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American Journal of Hypertension logoLink to American Journal of Hypertension
. 2013 Sep 12;27(4):610–619. doi: 10.1093/ajh/hpt144

Body Mass Index Modulates Blood Pressure Heritability: The Family Blood Pressure Program

Jeannette Simino 1,✉, Gang Shi 1, Alan Weder 2, Eric Boerwinkle 3, Steven C Hunt 4, Dabeeru C Rao 1,5–7,5–7,5–7
PMCID: PMC3958601  PMID: 24029162

Abstract

BACKGROUND

Candidate gene and twin studies suggest that interactions between body mass index (BMI) and genes contribute to the variability of blood pressure (BP). To determine whether there is evidence for gene–BMI interactions, we investigated the modulation of BP heritability by BMI using 4,153 blacks, 1,538 Asians, 4,013 whites, and 2,199 Hispanic Americans from the Family Blood Pressure Program.

METHODS

To capture the BP heritability dependence on BMI, we employed a generalized variance components model incorporating linear and Gaussian interactions between BMI and the genetic component. Within each race and network subgroup, we used the Akaike information criterion and likelihood ratio test to select the appropriate interaction function for each BP trait (systolic BP (SBP), diastolic BP (DBP), mean arterial pressure (MAP), and pulse pressure (PP)) and determine interaction significance, respectively.

RESULTS

BP heritabilities were significantly modified by BMI in the GenNet and SAPPHIRe Networks, which contained the youngest and least-obese participants, respectively. GenNet Whites had unimodal SBP, MAP, and PP heritabilities that peaked between BMI values of 33 and 37kg/m2. The SBP and MAP heritabilities in GenNet Hispanic Americans, as well as the PP heritability in GenNet blacks, were increasing functions of BMI. The DBP and SBP heritabilities in the SAPPHIRe Chinese and Japanese, respectively, were decreasing functions of BMI.

CONCLUSIONS

BP heritability differed by BMI in the youngest and least-obese networks, although the shape of this dependence differed by race. Use of nonlinear gene–BMI interactions may enhance BP gene discovery efforts in individuals of European ancestry.

Keywords: blood pressure, BMI, FBPP, heritability, hypertension, interactions.

High blood pressure (BP) contributes to 12.8% (or 7.5 million) of deaths worldwide.1 Although BP traits have a substantial genetic component (heritability estimates ranging 31%–68%),2 only a small fraction of the BP variation has been explained by significant single nucleotide polymorphisms identified through genome-wide association studies.3–11 The high prevalence of obesity (44.1% of black, 32.8% of White, and 37.9% of Hispanic American adults are obese (body mass index (BMI) ≥ 30kg/m2)),12 coupled with reports of significant interactions between genes and BMI from candidate gene and twin studies of BP,13–20 has inspired large-scale systematic investigations of gene–obesity interactions. However, meta-analyses of genome-wide association study results using massive sample sizes have yet to identify any genome-wide significant (P ≤ 5×10−8) single nucleotide polymorphism–BMI interactions. We investigated whether large and racially diverse family studies provided any evidence for gene–BMI interactions in hypertension pathophysiology and whether the shape (unimodal, linear increasing, linear decreasing) of this interaction was race dependent.

Specifically, we investigated the BP heritability dependence on BMI using 4,153 black, 1,538 Asian, 4,013 White, and 2,199 Hispanic American participants from the Family Blood Pressure Program (FBPP). Within each of 10 race and network subgroups, we employed the Shi and Rao21 generalized variance components model to allow the polygenic component, and hence BP heritability, to vary by BMI level. We modeled 2 polygenic–BMI interaction functions (linear and Gaussian) to permit a variety of heritability curve shapes (increasing, decreasing, unimodal) for each of 4 BP traits (systolic BP (SBP), diastolic BP (DBP), mean arterial pressure (MAP), and pulse pressure (PP)). The resulting heritability curves may provide insight into the genetic and environmental architecture of BP and is an important first step in exploring the existence and shape of gene–BMI interactions.

METHODS

Subjects

The FBPP data was pooled from 4 separate multicenter networks: GenNet, Genetic Epidemiology Network of Atherosclerosis (GENOA), Hypertension Genetic Epidemiology Network (HyperGEN), and the Stanford Asian Pacific Program in Hypertension and Insulin Resistance (SAPPHIRe). Detailed descriptions of each network can be found elsewhere.22 All networks recruited families via individuals with high or low BP. GenNet recruited black, White, and Hispanic American nuclear families ascertained through young/middle-aged probands with untreated high-normal BP (upper 20%–25% of race-/age-/sex-specific BP distributions).22 GENOA recruited black and White sibships containing at least 2 hypertensive members, Hispanic American sibships containing at least 2 adult-onset diabetics, and all full siblings of each index pair. HyperGEN recruited black and White hypertensive siblings, their parents, and 1 or more of a sibling’s untreated offspring, with preference given to sibships containing a severely hypertensive member. SAPPHIRe recruited Chinese and Japanese sibling pairs highly concordant or discordant for hypertension. The appropriate institutional review board approved each study; all participants provided written informed consent. We restricted our analysis to participants with genotyped microsatellite markers; these genetic markers were used to verify familial relationships using graphical representation of relationships and affected sibpair exclusion mapping.

Phenotype

We used the BP residuals from a prior FBPP meta- analysis.23 Briefly, we averaged 3 sitting SBP or DBP Dinamap measurements (2 Whites and 496 blacks from GenNet had Omron measurements instead). MAP was estimated by the sum of two-thirds the average DBP and one-third the average SBP. PP was computed as the difference between the average SBP and DBP. We excluded the BP phenotypes of participants with hypertension diagnosed after age 60 years or with BMI, SBP, DBP, MAP, or PP values ≥4 standard deviations from the mean of the subgroup (defined by network, race, and sex).

For each phenotype (SBP, DBP, MAP, and PP), we used 3 methods (called “raw,” “+10/5,” and “+15/10”) to adjust for antihypertensive medication status. The raw method used the observed BP values without any adjustment. The +10/5 medication-adjusted values24 were derived by adding 10mm Hg and 5mm Hg to the SBP and DBP, respectively, of those known to be taking antihypertensive or diuretic medications. Similarly, the +15/10 medication-adjusted values25 were obtained by adding 15mm Hg and 10mm Hg to the SBP and DBP, respectively, of those known to be taking antihypertensives or diuretics. The observed BP phenotypes were used for those untreated or with unknown medication status. The medication-adjusted PP and MAP were calculated from the medication-adjusted SBP and DBP values. Only 1 medication adjustment was necessary for PP because both +10/5 and +15/10 result in the same values for all individuals (5mm Hg added to PP for medication users).

Phenotypes were adjusted for age, age squared, age cubed, BMI, and field center within each subgroup (defined by network, race, and sex) using forward stepwise linear regression (retaining terms significant at 5%); thus we adjusted for any BMI main effect before the investigation of interactions between BMI and the polygenic component. This covariate adjustment methodology reflected our goal of finding genes which were primary for hypertension but not primary for BMI. We assumed the adjustment for BMI teased out the polygenic component that directly influenced BMI levels, leaving only the polygenic component that primarily influenced BP for the interaction analysis. After standardizing the residual phenotypes to a mean of 0 and SD of 1, we conducted a final outlier analysis. The male and female residuals within a race and network subgroup were pooled before statistical analysis.

Statistical methods

Within each race and network subgroup, we used the generalized variance components model proposed by Shi and Rao to vary the polygenic component, and hence BP heritability, as a function of BMI.21 Details of the specific models are given in the Supplementary Materials online. Guided by previous studies,20,26 we fit both linear and Gaussian interactions between BMI and the polygenic effect to allow flexibility in the BP heritability shape. The linear interaction function allowed the BP heritability to be strictly increasing or decreasing as a function of BMI; the Gaussian interaction function defined a unimodal BP heritability with parameters indicating the BMI at which the heritability peaked and the spread of this heritability peak. Assuming independence among pedigrees, we maximized the likelihood for each model in MATLAB (MathWorks, Inc., Natick, Massachusetts, U.S.A.) (QTLtrends package21) using kinship coefficients computed by MERLIN.27 Using the Akaike information criterion with a penalty of 2 per parameter, we selected the appropriate interaction function and performed a likelihood ratio test of the BMI interaction model vs. the traditional polygenic variance components model. The χ2 test statistic had 1 degree of freedom for the linear function and a 50:50 mixture of 1 and 2 degrees of freedom for the Gaussian interaction function. For each trait (SBP, DBP, MAP, PP), we applied a Bonferroni multiple testing adjustment for the number of medication adjustments and interaction functions. The significance cutoff for SBP, DBP, and MAP was 8.33×10−3 and for PP was 0.0125 in all race and network subgroups.

RESULTS

The descriptive statistics for each race stratified by network and sex are displayed in Table 1, and detailed BMI distributions are depicted in Figure 1. The GenNet participants have the youngest median age, the lowest hypertension prevalence, and the lowest antihypertensive/diuretic use rates within race and sex strata, as well as compared with the Asian groups. The Asian subgroups have the lowest obesity prevalence and the smallest range of BMI values (15–35kg/m2 in Chinese and 17–39kg/m2 in Japanese); the SAPPHIRe Japanese also have the smallest sample size (n = 378).

Table 1.

Descriptive statistics for the Family Blood Pressure Program stratified by race, network, and sex

Race Network No. of families Sex No. of participant Median age, y Median BMI, kg/m2 % Obese % Hyp % Meds Mean SBP, mm Hg Mean DBP, mm Hg Mean MAP, mm Hg Mean PP, mm Hg
Black GenNet 266 M 316 38 25.7 26 29 10 129 79 96 50
F 460 39 31.1 56 37 26 126 77 93 49
GENOA 518 M 505 58 27.8 30 67 53 131 74 93 57
F 1,144 58 31.3 57 73 64 130 69 89 61
HyperGEN 519 M 587 46 28.9 43 64 56 131 77 95 54
F 1,141 47 32.3 63 71 67 129 73 92 56
Asian SAPPHIRe Chinese 405 M 538 48 25.9 10 70 57 133 82 99 51
F 622 49 24.4 9 59 52 127 74 92 54
SAPPHIRe Japanese 155 M 158 55 26.8 23 86 77 136 83 101 52
F 220 54 25.8 20 82 76 133 75 95 58
White GenNet 226 M 429 42 28.3 39 34 23 127 73 91 54
F 529 44 27.8 39 32 25 120 68 85 52
GENOA 477 M 601 56 29.7 46 77 64 136 80 98 56
F 717 55 29.0 45 72 67 131 73 92 58
HyperGEN 427 M 839 53 28.5 37 60 55 123 73 89 50
F 898 55 28.2 40 60 57 120 67 85 54
Hispanic American GenNet 182 M 298 32 27.8 30 13 8 116 63 81 53
F 482 34 28.3 38 10 8 109 60 77 49
GENOA 378 M 568 54 29.2 42 41 23 129 74 93 55
F 851 54 30.9 57 45 36 127 68 88 59
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Obese is defined as having a body mass index ≥30kg/m2.

Abbreviations: BMI, body mass index; DBP, diastolic blood pressure; Hyp, hypertensive; MAP, meal arterial pressure; Meds, using medications (antihypertensives or diuretics); PP, pulse pressure; SBP, systolic blood pressure.

Figure 1.

Figure 1.

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Boxplots of the body mass index (BMI; kg/m2) distributions within each race, network, and sex strata. The diamond represents the mean BMI for that strata.

The shape of the interaction between BMI and the polygenic component differed for populations of European ancestry compared with those of African, Hispanic, or Asian ancestry. For each trait and medication adjustment within a race and network strata, we selected the interaction function that produced the lowest Akaike information criterion. Table 2 shows the relative frequency with which each interaction function was chosen by the Akaike information criterion in each race (1 model was fit for each trait, medication adjustment, and study-race subgroup for a total of 33 models in blacks and Whites and 22 models in Asians and Hispanic Americans). When an interaction model was chosen over the traditional variance components model, linear interactions tended to be chosen for the black, Asian, and Hispanic American cohorts, whereas Gaussian interactions were chosen for the White cohorts. The statistical significance, as indicated by the P value for the likelihood ratio test of the model with the interaction to that without, differed by up to 4 orders of magnitude for the Gaussian and linear interactions models in GenNet Whites (the Gaussian interaction had a P value of 2.14E−07 for raw PP compared with the linear interaction P value of 1.46E−03).

Table 2.

Functional form for the blood pressure heritability dependence on body mass index selected by the Akaike information criterion

Race Percentage of models selecting the following interaction functions
Linear Gaussian None
Black 30.3 3.0 66.7
Asian 68.2 0.0 31.8
White 9.1 36.4 54.5
Hispanic American 54.5 0.0 45.5
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There were 33 models fit to Whites and blacks and 22 models fit to Asians and Hispanic Americans (11 medication-adjusted traits combined with 3 networks for Whites and blacks and 2 networks for Asians and Hispanic Americans).

Table 3 displays the significant interactions (after appropriate Bonferroni correction) between BMI and the polygenic effect on BP using the generalized variance components models. Significant interactions were discovered in the GenNet and SAPPHIRe Networks, which were the youngest and most limited in BMI range, respectively. The heritability of DBP significantly differed by BMI in the SAPPHIRe Chinese, whereas the heritability of SBP, MAP, and PP differed by BMI in at least 2 racial groups (SBP in Asians, Whites, and Hispanic Americans; MAP in Whites and Hispanic Americans; PP in blacks and Whites). However, the Gaussian interaction was chosen for all significant traits in Whites, whereas the linear interaction was chosen in non–White cohorts. The most significant (P = 4.65×10−8) interaction was linear between BMI and the polygenic effect on raw PP in GenNet blacks.

Table 3.

Significant interactions between body mass index and the polygenic effect in Family Blood Pressure Program race and network subgroups

Race Network Trait Medication adjustment Akaike information criterion For the specified interaction function Selected interaction function χ 2 P value
None Linear Gaussian
Black GenNet PP Raw 796.93 769.07 771.18 Linear 29.86 4.65×10−8
+5 801.46 780.47 782.44 22.99 1.63×10−6
Asian SAPPHIRe Chinese DBP +5 1,173.95 1,169.09 1,169.29 Linear 6.86 8.79×10−3*
+10 1,162.55 1,155.61 1,156.01 8.95 2.78×10−3
SAPPHIRe Japanese SBP Raw 374.34 368.42 370.34 Linear 7.92 4.88×10−3
+10 367.40 362.01 363.98 7.39 6.54×10−3
+15 368.49 363.34 365.32 7.16 7.47×10−3
White GenNet PP Raw 1,046.20 1,038.08 1,020.61 Gaussian 29.59 2.14×10−7
+5 1,031.17 1,021.89 1,010.25 24.93 2.23×10−6
SBP Raw 1,016.57 1,010.93 1,009.64 Gaussian 10.93 2.59×10−3
+10 988.69 978.97 973.33 19.35 3.68×10−5
+15 1,001.40 987.31 978.39 27.00 7.86×10−7
MAP +10/5 927.34 923.73 921.66 Gaussian 9.68 4.88×10−3
+15/10 921.07 913.24 908.40 16.66 1.43×10−4
Hispanic American GenNet SBP Raw 760.69 744.96 747.26 Linear 17.73 2.55×10−5
+10 781.64 761.73 763.81 21.91 2.86×10−6
+15 811.78 788.93 791.18 24.85 6.20×10−7
MAP Raw 806.74 797.67 797.87 Linear 11.07 8.79×10−4
+10/5 769.66 757.23 757.73 14.43 1.45×10−4
+15/10 780.62 765.46 765.99 17.17 3.42×10−5
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+x, where x is 5, 10, or 15, indicates the observed blood pressure +x mm Hg for antihypertensive or diuretic users (observed used for nontakers). +x/y indicates that the mean arterial pressure was calculated from +x systolic blood pressure and +y diastolic blood pressure. χ2 was used to compare the interaction model with the traditional variance components models.

Abbreviations: DBP, diastolic blood pressure; MAP, mean arterial pressure; PP, pulse pressure. Raw, observed blood pressure; SBP, systolic blood pressure.

*The interaction is significant for the Gaussian function but not the selected linear (significance cutoff is 8.33×10−3 for DBP traits).

Figure 2 displays the BP heritability curves significantly modified by BMI in the GenNet and SAPPHIRe subgroups. (Supplementary Table S1 online contains the heritability values derived from traditional variance components models without any BMI dependence.) We overlaid the heritability curves for the +10/5 medication-adjusted traits from all races within each plot. The unimodal heritabilities of SBP, MAP, and PP in GenNet Whites peaked between BMI values of 33 and 37kg/m2 (irrespective of medication adjustment). The heritabilities of SBP and MAP in GenNet Hispanic Americans, as well as PP in GenNet blacks, were strictly increasing functions of BMI over the range of our data. The DBP and SBP heritability curves in the SAPPHIRe Chinese and Japanese participants, respectively, were decreasing functions of BMI. Thus the heritability of SBP had 3 different patterns (increasing, unimodal, decreasing) with respect to BMI in 3 different racial subgroups (Hispanic Americans, Whites, Asians), whereas MAP and PP had 2 different patterns (increasing, unimodal) in 2 different racial groups (MAP: Hispanic Americans and Whites; PP: blacks and Whites). Furthermore, the raw SBP and MAP heritabilities tended to be less than the corresponding medication-adjusted heritabilities (see Supplementary Figures S1 and S2 online); the PP heritability curves seemed least influenced by medication adjustment.

Figure 2.

Figure 2.

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Blood pressure heritability as a function of body mass index (BMI; kg/m2) for the GenNet and SAPPHIRe Networks. The heritability curves of each +10/5 medication-adjusted blood pressure (BP) trait were overlaid for all races where the BP differed significantly by BMI.

DISCUSSION

The heritabilities of BP traits varied as a function of BMI in the youngest and least-obese networks (GenNet and SAPPHIRe). The polygenic heritability of SBP, DBP, MAP, and PP depended on BMI in at least 1 race, although the shape of the dependence was race specific (European ancestry were Gaussian, African and Hispanic ancestry were linearly increasing, and the Asians were linearly decreasing). Most large-scale consortium analyses of BP are focused on individuals of European ancestry; thus examination of linear gene–BMI interactions may hinder the discovery of novel BP variants. Using a linear rather than Gaussian parameterization for the interactions resulted in a decrease of significance by up to 3 orders of magnitude in GenNet Whites.

The physiological mechanism through which BMI is associated with BP heritability is unknown. As BMI increases, it in turn leads to increased inflammation, insulin resistance, catecholamine levels, and induces other hormonal changes, all of which may explain significant changes in gene expression and increased BP. Dietary factors cause epigenetic modifications (including DNA methylation, histone modification, and alteration of microRNA expression),28,29 and differences in epigenetic modifications (differential methylation) by obesity status have been reported.30 Therefore, increasing BMI levels through poor diet (or other behaviors such as lack of exercise) may cause epigenetic modifications that alter BP gene expression patterns in the cell.31

The discovery of the BP heritability dependence on BMI in the GenNet network may have been enabled by that cohort’s young age distribution or low antihypertensive medication use. BP dissociates from obesity at advanced ages.32 Shi et al. found that the effect of genes on BP varied by age.33 Both a Japanese American family study and an Italian American longitudinal family study suggested that the heritability of SBP increases with age up to the mid-30s and early 40s and then falls to original values in the 40s.34 The median ages of the GenNet race and sex subgroups are all in the 30s to early 40s. Thus if BMI interacts with a BP-causing gene turned on only at a particular age, then the interaction would only be found in cohorts with a sufficient number of individuals in that age range. BMI, especially in Whites, tends to plateau in middle age,35 perhaps yielding the greatest interactions at these ages and explaining the choice of the Gaussian function as the best description of the interaction. The GenNet subgroups also have lower antihypertensive/diuretic use rates (observed prevalence of 15% less) than their GENOA and HyperGEN racial and sex counterparts. The medication adjustments applied are only approximations and may not properly impute the underlying (true) BPs, which may more severely confound the analysis of subgroups (such as HyperGEN and GENOA) with high medication rates.

There are several limitations to this study. The study sample may be biased and may overestimate BP heritability because FBPP probands were ascertained by BP. The estimation of heritability may be confounded by antihypertensive medications; we ignored the variability in type of antihypertensive medication taken and applied uniform medication adjustments to each trait. Our generalized variance components model did not decompose the heritability into shared familial environment and genetic components nor investigate modifications of the individual environment by BMI. The heritability is the proportion of total BP variance attributed to all additive familial effects (additive genetic effects and shared environment). Thus the heritability depends on BMI if the variance attributed to the additive genetic effect, shared environment effect, or individual environment effect (and thus the the total variance) depends on BMI. Using Han Chinese twins, Wu et al. showed that the SBP heritability was a decreasing function of BMI (replicated using our SAPPHIRe Japanese) due to modification of the common (shared by twins) and unique (individual) environments by BMI.20 Unimodal BP heritabilities, such as that found in GenNet Whites, may indicate that BP is more influenced by the interaction of BMI with individual environment instead of genes at extreme BMI levels. In contrast, the increasing heritability curves in GenNet blacks and Hispanic Americans may indicate that high BMI levels interact with the shared familial environment or genes to influence BP. Furthermore, a shared familial environment in 1 race may be an individual environment in another race. Factors such as disparities in living arrangements by race may contribute to differences in shared familial and individual environments; e.g., blacks and Hispanic Americans are significantly more likely to live in extended households (and perhaps share eating behaviors) than Whites (see, e.g., http://pewsocialtrends.org/files/2010/10/752-multi-generational-families.pdf).

Race-specific dietary epigenetic effects, race-specific genome-wide methylation patterns,36 and population-specific variants may also lead to differences in heritabilities curves across ethnicities. The National Heart, Lung, and Blood Institute’s Exome Sequencing Project reported that 82% of single nucleotide variants in coding regions were population specific.37 Further complicating matters, the same BMI value may correspond to a different biological environment across races. The lean mass and fat mass for a given BMI value may differ by race,38 perhaps altering the levels of inflammation and other BMI-associated sequelae. Unveiling the complex genetic architecture hindering the discovery of BP-influencing genes remains a monumental endeavor.

SUPPLEMENTARY MATERIAL

Supplementary materials are available at American Journal of Hypertension (http://ajh.oxfordjournals.org).http://ajh.oxfordjournals.org/lookup/suppl/doi:10.1093/ajh/hpt144/-/DC1

DISCLOSURE

The authors declared no conflict of interest.

Supplementary Material

Supplementary Data
supp_27_4_610__index.html (930B, html)

ACKNOWLEDGMENTS

We thank all the participants in GenNet, GENOA, HyperGEN, and SAPPHIRe. This research was partly supported by grants T32 HL091823, U01 HL54473, R21 HL095054, and R01 GM28719 from the National Heart, Lung, and Blood Institute . The authors fondly acknowledge the late J. David Curb for his assistance with the manuscript. The following investigators are associated with the Family Blood Pressure Program: GenNet Network: Alan B. Weder (Network Director), Lillian Gleiberman (Network Coordinator), Anne E. Kwitek, Aravinda Chakravarti, Richard S. Cooper, Carolina Delgado, Howard J. Jacob, and Nicholas J. Schork. GENOA Network: Eric Boerwinkle (Network Director), Tom Mosley, Alanna Morrison, Kathy Klos, Craig Hanis, Sharon Kardia, and Stephen Turner. HyperGEN Network: Steven C. Hunt (Network Director), Janet Hood, Donna Arnett, John H. Eckfeldt, R. Curtis Ellison, Chi Gu, Gerardo Heiss, Paul Hopkins, Aldi Kraja, Jean-Marc Lalouel, Mark Leppert, Albert Oberman, Michael A. Province, D. C. Rao, Treva Rice, and Robert Weiss. SAPPHIRe Network: David Curb (former Network Director), David Cox, Timothy Donlon, Victor Dzau, John Grove, Kamal Masaki, Richard Myers, Richard Olshen, Richard Pratt, Tom Quertermous, Neil Risch, and Beatriz Rodriguez. National Heart, Lung, and Blood Institute: Dina Paltoo and Cashell E. Jaquish.

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