(Hypertension. 2001;37:928.)
© 2001 American Heart Association, Inc.
Scientific Contributions |
Familial Factors of Blood Pressure and Adiposity Covariation
From the Department of Anatomy and Anthropology (G.L.), Sackler Faculty of Medicine, Tel Aviv University, Ramat Aviv, Tel Aviv, Israel; and Department of Public Health, Weill Medical College, Cornell University, New York, NY.
Correspondence to Dr Gregory Livshits, Department of Anatomy and Anthropology, Sackler Faculty of Medicine, Tel Aviv University, Ramat Aviv, Tel Aviv 69978, Israel. E-mail gregl{at}post.tau.ac.il
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Abstract |
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In the present study, we used the maximum likelihood approach as implemented by variance analysis and attempted to quantify genetic and environmental components of variance in systolic (SBP) and diastolic (DBP) blood pressure in 514 individuals who belonged to a total of 135 nuclear families of Chuvasha, Russia, ethnic origin. The extent to which these interindividual differences depend on age, gender, body mass index (BMI) and other anthropometric measurements was investigated. Major findings include the following. (1) The variation in both SBP and DBP was significantly affected by genetic factors (h2SBP=0.51±0.13, h2DBP=0.20±0.09), shared household environment, and age. These effects were stronger with respect to SBP, which also showed significant gender differences in baseline values and rate of SBP increase with age. (2) Genetic and common household factors, as well as undetected residual effects, were not completely independent. The respective 3 facets of correlation between SBP and DBP were significant: 0.66±0.10, 0.76±0.11, and 0.55±0.14. (3) SBP and DBP each showed significant phenotypic correlations with BMI and anthropometric factors. These correlations had a substantial genetic component but were not equal for SBP and DBP. SBP showed the highest genetic correlation with arm circumference (rG=0.63), whereas for DBP, this was found with hip skinfold (rG=0.88). (4) Bivariate heritability estimates, as well as adjustment of BP measurements for BMI and selected anthropometrics, indicated that DBP likely does not have independent genetic heritability. The residual genetic variance of adjusted SBP remained significant, although substantially lower in comparison with the nonadjusted h2.
Key Words: blood pressure • genetics • anthropometrics • body mass index
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Introduction |
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Extensive literature exists that documents the relationship between blood pressure (BP) and measures of body fat and fat distribution.1 2 3 4 Most studies report significant associations for men and women both within populations5 6 and between populations.7 8 Recent studies suggest this relationship has early beginnings, perhaps from birth or maybe prenatally.9 10 11
Measures of adiposity reported vary from study to study, as do their relationships to BP. The most commonly reported adiposity measures include weight, waist circumference, subscapular and triceps skinfold measures (as well as their sum), and indices such as body mass index (BMI), waist-to-hip (circumference) ratio (WHR), and various skinfold ratios. A recent review of studies that examined the associations of adiposity measures to BP found the vast majority reported significant relationships.6
The WHR has been found to be associated with BP in some studies12 13 but with diastolic BP (DBP) and not systolic BP (SBP) in other studies.14 In contrast, Gerber et al6 found that of the 7 adiposity measures examined, all were significantly related to BP except the WHR. Subscapular skinfold thickness was the best adiposity predictor of BP, but BMI was also found to be a good predictor. Seidell et al8 concluded from a multicenter study of women that among anthropometric (AP) variables, BMI was the best overall predictor of both SBP and DBP. The significant positive association between BMI and both SBP and DBP has been reported in studies of African-Americans,15 Chinese,16 17 Africans, and Caribbeans.18 The studies conducted in China, Africa, and the Caribbean are especially noteworthy because significant relationships held even in these lean populations.
Despite the large body of publications that clearly suggest a consistent and substantial relationship between the BP measurements and AP characteristics, relatively few studies have addressed the extent and relative contributions of genetic and environmental effects on this covariation. The conclusions reached in these studies vary from the position of substantial genetic correlation between BP and AP,19 20 21 which could be so great that heritability of BP is almost wholly attributable to genetic factors that affect obesity,22 to conclusions that reject any genetic contributions to this correlation.23 24
In the present study, we assessed a large number of AP traits and BP in 514 individuals who belonged to a total of 135 nuclear pedigrees. The genetic correlation between body composition measures and BP was analyzed in an attempt to quantify the contributions of heritability and environmental components to this association.
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Methods |
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Sample and Measurements
Pedigree data from Chuvasha (Russia) were gathered randomly from individuals who previously volunteered to participate in other studies not related to the present one. Further details on sample selection and data collection procedures were reported by Livshits et al.25
The sample contained 135 nuclear pedigrees composed of 527 observed individuals. The vast majority of pedigrees included 2 parents with 1 to 3 offspring each. The age of the individuals ranged from 18 to 91 years for men and from 18 to 86 years for women. The present data were collected in 1994 from individuals living in 40 villages near the city of Cheboksary as part of an epidemiological study on bone aging. The pedigree ascertainment was random with regard to health status and other assessed outcome variables.
Ethnically, Chuvasha consists of a mixed white population
who live in the Volga region of Russia; the inhabitants migrated to
these areas during the 17th and 18th centuries. The Chuvasha ethnic
group was formed during the last quarter of the first millennium
AD in the forested or hilly
portions of the Volga riverside. The Chuvasha ancestors were likely
Bulgars from the Volga and Kama riverside who intermarried with the
local Finno-Ugor tribes.26
The climate of Chuvasha where the present population lives is
moderately continental with average temperatures of -12°C in
January and 19°C in July and a mean annual precipitation of
450
mm.27
Consecutive BP measurements were taken on the left arm of each participant while in a seated position after a 10-minute rest. With a standard mercury sphygmomanometer and stethoscope, the measurements were taken twice at 5-minute intervals by the same nurse (see Livshits et al25 for further details). The average of the 2 blood pressure measurements was used as the estimate of SBP and DBP in the present analysis. AP measures taken from each individual included 8 skinfold measures from the body trunk and extremities: (1) chest (Che.sk), (2) abdomen (Abd.sk), (3) subscapular (Sub.sk), (4) hip (Hip.sk), (5) upper arm medial (Med.sk), (6) upper arm dorsal (Dor.sk), (7) lower arm (Low.sk), and (8) calf (Clf.sk); 9 circumference measures, including the chest and various levels of upper and lower extremities: (1) mesosternal chest minimal (Mmi.cr), (2) waist (Wai.cr), (3) hip (Hip.cr), (4) upper arm (Upp.cr), (5) lower arm (Low.cr), (6) wrist (Wri.cr), (7) thigh (Thi.cr), (8) calf (Clf.cr), and (9) ankle (Ank.cr); and the 2 indices of WHR and BMI. All measurements were obtained with standard AP techniques.28
Statistical Analysis
Pairwise correlation coefficients were calculated for
all AP variables with each other and with SBP and DBP measures.
Correlations between BP variables and adiposity measures were also
tested as stratified by generation and gender (ie, father, mother, son,
and daughter) with control for age. Two-tailed probability levels for
statistical significance are reported.
Statistical/Genetic Analysis
To examine genetic and environmental effects on the
variation of each of the selected traits in the study, variance
component analysis was performed using the FISHER statistical
package,29 with minor
modifications. The program finds the best-fitting and most parsimonious
model of the trait variability and produces maximum likelihood
estimates of genetic and various common family environment components
and corresponding standard errors on the basis of pedigree data. The
method allows one to partition the total phenotypic variation of
the study trait (VPH) into a number of
components, according to contributing factors: (1)
VAD is additive genetic component, (2)
VSP is shared spouse environment component, (3)
VHS is common household environment component,
(4) VSB is common household environment,
specific for sibs raised together, and (5) VRS
is individual specific residual component. The general
univariate model can be presented as the linear
equation
VPH=VAD+VSP+VHS+VSB+VRS
.
The effects of age and gender on each studied variable
were estimated simultaneously with the variance components,
using the linear regression function
µS=
S+ßS
(T), where S and ßS
are gender-specific intercept and regression slope on age, T. As is
customary in quantitative
genetics,30 we assume no
interaction between polygenic loci, between genetic and environmental
effects, and with dominance effect being negligible. To find the best
fitting and most parsimonious model, the likelihood ratio test has been
used:
2=-2(logLHG-logLHR),
where G and R are the general and restricted models,
respectively.31 The
most parsimonious model was constructed by sequential restriction of
the parameters to the expected values: for example, no
genetic determination of trait,
VAD=0.
To determine the possible extent of genetic and
environmental covariation between the BP measurements and AP traits,
covariance decomposition analysis (bivariate
analysis) was undertaken with the same FISHER package. In this
analysis, both genetic and environmental correlations between
all possible pairs of traits were estimated in a pairwise
manner.31 The bivariate
heritability estimate between traits X and Y was found using the
classic
formula30 p316:
rAD(XY)=COVAD(XY)/
COVAD(XX)COVAD(YY),
h2AD(XY)=
2COVAD(XY). The FISHER program computed all of
the components of this formula.
To avoid bias due to multiple testing, principal components analysis was also conducted. The results of both the univariate and bivariate analyses yielded very similar estimates to our variance decomposition approach and corroborated our findings for the effects of genetic factors, shared marital environment, and residual effects on single AP variables. Due to the greater ease in interpretation, only the latter results are presented.
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Results |
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Basic descriptive statistics for all study variables by generation and gender cohort are given in Table 1. The mean ages of the parent generation are 64 and 63 years, respectively, for fathers and mothers, and the mean age is 34 years for sons and 37 years for daughters. As expected, mean skinfold thicknesses were higher among women than among men, whereas the gender pattern in circumferences varied by adiposity measure.
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Correlation coefficients between AP characteristics and BP,
with control for age, are shown in
Table 2. At least 5 AP measures in each group show
significant (all r
0.25)
associations (all P
0.01) with
SBP, with BMI and upper arm circumference demonstrating significant
associations in all 4 groups.
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For DBP, hip skinfold had the most consistent correlation across all 4 groups. Correlations between AP variables and DBP were generally stronger for mothers than for fathers. Similarly, these relationships were greater for daughters than sons, with significance (P<0.05) achieved in 8 relationships among the sons and in 13 among the daughters.
Variance Analysis
Table 3 provides parameter estimates for each
of the 5 components examined, as obtained in the univariate
analyses. Each estimate of the most parsimonious model was
found a number of times. The optimization began with different randomly
selected initial values but converged finally to the same
parameter estimates. With respect to BP measurements,
variance analysis with simultaneous estimates for
gender and age effects showed (1) for SBP, 51% of the total variation
was attributable to genetic factors and almost 30% was accounted for
by shared household environment, and (2) for DBP, both
parameter estimates were lower, 20% and 22%,
respectively. In all instances, likelihood ratio tests indicated, with
P<0.01, that neither
VAD nor VHS can be
constrained to zero. Constraint of the 2 other variance components to
zero did not substantially change the likelihood of the corresponding
models.
Table 3 also shows strong significant correlations between
SBP and DBP with age. The correlation, however, was significantly
higher in women than in men for SBP. Men, however, had higher baseline
levels (intercept) for SBP (108.1±2.72 versus 91.4±3.5
correspondingly).
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Strong and significant gender and age influences were
detected for Upp.cr. Men had a substantially higher intercept
(309.8 versus 293.6 mm in women), and these values decreased with
age more rapidly for men than for women (-0.798 versus -0.410
mm/y). These differences cannot be accepted as models with equal
intercepts (M=F) or
equal slopes (ßM=ßF)
and were each significantly different from the general model at
P<0.05. The equalization of
both regression parameters simultaneously
(M=F and
ßM=ßF) produced much
worse likelihood of the model (2=43.6,
df=2,
P<0.001). The additive genetic
effects explained 32% of the total variation. A significant marital
correlation in Upp.cr was also found, likely due to common spousal
environment, and it accounted for >22% of the total
variance.
The similar estimate for VSP was also obtained for BMI (Table 3). For this index, almost 39% of interindividual variation can be attributed to genetic variation. Gender and age effects, however, were very weak and almost undetectable for BMI. They concerned only regression coefficient ß, which did not differ from zero in women but was significant, although small, in men (Table 3; BMI, ßM=-0.03±0.006, ßF=[0]).
Inference of the genetic and environmental effects for Hip.sk showed that only the former is significant and contributes almost 46% to the trait variance. Gender differences were also highly significant, in particular, with respect to intercepts. An age effect was found to be significant only in the female sample.
Bivariate Genetic Analysis
The next stage of the analysis assumed that SBP
and DBP might be genetically correlated with each another because
substantial phenotypic correlation was observed between them. The same
assumption was made with respect to SBP and BMI, and Upp.cr on the one
hand, and DBP and BMI, and Hip.sk on the other hand. The
parameter estimates obtained in the univariate
analyses were used as the initial values in the
corresponding bivariate analyses. In the subsequent model
fitting, estimates of the mean of each of the variables were
allowed to be different in men and women and linearly dependent on age.
In comparison with univariate analyses
(Table 3), only minor differences occurred in
parameter estimates of each of the studied variables.
We report, therefore, only bivariate components.
With respect to SBP and DBP, 3 significant components were inferred: RAD=0.656±0.103, RHS=0.755±0.113, and RRS=0.553±0.141. The results demonstrated that both shared genes and common household environment contribute substantially to phenotypic covariation of both BP measures. From these estimates, the bivariate genetic component of variance for both traits (h2SBP/DBP) can be estimated30 as 0.234 (P<0.01).
For all other pairwise analyses, only additive genetic correlation was found to be significant (Table 4). The strength of the correlation ranged from moderate and marginally significant (0.409±0.203) between DBP and BMI to a quite high and significant estimate between DBP and Hip.sk (0.88±0.17). As seen in Table 4, genetic correlations of SBP with both BMI and Upp.cr were of substantial magnitude and statistical significance. The analyses demonstrated significant bivariate heritability estimates for each pair of study traits as shown in Table 4. Approximately 50% of the SBP variation attributable to genetic factors is shared with those that determine adiposity variation. In particular, between 24% and 27% of SBP variation adjusted for gender and age effects was accounted for by genetic factors shared with Upp.cr and BMI, respectively. For DBP, the situation is different. The effects of genetic factors on the interindividual variation in Hip.sk and DBP simultaneously were of the higher magnitude as DBP-specific genetic effects. These are probably the same genes (h2DBP=0.20 versus h2DBP/HIP=0.26), and numerical differences can likely be explained by the relatively large standard error associated with the h2 estimate for DBP (Table 3). Bivariate h2 values for DBP and BMI, however, are considerably lower and only marginally significant (P=0.07).
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To examine the extent of SBP (and DBP) genetic variation independent of genetic sources that govern adiposity variation, our univariate model was slightly modified. The matrix of the observable covariates now included not only age, T, but also BMI and Upp.cr for SBP and BMI and HIP.cr for DBP, respectively. As seen in Tables 5 and 6, the obtained results are easily interpreted. First, concerning the covariates, age and gender effects, although they changed substantially quantitatively, they qualitatively showed the same trend as before. Correlation (regression) between SBP and Upp.cr was also strong and highly significant but gender independent (ß=0.151±0.024 for both genders). The independent effect of BMI on SBP was not observed. Finally, the contribution of the genetic factors on SBP variation dropped to 37.3% (from 51.4% in the previous analysis), whereas VHS did not change.
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A similar outcome to SBP was demonstrated for DBP (Table 6). In addition to previously detected gender and age effects, Hip.sk and BMI were retained in the model. Both effects were different in men and women. However, although the effect of Hip.sk was much higher in men than in women, as measured by regression coefficient ß (0.829 versus 0.321), the effect of BMI was negligible in men. In accordance with the above reported bivariate h2 estimates (Table 4), Table 6 demonstrates that almost no independent genetic effects on DBP were detected in the last model, VAD=14.8±11.8.
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Discussion |
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A vast amount of evidence suggests a strong and consistent relationship between BP levels and various measures of adiposity. Findings to date clearly indicate that both BP and AP are complex multifactorial traits that develop during the close interaction of social, economic, behavioral, physiological, and other factors. The large number of twin, adoption, and family studies also points out that genetic heritability accounts for a substantial portion of interindividual variation in each of these domains of the human phenotype.32 33 34
In the present study, we used the maximum likelihood approach as implemented by variance decomposition analysis to quantify genetic and environmental components of variance in SBP and DBP among families living in Chuvasha, Russia. We also investigated the extent to which these interindividual differences are dependent on age, gender, BMI, and some selected AP measurements. Our major findings can be summarized as follows: (1) The variation of both SBP and DBP was significantly affected by genetic factors, shared household environment, and age (Table 3). These effects were stronger with respect to SBP, which also showed significant gender differences in baseline values and rate of SBP increase with age. (2) Genetic and common household factors, as well as undetected residual effects, were not completely independent. The respective 3 facets of correlation between SBP and DBP were significant: 0.66±0.10, 0.76±0.11, and 0.55±0.14. (3) SBP and DBP each showed significant phenotypic correlations with BMI and many other AP measures (Table 2). These correlations had a substantial genetic component but were not equal for SBP and DBP. SBP showed the highest genetic correlation with Upp.cr (rG=0.63), whereas for DBP, this was found with Hip.sk (rG=0.88). (4) Bivariate heritability estimates, as well as adjustment of BP measurements for BMI and selected AP factors, indicated that DBP likely does not have independent genetic heritability (Table 6). The residual genetic variance of adjusted SBP remained significant, although substantially lower in comparison with the nonadjusted h2 (Table 5).
The strong genetic effect noted in this study is particularly noteworthy given the overall leanness of the sample. We can posit that if levels of adiposity were greater, there would be more variability seen in the adiposity measures with even greater genetic effects, with different effects on different measures. Another difference between this sample and most others is that high BP, for the most part, was not treated. This may help to explain the greater variability of BPs reported, especially in the parent generation.
There is a growing body of literature that suggests environmental factors that operate during fetal and early life may have profound effects on disease susceptibility in later life.35 Barker and colleagues9 36 37 suggested that maternal undernutrition during pregnancy leads to retarded intrauterine growth and increased risk of hypertension and cardiovascular disease. Another theory38 39 hypothesizes that nephron numbers are programmable in utero such that deficiencies in nephron endowment at birth result in hypertension in later life. An alternative to the direct effect of the intrauterine influence on BP is that accelerated growth sometimes observed in low birth weight babies during early infancy may be linked to an accelerated rate of increase in BP levels that persist over time.4 40
Circumstances operating in utero, and in early life, may affect the total interindividual variation of BP. It is unlikely that the induced variation would somehow simulate the genetic variation, which was assessed in our model through covariation between biological relatives. It could contribute to common family environment, which would be captured in our model under the category of the shared sibs environment, or through inequality of mother/offspring versus father/offspring covariation. The latter assumption is tested when the additive genetic component is estimated. Because none of these expectations were confirmed in our analysis, we believe that any environmental contribution to these components of variation was rather negligible.
Significant genetic effects on BP have been well established
in numerous studies published during the past 3 decades (for reviews,
see Ward32 and Livshits et
al34 ). The latter report
also suggests the possibility of a major gene effect on BP. This
statistically derived conclusion was recently confirmed in a linkage
study,41 which provided
strong evidence (P=0.005) for
the linkage of BP to the putative single gene located on chromosome 17.
Further, Cheng et al21 found
evidence of a major gene pleiotropic effect on SBP and BMI. However,
the majority of studies of BP mode of inheritance, and in particular
those that investigated covariation components, used linear statistical
methods, such as path and variance models. For example, Schork et
al42 used a technique
similar to that used in the present study. The results of both of
these studies are qualitatively in good agreement. For example, Schork
et al’s variance components showed a significant additive genetic
effect for both SBP and DBP. Our recalculations of data
presented in Schork et al’s
Table 3 indicated that additive genetic factors account for
30% and 18% of SBP and DBP variation adjusted for gender, age,
and other concomitant variables. Interestingly, 13% and 6%
of the adjusted variance were attributable to common household effects
for SBP and DBP, respectively. Their results of bivariate
analyses were also along the lines of the present study.
Schork et al42 found high
and significant genetic (0.90) and shared environment (0.70)
correlations between SBP and DBP. Bivariate analyses between BP
and AP measures in Schork et al’s study involved mean BP versus BMI
and body weight. Significant genetic correlations were found in these
analyses, which are in accord with the present
results.
The contribution of pleiotropy to BP and AP was found in studies by Majumder et al22 and Vinck et al,43 whereas in An et al,24 no genetic correlation was detected between the BP and AP. These 3 studies are useful to compare. All 3 used path analysis. Vinck’s team studied covariation in a twin design study, whereas the 2 other groups used mostly nuclear pedigrees. Vinck et al’s43 maximum likelihood estimates showed significant rG between both SBP and DBP and BMI, yet the residual h2 for both SBP and DBP remained significant, too. Majumder et al22 tested various hypotheses of BP transmission adjusted for AP variables and showed that there was no residual genetic heritability of adjusted SBP or DBP levels.
The discrepancy in the pleiotropic effects noted in these
studies, including the present one, may be attributed to the
following. (1) The basic familial resemblance (correlations) for BP
measures was low (or lower) in the studies where residual
h2 was not significant. For example, in
Majumder et al,22 maximum
likelihood estimates of some familial correlations were significant,
but some were not. Further analysis implementing path models
showed that observed familial resemblance of BP levels is primarily due
to cultural rather than to genetic inheritance. In our study, age- and
gender-adjusted h2DBP
(0.20) was much lower than
h2SBP (0.50)
(Table 3). (2) The magnitude of intraindividual (phenotypic)
correlations between BP and AP measurements was low in studies with
insignificant rG estimates. For example, in An
et al’s24 sample,
correlations between both BP measures and BMI, WAI.cr, WHR, and
other AP traits were 0.17. (3) As shown in the present study and
numerous other publications,4
many different AP traits correlate with BP. There are significant
phenotypic and genetic correlations between various AP
measures,44 45
but the magnitude of their covariation is different, as it is different
between BP and selected AP traits (see
Tables 2 and 4). The selection of the particular AP measure
in the specific population may lead to different results. This
heterogeneity can in turn be due to a number of
factors, of which the main should likely include differences in
measurement error, contribution of the genetic factors, and
physiological pathways for different AP traits.
Obviously, further studies, in particular those involving
molecular/genetic technique, are needed to clarify the extent and which
specific genes influence the BP/AP
relationship.
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Acknowledgments |
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This study was supported by a grant to Gregory Livshits from the Israeli Ministry of Health (agreement 4240).
Received August 15, 2000; first decision September 5, 2000; accepted September 14, 2000.
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