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Predictors of Postmenopausal Body Mass Index and Waist Hip Ratio in the Oklahoma Postmenopausal Health Disparities Study

Department of Pharmaceutical Sciences, University of Pittsburgh School of Pharmacy, Pittsburgh, Pennsylvania

Address reprint requests to: Judith S. Gavaler, Ph.D., University of Pittsburgh School of Pharmacy, Department of Pharmaceutical Sciences, 1014 Salk Hall, Pittsburgh, PA 15261. E-mail: gavaler{at}pitt.edu

	ABSTRACT

TOP FOOTNOTES ABSTRACT INTRODUCTION ACKNOWLEDGMENTS REFERENCES

 
Objective: The goal of the current study has been to examine systematically the respective roles of nutrition, exercise, menopausal weight gain, moderate drinking and smoking as determinants of body mass index (BMI) and waist hip ratio (WHR) in a setting in which the role of race or ethnic group could be simultaneously or individually evaluated as predictors of BMI and WHR. Because the use of estrogen replacement has been reported to affect estimates of body fat mass in postmenopausal women, endocrine factors have also been evaluated.

Methods: The design is cross-sectional with historical prospective elements. The study has a biomedical focus and is not an epidemiologic study. Data are from 649 women recruited into The Postmenopausal Health Disparities Study in Oklahoma. The study population was composed of 649 postmenopausal women: American Indian: 226 (34.9%), Asian: 21 (3.2%), Black: 78 (12.0%), Hispanics: 54 (8.3%) and Whites: 270 (41.6%). Recruitment occurred between 1994 and late 1999 in Oklahoma.

Results: In this multi-racial, multi-ethnic study population, there was statistical heterogeneity in all nutrition/dietary and exercise variables as well as in other potential determinants of BMI and WHR. In contrast to the literature available for postmenopausal women in which postmenopausal status, estrogen replacement and race have rarely been taken into account, the results of multi-linear regression revealed the following: Significant predictors for BMI, with or without WHR specified, included the neuroendocrine factors, menopausal weight gain, smoking, mean fitness (i.e., difficulty performing physical activities), fat as percent of total calories, moderate drinking and being Asian or Black. When WHR was not included, total calories and socioeconomic status also entered the model. The statistical predictors of WHR in the total study population with BMI in the equation included BMI and the neuroendocrine variables of FSH, E2, but not ERT, as well as the interaction of higher intensity exercise fitness with frequency, socioeconomic status and being American Indian or Asian. When BMI was not included in the model, in addition to the neuroendocrine factors, the interaction of lower intensity exercise fitness with frequency, fat as percent of total calories, age living alone and being American Indian or and Black were significant predictors of WHR. The predictors of both BMI and WHR were found to differ among individual racial and ethnic groups.

Conclusions: Given the role of increased body fat and obesity in disease risk and the substantial differences in life expectancy among the racial and ethnic groups, the findings of this study, particularly in contrast to literature reports, strongly suggest that a whole variety of factors including hormonal status and race need to be considered when examining the role of dietary factors and physical activity in relation to estimates of body fat mass and disease risk.

Key words: postmenopausal, determinants of body fat mass estimates, racial/ethnic minorities, nutrition, exercise, endocrinology, hormones, estrogen replacement therapy

	INTRODUCTION

TOP FOOTNOTES ABSTRACT INTRODUCTION ACKNOWLEDGMENTS REFERENCES

 
It is well established that the clinical consequences of increased body fat mass and obesity include osteoarthritis, gallbladder disease, diabetes, cardiovascular and pulmonary insufficiency, hypertension and atherosclerotic cardiovascular disease, all of which are dependent on the severity and duration of obesity [1,2]. Further, being overweight worsens all of the elements of the cardiovascular risk profile: hypertension, glucose intolerance, insulin-resistant glucose intolerance, dyslipemia, hyperuricemia, elevated fibrinogen and left-ventricular hypertrophy [3].

It is particularly important to note that there is substantial heterogeneity among races in the U.S. in the incidence, prevalence and mortality in these diseases. In this context, there is also substantial variation in female life expectancy among racial groups: American Indian and Black women: 74 years, Hispanic women: 78 years, White women: 80 years and Asian American women: 85 years [4].

A myriad of studies have examined the distribution of body fat mass and obesity in relation to disease [5–18]. Frequently, however, among the populations examined, the data have not been analyzed taking gender, age, race, and/or ethnicity into account [5,6]. Further, given that women mature from one distinct endocrinologic status (premenopausal with cyclic ovarian function) to another status (postmenopausal), the majority of studies in women have provided data neither about the subjects’ menopausal status and estrogen/hormone replacement therapy (ERT) use, nor have analyses statistically adjusted for these factors in analysis [7–18].

As has been the case for studies of obesity and disease, research examining the factors which influence the body mass index (BMI) and the waist hip ratio (WHR) have also frequently lacked specificity with respect to women [19–35]. There have been a few reports in older women using a clinical trial study design; while menopausal status and ERT have been taken into account in these few studies, race and ethnicity have not [22,25,26,30,35]. The absence of such information presents problems as some of the most important disease states of older women are related to the hormonal changes of the menopause (the two-species concept noted above) and the relationship of menopause to risk factors and disease [35].

The lack of studies in clearly defined postmenopausal (PMP) women with specific data about ERT use and race raises two serious questions. Can we assume that the endocrine status of women (i.e., the postmenopausal state with or without ERT) exerts no effect on estimates of body fat? And can we assume that findings in non-Hispanic Caucasian PMP women are generalizable to other races?

These two questions are highly relevant and serve to emphasize the growing need for research among minorities in general and among PMP aging minorities in particular. By 2050 the proportion of elderly (i.e., age 65+) minority women is expected to increase 2.53-fold, while in Whites a 0.77-fold decrease is anticipated [4,37]. Further, among the entire U.S. female population during the last fifteen years, compared to the 4.7% growth in White females of all ages, the overall growth among female minorities has been 33.8%, sevenfold higher [36,37].

The National Institutes of Health (NIH) instituted the requirement that women and minorities be included in clinical research supported by the NIH in 1994 [38]. Although women are increasingly being included in clinical studies, minority individuals to a considerable degree continue to be research "orphans."

With the above background in mind, the Postmenopausal Health Disparities Study has been designed to allow the many gaps in knowledge about postmenopausal minority women to begin to be addressed. Given the role of obesity in disease, we have started by identifying and evaluating the predictors of BMI and WHR and comparing potential determinants among endocrinologically-defined postmenopausal American Indian, Asian, Black, Hispanic and White women [39].

The goal of the current study has been to examine systematically the respective roles of nutrition, exercise, moderate drinking and smoking, as well as the role of postmenopausal endocrine factors including the use of ERT and hormonal status, in a setting in which race and ethnicity can simultaneously be evaluated as determinants of BMI and WHR.

METHODS
The Study Population
The study population was composed of 649 PMP women: 226 (34.9%) American Indians, 21 (3.2%), Asians, 78 (12.0%), Black, 54 (8.3%), Hispanics and 270 (41.6%) Whites. Recruitment occurred between 1994 and late 1999 in Oklahoma, primarily Oklahoma City, except for American Indians. The study was performed under a protocol approved by the Institutional Review Board at the Oklahoma Medical Research Foundation [39]. The Postmenopausal Health Disparities Study is a biomedical study not an epidemiological study, as we are evaluating underlying factors rather than end-points per se. The study population is a convenience sample composed of participants recruited using various methods including word of mouth, study brochures and posters as well as talks about menopause given to both small and large groups [39].

Data Collection
The study protocol has been described previously [39–45]. Briefly, nutritional data were obtained from three-day food records [46]; these were then analyzed by a trained nutritionist using The Nutritionist IV Software [47]. Total calories and percent of total calories as fat, protein and carbohydrates were used in the analyses. Data related to exercise frequency, the difficulty/fitness of performing various activities and ERT use, as well as other pertinent information, were all obtained using our standard questionnaires [39,41–43].

Fitness was based on self-reported difficulty in performing various tasks (4 = no problem, 1 = cannot do); data about exercise frequency was asked in terms of the previous month using categories: none, 1–2 times, 3–4 times, 5–6 times, or 7+ times per week. Higher intensity exercise included jogging or circuit training, leisure biking or ballroom dancing and walking at least a mile. Lower intensity activities included twisting, bending and reaching, crouching and stooping; lifting and carrying 10 pounds; walking a quarter of a mile or climbing 10 stair steps without stopping [39,41–43].

Blood samples were obtained by venipuncture; serum samples were stored in cryo-tubes at -70°C. At the time of the blood draw, weight, height and the circumference of waist and hips were measured. Estradiol (E2) and follicle stimulating hormone (FSH) levels were measured using standard radioimmunoassay methods established in our lab [41–45].

Database Management and Statistical Methods
Data were entered into a MS ACCESS database system. The analysis file was created as a SPSS-readable file. Yes/no variables were coded as 1, 0. Calculations were performed within the SPSS file for BMI (kg/m2), WHR and the normalizing transformations of hormone levels. Data are reported as percentages or as the mean ± SEM. Probabilities were considered to be statistically significant at the p < 0.05 level (2-tailed).

One-way analysis of variance with the Tukey multiple comparison procedure was used to evaluate differences among racial groups. Multivariate analyses were performed using multiple linear regression (MLR) in the stepwise mode so that the role of a given factor as a predictor of BMI or WHR could be assessed in a setting in which the role(s) of other specified variables could be taken into account and thus be statistically controlled. Predictor variables were those which entered the multiple linear regression models with a statistically significant standardized regression coefficient.

In the multiple linear regression analyses, the models used to determine the predictors of BMI and of WHR included the nutritional variables of total calories and percent of total calories as fat, as carbohydrates and as protein and the physical activity variables of mean fitness (difficulty preforming activity), fitness performing both higher intensity exercise and lower intensity exercise, as well as an interaction term with frequency for both. Menopausal weight gain, age, current smoking and current alcoholic beverage consumption were also included. Education and socioeconomic status were included because these factors could be postulated to influence the choice of foods consumed.

Given that the use of ERT has been shown to decrease estimates of body fat by some studies [21,22,30,32–35], but not by all [24], the possibility of a neuroendocrine component was assessed by including levels of FSH, E2 and testosterone in addition to ERT.

RESULTS
Analyses were hypothesis-driven in that the formal null hypothesis was that there would be no differences among the racial and ethnic groups of postmenopausal women. Notably, the null hypothesis was rejected for all variables except for total weekly drinks (TWD) among drinkers. Among the PMP women within each racial/ethnic group, there were significant differences (heterogeneity) not only in BMI and WHR, but also in potential predictor variables including the use of ERT, smoking and moderate drinking (Table 1). Note that in all racial and ethnic groups alcoholic beverage consumption was on mean less than one drink per day, below the maximum recommended alcohol consumption level for women in the U.S. [49]. Similarly, there were substantial racial differences in all nutrition, exercise frequency and exercise fitness variables (Table 2).

The statistical predictors of BMI are shown in Table 3 for the entire population as well as for each racial and ethnic group. In the total population, with and without WHR specified, the neuroendocrine factors were all significant predictors, as were menopausal weight gain, smoking, mean fitness (i.e., difficulty performing physical activities), fat as percent of total calories and moderate drinking in terms of total weekly drinks (TWD). In addition, being Asian or Black were also significant determinants of BMI. When WHR was not included in the model (NIM), total calories and socioeconomic status (SES) also entered the model.

Among Asian and Hispanic postmenopausal women, WHR did not enter the equation. Among Asian women, fat as percent of total calories and SES or education were significant predictors, while among Hispanic women, testosterone and menopausal weight gain were significant predictors of BMI. Among Black postmenopausal women, WHR and E2 were significant determinants of BMI, while E2 and smoking were significant determinants when WHR was not in the equation.

Among White postmenopausal women, WHR, FSH and E2, as well as carbohydrates as percent of total calories, moderate drinking and the interaction of frequency with lower intensity activities fitness, were significant determinants of BMI. When WHR was not specified in the model, testosterone, smoking, the interaction of frequency with higher intensity activities fitness were also significant predictors of BMI; moderate drinking, however, was no longer significant.

The statistical predictors of WHR are shown in Table 4. In the total study population with BMI in the equation, significant predictors included BMI and the neuroendocrine variables of FSH, E2, but not ERT, as well as the interaction of higher intensity exercise fitness with frequency, socioeconomic status and being American Indian or Asian postmenopausal women. When BMI was not included in the model, in addition to the neuroendocrine factors, the interaction of lower intensity exercise fitness with frequency, fat as percent of total calories, age, living alone and being American Indian or Black were significant predictors of WHR.

Among Black postmenopausal women, only BMI entered the equation, while, when BMI was not in the equation, carbohydrates as percent of total calories, moderate drinking and mean fitness were significant predictors of WHR. With BMI in the equation, only the interaction of lower intensity exercise fitness with frequency entered the equation in Hispanic women, while, with BMI not in the model, living alone was also a statistical predictor of BMI. Among White women, BMI and E2 were significant predictors of WHR; when BMI was not in the equation, in addition to E2, no ERT, smoking and the interaction of lower intensity exercise fitness with frequency were significant predictors of WHR.

DISCUSSION
For the first time, the statistical determinants of postmenopausal BMI and WHR values have been systematically assessed in a study population which included postmenopausal American Indian, Asian, Black and Hispanic women, as well as White women. In addition to multiple linear regression analyses in the total study population, the racial/ethnic groups have been also been analyzed as separate groups, thus allowing comparisons to be made across racial and ethnic groups in which the same data collection and analytic methods have been used.

Given the correlations of BMI with WHR, the finding that the models for BMI and WHR predictors changed with additional significant factors entering the equations when either BMI or WHR was not included in the model is not particularly surprising. Further, given the probability of a genetic component, it is not surprising that the models within individual racial and ethnic groups differed from each other. Because BMI provides an estimate of weight corrected for height while the WHR estimates abdominal fat mass, it is not surprising that the predictors of each are not identical.

Based on the literature that ERT has a negative effect on estimates and measures of body fat, we decided to include a neuroendocrine component in the regression models. As background for considering a neuroendocrine component as a determinant of both BMI and WHR, several facts need to be pointed out. We have already reported in The Postmenopausal Health Disparities Study that women treated with oral ERT levels do not uniformly achieve therapeutic E2 levels; specifically, the proportions of PMP women treated with oral ERT who achieved therapeutic E2 levels (45 pg/mL) ranged from 62.5% in Asians to 48.9% in Whites [41]; FSH levels decline in response to effective ERT treatment. In postmenopausal women not treated with ERT, FSH levels are increased in response to the low E2 concentrations of the PMP hormonal state. It is also important to note that in postmenopausal women not using ERT, E2 production occurs in body adipose tissue via the conversion/aromatization of androgen (testosterone) substrate.

In multivariate modeling in the total population, ERT use, E2 and FSH levels per se were included to examine the proposed neuroendocrine effect on body fat mass. Thus, "controlling" for known ERT and age effects, the roles of E2 and FSH levels could be examined separately and independently. In the inclusive multiple regression analysis, levels of both E2 and FSH were significant negative determinants of both BMI and WHR; the unexpected negative direction of both E2 and FSH effects in the presence of the positive effect of no ERT (i.e., lower E2 levels), when it might be expected that FSH might have had a negative effect, supports the concept of a neuroendocrine component as a determinant of both BMI and WHR levels. Interestingly, further support is partially and indirectly provided by the study of Lovejoy and colleagues [25], in which the effects of a weak androgen and an anti-estrogen were examined. Clearly, putative neuroendocrine effects on increased PMP body mass will require further examination.

This report in 649 women in The Postmenopausal Health Disparities Study has provided new data about the determinants of BMI and WHR which we hope will encourage other investigators to broaden their work in this area, particularly with respect to the inclusion of minority racial and ethnic groups. As The Postmenopausal Health Disparities Study is an ongoing project now being performed in a different geographic area (Pittsburgh, PA), future analyses may reveal additional determinants of body fat estimates such as geography [23,29]. Given the role of increased body fat mass and obesity in many diseases, including the major cause of death which is cardiovascular disease, and the substantial racial/ethnic differences in life expectancy, these new findings from The Postmenopausal Health Disparities Study provide specific data which may help to develop new weight loss strategies which could benefit all postmenopausal women.

	ACKNOWLEDGMENTS

TOP FOOTNOTES ABSTRACT INTRODUCTION ACKNOWLEDGMENTS REFERENCES

 
The authors thank Adrian Wyatt for the nutritional analyses and Barbara Dilettuso, MS, and Paula Boyle for ongoing technical assistance.

	FOOTNOTES

TOP FOOTNOTES ABSTRACT INTRODUCTION ACKNOWLEDGMENTS REFERENCES

 
This work has been supported in part by grants from the National Institute on Alcohol Abuse and Alcoholism (R01-11184 and R01-06672), the Office of Women’s Health Research and the Office of Minority Health Research.

Received June 14, 2002. Accepted September 27, 2002.

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