Heritability of Carotid Artery Structure and Function
The Strong Heart Family Study
Objective— Alterations in carotid artery structure and function may represent phenotypic measures of vascular disease that contain information beyond that which can be inferred from conventional cardiovascular disease risk assessment. However, apart from their associations with cardiovascular disease risk factors and outcome, the genetic basis of variations in carotid artery structure and function is largely unknown. The purpose of this study was to examine the genetic and environmental contributions to carotid artery structure and function in 3 large groups of American Indians.
Methods and Results— Approximately 950 men and women, aged ≥18 years, in 32 extended families were examined between 1997 and 1999. By use of a variance component approach and the program Sequential Oligogenic Linkage Analysis Routines, heritabilities for carotid artery structure and function phenotypes were estimated. After accounting for the effects of covariates (sex, age, diabetes, impaired glucose tolerance, smoking, cholesterol, body surface area, and hypertension), we detected significant heritabilities (given as h2 values) for common carotid artery diastolic diameter (h2<0.44), intimal-medial wall thickness (h2<0.21), vascular mass (h2<0.27), arterial stiffness (h2<0.23), and the augmentation index (h2<0.18).
Conclusions— These results suggest that the additive effects of genes explain a moderate proportion of the variability of carotid artery structure and function.
Numerous studies have demonstrated that carotid artery structure and function are influenced by cardiovascular disease (CVD) risk factors, most notably, age, hypertension, and diabetes.1–3⇓⇓ Moreover, alterations in carotid artery structure and function may represent phenotypic measures of vascular disease that contain information beyond that which can be inferred from conventional CVD risk factors and, thereby, may better guide aggressive preventive strategies.4 More important, numerous epidemiological studies have demonstrated the ability of abnormal carotid artery structure to predict future CVD events, most notably, myocardial infarction.5–8⇓⇓⇓ The recent documentation of the prognostic utility of arterial function has been largely confined to hypertensive populations.9,10⇓
Although carotid artery function and size in normotensive individuals are strongly influenced by age, blood pressure, and body habitus,11 much of carotid artery variability is unexplained and is likely attributable to genetic factors. However, apart from genetic contributions to CVD risk factors, the genetic basis of variations in carotid artery structure and function is largely unknown. Thus, the objective of the present study is to examine the genetic and environmental contributions to noninvasive measures of carotid artery structure and function in American Indians participating in the Strong Heart Family Study (SHFS) as a first step in our search for CVD risk factor genes.
Strong Heart Study
The Strong Heart Study (SHS) is a longitudinal population-based study of the prevalence and incidence of CVD and its risk factors in 13 American Indian communities in 3 geographic regions in North and South Dakota, Oklahoma, and Arizona, including 3 phases of clinical examination and ongoing mortality and morbidity surveillance of resident tribal members aged 45 to 74 years (the cohort). During the baseline examination, conducted between 1989 and 1991, 4549 tribal members were examined. The second examination, conducted between 1993 and 1995, included 89% of the surviving members of the original cohort, and the third examination, conducted in 1998 to 1999, included 88% of surviving members of the cohort. A pilot family study was initiated in the third examination, the SHFS, in which 8 to 12 extended families (>300 family members aged at least 18 years) were recruited and examined in each region. An extension of the pilot family study is currently in progress and will involve recruitment of ≈90 additional families, equally divided among regions.
Strong Heart Family Study
Men and women, aged ≥18 years, in 32 extended families were examined between 1997 and 1999 in the SHFS. Data were available for a total of 1011 individuals. All participants gave informed consent for the present study, which was approved by the Institutional Review Boards of all of the participating institutions.
Details of the 3 family study populations have been previously described12 but are briefly reviewed in the present study. In the Dakota center, participants have been primarily examined from the Cheyenne River Sioux tribe in Eagle Butte, SD. The Oklahoma center sampled members of 7 tribes: the Apache, Caddo, Comanche, Delaware, Fort Sill Apache, Kiowa, and Wichita tribes in southwestern Oklahoma. The Arizona Field center enrolled members of the Pima, Maricopa, and Papago (Tohono O’odham) Indian tribes13 on the Gila River and Salt River Reservations.14
Phenotypic, Demographic, and Lifestyle Data
The aim of SHFS is to investigate genetic determinants of CVD and its risk factors in American Indians. The SHFS examination consisted of a personal interview, physical examination, laboratory tests, computerized ECG, carotid ultrasonography, and assessment of arterial function with the use of applanation tonometry. During the physical examination, various categories of phenotypes were measured, including obesity, lipid profile, hypertension and diabetes status, and clotting indices.
Previous publications describe the standard protocols that were used for the collection of all data.13,14⇓ Briefly, fasting blood samples were obtained during the physical examination for the measurements of lipids, lipoproteins, apolipoproteins, insulin, glucose, plasma creatinine, plasma fibrinogen, and plasminogen activator inhibitor-1. All variables were assayed at the MedStar Research Institute, Washington, DC, by using standard laboratory methods, as previously described.13,14⇓ Diabetes status was determined by using the World Health Organization criteria.15 Hypertension status was defined by using Joint National Committee V criteria.16 Participants were considered hypertensive if they had a systolic blood pressure ≥140 mm Hg and/or a diastolic blood pressure >90 mm Hg or were using antihypertensive medications. Body surface area was calculated by using a standard nomogram. Information was also collected on demographic characteristics, lifestyle variables, medical history, and reproductive history. Smoking was defined as having had at least 100 cigarettes.
Carotid Ultrasound Measurements
Carotid ultrasound measurements were made by using previously described methods.1,17⇓ In brief, the extracranial segments of the right and left carotid arteries were extensively scanned by using a high-frequency 2D ultrasound probe. Atherosclerotic plaque was defined as the presence of focal thickening of >50% of the surrounding wall. The presence of significant obstruction was detected by Doppler techniques. Carotid artery structure was quantified from M-mode tracings of the distal common carotid artery obtained 1 to 2 cm proximal to the carotid bulb; M-mode tracings were never obtained at the level of a discrete plaque. M-mode tracings were acquired from videotape with the use of a frame grabber, and measurements were performed on digitized images with the assistance of custom software (ARTSS, Cornell University). Intimal-medial thickness (IMT) of the far wall was measured for several cycles at end diastole and averaged. Minimum (end-diastolic) and maximum (peak-systolic) diameters were obtained by continuous tracing of the lumen-intima interfaces of the near and far walls. Arterial cross-sectional area, an estimate of circumferential wall volume or vascular mass, was calculated as previously described.17
Arterial function was quantified by using methods that incorporate carotid artery imaging (systolic and diastolic arterial diameters) and arterial pressure waveforms.18,19⇓ Applanation tonometry was performed on the radial artery by using a high-fidelity solid-state transducer (Millar), and the central pressure waveform was generated by using a validated transfer function.20,21⇓ Two estimates of vascular stiffness were evaluated: the arterial stiffness index (β value), which is relatively independent of distending pressure,22,23⇓ and the augmentation index (AI), a method that quantifies the extent to which vascular stiffening augments late-systolic central pressure that is due to a more rapid return of waves reflected from the periphery.24,25⇓
Univariate quantitative genetic analyses were used to partition the phenotypic variance of carotid artery structure and function into their additive genetic and environmental variance components26,27⇓ by using maximum likelihood variance decomposition methods.28,29⇓ This approach was implemented in the computer program Sequential Oligogenic Linkage Analysis Routines (SOLAR)30 and allows for an explicit test of whether correlations among family members are in part due to genetic effects.
The likelihood of the phenotypes of the family members was assumed to follow a multivariate normal distribution, although the method is robust to violations of this assumption.31 The phenotypic covariance matrix was modeled as a function of the coefficient of relationship between individuals and the additive genetic and environmental variances. Once the expected mean and the covariance matrix of each pedigree were defined, the likelihood of a pedigree was evaluated by using the multivariate normal density function and summed over all pedigrees.30 Probability values for the heritability calculations were obtained by likelihood ratio tests, where the likelihood of a model is estimated and compared with the likelihood of the model in which the heritability is constrained to 0. Twice the difference in the natural logarithmic likelihood is asymptotically distributed as a 1/2:1/2 mixture of a χ2 variable with 1 df and a point mass at 0.32
The heritability of plaque was estimated by using a pedigree-based maximum likelihood method that models disease status by a liability threshold model.26,33⇓ Although disease status is usually considered a qualitative trait, with individuals scored either as affected or unaffected, it is generally assumed that there is an underlying quantitative liability that determines affection status. If an individual’s liability exceeds a specified threshold, disease ensues. In contrast, if an individual’s liability is below the threshold, the individual will remain unaffected. Liabilities and the threshold value itself are estimated by using sex- and age-specific parameters and the population prevalence of the disease trait. To use the threshold model, we assume that the underlying liability distribution is normal. The joint probability of observing the disease statuses of family members is calculated by using a multivariate normal distribution that allows for relatedness.
Application to SHFS Data
The measures of arterial structure that were analyzed included lumen diameter, IMT, cross-sectional area, and the presence of plaque. The first 3 structural parameters were assessed by using the average of values from the right and left common carotid arteries. The functional parameters analyzed were the average β value and AI.
To maximize our power to detect genetic effects on carotid artery structure and function, our initial analyses were based on the combined data from all 3 centers. However, center was included as a covariate by using 2 indicator variables in all analyses to obtain an estimate of between-center differences. When larger sample sizes become available, independent analyses can be conducted in each center.
The analysis of each phenotype was restricted to those individuals for whom all covariate data were complete. The data for arterial cross-sectional area and arterial stiffness were natural-logarithmically transformed. The initial analysis screened each carotid phenotype for linear fixed effects (statistically significant at the P≤0.10 level) for the following covariates: sex, age, diabetes status, impaired glucose tolerance status, smoking status, cholesterol, and hypertension status. Additionally, body surface area was also considered in analyses of arterial lumen diameter and cross-sectional area. Any covariates whose univariate linear effects were significant at the P≤0.10 level in the initial analysis were retained in subsequent analyses, even if the significance levels decreased after inclusion of other covariates. After the initial covariate screening, maximum likelihood methods were used to estimate the effects of covariates and additive effects of genes. We also experimentally substituted the covariates systolic blood pressure, diastolic blood pressure, and pulse pressure for hypertension status; however, because these substitutions did not substantially alter our findings, only the results using the covariate hypertension status will be reported.
A total of 518 female and 369 male SHFS participants had requisite carotid data for the present analyses. Because we recruited extended families, the sample of examined individuals included information on ≈12 800 relative pairs: nearly 1900 pairs of first-degree relatives, 3025 pairs of second-degree relatives, 4012 pairs of third-degree relatives, and 4863 pairs of relatives of fourth-degree of greater.12 The descriptive statistics for carotid artery structure and function of SHFS participants are reported in Table 1.
The descriptive statistics of estimated covariates are reported in Table 2. The mean±SD values of body surface area in men and women were 2.10±0.25 and 1.89±0.22 m2, respectively. The mean total cholesterol levels in men and women were 185±38 and 180±35 mg/dL, respectively. The prevalences of diabetes and hypertension were both ≈30% in men and women. Approximately 37% and 60% of men and 32% and 48% of women reported that they currently and had ever smoked, respectively.
The proportion of the total phenotypic variance accounted for by the measured covariates ranged from 36% (plaque) to 54% (arterial cross-sectional area, Table 3). For arterial structural and functional phenotypes, the proportion of variance accounted for by covariate effects was substantial, with sex, age, and body surface area significantly affecting all arterial measures. The effects of hypertension and diabetes status were statistically significant for most of the variables examined, whereas cholesterol levels, center effects, and smoking status were less often statistically significant.
Heritability of Carotid Artery Structure and Function
Table 3 presents the proportion of the variance that is due to the covariates and the heritability, measured as the proportion of residual phenotypic variance due to the additive effect of genes, after accounting for the effects of covariates, for carotid artery structure and function. The proportion of residual phenotypic variance due to the additive effects of genes ranged from 18% (AI) to 44% (lumen diameter), with all P≤0.001. In contrast, the heritability of liability to plaque was not significantly different from 0. The estimation of heritabilities was limited to that attributable to additive genetic effects. If other nonadditive sources of genetic variation exist, such as dominance or epistasis, then these observed heritabilities would represent lower bounds and are conservative.
The present study indicates that after simultaneously adjusting for sex, age, diabetes status, impaired glucose tolerance status, smoking status, cholesterol, hypertension status, and body surface area, when appropriate, the additive effects of genes explain a moderate proportion of the variability of carotid artery structure and function in American Indian participants in SHFS. To our knowledge, the present study is the first to report heritabilities of 4 of the 6 measures examined (ie, lumen diameter, arterial cross-sectional area [a measure of vascular mass], plaque, and β value) and to document the importance of genetic factors in influencing carotid artery structure and function. Duggirala et al34 reported a considerably higher heritability (given as h2 value) of carotid artery wall thickness (h2 0.92) among a small sample (n=88) of mestizo sibling pairs from Mexico City. However, the authors suggested that their findings should be interpreted with caution because the characteristics of their data (small sample of sibships only) could have inflated their heritability estimate. In addition, their study did not provide separate analyses of IMT and plaque, because plaque, whenever present, was incorporated into the measurement of wall thickness. In contrast, the findings from the present study are similar to those in 2 previous reports suggesting that genetic factors accounted for 30%35 and 34%36 of IMT variation in families, after adjustment for traditional CVD risk factors. Snieder et al37 reported a moderate heritability of AI (h2 0.37) among a large sample (n=1538) of female twins from the St. Thomas’ United Kingdom Adult Twin Registry. Although our heritability estimate for AI of 18% is lower, twin studies provide upper estimates of heritability, which may not accurately measure the degree of genetic control in the nontwin population.38
It should be noted that direct comparison of the heritability estimates from the present study with those obtained from other studies is problematic. Different methods of parameter estimation, study designs, population-specific environmental contributions to the phenotypic variance, and ascertainment schemes affect heritability estimates, possibly resulting in different heritabilities even when the genetic variance estimates in the different populations are similar.39,40⇓
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, eg, diabetes status and hypertension.41 Including such variables in the heritability calculation could reduce the heritabilities whenever there are pleiotropic effects of genes on the covariate and the phenotypic measure under study. Recognizing that American Indian populations have among the highest rates of diabetes worldwide, we reasoned that genes influencing diabetes status also may affect carotid artery structure and function and that by “factoring out” the effects of diabetes status, we may have removed some of the genetic components of variation. In fact, we have previously demonstrated pleiotropic and environmental interactions between diabetes status and several CVD risk factors.41 Nonetheless, it was important to include diabetes and hypertension as covariates in all estimates because they are substantial correlates of these measures.
This study was funded by a cooperative agreement that includes National Institutes of Health grants U01 HL-65520, U01 HL-41642, U01 HL-41652, U01 HL-41654, U01 HL-65521, and U01 MH-59590. We would like to thank the Strong Heart Family Study participants and the participating American Indian tribes. Without their participation, this project would not have been possible. In addition, the cooperation of the Indian Health Service hospitals and the coordinators of the SHS clinics, Betty Jarvis, Marcia O’Leary, Dr Tauqeer Ali, and the many collaborators and staff of the SHS have made this project possible. We would also like to thank Drs John Blangero, Tony Comuzzie, and Jeff Williams for assistance with analytical approaches and Tauqeer Ali, Rosinna Briones, Cherie Kessler, and Neil Sykes for their expert performance of the carotid ultrasound studies. This research was conducted while the first author (Dr North) was a postdoctoral fellow at the Southwest Foundation for Biomedical Research.
The views expressed in this study are those of the authors and do not necessarily reflect those of the Indian Health Service.
Received April 10, 2002; revision accepted July 9, 2002.
- ↵Roman MJ, Saba PS, Pini R, Spitzer M, Pickering TG, Rosen S, Alderman MH, Devereux RB. Parallel cardiac and vascular adaptation in hypertension. Circulation. 1992; 86: 1909–1918.
- ↵Folsom AR, Eckfeldt JH, Weitzmann S, Ma J, Chambless LE, Barnes RW, Cram KB, Hutchinson RG. Relation of carotid artery wall thickness to diabetes mellitus, fasting glucose and insulin, body size, and physical activity. Stroke. 1994; 25: 66–73.
- ↵Wagenknecht LE, D’Agostino R Jr, Savage PJ, O’Leary DH, Saad MF, Haffner SM. Duration of diabetes and carotid wall thickness: the Insulin Resistance Atherosclerosis Study (IRAS). Stroke. 1997; 28: 999–1005.
- ↵Greenland P, Abrams J, Aurigemma GP, Bond MG, Clark LT, Criqui MH, Crouse JR III, Friedman L, Fuster V, Herrington DM, Kuller LH, Ridker PM, Roberts WC, Stanford W, Stone N, Swan HJ, Taubert KA, Wexler L. Prevention Conference V: beyond secondary prevention: identifying the high-risk patient for primary prevention: noninvasive tests of atherosclerosis burden: Writing Group III. Circulation. 2000; 101: e16–e22.
- ↵Salonen JT, Salonen R. Ultrasonographically assessed carotid morphology and the risk of coronary heart disease. Arterioscler Thromb. 1991; 11: 1245–1249.
- ↵Belcaro G, Nicolaides AN, Laurora G, Cesarone MR, De Sanctis M, Incandela L, Barsotti A. Ultrasound morphology classification of the arterial wall and cardiovascular events in a 6-year follow-up study. Arterioscler Thromb Vasc Biol. 1996; 16: 851–856.
- ↵Chambless LE, Heiss G, Folsom AR, Rosamond W, Szklo M, Sharrett AR, Clegg LX. Association of coronary heart disease incidence with carotid arterial wall thickness and major risk factors: the Atherosclerosis Risk in Communities (ARIC) Study, 1987–1993. Am J Epidemiol. 1997; 146: 483–494.
- ↵de Simone G, Roman MJ, Koren MJ, Mensah GA, Ganau A, Devereux RB. Stroke volume/pulse pressure ratio and cardiovascular risk in arterial hypertension. Hypertension. 1999; 33: 800–805.
- ↵Laurent S, Boutouyrie P, Asmar R, Gautier I, Laloux B, Guize L, Ducimetiere P, Benetos A. Aortic stiffness is an independent predictor of all-cause and cardiovascular mortality in hypertensive patients. Hypertension. 2001; 37: 1236–1241.
- ↵Roman MJ, Pickering TG, Pini R, Schwartz JE, Devereux RB. Prevalence and determinants of cardiac and vascular hypertrophy in hypertension. Hypertension. 1995; 26: 369–373.
- ↵North KE, Howard BV, Welty TK, Best L, Lee ET, Yeh JL, Fabsitz RR, MacCluer JW. The Strong Heart Family Study: genetic and environmental contributions to cardiovascular disease risk in American Indians. Am J Epidemiol. In press.
- ↵Lee ET, Welty TK, Fabsitz RR, Cowan LD, Le N, Oopik AJ, Cucchiara AJ, Savage PJ, Howard BV. The Strong Heart Study: a study of cardiovascular disease in American Indians: design and methods. Am J Epidemiol. 1990; 132: 1141–1155.
- ↵Howard BV, Lee ET, Cowan L, Fabsitz RR, Howard WJ, Oopik AJ, Robbins DC, Savage PJ, Yeh JL, Welty TK. Coronary heart disease prevalence and its relation to heart disease in American Indians: the Strong Heart Study. Am J Epidemiol. 1995; 142: 254–268.
- ↵Roman MJ, Pini R, Pickering TG, Devereux RB. Non-invasive measurements of arterial compliance in hypertensive compared with normotensive adults. J Hypertens. 1992; 10 (suppl 6): S115–S118.
- ↵Chen C-H, Nevo E, Fetics B, Pak PH, Yin FCP, Maughan WL, Kass DA. Estimation of central aortic pressure waveform by mathematical transformation of radial tonometry pressure: validation of generalized transfer function. Circulation. 1997; 95: 1827–1836.
- ↵Pauca AL, O’Rourke MF, Kon ND. Prospective evaluation of a method for estimating ascending aortic pressure from the radial artery pressure waveform. Hypertension. 2001; 38: 932–937.
- ↵Hirai T, Sasayma S, Kawasaki T, Yagi S. Stiffness of systemic arteries in patients with myocardial infarction: a noninvasive method to predict severity of coronary atherosclerosis. Circulation. 1989; 80: 78–86.
- ↵Murgo JP, Westerhof N, Giolma JP, Altobelli SA. Aortic input impedance in normal man: relationship to pressure waveforms. Circulation. 1980; 62: 105–116.
- ↵Kelly R, Hayward C, Avolio A, O’Rourke M. Noninvasive determination of age-related changes in the human arterial pulse. Circulation. 1989; 80: 1652–1659.
- ↵Falconer DS. Introduction to Quantitative Genetics. 3rd ed. Essex, UK: Longman; 1989.
- ↵Lange K, Boehnke M. Extensions to pedigree analysis IV: covariance components models for multivariate traits. Am J Med Genet. 1983; 35: 816–826.
- ↵Duggirala R, Williams JT, Williams-Blangero S, Blangero J. A variance component approach to dichotomous trait linkage analysis using a threshold model. Gen Epidemiol. 1997; 14: 987–992.
- ↵Duggirala R, Villapando CG, O’Leary DH, Stern MP, Blangero J. Genetic basis of variation in carotid artery wall thickness. Stroke. 1996; 27: 833–837.
- ↵Xiang AH, Azen SP, Buchanan TA, Raffel LJ, Tan S, Cheng LS, Diaz J, Toscano E, Quinonnes M, Liu CR, Liu CH, Castellani LW, Hsueh WA, Rotter JI, Hodis HN. Heritability of subclinical atherosclerosis in Latino families ascertained through a hypertensive parent. Arterioscler Thromb Vasc Biol. 2002; 22: 843–848.
- ↵Snieder H, Hayward CS, Perks U, Kelly RP, Kelly PJ, Spector TD. Heritability of central systolic pressure augmentation: a twin study. Hypertension. 2000; 35: 574–579.
- ↵Khoury MJ, Beaty TH, Cohen BH, eds. Fundamentals of Genetic Epidemiology. New York, NY: Oxford University Press; 1993: 1–381.
- ↵Mitchell BD, Kammerer CM, Blangero J, Mahaney MC, Rainwater DL, Dyke B, Hixson JE, Henkel RD, Sharp RM, Comuzzie AG, Vandeberg JL, Stern MP. Genetic and environmental contributions to cardiovascular risk factors in Mexican Americans: the San Antonio Family Heart Study. Circulation. 1996; 94: 2159–2170.
- ↵Mahaney MC, Blangero J, Comuzzie AG, Vandeberg JL, Stern MP, MacCluer JW. Plasma HDL cholesterol, triglycerides, and adiposity: a quantitative test of the cojoint trait hypothesis in the San Antonio Family Heart Study. Circulation. 1995; 92: 3240–3248.
- ↵North KE, Williams JT, Best L, Lee ET, Howard BV, MacCluer JW. Joint action of genes on diabetes status and related quantitative CVD Risk factors in American Indians of the Strong Heart Family Study. Circulation. 2001; 104 (suppl II): II-827.Abstract.