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(Stroke. 1996;27:833-837.)
© 1996 American Heart Association, Inc.

Articles

Genetic Basis of Variation in Carotid Artery Wall Thickness

Ravindranath Duggirala, PhD; Clicerio González Villalpando, MD; Daniel H. O'Leary, MD; Michael P. Stern, MD John Blangero, PhD

From the Division of Clinical Epidemiology, Department of Medicine, University of Texas Health Science Center at San Antonio (R.D., M.P.S.), and the Department of Genetics, Southwest Foundation for Biomedical Research (J.B.), San Antonio, Tex; the Centro de Estudios en Diabetes, The American British Cowdray Hospital, Unidad de Investigación Médica en Enfermedades Metabólicas, Hospital "Bernardo Sepulveda" Centro Medico Nacional, and Instituto Mexicano del Seguro Social, Mexico City, Mexico (C.G.V.); and the Department of Radiology, New England Medical Center, Tufts University School of Medicine, Boston, Mass (D.H.O'L.).

Correspondence to Ravindranath Duggirala, PhD, Division of Clinical Epidemiology, Department of Medicine, University of Texas Health Science Center at San Antonio, 7703 Floyd Curl Dr, San Antonio, TX 78284-7873. E-mail ravi@gauss.uthscsa.edu.

	Abstract

Top Abstract Introduction Subjects and Methods Results Discussion References

 
Background and Purpose Other than the documented associations of risk factors and carotid artery wall thickness, the genetic basis of variation in carotid artery intimal-medial thickness (IMT) is unknown. The purpose of this study was to examine the extent to which variation in common carotid artery (CCA) IMT and internal carotid artery (ICA) IMT are under genetic control.

Methods The sibship data used for this analysis were part of an epidemiological survey in Mexico City. The CCA and ICA analyses were based on 46 and 44 sibships of various sizes, respectively. The CCA and ICA IMTs were measured with carotid ultrasonography. Using a robust variance decomposition method, we performed genetic analyses of CCA IMT and ICA IMT measurements with models incorporating several cardiovascular risk factors (eg, lipids, diabetes, blood pressure, and smoking) as covariates.

Results After accounting for the effects of covariates, we detected high heritabilities for CCA IMT (h²=0.92±0.05, P=.001) and ICA IMT (h²=0.86±0.13, P=.029). Genes accounted for 66.0% of the total variation in CCA IMT, whereas 27.7% of variation was attributable to covariates. For ICA IMT, genes explained a high proportion (74.9%) of total phenotypic variation. The covariates accounted for 11.5% of variation in ICA IMT.

Conclusions Our results suggest that substantial proportions of phenotypic variance in CCA IMT and ICA IMT are attributable to shared genetic factors.

Key Words: carotid arteries • genetics • ultrasonics

	Introduction

Top Abstract Introduction Subjects and Methods Results Discussion References

 
Although the molecular bases of atherosclerosis remain unclear, there have been recent efforts to examine quantitative intervening phenotypes that are involved in its pathogenesis.^{1 2} Given the strong correlations between atherosclerosis in various vascular beds,^{3 4 5} measurement of carotid artery wall IMT is becoming increasingly accepted as a marker of generalized arterial disease.^{6 7 8} Carotid ultrasonography is noninvasive and has proved to be a reproducible and accurate method of measuring arterial IMT.^{9 10 11 12}

Several studies have demonstrated that carotid artery IMT is associated with a number of cardiovascular risk factors, such as age, smoking, elevated concentrations of serum TC, low levels of HDL cholesterol, hypertension, and diabetes.^{6 13 14 15 16 17 18} Also, apolipoprotein genetic polymorphisms, including those involving the apolipoprotein A-I/C-III/A-IV gene cluster and apolipoprotein E, have been shown to be associated with carotid artery wall thickness.^{19 20 21} Recently, it has been demonstrated that HDL subfractions, rather than the concentrations of HDL cholesterol, and cholesteryl ester transfer protein are closely associated with carotid artery wall thickness.²²

According to some studies, systolic but not diastolic blood pressure is associated with IMT.^{6 23} Other studies, however, indicate that both systolic and diastolic blood pressure are associated with IMT in individuals with non–insulin-dependent⁶ diabetes mellitus¹² and also in relatively young and healthy subjects.²⁴ According to O'Leary et al,⁶ ICA IMT is more strongly correlated with a history of coronary disease, whereas the strongest correlate of stroke is CCA IMT. Carotid artery maximal wall thickness has been demonstrated to be associated with clinical manifestations of cerebral, coronary, and peripheral vascular atherosclerosis and with left ventricular hypertrophy.^{5 8 25 26 27}

Apart from its documented associations with risk factors and clinical end points, the genetic basis of variation in carotid artery wall thickness is unknown. In the present study, we examined the extent to which variation in ultrasonographically assessed CCA IMT and ICA IMT are under genetic control. Additionally, we examined the nature of the effects of cardiovascular disease risk factors such as lipid levels, blood pressure, diabetes, BMI, and smoking on the heritability estimates of IMT. To accomplish this aim, we used data on siblings studied as part of our epidemiological survey in Mexico City.

	Subjects and Methods

Top Abstract Introduction Subjects and Methods Results Discussion References

 
Population
We carried out an epidemiological survey in six low-income "colonias" (equivalent to census tracts) in Mexico City to examine the prevalence of diabetes and cardiovascular disease among the low-income residents. Like most of the population of Mexico, these individuals constitute a "Mestizo" group, which is characterized by Native American (59%), European (34.8%), and African (6.2%) ancestry.²⁸ The details of population sampling have been discussed in earlier publications.^{29 30 31} In brief, a total of 15 532 individuals were enumerated, and of these, 3505 subjects were considered eligible for the survey (35- to 64-year-old men and nonpregnant women). Of these individuals, a total of 2282 subjects underwent a baseline medical examination. Ultrasound imaging of the right and left CCA and ICA was performed on 1742 individuals who attended a follow-up examination 3.25 years later. All survey and examination procedures were approved by the institutional review board of the University of Texas Health Science Center at San Antonio, and all subjects gave informed consent.

Because the original study was not specifically designed for a genetic epidemiological investigation, the sibships, mainly from the first two of the six "colonias" in the original study, have been reconstructed for the present purpose. First, potential sibships were identified by examining the unique combinations of paternal and maternal surnames (Mexicans use two surnames, the first one from the father and the second one from the mother) used by individuals. The potential sibships thus identified were subsequently confirmed by interviews with the respective families. The CCA analyses were based on 46 sibships of various sizes (Table 1) involving 39 men and 49 women. The mean age of these individuals was 46 years, ranging from 35 to 63 years. The ICA analyses were based on 44 sibships of various sizes involving 34 men and 38 women, ranging in age from 35 to 61 years, with a mean age of 45 years. Sibships of size 1 were included because they contribute to the evaluation of covariate effects. In addition to the sibships, unrelated individuals from the first two "colonias" in whom carotid ultrasound measurements were available were also included in the analyses (449 for CCA IMT and 442 for ICA IMT) because they also provide information on covariate effects.

View this table: [in this window] [in a new window]   Table 1. Structure of Sibships and Sample Sizes Used for Genetic Analysis of CCA and ICA IMT Phenotypes

Ultrasonography and Carotid Artery IMT Phenotypes
Carotid arteries were imaged ultrasonographically by a single sonographer using a high-resolution ultrasound scanner (Toshiba SSA-279A, Toshiba American Medical Systems) and read at a central reading center by a single reader. The scanning and reading protocols used were identical to those used in several large multicenter NIH-sponsored epidemiological studies, including the Cardiovascular Health Study (CHS), the Insulin Resistance and Atherosclerosis Study (IRAS), and the Epidemiology of Diabetes Control (EDIC) Study.^{6 9 32}

All scans were performed by a single sonographer who underwent a standardized training course and was certified by the Ultrasound Reading Center at the Geisinger Clinic, Danville, Pa (since moved to Tufts–New England Medical Center, Boston, Mass). The scanning protocol required sonographers to obtain a single lateral view of the left and right CCA and three views (anterior, lateral, and posterior) of the left and right ICA centered on the site of maximum wall thickness. Doppler-derived peak systolic velocities were also determined. All studies were recorded on super-VHS videotape and sent weekly to the Ultrasound Reading Center for standardized readings by a single reader there. The images were digitized, and the reader, using a Wacom Tablet stylus, drew lines identifying six interfaces, three on the near wall and three on the far wall. Maximum IMT for each segment was determined using a specially designed arterial image-analysis computer program.

Since 1989, the Ultrasound Reading Center has served as the core ultrasound laboratory for numerous single-center and multicenter studies of carotid atherosclerosis and has analyzed over 25 000 carotid ultrasounds from approximately 40 sites throughout North America. In four major NIH-funded studies, CCA and ICA IMT measurements were successfully obtained for approximately 99% of participants. The number of lines drawn at interfaces is commonly used as a measure of quality of scans. Using this measure, our results compare favorably with those of large national studies such as the CHS, IRAS, and EDIC. For example, the percentage of lines drawn (for either right or left side of CCAs or ICAs) in the above three national studies ranged from 91.2% to 99.6%, whereas in our Mexico City study it ranged from 91.3% to 100.0%.

Following the practice of the CHS, IRAS, and EDIC, the multiple measures of carotid artery wall thickening were reduced to create two summary variables or phenotypes, one for the CCA and one for the ICA. In this study, the maximum wall thickness of the CCA (CCA IMT) was defined as the mean of the far walls of both the left and right CCAs. To quantify the maximum wall thickness of the ICA (ICA IMT), the mean of the maximum IMTs of the three views of the far wall of the right ICA were averaged with the corresponding mean of the left ICA. Both CCA IMT and ICA IMT phenotypes were analyzed as continuous variables in this study.

Other Measurements
For the Mexico City epidemiological study, several anthropometric, hemodynamic, metabolic, and behavioral variables were collected as described previously.³¹ Several of these were used as covariates in this study. The anthropometric variables (eg, height and weight) were collected using standardized anthropometric protocols.³³ BMI was calculated as weight (kilograms) divided by height (meters) squared. SBP (first phase) was measured to the nearest even digit with a random-zero sphygmomanometer (Hawksley-Gelman Lancing).

Fasting plasma glucose concentrations and fasting serum TC, triglycerides, and HDL cholesterol concentrations were measured with enzymatic procedures described previously.³⁴ Glucose concentrations were measured again 2 hours after a standardized oral glucose load.³⁵ Diabetes was diagnosed according to the World Health Organization criteria.³⁵ Subjects who did not meet these criteria but who gave a history of diabetes and were under treatment with oral antidiabetic agents or insulin were also considered to have diabetes. Current smoking status was defined as those subjects who currently smoked cigarettes. The variables of BMI, SBP, TC, triglycerides, and HDL cholesterol were analyzed as continuous variables, and diabetes status and current smoking status were considered dichotomous traits (yes or no). For example, of the 88 sibs involved in the common carotid analyses, 29 were diabetic (14 men and 15 women) and 33 were current smokers (21 men and 12 women). Of the 72 sibs involved in the internal carotid analyses, 24 were diabetic (13 men and 11 women) and 30 were current smokers (19 men and 11 women).

Statistical Genetic Analysis
A number of statistical genetic models that use covariances or correlations among biological relatives are available to decompose the total phenotypic variance of a trait into its genetic and environmental components. In this study, we used a robust variance component method, which is not constrained by the assumption of multivariate normality of the phenotype, to partition phenotypic variation into its genetic and environmental components.³⁶ The proportion of phenotypic variance that can be attributed to (additive) genetic effects, which is termed the heritability (h²), can be estimated from the components of variance.^{37 38}

We estimated both variance components and covariate effects simultaneously by maximum likelihood estimation. In addition to the age and sex effects, our model included the following cardiovascular risk factors as covariates: diabetes mellitus, SBP, BMI, current smoking status, triglycerides, TC, and HDL cholesterol levels as covariates. As a working model, a likelihood function based on multivariate normal density was numerically maximized to obtain parameter estimates. Twelve parameters were estimated: µ, the phenotypic mean; , the phenotypic standard deviation; ß_sex, the regression coefficient for sex; ß_age, the regression coefficient for age; ß_{triglycerides}, the regression coefficient for triglycerides; ß_TC, the regression coefficient for TC; ß_smoke, the regression coefficient for smoking status; ß_diabetes, the regression coefficient for diabetes status; ß_HDL, the regression coefficient for HDL cholesterol; ß_SBP, the regression coefficient for SBP; ß_BMI, the regression coefficient for BMI; and h², the heritability of the phenotype.

Although the final estimators can be consistently obtained using likelihood techniques even when the assumption of multivariate normality is violated, the estimation of standard errors for these estimators can be problematic under these circumstances.

Robust estimators for standard errors, however, were computed by a modification of the covariance matrix of the parameters involving specification of the first two moments of the underlying distribution. In a similar way, a robust z score statistic was obtained to test whether a given variance component was zero. This robust test statistic was obtained as the product of the score (ie, first derivative) and the square root of the diagonal element of the inverted covariance matrix.³⁶ The critical value of this robust score test was obtained from a standard normal distribution. The robust variance decomposition procedure has been implemented by us using the computer program FISHER.³⁹

	Results

Top Abstract Introduction Subjects and Methods Results Discussion References

 
Table 2 contains the results from the variance component analysis of CCA IMT. After accounting for the effects of the covariates on CCA IMT, we detected a very high heritability for CCA IMT (h²=0.92±0.05), which is highly statistically significant (P=.0012). The variables of age, diabetes mellitus, SBP, TC, and HDL cholesterol also showed highly significant effects on CCA IMT. These effects were in the expected directions. The effects of triglycerides and sex on CCA IMT were marginal. The associations between CCA IMT and current smoking status and BMI were insignificant. When covariate effects were not included in the models, heritability for CCA IMT was 0.86±0.05. It is interesting that after the correction for the covariate effects the heritability estimate increased slightly, probably indicating that the effects of the covariates used in this analysis are independent of the genes responsible for the observed high heritability for CCA IMT.

View this table: [in this window] [in a new window]   Table 2. Estimates of Heritability and Other Parameters With Associated Robust Standard Errors, Robust Test Scores, and Significance Levels for CCA and ICA IMT

Table 2 shows a very high heritability (h²=0.86±0.13) for ICA IMT, which is also statistically significant (P=.029). As in the case of CCA IMT, ICA IMT was positively and significantly associated with age (P<.0001). The covariates SBP, triglycerides, and TC were significantly associated with ICA IMT. The relationships between ICA IMT and the covariates diabetes, smoking, and sex were marginally significant. The effects of BMI and HDL cholesterol on ICA IMT were statistically insignificant. As in the case of CCA IMT, the heritability estimate for ICA IMT without accounting for the covariate effects (h²=0.87±0.10) was similar to the heritability estimate obtained after correcting for covariate effects (h²=0.86±0.13). This finding again suggests that the observed genetic variation in ICA IMT is due to genes other than those responsible for variation in the covariates used in the analysis.

The results of the analysis of variance components for CCA IMT and ICA IMT are reported in Table 3. Table 3 indicates that genes account for 66.0±8.6% of the total CCA IMT variation, whereas 27.74±7.5% of variation is attributable to the covariates age, sex, triglycerides, TC, diabetes, HDL cholesterol, and SBP. Age alone explains about two thirds of the observed variation in CCA IMT due to the covariates. Unspecified environmental factors account for 6.3% of total variation. For the ICA IMT, a substantial proportion of variation is under the control of genes (74.9±19.1%). In combination, the covariates age, sex, triglycerides, TC, smoking status, diabetes, and SBP explain 11.5±5.6% of variation in ICA IMT. Of the covariates, age accounts for approximately two thirds of the variation in ICA IMT explained by the covariates; 13.6% of variation is attributable to other unspecified environmental factors.

View this table: [in this window] [in a new window]   Table 3. Partitioning of Phenotypic Variances of CCA and ICA IMT Into Genetic and Environmental Components

	Discussion

Top Abstract Introduction Subjects and Methods Results Discussion References

 
Aside from indications that certain structural defects of arterial wall (ie, intracranial aneurysms and cervical artery dissections) may be due to genetic predisposition,^{40 41} the genetic basis of variation in carotid artery IMT has not, to our knowledge, been explored. To assess the extent to which variation in carotid artery wall thickness is under genetic control, we performed quantitative genetic analyses of CCA IMT and ICA IMT measurements obtained from siblings. The results provide evidence that these quantitative phenotypes are strongly influenced by genetic factors. The present analysis does not, however, permit identification of the mode of genetic transmission of these phenotypes.

To verify whether classic cardiovascular risk factors would have an impact on the genetic variation in carotid artery IMT, our genetic models included known correlates of carotid artery wall thickness such as TC, triglycerides, HDL cholesterol, diabetes mellitus, SBP, and BMI. These factors are known to aggregate in families under the influences of both genes and environment.^{42 43 44 45} Our results suggest that their effects on carotid artery wall thickness are independent of the genetic sources accounting for the observed high heritabilities for both CCA IMT and ICA IMT.

It has been suggested that lipoprotein(a) may have thrombogenic plus atherogenic properties.⁴⁶ On the basis of a random sample of middle-aged eastern Finnish men, Rankinen et al⁴⁷ demonstrated that IMT in the carotid bifurcation, although not in the common carotid, is associated with serum apolipoprotein(a) concentrations. Additionally, these authors found positive univariate associations of common carotid IMT and carotid bifurcation IMT with the levels of fibrinopeptide A. On the other hand, two studies have shown no association between carotid artery IMT and plasma fibrinogen, a determinant of arterial thrombosis.^{24 47} In fact, a univariate association between fibrinogen and carotid artery IMT was discernible in one of these studies,²⁴ but it largely disappeared in multivariate analysis.

Plasma ACE concentrations have been shown to be associated with structural changes in the carotid arterial wall,⁴⁸ and there is evidence of a familial resemblance in plasma ACE activity.⁴⁹ It has been demonstrated that in the absence of conventional risk factors for atherosclerosis, chronic exposure to high concentrations of plasma ACE may have an independent effect on the development of carotid artery IMT.⁴⁸ Given that the smooth muscle cell is the main cellular component of atheromatous plaques, differential expression of c-myc proto-oncogene may have relevance to the pathogenesis of carotid atherosclerosis, since the proto-oncogene c-myc is implicated in the induction of cell proliferation and differentiation.⁵⁰

In view of the above observations, one may speculate that a variety of (known or unknown) shared genetic factors, with either individual or cumulative effects, could have contributed to the high heritabilities for CCA IMT and ICA IMT observed in this study. Nonetheless, the present results should be interpreted cautiously for the following reasons. First, the analyses are based on relatively small sample sizes, especially in the case of ICA IMT. Secondly, any heritability estimate based on sib analysis could be inflated, since such data do not account for either dominance effects or shared environmental factors; hence, our estimates should be considered as an upper limit to the heritability of these traits.³⁷

In summary, the present study suggests that a high proportion of the phenotypic variance in quantitative measures of CCA IMT and ICA IMT is attributable to genetic factors. These observations are of potential importance in understanding the etiology of carotid atherosclerosis.

	Selected Abbreviations and Acronyms

 

ACE = angiotensin-converting enzyme BMI = body mass index CCA = common carotid artery HDL = high-density lipoprotein ICA = internal carotid artery IMT = intimal-medial thickness NIH = National Institutes of Health SBP = systolic blood pressure TC = total cholesterol

	Acknowledgments

 
This research was supported by grants from the National Heart, Lung, and Blood Institute (NHLBI; RO1 HL-24799), the Consejo Nacional de Ciencia y Tecnología (CONACYT) (2029/M9303), the Fundación Mexicana para la Salud, and the American Diabetes Association mentor-based postdoctoral fellowship program (Dr Duggirala). Partial support for the analysis was provided by grant PO1 HL-45522 from NHLBI.

Received August 24, 1995; revision received February 12, 1996; accepted February 12, 1996.

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