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American Journal of Clinical Nutrition, Vol. 88, No. 1, 95-104, July 2008
© 2008 American Society for Nutrition

ORIGINAL RESEARCH COMMUNICATION

Lifestyle factors associated with age-related differences in body composition: the Florey Adelaide Male Aging Study1,2,3

Evan Atlantis, Sean A Martin, Matthew T Haren, Anne W Taylor, Gary A Wittert for the Florey Adelaide Male Aging Study

1 From the Discipline of Medicine, University of Adelaide, Adelaide, Australia (SAM, MTH, AWT, and GAW); the Spencer Gulf Rural Health School & Centre for Rural Health and Community Development, University of South Australia, Whyalla, Australia (MTH); and the Exercise, Health and Performance Faculty Research Group, Faculty of Health Sciences, University of Sydney, Sydney, Australia (EA)

2 Supported by the University of Adelaide Florey Foundation, the South Australian Premier's Science and Research Fund, and a National Health and Medical Research Council postdoctoral fellowship (Public Health) award (to MTH).

3 Reprints not available. Address correspondence to E Atlantis, Exercise, Health and Performance Faculty Research Group, Faculty of Health Sciences, The University of Sydney, 75 East Street, Lidcombe, NSW 2141, Australia. E-mail: e.atlantis{at}usyd.edu.au.


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 SUBJECTS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Background: Age-related change in body composition is associated with adverse health outcomes, including functional decline, disability, morbidity, and early mortality. Prevention of age-related changes requires a greater understanding of the associations among age, lifestyle factors, and body composition.

Objective: We aimed to comprehensively determine lifestyle factors associated with age-related differences in body composition assessed by using dual-energy X-ray absorptiometry.

Design: We analyzed baseline (cross-sectional) data collected from 2002 to 2005 for {approx}1200 men in the Florey Adelaide Male Aging Study, a regionally representative cohort of Australian men aged 35–81 y.

Results: Mean values for whole-body lean mass (LM) and areal bone mineral density (aBMD) decreased, whereas mean values for abdominal fat mass (FM) and whole-body and abdominal percentage FM (%FM) increased with age. No significant age-related differences were found for whole-body FM. Multiple adjusted odds of being in the highest tertiles for whole-body and abdominal %FM decreased for smokers (63–71%) but increased with age group and for lowest energy (43–50%), carbohydrate (92–107%), and fiber (107%) intake tertiles. Multiple adjusted odds of being in the highest aBMD tertile decreased for lowest body mass (92%) and carbohydrate intake (63%) tertiles and for men aged ≥75 y (78%) but increased for Australian birth (58%) and for participation in vigorous physical activities (82%).

Conclusions: Age-related differences in body composition indicate that whole-body FM remains stable but increases viscerally and that whole-body %FM is confounded by LM, whereas aBMD decreases with age. Age-related differences in %FM and aBMD are associated with demographic and lifestyle factors.


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
SUBJECTS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Obesity is a well-known cause of cardiovascular disease (1) and early mortality (2) and is associated with functional decline and disability among older adults (3). The etiologic mechanisms of excess body weight are not well known. Age-related changes in body composition, namely reductions in skeletal tissue (lean body mass) and relative increases in adipose tissue (percentage body fat mass), particularly in the abdominal region (often termed central obesity), are believed to be key risk factors for functional decline, disability, morbidity, and early mortality (4). Studies have shown that relative lean body mass is inversely associated with functional impairment (5), that central obesity predicts early mortality far better than does body mass index (6, 7), and that elevated blood pressure and cholesterol concentrations are associated with higher percentage body fat and lower lean body mass in overweight men (8). Thus, age-related changes in whole-body and regional body composition suggest several plausible explanations for the heightened risk of disease, disability, and early mortality found with obesity.

Describing the aging effect on body composition provides important epidemiologic information for identifying possible etiologic mechanisms as targets for treatment and prevention of such adverse health outcomes, given the worldwide growth rates of obesity (9) and the increase of the aging population (10). Our present knowledge of age-related differences in body composition is insufficient, because the existing body of evidence does not comprehensively describe regional as well as whole-body composition, and unreliable, because results are mostly drawn from study samples consisting of participants who were not randomly selected. We aimed to comprehensively define age-related differences in body composition in a random sample of men aged 35–81 y from the Florey Adelaide Male Aging Study (FAMAS). Furthermore, we tested whether demographic and lifestyle variables were associated with age-related differences in whole-body and regional body composition, as potential risk factors.


   SUBJECTS AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 SUBJECTS AND METHODS
RESULTS
 DISCUSSION
 REFERENCES
 
Study sample and design
We analyzed baseline (cross-sectional) data from the FAMAS, a regionally representative cohort study of 1195 men aged 35–80 y at recruitment and living in the northwest section of Adelaide, Australia. The FAMAS aims to investigate the health of aging men in Australia. A sample size of 1200 was initially targeted for chronic diseases, which allows for a minimum detectable relative risk of disease of >1 of 1.52 and <1 of 0.61 between groups of participants with and those without risk factor exposure, by using the following assumptions: 1) a sample power of 80%, 2) a 2-tailed test; 3) a 5% chance of type 1 error; 4) an estimated risk factor prevalence of 30% (conservative estimate for behavioral risk factors such as physical inactivity); and 5) an estimated disease prevalence in the reference group of 15% (conservative estimate for prevalence of ≥1 main chronic disease).

Details of the FAMAS design, procedures, and participants were published elsewhere (11). Briefly, study eligibility required participants to be male, between 35 and 80 y old at the time of recruitment, and living in the defined catchment area (the Australian Bureau of Statistics North and West Statistical Division of Adelaide) and to have a connected telephone number listed in the Electronic White Pages. Telephone numbers for noncommunity-dwelling units (ie, businesses, institutions, and residential-care facilities) were excluded from the sampling frame. An introductory mailing to randomly selected households was followed by computer-assisted telephone interview (CATI) recruitment by trained interviewers. The adult male (aged ≥35 y) in the household to last have a birthday was invited to attend the clinic for baseline assessment of a variety of biomedical and sociodemographic factors. A response rate of 45.1% (1195/2650) was calculated by dividing the number of households of participants who attended clinical assessments by the total number of randomly sampled households with eligible participants (12). Recruitment of participants occurred during 2 phases, from August 2002 to July 2003 and from June 2004 to May 2005, and complete clinical follow-up is scheduled to recur every 5 y from baseline throughout the life of the cohort. Follow-up self-reported information from questionnaires is collected annually.

All participants gave written informed consent. All protocols and procedures were approved by the Royal Adelaide Hospital Research Ethics committee and, when appropriate, by the Aboriginal Health Research Ethics Committee of South Australia.

Anthropometric variables
Anthropometric measurements were performed by using standard protocols (13) in the morning, before subjects broke an overnight fast and while they were barefoot and wearing light clothing. Height was measured by using a wall-mounted stadiometer (model no. 220; SECA, Hamburg, Germany). Body weight was obtained by using digital platform scales (Wedderburn UW OFWB Series—Digital Platform Scale, Taipei, Taiwan, China), which were maintained by on-site engineering services. Waist and hip circumferences were assessed by using a fiberglass tape measure (Gulik II; Country Technology, Gays Mills, WI). Waist circumference was measured 3 times, taken at the level of the narrowest point (or midway) between the lower costal border and the top of the iliac crest, and it was read in the midaxillary line. Hip circumference was measured 3 times at the level of the greatest posterior protuberance of the buttocks with the tape placed in the horizontal plane. The mean of the 3 measurements was used in analyses. CVs for triplicate waist and hip circumference measurements were <2.2% for 99.7% of the sample. Waist-to-hip ratio was computed as waist circumference divided by hip circumference.

Body composition variables
Whole-body and regional body composition was measured by using dual-energy X-ray absorptiometry (DXA) performed either on a fan-beam (Prodigy DF + 14759; ENCORE software, version 9.15) or a pencil-beam (DPX+; LUNAR software, version 4.7e) densitometer (both machines and software programs: GE Lunar, Madison, WI). System accuracy and stability checks were performed regularly throughout the study by using a customized phantom, and quality-assurance checks were performed at the beginning of each scan day to calibrate and verify correct operation of the scanner. A cross-calibration analysis previously showed no significant differences between densitometers (14).

Fat mass (FM), lean mass (LM), bone mineral content (BMC; in kg), and areal bone mineral density (aBMD; in g/m2) were defined for whole body, arms, legs, trunk, and spine regions by using default settings. For assessment of soft tissue composition in the abdominal region, the top of lumbar vertebrae L2 to the bottom of L4 and extending outwards to a vertical line touching the inner edges of the rib cage were adopted as customized anatomical settings. Percentage FM (%FM) was computed with the following equation:

Formula 1(1)

Demographic and lifestyle variables
Demographic and lifestyle information was obtained by self-report questionnaires. Smoking status was determined by using question items from recent Australian National Health Surveys. Leisure-time physical activity during the past 2 wk was determined by using question items from the 1999 National Physical Activity Survey (15). Question items and descriptors used in the 1999 National Physical Activity Survey to define physical activity duration, frequency, and intensity are similar to those of the International Physical Activity Questionnaire (16), which showed good repeatability coefficients and criterion validity for classifying those who achieved sufficient physical activity for health benefits compared with accelerometer methodology. Total time spent in leisure-time physical activities was multiplied by intensity weights (3.5 for walking and 5.0 for moderate- and 7.5 for vigorous- intensity exercise) to compute metabolic equivalent hours (MET-h). The MET is a proxy estimate of total energy expenditure during exercise expressed as multiples of standard resting energy expenditure (equivalent to 1 MET unit) (17). The semiquantitative food-frequency questionnaire (FFQ) developed by the Cancer Council of Victoria was used to estimate usual energy-providing macronutrient intakes (18). The FFQ has 74 items with 10 frequency response options, and it contains 3 photographs of scaled portions for 4 foods. Questionnaires were processed by the Cancer Council of Victoria's Nutrition Assessment Office to compute macronutrient intakes. Correlation coefficients for energy, protein, fat, carbohydrate, fiber, and alcohol intakes ranged from 0.23 to 0.60 for relative validity assessment of the FFQ compared with 7-d weighed food records methodology (19).

Statistical analysis
Statistical analyses were performed with SPSS software (version 15.0; SPSS Inc, Chicago, IL). Data are presented as meansand SDs unless otherwise specified. Age was categorized into the following groups: 35–44, 45–54, 55–64, 65–74, and ≥75 y old. Between-group differences for categorical variables were compared by using chi-square analysis. Between-group differences for continuous variables were compared by using analysis of variance (ANOVA). If overall ANOVAs were statistically significant, Dunnett's 2-tailed tests for post hoc contrasts were performed by using the youngest age group (35–44 y old) as the reference. Because many comparisons were necessary, a conservative P < 0.001 was adopted for determining statistical significance, as recommended, to decrease the probability of type 1 errors (20). Prevalence odd ratios (ORs) (and 95% CIs) for highest to lowest tertile (reference) for whole-body and abdominal %FM and for whole-body aBMD were determined by using unadjusted, age-adjusted, and multiple risk factor–adjusted (includes demographic and lifestyle variables) logistic regression analyses. Subtypes of fat (saturated, polyunsaturated, and monounsaturated) and carbohydrate (sugar and starch) intake were excluded from these models because of concerns regarding multicollinearity. Body mass was included as a covariate in logistic regression models to adjust for confounding effects of weight status on aBMD (21).


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 SUBJECTS AND METHODS
 RESULTS
DISCUSSION
 REFERENCES
 
Age-related differences in means by age group for anthropometric and body composition variables appear in Table 1. Compared with the values in men aged 35–44 y, mean values were smaller for height in men aged ≥55 y and for weight in men aged ≥75 y, and there was a trend showing smaller mean values for BMI in men aged ≥65 y, with increasing age group. Compared with men aged 35–44 y, mean values were larger for waist circumference and waist-to-hip ratio in men aged 55–64 y, and 65–74 y, and, although the differences were not significant, mean values for hip circumference tended to be larger in each older age group. Larger mean values for waist-to-hip ratio across these age groups therefore are mostly due to differences in waist circumference, which increases with age category, whereas the null finding for BMI between age groups is likely confounded by age-related differences in height.


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  		TABLE 1 Age-related differences per decade for anthropometric and body-composition variables obtained by using dual-energy X-ray absorptiometry in Australian men aged 35–81 y1

 
Mean values were smaller for whole-body LM in men aged ≥65 y and larger for %FM in men aged 65–74 y than in men aged 35–44 y. Because no significant differences across age groups were found for FM, larger mean values for whole-body %FM per decade are due mostly to smaller mean values for LM and to a lesser degree to those for BMC, considering their relative contribution to body mass. Results for abdominal body composition were contrasting. Mean values for abdominal FM were larger in men aged 55–64 y and 65–74 y and for LM in men aged 55–64 y than in men aged 35–34 y, which indicates that larger mean values for abdominal %FM actually reflect larger values for abdominal FM. Therefore, whole-body FM remains stable but is redistributed viscerally, and larger mean values for whole-body %FM are confounded by smaller mean values for whole-body LM with increasing age group.

Compared with men aged 35–44 y, mean values were smaller for whole-body BMC and aBMD in men aged ≥55 and 65 y. Age-related differences for whole-body BMC and aBMD appear to be mostly due to smaller mean values for the legs and arms and partly for the trunk. No significant differences across age groups were found for aBMD values for the spine. No significant age-related differences were observed for FM in other body regions, and mean values for LM were smaller for arms in men aged ≥65 y and smaller for legs in men aged ≥55 y than in men aged 35–44 y.

Age-related differences in means per decade for demographic and lifestyle variables appear in Table 2. Increasing age category was associated with lower household income, non-Australian birth, and nonsmoking status categories. Compared with values in men aged 35–44 y, mean values for total energy intake and for monounsaturated and saturated fat intakes as proportions of macronutrient intakes were smaller in men aged ≥65 y and 55–74 y, respectively. In contrast, mean values for sugars and fiber intake as proportions of macronutrient intake were larger in men aged 65–74 y and ≥55 y, respectively, than in men aged 35–44 y. Although no significant differences between age groups were found for physical activity, protein, total fat, polyunsaturated fat, total carbohydrate starch, and alcohol variables, trends suggested that mean values were smaller for total fat and alcohol and larger for total carbohydrate intake with increasing age group.


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  		TABLE 2 Age-related differences per decade for demographic and lifestyle risk factors in Australian men aged 35–81 y1

 
Prevalence ORs for highest to lowest (reference) whole-body %FM tertile associated with demographic and lifestyle factors are shown in Table 3. The odds of being in the highest %FM tertile increased with age category and for men born in Australia (61%) but decreased for current smokers (63%) in multiple adjusted analyses. For macronutrients, the odds of being in the highest %FM tertile increased for men in the lowest energy (50%), carbohydrate (92%), and fiber (107%) intake tertiles in multiple-adjusted analyses. No significant associations were found for household income, physical activity, protein, fat, and alcohol variables in multiple-adjusted analyses.


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  		TABLE 3 Prevalence odds ratios (ORs) for highest to lowest (reference) percentage whole-body fat mass (%FM) tertile associated with demographic and lifestyle risk factors in Australian men aged 35–81 y1

 
Prevalence ORs for highest to lowest (reference) abdominal %FM associated tertile with demographic and lifestyle factors are shown in Table 4. As with whole-body %FM, the odds of being in the highest abdominal %FM tertile increased with age and decreased for current smokers (71%) in multiple-adjusted analyses. For macronutrients, the odds of being in the highest abdominal %FM tertile increased for men in the lowest energy (57%), carbohydrate (107%), and fiber (107%) intake tertiles in multiple-adjusted analyses. No significant associations were found for household income, Australian birth, physical activity, protein, fat, and alcohol variables in multiple-adjusted analyses.


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  		TABLE 4 Prevalence odds ratios (ORs) for highest to lowest (reference) percentage abdominal fat mass (abdominal %FM) tertile associated with demographic and lifestyle risk factors in Australian men aged 35–81 y1

 
Prevalence ORs for highest to lowest (reference) whole-body aBMD tertile associated with demographic and lifestyle factors are shown in Table 5. The odds of being in the highest aBMD tertile were most consistently decreased for men in the lowest body mass tertile (91–92%) across unadjusted and adjusted analyses but were also decreased for men aged ≥75 y (78%) and for those in the lowest carbohydrate intake tertile (63%). In contrast, the odds of being in the highest aBMD tertile increased for men born in Australia (58%) and for those who reported participating in vigorous physical activities (82%) in multiple-adjusted analyses.


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  		TABLE 5 Prevalence odds ratios (ORs) for highest to lowest (reference) whole-body areal bone mineral density (aBMD) tertile associated with demographic and lifestyle risk factors in Australian men aged 35–81 y1

 

   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 SUBJECTS AND METHODS
 RESULTS
 DISCUSSION
REFERENCES
 
This report comes from the most comprehensive study to have investigated all of the important lifestyle factors associated with age-related differences in whole-body and regional body composition in a large random sample that was regionally representative of Australian men aged 35–81 y. We first defined age-related differences in anthropometric and body composition variables. Compared with values in men aged 35–44 y, the values for height decrease linearly with age, as reported previously for men and women (22–25), whereas the values for weight tend first to increase and then to decrease, notably in older men, in whom it displays an inverted "U" curve, which was previously reported also in men and women (22–25). Waist circumference and waist-to-hip ratio tend to increase and then stabilize or decrease marginally, consistent with some studies in men and women (23, 26, 27), whereas other studies (24, 26, 28, 29) showed continuous increases with age. The BMI tends to stabilize or decrease marginally among older men, consistent with several other studies in men (22, 23, 25, 28, 29) and women (22, 23, 25, 26), but it may continue to increase among women in other populations (24, 28, 29), whereas the hip circumference tends to stabilize with age, consistent with other studies in men and women (23, 24, 26). Inconsistent findings compared with some studies suggest that the between-study variation in age-related differences in some anthropometric variables are partly due to sex and ethnicity effects. Nonetheless, the findings of the present study and those of these other studies suggest that age-related changes in height may weaken the BMI's ability to adequately assess the risk factor status of excess whole-body weight, particularly in older adults, and that the waist circumference may be a better anthropometric measure than waist-to-hip ratio or BMI for assessing the risk factor status of central obesity with aging. Evidence supporting this notion stems from prospective cohort studies that show a stronger and more consistent association between central obesity measures and early mortality than does the BMI in both men and women (6, 7).

Compared with values in men aged 35–44 y, the values for LM, BMC, and aBMD decrease but %FM increases with age. Increases in mean values for %FM are clearly confounded by decreases in LM, because no significant age-related differences were seen for FM, which is most apparent for older men, consistent with other cross-sectional (24–26, 29) and prospective cohort studies (30). Thus, the greater risk of early mortality observed in older adults with lower BMI (2, 31) may be partly caused by changes in LM. In contrast, increases in abdominal %FM with age were due to true differences in abdominal FM, whereas no differences or larger mean values for abdominal LM were observed. Although these findings contrast with those for whole-body composition, overall obesity (BMI) and central obesity are hazardous to health even after adjustment for age, and therefore age-related changes in whole-body and abdominal body composition are likely to be independent etiologic risk factors. Well-designed prospective cohort studies are needed to discover biological causes of age-related changes in body composition and the main chronic diseases and the associated mortality burden.

For BMC and aBMD, age-related differences are most apparent in men aged ≥65 y, which is similar to findings in other populations (23), and which is clinically important, given that the risk of osteoporotic fractures increases with decreasing aBMD and with age (32). Osteoporotic fractures occur at clinically important rates for men and women aged ≥70 y, but is not a well-recognized men's health issue, even though the associated increased risk of early mortality is strongest for men (33). To help avert the projected economic burden of osteoporotic fractures, national awareness campaigns targeting healthy aging should include recommendations for aBMD assessment for all men aged ≥65 y to identify and treat those at risk (34).

We further tested whether demographic and potentially modifiable (lifestyle) variables were associated, as potential risk factors, with age-related differences in body composition. Increasing age category, Australian birth, and lowest tertiles for energy, carbohydrate, and fiber intake in particular were found to be significant risk factors, whereas smoking was a strong protective factor for being in the highest tertile for whole-body and abdominal %FM. Because most immigrants were born in Europe (11), where the diet is inversely associated with obesity (35), obesity rates in countries of origin (9) or less exposure to the "Westernized" Australian culture may partly explain the greater risk associated with being born in Australia.

Low fiber intake was one of the most reliable factors found to increase the risk of being in the highest tertile for %FM. Although we found no previous epidemiologic studies that comprehensively assessed energy-providing macronutrients and body composition, there is evidence that fiber intake is associated with less gain in weight and waist circumference (36, 37). Our finding of no positive association between energy intake and body composition accords with the findings of others (38) but contradicts the conventional viewpoint with respect to energy balance and obesity. Experimental evidence shows that dietary energy restriction causes substantial loss of both LM and FM (39), which may explain this apparent inconsistency. Furthermore, the long-term effect of low energy intake on %FM is currently unknown. Thus, we hypothesize that chronic low energy intake may cause unfavorable body composition changes with aging, resulting in greater %FM due to substantial losses of LM.

The finding of an inverse association between carbohydrate intake and %FM is consistent with one cohort study that found high carbohydrate intake to be associated with less annual gain in BMI and waist circumference (40), and it suggests that a high-carbohydrate diet may be associated with a lower gain in %FM with aging. Smoking was found to be a protective factor against being in the highest tertile %FM, which is consistent with findings for other populations (41, 42), and is a finding that may be due to greater fat utilization and appetite suppression compared with not smoking (43). Our findings of no association between both total physical activity and alcohol intake variables and body composition are broadly consistent with the existing epidemiologic literature (44–46).

Increasing age category, smoking, low carbohydrate intake, and low body weight in particular were significant risk factors, whereas Australian birth and vigorous physical activity were protective factors for being in the highest tertile for aBMD. Ethnicity effects for those born in other countries may explain some of the age-related differences in aBMD (47). The increased risk of being in the lowest tertile for aBMD associated with smoking, even after adjustment for body weight, is consistent with the existing body of evidence (48), although the mechanisms are not yet known. Our finding of a positive association between carbohydrate intake and aBMD is also consistent with the findings of others (49). It has been hypothesized that low carbohydrate intake causes acidosis, which stimulates excessive bone resorption to restore the blood's pH, as a buffer. Furthermore, the lack of evidence for long-term safety (50) and the potential risk of decreasing aBMD associated with low carbohydrate intake should be of concern to nutritionists and medical practitioners. Finally, vigorous physical activity was consistently found to be a protective factor associated with aBMD, which is in broad agreement with the findings of others (49). Vigorous physical activity may be more effective than moderate physical activity in stimulating bone deposition, because of the greater mechanical and compressive stresses on the skeletal system, similar to the hypothesized weight-bearing mechanisms of body weight on aBMD. Thus, several demographic and lifestyle variables were found to be risks as well as protective factors associated with age-related differences in body composition variables.

The strengths of this study include a large random sample similar in characteristics to men from the general Australian population, precise measures of body composition, and measured anthropometry. Further, the study population and the characteristics of dropouts and nonresponders have been sufficiently described, and statistical models were adjusted for many important covariates. Limitations of this study include the cross-sectional design, the responder bias [although few differences were found between responders, nonresponders, and drop-outs (11)], seasonal effects from different periods of data collection, self-report questionnaires used to collect information on all lifestyle factors, generalizations restricted to men, missing risk factor variables not yet known, and different types of physical activity that could not be assessed (eg, aerobic compared with resistance).

In summary, we defined age-related differences in anthropometric and body composition variables associated with lifestyle factors in a random sample of men aged 35–81 y. Age-related differences in whole-body %FM are mostly due to less LM, whereas differences in abdominal %FM are actually due to more FM. For whole-body and abdominal %FM, increasing age category, Australian birth, and the lowest tertiles for energy, carbohydrate, and fiber intake in particular were found to be significant risk factors, whereas smoking was a strong protective factor. For aBMD, increasing age category, smoking, and the lowest tertiles for carbohydrate intake and, in particular, body mass were significant risk factors, whereas Australian birth and vigorous physical activity were protective factors. To promote healthy body composition changes with aging, age-related differences in %FM and aBMD are therefore associated with demographic and lifestyle factors that should be targeted for intervention.


   ACKNOWLEDGMENTS
 
The authors acknowledge the clinic and recruitment staffs for their invaluable efforts. We extend particular thanks to Janet Grant, Sandy Pickering, and the staff of the North West Adelaide Health Study for all their assistance. We also thank Chris Seaborn and Erika Bowden and the staff at the Department of Nuclear Medicine, The Queen Elizabeth Hospital, for providing expertise and assistance with dual-energy X-ray absorptiometry procedures.

The authors' responsibilities were as follows: KA: data analysis and writing of the manuscript; SAM, MTH, and AWT: data collection and contributions to the writing of the manuscript; GAW: the study design, securing of funding, and contributions to the writing of the manuscript. None of the authors had a personal or financial conflict of interest.


   REFERENCES
TOP
 ABSTRACT
 INTRODUCTION
 SUBJECTS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 

  1. Bogers RP, Bemelmans WJE, Hoogenveen RT, et al. Association of overweight with increased risk of coronary heart disease partly independent of blood pressure and cholesterol levels: a meta-analysis of 21 cohort studies including more than 300 000 persons. Arch Intern Med 2007;167:1720–8.[Abstract/Free Full Text]
  2. Flegal KM, Graubard BI, Williamson DF, Gail MH. Cause-specific excess deaths associated with underweight, overweight, and obesity. JAMA 2007;298:2028–37.[Abstract/Free Full Text]
  3. Alley DE, Chang VW. The changing relationship of obesity and disability, 1988–2004. JAMA 2007;298:2020–7.[Abstract/Free Full Text]
  4. Zamboni M, Mazzali G, Zoico E, et al. Health consequences of obesity in the elderly: a review of four unresolved questions. Int J Obes (Lond) 2005;29:1011–29.[Medline]
  5. Janssen I, Heymsfield SB, Ross R. Low relative skeletal muscle mass (sarcopenia) in older persons is associated with functional impairment and physical disability. J Am Geriatr Soc 2002;50:889–96.[Medline]
  6. Simpson JA, MacInnis RJ, Peeters A, Hopper JL, Giles GG, English DR. A comparison of adiposity measures as predictors of all-cause mortality: the Melbourne Collaborative Cohort Study. Obesity 2007;15:994–1003.[Medline]
  7. Lahmann PH, Lissner L, Gullberg B, Berglund G. A prospective study of adiposity and all-cause mortality: the Malmo Diet and Cancer Study. Obes Res 2002;10:361–9.[Medline]
  8. Segal KR, Dunaif A, Gutin B, Albu J, Nyman A, Pi-Sunyer FX. Body composition, not body weight, is related to cardiovascular disease risk factors and sex hormone levels in men. J Clin Invest 1987;80:1050–5.[Medline]
  9. Silventoinen K, Sans S, Tolonen H, et al. Trends in obesity and energy supply in the WHO MONICA Project. Int J Obes Relat Metab Disord 2004;28:710–8.[Medline]
  10. United Nations. World population ageing 2007. New York, NY: United Nations Publications, 2007. Internet: http://www.un.org/esa/population/publications/WPA2007/wpp2007.htm (accessed 12 November 2007).
  11. Martin SA, Haren MT, Middleton SM, Wittert GA, Members of the Florey Adelaide Male Ageing S. The Florey Adelaide Male Ageing Study (FAMAS): design, procedures, participants. BMC Public Health 2007;7:126.[Medline]
  12. American Association for Public Opinion Research. Standard definitions: final dispositions of cases codes and outcome rates for surveys. 4th ed. Lenexa, KS: American Association for Public Opinion Research, 2006.
  13. Australian Sports Commission. (Olds T, Norton K, eds.) Anthropometrica: a textbook of body measurement for sports and health courses. Sydney, Australia: UNSW Press, 1996.
  14. Mazess RB, Barden HS. Evaluation of differences between fan-beam and pencil-beam densitometers. Calc Tissue Int 2000;67:291–6.[Medline]
  15. Armstrong T, Bauman A, Davies J. Physical activity patterns of Australian adults. Results from the 1999 National Physical Activity Survey. Canberra: Australian Institute of Health and Welfare, 2000.
  16. Craig CL, Marshall AL, Sjostrom M, et al. International physical activity questionnaire: 12-country reliability and validity. Med Sci Sports Exerc 2003;35:1381–95.[Medline]
  17. Ainsworth BE, Haskell WL, Whitt MC, et al. Compendium of physical activities: an update of activity codes and MET intensities. Med Sci Sports Exerc 2000;32(suppl):S498–504.[Medline]
  18. Giles GG, Ireland PD. Dietary questionnaire for epidemiological studies (version 2). Melbourne, Australia: The Cancer Council of Victoria, 1996.
  19. Hodge A, Patterson AJ, Brown WJ, et al. The Anti Cancer Council of Victoria FFQ: relative validity of nutrient intakes compared with weighed food records in young to middle-aged women in a study of iron supplementation. Austr N Z Public Health 2000;24:576–83. (Published erratum appears in Aust N Z J Public Health 2003;27:468.)
  20. Woodward M. Modelling quantitative outcome variables. In: Epidemiology. Study design and data analysis. 2nd ed. Boca Raton, FL: Chapman & Hall/CRC, 2005.
  21. De Laet C, Kanis JA, Oden A, et al. Body mass index as a predictor of fracture risk: a meta-analysis. Osteopor Int 2005;16:1330–8.[Medline]
  22. Chumlea WC, Guo SS, Kuczmarski RJ, et al. Body composition estimates from NHANES III bioelectrical impedance data. Int J Obes Relat Metab Disord 2002;26:1596–609.[Medline]
  23. Ito H, Ohshima A, Ohto N, et al. Relation between body composition and age in healthy Japanese subjects. Eur J Clin Nutr 2001;55:462–70.[Medline]
  24. Heitmann BL. Body fat in the adult Danish population aged 35–65 years: an epidemiological study. Int J Obes 1991;15:535–45.[Medline]
  25. Kyle UG, Genton L, Hans D, Karsegard L, Slosman DO, Pichard C. Age-related differences in fat-free mass, skeletal muscle, body cell mass and fat mass between 18 and 94 years. Eur J Clin Nutr 2001;55:663–72.[Medline]
  26. Shaw KA, Srikanth VK, Fryer JL, Blizzard L, Dwyer T, Venn AJ. Dual energy X-ray absorptiometry body composition and aging in a population-based older cohort. Int J Obes 2007;31:279–84.[Medline]
  27. Welch GW, Sowers MR. The interrelationship between body topology and body composition varies with age among women. J Nutr 2000;130:2371–7.[Abstract/Free Full Text]
  28. Thomas GN, Tomlinson B, Hong AWL, Hui SSC. Age-related anthropometric remodelling resulting in increased and redistributed adiposity is associated with increases in the prevalence of cardiovascular risk factors in Chinese subjects. Diabetes Metab Res Rev 2006;22:72–8.[Medline]
  29. Gomez JM, Maravall FJ, Gomez N, Navarro MA, Casamitjana R, Soler J. Interactions between serum leptin, the insulin-like growth factor-I system, and sex, age, anthropometric and body composition variables in a healthy population randomly selected. Clin Endocrinol 2003;58:213–9.[Medline]
  30. Woo J, Ho SC, Sham A. Longitudinal changes in body mass index and body composition over 3 years and relationship to health outcomes in Hong Kong Chinese age 70 and older. J Am Geriatr Soc 2001;49:737–46.[Medline]
  31. Wannamethee SG, Shaper AG, Lennon L, Whincup PH. Decreased muscle mass and increased central adiposity are independently related to mortality in older men. Am J Clin Nutr 2007;86:1339–46.[Abstract/Free Full Text]
  32. Johnell O, Kanis JA, Oden A, et al. Predictive value of BMD for hip and other fractures. J Bone Miner Res 2005;20:1185–94. (Published erratum appears J Bone Miner Res 2007;22:774.)[Medline]
  33. Center JR, Nguyen TV, Schneider D, Sambrook PN, Eisman JA. Mortality after all major types of osteoporotic fracture in men and women: an observational study. Lancet 1999;353:878–82.[Medline]
  34. Burge R, Dawson-Hughes B, Solomon DH, Wong JB, King A, Tosteson A. Incidence and economic burden of osteoporosis-related fractures in the United States, 2005–2025. J Bone Miner Res 2007;22:465–75.[Medline]
  35. Panagiotakos DB, Chrysohoou C, Pitsavos C, Stefanadis C. Association between the prevalence of obesity and adherence to the Mediterranean diet: the ATTICA study. Nutrition 2006;22:449–56.[Medline]
  36. Koh-Banerjee P, Chu N-F, Spiegelman D, et al. Prospective study of the association of changes in dietary intake, physical activity, alcohol consumption, and smoking with 9-y gain in waist circumference among 16 587 US men. Am J Clin Nutr 2003;78:719–27.[Abstract/Free Full Text]
  37. Ludwig DS, Pereira MA, Kroenke CH, et al. Dietary fiber, weight gain, and cardiovascular disease risk factors in young adults. JAMA 1999;282:1539–46.[Abstract/Free Full Text]
  38. Mitchell D, Haan MN, Steinberg FM, Visser M. Body composition in the elderly: the influence of nutritional factors and physical activity. J Nutr Health Aging 2003;7:130–9.[Medline]
  39. Pritchard JE, Nowson CA, Wark JD. A worksite program for overweight middle-aged men achieves lesser weight loss with exercise than with dietary change. J Am Dietet Assoc 1997;97:37–42.[Medline]
  40. Newby PK, Muller D, Hallfrisch J, Qiao N, Andres R, Tucker KL. Dietary patterns and changes in body mass index and waist circumference in adults. Am J Clin Nutr 2003;77:1417–25.[Abstract/Free Full Text]
  41. Elmstahl S, Jarnblad G, Stavenow L, et al. Body composition and dietary habits in 80-year-old smoking men without cardiovascular disease. Aging Clin Exp Res 1991;3:269–77.
  42. Samaras K, Kelly PJ, Spector TD, Chiano MN, Campbell LV. Tobacco smoking and oestrogen replacement are associated with lower total and central fat in monozygotic twins. Int J Obes Relat Metab Disord 1998;22:149–56.[Medline]
  43. Bishop C, Parker GC, Coscina DV. Systemic nicotine alters whole-body fat utilization in female rats. Physiol Behav 2004;80:563–7.[Medline]
  44. Raguso CA, Kyle U, Kossovsky MP, et al. A 3-year longitudinal study on body composition changes in the elderly: role of physical exercise. Clin Nutr 2006;25:573–80.[Medline]
  45. Hughes VA, Frontera WR, Roubenoff R, Evans WJ, Singh MAF. Longitudinal changes in body composition in older men and women: role of body weight change and physical activity. Am J Clin Nutr 2002;76:473–81.[Abstract/Free Full Text]
  46. Starling RD, Ades PA, Poehlman ET. Physical activity, protein intake, and appendicular skeletal muscle mass in older men. Am J Clin Nutr 1999;70:91–6.[Abstract/Free Full Text]
  47. Araujo AB, Travison TG, Harris SS, Holick MF, Turner AK, McKinlay JB. Race/ethnic differences in bone mineral density in men. Osteoporos Int 2007;18:943–53.[Medline]
  48. Ward KD, Klesges RC. A meta-analysis of the effects of cigarette smoking on bone mineral density. Calc Tissue Int 2001;68:259–70.[Medline]
  49. McGuigan FEA, Murray L, Gallagher A, et al. Genetic and environmental determinants of peak bone mass in young men and women. J Bone Miner Res 2002;17:1273–9.[Medline]
  50. Adam-Perrot A, Clifton P, Brouns F. Low-carbohydrate diets: nutritional and physiological aspects. Obes Rev 2006;7:49–58.[Medline]
Received for publication January 14, 2008. Accepted for publication March 31, 2008.





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