Associations among 5-Year Changes in Weight, Physical Activity, and Cardiovascular Disease Risk Factors in Mexican Americans

David L. Rainwater1, Braxton D. Mitchell1, Anthony G. Comuzzie1, John L. VandeBerg1, Michael P. Stern2 and Jean W. MacCluer1

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.


    ABSTRACT
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Recent changes in lifestyle have led to a global epidemic of obesity. To determine the associations of these changes with cardiovascular disease (CVD) risk, the authors correlated changes in CVD risk factors with changes in weight and physical activity in a population-based sample of 539 Mexican Americans in the San Antonio Heart Study in 1992–1999 who were examined twice approximately 5 years apart. Average weight change during that interval was 2.7 kg. While change in physical activity (expressed as percent change) was associated modestly only with change in low density lipoprotein cholesterol median diameter (p = 0.017), weight change was strongly and positively associated with unfavorable changes in lipid and lipoprotein traits, insulin levels, and blood pressure, explaining 2–10% of the variation in the risk factor changes during the interval. The unfavorable associations with weight gain tended to be more pronounced in lean compared with obese individuals and in men compared with women. However, the associations were significant for most CVD risk factors in all groups. In Mexican Americans, a population at high risk for obesity, weight change was positively correlated with metabolic variables associated with risk of CVD. Therefore, increasing adiposity in this population may tend to slow, or even reverse, the decline in CVD morbidity and mortality. Am J Epidemiol 2000;152:974–82.

lipoproteins; Mexican Americans; obesity; risk factors

Abbreviations: %{Delta}, percent change; BMI, body mass index; CVD, cardiovascular disease; h2, heritability; HDL cholesterol, high density lipoprotein cholesterol; LDL cholesterol, low density lipoprotein cholesterol


    INTRODUCTION
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
The prevalence of obesity has increased in recent years to the point that it has been recognized as a "global epidemic" (1Go). The obesity epidemic is particularly acute in many minority populations, including Mexican Americans (2Go). This epidemic has been attributed to several factors, including a decline in the physical activity level (3Go).

Obesity has long been known to be associated with increased risk of cardiovascular disease (CVD) morbidity and mortality (1Go, 4Go, 5Go). 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 (6Go). 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 (7Go). 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 (8GoGo–10Go).

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.


    MATERIALS AND METHODS
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Subjects and blood samples
The San Antonio Family Heart Study is an investigation of risk factors for CVD in Mexican-American families performed in 1992–1999. Probands were selected from a random sampling of a low-income neighborhood in San Antonio, Texas, and all first-, second-, and third-degree relatives of the proband and his or her spouse were invited to participate (11Go). Subjects for this study included those participants who visited the clinic in the initial screening phase and in a recall study approximately 5 years later. At each clinic visit, participants were given a physical examination, were interviewed about their lifestyle practices, and provided a blood sample (after an overnight fast). Serum was prepared from blood after clotting, and plasma was isolated by low-speed centrifugation; samples were stored at -80°C in single-use aliquots prior to analyses. All procedures were approved by an Institutional Review Board (University of Texas Health Science Center at San Antonio, San Antonio, Texas).

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 (12Go, 13Go). 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 (11Go), 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+ (14Go), 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 (15Go, 16Go). 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 (%{Delta}) 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 %{Delta}s in weight and CVD risk factor was assessed by constructing linear models of the following form:



As indicated in the above model, we partitioned the variance in each %{Delta}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 %{Delta}weight. The residual variance in %{Delta}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 %{Delta}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 %{Delta}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 %{Delta}physical activity and %{Delta}CVD risk factors and between %{Delta}fat mass (and %{Delta}lean mass) and %{Delta}CVD risk factors. We estimated the independent effects of %{Delta}weight and %{Delta}physical activity (and of %{Delta}fat mass and %{Delta}lean mass) by including both terms in the model. All analyses were conducted using the SOLAR software package (17Go).

We evaluated whether the correlation between %{Delta}weight and %{Delta}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 %{Delta}weight and sex x %{Delta}weight, respectively. Significance was evaluated by the likelihood ratio test (i.e., by comparing likelihoods between models with and those without the interaction term).


    RESULTS
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Characteristics of the population
A total of 539 individuals (321 women and 218 men) visited the clinic on two occasions approximately 4.6 years apart (standard deviation = 1.34). Table 1 presents mean levels of CVD risk factors in the study population at the first visit, the mean changes during the follow-up period (expressed as a percent of the baseline value (i.e., %{Delta})), and heritability for those changes. On average, individuals gained 2.7 kg during this interval, an amount corresponding to an average change of approximately 4 percent. Although most participants gained weight between the two clinic visits (64 percent gained weight, and 39 percent increased weight by more than 5 percent), a substantial proportion of individuals (36 percent) actually lost weight, and 25 percent lost more than 5 percent of their baseline body weight. Weight change was age dependent: Younger people tended to gain weight, and older people tended to lose it, although there were those who gained or lost weight throughout the spectrum of ages (figure 1).


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TABLE 1. Characteristics of the population at the first visit and changes occurring prior to the second visit,{dagger} San Antonio Family Heart Study, 1992–1999

 


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FIGURE 1. Scatterplot of percent weight change as a function of age in women and men, San Antonio Heart Study, 1992–1999.

 
Mean levels of reported physical activity declined by approximately 2 percent during the follow-up interval (p < 0.001). In addition, during the interval, there was an 11.3 percent increase in mean glucose level and 4.6 and 3.1 percent increases in mean systolic and diastolic pressures, respectively (p < 0.001 for all increases). Insulin concentrations increased in both men (by 24.5 percent) and women (by 14.5 percent), with the increase in men significantly greater than that in women (p = 0.005 for the sex x %{Delta}insulin interaction). In addition, HDL cholesterol levels declined by 3.7 percent (p < 0.001), and non-HDL cholesterol levels declined by 2.7 percent (p < 0.05). There were no significant changes in LDL cholesterol and HDL cholesterol size distributions (median diameters).

Heritabilities of %{Delta}weight, %{Delta}physical activity, and each %{Delta}CVD risk factor are also shown in table 1. Heritability of %{Delta}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 %{Delta}HDL cholesterol, which was also not heritable, additive effects of genes accounted for 13–30 percent for the various %{Delta}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 %{Delta}weight and %{Delta}physical activity did not differ significantly from zero (r2 = -0.004; p > 0.90).

We evaluated the associations between %{Delta}weight (and %{Delta}physical activity) and %{Delta}CVD risk factors by using multivariate genetic models. For each model, we estimated the association between %{Delta}weight (or %{Delta}physical activity, i.e., the independent variable) and %{Delta} 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 %{Delta}weight and %{Delta}physical activity (scaled to a 10 percent change), their statistical significance (as determined by likelihood ratio tests), and the percent of variance in %{Delta} in each CVD risk factor explained by %{Delta}weight (or %{Delta}physical activity). Weight change explained a significant proportion of variation in all measures of lipoprotein metabolism, including two (%{Delta}HDL cholesterol median diameter and %{Delta}LDL cholesterol median diameter) whose mean values did not change significantly over time (table 1). %{Delta}Weight accounted for a significant proportion of total variation in %{Delta}insulin and %{Delta}blood pressure, but not %{Delta}glucose. In contrast, %{Delta}physical activity was not associated with changes in any of the CVD risk factors, except for a modest association (p = 0.017) with %{Delta}LDL cholesterol median diameter.


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TABLE 2. Mean coefficient (SE{dagger}) and percent of trait variance explained by %{Delta}weight and %{Delta}physical activity{ddagger}, San Antonio Family Heart Study, 1992–1999

 
Associations of fat and lean weight change with changes in CVD risk factors
To determine whether a particular component of weight change (i.e., fat or lean mass) was primarily responsible for the significant associations of %{Delta}weight and the CVD risk factors, we estimated the correlations between %{Delta}fat weight and %{Delta}lean weight (as determined by bioimpedance) on each %{Delta}CVD risk factor. Analyses were conducted for each of these weight variables separately (i.e., without the other in the model) and also with both variables included in the same model. Table 3 gives the coefficients from regression models for %{Delta}fat weight and %{Delta}lean weight. In general, the correlations of each of these variables with the %{Delta}CVD risk factors were weaker than for %{Delta}weight, and in no case did either of these variables explain as much of variation in %{Delta}CVD risk factors as did %{Delta}weight alone. Changes in fat and lean weight were significantly correlated with changes in triglycerides, non-HDL cholesterol, HDL cholesterol, and HDL cholesterol median diameter, while %{Delta}fat weight, but not %{Delta}lean weight, was also significantly associated with %{Delta}systolic pressure. Neither %{Delta}fat nor %{Delta}lean was associated with changes in LDL cholesterol median diameter, insulin, or diastolic pressure, all of which were associated with %{Delta}weight. Overall, the correlations with %{Delta}fat weight tended to be higher than those with %{Delta}lean. In no case did the direction of correlation for %{Delta}fat or %{Delta}lean differ significantly from that of %{Delta}weight.


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TABLE 3. Mean coefficient (SE{dagger}) and percent of trait variance explained by %{Delta}fat and %{Delta}lean weights and coefficients for {Delta}%fat and %{Delta}lean in a bivariate model{ddagger}, San Antonio Heart Study, 1992–1999

 
Bivariate analyses, in which %{Delta}s for both fat and lean mass were included in the same model, were conducted to determine whether the two variables were independently associated with %{Delta}CVD risk factors. Inclusion of both %{Delta}fat and %{Delta}lean in the same analysis did not substantially diminish any of the observed correlations, suggesting that neither component was primarily responsible for the observed association of %{Delta}weight and %{Delta}CVD risk. Thus, %{Delta}fat and %{Delta}lean weights were independently correlated with %{Delta}triglyceride, %{Delta}non-HDL cholesterol, %{Delta}HDL cholesterol, and %{Delta}HDL cholesterol median diameter, and %{Delta}fat weight alone was correlated with %{Delta}systolic pressure. In contrast, when both percent total weight change and percent fat weight change were included in a bivariate model, %{Delta}Weight remained significantly related to the %{Delta}CVD risk factors, but %{Delta}fat weight did not (results not shown).

Weight change-associated changes in CVD risk factors for subgroups stratified on adiposity
To determine whether the correlation between %{Delta}weight and %{Delta}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, %{Delta}weight was more strongly correlated with %{Delta}CVD risk in the low BMI group compared with the high BMI group. In contrast, %{Delta}weight was more strongly correlated with %{Delta}insulin in the high BMI group. Surprisingly, %{Delta}weight showed opposite correlations with %{Delta}glucose in the two groups—positive 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 %{Delta}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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TABLE 4. Trait means (SE{dagger}) and mean coefficients (SE) for weight change in low and high BMI{dagger} subgroups and significance level for BMI weight interaction{ddagger}, San Antonio Family Heart Study, 1992–1999

 
Weight change-associated changes in CVD risk factors for subgroups stratified on sex
We also tested whether the correlations between %{Delta}weight and %{Delta}CVD risk factors differed according to sex. Table 5 shows means and the associations of %{Delta}weight with the %{Delta}CVD risk factor traits in women and men separately. %{Delta}Weight was significantly correlated with %{Delta}triglyceride, %{Delta}non-HDL cholesterol, and %{Delta}systolic pressure in both men and women, but was correlated with %{Delta}HDL cholesterol, %{Delta}HDL cholesterol median diameter, %{Delta}insulin, and %{Delta}diastolic pressure in women only and with %{Delta}LDL cholesterol median diameter and %{Delta}glucose in men only. %{Delta}Weight showed opposite correlations with %{Delta}glucose in the two sexes (negative in women, p = 0.066, and positive in men, p = 0.038), suggesting a sex-specific relation between fat and carbohydrate metabolism. We tested for a sex x %{delta}{Delta}weight interaction in the CVD risk traits and found significant sex interactions for %{Delta}triglyceride, %{Delta}non-HDL cholesterol, %{Delta}LDL cholesterol median diameter, and %{Delta}glucose. In general, the associations of %{Delta}weight and %{Delta}CVD risk factors appeared to be stronger in men than in women (table 5).


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TABLE 5. Trait means (SE{dagger}) and mean coefficients (SE) for weight change in women and men, and significance level for sex x weight interaction{ddagger}, San Antonio Family Heart Study, 1992–1999

 

    DISCUSSION
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
In this sample of randomly ascertained families, weight increased significantly by an average of 4 percent over the 5-year study period, and reported physical activity levels decreased by an average of 2 percent. Weight change was correlated with changes in a variety of CVD risk factors, including lipids and lipoproteins, blood pressure, and insulin, and accounted for 2–5 percent of the variance associated with blood pressure change and 2–10 percent of the variance associated with change in lipids and lipoproteins. These surprisingly strong associations may have profound public health implications. That is, concomitant with the increasing prevalence of obesity worldwide is a worsening of the CVD risk profile, at least in terms of the traditional risk factors characterized in this study. The strength of the associations we have observed suggests that a possible slowing in the rate of decline of CVD in the United States (18Go, 19Go) may be on the horizon. This concern highlights the importance of better understanding the factors driving the obesity epidemic.

An overall decline in physical activity levels in the population may be an important factor contributing to the obesity epidemic (3Go). 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 %{Delta}LDL cholesterol median diameter). Although a few studies have detected significant correlations between self-reported physical activity levels and CVD risk factors (20Go, 21Go), many others have not. One possibility is that the failure to detect an association between %{Delta}physical activity and %{Delta}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 (12Go, 13Go), 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 (22Go).

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 (23GoGoGo–26Go). 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 (2Go). 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. (27Go) 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 (28GoGoGo–31Go).

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 (32GoGo–34Go). 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 (35GoGo–37Go). 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 (10Go, 38GoGo–40Go), as well as in Mexican Americans (41Go), 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 (23Go, 27Go, 42Go, 43Go). 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) (5Go). 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.


    ACKNOWLEDGMENTS
 
Supported by National Institutes of Health grants HL-45522 and AR-43351 and Frederic C. Bartter General Clinical Research Center grant M01-RR-01346.

The authors thank Perry H. Moore, Jr., Mahmood Poushesh, Wendy Shelledy, and Jane F. VandeBerg.


    NOTES
 
Reprint requests to Dr. Braxton D. Mitchell, Department of Genetics, Southwest Foundation for Biomedical Research, P. O. Box 760549, San Antonio, TX 78245–0549 (e-mail: bmitchel{at}darwin.sfbr.org).


    REFERENCES
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 

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Received for publication August 23, 1999. Accepted for publication February 4, 2000.