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The combined relations of adiposity and smoking on mortality^1,2,3,4

¹ From the Laboratory of Epidemiology, Demography, and Biometry, National Institute on Aging, Bethesda, MD (AK and TBH); the Faculty of Health, Medicine and Life Sciences, Universiteit Maastricht, Netherlands (AK and JTMvE); the Nutritional Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD (MFL, AS, and KFA); the AARP, Washington DC (AH)

² The views expressed herein are solely those of the authors and do not necessarily reflect those of the contractor or DOH.

³ Supported in part by the Intramural Research Program of the NIH, National Cancer Institute, and the Intramural Research Program of the NIH, National Institute on Aging.

⁴ Reprints not available. Address correspondence to A Koster, National Institute on Aging, 7201 Wisconsin Avenue, Gateway Building, Suite 3C309, Bethesda, MD 20892. E-mail: kostera{at}mail.nih.gov.

	ABSTRACT

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Background: Smoking and high adiposity are strong independent health risk factors but are also interrelated. Smoking is related to a lower body mass index (BMI) but not necessarily with a smaller waist circumference. Smoking cessation is associated with increased body weight and a substantial increase in waist circumference. How this affects mortality risk is unknown.

Objective: This study examined the combined relations of smoking status with BMI and waist circumference and smoking status to all-cause mortality.

Design: Data were from 149 502 men and 88 184 women aged 51–72 y participating in the National Institutes of Health–AARP Diet and Health Study. All-cause mortality was assessed over 10 y of follow-up from 1996 to 2006.

Results: Current smokers with a BMI (in kg/m²) <18.5 or 35 had a mortality risk 6–8 times that of persons within the normal BMI range who never smoked. Current smokers with a large waist circumference had a mortality risk about 5 times that of never smokers with a waist circumference in the second quintile.

Conclusion: Both smoking and adiposity are independent predictors of mortality, but the combination of current or recent smoking with a BMI 35 or a large waist circumference is related to an especially high mortality risk.

	INTRODUCTION

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The prevalence of overweight and obesity is increasing across the age spectrum (1, 2). Overweight and obesity have been shown to be associated with an increased risk of diabetes, heart disease, arthritis, and cancer (3-5). However, the association between body weight and mortality remains controversial. Most previous studies have found an increased risk of mortality among underweight and obese people (6-8). However, not all studies have found an increased risk of mortality among overweight persons. In addition to total body fat, fat distribution may be an important determinant of morbidity and mortality. High amounts of abdominal fat have been shown to be associated with metabolic disease risk (9, 10), independent of overall adiposity (11, 12). A large waist circumference has also been related to increased mortality risk (13, 14).

Cigarette smoking is a major lifestyle risk factor strongly associated with morbidity and mortality (15). Current smoking and adiposity are independent health risk factors but are also interrelated. Smoking is associated with both a lower body weight (16, 17) and an increased risk of death and therefore plays an important role in the association between adiposity and mortality. There seems to be only a weak association between BMI and mortality in current smokers, whereas the association is much stronger in nonsmokers (6, 7). Smoking cessation is associated with increased body weight due to increased body fat (18). Although current smoking is related to a lower body mass index (BMI), it is not necessarily associated with a smaller waist circumference (16, 19, 20). In fact, some evidence suggests that smoking is related to visceral fat accumulation (21). A recent study showed that smoking cessation is associated with a substantial increase in waist circumference (22). How this affects mortality risk and whether this is independent of total adiposity is unknown. The combined relations of BMI and smoking on mortality have not been studied extensively (23) and to our knowledge the combined effects of waist circumference and smoking have not been previously studied.

Using data from the National Institutes of Health–AARP (formerly known as the American Association of Retired Persons) Diet and Health Study, we examined the joint effects of BMI and smoking status and waist circumference and smoking status on all-cause mortality. The relations with waist circumference were examined after adjustment for BMI to determine whether the combined relations of waist circumference and smoking on mortality are independent of overall adiposity.

	SUBJECTS AND METHODS

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Study population
The National Institutes of Health–AARP Diet and Health Study was initiated in 1995–1996, when an extensive baseline questionnaire was mailed to 3.5 million AARP members between 50 and 71 y of age who resided in 1 of 6 US states (California, Florida, Louisiana, New Jersey, North Carolina, and Pennsylvania) or in 2 metropolitan areas (Atlanta, GA, and Detroit, MI) (24). Of 617 119 (17.6%) questionnaires returned, 567 169 (16.2%) were satisfactorily completed. In 1996–1997, a second questionnaire was sent to participants who successfully completed the baseline questionnaire to collect additional information on diet, family history of cancer, anthropometric measures (including waist circumference), physical activity, and use of menopausal hormones. A total of 334 908 respondents completed the second questionnaire. We excluded 83 860 persons who provided no data on waist circumference, those with a waist circumference <60 cm (n = 549), those with missing data on height or weight (n = 4425), and those with a BMI (kg/m²) <15 or >60 (n = 543). Persons with extreme values for waist circumference and BMI were excluded because of biological plausibility. Furthermore, 7845 persons with missing smoking data were excluded, which resulted in 237 686 participants for the present analysis. The NIH-AARP Diet and Health Study was approved by the Special Studies Institutional Review Board of the US. National Cancer Institute.

Mortality
From 1996 to 1997 through 31 December 2006, vital status was determined through an annual linkage of the cohort to the Social Security Administration Death Master File on deaths in the United States (25) and follow-up searches of the National Death Index. We estimated that the follow-up for deaths in our cohort was >93% complete (26, 27).

Body mass index
Current height was reported in feet and inches, and weight was reported to the nearest pound. BMI was calculated as weight in kilograms divided by height squared in meters and divided into 6 categories: <18.5, 18.5 to <23.5, 23.5 to <25, 25 to <30, 30 to <35, and 35. In accordance with the article by Adams et al (6), we used the group with a BMI between 23.5 and 25 as the reference group.

Waist circumference
Using pictured instruction, the participants were requested to measure their waist with a tape measure 1 inch (2.54 cm) above the navel while standing and to report values to the nearest quarter inch. Self-reported waist circumference has been found to be a valid assessment of measured waist (28, 29). For example, a study of 123 men aged 40–75 y and 140 women aged 41–65 y reported crude Pearson correlation coefficients comparing self-reported and measured waist circumference of 0.95 for men and 0.89 for women (30). Sex-specific quintiles of waist circumference were created, and the second quintile was used as the reference group (31, 32).

Smoking
Information on cigarette smoking included the number of years and the number of cigarettes per day a person smoked. Smoking status was categorized as never smoker, former smoker who stopped smoking 10 y ago, former smoker who stopped smoking <10 y ago, and current smoker. Smoking intensity, defined as the number of cigarettes per day a person smoked, was categorized as 1–10, 11–20, 21–30, 31–40, 41–60, and 61.

Covariates
Information on covariates was collected with the use of a self-administered mailed questionnaire. Sociodemographic variables included age and race or ethnic group (non-Hispanic white, non-Hispanic black, Hispanic, Asian, and Pacific Islander or American Indian). Education level was categorized as 11 y, 12 y or completed high school, post-high school or some college, college graduate, or postgraduate. Physical activity was assessed by a question in the baseline questionnaire about how often a person participated in physical activities at work or home including exercise, sports, and activities such as carrying heavy loads for 20 min that caused increases in breathing, heart rate, or working up a sweat during a typical month in the past 12 mo. Categories of physical activity were never, rarely, 1–3 times/mo, 1–2 times/wk, 3–4 times/wk, and 5 times/wk. Alcohol consumption over the past 12 mo was assessed as part of a food-frequency questionnaire (24). From the total alcohol intake (in g/d), 4 categories were created: 0, 0 to <5, 5 to <15, and 15 g/d. Information on chronic diseases was collected by means of the following question: "Have you ever been told by a doctor that you had any of the following conditions?" Diseases included cancer, heart disease, stroke, diabetes, emphysema, and renal failure.

Statistical analysis
Differences in baseline characteristics between BMI, waist circumference, or smoking status groups were tested by using chi-square tests for categorical variables, and analysis of variance was used for continuous variables. Age-standardized mortality rates were calculated standardized to the age distribution of the cohort in men and women by using 5-y age categories. Cox proportional hazard models were fitted to study the individual and joint effects of BMI and smoking and waist circumference and smoking on time to death in men and women. The participants were cross-classified on the basis of smoking status and BMI or waist circumference group. The group "never smokers" with a normal BMI or waist circumference was used as the reference group. Analyses were adjusted for age, race or ethnic group, smoking intensity, education, physical activity, and alcohol consumption. For the analyses with waist circumference, we additionally adjusted for height and BMI. In additional analyses, persons with chronic diseases at baseline were excluded. We further excluded the first 2 y of follow-up to exclude persons who died during the first 2 y. The proportional hazards assumption was investigated by testing the constancy of the log hazard ratio over time by means of log-minus-log survival plots; according to the test, the proportional hazard assumption was not violated. Analyses were performed by using SPSS, version 15.0 (SPSS Inc, Chicago, IL).

	RESULTS

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During 10 y of follow-up, 19 699 men and 7371 women died. The baseline characteristics of the study population according to BMI, waist circumference, and smoking status are shown in Table 1. Men and women with the highest BMI or the largest waist circumference had a lower education, were less likely to currently smoke, were less physically active, and had a lower alcohol intake than did those with a normal BMI (18.5 to <25) or a smaller waist circumference (all P < 0.01). Current smokers had a lower education, a lower physical activity level, and a higher alcohol intake than did never smokers (all P < 0.01).

Age-standardized mortality rates and adjusted hazard ratios of mortality for the combined relations of smoking and BMI are shown in Table 2. Overall mortality rates were higher in the very-low- and high-BMI groups. In each BMI group, mortality rates were highest in former smokers who stopped smoking <10 y ago and current smokers. Compared with never smoking men with a BMI of 23.5 to <25, never smokers with a BMI between 25 and 30 had an increased mortality risk (HR: 1.11; 95% CI: 1.01, 1.22) as did men with a BMI between 30 and 35 (HR: 1.41; 95% CI: 1.26, 1.59) or a BMI 35 (HR: 2.44; 95% CI: 2.10, 2.82). A significantly higher mortality risk was also found in men in the normal BMI range (23.5 to <25) who were former smokers and had stopped smoking 10 y ago (HR: 2.15; 95% CI: 1.89, 2.45), former smokers who quit smoking <10 y ago (HR: 4.09; 95% CI: 3.53, 4.74), and current smokers (HR: 6.15; 95% CI: 5.34, 7.08). The highest mortality risks were found in current smokers and particularly in current smokers who were underweight (BMI < 18.5) (HR: 8.36; 95% CI: 6.27, 11.15) and those who were morbidly obese (BMI 35) (HR: 8.13; 95% CI: 6.61, 10.01). A similar pattern was found in women. In additional analyses, we excluded persons with chronic diseases at baseline. In this healthy group, the results were similar and those with a BMI < 18.5 still had a significantly higher mortality risk across all smoking groups (data not shown). In additional analyses we further excluded the first 2 y of follow-up to exclude persons who died during the first 2 y; very similar results were found (data not shown).

Table 3

	DISCUSSION

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Several previous studies have shown the BMI mortality association stratified by smoking status (6, 7). However, only a few studies specifically presented the joint effects of BMI and smoking on mortality (23); the joint effects of waist circumference and smoking on mortality risk have not been shown previously. A study of Japanese American men reported that, compared with never smokers with a BMI between 21.2 and 26.3, mortality risk was significantly higher in men who smoked, regardless of BMI level (33). Another recent study showed the highest mortality risk among obese persons who smoked; risks were especially high for circulatory disease mortality in persons aged <65 y (23). That study did not show results for the group with a BMI < 18.5.

Previous studies that examined the BMI-mortality association showed significantly higher mortality rates among underweight persons (8, 28). One of the explanations for the higher mortality risk among underweight people was residual confounding by smoking. Persons who smoke have a higher mortality risk and are more often underweight. In the present study, we observed a higher mortality risk in low-BMI groups across all smoking groups than in never smokers within a BMI in the normal range. That smoking distorts the relation between BMI and mortality is seen among former smokers who stopped smoking <10 y ago and among current smokers, in whom mortality rates were not markedly different between normal-weight, overweight, and obese (BMI: 30 to <35) persons. This might have been due to residual confounding by smoking, which cannot be completely ruled out even though we additionally adjusted for smoking intensity. For example, depth of inhalation or genetic susceptibility could influence the effect of smoking on weight and mortality and were not accounted for. A further explanation for the higher mortality risk in persons with a low BMI is reverse causation by prevalent chronic disease (6). However, when we excluded persons with chronic diseases or excluded the first 2 y of follow-up, persons with a BMI < 18.5 still had a significantly higher mortality risk.

For waist circumference, a nonlinear pattern was found in former smokers who stopped smoking <10 y ago and in current smokers; particularly high mortality rates were found among those with a very small or a very large waist circumference. This might have been due to a higher prevalence of chronic disease in these groups, which may have caused weight loss. However, a similar pattern was found in persons without chronic conditions or after the exclusion of data from the first 2 y of follow-up. Information on preexisting chronic diseases was based on self-report, and the comprehensiveness of the panel of diseases considered was limited. In a better-defined healthy group, a stronger linear association with mortality across categories of BMI and waist circumference and mortality may have been found.

The analyses on the combined effects of waist circumference and smoking on mortality were adjusted for BMI. Thus, the effect of waist circumference across smoking groups was independent of total adiposity. Previous studies showed that smokers have more central obesity than do nonsmokers (16, 19, 20) and some evidence indicates that current smoking is related to visceral fat accumulation (21). In the present study, we did not find a larger waist circumference among current smokers than in never smokers, and our data do not suggest that waist circumference has a stronger relation with mortality in current smokers than in never smokers.

Current smokers had the highest mortality rates across all BMI and waist circumference groups. Smoking cessation was associated with a significantly lower mortality risk in every BMI or waist circumference group, and the longer someone was a former smoker, the lower the mortality risk (34). Losing weight may decrease mortality risk in current smokers; however, smoking cessation will be related to a stronger decrease in risk. Smoking cessation has been associated with weight gain (35) and an increase in waist circumference (22), possibly because of a decreased metabolic rate and increased caloric intake (18). However, weight gain is not likely to counteract the health benefits of smoking cessation. In the present study we observed lower mortality rates among former smokers with a high BMI or a large waist circumference than in current smokers within the normal BMI range or a normal waist circumference. A recent study that used data from the National Health and Nutrition Examination Survey showed that a substantial decrease in smoking prevalence had only a small effect on increases in the prevalence of obesity and decreases in the prevalence of healthy weight (36).

This study had some limitations. Height and weight were self-reported, and waist circumference was self-measured by participants. Self reported height and weight are generally known to be accurate, although heavy persons are more likely to underreport their weight (37). Previous research also shows that the validity of self-measured waist circumference is fairly high (30).

In conclusion, both smoking and adiposity are independent predictors of mortality, but the combination of current or recent smoking with a BMI 35 or a large waist circumference is related to an especially high mortality risk.

	ACKNOWLEDGMENTS

 
Cancer incidence data from the Atlanta metropolitan area were collected by the Georgia Center for Cancer Statistics, Department of Epidemiology, Rollins School of Public Health, Emory University. Cancer incidence data from California were collected by the California Department of Health Services, Cancer Surveillance Section. Cancer incidence data from the Detroit metropolitan area were collected by the Michigan Cancer Surveillance Program, Community Health Administration, State of Michigan. The Florida cancer incidence data used in this report were collected by the Florida Cancer Data System under contract to the Department of Health (DOH). Cancer incidence data from Louisiana were collected by the Louisiana Tumor Registry, Louisiana State University Medical Center in New Orleans. Cancer incidence data from New Jersey were collected by the New Jersey State Cancer Registry, Cancer Epidemiology Services, New Jersey State Department of Health and Senior Services. Cancer incidence data from North Carolina were collected by the North Carolina Central Cancer Registry. Cancer incidence data from Pennsylvania were supplied by the Division of Health Statistics and Research, Pennsylvania Department of Health, Harrisburg, PA. The Pennsylvania Department of Health specifically disclaims responsibility for any analyses, interpretations, or conclusions.

The authors' responsibilities were as follows—AK: conceptualized the idea, analyzed the data, and wrote the first draft of the paper; MFL: conceptualized the idea, contributed to the data analyses, interpreted the data, and contributed to drafts of the article; AS: initiated the NIH-AARP Diet and Health Study, interpreted the statistical analyses, and reviewed drafts of the manuscript; KFA and JTMvE: interpreted the statistical analyses and reviewed drafts of the manuscript; ARH: helped conceptualize the NIH-AARP Diet and Health Study, interpreted the statistical analyses, and reviewed drafts of the manuscript; TBH: contributed to the conceptualization of the idea, interpreted the data, and contributed to drafts of the article. No conflicts of interest were declared

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