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the Departments of Epidemiology & Biostatistics (F.A.S.-T., M.J.E.v.R., Y.S.A., E.A.C., M.C.Z., J.C.M.W., C.M.v.D.), Internal Medicine (A.F.C.S., M.C.Z., H.A.P.P.), and Clinical Genetics (B.A.O.), Erasmus Medical Centre, Rotterdam, The Netherlands.
Abstract
Background and Purpose— Using 930 individuals connected in a single pedigree from an isolated population, participants of the Erasmus Rucphen Family (ERF) study, we investigated the heritability of carotid–femoral pulse wave velocity (PWV), carotid intima media thickness (IMT), and carotid plaque score.
Methods— PWV was measured between the carotid and femoral arteries as an indicator of aortic stiffness. Common carotid IMT and plaque score, quantifying alterations in arterial wall structure, were measured by ultrasonography.
Results— All 3 traits were significantly associated with classic cardiovascular risk factors. Age- and gender-adjusted heritability estimates were 0.36 for PWV, 0.41 for carotid IMT, and 0.28 for plaque score. After adjustment for appropriate risk factors, the heritabilities were 0.26, 0.35, and 0.21 for PWV, IMT, and plaque score, respectively. All heritability estimates were statistically significant (P<0.001). Taking into account different proportions of variance associated with covariates for each trait, genetic factors explained &12% of the total variability for each of the phenotypes.
Conclusions— To our knowledge, this is the first report on the heritability of PWV. The heritability estimates of IMT and plaque score were similar to those in previous reports. We conclude that genetic factors significantly contribute to arterial structure and function in this isolated population, presenting the opportunity to locate susceptibility genes related to cardiovascular disorders.
Key Words: atherosclerosis blood flow velocity carotid arteries genetics
Introduction
Arterial stiffness and atherosclerosis are major factors in pathophysiological pathways leading to various cardiovascular diseases. In Western society, these diseases are the underlying cause of &50% of all deaths.1 Aortic stiffness, as measured by carotid–femoral pulse wave velocity (PWV), has been shown to be an independent predictor of cardiovascular morbidity and mortality in patients with essential hypertension2,3 and end-stage renal disease,4,5 and has been shown to be strongly associated with atherosclerosis at various sites in the vascular tree.6
Carotid intima media thickness (IMT) is used as a proxy for atherosclerosis. It has been shown to be correlated with atherosclerosis in other arterial sites7 and a strong predictor of myocardial infarction.8 In addition, quantitative assessment of plaques in carotid arterial walls can also be used as an indicator of atherosclerosis.9
Genetic factors play a major role in alterations of arterial wall function and structure.10 Prior findings, however, on the extent to which genetic factors may explain the variance of these traits (heritability) are highly variable and limited in number. Heritability estimates (h2) of carotid IMT published thus far range widely from 0.21 to 0.92,11,12 but most of the studies reported heritabilities around 0.30 and 0.40.13–17 To our knowledge, only 2 studies on the heritability of carotid plaques were performed, one yielding a nonsignificant heritability18 and the other an estimate of 0.23,19 and no heritability estimate for PWV has been published to date.
We studied the heritability of carotid–femoral PWV, carotid IMT, and carotid plaques in an extended pedigree from an isolated population in The Netherlands.
Materials and Methods
Study Population
Subjects were participants of the Erasmus Rucphen Family (ERF) study. This is an ongoing large, family-based study, which began in June 2002 in a genetically isolated population located in the southwest of The Netherlands. This population was founded in the middle of the 18th century by &150 individuals and was isolated until the last few decades. An extensive genealogic database, including >63 000 individuals is available for this population.
For the ERF study, 20 couples were selected that had at least 6 children baptized in the community church between 1880 and 1900. All living descendants of the selected couples and their spouses (&2500) were invited to participate in the study. The pedigree members were not selected based on any disease status. This study is based on the first 930 participants for whom complete phenotypic data were available at the time of the analyses. The Medical Ethics Committee of the Erasmus Medical Centre Rotterdam approved the study and informed consent was obtained from all participants.
Data Collection
At the research center, located within the community, extensive clinical examinations were done, including the collection of fasting blood samples, anthropometric measurements, cardiovascular assessments, and personal interviews. The interviews were performed by medical practitioners and included questions on education level, smoking status, current medication use, and medical history.
Lipids and glucose levels were measured from the fasting blood sample according to standard procedures.20,21 Height and weight were measured with the participant in light underclothing and body mass index (kg/m2) was computed. Blood pressure was measured twice on the right arm in a sitting position after at least 5 min rest using an automated device (OMRON 711). The average of the 2 measures was used in the analyses. Mean arterial pressure (MAP) was calculated as one third systolic blood pressure+two thirds diastolic blood pressure.
During the cardiovascular assessment, carotid–femoral PWV was measured by means of an automatic Complior SP device with the subjects in a supine position. The time delay between the rapid upstroke from the base point of simultaneously recorded pulse wave curves in the carotid and the femoral arteries were assessed, and the distance between the carotid and the femoral arteries was measured over the surface of the body with a tape measure. PWV was calculated as the ratio between the distance traveled by the pulse wave and the time delay and expressed in meters per second.
A duplex scan ultrasonographic investigation of the carotid arteries was made using a 7.5-MHz linear array transducer (ATL; Ultramark IV). Three optimal still images were recorded on the videotapes from each artery site (common carotid, bifurcation, and internal carotid arteries at the right and left sides). Measurements of IMT were performed offline using the still images. We used measurements of common carotid IMT in this study. The interfaces of the far and near wall of the distal common carotid artery are marked by an automated method over a length of 10 mm, and maximum IMT is measured on the 3 still images of the near and far wall from both the left and right arteries. The mean value of these measurements is used in the analyses. When a plaque was present at the 10-mm measurement site, IMT was measured at the region closest to the plaque.
During the ultrasound, the common carotid artery, carotid bifurcation, and internal carotid artery were also visualized over the longest segment possible in both the left and right sides for the presence of plaques. Plaques were defined as local widening of the arterial wall relative to the adjacent segment with protrusion into the lumen. The total plaque score reflected the total number of sites with plaques and ranged from zero to 12 (considering the far and near walls of the artery in each of the 3 different arterial sites on both the left and right sides).
Statistical Analyses
General characteristics were compared between men and women using Student t test for continuous variables and 2 test for dichotomous variables with SPSS 11.0 for Windows. Inbreeding coefficients were calculated using the PEDIG software22 based on a pedigree of the total population. The inbreeding coefficient equals the probability that 2 identical alleles at a given locus in an individual are identical by descent.
For the heritability analyses, we first performed univariable and multivariable regression analyses using SPSS. All covariates that were significant at the 0.10 level in the multivariable analysis were retained in the final model for heritability estimation. To obtain a normal distribution of the regression residuals for the traits under study, we used the natural logarithm transformation of (PWV-3) and (IMT-3). Because mean arterial pressure is shown to strongly influence arterial stiffness,23 it was used instead of systolic and diastolic blood pressures in the analyses related to PWV. In the multivariable regression models for IMT and plaque score, the beta coefficient for diastolic blood pressure is reported from a model without systolic blood pressure to avoid the multicollinearity effect. Both variables were used in the final analyses.
A variance component maximum likelihood method implemented in the SOLAR 2.1.2 software package24 was used to partition the phenotypic variance of PWV, IMT, and plaque score into their additive genetic and environmental elements. The contribution of genetic factors to these traits was then estimated as the heritability, defined as the proportion of variance (after correction for covariates) explained by additive genetic components. Heritability estimates were calculated using a model with only age and gender and a full model with all significant covariates from the regression analyses. We also presented the contribution of the genetic factors to the total variance of each trait in different models, calculated as: [(1–proportion of variance explained by covariates)xheritability estimate]. Significance was determined by likelihood ratio tests.
Results
The gender-specific characteristics of the participants are shown in Table 1. All 930 participants were part of one extended pedigree. Almost all of the established cardiovascular risk factors were higher in men, with the exception of low-density lipoprotein (LDL) cholesterol, which showed no differences between genders, and current smoking, which was higher in women (Table 1).
Table 2 shows the association between risk factors and PWV in univariable and multivariable models. In the univariable model, the association between gender and the outcome variables was adjusted for age, and the association between age and the outcomes was adjusted for gender. All other associations were adjusted for both age and gender. In the multivariable model, the only factors that significantly determined PWV were age, gender, mean arterial pressure, LDL cholesterol, and heart rate (Table 2). These factors were included in the multivariable adjusted model for the heritability analyses. Because fasting glucose levels have been associated with PWV,25 this variable was also included in the final model.
Table 3 presents the results of the univariable and multivariable regression analyses for IMT. The multivariable model showed that only triglycerides, low education, and inbreeding coefficient were not significantly associated with IMT. For plaque score, diastolic blood pressure, triglycerides, glucose levels, low education, heart rate, and inbreeding coefficient were not significantly associated in the multivariable model (Table 4).
The estimated components of variance for PWV, IMT, and plaque score are presented in Table 5. All estimates of heritability were statistically significant. Adjusted for age and gender, the heritability estimate was 0.36 for PWV, indicating that the additive effects of genes account for 36% of the variation in PWV that is not explained by age and gender. With 0.45 of the variance being explained by age and gender, genetic factors account for [(1.0 – 0.45)x0.36]&0.20 of the total variance in the PWV trait. After further adjustment for additional covariates, the estimate of heritability was 0.26, meaning that [(1.0 – 0.53)x0.26]&0.12 of the total variance of the PWV trait is explained by genetic factors. Because the other risk factors used as covariates in the model may be in part genetically determined, this result indicates that 12% of the total variance of PWV is explained by unknown genetic factors that contribute to PWV variance independently of the covariates in the model. For IMT, the heritability was 0.41 in the first model and 0.35 in the second model, suggesting that genetic factors explain &0.16 and &0.12 of the total variance of IMT in the first and the second models, respectively. Heritability estimates of plaque score were 0.28 and 0.21, corresponding to &0.17 and &0.12 of the total variance explained by genetic factors.
Discussion
In this large family-based study in an isolated population, we investigated the contribution of genetic and environmental factors to carotid–femoral pulse wave velocity, common carotid intima media thickness, and carotid plaque score. Our study indicated that after adjusting for appropriate risk factors, the additive effects of genes explain significant proportions of the variability in the function and structure of the arterial wall in this population.
To our knowledge, the present study is the first to report on the heritability of PWV, which mainly measures aortic stiffness. In the Rotterdam Study, we previously showed that arterial stiffness is strongly associated with common carotid intima media thickness, severity of plaques in the carotid artery, and severity of plaques in the aorta.6 The associations between cardiovascular disease risk factors and PWV that we observed in this isolate were in line with previous findings.26 In this study, we investigated to what extent genetic factors contribute to the variation of PWV in this population and if this is comparable to the contribution of genetic factors to the variance of atherosclerosis measurements, carotid IMT and plaque score. For PWV, we found a heritability of 0.36. This estimate is quite similar to the heritability of IMT in our study. After adjustment for appropriate cardiovascular risk factors, the PWV heritability was reduced to 0.26. Although this reduction in the heritability estimate is greater than that observed for other traits in our study, we also observed a greater increase in the proportion of variance associated with the covariates. Some of these covariates are genetically mediated themselves such as blood pressure (presented in this analysis as mean arterial pressure).
Duggirala et al12 reported a considerably higher heritability of carotid artery IMT (h2=0.92) among a small sample (46 sibships of various sizes) from Mexico City; however, the authors suggested that their findings should be interpreted with caution because using small sample of sibships only might have inflated their heritability estimate. In contrast, the findings from the present study are similar to those in previous reports, suggesting that genetic factors account for 0.30 to 0.40 of IMT variation in families after adjustment for traditional CVD risk factors.14–17 In this study, we also considered the genetic basis of another marker of subclinical atherosclerosis, the carotid plaque score. The observed associations between cardiovascular disease risk factors and plaque score in our study were comparable to previous findings.19,27 We found a heritability estimate of 0.28, which decreased to 0.21 after adjusting for the established cardiovascular risk factors. These heritability estimates were fairly similar to an earlier published study19 performed in the San Antonio Family Heart Study, also using a randomly ascertained study population. Moskau et al,18 who reported no significant heritability of plaque score, however, used a limited sample size of families that were ascertained by a parent affected with manifest atherosclerosis. It makes those results not directly comparable to results obtained in randomly selected, healthy populations.
It should be noted that direct comparison of the heritability estimates from the present study with those obtained from other studies is problematic. Different study designs, adjustments for covariates, and population-specific environmental contributions to the phenotypic variance might result in different heritabilities even when the genetic variance estimates in the different populations are similar. In the present study, we used a huge single pedigree, the largest pedigree used so far to study heritability estimates. Previously published studies were mainly based on sibpair analyses or multiple families selected on the basis of disease status with relatively small sizes. In contrast, the members of our extended pedigree represent a random sample of our study population and were not ascertained through persons with a specific disease status. This allows us to make inferences about these heritabilities at the population level. At the same time, it is important to realize that the genetic contribution to a specific trait may not be constant between populations, even when they inherited the same genetic makeup. For instance, genes involved in salt sensitivity will not express in a population with low salt intake. Presence or absence of an environmental factor (diet, physical activity, lifestyle, and so on) may indicate whether a certain genetic makeup plays an important role in the variability of a trait.
Inbreeding coefficient was not significantly related to any of the traits under the study. Excessive inbreeding may lead to a lower heritability as a result of an increase in homozygosity. However, we do not expect an effect of inbreeding on heritability estimates in this population, because this is a young, isolated population and fluctuations for common genetic variants (>1%) in this population are therefore small.28 The inclusion of covariates that are known to aggregate in families may have affected our results. Indeed, some of the covariates that we included are themselves genetically mediated, for example, blood pressures and plasma lipid levels. Including such variables in the heritability calculations could reduce the heritability estimates whenever there are pleiotropic effects of genes on the covariates and the phenotypic measures under study. The heritability estimates derived from a model adjusted for age and gender indicate to what extent the genetic factors (directly or through other covariates) contribute to part of the variance of the trait unexplained by age and gender. On the other hand, estimated heritabilities from a fully adjusted model represent only the contribution of the genes that are acting independently of the considered covariates.
In summary, we report for the first time that a substantial proportion of the variability in PWV is explained by genetic factors. This heritability was quite similar to the heritability estimates of IMT and plaque score in our study. Our findings stimulate the search for genes responsible for arterial stiffness.
Acknowledgments
This study was supported by the Netherlands Organization for Health Research and Development (ZonMw), grant 904-61-196.
A report on heritability of PWV has been published since submission of this article: Mitchell et al. Heritability and a genome-wide linkage scan for arterial stiffness, wave reflection, and mean arterial pressure: the Framingham Heart Study. Circulation. 2005;112:194–199.
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