Differential Influence of Family History of Hypertension and Premature Myocardial Infarction on Systolic Blood Pressure and Left Ventricular Mass Trajectories in Youth
* Georgia Prevention Institute, Department of Pediatrics, Medical College of Georgia, Augusta, Georgia
Twin Research and Genetic Epidemiology Unit, St Thomas’ Hospital, London, United Kingdom
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ABSTRACT |
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Objective. To examine the influence of genetic susceptibility to essential hypertension (EH) and the genetic susceptibility to premature myocardial infarction (MI) on longitudinal development of systolic blood pressure (SBP) and left ventricular mass (LVM) in youth.
Methods. Individual SBP and LVM growth curves across age were created for a sample of 745 subjects (age range: 4.9–27.5 years) and a sample of 687 subjects (age range: 8.2–27.5 years), respectively. Each sample had an approximately equal proportion of African American and European American males and females, with annual assessments over a 10-year period. Family history (FH) of EH and FH of premature MI were used as measures of genetic susceptibility to EH and to premature MI, respectively. Positive FH (FH+) of EH and of premature MI were defined, respectively, as verified EH in 1 or both biological parents, and verified MI in any biological parent or grandparent before 55 years of age.
Results. Subjects with an FH+ of EH had higher SBP levels and stronger increases in SBP over time than subjects with a negative FH (FH–) of EH. Subjects with an FH+ of EH also showed higher LVM levels than subjects with an FH– of EH. In addition, the effect of an FH+ of EH on LVM was stronger in females than males. The effects of FH of EH on SBP and LVM could not be explained by differences in socioeconomic status, but the effect on LVM was no longer significant after adjustment for BMI. FH of MI had no significant effects on SBP or LVM.
Conclusions. Effects of genetic susceptibility to EH on SBP and LVM trajectories were observed in childhood, whereas no such effects were found for FH of MI. Genetic markers of EH may improve the understanding of individual differences in susceptibility to develop hypertension and LV hypertrophy.
Key Words: family history • systolic blood pressure • essential hypertension • myocardial infarction • ethnicity • sex • multilevel modeling
Abbreviations: AA, African American • BMI, body mass index • BP, blood pressure • CV, cardiovascular • CVD, cardiovascular disease • DBP, diastolic blood pressure • EA, European American • EH, essential hypertension • FH, family history • FH+, positive family history • FH–, negative family history • LVM, left ventricular mass • MI, myocardial infarction • SBP, systolic blood pressure • SES, socioeconomic status
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONCONCLUSIONSREFERENCES |
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Elevated blood pressure (BP)1 and increased left ventricular mass (LVM)2 are independent risk factors for cardiovascular (CV) morbidity and mortality, such as myocardial infarction (MI) and congestive heart failure.2,3 African Americans (AAs) and males have higher BP levels3 and greater LVM4 compared with European Americans (EAs) and females, respectively. Several anthropometric, hemodynamic, and/or sociodemographic variables have been found to be associated with BP and LVM levels, accounting for considerable proportions, but not all, of the between-subject variance in systolic BP (SBP) and LVM.5–8 This suggests that additional factors are needed to explain individual differences in SBP and LVM. Because both LVM and SBP are known to be in part genetically determined,9,10 it is conceivable that genetic susceptibility may explain at least part of the remaining variance.
Family history (FH) of CV disease (CVD) is a proxy for genetic influence. Normotensive offspring of hypertensive subjects have been found to be at higher risk for development of essential hypertension (EH)11,12 and increased LVM.12,13 Besides their increased risk to develop EH, it can be hypothesized that subjects with increased LVM and/or elevated SBP are also at higher risk to develop ischemic heart disease (ie, MI, angina). Therefore, an FH of MI is another proxy measure of genetic susceptibility that may act via SBP elevation and increased LVM.
Children with a positive FH (FH+) of EH show higher SBP levels than subjects with a negative FH (FH–).12,14 Similar findings have been observed in adolescents12,14 and young adults.11 In addition, Lauer et al11 found higher SBP levels in young adults with an FH+ of ischemic heart disease, but not in children. Likewise, a higher LVM adjusted for body surface area has been observed in adolescents12,15 and young adults13,16 from hypertensive parents compared with those from normotensive parents.
Some studies have found that subjects from a low socioeconomic status (SES) background show higher SBP levels and increased LVM compared with subjects from a high SES background.12,14 It is conceivable that subjects that are genetically predisposed to EH based on FH who also come from low SES backgrounds may be at an even higher risk of developing EH and increased LVM. To date, possible interactive effects of genetic susceptibility and SES have not been examined.
Although studies have addressed the effect of genetic susceptibility to EH on SBP and LVM, only a few of them included AAs.12,14 Examination of early determinants of preclinical CVD is especially needed in AAs because of their increased risk to develop CVD. In addition, because the pathophysiology of CVD has its origin in childhood, it is important to identify the impact of FH on CV risk factors during that time period. However, only 1 FH study included young children.14 Moreover, all FH studies but 114 were cross-sectional in design, which precludes evaluation of the effect of genetic predisposition on the longitudinal development of SBP and LVM. Also, none of the FH studies examined the effects of FH of EH and FH of MI simultaneously.
To the best of our knowledge, this is the first study to explore the effects of genetic susceptibility to EH as well as genetic susceptibility to premature MI on SBP and LVM trajectories from childhood into adulthood in AA and EA youth. The primary aim of our study was to test the effects of FH of EH and of premature MI on SBP and LVM trajectories characterized by their levels and rates of change. The secondary aims were to examine whether the effects of FH were moderated by ethnicity, sex, and/or SES.
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METHODS |
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Participants
Subjects were participants in ongoing longitudinal studies evaluating the development of CV risk factors in youth.17–19 The data encompass a 10-year period from 1989–1999 in which annual measurements were collected. Participants in this study were derived from a sample of 745 subjects (164 AA males, 185 AA females, 205 EA males, and 191 EA males; mean age at first visit 12.0 years, range: 4.9–23.9 years) for whom annual SBP measurements were available. Among this sample, a subsample of 687 subjects (152 AA males, 176 AA females, 188 EA males, and 171 EA males; mean age at first visit 14.0 years, range: 8.2–25.2 years) had annual LVM measurements. Descriptive characteristics of the SBP and LVM samples by FH group status are presented in Tables 1 and 2, respectively. FH+ of EH was defined as the occurrence of EH (ie, SBP
140 mm Hg and/or diastolic BP (DBP) 90 mm Hg or antihypertensive medication) in 1 or both biological parents, and an FH+ of premature MI was defined as the occurrence of MI in either biological parent or any biological grandparent before 55 years of age. Diagnosis of EH and of premature MI was verified by the individual’s physician or medical records.17–19 On the baseline evaluation, subjects were normotensive for age and sex and apparently healthy, based on parental report of the child’s medical history. Information on FH+ of EH and FH+ of premature MI was unknown for 6 and 28 subjects, respectively. These subjects were omitted from the analyses in which these respective variables were included.
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Subjects were classified as AA or EA according to criteria described previously.20 The Human Subjects Review Committee at the Medical College of Georgia had given approval for the study. Informed written consent was provided by 1 of the parents and the child.
Information on subject recruitment and evaluation that started in 1989 has been previously described.17–19 The annualized attrition rate has been <4% per year and has mostly been caused by subjects moving out of the region. There have been no significant differences in age, ethnicity, and sex distributions between dropouts and the subjects that remained in the study.
In the SBP and LVM samples, respectively, 153 and 149 of the total number of subjects were siblings. Siblings share genes and environment and consequently will be more alike than subjects from different families. Although this dependency between siblings does not lead to biased estimates, it may have overestimated significance of observed effects.21 However, when siblings were excluded from the analyses, the pattern of significant results was virtually identical, so results for the entire sample are reported here.
Procedure and Measurements
On each annual laboratory visit during the 10-year period, anthropometric, resting hemodynamic, and cardiac structure evaluations were conducted.
Subjects’ height and weight were measured with a Healthometer medical scale that was calibrated daily. Body mass index (BMI; weight/height2) was calculated as a measure of general obesity. BP was measured with an automated oscillatory system (Dinamap Vital Signs Monitor, Model 1846 SX; Criticon Incorporated, Tampa, FL), using an appropriately sized BP cuff placed on the subject’s right arm. BP measurements were taken at 11, 13, and 15 minutes, during a 15-minute supine relaxation period. The average of the last 2 readings was used to represent SBP and DBP values. The effect of FH on longitudinal development of BP in this study was restricted to SBP, because SBP can be measured with greater accuracy in children,22 and increasing evidence suggests that SBP is a better predictor of coronary heart disease mortality than DBP.23,24
The protocol used to perform echocardiographic examinations has been described elsewhere.25 LVM was calculated using the necropsy-validated formula of Devereux et al.26 Intra- and interrater coefficients of variation for all cardiac structures assessed were <10%.
Because father’s education level had been found to be the only socioeconomic variable affecting LVM and SBP in previous studies involving this cohort,5,6 SES was represented by father’s education level. Father’s education level, as measured at the midpoint of the study, was taken as representative for the whole study period. Education level was measured on a 7-point scale, ranging from less than high school to postgraduate education.
Marital status was assessed as another indicator of SES, because the percentage of female-headed households is increased in low-SES neighborhoods.27 Marital status was divided into single-parent household (single, divorced, widowed, separated) and 2-parent household (married), with single-parent household being considered as an index of lower SES compared with a 2-parent household.
Statistical Analyses
The effect of FH of EH and of a FH of premature MI on the development of LVM and SBP from childhood to adulthood was examined using individual growth curve modeling within a multilevel framework,28 which is a data analysis technique especially designed for longitudinal data. The method has a number of advantages over traditional statistical methods for analysis of quantitative longitudinal data, which are described elsewhere.6
We chose individual growth curve modeling over ordinary regression analysis, because the former method accounts for the dependency of the data attributed to clustering.28 Longitudinal data may be regarded as a 2-level hierarchy, with repeated (SBP or LVM) measurements (or waves) at level 1 (within-subject level) clustered within subjects, representing level 2 (between-subject level). Ordinary regression analysis would estimate a single equation for all data, whereas individual growth curve modeling fits a regression curve for each individual subject by modeling the dependent variable (SBP or LVM) as a function of the independent variables (age, age2, ethnicity, sex, etc). These individual curves (eg, SBP development with age) are characterized by their intercept (or level) and slope (rate of change). Thus, addition of independent variables to the model, such as FH of EH and FH of premature MI, is aimed at explaining between-subject variation (in level and slope) of the growth curves.
Analytical Strategy
The aims of our study were 3-fold: 1) to test the effects of FH of EH and FH of premature MI on both SBP and LVM growth curves (ie, level as well as rate of change) over up to 10 years; 2) whether the effect of FH of EH or of premature MI was moderated by ethnicity and/or sex; and 3) whether the effect of genetic susceptibility to EH or to premature MI was moderated by SES. Finally, we wanted to know whether any effects of FH of EH and FH of MI on SBP and LVM were mediated by BMI, because BMI is an important predictor of both SBP and LVM, as illustrated by our previous studies.5,6
Modeling of age, age2, ethnicity, and sex effects on the development of SBP and LVM of the present sample sizes have been described in detail previously.5,6 To test the effects of FH of EH and FH of premature MI on SBP and LVM growth curves, FH of EH and FH of premature MI were separately added to the ethnicity and sex models of SBP and LVM. Main effects of FH of EH and FH of premature MI represent effects on the growth curve level. Effects on the rate of change of LVM and SBP were modeled as interactions with age and age2.
In the next step, the interactions of FH of EH and of FH of premature MI with ethnicity and sex were modeled to examine whether the effect of genetic susceptibility to EH and to MI was different for males and females, and for AAs and EAs. To test whether the effects of FH of EH and of FH of premature MI were mediated by obesity, the previous 2 steps were repeated with the difference that BMI (including any significant interactions of BMI with age, age2, race and sex) was also entered into the models.
In the final step, father’s education level and marital status were separately added to the model, followed by their respective interactions with age, as well as their respective interactions with FH of EH and with FH of premature MI, to test whether the effect of genetic susceptibility was moderated by individual differences in SES.
Hierarchic 
2 tests were used to determine the significance of the fixed and random effects that were added to the model in each of the analyses steps.29 All multilevel modeling was performed using MLwiN software.30
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RESULTS |
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SBP
FH of EH had a significant effect on SBP level (b = 3.4, P < .001) and on the rate of change of SBP (b = .23, P < .05), indicating that subjects genetically predisposed to EH had higher SBP levels and a stronger increase in SBP over time. When adjusted for BMI, the effect of FH of EH on SBP (b = 2.9, P < .001) and on the rate of change of SBP (b = .20, P < .05) remained significant. Compared with the ethnicity and sex model, FH of EH accounted for an additional 4.0% of the between-subject variance in SBP.
Fig 1 shows the mean values of raw SBP data from childhood into adulthood by FH of EH status. SBP differences between subjects with and without a genetic susceptibility to EH already exist in childhood and tend to increase with age. No significant interactions were shown with ethnicity or sex (P > .62).
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Children from single-parent households had higher SBP levels (b = –.88, P < .05) than children from 2-parent households. The interaction between marital status and FH of EH was not significant (b = .62, P = .41), indicating that the effect of genetic susceptibility to EH on SBP was not moderated by SES. Father’s education level was not a significant predictor of SBP. FH of premature MI had no significant effects on SBP.
LVM
FH of EH showed a significant effect on LVM level (b = 8.1, P < .001), indicating that subjects genetically predisposed to EH had greater LVM. FH of EH had no significant effect on the rate of change of LVM (P = .20). In Fig 2, the mean raw LVM values against age are presented for subjects with an FH+ and with an FH– of EH. From early adolescence into early adulthood, subjects genetically predisposed to EH have a higher LVM than subjects without this genetic predisposition, and the difference in LVM between the 2 groups seems to become more pronounced after age 16. In addition, a significant interaction between FH of EH and sex was observed (b = 11.1, P < .025), indicating that the genetic influence of EH on LVM is stronger in females than in males. This interaction is visually displayed in Fig 3. As opposed to females, the effect of FH+ of EH in males only becomes apparent in late adolescence. The FH of EH model accounted for an additional 2% of the between-subject variance in LVM compared with the ethnicity and sex model. No significant interaction with ethnicity was found, suggesting that the effect of genetic susceptibility to EH is not different for AAs and EAs. When adjusted for BMI, the effect of FH of EH on LVM (b = 3.4, P = .07) and its interaction with sex (b = 4.2, P = .24) were no longer significant, ie, the effect of FH of EH on LVM appears to be mediated by obesity.
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Father’s education level was a significant predictor of LVM (b = –.90, P < .05), indicating that subjects whose fathers had lower education levels had higher LVM. No significant interaction between father’s education level and FH of EH was observed (b = –.61, P = .54), indicating that the effect of genetic susceptibility to EH on LVM was not moderated by SES. Marital status was not a significant predictor of LVM (b = –1.40, P = .38).
Similar to SBP, FH of premature MI showed no significant effects on LVM (data not shown).
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DISCUSSION |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONCONCLUSIONSREFERENCES |
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The primary aim of this study was to evaluate the effects of genetic susceptibility to EH and to premature MI on SBP and LVM trajectories across a 10-year period in youth initially 4.9 to 23.9 years old. Our longitudinal findings showed that subjects with a genetic predisposition to EH had higher SBP levels from childhood to early adulthood. Similar results were observed by others in childhood, adolescence,12,14 and young adults.11 Moreover, subjects with an FH+ of EH also showed a stronger increase of SBP with age, suggesting that they are likely to develop EH at a younger age than subjects without such a genetic susceptibility. The effect of genetic susceptibility to EH on SBP growth curves did not differ for males versus females and AAs versus EAs, suggesting that subjects with a FH+ of EH have a higher risk to develop future EH, independent of sex and ethnicity.
Comparable to SBP results, from early adolescence onward, subjects genetically predisposed to EH had greater LVM than subjects without such a genetic predisposition. This is in line with the cross-sectional findings of higher LVM in subjects with an FH+ of EH, observed in adolescents12,15 and young adults.13,16 Moreover, the effect of genetic susceptibility to EH on LVM was stronger in females than males.
The effect of FH of EH on LVM was mediated by BMI, whereas this was not the case for the effect on SBP. These findings indicate that the increase in LVM in young individuals genetically susceptible to EH is largely mediated by obesity. One possible explanation could be that a FH of EH partly represents a familial susceptibility to develop obesity, subsequently leading to an increase in LVM.
Our results showed that subjects from an adverse environment had higher SBP and LVM. Father’s education level was a significant predictor of LVM, with lower education levels predictive of higher LVM. Negative associations between SES and LVM in AA and EA children and adolescents have been observed previously,31 although the associations were mediated by CV reactivity and/or hostility. Marital status was a significant predictor of SBP in our study. In line with evidence of higher prevalence of CVD in low SES subjects, presumably mediated by chronic stress and health-related lifestyle/behavior,32 subjects from single-parent households may experience more stress and engage in less healthy behavior, resulting in higher SBP levels.
In a previous study from our group,5 father’s education level was a significant predictor of SBP in males. In the present study, father’s education level was not a significant predictor of SBP when it was entered into the model after FH of EH, but we did observe a significant effect of father’s education on SBP before FH of EH was entered into the model (data not shown). Apparently, FH of EH accounts for some of the between-subject variance in SBP explained by father’s education level, probably attributed to the lower education level of the fathers in subjects with an FH+ (see Table 1). These results indicate that FH is not a pure measure of genetic susceptibility, but may partly reflect familial environmental factors such as SES as well.
For both SBP and LVM, the effect of FH of EH on SBP and LVM was not moderated by SES. Thus, the results do not support our hypothesis that a combination of a disadvantageous SES and a genetic predisposition to EH heightens the risk of developing increased SBP and LVM.
Interestingly, but unsupportive of our hypothesis, our results indicate that a genetic predisposition to premature MI is not predictive of higher SBP or LVM, at least not in the period from childhood into adulthood. Lauer et al11 also failed to observe a significant relationship between SBP levels and FH of ischemic heart disease in children. Possibly, the increased risk to develop MI in subjects genetically predisposed to premature MI is mediated by other risk factors for coronary heart disease such as cholesterol levels, obesity, and insulin-resistance.
Great care was given in this study to accurately measure FH of EH and FH of premature MI, with diagnoses of EH and of premature MI in parent and/or grandparent verified by the individuals’ physician or medical records.17–19 However, FH+ of EH and FH of MI are only crude measures that may have underestimated the real effect of genetic susceptibility in our study. As mentioned, they may also reflect familial environment and individuals with an initially FH– may become positive over time as their parents and/or grandparents develop EH or MI. An increasing number of candidate genes for EH and MI is expected to become available with the recent completion of the first draft of the human genome. Studies that measure common variations in these genes (polymorphisms) are expected to supersede FH studies and should help elucidate pathophysiological pathways leading to EH and MI.
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CONCLUSIONS |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONCONCLUSIONSREFERENCES |
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Our results show that subjects genetically predisposed to EH are at higher risk to develop hypertension and LV hypertrophy. Because FH of EH is a proxy for genetic susceptibility and is likely confounded by SES-related lifestyle factors, such as dietary intake or physical activity level, we do not know to what extent the effect of FH of EH on SBP and LVM is affected by such environmental factors. Longitudinal studies involving more specific measures of genetic predisposition such as genetic markers may improve the understanding of individual differences in susceptibility to develop EH and LV hypertrophy.
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ACKNOWLEDGMENTS |
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This work was supported in part by grants HL 69999, HL 35073, and HL 41781 from the National Institutes of Health.
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FOOTNOTES |
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Received for publication May 28, 2002; Accepted Nov 18, 2002.
Address correspondence to Harold Snieder, PhD, Georgia Prevention Institute, Medical College of Georgia, Building HS-1640, Augusta, GA 30912-3710. E-mail: hsnieder{at}mcg.edu
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