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Original Contribution |

Mortality and Cardiac and Vascular Outcomes in Extremely Obese Women FREE

Kathleen McTigue, MD, MS, MPH; Joseph C. Larson, MS; Alice Valoski, MS, RD; Greg Burke, MD, MS; Jane Kotchen, MD, MPH; Cora E. Lewis, MD, MSPH; Marcia L. Stefanick, PhD; Linda Van Horn, PhD, RD; Lewis Kuller, MD, DrPH
[+] Author Affiliations

Author Affiliations: Division of General Internal Medicine, Department of Medicine (Dr McTigue) and Department of Epidemiology (Ms Valoski and Dr Kuller), University of Pittsburgh, Pittsburgh, Pa; Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Wash (Mr Larson); Department of Public Health Sciences, Wake Forest University, Winston-Salem, NC (Dr Burke); Division of Epidemiology, Health Policy Institute, Medical College of Wisconsin, Milwaukee (Dr Kotchen); Division of Preventive Medicine, Department of Medicine, University of Alabama at Birmingham, Birmingham (Dr Lewis); Stanford Prevention Research Center, Department of Medicine, Stanford University School of Medicine, Stanford, Calif (Dr Stefanick); and Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, Ill (Dr Van Horn).

More Author Information
JAMA. 2006;296(1):79-86. doi:10.1001/jama.296.1.79.
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Published online

Context Obesity, typically measured as body mass index of 30 or higher, has 3 subclasses: obesity 1 (30-34.9); obesity 2 (35-39.9); and extreme obesity (≥40). Extreme obesity is increasing particularly rapidly in the United States, yet its health risks are not well characterized.

Objective To determine how cardiovascular and mortality risks differ across clinical weight categories in women, with a focus on extreme obesity.

Design, Setting, and Participants We examined incident mortality and cardiovascular outcomes by weight status in 90 185 women recruited from 40 US centers for the Women's Health Initiative Observational Study and followed up for an average of 7.0 years (October 1, 1993 to August 31, 2004).

Main Outcome Measures Incidence of mortality, coronary heart disease, diabetes, and hypertension.

Results Extreme obesity prevalence differed with race/ethnicity, from 1% among Asian and Pacific Islanders to 10% among black women. All-cause mortality rates per 10 000 person-years were 68.39 (95% confidence interval [CI], 65.26-71.68) for normal body mass index, 71.16 (95% CI, 67.68-74.82) for overweight, 84.47 (95% CI, 78.90-90.42) for obesity 1, 102.85 (95% CI, 92.90-113.86) for obesity 2, and 116.85 (95% CI, 103.36-132.11) for extreme obesity. Analyses adjusted for age, smoking, educational achievement, US region, and physical activity levels showed that weight-related risk for all-cause mortality, coronary heart disease mortality, and coronary heart disease incidence did not differ by race/ethnicity. Adjusted analyses among white and black participants showed positive trends in all-cause mortality and coronary heart disease incidence with increasing weight category. Much of the obesity-related mortality and coronary heart disease risk was mediated by diabetes, hypertension, and hyperlipidemia. In white women, weight-related all-cause mortality risk was modified by age, with obesity conferring less risk among older women.

Conclusions Considering obesity as a body mass index of 30 or higher may lead to misinterpretation of individual and population risks. Escalating extreme obesity may exacerbate health effects and costs of the obesity epidemic.

Figures in this Article

Obesity diagnosis and treatment are typically based on body mass index (BMI), calculated as weight in kilograms divided by height in meters squared, of at least 30. However, 3 categories of obesity are defined: obesity 1 (30-34.9); obesity 2 (35-39.9); and extreme obesity (≥40).1 The latter 2, sometimes termed severe obesity,2,3 are reported to be increasing especially rapidly in the United States,4,5 particularly among women, and tend to be persistent.6 From 1986 to 2000, prevalence of BMI of 30 or higher approximately doubled, while that of BMI of 40 or higher quadrupled and that of BMI of 50 or higher increased 5-fold.5 In 2000, 2.8% of all US women, and 6% of black women reported measurements consistent with extreme obesity.4

Although sound public health and clinical decisions regarding obesity must incorporate its expected health risks, estimates of obesity-related risks in women have generally been based on weight data that preceded the increase in extreme obesity.713 It is unclear whether health risk increases or plateaus as body weight increases throughout the obese range. Mortality risk has been shown to be highest for the most obese individuals,3,9,14,15 and extreme obesity has been linked with impaired quality of life16 and health care spending.17 Limited data show high comorbidity prevalence among extremely obese patients.18,19 However, prospective data are limited, based on select (often surgical) samples, and often focus on diminished morbidity following bariatric surgery rather than expected comorbidity incidence in the absence of weight loss intervention.

We sought to examine the relationship between weight category and mortality and coronary heart disease (CHD) in a large population-based sample of US women, focusing on risk across degree of obesity. We focus on race-specific risk estimates and assess for racial differences in mortality risk in order to better understand how obesity may be related to the generation of excess cardiovascular risk in black women. For example, the strong established link between obesity and CHD20 suggests that prevalent obesity among black individuals may be imparting significant health risk; alternatively, published data show a weaker association between obesity and mortality for black vs white samples.21,22 The racial/ethnic diversity, broad baseline weight range, and prospective follow-up data of the Women's Health Initiative Observational Study (WHI-OS) make it a valuable setting for addressing these issues.

As described in detail elsewhere,23 the WHI-OS involved women who were aged 50 through 79 years, were recruited from 40 US clinical centers, and had signed consent forms approved by institutional review boards of collaborating institutions. Participants included women who were ineligible for or chose not to participate in the WHI diet or hormone trials. Recruitment was completed in December 1998, and 3.7% of participants were subsequently lost to follow-up.

During baseline and 3-year follow-up clinic visits, WHI-OS data were collected on demographics, measured height, health history, tobacco use, and frequency and duration of a variety of recreational physical activities. Metabolic equivalent scores were computed as the product of days per week, minutes per day, and metabolic equivalent values for each activity.24 Self-reported health outcomes were assessed by annual mailed survey; incident cardiovascular outcomes (but not diabetes or hypertension) were verified by medical records.25 Outcomes through August 31, 2004, are reported herein, representing an average follow-up of 7.0 years (range, 0.0-9.9 years).

We used BMI-based criteria to assign women to 5 weight categories: normal (18.5-24.9), overweight (25.0-29.9), obesity 1 (30.0-34.9), obesity 2 (35.0-39.9), and extreme obesity (≥40).20 Because we were interested in the effects of excess body weight, we excluded underweight women. We included women who had a baseline BMI of at least 18.5 and listed their primary ethnicity as white, African American, Hispanic, Asian/Pacific Islander, or Native American. Women self-identified race/ethnicity with a questionnaire, which included these 5 options and “other.”

We examined mortality rates by race (black vs non-Hispanic white), age, race/ethnicity, baseline smoking status, and presence of diabetes or hypertension. As a first step toward examining how much these risk factors contribute to obesity-related mortality risk, we also examined all-cause mortality in women without smoking, hypertension, or diabetes at baseline.

For each BMI category, we also examined race-specific (CHD) mortality. We calculated the incidence of diabetes or hypertension among women without each condition at baseline. Among women without a history of CHD (defined as including clinical myocardial infarction, angina, and cardiac revascularization), we calculated the incidence of CHD and its components.

We used Cox proportional hazard modeling to examine relationships between body size and 3 major outcomes (all-cause mortality, CHD mortality, and CHD incidence) in white and black women. Coronary heart disease analyses included fatal and nonfatal disease and were restricted to women without baseline CHD.

To determine whether weight-related risk was significantly modified by race/ethnicity and age, we initially assessed interaction models between continuous BMI and race/ethnicity among all women. Within the 2 largest groups (white and black), we also examined interactions between continuous BMI and age or smoking status. Each model was adjusted for age, smoking status, educational achievement, US region, and physical activity level; the model combining all women was also adjusted for race/ethnicity. For interaction terms with P values significant at P<.05, we further examined subset data (eg, by age strata for a significant age × BMI interaction) to determine the clinical significance of the interaction.

In the main modeling analyses, black and white women were analyzed separately and BMI categories were the predictor variables, for ease of application to clinical settings. We first adjusted the models for age (model 1), then for age, current smoking, educational achievement, US region, and recreational physical activity (model 2). As a secondary analysis to see how much of the total weight-related risk was explained by the weight-related CHD risk factors, we further adjusted for baseline report of diabetes, hypertension, and pharmacotherapy-treated hyperlipidemia. In doing so, we lost 1663 participants from our sample who did not provide these data. P values <.05 indicated statistical significance. For each of the relationships examined in the Cox proportional hazard models, we also examined tests of trend by running the proportional hazards model with an orthogonal variable of the BMI levels and present the P values for the BMI term.

All proportional hazards models were complete case and excluded 2729 participants with missing data for adjustment covariates. The proportional hazards assumption was checked by modeling a time × BMI interaction term in a proportional hazards model along with the BMI term. All analyses were conducted using SAS software version 9.1 (SAS Institute Inc, Cary, NC).

Of 93 676 WHI-OS women, we excluded 3491 (1108 with inadequate data to calculate BMI, 1107 who were underweight, and 1308 with unknown, or missing data or being of another ethnicity), resulting in a sample of 90 185 women for these analyses (96.3% of the original sample). Some women had more than 1 reason for being excluded from the study.

Baseline Characteristics

As shown in Table 1, the prevalence of extreme obesity was highest among black (9.6%) and lowest among Asian (0.9%) women. Women in higher weight categories reported having less education than those in lower weight categories. In general, participants reported low levels of recreational physical activity and degree of activity varied inversely with BMI. For example, 49% of women with normal BMI were in the highest 2 quintiles of physical activity, while only 16% of women with extreme obesity were in these 2 quintiles.

Table Graphic Jump LocationTable 1. Distribution of Baseline Characteristics

We found significant trends for increasing baseline prevalence of hypertension, diabetes, and hyperlipidemia across increasing weight class, although point estimates suggest that the relationship is weaker for hyperlipidemia. Although current smoking status also differed by weight class, with women in higher weight categories reporting less smoking, tobacco use was uncommon and the gradient across weight classes was small. Baseline prevalence of cardiovascular diagnoses all showed significant positive trends across weight categories.

Mortality and Weight Category

A total of 1743 women who were in the normal BMI range, 1527 in the overweight range, 827 in the obese 1 range, 371 in the obese 2 range, and 255 in the extreme obese category died during our follow-up period. As shown in Table 2, all-cause mortality (deaths per 10 000 person-years) increased with increasing weight category and ranged from 68.39 in women with normal BMI to 116.85 in women with extreme obesity. Point estimates of CHD-related mortality among women without CHD at baseline were also highest in women with extreme obesity; CHD-related mortality for overweight women was similar to that of women with normal BMI.

Table Graphic Jump LocationTable 2. Mortality by Clinical Weight Categories*

At each decade of baseline age, rates of all-cause mortality were higher in the obese vs normal BMI weight categories. However, the relative increase in mortality rates with weight category decreased with age.

Limited sample size led to less-precise race/ethnicity–specific estimates of risk, but in white (all obesity classes), Hispanic (extreme obesity), and black (obesity 2) women, mortality was clearly higher for obese women than those with normal BMI. Point estimates were consistent with considerable obesity-related mortality in Asian and Native American women, but the confidence intervals (CIs) are wide.

Mortality rates were higher among those with CHD risk factors (smoking, diabetes, or hypertension) at baseline than those without such risk factors, regardless of race/ethnicity. Although sample size limits evaluation of change across weight categories, and these rates are not adjusted for possible confounders, the most clear-cut dose-response pattern in the 7-year all-cause mortality was found across weight classes for women without any current smoking, diabetes, or hypertension.

Severely obese black and white women had higher incidence rates of diabetes and hypertension than women in the lower weight classes (Figure).

Figure. Incidence of Diabetes and Hypertension by Race Among Individuals Who Did Not Report Baseline Diagnoses, by Baseline Body Mass Index Category
Graphic Jump Location

Error bars represent 95% confidence intervals.

Modeling Outcomes

The proportional hazards assumption was not violated in any of the modeling analyses, although it was of borderline significance (P = .049) for the all-cause mortality outcome. The interaction models showed that the relationship between BMI and all-cause mortality, CHD mortality, and CHD incidence did not differ by race/ethnicity (P>.05). However, among white women, a significant interaction was found between BMI and age (P<.001), and among black women, a significant interaction was noted between BMI and smoking (P = .03).

In the main modeling analyses, all obesity classes (but not overweight) were significantly associated with increased all-cause mortality in the age-adjusted model for white women (Table 3). Compared with BMI in the normal range, risk for mortality was 18% higher for obesity 1, 49% higher for obesity 2, and more than doubled for extreme obesity. Adjusting for tobacco use, educational achievement, US region, and physical activity reduced hazard ratios (HRs) slightly, and did not alter significance; a significant trend was found for increasing risk across weight classes. Similarly, among black women, all-cause mortality HRs were significantly higher for all obesity classes than for the normal BMI class in both the age-adjusted and fully adjusted models. Model 2 again showed a significant trend in risk across weight classes.

Table Graphic Jump LocationTable 3. Predicting Mortality or Incident Coronary Heart Disease by Body Mass Index Category*

When we modeled CHD mortality among white women without baseline CHD, all obesity classes were associated with increased risk, with HRs showing a dose-response pattern throughout the obese range (Table 3). Adjusting the model for confounders resulted in minimally changed HRs, with an obesity 1 HR of 1.41 (95% CI, 1.02-1.94) and an extreme obesity HR of 2.59 (95% CI, 1.56-4.32), and a positive trend across weight categories. Among black women, only obesity 2 was significantly linked with CHD mortality, and the trend analysis was of borderline significance.

Coronary heart disease incidence was strongly related to weight class, regardless of race, with elevated risk appearing in the overweight range (HRs for white women, 1.39; for black women, 1.75). Although a trend for increasing risk across weight categories was noted for both white and black women, extreme obesity lost significance among black women in the fully adjusted model. Further adjusting the modeling analyses for baseline diabetes, hypertension, or hyperlipidemia indicated that much of the weight-related mortality risk was mediated by these factors. Most HRs for all-cause mortality approached 1, remaining significantly elevated only for white women in the extreme obesity category (HR, 1.42; 95% CI, 1.21-1.67), and black women in the obesity 2 range (HR, 1.56; 95% CI, 1.13-2.17). Regardless of race, adjustment for these weight-related cardiovascular risk factors eliminated any significant associations between weight class and CHD mortality; HRs were generally somewhat reduced and CIs were wide. However, adjusting models of CHD incidence for diabetes, hypertension, and hyperlipidemia reduced HRs modestly while minimally affecting significance. For example, among white women, HRs were 1.19 (95% CI, 1.08-1.31) for overweight, 1.27 (95% CI, 1.13-1.43) for obesity 1, 1.27 (95% CI, 1.07-1.51) for obesity 2, and 1.24 (95% CI, 1.00-1.55) for extreme obesity. Likewise, among black women, overweight (HR, 1.73; 95% CI, 1.09-2.75) and obesity 2 (HR, 2.04; 95% CI, 1.18-3.51) remained significantly greater than 1.

Removing deaths during the first year of follow-up from model 2 for either mortality outcome had little effect on HRs and did not change the significance of any relationships.

When we repeated the all-cause mortality analyses stratified by age for white women (Table 4), we found that weight class was not as strong a predictor for total mortality in older women as in younger women and that for the oldest women, overweight was slightly protective of mortality. Although a BMI × smoking interaction showed a P value of <.05 in black women, the limited number of smokers in this sample, in particular, among black women with extreme obesity, did not allow estimation of obesity-related risk according to smoking subsets in this racial group.

Table Graphic Jump LocationTable 4. Predicting All-Cause Mortality in White Women by Age at Baseline*

In this diverse population-based sample of older women, we found that obesity was linked with considerable health risk and that accounting for degree of excess weight is important in understanding weight-related health risk. Overall, extremely obese women were more likely to die over the average 7.0 years of follow-up than were women in other examined weight categories. Modeling analyses adjusted for age, smoking status, educational achievement, US region, and physical activity level showed that weight-related risk for all-cause mortality, CHD mortality, and CHD incidence did not differ by race/ethnicity. There was a positive trend in all-cause mortality risk and CHD incidence with increasing weight category. This trend had borderline significance for CHD mortality among black women, likely reflecting sample size limitations. Much of the obesity-related mortality and CHD risk was mediated by diabetes, hypertension, and hyperlipidemia. In white women, as other studies have found,26 weight-related all-cause mortality risk was modified by age, with obesity conferring less risk among older women. Smoking may modify weight-related risk in black women, but further study is needed to understand the nature of this relationship.

Several limitations deserve mention. First, although the WHI-OS is the largest ethnically diverse sample to date to allow examination of extreme obesity-related mortality and morbidity, sample size was still inadequate to fully compare risks across all racial and ethnic groups and within smaller groups. Second, the low prevalence of smokers in this sample and the difficulty of measuring physical activity limit evaluation of the role of these behaviors in the weight-health relationship. Third, the outcomes were self-reported. However, bias was lessened by the use of standardized questionnaires and review of medical records. Because diabetes, hypertension, and hyperlipidemia are often asymptomatic, their estimates are particularly likely to be conservative and reflect health care access. Fourth, follow-up is limited to 7.0 years, which may be insufficient to detect the development of certain weight-related outcomes or those due to less-extreme degrees of excess weight. Likewise, longer follow-up may minimize the effect of preexisting disease on estimates of weight-related health risk.

The lack of interaction between race/ethnicity and BMI for any of the 3 main outcomes suggests that lower point estimates for hazard between extreme obesity and all-cause or CHD mortality in black women (vs white women) are due to the limited sample size of extreme obesity in that group. The small number of smokers in this subset may also artificially depress risk. The clear link between obesity and mortality or CHD incidence among black women in this sample emphasizes the importance of including adequate diversity in research studies and highlights the potential for preventive medicine practices to help reduce the disproportionate risk for CHD and mortality in the black community.

As have other studies,27 we found that being overweight but not obese is not associated with mortality over the time-frame of our analysis. However, particularly for the younger women, its role in determining health risk should not be dismissed because overweight was associated with substantially increased risk of CHD incidence. Given the considerable mortality and morbidity associated with CHD, it is likely that 7.0 years is insufficient to detect the full effect of overweight on health.

These data suggest that diabetes, hypertension, and hyperlipidemia may mediate much of women's 7.0-year weight-related health risk. The finding does not lessen health implications of the obesity epidemic, for excess weight promotes development of these conditions.20 However, it emphasizes the importance of aggressive diagnosis and treatment of diabetes, hypertension, and hyperlipidemia among obese individuals.

Reported recreational physical activity minimally influenced obesity's health hazard in this sample. Vigorous activity was seldom reported, so its effect may not be represented. Because the WHI-OS is diverse ethnically and geographically and because sedentary behavior is common,28 it is plausible that sufficient physical activity to mitigate obesity's adverse health effects29 may be rare in the older US population.

Our findings have important clinical and policy implications. The escalating prevalence of extreme obesity may exacerbate the health effects and health-related expenditures resulting from the US obesity epidemic. Calculating the weight-related risks of morbidity and mortality based on findings in earlier population samples, which tended to reflect lower degrees of obesity, may underestimate the risks for extremely obese individuals and overestimate the risks for mildly obese individuals in diverse groups. The consideration of risks related to specific categories of obesity may improve policy decision making in general and may help patients and clinicians more accurately assess the potential risks and benefits of weight loss interventions in particular patients.

In summary, we found that considering obesity as a homogenous condition with fixed risk is inappropriate. Weight-related health risk clearly varies with degree of excess weight. The distribution of body weight, age, and cardiac risk factor status also alter weight-related risk. Although prevalence of different weight categories differs by race/ethnicity, we found similar weight-related health risk in white and black women after accounting for BMI category, smoking status, educational achievement, US region, and physical activity level. More accurately assessing weight-related health risk may both improve policy decisions about obesity and assist women in making informed decisions about their health.

Corresponding Author: Kathleen McTigue, MD, MS, MPH, Departments of Medicine and Epidemiology, 230 McKee Pl, Suite 600, University of Pittsburgh, Pittsburgh, PA 15213 (mctiguem@.edu).

Author Contributions: Dr McTigue had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Study concept and design: McTigue, Kuller.

Acquisition of data: Valoski, Burke, Kotchen, Lewis, Stefanick, Van Horn, Kuller.

Analysis and interpretation of data: McTigue, Larson, Burke, Lewis, Stefanick, Kuller.

Drafting of the manuscript: McTigue, Larson, Valoski, Kotchen, Van Horn, Kuller.

Critical revision of the manuscript for important intellectual content: McTigue, Larson, Burke, Kotchen, Lewis, Stefanick, Kuller.

Statistical analysis: Larson, Kuller.

Obtained funding: Burke, Lewis, Stefanick, Kuller.

Administrative, technical, or material support: McTigue, Valoski, Kotchen, Lewis, Kuller.

Study supervision: Van Horn, Kuller.

Financial Disclosures: None reported.

Program Office: National Heart, Lung, and Blood Institute, Bethesda, Md: Barbara Alving, Jacques Rossouw, Shari Ludlam, Linda Pottern, Joan McGowan, Leslie Ford, and Nancy Geller.

Clinical Coordinating Center: Fred Hutchinson Cancer Research Center, Seattle, Wash: Ross Prentice, Garnet Anderson, Andrea LaCroix, Charles L. Kooperberg, Ruth E. Patterson, Anne McTiernan; Wake Forest University School of Medicine, Winston-Salem, NC: Sally Shumaker; Medical Research Labs, Highland Heights, Ky: Evan Stein; University of California, San Francisco, Steven Cummings.

Clinical Centers: Albert Einstein College of Medicine, Bronx, NY: Sylvia Wassertheil-Smoller; Baylor College of Medicine, Houston, Tex: Jennifer Hays; Brigham and Women's Hospital, Harvard Medical School, Boston, Mass: JoAnn Manson; Brown University, Providence, RI: Annlouise R. Assaf; Emory University, Atlanta, Ga: Lawrence Phillips; Fred Hutchinson Cancer Research Center, Seattle, Wash: Shirley Beresford; George Washington University Medical Center, Washington, DC: Judith Hsia; Harbor-UCLA Research and Education Institute, Torrance, Calif: Rowan Chlebowski; Kaiser Permanente Center for Health Research, Portland, Ore: Evelyn Whitlock; Kaiser Permanente Division of Research, Oakland, Calif: Bette Caan; Medical College of Wisconsin, Milwaukee: Jane Morley Kotchen; MedStar Research Institute/Howard University, Washington, DC: Barbara V. Howard; Northwestern University, Chicago, Ill: Linda Van Horn; Rush Medical Center, Chicago, Ill: Henry Black; Stanford Prevention Research Center, Stanford, Calif: Marcia L. Stefanick; State University of New York at Stony Brook, Stony Brook: Dorothy Lane; Ohio State University, Columbus: Rebecca Jackson; University of Alabama at Birmingham, Birmingham: Cora E. Lewis; University of Arizona, Phoenix: Tamsen Bassford; University at Buffalo, the State University of New York, Buffalo: Jean Wactawski-Wende; University of California at Davis, Sacramento: John Robbins; University of California at Irvine, Irvine: F. Allan Hubbell; University of California at Los Angeles, Los Angeles: Howard Judd; University of California at San Diego, LaJolla: Robert D. Langer; University of Cincinnati, Cincinnati, Ohio: Margery Gass; University of Florida, Gainesville: Marian Limacher; University of Hawaii, Honolulu: David Curb; University of Iowa, Iowa City: Robert Wallace; University of Massachusetts and Fallon Clinic, Worcester: Judith Ockene; University of Medicine and Dentistry of New Jersey, Newark: Norman Lasser; University of Miami, Miami, Fla: Mary Jo O’Sullivan; University of Minnesota, Minneapolis: Karen Margolis; University of Nevada, Reno: Robert Brunner; University of North Carolina, Chapel Hill: Gerardo Heiss; University of Pittsburgh, Pittsburgh, Pa: Lewis Kuller; University of Tennessee, Memphis: Karen C. Johnson; University of Texas Health Science Center, San Antonio: Robert Brzyski; University of Wisconsin, Madison: Gloria E. Sarto; Wake Forest University School of Medicine, Winston-Salem, NC: Denise Bonds; Wayne State University School of Medicine/Hutzel Hospital, Detroit, Mich: Susan Hendrix.

Funding/Support: The Women's Health Initiative program is funded by the National Heart, Lung, and Blood Institute, US Department of Health and Human Services. Dr McTigue was supported by grant 1 K08 DK067192-01 from the National Institute of Diabetes and Digestive and Kidney Diseases.

Role of the Sponsor: The representatives of the National Heart, Lung, and Blood Institute played a role in the design and conduct of the study and management and interpretation of the data, as well as review of the manuscript. They played no role in the collection or analysis of the data or preparation or approval of the manuscript. Representatives of the National Institute of Diabetes and Digestive and Kidney Diseases did not play a role in the design or conduct of the study, collection, management, analysis, and interpretation of the data, or preparation, review, or approval of the manuscript.

Previous Presentation: An abstract of an earlier version of this analysis was presented as a poster at an American Heart Association meeting in spring of 2005. It was published in a Circulation supplement at the time.

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Langer RD, White E, Lewis CE, Kotchen JM, Hendrix SL, Trevisan M. The Women's Health Initiative Observational Study: baseline characteristics of participants and reliability of baseline measures.  Ann Epidemiol. 2003;13:(9 suppl)  S107-S121
PubMed   |  Link to Article
Ainsworth BE, Haskell WL, Leon AS.  et al.  Compendium of physical activities: classification of energy costs of human physical activities.  Med Sci Sports Exerc. 1993;25:71-80
PubMed   |  Link to Article
Curb JD, McTiernan A, Heckbert SR.  et al.  Outcomes ascertainment and adjudication methods in the Women's Health Initiative.  Ann Epidemiol. 2003;13:(9 suppl)  S122-S128
PubMed   |  Link to Article
Stevens J, Cai J, Pamuk ER, Williamson DF, Thun MJ, Wood JL. The effect of age on the association between body-mass index and mortality.  N Engl J Med. 1998;338:1-7
PubMed   |  Link to Article
McGee DL.Diverse Populations Collaboration.  Body mass index and mortality: a meta-analysis based on person-level data from twenty-six observational studies.  Ann Epidemiol. 2005;15:87-97
PubMed   |  Link to Article
US Department of Health and Human Services.  Physical Activity and Health: A Report of the Surgeon GeneralAtlanta, Ga: Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion; 1996. http://www.cdc.gov/nccdphp/sgr/pdf/prerep.pdf. Accessed June 8, 2006.
Blair SN. Revisiting fitness and fatness as predictors of mortality.  Clin J Sport Med. 2003;13:319-320
PubMed   |  Link to Article

Figures

Figure. Incidence of Diabetes and Hypertension by Race Among Individuals Who Did Not Report Baseline Diagnoses, by Baseline Body Mass Index Category
Graphic Jump Location

Error bars represent 95% confidence intervals.

Tables

Table Graphic Jump LocationTable 1. Distribution of Baseline Characteristics
Table Graphic Jump LocationTable 2. Mortality by Clinical Weight Categories*
Table Graphic Jump LocationTable 3. Predicting Mortality or Incident Coronary Heart Disease by Body Mass Index Category*
Table Graphic Jump LocationTable 4. Predicting All-Cause Mortality in White Women by Age at Baseline*

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Durazo-Arvizu R, Cooper RS, Luke A, Prewitt TE, Liao Y, McGee DL. Relative weight and mortality in U.S. blacks and whites: findings from representative national population samples.  Ann Epidemiol. 1997;7:383-395
PubMed   |  Link to Article
Stevens J. Obesity and mortality in Africans-Americans.  Nutr Rev. 2000;58:346-353
PubMed   |  Link to Article
Langer RD, White E, Lewis CE, Kotchen JM, Hendrix SL, Trevisan M. The Women's Health Initiative Observational Study: baseline characteristics of participants and reliability of baseline measures.  Ann Epidemiol. 2003;13:(9 suppl)  S107-S121
PubMed   |  Link to Article
Ainsworth BE, Haskell WL, Leon AS.  et al.  Compendium of physical activities: classification of energy costs of human physical activities.  Med Sci Sports Exerc. 1993;25:71-80
PubMed   |  Link to Article
Curb JD, McTiernan A, Heckbert SR.  et al.  Outcomes ascertainment and adjudication methods in the Women's Health Initiative.  Ann Epidemiol. 2003;13:(9 suppl)  S122-S128
PubMed   |  Link to Article
Stevens J, Cai J, Pamuk ER, Williamson DF, Thun MJ, Wood JL. The effect of age on the association between body-mass index and mortality.  N Engl J Med. 1998;338:1-7
PubMed   |  Link to Article
McGee DL.Diverse Populations Collaboration.  Body mass index and mortality: a meta-analysis based on person-level data from twenty-six observational studies.  Ann Epidemiol. 2005;15:87-97
PubMed   |  Link to Article
US Department of Health and Human Services.  Physical Activity and Health: A Report of the Surgeon GeneralAtlanta, Ga: Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion; 1996. http://www.cdc.gov/nccdphp/sgr/pdf/prerep.pdf. Accessed June 8, 2006.
Blair SN. Revisiting fitness and fatness as predictors of mortality.  Clin J Sport Med. 2003;13:319-320
PubMed   |  Link to Article
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