non-imputed dataset, total body fat and height are found to be positively associated with SBP (p=0.007 and 0.03, respectively) among all subjects (see Table 3). When stratified by race/ethnicity, total body fat (p=0.0005) was positively associated with SBP among EA. Among AA, Amerindian and African admixture are negatively associated with SBP (p=0.015 and 0.038 respectively), whereas perceived racial discrimination and SES are positively associated with SBP (p=0.02 and 0.035, respectively). In order to further examine the stability of some of our results, we removed the perceived racial discrimination variable from the dataset, thus increasing our sample size for regression analysis. In the entire sample, we find that African admixture and total body fat are positively associated with SBP (p=0.008 and 0.0014, respectively). Among HA and EA, but not among AA, total body fat is positively associated with SBP (p=0.005 and 0.0071, respectively). Among AA, physical activity is significantly positively associated with SBP (p=0.0017).
Table 3
Table 3
Results of multiple regression for SBP without imputed data. The first panel is for the sample with values for all variables, followed by the sample with the perceived discrimination variable removed. Shown are standardized coefficients and p-value in (more ...)
The results for DBP are shown in Table 4. Using only individuals with non-missing values for all potential predictors (n=132), and the models identified by AICC, we find that height (p=0.021) and African admixture (p=0.026) are positively associated with blood pressure. Among EA, SES (p= 0.001) and perceived racial discrimination (p=0.009) are negatively associated with DBP. Among AA, height (p=0.016), and perceived racial discrimination (p=0.029) are positively associated with DBP, while Tanner stage is negatively associated with DBP (p=0.008). Among HA, addition of predictors did not improve the fit of the model, according to AICC. When we remove the perceived racial discrimination variable from the analysis, we find that SES is negatively associated with DBP among the entire sample and among EA (p=0.014 and 0.015, respectively), total fat is positively associated with DBP among EA (p=0.34), and that among the entire sample, Amerindian admixture is negatively associated with DBP (p=0.0027), while African admixture is positively associated with DBP (p=0.0086). Among HA and AA, addition of predictors did not improve the fit of the model, according to AICC.
Table 4
Table 4
Results of multiple regression for DBP without imputed data. The first panel is for the sample with values for all variables, followed by the sample with the perceived discrimination variable removed. Shown are standardized coefficients and p-value in (more ...)
Single Marker Associations
We tested the relationship between each of the 142 AIMs and SBP and DBP for the entire sample and for each ethnic group separately. For SBP, we find a strong association for marker rs832173 on chromosome 1. Among the entire sample, the risk allele (G) is associated with higher SBP in the additive model (p=0.0060), the recessive model (p=0.00047), and the genotypic model (p=0.00096). Among EA only, the p-value corresponding to this association in the additive model is 0.0069. However the p-values are much lower for the recessive model (p=6.16 × 10−6), and the genotypic model (p=8.97 × 10−6), and are statistically significant after applying the conservative Bonferroni correction as described in the Methods section. Using the imputed dataset containing perceived racial discrimination, physical activity and SES as additional covariates, we obtain very similar results. For example, among EA in the recessive and genotypic model, the p-values for marker rs832173 are 4.03 × 10−6 and 5.6 × 10−6, respectively. In the recessive model among EA, marker rs832173 accounts for 15% of the variance in SBP, but it accounts for a small proportion (2%) of differences in SBP between AA and non-AA. In all the above cases, the G allele is associated with higher SBP. In the entire sample the frequency of the risk allele is 0.41. In European Americans, the frequency of the risk allele is 0.208, and the SNP is in Hardy-Weinberg equilibrium (p= 0.5665). Among the parental populations used in the admixture estimation, the G allele is highest in W. Africans (0.889) compared to Europeans (0.179) and Native Americans (0.207). For DBP, this SNP was the highest ranked SNP according to p-values, and was nominally significant (0.006) in the additive model, for the entire sample as well as for EA (p=0.0036). The G allele was associated with higher DBP in all cases. The region near this SNP has been implicated in blood pressure in a recent genome-wide association study on African Americans, in which rs12748299 was found to be associated with hypertension as a binary trait [45]. This SNP is approximately 2.27 Mb upstream of rs832173, and these two SNPs may therefore be in admixture linkage disequilibrium.
We have examined the associations of various genetic, social, physiological, and behavioral factors in relationship to blood pressure in a multi-racial/ethnic sample of healthy children. Evaluating the potential contributors to hypertension in childhood may be an ideal model as additional confounding factors (smoking, alcohol consumption, etc…) are less likely to have occurred in relatively young populations. Our findings suggest that SES, perceived racial discrimination, and physical activity are predictors of SBP among AA, while we find little evidence for an association with genetic admixture within groups. Among EA and HA, total body fat is a highly significant predictor of SBP in all analyses, while it is not among AA. We find that HA report greater perceived racial discrimination than the other groups. As Viruell-Fuentes notes [46], Hispanic children of this generation find out about their lower racial/ethnic status through their interactions with individuals from other groups in the United States. However, none of the measured social or behavioral factors are significantly associated with SBP among EA or HA. We observed an overall similar trend for DBP as for SBP, with the main differences being that total body fat is not as strong of a predictor of DBP as it is for SBP, and that perceived racial discrimination and SES are negatively associated with DBP among EA, and that physical activity is not significantly associated with DBP among AA.
We have tested a wide range of potential predictors of elevated blood pressure. First, the results suggest that the etiology of elevated blood pressure is significantly different among groups. Second, they suggest that the relationship between genetic admixture and blood pressure may be driven by social and behavioral factors that may differ among and within groups. Most notably, perceived racial discrimination and physical activity are associated with SBP among AA, but not among EA or HA. Conversely, the association of total body fat and both SBP and DBP appears to be much stronger among EA and HA than it is among AA. This result is consistent with other studies that suggest a similar difference among groups [4,47-49].
Although in some cases we find that African genetic admixture is positively associated with SBP and DBP in the entire sample, it is usually not significantly positively associated with these traits within racial/ethnic groups. This absence of a significant relationship with genetic admixture may be attributed to the possibility that after adjusting for several social, behavioral and environmental variables, admixture is not an important predictor of high blood pressure within racial/ethnic groups, and the relationship in the entire sample is driven principally by other (i.e. non-genetic) differences among racial/ethnic groups (e.g. unknown or poorly measured environmental factors, or confounding epigenetic modifications). The AIM rs832173 on chromosome 1 that we have identified as being associated with SBP is in a region identified in a recent GWAS among African Americans, and could be an interesting candidate for explaining a genetic basis, if any, for group differences in blood pressure. Although the effect size of this variant may appear large compared to other genetic association studies, the use of refined phenotypes and the inclusion of many covariates may have increased our ability to detect an effect size of this magnitude.
This study is strengthened by the robust measures of body composition, objective measure of physical activity, as well as the inclusion of various social factors. However, it is not without limitations. These limitations lie mainly in our reduced sample size due to missing data for some of the perceived racial discrimination and physical activity measures, thus precluding an analysis on the entire dataset with all variables having non-missing values. Nevertheless, we are able to detect significant and important relationships that are consistent with and without imputation of missing values. The measure of perceived racial discrimination is limited in that it relies on how “willing and able” subjects are to report discrimination [50], and the accuracy of their responses. However, this measure could also be considered a strength, given that we are considering individual perception. The measure of sodium consumption also relies on the measurements of self-report; however recall bias would likely be similar across populations and under/over reported similarly across groups. Finally, there are several other factors that we have not considered that could account for the differences in mean blood pressure between groups. For example, differences in early-life factors have been reported to be associated with blood pressure variation [51].
Our findings indicate that the risk factors for hypertension and racial/ethnic disparities develop early in the lifespan, indicating that prevention and treatment strategies should begin during early childhood. Our findings also demonstrate that the etiology of elevated blood pressure differs among groups, with respect to total body fat, and social and behavioral factors. These different etiologies could be due to the different distribution of risk factors resulting in different interactions among them. However, it is also possible that the underlying pathophysiology of elevated blood pressure is different among groups. We have also identified a potential genetic locus that may constitute a racial/ethnic-specific risk factor. These findings begin to shed light on a more comprehensive understanding of the etiology and early-life development of population differences in blood pressure, and the resulting disparities in hypertension.
Table 5
Table 5
Summary
Supplementary Material
Supplemental Material
Acknowledgments
We thank the participants of the AMERICO study and their families. Funded in part by NIH grants R01-DK067426, T32-HL007457, P30-DK56336, CA47888, and P60-DK079626. The opinions expressed are those of the authors and not necessarily those of the NIH or any other organization with which the authors are affiliated.
Footnotes
The authors declare no conflict of interest.
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