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RESEARCH AND PRACTICE |
At the time of the study, Chantal Matkin Dolan and Jennifer L. Kelsey were with the Department of Health Research and Policy, Stanford University School of Medicine, Palo Alto, Calif. Helena Kraemer was with the Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Palo Alto. Warren Browner is with the California Pacific Medical Center Research Institute, San Francisco, and the University of California, San Francisco. Kristine Ensrud is with the Division of Epidemiology, School of Public Health, University of Minnesota, Minneapolis.
Correspondence: Requests for reprints should be sent to Chantal Matkin Dolan, PhD, MPH, PO Box 448, Palo Alto, CA 94302 (e-mail: matkin{at}comcast.net).
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ABSTRACT |
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Objectives. We examined the relation between measures of body size and mortality in a predominantly White cohort of 8029 women aged 65 years and older who were participating in the Study of Osteoporotic Fractures.
Methods. Body composition measures (fat and lean mass and percentage body fat) were calculated by bioelectrical impedance analysis. Anthropometric measures were body mass index (BMI; kg/m2) and waist circumference.
Results. During 8 years of follow-up, there were 945 deaths. Mortality was lowest among women in the middle of the distribution of each body size measure. For BMI, the lowest mortality rates were in the range 24.6 to 29.8 kg/m2. The U-shaped relations were seen throughout the age ranges included in this study and were not attributable to smoking or measures of preexisting illness. Body composition measures were not better predictors of mortality than BMI or waist girth.
Conclusions. Our results do not support applying the National Institutes of Health categorization of BMI from 25 to 29.9 kg/m2 as overweight in older women, because women with BMIs in this range had the lowest mortality.
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONReferences |
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30.0 kg/m2.1 However, applying a single set of cutpoints to define overweight and obesity in different age groups may not be appropriate. Several studies have suggested that the relative risk of mortality associated with increased BMI is greater among younger women than older women.2–7 The shape of the relation between BMI and mortality is also controversial. One large prospective study showed a positive linear association between BMI and mortality in women aged 30 to 55 years who had been followed for 16 years8; several other studies of women at various ages have reported a U-shaped relation,2,5,9–18 which indicates an elevated mortality risk among those with low BMI and those with high BMI. Some evidence suggests that this nonlinear association may be the result of not controlling for confounding by smoking or preexisting illness,19 but other studies have observed a U-shaped distribution even when adjusting for these variables.5,9,10,12–16
We used data from the Study of Osteoporotic Fractures, a large prospective cohort study of predominantly White women aged 65 years and older, to examine the relation between measures of obesity and mortality during an 8-year-average follow-up period. Body composition was measured directly by bioelectrical impedance analysis (BIA) as well as by traditional measures of adiposity, including BMI and waist circumference.
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METHODS |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONReferences |
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More than 98% of the participants were White. Of the 9704 women who entered the study at baseline, 85% of the surviving cohort (n = 8082) completed a follow-up clinic visit at year 2 (visit 2) between January 1989 and January 1991 (when they were at least 67 years old). Bioelectric impedance measurements were made only at visit 2. We included the 8029 women who had complete bioelectric impedance measurements so that we could estimate lean mass, fat mass, and fat mass percentage.
All body composition and body size measurements were made at visit 2. Participants were instructed to maintain a normal fluid balance and to abstain from vigorous physical activity and ingestion of alcohol and caffeine for 12 hours prior to the clinic visit.
Women were weighed while wearing indoor clothing without shoes; weight was measured with a balance beam scale. Height was measured with a wall-mounted stadiometer. BMI was calculated from weight and height at visit 2. Waist girth was measured with an inelastic tape measure during the visit 2 examination.
Lean mass was estimated from BIA as 0.470x (Height2/Resistance) + (0.170x Weight) + (0.03 x Reactance) + 5.7.21 Fat mass was calculated as the difference between total body weight and lean mass. Percentage body fat was fat mass expressed as a percentage of total weight.
A validation substudy of 205 women demonstrated that estimates from BIA were well correlated with dual x-ray absorptiometry (DXA; Hologic QDR 1000, Hologic Inc, Waltham, Mass) measures of fat mass (r=0.89) and lean mass (r = 0.79).22 These correlations were consistent across all the age categories and were observed despite an average of 2 years’ difference between the BIA and DXA measures. DXA has been validated as a precise measure of body composition.23
Study participants were contacted every 4 months, and follow-up for mortality was more than 99% complete.24 Because of relatively small numbers in specific cause-of-death categories, overall mortality was used as the end point. The average time from visit 2 until the end of follow-up for this analysis (November 1997) was 8 years.
Potential Confounding Variables
Information on most demographic, lifestyle, and clinical covariates of interest was obtained at visit 2 by interview (alcohol consumption, marital status, use of hormones, use of diuretics, and reproductive history) or by examination (muscle strength, including grip strength and femoral neck bone mineral density with DXA). Femoral neck bone mineral density has been associated with both obesity and mortality.25
Some covariates were measured only at baseline, including walking for exercise, cigarette smoking (never, former, current), education, self-reported health compared with others the same age (excellent, good, fair, poor, very poor), diabetes, and hypertension.
Analyses
Descriptive statistical analyses were performed to identify potential confounding variables for inclusion in multivariate models. For continuous variables, analysis of covariance was used to estimate age-adjusted means and standard deviations among survivors and among those who died during follow-up. For categorical variables, percentages were adjusted to the age distribution of the entire cohort (n = 8029) at visit 2 by the direct method.26 Pearson correlation coefficients were calculated to determine the correlations between the anthropometric main variables of interest.
Cox proportional hazards models were used to estimate the associations between anthropometric variables and rate of mortality. Models were run for all women, adjusting for age only; for all women, adjusting for multiple potential confounders; and for nonsmokers only, adjusting for multiple potential confounders. The censor date was either the date of death or the end of the follow-up period. Each body size measure was included in a Cox regression model with a quadratic term because the association between each anthropometric measure and mortality was curvilinear. Proportionality assumptions of the models were checked by plotting the log(–log) survival curves. Interaction terms between each body size measure and age were included, but no interactions were apparent.
The optimal value (nadir of the curve)27 of each body size variable was stable in all age groups (66–69, 70–74, 75–79, 80–84, and 85 years), so all age groups were combined in the results presented here. We controlled for the effects of age by including it as a continuously distributed covariate.
To depict the curvilinear associations between the body size measures and mortality, each body size measure was categorized into 5 equally sized quintiles (on the basis of the distribution in the entire sample at visit 2). Mortality rate ratios were calculated for each quintile relative to the lowest quintile.
All statistical analyses were carried out with the SAS version 6.0 (SAS Institute, Cary, NC) and EGRET (Statistics and Epidemiology Research Corp, Seattle, Wash, and Cytel Inc, Cambridge, Mass) statistical programming packages.
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RESULTS |
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gives the means and standard deviations for the various body size measures at visit 2. The table also shows that all of the measures of body composition and anthropometry were highly correlated with each other.
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). Those who died during follow-up were less well educated, were less likely to be married or to walk for exercise, had lower grip strength, and were more likely to be smokers, to report their health status as fair or poor, and to use nonthiazide diuretics. Age, smoking status, self-reported health status, grip strength, nonthiazide diuretic use, and femoral bone mineral density were included in the multivariate analyses as potential confounding variables because they were also independently associated with mortality.
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and Figure 1 show multivariate-adjusted mortality rates according to quintile of body size indicator. Table 4 presents the quintiles of the body composition and body size measures in this cohort. The lowest mortality rates consistently occurred in the middle of the distributions of body size indicators, and the highest mortality rates were at either end. The quadratic term was significant for all body size variables when data were stratified into 5-year age groups.
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Effects of Potential Confounders
Among nonsmokers, the patterns of associations between quintile of body size measures and mortality were similar to the results for the entire cohort (Table 3
), confirming that the U-shaped association between body size measures and mortality is not explained by uncontrolled confounding from smoking status. Among the nonsmokers, the highest mortality consistently occurred among women in the highest quintile of body size.
Because of the concern that preexisting illness could influence the associations between body size and mortality, analyses were also adjusted for hypertension and diabetes. Again, the U-shaped relation between body size measures and mortality was similar to that seen in the unadjusted results (data not shown). Furthermore, the U shape was observed for the measures of body size when we excluded women who died within the first 2 years of follow-up or those had lost more than 10% of their body weight since age 50 years.
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DISCUSSION |
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Our results are consistent with several large prospective cohort studies that have reported a U-shaped relation between body size and mortality among adult women of various age groups.2,5,9–18 Both the American Cancer Society study, which included more than 400 000 women aged 30 years and older2 and a study from Norway of more than 900 000 women aged 15 to 90 years11 reported U-shaped relations between BMI and mortality. However, neither the American Cancer Society study nor the Norwegian study adjusted for smoking. The Nurses Health Study, which includes more than 115000 women aged 30 to 55 years at baseline (followed for 16 years), reported that after adjusting for smoking, the association between BMI and mortality was linear.19 However, other studies of women in age groups more comparable to the Nurses Health Study have adjusted for smoking and still reported a U-shaped relation between body size and mortality.9,10,14,16,28 Recently, the Leisure World Cohort (including more than 8000 women, mean age 73 years, followed over a 23-year period) reported a reverse-J–shaped relation between BMI and mortality, with controls for age at entry and smoking. Although obese women were at higher risk of mortality than were "normal-weight" women, the highest risk of mortality was observed among underweight women, and thus a reverse-J–shaped relation.18
It is not surprising that the women at lowest risk for mortality are neither the thinnest nor the most obese. However, the levels of BMI associated with the lowest risk of mortality in our study merit comment. The BMI levels for the 2 quintiles at lowest risk were between 24.6 and 29.8 kg/m2, and the optimal value was estimated as 29.2 kg/m2. According to the recent NHLBI guidelines, the majority of these women would be classified as overweight and almost obese. The Clinical Guidelines on the Identification, Evaluation, and Treatment of Overweight and Obesity in Adults recommends that "all overweight and obese adults (age 18 years of age or older) with a BMI of 25 kg/m2 or higher are considered at risk."1 Our results suggest that these guidelines are not appropriate for older women and that classifying women over 65 years of age with BMI from 24.6 to 29.8 kg/m2 as overweight and therefore at increased risk for mortality may be incorrect.
Other studies have reported that the association between obesity and mortality is different for older and younger women.3,4,28–30 Perhaps a certain amount of adiposity confers a survival advantage in elderly women. Some studies have suggested that the association between body size and mortality in older women is explained either by preexisting poor health status2,3,8,11 or weight loss.31–34 We found that the U-shaped relation between body size and mortality remained when we adjusted for self-reported health status or excluded early deaths as well as when we excluded women who had lost more than 10% of their body weight since they were aged 50 years.
Although it is difficult to conclude which of the measures of body composition and anthropometry best predicts mortality, we can draw a few practical conclusions. First, the U-shaped relation between body size and mortality is consistent among these various highly correlated measures. Second, the more specific measures of obesity (BIA-measured lean mass, fat mass, and percentage body fat) do not provide an obvious advantage over the more general and less expensive indicators of obesity (BMI, waist circumference) for predicting mortality. In the absence of a clear advantage of the BIA measures in predicting mortality, lower cost and ease of measurement favor the use of BMI or waist circumference.
Although this large community-based study of mortality in older women has many strengths, it has some limitations. We did not enroll a probability sample of a defined population, and almost all the women were White. We cannot address possible variations in the association between obesity and mortality by race or ethnicity. These results do not address mortality risk among women categorized as underweight according to NHLBI criteria (BMI<18.0 kg/m2), because there were few such women in our sample (women in the lowest BMI quintile had BMI
22.38 kg/m2). Also, this cohort is not representative of all older women, as those unable to walk or with bilateral hip replacements were excluded. Some error may have been introduced by the 2-year time difference between visit 2 (when body size measurements were obtained) and baseline (when some confounding variables were measured). We did not have an estimate of total caloric intake or information on dietary patterns during the study or earlier in life. Finally, we were unable to examine the association between body size measures and specific causes of death because of relatively small numbers in individual cause-of-death categories.
Nevertheless, this is the largest prospective study of obesity and mortality that has included estimates of lean mass and fat mass in older women. Previous studies have measured only BMI, weight, or weight change or used a measure of waist circumference. In addition, until recently there have been relatively few studies of the association between obesity or body size and mortality in older women.
Our results showing minimum mortality in the middle of the distribution of body composition levels are consistent with the results of the National Health and Nutrition Examination Survey I,13 which reported that a broad range of BMI values was associated with lower mortality, as well as with other studies that have suggested that women classified as overweight may not be at excess risk for mortality, particularly in older age groups.16,35–39 Furthermore, in a study combining data from 5 prospective cohorts in the United States, more than 80% of deaths attributable to excess weight were among those with a BMI greater than 30 kg/m2.40 A meta-analysis of BMI and mortality (not limited to older adults) also reported an increased relative risk of mortality among the obese but little evidence of increased risk among those classified as overweight. 39
Our results provide evidence of the U-shaped association between measures of obesity and mortality in older White women and extend these findings to specific measures of fat and lean mass. Few studies have reported on the prediction of mortality from body size measures in older women. The shape of the relation was not attributable to smoking, preexisting illness, or any other factors measured in this study. The patterns of risk were similar for the different body size measures. Using more complicated and expensive measures of body size such as BIA did not provide an advantage over easier and less expensive measures such as BMI and waist circumference. Finally, our results do not support the application of the NHLBI guidelines for the classification and treatment of overweight to older women with BMIs of 25.0 to 29.9 kg/m2, because these women had the lowest rates of mortality for their age.
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Acknowledgments |
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University of California, San Francisco (coordinating center): S. R. Cummings (principal investigator), M. C. Nevitt (coinvestigator), K. L. Stone (coinvestigator), D. C. Bauer (coinvestigator), D. M. Black (study statistician), H.K. Genant (director, central radiology laboratory), R. Benard, T. Blackwell, W.S. Browner, M. Dockrell, S. Ewing, C. Fox, R. Fullman, D. Kimmel, S. Litwack, L.Y. Lui, J. Maeda, P. Mannen, L. Nusgarten, L. Palermo, M. Rahorst, C. Schambach, J. Schneider, R. Scott, D. Tanaka, C. Yeung.
University of Maryland: M. C. Hochber (principal investigator), L. Makell (project director), R. Nichols, C. Boehm, L. Finazzo, T. Page, S. Trusty, B. Whitkop.
University of Minnesota: K. E. Ensrud (principal investigator), K. Margolis (coinvestigator), P. Schreiner (coinvestigator), K. Worzala (coinvestigator), S. Love (clinical research director), E. Mitson (clinic coordinator), C. Bird, D. Blanks, F. Imker-Witte, K. Jacobson, K. Knauth, N. Nelson, E. Penland-Miller, G. Saecker.
University of Pittsburgh: J. A. Cauley (principal investigator), L. H. Kuller (coprincipal investigator), M. Vogt (coinvestigator), L. Harper (project director), L. Buck (clinic coordinator), C. Bashada, D. Cusick, G. Engleka, A. Flaugh, A. Githens, M. Gorecki, D. Medve, M. Nasim, C. Newman, S. Rudovsky, N. Watson, D. Lee.
Kaiser Permanente Center for Health Research, Portland, Ore: T. Hillier (principal investigator), E. Harris (coprincipal investigator), E. Orwoll (coinvestigator), H. Nelson (coinvestigator), M. Aiken (biostatistician), J. Van Marter (project administrator), M. Rix (clinic coordinator), J. Wallace, K. Snider, K. Canova, K Pedula, J. Rizzo.
Human Participant Protection
The institutional review boards at each institution approved the study. All women provided written informed consent at study entry and at each clinical examination.
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Footnotes |
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Contributors
C. M. Dolan developed the research proposal, analyzed the data, and led the writing of the article. H. Kraemer assisted in the development of the statistical analysis plan and interpretation of the data. W. Browner and K. Ensrud contributed to the development of the hypothesis and the interpretation of the data. J. L. Kelsey contributed to the development and design of the study, the analysis, and the interpretation of the results. All authors reviewed and edited drafts of the article.
Accepted for publication May 10, 2006.
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References |
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