Heritability of Cardiovascular Risk Factors in a Chinese Population – Taichung Community Health Study and Family Cohort
Cheng-Chieh Lin, 1,2,3 Patricia A. Peyser, 4§ Sharon L.R. Kardia, 4,5 §, Chia-Ing Li, 2,3 Chiu-Shong Liu, 1,2,3 Julia S. Chu,4 Wen-Yuan Lin,1,2,5 Tsai-Chung Li, * 6,7
1. Department of Family Medicine, China Medical University Hospital, Taichung, Taiwan
2. School of Medicine, College of Medicine, China Medical University, Taichung, Taiwan
3. Department of Medical Research, China Medical University Hospital, Taichung, Taiwan
4. Department of Epidemiology, University of Michigan, Ann Arbor, Michigan, USA
5. Graduate Institute of Clinical Medical Science, College of Medicine, China Medical University, Taichung, Taiwan
6. Graduate Institute of Biostatistics, College of Management, China Medical University, Taichung, Taiwan
7. Department of Healthcare Administration, College of Health Science, Asia University, Taichung, Taiwan
Short title: Heritability of cardiovascular risk factors §: Authors equally contributed to the first author. Correspondence to: Tsai-Chung Li
China Medical University, 91 Hsueh-Shih Road, Taichung, 40421, Taiwan, Tel: 886-4-2205-3366 ext. 6605, Fax: 886-4-2207-8539, e-mail: tcli@mail.cmu.edu.tw
Abstract
Background and aim: Previous studies reporting on estimates of heritability of cardiovascular risk factors in Chinese are limited. This study aims to estimate the heritability of cardiovascular risk factors in relatives of residents who participated in the Taichung Community Health Study (TCHS) and Family Cohort (TCHS-FC) while controlling as many potential confounders as possible.
Methods: A total of 1564 study subjects from 494 families with members aged 12 to 91 years were enrolled from a random sample of participants of TCHS and their family members (TCHS-FC) from 2009 to 2012. Anthropometric measurement, body composition, blood pressure, plasma lipids, fasting glucose, insulin, highly sensitive C-reactive protein (hs-CRP), brachial-ankle pulse wave velocity (baPWV), and the ankle-brachial index (ABI), as well as a questionnaire interview, were obtained from each participant.
Results: Cardiovascular risk factors with estimates of heritability greater than 30% after multivariate adjustment were triglyceride (h2=0.41), HDL-C (h2=0.49), LDL-C (h2=0.47), total cholesterol (h2=0.46), hip circumference (h2=0.44), weight (h2=0.42), insulin (h2=0.39), hs-CRP (h2=0.38), BMI (h2=0.38), and percent body fat mass (h2=0.35). Correlation coefficients for significant sibling varied from 0.10 for weight to 0.47 for LDL-C whereas those for significant parent-offspring varied from 0.09 for fasting plasma glucose to 0.43 for baPWV.
Conclusions: This study demonstrated significant heritability and familial aggregation of cardiovascular risk factors in a random sample of ethnic Chinese population.
Key words: heritability; cardiovascular risk factors; family study; highly sensitive C-reactive protein; brachial-ankle pulse wave velocity
1. Introduction
Cerebrovascular disease (CVD), cardiovascular heart disease (CHD), and diabetes are the most prominent causes of death worldwide. In Taiwan, CHD, CVD, and diabetes were the second, third, and fourth leading causes of death in men and women in 2011, accounting for more than 22.3% and 26.6% of total deaths, respectively [1]. Cardiovascular risk factors associated with an increased risk of developing CHD that tend to cluster in individuals include abdominal obesity, high blood pressure, a low level of high-density lipoprotein cholesterol (HDL-C), a high triglyceride (TG) level, and a high plasma glucose concentration [2, 3]. These associated risk factors are referred to as the metabolic syndrome (MetS). Insulin resistance due to obesity or an inherited genetic defect was hypothesized as the mechanism underlying MetS.
MetS has been shown to increase the risk of CVD mortality and all-cause mortality [3, 4]. The role of environmental factors in the aetiology of CVD is well established. Inherited susceptibility to CVD is one of a number of possible risk factors that could account for this finding. Previous studies investigating genetic factors of cardiovascular risk factors include familial aggregation study [5-19], candidate gene study [20], and GWA study [20-24]. Studies of familial aggregation of MetS and its components focused on correlation of these phenotypes among family members [9-12]. Most studies that reported heritability of cardiovascular risk factors based on studies on twins either in white [15–17] or Asian populations [5–8 16]. Those studies using twin study design in Asian populations either report cross-trait correlations or diseases-discordant rates [5–8, 16]. Only one study reported heritability estimates of C-reactive protein (CRP) and body fat composition markers in Chinese adolescent twins [16]. Reported studies on estimates of heritability of cardiovascular risk factors
in Chinese using family study design are limited [12]. A previous study based on Chinese adolescent probands and their relatives in a rural area reported that
heritability was highest in LDL (0.36), then total cholesterol (0.33), and systolic blood pressure (0.32) [12]. Yet highly sensitive CRP (hs-CRP) and indicators for arterial stiffness and peripheral vascular disease (PVD) in Chinese adults have not been studied. Hs-CRP is a marker of systemic inflammation. Mild chronic elevations of hs-CRP concentrations are independently predictive of future cardiovascular events [17]. Arterial stiffness and PVD are two independent predictors of cardiovascular mortality and morbidity [18].
One problem encountered when attempting to assess the importance of genetic factors is the difficulty in determining how much of observed familial aggregation of CVD cases can be explained by shared environmental factors and how much is due to genetic predisposition. Failure to control several environmental risk factors while examining familial clustering was a major weakness of many previous studies. These factors include exposure to environmental tobacco smoke and physical activity, both of which have been confirmed to be associated with cardiovascular risk factors. The purpose of this study was to estimate heritability of cardiovascular risk factors in relatives of residents who participated in the Taichung Community Health Study and Family Cohort (TCHS-FC) while controlling as many potential confounders as possible. If familial aggregation exists, this study would provide the rationale to investigate both genetic and intra-familial environmental causes.
2. Methods 2.1 Participants
This community-based cross-sectional study was based on data from
Detailed TCHS methodology is described elsewhere [25]. Briefly, target population consisted of residents aged 40 and over in Taichung City, Taiwan in October 2004. A two-stage sampling design was used to draw residents, with a sampling rate
proportional to sample size within sampling units of each stage. At each stage, simple random sampling was performed. A total of 3,530 subjects were eligible, and 2,359 agreed to participate with an overall response rate of 66.83%. The current study had a total of 1564 study subjects. Among them, 484 were TCHS participants and 1080 were their family members. Considering that several measurements, such as hs-CRP and insulin, were only determined in the first 1305 subjects of the TCHS participants, these measurements were only available in 607 to 978 participants. Their ages were between 12 and 91 years and 707 were males, which corresponds to a proportion of 45.2%. Mean age of men was 49.84 years and that of women was 48.88 years. The 1,564 subjects belonged to 494 families: 5 families of size 1, 181 families of size 2, 163 of size 3, 83 of size 4, 31 of size 5, and 31 more than size 6. Relationships
consisted of sibships (178 sib pairs), 641 parent-offspring pairs, and 9 avuncular pairs. 2.2 Measures
Laboratory examination
Blood was drawn from an antecubital vein in the morning after a 12-h overnight
fasting and was sent for analysis within 4 h of blood collection. Biochemical markers,
such as fasting plasma glucose, HDL-C, and triglyceride, were analyzed by a
biochemical autoanalyzer (Beckman Coulter Synchron system, Lx-20, Fullerton, CA,
USA) at the Clinical Laboratory Department of China Medical University Hospital.
TUBE contains 5mg sodium fluoride to inhibit glucose metabolism and 4mg
potassium oxalate to chelate calcium and to prevent coagulation. Inter- and intra-assay
coefficient of variations (CVs) for fasting plasma glucose were both 4%. We
measured cholesterol and triglyceride in serum mode. Triglyceride levels were
determined by an enzymatic colorimetric method. Inter- and intra-assay CVs for
triglyceride were 6.8% and 5%, respectively. HDL-C level was measured by a direct
HDL-C method and the interassay and intraassay CVs were both 4.5%. Low-density
lipoprotein cholesterol (LDL-C) level was also measured by a direct LDL-C method
and inter- and intra-assay CVs were 4.5% and 3%, respectively. Hs-CRP levels were
measured by nephelometry, a latex particle-enhanced immunoassay (TBA-200FR,
Tokyo, Japan). Inter- and intra-assay CVs were <2.0% and <1.9%, respectively.
Lower detection limit of the assay was 0.1 mg/L. Serum insulin level was measured
by a commercial enzyme-linked immunosorbent assay kit (Diagnostic Products, Los
Angeles, CA). Inter-assay CV for insulin was 8.7% and intra-assay CV was 3.4%.
Brachial-ankle pulse wave velocity (baPWV) and ankle-brachial index (ABI) were
determined non-invasively with subjects in supine position and cuffs wrapped on both
brachia and ankles with a VP-1000 automated PWV/ABI analyzer (PWV/ABI; Colin
Co. Ltd., Komaki, Japan). BaPWV is calculated as the distance traveled by pulse
well-documented validity and reproducibility (coefficient of variation [CV]=3.31%
and reproducibility coefficient=0.947). For every subject, maximum of left and right
baPWV was chosen. Higher baPWV values indicated more severe arterial stiffness.
Lower ABI values indicated more severe PVD. Trained staff measured blood pressure
(BP) in the right arm using an appropriately sized cuff and a standard mercury
sphygmomanometer in a seated position. Physicians measured BP using the same
method in the same arm while they did the physical examination. If differences of BP
measured between trained staff and physicians exceed 5 mmHg (either systolic BP or
diastolic BP), then BP measurement was taken for a third time by the same physician.
Anthropometric measurement, demographic factors, and lifestyle behaviors
Anthropometric measurements were obtained from complete physical
examination. Weight and height were obtained on an autoanthropometer (super-view, HW-666), with subjects shoeless and wearing light clothing. With participant
standing, waist circumference was measured midway between superior iliac crest and costal margin by fitting the tape snugly but not compressing soft tissues. Hip
circumference was measured at the maximum protrusion point of buttocks around pelvis. Circumference was measured to the nearest 0.1cm. Then, waist-to-hip ratio (WHR) was calculated as a measure of regional fat distribution. WHR and waist-to height ratio (WhtR) were calculated by dividing WC by hip circumference and body height, respectively. WHtR corrects WC for an individual’s height and is another measure of central obesity. Body mass index (BMI) was derived from the formula of weight (kg) (height)2 (m2). Percent body fat mass (%FM) was determined by BIA
for total body, which was assessed by a body composition analyzer (Tanita BC-418, Arlington Heights, Illinois, USA).Amount of body fat was expressed as a percentage of total weight. Previous studies confirmed validity of BIA in estimating body
composition compared with dual energy X-ray absorptiometry (DEXA) [26].BIA was a good predictor of DEXA-derived fat-free mass (r=0.85 to 0.88) and was superior to BMI in measuring body fat [27]. It has been shown that BIA, as a simple method, has practice value in measuring anthropometric attributes for estimating their heritability [27].
Information on smoking, alcohol drinking, physical activity, and betel nut chewing were obtained by questionnaire when participants underwent a complete physical examination. Smoking, alcohol drinking, physical activity, and betel nut chewing were dichotomized into two groups. Those in non-smoking group did not ever smoke or ever smoked less than 100 cigarettes during their lifetime, whereas those in smoking group smoked currently or ever smoked greater than or equal to 100 cigarettes during their lifetime. Individuals who self-reported having alcohol drinking, betel nut chewing, or physical activity were classified into the group with this specific characteristic. Participants who were defined as having physical activity were those who had self-reported that they had engaged in physical activities regularly at least twice a week and had spent at least 20 minutes each time for 6 months.
2.3 Statistical analysis
Simple descriptive analyses, such as mean, standard deviation, and proportion, were employed to analyze data when appropriate. Intraclass correlations for sibling pairs, parent-offspring and spouse pairs were estimated for all cardiovascular risk factors. To determine contribution of genetic factors to cardiovascular risk factors, heritability estimates (h2) were calculated using variance components approach, which is
implemented in SOLAR [28]. To determine contribution of genetic factors to cardiovascular risk factors, a quantitative trait, y, for individual i was modeled as
y
i=
μ+
∑
β
jX
ij+
g
i+
e
i, where μ is trait mean,
X
ij is the j-th covariate withregression coefficient
β
j ,g
i is an additive genetic effect normally distributedwith mean of 0 and variance of 2 g
, and e is a random residual effect which is i normally distributed with a mean of 0 and variance of . Any non-additive genetic, e2 unmeasured measurement, and random error are incorporated into e . Sum of i
2 g and 2 e
is equal to 1. Heritability is estimated by 2 g
, which is defined as relative proportion of residual variance in the quantitative trait explained by additive genetic factors divided by residual variance after adjustment for covariates. Maximum likelihood methods were used to simultaneously estimate mean and variances as well as the covariate and genetic effects. Significance of covariate effects was assessed by likelihood ratio tests, which are used to assess significance of a parameter of interest by comparing log-likelihood of the model in which parameter is estimated with that of the model in which parameter is constrained to zero. Heritability estimates for
cardiovascular risk factors were calculated by adjusting for age, sex, smoking, alcohol drinking, physical activity, and betel nut chewing. Estimates of covariate variance obtained were used to estimate percentage of total variation explained by genetic factors: [(1−proportion of variance explained by covariates) × h2] × 100.
Demographic and laboratory characteristics are summarized in Table 1. Estimates of heritability, proportion of variance attributed to covariates, and proportion of variance attributed to genetic factors are shown in Table 2. Cardiovascular risk factors with estimates of heritability greater than 40% after multivariate adjustment were TG (h2=0.41; p<0.001), HDL-C (h2=0.49; p<0.001), LDL-C (h2=0.47; p<0.001), total cholesterol (h2=0.46; p<0.001), hip circumference (h2=0.44; p<0.001), and weight (h2=0.42; p<0.001). Cardiovascular risk factors with estimates of heritability between 20% and 40% included insulin (h2=0.39; p<0.001), SBP (h2=0.24; p<0.001), ABI (h2=0.25; p=0.0011), hs-CRP (h2=0.38; p=0.0011), waist circumference (h2=0.26; p<0.001), BMI (h2=0.38; p<0.001), %FM (h2=0.35; p<0.001), and WHtR (h2=0.24; p<0.001). Cardiovascular risk factors with more than 20% of variance attributed to all covariates included SBP (36.26%), baPWV
(48.24%), waist circumference (33.09%), %FM (39.81%), weight (33.46%), WHR (45.15%), and WHtR (25.05%). Cardiovascular risk factors with more than 20% of variance attributed to genetic factors included insulin (38.57%), TG (34.77%), HDL-C (40.41%), LDL-HDL-C (46.63%), total cholesterol (44.58%), ABI (25.06%), hs-HDL-CRP (35.88%), BMI (34.98%), %FM (21.20%), hip circumference (41.99%), and weight (28.23%).
Table 3 shows results of significant covariates including age, gender, smoking, alcohol drinking, physical activity, and betel nut chewing, which were associated with cardiovascular risk factors. Age and gender were significant factors explaining
variation of all cardiovascular risk factors, except for age in insulin and gender in total cholesterol. Smoking was associated with SBP, DBP, TG, HDL-C, and WHR; alcohol drinking with FPG, insulin, HDL-C, and baPWV; physical activity with insulin, TG, ABI, waist circumference, WHR, and WhtR; betel nut chewing with insulin, SBP,
DBP, TG, HDL-C, waist, BMI, %FM, WHR, and WHtR. These findings indicate that environmental factors were also significant predictors of variation in cardiovascular risk factors.
Table 4 shows intra-class correlation for spouse, sibling, and parent-offspring relationship. Significant weak or moderate familial correlation coefficients for spouse were HDL-C, baPWV, %FM, hip circumference, and weight. Cardiovascular risk factors with significant and moderate sibling correlation coefficients (>0.2) were HDL-C, LDL-C, total cholesterol, baPWV, %FM, hip, and WHtR. All parent-offspring correlation coefficients were significant except for ABI. Those with
moderate correlation coefficients were insulin, TG, HDL-C, LDL-C, total cholesterol, baPWV, and hip circumference.
4. Discussion
In our community-based sample of middle-aged adults and their family
members, we found that most cardiovascular risk factors are heritable. Results reveal a significant and moderate magnitude of heritability for insulin, TG, HDL-C, LDL-C, total cholesterol, hs-CRP, BMI, and hip circumference and a weak magnitude of heritability for FPG, SBP, DBP, baPWV, and WHR. Although these cardiovascular risk factors have a complex pathogenesis that is likely influenced by the interaction of numerous environmental and genetic factors, evidence for genetic effects suggests a substantial genetic component for variation in cardiovascular risk factors, even after accounting for effects of lifestyle factors. Prior studies have demonstrated that genetic factors contribute to variation in levels of cardiovascular disease-related variables, including hypertension [21], diabetes [22], cholesterol levels [23], and measures of obesity [24]. To our knowledge, our study using a random sampling method for participant selection represent the first large population-based estimate of heritability
for a large number of cardiovascular risk factors in Chinese adults who resided in community with integrated on-the-site screenings for hypertension, diabetes, and hyperlipidemia for adults aged 40 years and over. Our results suggest that these cardiovascular risk factors may be useful measures for further exploring genetics of atherosclerosis and clinical cardiovascular disease.
Although several studies have reported familial correlation and heritability of cardiovascular risk factors among different populations [9–15, 27, 29, 30], little has been performed on Chinese population [9, 12, 30]. Chien et al. [12] estimated heritability and familial correlation of parent-offspring and sibling on MetS-related quantitative components on ethnic Chinese community based on adolescent probands [12]. Feng et al. [9] reported sibling intra-class correlation in a Chinese population. Wu et al. [30] estimated heritability of metabolic factors and insulin variables based on siblings with Japanese or Chinese families with hypertension. Consistent with findings of previous studies [9, 10, 12, 13, 15], we demonstrated the strongest familial correlation and heritability in lipid-related quantitative components. On the contrary, estimates of heritability and familiar correlation in our study were either higher or lower than those estimated in other populations [9-13, 15, 27, 31, 32]. Compared with studies conducted among Chinese, estimates of heritability and intra-class correlation for parental offspring and sibling relationships in our study were close to those estimated by Chien et al. [12] and Feng et al. [9]. Our study further provides estimates of familial correlation and heritability of insulin, hs-CRP, baPWV, ABI, WHR, and WHtR. We demonstrated moderate heritability (>30%) in insulin, ABI, hs-CRP, and hip circumference; strong sibling correlation (>0.2) in baPWV, %FM, hip
circumference, and WHtR; and strong parent-offspring correlation in insulin, baPWV, and hip circumference. Heritability of ABI, baPWV, and hs-CRP has not been
reported in Chinese adult population. With exception of sibling correlation of insulin and %FM in a Chinese population reported by Feng et al. [9], these significant familiar correlations are reported for the first time in the current paper. Our study results strongly suggest genetic susceptibility in cardiovascular risk factors.
Our findings regarding heritability of lipid profiles are consistent with those reported by Chien et al based on Chinese adolescent probands and their relatives in a rural area [12], that estimated heritability was highest in lipid profiles. Results about percentage of variance attributable to genetic factors is so much higher for lipids than blood pressure are consistent with findings of previous studies which demonstrated so many loci have been found for lipids [33] versus blood pressure phenotypes [34]. On the contrary, our finding about high percentage of variance attributable to genetic factors for lipids was inconsistent with those reported by Jermendy et al [35]. In Jermendy’s study, 63 monozygotic and 38 dizygotic adult twin pairs were
investigated the effect of genetic and environmental influences on cardio-metabolic risk factors and they found lipid profiles were low heritable, but shared and unique environmental influences had the highest proportion of total phenotypic variance in lipid profiles. The possible explanation for the differences may be due to our study did not consider shared environmental influences.
This study had three advantages. First, study subjects were ascertained from a representative sample of well-defined geographic population and distributions of demographic factors are similar to our target population. Our estimates of heritability and familiar correlation are thus more reflective of conditions in our population. Heritability studies using twin design are based upon assumption that twins are representative of general population for outcomes being studies. Although twin design has advantage of allowing greater control over observable environmental risk factors,
it has several limitations. A major limitation is that twin study may restrict inferences to general population because clusters themselves may not be representative. In addition, twins reared together have a unique social and behavioral development experience because they grow up with a co-twin, while twins reared apart are likely to have been involved in unusual situations leading to their separation. Furthermore, recruitment of twin is difficult and there is substantial self-selection. This may result in differential participation rates that may lead to selection bias. Second, our study provides intra-class correlation for spouses, siblings, and parent-offspring whereas previous twin studies can only report intra-class correlation for siblings. Third, we adjusted for many lifestyle behaviors, such as physical activity, smoking, alcohol drinking, and betel nut chewing, some of which were neglected by many previous studies. However, we did not consider dietary habits, which were important determinants for cardiovascular risk factors.
Our study had several important limitations that must be stated. First, given limited funding, some measurements were restricted to the first 1305 TCHS subjects entering the current study, indicating that potential selection bias might exist. To assess this possibility, we examined demographic characteristics of individuals with and without these measurements by comparing age, sex, and administrative unit, and similar distributions were found. Non-differential distributions in age, sex, and administrative unit indicate that this type of selection error might be random. Thus, biased results in the effect may be toward the null, a lesser threat to validity. Second, the models we used assumed that there is no gene-covariate interaction, i.e.
heritability for a marker is constant across covariate spectrum such as age, and gender, which is commonly adopted by most studies. However, previous studies indicate that correlation among family members for lipids and blood pressure
depended on age, i.e., gene-age interactions [36, 37].
Third, we did not consider passive smoking status, dietary factors, and status of
hypertension, diabetes, and hyperlipidemia or their medication in our analysis.
Passive smoking status was an important determinant for cardiovascular risk factors,
but we did collect such information. Data from Adult smoking behavior Surveillance
system in Taiwan reported the prevalence of passive smoking in Taiwan was 24.9% in
family and 15.7% in occupation setting in 2010 [38]. In Taiwan, a policy regarding
prohibiting smoking in public settings was implemented, thus we believed prevalence
of passive smoking would become lower. Due to low prevalence of passive smoking,
its confounding effect on our heritability estimates would be small. For dietary
factors, a twin study indicated that genetic variants in key leukotriene enzymes are
associated with atherosclerosis, and dietary intake of polyunsaturated fatty acids
modifies the effect of leukotriene variants on atherosclerosis [39, 40]. Furthermore,
dietary factor is also associated with a favorable change in distribution of HDL
subspecies. Thus, no considering dietary factors may have impact on estimates of
heritability of baPWV, ABI, and HDL-C in our study. Regarding that we also did not
consider status of hypertension, diabetes, and hyperlipidemia and their medication in
our analysis, it is because prevalence of these diseases’ medication is low (<20%) and
results of heritability of metabolic variables were found no matter antihypertensive
medication was considered or not [30]. Fourth, our sample was primarily Chinese,
limiting generalizability of our findings to other racial and ethnic groups. Studies of
racially diverse samples observed a high variation in prevalence of cardiovascular risk
factors among Blacks, Whites, Chinese, and South Asia [41]. Thus, heritability
estimates from our results may not be extended to other racial/ethnic groups or Asian
populations. Fifth, a limitation of measuring the brachial–ankle PWV is this
parameter reflects not only elastic arterial stiffness, but also muscular arterial stiffness
[42]. Even so, previous study demonstrated brachial–ankle PWV shows a close
correlation with aortic PWV [42] and carotid–femoral PWV [43], the gold standard
for assessing central arterial stiffness [44]. Due to a high level of skill and exposure of
inguinal region being required for carotid–femoral PWV measurement, so its
applicability is mostly limited to research institutes [45], not in clinical practice. On
the contrary, baPWV has been shown to be a promising technique to assess arterial
stiffness conveniently in clinical practice. As for ABI, it has been reported its
reliability was 0.61, lower than minimal requirement of 0.7. Thus, it was suggested
there was a need for repeated measures of ABI in clinical practice [46]. Sixth, we did
not consider shared environment and epigenetic factors. For shared environment,
because we did not measure shared environments. However, 87.62% of siblings
indicated that they lived in separate households either from one another or from their
parents at the time of the study. Shared environments early in life may possibly
contribute to correlations for these cardiovascular risk factors among adult relatives.
For spouses, we cannot separate how much of their correlation is attributable to
shared environments and how much is attributable to assortative mating for factors
related to these cardiovascular risk factors. Spouse correlation coefficients for all
cardiovascular risk factors were much weaker than those of sibling and
parent-offspring, except for HDL-C and baPWV. We could assume that spouse correlation is
fully attributable to shared environments, which is likely, because of their weak
association. According to our findings, genetic factors would be stronger factors that
explain variation of these cardiovascular risk factors because of high estimate of
heritability, especially for lipid profiles and obesity markers. Recent studies explored
epigenetic effect on phenotypes using dizygotic and monozygotic twin studies
[48-50]. It has been shown that a difference in environment can produce different
developmental monozygotic twin subtypes with respective to their susceptibility,
resulting in discordant in autoimmune disease status [48]. In a high-throughput study
of epigenetic differences between monozygotic twins, they found there is an
[49]. On the contrary, a epigenetic study of DNA methylation status of about 6,000
unique genomic regions provide evidence that epigenetic similarity contribute to
phenotypic similarities in monozygotic co-twins [50]. Due to no study examining the
effect of epigenetic factors on heritability of cardiovascular risk factors, it is hard to
know the impact of epigenetic factors on our findings. In the future, studies estimating
heritability of cardiovascular risk factors may take into account of epigenetic factors. 6. Conclusion
In conclusion, a low or modest to high proportion of variability in
cardiovascular risk factors is attributed to genetic factors. Further studies of genetic linkage, candidate gene association, or GWAS association considering shared
environment factors are necessary to identify specific genetic variants associated with this important predictor of cardiovascular disease events and mortality. These
cardiovascular risk factors are also associated with environment factors including smoking, alcohol drinking, physical activity, or betel nut chewing.
Author contributions
CCL, PAP and TCL developed the hypotheses tested in the study. SLRK CCL, CSL and WYL contributed to study design, planned and conducted the study, performed the vascular measurements, developed diet materials, performed the statistical analyses, interpreted the results and wrote the manuscript. TCL, PAP, CIL and JSC contributed to statistical analyses, interpretation of the data and critically reviewed the manuscript.
Disclosures None.
Conflict of interest
The authors report no relationships that could be construed as a conflict of interest.
Acknowledgements
This study was supported primarily by the Ministry of Science and Technology of Taiwan (National Science Council) (NSC94-2314-B039-019, NSC95-2314-B-039-009, NSC97-2314-B-039-019 & NSC99-2628-B039-007-MY3), the China Medical University Hospital (DMR101-110), and Taiwan Ministry of Health and Welfare Clinical Trial and Research Center of Excellence (DOH102-TD-B-111-004).
Reference
1. Ten leading causes of death from Ministry of Health and Welfare, R.O.C: http://www.mohw.gov.tw/EN/Ministry/Statistic.aspx?f_list_no=474.
2. Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults. Executive summary of the third report of the National Cholesterol Education Program (NCEP) expert panel on detection, evaluation, and treatment of high blood cholesterol in adults (Adult Treatment Panel III). JAMA
2001;285:2486-97.
3. Isomaa B, Almgren P, Tuomi T, Forsén B, Lahti K, Nissén M, et al.
Cardiovascular morbidity and mortality associated with the metabolic syndrome.
Diabetes Care 2001;24:683-9.
4. Klein BE, Klein R, Lee KE. Components of the metabolic syndrome and risk of cardiovascular disease and diabetes in beaver dam. Diabetes Care 2002;25:1790-4.
5. Yang H, Li X, Cao W, Lu J, Wang T, Zhan S, et al. Chinese National Twin Registry as a resource for genetic epidemiologic studies of common and complex diseases in China. Twin Res. 2002;5(5):347-51.
6. Lee K, Song YM, Sung J. Genetic and environmental associations between C-reactive protein and components of the metabolic syndrome. Metab Syndr Relat
Disord. 2013;11(2):136-42.
7. Lee K, Sung J, Lee SC, Park SW, Kim YS, Lee JY, et al. Segment-specific carotid intima-media thickness and cardiovascular risk factors in Koreans: the Healthy Twin Study. Eur J Prev Cardiol. 2012;19(5):1161-72.
8. Li L, Gao W, Yu C, Lv J, Cao W, Zhan S, et al. The Chinese National Twin Registry: an update. Twin Res Hum Genet. 2013;16(1):86-90.
9. Feng Y , Zang T, Xu X, Xu X. Familial aggregation of metabolic syndrome and its components in a large Chinese population. Obesity (Silver Spring).
2008;16(1):125-9.
10. Lee KE, Klein BE, Klein R. Familial aggregation of components of the multiple metabolic syndrome in the Framingham Heart and Offspring Cohorts: Genetic Analysis Workshop Problem 1. BMC Genet. 2003;4 Suppl 1:S94.
11. Park HS , Park JY, Cho SI. Familial aggregation of the metabolic syndrome in Korean families with adolescents. Atherosclerosis. 2006;186(1):215-21.
12. Chien KL, Hsu HC, Chen WJ, Chen MF, Su TC, Lee YT. Familial aggregation of metabolic syndrome among the Chinese: report from the Chin-Shan
13. Austin MA, King MC, Bawol RD, Hulley SB, Friedman GD. Risk factors for coronary heart disease in adult female twins. Genetic heritability and shared environmental influences. Am J Epidemiol. 1987;125(2):308-18.
14. Edwards KL, Newman B, Mayer E, Selby JV, Krauss RM, Austin MA.
Heritability of factors of the insulin resistance syndrome in women twins. Genet
Epidemiol. 1997;14(3):241-53.
15. Poulsen P, Vaag A, Kyvik K, Beck-Nielsen H. Genetic versus environmental aetiology of the metabolic syndrome among male and female twins.
Diabetologia. 2001;44(5):537-43.
16. Wang G, Christoffel KK, Brickman WJ, Hong X, Arguelles L, Zhang S, et al. C-reactive protein in adolescent twins: patterns and relationship to adiposity. J Clin
Endocrinol Metab. 2011;96(10):3226-33.
17. Ridker PM, Rifai N, Rose L, Buring JE, Cook NR. Comparison of C-reactive protein and low-density lipoprotein cholesterol levels in the prediction of first cardiovascular events. N Engl J Med 2002;347:1557-65.
18. Blacher J, Asmar R, Djane S, London GM, Safar ME. Aortic pulse wave velocity as a marker of cardiovascular risk in hypertensive patients. Hypertension
1999;33:1111-7.
19. Edwards KL, Newman B, Mayer E, Selby JV, Krauss RM, Austin MA.
Heritability of factors of the insulin resistance syndrome in women twins. Genet
Epidemiol. 1997;14(3):241-53.
20. Gianfagna F, Cugino D, Santimone I, Iacoviello L. From candidate gene to genome-wide association studies in cardiovascular disease. Thromb Res. 2012;129(3):320-4.
21. Levy D, DeStefano AL, Larson MG, O'Donnell CJ, Lifton RP, Gavras H, et al. Evidence for a gene influencing blood pressure on chromosome 17. Genome scan linkage results for longitudinal blood pressure phenotypes in subjects from the Framingham Heart Study. Hypertension 2000;36:477-83.
22. Meigs JB, Panhuysen CI, Myers RH, Wilson PW, Cupples LA. A genome-wide scan for loci linked to plasma levels of glucose and HbA(1c) in a community-based sample of Caucasian pedigrees: The Framingham Offspring Study.
Diabetes 2002;51:833-40.
23. Shearman AM, Ordovas JM, Cupples LA, Schaefer EJ, Harmon MD, Shao Y, et al. Evidence for a gene influencing the TG/HDL-C ratio on chromosome 7q32.3-qter: a genome-wide scan in the Framingham Study. Hum Mol Genet
2000;9:1315-20.
24. Atwood LD, Heard-Costa NL, Cupples LA, Jaquish CE, Wilson PW, D'Agostino RB. Genomewide linkage analysis of body mass index across 28 years of the
Framingham Heart Study. Am J Hum Genet 2002;71:1044-50.
25. Lin CC, Liu CS, Lai MM, Li CI, Chen CC, Chang PC, et al. Metabolic syndrome and its associated risk factors in a Taiwanese metropolitan adult population.
BMC Public Health 2007;7(1):739-43.
26. Roubenoff R. Applications of bioelectrical impedance analysis for body
composition to epidemiologic studies. Am J Clin Nur 1996;64(suppl):459S-62S. 27. Tarnokia AD, Tarnokia DL, Meddab E, Cotichini R, Stazi MA, Fagnani C, et al.
Bioimpedance analysis of body composition in an international twin cohort.
Obes Res Clin Pract 2012;8(3):e291-7.
28. Almasy L, Blangero J. Multipoint quantitative-trait linkage analysis in general pedigrees. Am J Hum Genet. 1998;62:1198-211.
29. Murabito JM, Guo CY, Fox CS, D'Agostino RB. Heritability of the ankle-brachial index: the Framingham Offspring study. Am J Epidemiol.
2006;164(10):963-8.
30. Wu KD, Hsiao CF, Ho LT, Sheu WH, Pei D, Chuang LM, et al. Clustering and heritability of insulin resistance in Chinese and Japanese hypertensive families: a Stanford-Asian Pacific Program in Hypertension and Insulin Resistance sibling study. Hypertens Res. 2002;25(4):529-36.
31. Hong Y, Pedersen NL, Brismar K, de Faire U. Genetic and environmental architecture of the features of the insulin-resistance syndrome. Am J Hum Genet. 1997;60(1):143-52.
32. Carmelli D, Cardon LR, Fabsitz R. Clustering of hypertension, diabetes, and obesity in adult male twins: same genes or same environments? Am J Hum
Genet. 1994;55(3):566-73.
33. Global Lipids Genetics Consortium, Willer CJ, Schmidt EM, Sengupta S, Peloso GM, Gustafsson S, et al. Discovery and refinement of loci associated with lipid levels. Nat Genet. 2013;45(11):1274-83.
34. Tragante V, Barnes MR, Ganesh SK, Lanktree MB, Guo W, Franceschini N, et al. Gene-centric meta-analysis in 87,736 individuals of European ancestry identifies multiple blood-pressure-related loci. Am J Hum Genet.
2014;94(3):349-60.
35. Jermendy G, Horváth T, Littvay L, Steinbach R, Jermendy AL, Tárnoki AD, et al. Effect of genetic and environmental influences on cardiometabolic risk factors: a twin study. Cardiovasc Diabetol. 2011;10:96.
36. Shi G, Gu CC, Kraja AT, Arnett DK, Myers RH, Pankow JS, et al. Genetic effect on blood pressure is modulated by age: the Hypertension Genetic Epidemiology Network Study. Hypertension. 2009;53(1):35-41.
expression for plasma lipids, lipoproteins, and apolipoproteins. Am J Hum
Genet. 1997;60(3):638-50.
38. Adult Smoking Behavior Surveillance System from Health Promotion Administration, Ministry of Health and Welfare, R.O.C:
http://tobacco.hpa.gov.tw/Show.aspx?MenuId=581.
39. Zhao J, Goldberg J, Vaccarino V. Leukotriene A4 hydrolase haplotype, diet and atherosclerosis: a twin study. Atherosclerosis. 2013;226(1):238-44.
40. Bogl LH, Maranghi M, Rissanen A, Kaprio J, Taskinen MR, Pietiläinen KH. Dietary omega-3 polyunsaturated fatty acid intake is related to a protective high-density lipoprotein subspecies profile independent of genetic effects: a
monozygotic twin pair study. Atherosclerosis. 2011;219(2):880-6.
41. Chiu M, Austin PC, Manuel DG, Tu JV. Comparison of cardiovascular risk profiles among ethnic groups using population health surveys between 1996 and 2007. CMAJ. 2010;182(8):E301-10.
42. Yamashina A, Tomiyama H, Takeda K, Tsuda H, Arai T, Hirose K, et al. Validity, reproducibility, and clinical significance of noninvasive brachial-ankle pulse wave velocity measurement. Hypertens Res. 2002;25(3):359-64.
43. Sugawara J, Hayashi K, Yokoi T, Cortez-Cooper MY, DeVan AE, Anton MA, et al. Brachial-ankle pulse wave velocity: an index of central arterial stiffness? J
Hum Hypertens. 2005;19(5):401-6.
44. Adams MR, Robinson J, McCredie R, Seale JP, Sorensen KE, Deanfield JE, et al. Smooth muscle dysfunction occurs independently of impaired endothelium-dependent dilation in adults at risk of atherosclerosis. J Am Coll Cardiol 1998;32:123-7.
45. Bonny A, Lacombe F, Yitemben M, Discazeaux B, Donetti J, Fahri P, et al. The 2007 ESH/ESC guidelines for the management of arterial hypertension. J
Hypertens. 2008;26(4):825.
46. Weatherley BD, Chambless LE, Heiss G, Catellier DJ, Ellison CR. The
reliability of the ankle-brachial index in the Atherosclerosis Risk in Communities (ARIC) study and the NHLBI Family Heart Study (FHS). BMC Cardiovasc
Disord. 2006;6:7.
47. Tarnoki AD, Tarnoki DL, Bogl LH, Medda E, Fagnani C, Nisticò L, et al. Association of body mass index with arterial stiffness and blood pressure components: a twin study. Atherosclerosis. 2013;229(2):388-95.
48. Ballestar, E. Epigenetics lessons from twins: prospects for autoimmune disease.
49. Fraga MF, Ballestar E, Paz MF, Ropero S, Setien F, Ballestar ML, et al.
Epigenetic differences arise during the lifetime of monozygotic twins. Proc Natl
Acad Sci U S A 2005;102:10604-9.
50. Kaminsky ZA, Tang T, Wang SC, Ptak C, Oh GH, Wong AH, et al. DNA methylation profiles in monozygotic and dizygotic twins. Nat Genet 2009;41:240-5.
