Family Practice Vol. 20, No. 4, 401-409
© Oxford University Press 2003
Womens' Health |
Weight gain among women in the late reproductive years
Department of Biostatistics and Epidemiology and Center for Clinical Epidemiology and Biostatistics,
a Departments of Obstetrics and Gynecology, and Psychiatry,
b Center for Research in Reproduction and Women’s Health, and
c Obstetrics and Gynecology, Hospital of the University of Pennsylvania, University of Pennsylvania School of Medicine, Philadelphia, PA, USA.
Correspondence to Mary D Sammel, Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania School of Medicine, 423 Guardian Drive, 605 Blockly Hall, Philadelphia, PA 19104, USA. E-mail: msammel{at}cceb.upenn.edu
Sammel MD, Grisso JA, Freeman EW, Hollander L, Liu L, Liu S, Nelson DB and Battistini M. Weight gain among women in the late reproductive years. Family Practice 2003; 20: 401-409.
Received 5 September 2002; Revised 25 February 2003; Accepted 28 March 2003.
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Abstract |
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 Top Abstract IntroductionMethodsResultsDiscussionAppendix AAppendix BReferences |
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Background. Given the impact of obesity on mortality and morbidity in women, we evaluated correlates of weight gain in women ages 35–47 years.
Methods. Three hundred and thirty-six African American and Caucasian American women, randomly selected from among urban residents aged 35–47 years and pre-menopausal at baseline, were included in the prospective cohort study. Participants were followed over a 4-year period. Baseline measures included anthropometric variables, socio-demographic factors, measures of anxiety, depressed mood, quality of life, and self-reported measures of diet, vigorous physical activity, alcohol consumption and cigarette smoking. Hormone measurements were obtained during the follicular phase of the menstrual cycle. Weight gain was assessed by comparing the baseline weight with weight measured at the end of the 4-year period.
Results. Over 25% of the cohort gained 
10 lb during follow-up. Five of the 14 women (36%) who were considered menopausal gained weight. Women aged 45–47 were 61% less likely to gain 10 lb compared with women aged 35–39 [odds ratio (OR) = 0.39, 95% confidence interval (CI) 0.18–0.87]. Depressed mood was a major correlate of weight gain (OR = 1.9, 95% CI 1.09–3.31). Other psychological measures, including anxiety and quality of life, were similarly correlated with weight gain. No association was detected for levels of sex hormones or self-reported measures of physical activity. Most recalled dietary factors were not predictive of subsequent weight gain in our population.
Conclusions. In this population-based sample of women aged 35–47 years, psychological factors were the major predictors of gaining 10 lb during a 4-year follow-up period. Few of the other measures, including baseline hormone values, were correlated with subsequent weight gain. These findings suggest that screening for depression and anxiety may be important clinical assessments to identify women at increased risk of substantial weight gain.
Keywords. Pre-menopausal women, weight gain.
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Introduction |
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TopAbstractIntroductionMethodsResultsDiscussionAppendix AAppendix BReferences |
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Numerous studies have demonstrated a role for body weight in morbidity and mortality risk among women, including diabetes, cardiovascular disease and arthritis.1 The prevalence of being overweight increases from the age of 20 to 60 years, but relatively little is known about the aetiology of this age-related weight gain.2 We considered characteristics of subjects upon enrolment into a prospective cohort to identify correlates of significant weight gain (
10 lb) over a 4-year period in Caucasian American and African American women who were pre-menopausal and 35–47 years of age at baseline. Three subgroups of characteristics are considered: demographic, behavioural/clinical and psychological. First, the prevalence of obesity has been demonstrated to vary according to demographic factors such as race, education and socio-economic status. These characteristics are potential modifiers of postulated associations between psychological factors and obesity.3 Most research has been in clinical samples, with little information available from representative population-based samples. Racial differences have also not been described.4 Behavioural and clinical features are of interest to provide insight into possible intervention strategies. Sex hormone changes during the menopausal transition are thought to have an important impact on weight gain, in addition to diet and decreased levels of physical activity.5–7 Numerous cross-sectional studies have demonstrated an association between obesity and psychological factors such as depression,8 while prospective evaluation has been limited. It has largely been assumed that relative body weight is unrelated to depression in the population.3 However, one prospective study did report an increase in adult body mass index (BMI) in subjects with adolescent depression.9
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Methods |
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TopAbstractIntroductionMethodsResultsDiscussionAppendix AAppendix BReferences |
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The Penn Study of Ovarian Aging is a longitudinal study to identify hormonal, clinical, behavioural and demographic factors associated with ovarian ageing. A population-based sample of women was identified through random digit dialling. Eligible women were African American or Caucasian between 35 and 47 years of age, reported menstrual cycles in the normal range (22–35 days) for the previous 3 months, and had at least one intact ovary. Exclusion criteria included any serious illness that might compromise ovarian or hormonal function, such as diabetes, liver disease, breast or endometrial cancer; use of exogenous hormones or psychotropic drugs; alcohol or drug use within the past year; and pregnancy, lactation or intent to become pregnant. The Institutional Review Board of the University of Pennsylvania approved the study, and all women provided informed consent.
A total of 1420 women in Philadelphia were identified; of these, 402 refused further screening, 580 were eligible and 438 ineligible. Seventy-five percent (436/580) of the eligible women participated; the stratified sampling by race yielded 218 subjects in each racial group. Three hundred and fifty-three women completed the sixth interview ~4 years after enrolment. Of these participants, 17 did not provide blood samples for the hormone assessment, leaving 336 for this analysis.
Subjects participated in six follow-up assessment periods at ~8-month intervals over 4 years. Within each assessment period, there were two visits 1 month apart to obtain blood samples for hormone measurements, providing a maximum of 12 blood samples per subject. All visits were scheduled within the first 6 days of the menstrual cycle and included anthropometric measures, completion of standardized questionnaires, and blood samples. Study co-ordinators conducted the measurements and were rotated among the study participants to eliminate the influence of interviewer bias.
Information on the following variables obtained at baseline were included in this report.
Behavioural factors
Alcohol use was assessed by self-reported typical weekly consumption over the past year. Current and past cigarette use, the average number of cigarettes smoked per day and the duration of smoking were captured. Women completed a food frequency questionnaire in which they were asked to estimate the average servings of food consumed over the previous year (see Appendix A). Subjects recorded an average serving by checking the box indicating the range of servings. No direction was given with regard to interpretation of serving size or validation of self-report. The following food categories were created: protein, dairy foods, fruit/vegetables, sweets, breads and cereals, and high fat foods. Women were also asked if they currently were on a diet.
The questionnaire included several items regarding self-reported physical activity per weekday and per weekend, recognizing the change in physical activity level during the weekend. Women were asked the number of blocks walked, and the number of flights of stairs climbed, the number of hours spent sleeping or reclining, sitting, doing light or moderate activities, or vigorous activities (see Appendix B). Averages of weekday and weekend activity were computed.
Using standardized procedures, body weight, height, and waist and hip circumference were measured by the study co-ordinators. Body weight (in light clothing) was measured to the nearest 0.2 kg with a calibrated scale. Height (without shoes) was measured to the nearest 0.5 cm with a vertical ruler. Waist circumference was measured at the maximum abdominal girth, in duplicate to the nearest 0.5 cm. Hip circumference was measured at the maximal protrusion of the hips at the level of the symphysis pubica, in duplicate, to the nearest 0.5 cm. The average of two measures was used. The primary outcome variable for this analysis was defined as a net weight gain of 10 lb at the final assessment compared with the baseline.
Clinical data
Blood samples were obtained during the early follicular phase of the menstrual cycle. As women became irregular, blood draws were still conducted using the same intervals, with the cycle day information coded as missing. Blood samples were centrifuged and plasma frozen in aliquots at –70°C. Assays were conducted in the General Clinical Research Center at the Hospital of the University of Pennsylvania in batches that included four visits per subject in order to reduce the within-subject variability due to assay conditions. Oestradiol (E2), follicle-stimulating hormone (FSH), leutinizing hormone (LH), dehydroepiandrosterone sulfate (DHEAS) and testosterone were measured by radioimmunoassay using Coat-A-Count commercial kits (Diagnostic Products, Los Angeles, CA). Assays were performed in duplicate and repeated if values differed by >15%. The inter- and intra-assay coefficients of variation were: E2, 1.31%, 0.74%; FSH, 1.74%, 0.94%; LH, 2.16%, 1.85%; testosterone, 3.95%, 3.50%; and DHEAS, 2.03%, 0.81%. To minimize the variability and propensity to skewness inherent in hormone values, the natural logarithms and the average value of two transformed values obtained 1 month apart were used in the analysis. For this evaluation, we examined average levels at baseline and at the first follow-up period. We restricted hormone information to this time frame to reflect what would be available in a clinical setting, adding to the clinical usefulness of our findings.
Psychological factors
Standard psychosocial instruments were administered including the Center for Epidemiological Studies’ Depression Scale (CES-D) to assess depressive symptoms,10 the Zung Anxiety Scale11 and Cohen’s Perceived Stress Scale (PSS).12 In the CES-D, subjects rated 20 items that relate to depressed mood from 0 (rarely or none of the time) to 4 (most of the time). Positive item scores were reversed and the ratings were summed for a total score, with scores of 16 indicating depressed mood. The Zung Anxiety Scale11 is a validated self-report measure of anxiety and includes 20 items that are sensitive to the frequency of affective and somatic anxiety symptoms.11 Subjects rated each item from 1 (none or a little of the time) to 4 (most or all of the time), and the ratings were summed for a total anxiety score. The PSS12 is a 14-item validated self-report measure to assess the degree to which situations are appraised as stressful. Subjects rated each item from 0 (never) to 4 (very often), and total scores were obtained by reverse-scoring positive items and summing all ratings.
Analytic strategy
Multivariate logistic regression models were used to estimate the effects of co-variates measured at baseline on subsequent weight gain. The initial model included all potential predictors with P-values <0.20 after adjustment for baseline BMI categorized as <21, 21–24, 25–29, 30, with 21–24 considered the reference group. Interactions between risk factors of interest and BMI were also evaluated to determine whether associations were modified by BMI. Interactions were considered further if the interaction P-value was <0.05. We found that all the psychological measures were associated with weight gain but they were also significantly correlated with each other. Thus, separate multivariate models were conducted examining each of the psychological measures. The psychological measure of depressed mood, using the CES-D, was selected for the final multivariate model and reported herein because the association with weight gain was greatest among women with depressed mood. We developed the final model using backward selection and included co-variates based on whether the variable remained statistically significant at the P 
0.05 level and whether the inclusion of the variable modified other significant associations in the model by 15% or more.
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Results |
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TopAbstractIntroductionMethodsResultsDiscussionAppendix AAppendix BReferences |
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The average age at baseline was 41.0 years (SD = 3.5), 49% were African American, the majority of women were married (59%) and 90% had completed high school, some post-high school training or some college. At baseline, the mean BMI was 29.3 (SD = 8.2), with 38% of women having normal or low body weight (BMI
24), 25% overweight (BMI 25–29) and 37% obese (BMI 30). BMI also varied significantly by race; 52% of African American women were obese compared with 25% of Caucasian women (P = 0.001).
Women gained a median of 2.4 lb over the 4-year period, increasing the percentage of overweight women from 62 to 68%. A substantial proportion of women (25%) gained 10 lb during the follow-up period, with women whose weight was in the normal range at baseline (BMI 21–24) most likely to gain 10 lb (31%), followed by overweight women (27%), and only 7% of women with BMI <21 gained 10 lb. These proportions were not statistically significant (Table 1
). For women who were overweight or obese at baseline, their risk of weight gain was slightly less than that of normal women, but not significantly.
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Although there were marked differences by race in BMI at baseline, there were no differences in the proportion of African American and Caucasian American women who gained
10 lb (Table 1
). Age was related to weight gain, with women in the oldest age category (45–49 years) less likely than women in the other age groups to gain substantial weight.
Associations with demographic and behavioural characteristics
Cigarette smoking, alcohol consumption and most dietary factors did not affect the risk of substantial weight gain (Table 1). Increased consumption of fruit and vegetables, and sweets were both associated with a decreased risk of gaining 10 lb, although only consumption of sweets remained significant in the final multivariate model. Women who reported being on a diet at baseline were slightly more likely to gain weight compared with women who did not report being on a diet (35% versus 23%, P = 0.08). We evaluated multiple self-reported measures of physical activity, including the number of stairs climbed, blocks walked and self-reported vigorous activity. None of the measures were correlated with the risk of gaining 10 lb. In addition, we assessed the number of hours spent lying down or sitting per day, but these measures were also not associated with subsequent weight gain (data not shown).
Associations with clinical characteristics
Table 2 includes hormone values at baseline among women grouped according to weight gain. There were no differences between the two groups for any of the hormone measures, including testosterone and DHEAS, hormones which might be considered androgenic and associated with greater body mass. Eight percent of subjects reported sporadic use of exogenous hormones during the study period, but this was not associated with weight gain (P = 0.68, data not shown). During the follow-up period, 14 women reached clinical menopause (defined by 12 months without menses). Among these women, 36% had a weight gain of 10 lb, and 32% of women who had 3–11 months of amenorrhoea also had significant weight gain. Although these rates were slightly higher than the overall study population, the difference was not statistically significant.
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Table 3
shows the final multivariate model, including socio-demographic variables, BMI at baseline and factors independently associated with the risk of weight gain. As shown, a CES-D score of 16 was associated with nearly a 2-fold risk of gaining 10 lb over the 4-year period [odds ratio (OR) = 1.9, 95% confidence interval (CI) 1.09–3.31]. It is of note that all the psychological measures were predictive of subsequent weight gain for women in each BMI category. Antidepressant medications were reported for 30 subjects (9%) during the course of the study, but only eight (26.7%) exhibited substantial weight gain. This is similar to the overall proportion of subjects who gained weight in the 4-year time period (25%). Antidepressant medication was not associated with weight gain when added to our final model, P = 0.78; however, our power to evaluate this association may be limited by small sample size.
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On the multivariate level, baseline BMI continued to be associated with 4-year weight gain (P = 0.008), as well as age, but the significance was marginal (P = 0.054). Increased consumption of fruit, vegetables and sweets was also associated with a decreased risk of gaining 10 lb, although only sweets remained statistically significant in the final model, P < 0.001 (Table 3
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Discussion |
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TopAbstractIntroductionMethodsResultsDiscussionAppendix AAppendix BReferences |
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In this cohort of urban women in their early forties, 25% were overweight and 37% were obese at baseline. The proportion of overweight and obese women in our population is slightly lower than the reported US average, where 56% of women were overweight and 28% were obese.13 Over a 4-year period, substantial weight gain occurred (average gain of 2.4 lb) and 25% gained
10 lb or more, representing a clinically significant increase. These results are consistent with another 5-year prospective study of women ages 46–57 years, that found a mean weight gain of 4.63 lb, with weight gain not related to exercise, alcohol intake, smoking or menopausal status.14
The primary goal of this study was to identify factors that correlate with clinically significant weight gain. Given the difficulty of losing weight, it is perhaps most important for health care providers to identify correlates of weight gain. Early prevention of weight gain may ultimately prove more effective than our current modalities of initiating weight loss. Although many of the factors were measured repeatedly over the 4-year period, only the baseline values were used to mimic measurements available in a clinical setting. Our findings indicate that depressed mood scores, anxiety and quality of life measures were the major correlates of gaining 10 lb. These factors remained independent predictors of weight gain after adjusting for endogenous hormones and a variety of other lifestyle and demographic measures including age, diet and exercise. We found the odds of gaining 10 lb in a 4-year period to be twice as high for subjects presenting with depressive symptoms. Again it is noteworthy that a similar 2-fold increase was also reported by Goodman and Whitaker15 for adolescents who were followed into adulthood.
These findings add support to other reports of the association between depressive symptoms and weight gain. Studies have reported a high prevalence of mood and anxiety disorders among obese women, and weight gain is a common symptom among persons who overeat when depressed.16 Depressive symptoms may also play a significant role in the lack of success of weight loss efforts.17 In addition, mood disorders have been associated with poor responses to treatment and diminished compliance with therapy.18,19 Depression and depressive symptoms currently are under-recognized and under-diagnosed in the clinical setting, and this relationship has received little attention to date. While this study was not designed to assess whether an intervention with antidepressants will prevent weight gain, given the adverse health effects of weight gain and the relatively low risk of antidepressant use, this study provides an added reason to initiate treatment early in the disease of depression.
Because weight gain is a relatively common problem during treatment with antidepressant medication,16 we included the 30 women (9%) who reported antidepressant medication use in the model. However, including this variable in the model did not change the results. Another possible explanation for the association between depression and weight gain could be that depressed women lost weight prior to the baseline measure, and that subsequent weight gain marked a return to their usual weight. To address this possibility, we evaluated the difference between reported weight at baseline and the subject’s recalled weight 5 years earlier. Women with high depression scores (41%) gained 15 lb (SD = 25.0) in the 4-year interval compared with a gain of 13.6 lb (SD = 22.9) among the non-depressed women (P = 0.58).
Women with normal BMI values at baseline were most likely to gain substantial weight over the follow-up period. Thirty-one percent of this group gained 10 lb compared with 23% of obese women and 7% of women with baseline BMIs <21, although these differences were not statistically significant. Women who reported being on a diet were slightly more likely to gain 10 lb over the 4-year period, even after adjusting for baseline BMI values. To evaluate the influence of dieters in the present findings, we removed the 80 subjects (24%) who reported dieting at baseline and reran the final model. With omission of the dieters, depression was more strongly associated with weight gain.
Although numerous studies have demonstrated the importance of diet and exercise associated with weight gain,5–7 this study did not find a significant association between diet or exercise and weight gain among our group of women. We found no association between high fat foods, bread, cereal and salty snack consumption and weight gain, but did find an inverse relationship between consumption of sweets and weight gain, an unexpected finding. Although this study had adequate power to detect differences between the groups, these findings may reflect inaccuracies associated with food frequency data compared with data collected by daily food diaries that incorporate precise measurement of portion sizes. Similarly, none of the measures of physical activity predicted subsequent weight change. We do recognize that our failure to detect a significant association of dietary factors and exercise with weight gain may be a problem of recall bias. It cannot be concluded from the present study that exercise is not important, but only that there was no association in this cohort.
Our results do not support a primary role of reproductive hormones in the aetiology of weight gain among women in their late thirties and forties. Other studies have not detected an association between menstrual status and weight gain among women in the menopausal transition.20 For example, in the Massachusetts Women’s Health Study, weight gain among women aged 50–60 years was not associated with menopause and, in a study of women in the perimenopausal transition, neither the menopausal transition nor the use of oestrogen was significantly associated with weight gain.21,22 However, it is possible that other hormones or neurotransmitters play a role in weight gain. Physiologically, depression has been associated with both low serotonin and high cortisol in some patients. Obesity is associated with high cortisol and perhaps low serotonin.23–26 Given these potential associations, the role of ageing in these processes remains unknown. Further investigations of these associations with depression and obesity in perimenopausal women are warranted.
There are several limitations to this study. At the 4-year follow up, 100 (23%) women in the cohort were not included because of poor participation or insufficient hormone data, raising the question of non-participation bias. We conducted extensive analyses to compare the continuing subjects with subjects who missed visits and subjects who withdrew from the study. There were no significant differences among these three groups in either weight gain or depression scores during the time of their study participation.
Many of the measures were based on self-reported data. However, women’s self-reported information on physical activity and diet is similar to what would be captured in the physician’s office and is consistent with our goal to assist health care providers in identifying women who are at increased risk of weight gain. Our diet instrument did not include complete information on carbohydrate consumption and may not be a measure comparable with daily food diaries. Finally, the participants represented a population-based sample of African American and Caucasian women, but other ethnic and racial groups were not represented nor were women from non-urban areas.
Methodological advantages of the study include the objective measures of weight, hip and abdominal girth, standardized measures of depressed mood and reproductive hormones, and the longitudinal evaluation of weight gain.
In summary, in this cohort of women in their late reproductive years, we observed that a substantial proportion of women gained 10 lb during a 4-year period of observation, and the major predictors of weight gain were psychological factors, including depressed mood, anxiety and quality of life. These factors were not correlated with weight at baseline and were predictive of weight gain in women with normal BMI as well as those who were overweight. If these findings are replicated, screening for depression and anxiety may be a useful and important clinical assessment to identify women at risk for substantial weight gain.
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Appendix A |
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TopAbstractIntroductionMethodsResultsDiscussionAppendix AAppendix BReferences |
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Appendix B |
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TopAbstractIntroductionMethodsResultsDiscussionAppendix AAppendix BReferences |
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Acknowledgments |
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This study was supported by grants R01-AG12745 and 2M01RR-00040-37 from the National Institutes of Health, General Clinical Research Center.
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References |
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TopAbstractIntroductionMethodsResultsDiscussionAppendix AAppendix BReferences |
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