1 Department of Genetics, Southwest Foundation for Biomedical Research, San Antonio, TX.
2 Department of Medicine/Epidemiology, University of Texas Health Science Center, San Antonio, TX.
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ABSTRACT |
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lipoproteins; Mexican Americans; obesity; risk factors
Abbreviations:
%, percent change; BMI, body mass index; CVD, cardiovascular disease; h2, heritability; HDL cholesterol, high density lipoprotein cholesterol; LDL cholesterol, low density lipoprotein cholesterol
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INTRODUCTION |
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Obesity has long been known to be associated with increased risk of cardiovascular disease (CVD) morbidity and mortality (1, 4
, 5
). Among the pathways that might potentially explain this relation, the contribution of body fat to the progression of atherosclerosis is possibly the most direct. One recent study demonstrated an association between weight change since age 25 years and carotid artery wall thickness in middle-aged adults, with the association remaining significant even after adjustment for smoking, education, and sports activity level (6
). A number of obesity-related dyslipidemias, which may, in turn, help to explain the association between obesity and atherosclerosis, have been reported in cross-sectional studies (7
). Particularly noteworthy is the positive relation of adiposity with triglyceride and the inverse relations of adiposity with high density lipoprotein cholesterol (HDL cholesterol) and low density lipoprotein cholesterol (LDL cholesterol) particle size. In addition to dyslipidemia, obesity is associated with a metabolic syndrome of other risk factors for CVD, including hypertension and insulin resistance (8
10
).
To understand better the association of obesity and CVD, we have evaluated the relation between changes in weight and reported physical activity levels and changes in CVD risk factors over approximately a 5-year period in a randomly selected population of Mexican Americans, a minority population at high risk for obesity and diabetes. After finding associations between changes in weight and CVD risk factors, we partitioned weight change into fat and lean tissue and reevaluated these relations. Finally, we also investigated the associations of weight change with change in CVD risk in subgroups stratified on the basis of body mass index (BMI) and sex.
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MATERIALS AND METHODS |
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Physical measurements
BMI was calculated as weight divided by height squared (kg/m2). Bioimpedance (Valhalla Scientific, Inc., San Diego, California) was used to estimate relative proportions of lean and fat mass, which were converted to mass (kilograms) in each compartment for analysis. Bioimpedance estimates of fat mass were based on manufacturer's proprietary equations, which may not have been validated for Mexican Americans or other populations with a high prevalence of obesity. However, the correlation between fat mass as estimated by bioimpedance and by dual energy X-ray absorptiometry is strong in this population (r2 = 0.70, n = 640; B. D. Mitchell, unpublished observations, 1999).
The systolic (first-phase) and diastolic (fifth-phase) blood pressures were determined to the nearest even digit by using a random-zero sphygmomanometer on the right arm of the seated subject. Three readings were taken, and the last two were averaged. Reported intensity of physical activity was assessed by using a modified version of the Stanford Seven-Day Physical Activity Recall Instrument (12, 13
). With a structured interview, subjects reported the weekly number of hours they slept and engaged in moderately strenuous, heavy, and very heavy physical activities. Examples of activities corresponding to each category were provided to assist the subject's responses. Each category of physical activity is scored in weekly metabolic equivalents, where one metabolic equivalent equals the energy expenditure at rest of 1 kg of body weight per hour or an oxygen uptake of 3.5 ml per kg per minute.
Biochemical measurements
Plasma glucose was measured with an Abbott V/P Analyzer (Abbott Laboratories, Abbott Park, Illinois), and serum insulin concentrations were determined by use of a commercial radioimmunoassay kit (Diagnostic Products Corp., Los Angeles, California). As reported previously (11), coefficients of variation for these assays were 6.5 percent for glucose and 8.0 percent for insulin.
Cholesterol and triglyceride concentrations were measured with a Ciba-Corning Express Plus clinical chemistry analyzer by using enzymatic kits supplied by Boehringer-Mannheim Diagnostics (Indianapolis, Indiana) and Stanbio (San Antonio, Texas), respectively. HDL cholesterol was measured in the supernatant after precipitating apoB-containing lipoproteins with dextran sulfate-Mg2+ (14), and non-HDL cholesterol was calculated as the difference between total plasma cholesterol and HDL cholesterol. Coefficients of variation for control products in these assays were 2.1 percent for cholesterol and 6.2 percent for triglyceride.
Plasma lipoprotein size distributions were analyzed in composite gradient gels, which allow analysis of LDL cholesterols and HDL cholesterols in the same sample lane, as described (15, 16
). To minimize effects of gel-to-gel variation, the paired samples from each individual were run on adjacent lanes. Cholesterol was stained with Sudan black B; particles between 21 and 29 nm were defined as LDL cholesterols, and those between 7.2 and 13 nm were defined as HDL cholesterols. LDL cholesterol and HDL cholesterol median diameters were estimated as the particle diameter, where half of the LDL cholesterol (or HDL cholesterol) absorbance was on larger particles and half was on smaller particles. Coefficients of variation for a control product run on each gel were 0.5 percent for LDL cholesterol median diameter (mean, 26.8 nm; n = 179) and 2.7 percent for HDL cholesterol median diameter (mean, 9.6 nm; n = 180).
Statistical analyses
We estimated percent change (%) in weight over the approximately 5-year period as the difference between the subject's weight at the baseline and follow-up visits, expressed as a percentage of the baseline weight (i.e., (visit 2 value visit 1 value)/visit 1 value). Similarly, we also estimated the 5-year change in cardiovascular risk factors as a percentage of the baseline value. Insulin and triglyceride values were loge-transformed to remove skewness so that the percentage change in these variables represents the change in transformed values. Subjects who were taking antilipid medications at either visit were excluded from analyses involving lipid levels (n = 26), subjects who were currently taking antihypertension medications at either visit were excluded from analyses involving blood pressure (n = 101), and those with diagnosed diabetes at either visit were excluded from analyses involving insulin (n = 108).
Using variance component methods, we estimated trait means and heritabilities while simultaneously estimating the effects of sex and sex-specific effects of age and age-squared. Parameter estimates and their standard errors were obtained by maximum likelihood methods, and all analyses were conditioned on the family structure to account for the dependencies among related family members.
The relation between %s in weight and CVD risk factor was assessed by constructing linear models of the following form:
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As indicated in the above model, we partitioned the variance in each %CVD risk factor (the dependent variable) into components attributable to a mean effect, effects of sex and sex-adjusted age and age squared, an effect due to the baseline level of the CVD risk factor, and an effect due to %
weight. The residual variance in %
CVD risk factor that was attributable to the effects of shared genes (i.e., heritability (h2)) was also estimated as a random effect. We used the likelihood ratio test to estimate the significance of the effect due to %
weight; that is, we compared the likelihood of the pedigree data for a model in which all parameters were estimated with the likelihood of the pedigree data for a nested model in which the effect due to %
weight was constrained to be zero. Minus two times the difference in loge likelihoods between these two models (which differ by a single parameter) is distributed approximately as a chi-squared statistic with one degree of freedom. In analogous manner, we also estimated the correlations between %
physical activity and %
CVD risk factors and between %
fat mass (and %
lean mass) and %
CVD risk factors. We estimated the independent effects of %
weight and %
physical activity (and of %
fat mass and %
lean mass) by including both terms in the model. All analyses were conducted using the SOLAR software package (17
).
We evaluated whether the correlation between %weight and %
CVD risk factor was consistent for subjects below and those above the population mean for BMI and for men and women by stratifying our analyses on these variables. Tests for interaction were conducted by including in the model additional terms for baseline BMI x %
weight and sex x %
weight, respectively. Significance was evaluated by the likelihood ratio test (i.e., by comparing likelihoods between models with and those without the interaction term).
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RESULTS |
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Heritabilities of %weight, %
physical activity, and each %
CVD risk factor are also shown in table 1. Heritability of %
weight, which reflects the proportion of the variance accounted for by the additive effects of genes, was 11.3 percent. However, change in physical activity was not heritable. Except for %
HDL cholesterol, which was also not heritable, additive effects of genes accounted for 1330 percent for the various %
CVD risk factors.
Associations of change in weight and physical activity with changes in CVD risk factors
After adjustment for the effects of age and sex, the partial correlation between %weight and %
physical activity did not differ significantly from zero (r2 = -0.004; p > 0.90).
We evaluated the associations between %weight (and %
physical activity) and %
CVD risk factors by using multivariate genetic models. For each model, we estimated the association between %
weight (or %
physical activity, i.e., the independent variable) and %
for each CVD risk factor (the dependent variable), while adjusting for the effects of sex, age, and the baseline value (visit 1) for the CVD risk trait. Table 2 shows the regression coefficients for %
weight and %
physical activity (scaled to a 10 percent change), their statistical significance (as determined by likelihood ratio tests), and the percent of variance in %
in each CVD risk factor explained by %
weight (or %
physical activity). Weight change explained a significant proportion of variation in all measures of lipoprotein metabolism, including two (%
HDL cholesterol median diameter and %
LDL cholesterol median diameter) whose mean values did not change significantly over time (table 1). %
Weight accounted for a significant proportion of total variation in %
insulin and %
blood pressure, but not %
glucose. In contrast, %
physical activity was not associated with changes in any of the CVD risk factors, except for a modest association (p = 0.017) with %
LDL cholesterol median diameter.
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Weight change-associated changes in CVD risk factors for subgroups stratified on adiposity
To determine whether the correlation between %weight and %
CVD risk factors differed between obese and lean subjects, we stratified individuals according to whether their BMI was above or below mean BMI for the population (29.1 kg/m2). Weight change was significantly correlated with change in most of the traits for each adiposity group (table 4), suggesting that weight and CVD risk changes were associated in both obese and lean people. However, for most of the lipid and blood pressure traits, %
weight was more strongly correlated with %
CVD risk in the low BMI group compared with the high BMI group. In contrast, %
weight was more strongly correlated with %
insulin in the high BMI group. Surprisingly, %
weight showed opposite correlations with %
glucose in the two groupspositive in the low BMI group (p = 0.0007) and negative in the high BMI group (p = 0.063)which may help to account for lack of a significant association in the total population (table 2). We tested for BMI x %
weight interaction (by including BMI as a continuous, rather than a dichotomous, trait) and found significant interactions for all traits, particularly glucose, insulin, and blood pressure (table 4).
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DISCUSSION |
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An overall decline in physical activity levels in the population may be an important factor contributing to the obesity epidemic (3). We therefore investigated the associations of physical activity with CVD risk factors. However, reported changes in level of physical activity did not correlate with weight change in this study, nor did they correlate with changes in any of the CVD risk factors (except for a modest correlation (p = 0.017) with %
LDL cholesterol median diameter). Although a few studies have detected significant correlations between self-reported physical activity levels and CVD risk factors (20
, 21
), many others have not. One possibility is that the failure to detect an association between %
physical activity and %
CVD risk in our study may be due to a poor correlation between reported physical activity and actual physical fitness. Although the physical activity questionnaire has been validated in some research settings (12
, 13
), it may suffer from excessive variation when used to document change within individuals. In the CARDIA Study, for example, correlations were observed between 7-year changes in physical fitness, as measured by the graded treadmill exercise test, and changes in HDL cholesterol, LDL cholesterol, triglycerides, and total cholesterol, but not between reported physical activity and these lipid and lipoprotein measures (22
).
The results of this study confirm previous reports of significant relations between change in weight and in one or more of the CVD risk factors (2326
). What distinguishes this study from the earlier ones is that we have characterized these relations in Mexican Americans, a biethnic population with a high prevalence of obesity (2
). A potential limitation of this study is our assumption that each subject is in metabolic equilibrium with current weight at the time of the clinic visit. This assumption may not have been true for a subset of individuals who were in the midst of major changes in body weight (i.e., due to crash dieting or binge eating). We have no information that would help to identify such individuals. However, we believe that violation of this assumption would tend to degrade true associations rather than create false ones. Thus, the strength of the associations we report here should be considered as minimal estimates.
The failure of this and other studies to show strong correlations between reported physical activity and CVD risk notwithstanding, it remains controversial whether the alterations in CVD risk that accompany weight change are mediated in part by changes in actual physical activity and/or fitness. It is also possible that changes in the distribution of body fat (e.g., abdominal visceral fat) or in the relative amount of fat versus lean tissue are particularly atherogenic. With respect to the latter hypothesis, Allison et al. (27) evaluated the effects of changes in fat and lean body mass on mortality in the Framingham and Tecumseh studies and observed that while change in fat body mass correlated positively with both total and cardiovascular mortality, the correlation between change in lean body mass and mortality was inverse. In our study, we partitioned the total weight into fat and lean body mass and evaluated the associations for each compartment separately. However, we did not find strong evidence that the atherogenic changes associated with weight gain could be ascribed primarily to one or the other of these components, although it is possible that this result could be due to imprecision associated with use of bioimpedance to measure fat mass in this Mexican-American population. We did not have a good measure of abdominal visceral fat in our study and thus were unable to evaluate whether the atherogenic changes associated with weight change could be attributed primarily to changes in body fat distribution.
It is possible that because Mexican Americans have a high incidence of obesity, they might respond to weight change differently than do other, leaner populations. To evaluate whether the response of CVD risk factors to weight change differed between lean and obese subjects, we stratified study subjects on the basis of their BMI at baseline and evaluated the correlations between weight change and CVD risk change in each group separately. Although the associations were generally stronger in lean people, weight change was also significantly correlated with change in CVD risk factors among subjects in the obese group. We therefore speculate that the associations of weight change and CVD risk reported in this relatively obese population will be at least as strong, if not stronger, in leaner populations.
We observed that the association of weight change with change in CVD risk factors appeared to be greater in men than in women for several of the CVD risk factors, including triglycerides, non-HDL cholesterol, LDL cholesterol median diameter, and glucose. This is consistent with other published results showing that the effect of weight change on total cholesterol, HDL cholesterol, and LDL cholesterol may be greater in men than in women (2831
).
Another unique feature of this study is the inclusion of variables to represent the distributions of cholesterol among the various sizes of HDL cholesterol and LDL cholesterol. A small LDL cholesterol phenotype is generally considered to be a risk factor for CVD (3234
). However, the relation of HDL cholesterol size phenotype with CVD is not well understood, although several studies have noted a significant positive correlation of sizes of HDL cholesterol and LDL cholesterol (35
37
). In this study, we found significant reductions in LDL cholesterol and HDL cholesterol median diameters with increasing weight and also found the same trend for LDL cholesterol predominant particle diameter (data not shown). Thus, as with the other CVD risk factors, weight gain tends to increase CVD risk associated with LDL cholesterol size phenotype. The association of weight change with change in LDL cholesterol particle size phenotype was significantly greater in men (p < 0.0001) than in women (p = 0.54). Several studies have reported that BMI and other measures of adiposity are inversely correlated with LDL cholesterol and HDL cholesterol particle size phenotypes in other populations (10
, 38
40
), as well as in Mexican Americans (41
), but to our knowledge this is the first study to investigate the relation between weight change and change in lipoprotein distribution.
Paradoxically, some studies have reported that weight loss, rather that weight gain, in unselected populations is associated with increased overall CVD mortality (23, 27
, 42
, 43
). Controversy may exist because of confounding effects in smokers (who tend to be lean) and the difficulty of accounting for unintentional weight loss (which tends to be associated with poor outcome) (5
). In addition, weight loss may have differential effects depending on whether it occurs primarily in lean tissue (differentially higher among the elderly) or in fat tissue (differentially higher among younger individuals). Thus, despite these controversies, there continues to be substantial evidence to suggest that the atherogenic changes that accompany age-related increases in weight are likely to increase mortality and morbidity risk.
In summary, we observed that weight change is significantly correlated with an adverse change in numerous CVD risk factors in a randomly selected population of Mexican Americans. In general, weight gain was associated with an unfavorable shift in the CVD risk factor variables, whereas CVD risk could be reduced with weight loss. However, these findings do not address the issue of whether the associations result from general weight increase or from increases in specific fat depots. As a result, we would propose to focus future work on the examination of specific changes in body composition on this set of CVD risk factors. In addition, given what appears to be a disproportionate increase in CVD risk factors with increasing weight in lean (as opposed to obese) individuals, we would also propose that future work should examine this issue in greater detail. Specifically, does weight gain in individuals who are genetically prone to be leaner have a more adverse effect than in those individuals genetically prone to being heavier? Finally, we found that changes in many of the CVD risk factors had significant heritabilities, suggesting the contributions of one or more genes to their variation. Because these studies were done on extended families, it may be possible to identify the genes (and mechanisms) that mediate some of these changes. Exploring such issues as these in future studies will allow us to better characterize the principal contributors to the adverse relations between weight gain and CVD risk.
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ACKNOWLEDGMENTS |
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The authors thank Perry H. Moore, Jr., Mahmood Poushesh, Wendy Shelledy, and Jane F. VandeBerg.
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NOTES |
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REFERENCES |
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