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J Am Coll Cardiol, 2003; 42:264-270, doi:10.1016/S0735-1097(03)00631-4
© 2003 by the American College of Cardiology Foundation
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CLINICAL RESEARCH: ATHEROSCLEROSIS RISK

Physical activity reduces genetic susceptibility to increased central systolic pressure augmentation: a study of female twins

Jerry R. Greenfield, MBBS, BSc (Med), FRACP*,*, Katherine Samaras, MBBS, PhD, FRACP*, Lesley V. Campbell, MBBS, FRCP, FRACP*{dagger}, Arthur B. Jenkins, BSc, PhD§, Paul J. Kelly, MBBS, MD, FRACP||, Tim D. Spector, MSc, MD, FRCP and Christopher S. Hayward, BMedSc, MBBS, MD, FRACP{ddagger}#

* Department of Endocrinology, St. Vincent’s Hospital, Sydney, Australia
{dagger} Diabetes Centre, St. Vincent’s Hospital, Sydney, Australia
{ddagger} Department of Cardiology, St. Vincent’s Hospital, Sydney, Australia
§ Department of Biomedical Science, University of Wollongong, Wollongong, Australia
|| Sequenom, San Diego, California, USA
Twin Research and Genetic Epidemiology Unit, St. Thomas’ Hospital, London, United Kingdom
# Victor Chang Cardiac Research Institute, Sydney, Australia

Manuscript received January 19, 2003; revised manuscript received April 10, 2003, accepted April 17, 2003.

* Reprint requests and correspondence: Dr. Jerry R. Greenfield, Department of Endocrinology, St. Vincent’s Hospital, Darlinghurst, Sydney, NSW 2010, Australia.
j.greenfield{at}garvan.org.au


   Abstract
Top
 Abstract
Methods
 Results
 Discussion
 References
 
OBJECTIVES: We sought to examine associations between the augmentation index (AI) and metabolic, adiposity, and lifestyle factors, independent of genetic influences, and to determine whether gene-environment interactions modulate these relationships.

BACKGROUND: Reported associations between AI, an index of systemic arterial stiffness, and metabolic, adiposity, and lifestyle factors remain contradictory. The modulating effect of genetic risk is unknown.

METHODS: We studied 684 female twins (age 18 to 71 years); AI was derived from the pressure waveform measured at the radial artery by applanation tonometry. Percentage of total body fat (TBF) and percentage of central abdominal fat (CAF) were assessed by dual-energy X-ray absorptiometry.

RESULTS: In univariate analysis, age-adjusted AI was significantly associated with fasting triglyceride levels (r = 0.1, p = 0.03), apolipoprotein-B/A1 (r = 0.1, p = 0.04), percentage of TBF (r = 0.11, p = 0.006), and percentage of CAF (r = 0.11, p = 0.004). In co-twin case-control (monozygotic twin) analysis, a 3.1% absolute within-pair difference in percentage of CAF accounted for a 6% within-pair difference in AI, independent of genetic effects. Smokers and subjects with alcohol intakes >15 U/week had higher AI than nonsmokers (p = 0.01) and nondrinkers (p = 0.02), respectively. Forty percent of the variance in AI was explained by age, central mean arterial pressure, heart rate, height, percentage of CAF, and smoking. In gene-environment interaction analysis, subjects at high genetic risk of increased AI participating in regular leisure-time physical activity had AI values similar to low genetic risk subjects.

CONCLUSIONS: Central abdominal adiposity is a significant determinant of AI in female twins, independent of hemodynamic, lifestyle, and, importantly, genetic effects. Smoking is associated with increased AI, even after controlling for abdominal obesity and other AI determinants. Physical activity reduces genetic predisposition to increased AI.

Abbreviations and Acronyms
  AI
  augmentation index
  BMI
  body mass index
  BP
  blood pressure
  CAF
  central abdominal fat
  CMAP
  central mean arterial pressure
  DXA
  dual-energy X-ray absorptiometry
  DZ
  dizygotic
  HOMA
  homeostasis model assessment
  HRT
  hormone replacement therapy
  MZ
  monozygotic
  TBF
  total body fat

Central arterial stiffness predicts cardiovascular mortality, independent of traditional cardiovascular risk factors (1,2). With age, arterial stiffening increases pulse wave velocity, resulting in premature reflection of the central systolic pressure wave (3). Reflection in late systole (rather than in diastole) "augments" central systolic arterial pressure, reduces diastolic coronary perfusion, and increases cardiac workload (3). The degree of augmentation, expressed as the "augmentation index" (AI), is derived noninvasively by applanation tonometry of the radial, carotid, or femoral arteries and yields information on systemic arterial stiffness (4–6). Augmentation index is an important marker of coronary risk (7,8) and independently predicts total and cardiovascular mortality in end-stage renal failure (1).

Although 18% to 37% of the population variance in AI is heritable (9,10), several determinants of AI have been reported, including age (4,6,9,11,12), gender (1,6,7,11), blood pressure (BP) (6,7,9,11), heart rate (6,7,9,11,13), height (6,9,11), and type 2 diabetes (13). Augmentation index has also been associated with abdominal obesity, albeit estimated anthropometrically (14,15). To our knowledge, the relationship between directly measured central abdominal fat (CAF) (by dual-energy X-ray absorptiometry [DXA]) and AI has not been reported. Such a relationship may mediate, at least partly, the increased cardiovascular risk associated with the "Metabolic Syndrome" and type 2 diabetes.

No previous study has assessed the effect of lifestyle and physical factors on AI independent of genetic influences, important confounders that can only be controlled for by studying twins. Furthermore, whether individuals genetically predisposed to arterial stiffness are more susceptible to the effects of physical, lifestyle, or metabolic factors has not been previously examined. Such gene-environment interactions may play an important role in altering phenotypic expression in genetically predisposed individuals. The twin model specifically allows examination of the influence of individual environmental factors, independent of genetic effects, and permits investigation of gene-environment interactions.

In a large cohort of healthy, pre- and postmenopausal female twins, we examined associations between AI and total and central abdominal adiposity, lipid and glycemic parameters, and lifestyle factors (smoking, alcohol consumption, physical activity, and hormone replacement therapy [HRT]). Studying twins also enabled us to quantify the contribution of these factors to AI independent of genetic influences and to investigate the modulating effect of genetic risk on these relationships.


   Methods
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 Abstract
 Methods
Results
 Discussion
 References
 
Subject characteristics.   The study group comprised 684 healthy Caucasian female twins (53 monozygotic [MZ] and 262 dizygotic [DZ] pairs and 54 singletons [whose co-twin was excluded or had incomplete data]) recruited from the general population through media advertisements (via the St. Thomas’ U.K. Adult Twin Registry) (9). Ischemic heart disease, stroke, diabetes mellitus, and treatment with lipid-lowering or antihypertensive medications were exclusion criteria. Subjects were included in the study if their fasting plasma glucose level was <6.1 mmol/l, and, in subjects who had a 75 g oral glucose tolerance test (n = 467), 2-h plasma glucose levels were <7.8 mmol/l. The study was approved by institutional ethics committees at St. Thomas’ Hospital, London, UK (phenotypic data collection) and St. Vincent’s Hospital, Sydney, Australia (data validation, analysis, and interpretation). All participants provided written informed consent. Zygosity was ascertained through questionnaire (16), confirmed by multiplex deoxyribonucleic acid fingerprinting (PE Applied Biosystems, Foster City, California) if uncertain. Menopause was defined as amenorrhea for ≥12 months in women ≥40 years of age. Information on medication use, lifestyle, and demographic characteristics were ascertained by standardized questionnaires. Subjects were categorized as current smokers, ex-smokers, or lifetime nonsmokers. Subjects reported participation in regular leisure-time physical activity and time spent in weight-bearing and non–weight-bearing sports of moderate and vigorous intensity (17,18). Alcohol intake was recorded as never (6%), social (28%), 1 to 5 (32%), 6 to 10 (16%), 11 to 15 (8%), 16 to 20 (5%), 21 to 40 (4%), and >40 (1%) U/week (1 U = 8 g of alcohol). As only five subjects were in the latter category, data on this group was not reported.

Hemodynamic indexes.   Brachial BP was measured twice using an automated cuff sphygmomanometer (OMRON HEM713C, Tokyo, Japan). Radial artery pressure waveforms were measured supine by applanation tonometry (9,19) and were analyzed using commercially available software (SphygomoCor, AtCor Medical, Sydney, Australia). Pressure was applied to the radial artery by a probe containing a high-fidelity transducer, through which pressure waves were recorded (4,5,9). This technique provides intra-arterial pressure measurements similar to those obtained invasively (5). An aortic pressure waveform was generated from peripheral waveforms using a validated transfer function (20,21). Augmentation index was calculated by dividing the pressure difference between the second systolic peak and the diastolic pressure by the difference between the first systolic peak and the diastolic pressure (x100%) (Fig. 1) (6). As previously described, intra-operator and inter-operator reproducibility (expressed as intra-class correlations) were 0.82 and 0.84, respectively (9).



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  		Figure 1 Graphical representation of central arterial pressure waveform. P1 = first systolic peak; P2 = second systolic peak (systolic pressure); D = diastolic pressure; Augmentation index = (P2 – D)/(P1 – D) x 100%.

 
Anthropometry and body composition.   Weight (kg) was measured with subjects in light street clothing and height (m) by stadiometer. Body mass index (BMI) was calculated (kg/m2). Waist circumference was measured as the narrowest circumference between the lower rib margin and anterior superior iliac spines. Dual-energy X-ray absorptiometry (Lunar DPXL, Madison, Wisconsin) was used to measure total body fat (TBF) and CAF (in kg and %) (18); CAF was defined as the adipose tissue content of a window bordered by the upper margin of the second and lower margin of the fourth lumbar vertebral bodies and the inner aspect of the ribs (by a single observer) (18).

Biochemical analysis.   Fasting plasma glucose was measured by a glucose-oxidase method and fasting plasma insulin by radioimmunoassay (Immunodiagnostics Enzum Test, Boehringer Mannheim, Germany) (n = 558). Insulin resistance (R’) and insulin secretion (ß’) were estimated by modified homeostasis model assessment (HOMA) (22). Fasting lipid and lipoprotein levels were measured in 579 subjects as previously described (23).

Statistical methods.   Data are mean ± SEM unless otherwise stated. The generalized estimating equation was used to correct for intra-pair phenotypic correlations, as standard statistical analysis of paired data may underestimate standard errors and overestimate p values (24). Fasting triglyceride and insulin data was loge-transformed to normalize its distribution. Due to the strong relationship between AI and age (r = 0.43, p < 0.001), AI was age-adjusted in all analyses by calculating age-residuals or by including age as a covariate in analysis of covariance. Stepwise multiple regression models were constructed (correcting for intra-pair correlations using the generalized estimating equation) with AI as the dependent variable and significant factors in univariate analyses as independent variables. A value of p < 0.05 was considered significant. Data were analyzed by Statview 5 (SAS Institute Inc., Cary, North Carolina) and STATA Statistical Software, release 7.0 (StataCorp, College Station, Texas).

Co-twin case-control (MZ twin) analysis is a unique and established model by which the effect of environmental and physical factors on a measured trait can be quantified independent of genetic influences (18,25). As MZ twins have 100% genetic concordance, this model allows potentially confounding genetic factors to be controlled for. Therefore, any differences within MZ twin pairs for a measured variable must be due to the environmental or physical factors for which the twin pairs are discordant. For example, if MZ twin pairs discordant for a measure of adiposity have greater within-pair differences in AI than concordant pairs, adiposity has an effect on AI independent of genetic influences.

In the current study, we used the co-twin case-control study design to examine the contribution of body fat distribution and lifestyle factors (smoking and alcohol intake) to AI, independent of genetic effects. Only factors significantly associated with AI in univariate analysis were examined in MZ models. Within-pair differences in MZ pairs discordant for these factors were compared to concordant twin pairs using the Mann-Whitney U test.

Gene-environment interaction analysis was undertaken to investigate whether univariate associations between environmental factors and AI were modulated by genetic susceptibility to increased AI (18,25). The method used was as follows: MZ and DZ twins were grouped into tertiles of age-adjusted AI. Genetic risk of increased AI was assigned to a randomly selected twin from each pair based on the tertile of her co-twin. Each environmental factor and genetic risk category (high and low) were entered into a two-factor analysis of variance (with age-adjusted AI as the dependent variable) to determine if there was a statistically significant interaction between environmental factor and genetic risk.


   Results
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 Abstract
 Methods
 Results
Discussion
 References
 
Mean age of the 684 subjects was 42.6 years (range 18 to 71 years). Of the 35% who were postmenopausal, 44% used HRT. Of the subjects, 154 (23%) were current smokers and 144 (21%) ex-smokers. Body composition, hemodynamic, and fasting biochemical variables are presented in Table 1.


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  		Table 1 Body Composition Data, Peripheral and Central Hemodynamic Indexes, and Fasting Biochemical Parameters in Healthy Female Twins

 
Univariate analysis.   As previously shown in this population (9), AI was directly related to age and central mean arterial pressure (CMAP), and inversely to heart rate in unpaired analysis. Although AI was higher in postmenopausal than premenopausal women (142 ± 1% vs. 124 ± 1%, p < 0.001), this was attenuated (p = 0.18) after adjusting for age. Triglyceride levels (r = 0.1, p = 0.03) and apolipoprotein-B/A1 (r = 0.1, p = 0.04) were the only lipid variables associated with AI after adjusting for age. There were no relationships between AI and fasting levels of glucose, insulin, HOMA-ß’, or HOMA-R’ (not shown). Relationships between adiposity measures and AI are shown in Table 2.


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  		Table 2 Univariate Correlation Coefficients Between Age-Adjusted Augmentation Index and Anthropometric and Direct Measures of Total Body and Central Abdominal Adiposity in Healthy Female Twins (n = 684)

 
Lifestyle determinants of AI.   Despite similar heart rate and brachial BP, current smokers had higher AI than nonsmokers (134 ± 2% vs. 129 ± 1%, p = 0.01). Never smokers and ex-smokers had similar AI (129 ± 1% vs. 130 ± 2%). Using abstainers as the reference group, subjects drinking 16 to 20 (138 ± 4%) U/week and 21 to 40 (137 ± 4%) U/week had higher AI than abstainers (125 ± 3%, both p = 0.02). These differences remained statistically significant after controlling for smoking. The 282 subjects who participated in regular leisure-time physical activity had similar AI to those who did not (p = 0.32). There was no relationship between duration of weekly activity and AI in physically active subjects (not shown). Among postmenopausal women, HRT users and nonusers had similar AI (141 ± 2 vs. 143 ± 2). Although premenopausal women taking the oral contraceptive pill (n = 83) had lower AI than those who did not (117 ± 3 vs. 126 ± 1, p = 0.05), there was no difference after adjusting for age (p = 0.65).

Stepwise multiple regression analysis.   In stepwise multiple regression analysis, age, CMAP, heart rate, height, percentage of CAF, and smoking explained 40% of the variance in AI (Table 3). Apolipoprotein-B/A1, triglyceride levels, and alcohol intake (>15 vs. ≤15 U/week) were not significant predictors of AI in this model.


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  		Table 3 Independent Determinants of Augmentation Index in Healthy Female Twins: Multiple Regression Analysis (n = 681)

 
Co-twin case-control (MZ twin) analysis.   Co-twin case-control analysis was performed in MZ twins to examine the discrete contribution of total and central obesity, smoking, and alcohol intake to AI independent of genetic effects. Discordance for AI was defined as a within-pair difference of ≥6%. Discordant twin pairs (n = 37) had greater within-pair differences in CAF (0.32 ± 0.04 kg vs. 0.15 ± 0.03 kg, p = 0.02) and percentage of CAF (5.9 ± 0.8% vs. 2.8 ± 0.7%, p = 0.006) than concordant pairs (n = 16). Therefore, 170 g and 3.1% absolute within-pair differences in CAF and percentage of CAF, respectively, accounted for a 6% within-pair difference in AI. Twin pairs discordant and concordant for TBF and percentage of TBF had similar within-pair differences in AI (not shown). Therefore, our data do not support any impact of TBF on AI after controlling for genetic effects. There were no differences in AI between twin pairs concordant and discordant for smoking or heavy alcohol consumption (not shown).

Gene-environment interaction analysis.   Gene-environment interaction analysis was performed to investigate whether relationships between AI and environmental factors were modulated by genetic susceptibility to increased AI. No gene-environment interactions were found with smoking, alcohol consumption, or HRT. However, a significant interaction between physical activity and genetic risk of increased AI was found (p = 0.01) (Fig. 2). In subjects at high genetic risk of increased AI, participation in regular leisure-time physical activity was associated with significantly lower AI than nonparticipation (125 ± 3% vs. 138 ± 3%, p= 0.008). In contrast, in subjects at low genetic risk, physically active and inactive subjects had similar AI (126 ± 3% vs. 123 ± 2%, p = 0.46). This means that subjects genetically predisposed to increased AI who participated in regular physical activity had AI values equivalent to subjects at low genetic risk. When analysis of covariance was performed in high genetic risk subjects, the difference in AI remained significant when CMAP (p = 0.02), heart rate (p = 0.002), height (p = 0.02), percentage of CAF (p = 0.02), and smoking (p = 0.03) were included as covariates. Notably, there were no gene-environment interactions between smoking and genetic risk of systolic or diastolic hypertension.



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  		Figure 2 Gene-environment interaction analysis: the association between regular leisure-time physical activity and augmentation index in subjects at low and high genetic risk of increased augmentation index. Open bars = regular physical activity; hatched bars = no regular physical activity.

Data are mean ± SEM. Interaction p = 0.01. *p = 0.008 compared to subjects at high genetic risk not participating in regular physical activity.

 

   Discussion
Top
 Abstract
 Methods
 Results
 Discussion
References
 
In 684 female twins, we examined the relationship between AI and accurately measured hemodynamic, body composition, and environmental parameters. We specifically studied twins to examine these relationships independent of genetic effects and to evaluate any modulating effect of genetic susceptibility. A major and novel finding of the current study was that central abdominal adiposity was a significant determinant of AI, independent of age, CMAP, heart rate, height, smoking, and, in co-twin case-control analysis, genetic factors. It is of some importance that 40% of the variance in AI could be explained by age, CMAP, heart rate, height, percentage of CAF, and smoking, as we have previously shown the heritability of AI to be around 40% (9). Another important finding was that regular physical activity reduced the phenotypic expression of genetic susceptibility to increased AI; in gene-environment interaction analysis, subjects at high genetic risk of increased AI who participated in regular activity had similar AI values to subjects at low genetic risk.

To our knowledge, no previous study has specifically examined the relationship between central abdominal adiposity (measured by DXA) and AI in healthy pre- and postmenopausal women. Previous reports concerning the relationship between obesity and arterial stiffness have been contradictory, related, in part, to the less accurate methods used to measure body fat and its distribution. Studies have reported disparate associations between AI and total adiposity estimated by weight, BMI, and hydrostatic weighing (7,9,11,12,14,15,26). In relation to body fat distribution, although surrogate estimates of abdominal fat have been found to correlate directly with AI (14,15) and inversely with arterial distensibility (27), the former were not significant after adjusting for age (15) and other AI determinants (14). In studies in which abdominal fat was measured by magnetic resonance imaging, despite no correlation with anthropometric estimates or subcutaneous abdominal fat, inverse relationships were found between visceral abdominal fat and aortic (28) and carotid artery (29) distensibility, the latter, however, explained by arterial diameter. Additionally, recent experiments found that the reduction in AI induced by infused insulin in normal individuals (30), although impaired in insulin-resistant obese (31) and type 2 diabetic subjects (32), is inversely related to total (15,31) and abdominal obesity (15). Together with the current study, these reports highlight the importance of body fat distribution in determining systemic arterial stiffness and offer a potential mechanism linking these factors to adverse cardiovascular outcomes.

Despite no overall relationship between leisure-time physical activity and AI, we found that physically active subjects at high genetic risk of increased AI had similar AI to subjects at low genetic risk. In other words, the phenotypic expression of genetic susceptibility to increased AI was reduced by regular physical activity. The reported effect of physical activity on arterial stiffness and compliance is inconsistent, related, in part, to differences between studies in exercise regimens, measures of arterial stiffness and compliance, cohort characteristics, and the completeness of covariate adjustment (33–37). Using AI, it was reported that endurance training partially protected against age-related increases in arterial stiffness in men (12) and women (14). In contrast, a recent study found that a three-month training program had no effect on AI in postmenopausal women (38). Our study raises the possibility that the relationship between exercise and AI may depend on genetic risk. This important protective effect on a known cardiac predictor provides a further mechanism for understanding the beneficial effect of physical activity on heart disease risk.

To our knowledge, this is the largest examination of the relationship between chronic smoking and AI in healthy pre- and postmenopausal women. Despite similar brachial BP and HR, smokers had higher AI than nonsmokers, and smoking was a significant independent determinant of AI in multiple regression models. Our findings confirm some (13,39–41), but not all (7), previous studies using AI. Although the effect of smoking was not quantifiable in co-twin case-control analysis, this may relate to small numbers. We also found that ex-smokers had similar AI to never smokers, implying that negative effects may be reversible once smoking is ceased.

The finding that women drinking >15 U of alcohol per week had higher AI than abstainers contradicts a recent report (42). In the current study, alcohol intake was not a significant independent determinant of AI in multiple regression analysis, and discordance in alcohol consumption was not associated with greater differences in AI than concordance in co-twin case-control analysis. Considering epidemiologic evidence linking moderate alcohol consumption to cardiovascular protection (43), an important negative observation from the current analysis was that moderate alcohol consumers had similar AI to abstainers.

We found no difference in AI between postmenopausal women using and not using HRT, consistent with some (44), but not all (26), previous cross-sectional studies. In a randomized study of subjects with type 2 diabetes, we reported no effect of HRT on AI, although half of these subjects were hypertensive and already receiving vasodilatory therapy (45).

The main strength of this paper relates to the simultaneous accurate measurement of hemodynamic, metabolic, and body composition parameters in a large cohort of healthy pre- and postmenopausal females. The unique use of the twin model extends previous epidemiologic studies by allowing examination of associations between body fat and AI independent of genetic influences and the modulating effect of genetic susceptibility on these relationships. Limitations include the study’s cross-sectional design, the self-reported nature of lifestyle factors, and our inability to control for dietary sodium intake, which has been shown to influence AI (38). We are unable to comment on the individual effects of estrogen and progesterone on AI. Our results may not be applicable to non-Caucasian females, males, or individuals with glucose intolerance or diabetes.

In conclusion, using accurate measures and the twin study design, we investigated the relationship between AI and metabolic, body fat, and lifestyle variables in female twins. Central abdominal obesity was an important determinant of AI, independent of age, CMAP, heart rate, height, smoking, and, in co-twin case-control analysis, genetic effects. In addition, smoking was independently associated with elevated AI, while physical activity reduced the phenotypic expression of genetic susceptibility to increased AI. Our findings raise the possibility that AI is an intermediary variable mediating the association between abdominal obesity and cardiovascular morbidity and mortality and the means by which smoking increases, and physical activity decreases (particularly in genetically susceptible populations), coronary disease risk.


   Acknowledgments
 
The authors thank Dr. Mathias Chiano for his assistance and statistical advice. We are grateful to the twins from the Twin Research and Genetic Epidemiology Unit, St. Thomas’ Hospital, London, United Kingdom, for their generous participation in this study.


   Footnotes
 
Dr. Greenfield was supported by a Postgraduate Medical Scholarship from the National Health and Medical Research Council of Australia and Dr. Samaras by the Royal Australasian College of Physicians Diabetes Australia Fellowship. St. Vincent’s Clinic Foundation, Sydney, Australia supplied funds that supported data analysis. The Twin Research and Genetic Epidemiology Unit, St. Thomas’ Hospital, London, United Kingdom, is supported by the Wellcome Trust, Chronic Diseases Research Foundation, British Heart Foundation, United Kingdom and Sequenom, San Diego, California.


   References
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