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American Journal of Epidemiology Advance Access originally published online on July 3, 2006
American Journal of Epidemiology 2006 164(5):459-469; doi:10.1093/aje/kwj217
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American Journal of Epidemiology Copyright © 2006 by the Johns Hopkins Bloomberg School of Public Health All rights reserved; printed in U.S.A.

Original Contribution

The Relations of Body Composition and Adiposity Measures to Ill Health and Physical Disability in Elderly Men

Sheena E. Ramsay1, Peter H. Whincup2, A. G. Shaper1 and S. G. Wannamethee1

1 Department of Primary Care and Population Sciences, Royal Free Hospital and University College Medical School, London, United Kingdom
2 Department of Community Health Sciences, St. George's University of London, London, United Kingdom

Correspondence to Dr. Sheena E. Ramsay, Department of Primary Care and Population Sciences, Royal Free Hospital and University College Medical School, Rowland Hill Street, London NW3 2PF, United Kingdom (e-mail: s.ramsay{at}pcps.ucl.ac.uk).

Received for publication November 1, 2005. Accepted for publication February 22, 2006.


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 References
 
Although body build is related to disability and mortality in older people, the independent contributions of adiposity and lean mass are not fully defined. The authors examined the relations of body composition (fat mass index, fat-free mass index) and adiposity (body mass index, waist circumference) to ill health and physical disability in a cross-sectional study of 4,252 British men aged 60–79 years in 1998–2000. Increased body mass index, waist circumference, and fat mass index were associated with increased prevalence of cardiovascular disease, overall ill health, and disability. Adjusted odds ratios of cardiovascular disease (top vs. bottom fifth) were 1.58 (95% confidence interval (CI): 1.23, 2.03) for fat mass index, 1.45 (95% CI: 1.14, 1.86) for body mass index, and 1.27 (95% CI: 0.99, 1.62) for waist circumference. For overall "poor/fair" health, the corresponding odds ratios were 1.71 (95% CI: 1.33, 2.21), 1.49 (95% CI: 1.17, 1.90), and 1.64 (95% CI: 1.28, 2.09) and, for mobility limitation, they were 1.56 (95% CI: 1.17, 2.06), 1.96 (95% CI: 1.48, 2.56), and 1.88 (95% CI: 1.42, 2.49). A high fat-free mass index was associated with only a decreased prevalence of respiratory problems and cancer (odds ratios = 0.45 (95% CI: 0.33, 0.62) and 0.62 (95% CI: 0.42, 0.94), respectively). Body fatness, not fat-free mass, is associated with cardiovascular disease and disability in older men. Simple measures of overweight, such as body mass index and waist circumference, are good indicators of the likelihood of morbidity in older men. Prevention of weight gain with increasing age is likely to reduce morbidity and disability among older men.

body composition; body mass index; chronic disease; mobility limitation

Abbreviations: BNF, British National Formulary; CI, confidence interval; FEV1, forced expiratory volume in 1 second; HDL-C, high density lipoprotein cholesterol


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 References
 
Obesity in many developed countries is a major public health problem. The health problems associated with an increase in the proportion of elderly in the population are further compounded by the increasing prevalence of obesity and overweight in older people (1Go). It is now well established that overweight and obesity are associated with an increased burden of cardiovascular disease, other chronic diseases, and disability (2Go, 3Go). Both body mass index and waist circumference have been used as markers of obesity and adiposity to study their relation to chronic diseases (2Go, 4Go, 5Go). The limitation of these measurements is that they fail to indicate the extent to which body fat and muscle mass independently contribute to disease or disability (6Go). Recently, there has been more research on exploring the role of body composition measures—fat mass and fat-free mass—in disability and mortality (4Go, 7Go–12Go). Fat mass (positively) and fat-free mass (inversely) have been shown to be associated with disability and mortality. Attention has been drawn to the opposing effects of these measures, which may help to explain the U-shaped association seen between body mass index and mortality, particularly in the elderly (13Go), where aging is associated with not only a tendency to increased adiposity but also a loss of muscle mass (sarcopenia) (14Go, 15Go). There has thus been growing interest in the role of fat mass and fat-free mass and their contribution to disease and disability.

However, few studies have examined the relations of body composition and adiposity measures to physical disability and morbidity in older subjects. Previous reports based on our study population have examined the burden of disease and disability related to overweight and obesity in the elderly using body mass index (2Go) and the relation of body composition to lung function (16Go). The purpose of this study was to further explore the extent to which body composition measures (fat mass and fat-free mass) and markers of adiposity (body mass index and waist circumference) are associated with disability and morbidity in a population-based study of older British men. We aimed to explore any difference or similarity in these associations to understand which of these markers are the best indicators of adiposity-related problems and whether lean mass is also related.


   MATERIALS AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
RESULTS
 DISCUSSION
 References
 
The British Regional Heart Study is a prospective study of cardiovascular disease and other outcomes in a socially and geographically representative sample of 7,735 men aged 40–59 years from one general practice in each of 24 towns representing all major British regions and who were initially examined in 1978–1980 (17Go). Ethical approval was provided by all relevant local research ethics committees. All men provided written, informed consent to the investigations, which were carried out in accordance with the Declaration of Helsinki. In 1998–2000, all surviving men, now aged 60–79 years, were invited to a 20th year reexamination. All men completed a mailed questionnaire providing information on their lifestyle and medical history, had a physical examination, and provided a fasting blood sample. The men were requested to fast for a minimum of 6 hours to attend a measurement session at a specified time between 8 a.m. and 6 p.m. A total of 4,252 men (77 percent) attended the examination.

Measurements
Physical examination included anthropometric and physiologic measurements. Details of height, weight, body mass index, and waist circumference measurements have been described (18Go). Fat mass and fat-free mass were calculated by use of bioelectrical impedance analysis with a Bodystat model 500 apparatus (Bodystat, Ltd., Douglas, United Kingdom). The equation by Deurenberg et al. (19Go) designed for people greater than 60 years of age was used to calculate fat mass. Fat-free mass was calculated as 6,710 x height (m)2/resistance ({Omega}) + 7. Fat mass was calculated as body weight – fat-free mass. The correlations between measurements taken 1 week apart were 0.67 for fat-free mass and 0.75 for fat mass. To permit comparison of subjects with different height, fat mass and fat-free mass measures were normalized for height by dividing them by (height)2 to obtain the fat mass index and fat-free mass index (6Go). Blood pressure was measured twice in the right arm with a Dinamap model 1846 oscillometric blood pressure recorder (Critikon, Inc., Tampa, Florida). Forced expiratory volume in 1 second (FEV1) was measured as part of lung function tests. FEV1 was recorded for the best test, defined in accordance with American Thoracic Society recommendations (20Go). Cole (21Go) has shown that dividing by (height)2 is the most appropriate way of standardizing lung function for stature. FEV1 was height standardized to the average height, 1.73 m, in the study. Thus, height-standardized FEV1 = FEV1 x (1.73/height)2. Low FEV1 was defined as being in the lowest quartile of FEV1.

Ill health
Men were asked to describe their health status as excellent, good, or fair/poor. In addition, subjects were asked to report any physician diagnosis of the following conditions: history of cardiovascular disease (heart attack (i.e., coronary thrombosis or myocardial infarction), angina, or stroke); diabetes; cancer; and history of taking medication for respiratory or musculoskeletal disease. Reported medication use was coded according to British National Formulary (BNF) codes (22Go). Respiratory medication included BNF codes 3.1–3.10, and musculoskeletal medication included BNF codes 10.1–10.3 (all drugs used in the treatment of rheumatic diseases, neuromuscular disorders, and soft tissue inflammation).

Physical disability
Information on three different aspects of physical disability—problems with mobility, carrying out usual activities, and self-care—was collected using a questionnaire. Mobility limitation was determined by asking subjects whether they currently had difficulty carrying out any of the following activities on their own as a result of a long-term health problem: 1) difficulty going up or down stairs or 2) difficulty walking for a quarter of a mile (0.40 km) on the level. Those who answered positively for any of these questions were classified as having mobility limitation. Subjects were asked if they had some problems with or were unable to carry out their usual activities. To ascertain problems with self-care, subjects were asked if they had some problems with or were unable to wash and dress themselves.

Lifestyle factors
Subjects were asked detailed questions on their smoking and drinking habits. The men were classified into groups based on their alcohol intake—none, occasional, light, moderate, and heavy. Heavy drinking was defined as drinking more than 6 units (1 United Kingdom unit = 10 g) of alcohol daily or on most days in the week. On the questionnaire, subjects were also asked to report their pattern of physical activity, such as walking, cycling, and other sporting activities. Physical activity scores were assigned on the basis of frequency and type of activity, and the men were divided into six groups: none, occasional, light, moderate, moderately vigorous, and vigorous. Subjects who reported none or occasional activity were classified as "inactive."

Social class
Social class was derived from the longest held occupation recorded at the time of the baseline questionnaire (1978–1980) using the Registrar General's classification of occupations, with categories grouped as nonmanual (I, II, and III nonmanual) and manual (III manual, IV, and V).

Metabolic risk factors
Details of blood lipid, blood glucose, and insulin measurements have been described (2Go, 23Go). Insulin was adjusted for the effects of fasting duration and time of day (23Go). Blood pressure was adjusted for observer variation (24Go). The International Society for Hypertension guidelines were used to identify patients with hypertension (25Go). Hypertensive patients were those with systolic blood pressure of greater than or equal to 160 mmHg or diastolic blood pressure of greater than or equal to 90 mmHg or those taking antihypertensive treatment. High cholesterol was defined as total cholesterol of greater than or equal to 6.2 mmol/liter, and low high-density lipoprotein cholesterol (HDL-C) was defined as levels of less than or equal to 1.0 mmol/liter. Insulin resistance was estimated by use of homeostasis model assessment as the product of fasting glucose (mmol/liter) and insulin (µU/ml) divided by the constant 22.5 (26Go). High homeostasis model assessment was defined as being in the top fifth of the distribution.

Statistical analysis
Correlation coefficients were calculated for body mass index, waist circumference, fat mass index, and fat-free mass index. Descriptive statistics (age, inactivity, smoking, alcohol intake, and social class) for subjects were computed according to fifths of body mass index, waist circumference, fat mass index, and fat-free mass index. Multiple logistic regression was used to assess the relation of these measures to metabolic risk factors (hypertension, high cholesterol, low HDL-C, and high homeostasis model assessment) and low FEV1; ill health (fair/poor health, cardiovascular disease, diabetes, cancer, and use of respiratory or musculoskeletal medication); and physical disability (mobility limitation, problem with usual activities, or problem with self-care). Odds ratios with 95 percent confidence intervals for these outcome measures were obtained for the adiposity and body composition measures by use of the lowest fifth as the referent category.

Models for the various cardiovascular disease risk factors and chronic diseases were adjusted for age, social class, smoking, alcohol intake, and physical activity. For the adjustment, age was fitted as a continuous variable; social class (six levels), smoking (six levels), alcohol intake (five levels), and physical activity (five levels) were fitted as categorical variables. The odds ratios for physical disability were adjusted for age, social class, smoking, alcohol intake, physical activity, and morbidity. Tests for trends were carried out fitting body mass index, waist circumference, fat mass index, and fat-free mass index as continuous variables. To explore the effect of fat mass index on the relation of body mass index to physical disability, the odds ratios for body mass index were additionally adjusted for fat mass index. Statistical analyses were performed using SAS, version 8.2, software (SAS Institute, Inc., Cary, North Carolina).

Receiver operating characteristic curve analysis
Receiver operating characteristic analysis was used to compare the association of adiposity measures (body mass index, waist circumference, and fat mass index) with measures of ill health and physical disability. Tests for differences between the curves were performed by using STATA, version 7.0, software (Stata Corp., College Station, Texas). The receiver operating characteristic curve tests the ability of a variable to predict an outcome by plotting sensitivity against 1 – specificity, and it simultaneously compares this in different variables. The area under the curve is the summary statistics from the receiver operating characteristic curve that ranges from 0 to 1, with 0.5 indicating no predictive power and 1 indicating perfect predictive power. The area under the curve with 95 percent confidence intervals was calculated.


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
DISCUSSION
 References
 
Table 1 shows the correlation of the adiposity and body composition measures. Body mass index, waist circumference, and fat mass index had a strong positive correlation with each other. The fat-free mass index was positively correlated with body mass index and waist circumference and showed a small but significant inverse association with fat mass index.


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  		TABLE 1. Correlation of adiposity and body composition measures in a cross-sectional study of British men aged 60–79 years in 1998–2000*

 
Details of the characteristics of the subjects are given in table 2. With increasing age, the fat-free mass index, fat mass index, body mass index, and waist circumference decreased significantly. The percentage of manual group and inactive subjects increased with increasing body mass index, waist circumference, and fat mass index. The percentage of current smokers decreased with increasing fat-free mass index, fat mass index, body mass index, and waist circumference. The prevalence of never smokers decreased with increasing body mass index, waist circumference, and fat mass index (ptrend < 0.001 for all) but increased with increasing fat-free mass index (ptrend < 0.001). The prevalence of heavy drinking rose with increasing waist circumference but varied little for other body measures.


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  		TABLE 2. Characteristics (%) of body mass index, waist circumference, fat mass index, and fat-free mass index in a cross-sectional study of British men aged 60–79 years in 1998–2000

 
Table 3 shows the relation of body mass index, waist circumference, fat mass index, and fat-free mass index to metabolic risk factors and FEV1. The likelihood of having hypertension was similar for increasing fifths of fat mass index, body mass index, and waist circumference, with a progressive increase in the odds of hypertension (ptrend < 0.001). Fat mass index, body mass index, and waist circumference all showed strong positive associations with insulin resistance (high homeostasis model assessment; ptrend < 0.001 for all). Those in the higher fifths of fat-free mass index showed significantly increased odds of having hypertension, low HDL-C, and high homeostasis model assessment compared with those in the bottom fifth. However, since those with a high fat-free mass index tended to have higher waist circumferences, we further adjusted for waist circumference in the model for fat-free mass index to explore whether waist circumference explained these relations of fat-free mass index. Adjustment for waist circumference abolished the relations of fat-free mass index to hypertension, low HDL-C, and high homeostasis model assessment. The odds ratios of those in the top fifth compared with those in the bottom fifth of fat-free mass index were 1.16 (95 percent confidence interval (CI): 0.94, 1.45) for hypertension, 1.22 (95 percent CI: 0.91, 1.65) for low HDL-C, and 0.90 (95 percent CI: 0.68, 1.18) for high homeostasis model assessment. No association was seen between the measures of adiposity or body composition and high cholesterol. Waist circumference was positively associated with low lung function, but little association was seen among fat mass index, body mass index, and low lung function. By contrast, a low fat-free mass index was associated with increased odds of having low lung function (ptrend < 0.001).


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  		TABLE 3. Prevalence of metabolic risk factors and low forced expiratory volume in 1 second and odds ratios with 95% confidence intervals according to fifths of body mass index, waist circumference, fat mass index, and fat-free mass index in a cross-sectional study of British men aged 60–79 years in 1998–2000

 
Table 4 shows the prevalence and adjusted odds ratios of having ill health according to fifths of body mass index, waist circumference, fat mass index, and fat-free mass index. The odds of reporting fair/poor health increased with body mass index, waist circumference, and fat mass index levels, although these increases were statistically significant only in the top fifths of the body size measures (about 50 percent or more increased odds of reporting fair/poor health; ptrend < 0.001 for all). The odds of cardiovascular disease increased with fat mass index and body mass index levels (ptrend < 0.001). Subjects in the fourth and top fifth of fat mass index were 33 percent and 58 percent, respectively, more likely to have cardiovascular disease compared with those in the bottom fifth of fat mass index. Fat-free mass index was not significantly associated with having cardiovascular disease or reporting fair/poor health.


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  		TABLE 4. Prevalence of ill health and odds ratios with 95% confidence intervals according to fifths of body mass index, waist circumference, fat mass index, and fat-free mass index in a cross-sectional study of British men aged 60–79 years in 1998–2000

 
With increasing body mass index, waist circumference, fat mass index, and fat-free mass index, subjects were more likely to be taking musculoskeletal medication and to have diabetes (table 4). To explore whether the higher waist circumference explained these relations between fat-free mass index and morbidity indicators, we further adjusted for waist circumference in the model for fat-free mass index. The relations of fat-free mass index to use of musculoskeletal medication and diabetes were attenuated when adjusted for waist circumference and were no longer significant. The odds ratios for those in the top fifth compared with those in the bottom fifth of fat-free mass index were 1.32 (95 percent CI: 0.96, 1.82) for having diabetes and 1.22 (95 percent CI: 0.88, 1.69) for taking musculoskeletal medication. However, with increasing fat-free mass index, there was a significant reduction in the likelihood of taking respiratory medication (ptrend < 0.001). Those in the bottom fifth of fat-free mass index were more than twice as likely to take respiratory medication compared with those in the top fifth of fat-free mass index. Adjustment for waist circumference slightly strengthened the inverse association seen (odds ratio = 0.41, 95 percent CI: 0.29, 0.57). With increasing fat-free mass index, the subjects were less likely to have cancer (ptrend = 0.02).

Overall, 30 percent of the men reported some form of physical disability. Table 5 shows the prevalence and adjusted odds ratios of having physical disability by fifths of body mass index, waist circumference, fat mass index, and fat-free mass index. Fat-free mass index demonstrated a weak but significant positive relation with mobility limitation, but this was attenuated and no longer significant after adjusting for waist circumference (odds ratio = 0.92, 95 percent CI: 0.68, 1.24; ptrend = 0.46). Body mass index, waist circumference, and fat mass index showed a significant positive relation with mobility limitation, with those in the top fifth showing substantially increased risk compared with those in the bottom fifth. Increased body mass index and waist circumference, and to a lesser extent fat mass index, were significantly associated with problems of usual activities. To assess whether fat mass index was responsible for the associations of body mass index and disability, we examined these associations adjusting in addition for fat mass index. The positive association of body mass index with physical disability was slightly attenuated when it was further adjusted for fat mass index (for the top fifth of body mass index, the odds ratios were 1.45 (95 percent CI: 0.96, 2.18) for mobility limitation and 1.13 (95 percent CI: 0.78, 1.62) for problems with usual activities).


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  		TABLE 5. Prevalence and odds ratios with 95% confidence intervals for disability according to fifths of body mass index, waist circumference, fat mass index, and fat-free mass index in a cross-sectional study of British men aged 60–79 years in 1998–2000

 
Receiver operating characteristic analyses for the relations of body mass index, waist circumference, and fat mass index to ill health and physical disability are shown in table 6. Areas under the curve for cardiovascular disease, diabetes, musculoskeletal medication use, mobility limitations, and problems with usual activities were very similar for body mass index, waist circumference, and fat mass index. The receiver operating characteristic analyses give results similar to those already observed in the logistic regression analyses. However, waist circumference and fat mass index had slightly higher areas under the curve than did body mass index for fair/poor health.


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  		TABLE 6. Analysis results of receiver operating characteristics with area under the curve and 95% confidence intervals, p values, {chi}2, and degrees of freedom in a cross-sectional study of British men aged 60–79 years in 1998–2000

 

   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
References
 
This study in a cohort of British men aged 60–79 years examines the relation of body composition and body fat measures to ill health and physical disability. Fat mass index, body mass index, and waist circumference had similar patterns of relations to ill health and physical disability. Our results suggest that increasing fat mass, body mass index, and waist circumference are associated with physical disability, ill health (cardiovascular disease, diabetes, taking musculoskeletal medication, and reporting fair/poor health), and metabolic risk factors, that is, hypertension, low HDL-C, and insulin resistance (high homeostasis model assessment). Overall, the fat mass index contributed to a wider range of disease than did the fat-free mass index. Low fat-free mass was associated with poor respiratory function and cancer but was not independently associated with metabolic risk factors, reporting of poor/fair health, cardiovascular disease, taking musculoskeletal medication, or physical disability.

Body composition and ill health
It has been suggested that the relation of body mass index to chronic disease masks the underlying opposing relations of fat mass and fat-free mass to disease risk (13Go, 27Go). The opposing effects of fat mass and fat-free mass on mortality were seen to explain the U-shaped association of body mass index with mortality (27Go). Studies by Allison et al. (27Go) and others (7Go) demonstrate a relation between fat mass (positive) and fat-free mass (inverse) with mortality. To our knowledge, the current literature on the relations of fat mass and fat-free mass to morbidity, however, is very limited. The positive relation of fat mass index to cardiovascular disease and other chronic diseases and the inverse association between fat-free mass and respiratory disease observed in our study could explain the mortality pattern of body composition reported by other studies, in particular, the opposing effects of fat-free mass and fat mass on all-cause mortality. The inverse relation of fat-free mass index and cancer observed in our results could reflect the loss of muscle mass among subjects with cancer. Such a loss of muscle mass in cancer patients or cachexia has been reported (28Go, 29Go). Therefore, loss of muscle mass could have been a consequence of cancer rather than being a cause of it.

Adiposity measures and morbidity
There has been controversy as to whether body mass index is an adequate marker of adiposity-related problems and whether indicators such as waist circumference or body composition should be used instead (5Go, 14Go). We have shown previously that body mass index is a good indicator of adiposity-related problems and that body mass index and waist circumference were the adiposity measures most strongly associated with metabolic risk factors (hypertension, dyslipidemia, and glucose), insulin resistance, and the metabolic syndrome (2Go, 18Go). In this report, we extend our findings to examining the associations between waist circumference, body mass index, and fat mass and indicators of morbidity and disability. The measures of adiposity in this study (body mass index, waist circumference, and fat mass index) all showed strong associations with metabolic risk factors and many of the chronic diseases and disability. Our study shows that the pattern of relation of body mass index and waist circumference to indicators of ill health is similar to that of fat mass index. In particular, all showed significant positive associations with cardiovascular disease morbidity, musculoskeletal problems, and overall ill health, although fat mass index showed a more consistent positive association with cardiovascular disease morbidity but weaker association with diabetes than did waist circumference and body mass index. Overall, body mass index and waist circumference were good indicators of obesity-related problems, and both showed similar relations.

Body composition, adiposity measures, and physical disability
Although studies have reported that overweight or obesity is associated with disability (1Go, 2Go), it was possible in our study to compare the relation of adiposity measures and disability to that of body composition measures. Our results show that fat mass, rather than fat-free mass, was associated with physical disability. The finding that fat mass was more strongly associated with physical disability than was fat-free mass has been reported in other studies (9Go, 30Go–33Go). We also observed in our study that body mass index and waist circumference had a positive association with physical disability, which was similar to that of fat mass. The attenuation of the association of body mass index with indicators of physical disability when adjusted for fat mass index suggests that fat mass contributes to the association with body mass index. Some studies, on the other hand, have shown fat-free mass to be inversely related to physical disability (8Go, 34Go). In this study, the fat-free mass index appeared to be positively associated with mobility limitation, but this was largely due to the higher waist circumference seen in those with a high fat-free mass index. Adjustment for waist circumference abolished this relation, suggesting that fat-free mass index per se has little association with mobility limitation. Although low muscle mass may lead to frailty in older people, in this study lower lean mass was not strongly related to disability, although it was related to an increased prevalence of respiratory disease, independent of smoking.

Strengths and weaknesses of study
The results of this paper are based on a population-based study, making the findings largely generalizable to older men. Our study adds to the current literature on body composition in several ways. We have made comparisons of the relation of adiposity and body composition measures to health outcomes in the same sample and at the same time point. In addition to disability, we have also explored the relation of these measures to a wide range of conditions related to quality of life, such as musculoskeletal and respiratory problems, problems with usual activities, and problems with self-care, which were lacking in previous reports (35Go). Finally, we took into account the need to control for body size (14Go) by dividing fat mass and fat-free mass by (height)2 (6Go).

However, there are limitations that need to be addressed. First, from our study, it is not possible to establish a cause-and-effect relation of the associations observed, since the data used are cross-sectional. Yet, our findings are similar to those of a prospective study that found fat mass to be more predictive of disability than fat-free mass (30Go). Second, there is also a possibility that disabled or very ill subjects may not have attended the physical examination or returned the questionnaire. However, this should not have had any marked effect on the strengths of associations observed; any effect would have tended to lead to underestimation rather than overestimation of associations. Third, some studies have noted inaccuracies and little within-subject variation when using bioelectrical impedance analysis to assess fat-free mass in the elderly (36Go, 37Go). Fat-free mass measurements (a surrogate measure of muscle mass) using bioelectrical impedance analysis may be subject to imprecision because of variation in hydration status (36Go), and they may not be a very accurate measure of muscle mass, which is reflected in the weak 1-week correlation of fat mass and fat-free mass. This imprecision in measurement of fat mass and fat-free mass could have led to underestimation of associations. However, our bioelectrical impedance analysis was based on the equation of Deurenberg et al. (19Go) that has been validated in an elderly population. Studies have also validated the use of bioelectrical impedance analysis with other measures, such as magnetic resonance imaging (38Go), dual-energy x-ray absorptiometry (39Go), and computerized tomography (40Go). We report a strong inverse association with respiratory disease, which is consistent with previous reports (16Go, 41Go). Last, the presence of chronic diseases was based on the subject's reporting of physician diagnosis in the questionnaire, which could be seen as an inaccurate measure of prevalence of disease. However, data from previous studies (42Go–46Go) show that patients' recall of diagnosis of cardiovascular disease is a valid method of recording diseases. The kappa statistic in a study on our cohort comparing record review with patients' recall of ischemic heart disease was 0.82 (42Go).

Implications of this study
In this paper, we have attempted to assess the relation of fat mass, fat-free mass, and adiposity measures to ill health. Our study shows that body mass index, waist circumference, and fat mass are strongly related to ill health and disability in elderly men to a similar degree and suggests that control of adiposity in the population will help to reduce the burden of disease and disability. It is clear in our study that measures of body fat, such as body mass index and waist circumference, are good indicators of adverse health outcomes. Although body mass index is considered a surrogate marker of body fat and fat mass measured by bioelectrical impedance analysis is a more precise indicator of body fat (47Go), the relation of the two measures to adverse health outcomes is similar. Low lean mass, on the other hand, although related to an increase in respiratory morbidity, was not related to other markers of ill health or disability function.

Conclusions
Body fatness was observed to be associated with morbidity and physical disability, while fat-free mass was related to respiratory function and cancer. Body mass index and waist circumference are adequate measures of overweight and obesity and the likelihood of ill health in older men. Therefore, body mass index and waist circumference, which are routine and easily used measures, are of practical value in identifying people at risk of developing health problems without the need to undertake complex tests, such as bioelectrical impedance analysis. Our study provides further evidence in support of the use of body mass index and waist circumference in clinical and research settings (35Go, 48Go, 49Go). Using these simple measures of adiposity should be encouraged to reduce the public health burden of obesity and overweight in the elderly, by the promotion of lifestyles that decrease the weight gain accompanying the aging process.


   ACKNOWLEDGMENTS
 
The British Regional Heart Study is funded by the British Heart Foundation and receives additional support from the Department of Health.

The opinions expressed in the paper are those of the authors and not necessarily those of the funding bodies.

Conflict of interest: none declared.


   References
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 References
 

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