Osteoporosis is an important cause of age-related mortality and morbidity. PURPOSE The purpose of this study was to evaluate differences in body composition and in radial, spinal, and femoral BMD (g/cm2) in a sample of men, divided into five age categories (65–69, 70–74, 75–79, 80–84 and 85+ years). World Health Organization T-values were used to classify BMD as normal, osteopenic, or osteoporotic. METHODS Body tissues, (lean, fat and total) were assessed with anthropometry (skinfolds and body circumferences) and with absorptiometry (Hologic QDR-2000 DEXA). ANOVAs were used to assess differences across age groups while partial correlations (with affect of age removed) were used to assess relationships between body composition and BMD variables. Frequency counts and percentages evaluated the incidence of osteopenia and osteoporosis. RESULTS Results revealed significant decreases in body weight, lean tissue, arm circumferences, and forearm BMD with increasing age. Total lean tissue was significantly related to radial, spinal, and hip BMD values. T-scores revealed that 41 men (80%), 27 men (53%), and 17 men (33%) exhibited osteopenic or osteoporotic BMD values at femoral neck, radius, and spine, respectively. CONCLUSION In conclusion, elderly men should be encouraged to be physically active to maintain adequate lean tissue because of its association with increased BMD values. Systematic BMD screening is recommended in this population.

1566 We calculated principal component (PC) scores for N=101 postmenopausal subjects (Ss) from anthropometry: 8 skinfolds, AGE (yrs), standing and sitting height (HT1, HT2; cm), body weight (WT; kg), body mass index (BMI; kg/m**2), and gluteal girth (GG; cm). Uncorrelated PC scores, arranged in descending order of (maximized) variability, were examined separately and as surrogate predictors for raw variables in multiple regression of body density (BD). Such models predict body composition when hydrostatic weightin is not feasible. The separate PC analysis was validated against N=139 Ss, restricting ages to 50-70 in both groups. Considering skinfold sites only, PC1 was approximately proportional to the sum of all 8 skinfolds and explained 60.9% of the total system variability (TSV). PC2 contrasted subscapular, chest, midaxillary, suprailiac sites vs. triceps, abdominal thing, and calf (13.2% TSV). Similar patterns were seen in the validation sample: PC1 (69.9% TSV), PC2 (9.6% TSV), although the sign changed for the abdominal site in PC2. Among non-skinfold variables alone, each positively loaded on PC1 (47.0% TSV), and PC2 contrasted HT1 and HT2 vs. the other non-skinfold variables (32.7% TSV). Combining variables, the results were less interpretable, although each variable positively loaded on PC1 (47.2% TSV) while PC2 was dominated by a contrast between HT1 and HT2 vs. most of the skinfolds (except for chest and triceps) and explained appreciably more variation (15.7% TSV). Whether to include PC3 was marginal (9.3% TSV). In the PC regression of BD using these combined PCs as independent variables, PC1 was the best single surrogate predictor(R2=55.3%), while BMI was the best single raw predictor (R2=46.2%). For two-predictor models, PC1 and PC2 together explained very little additional variation in BD (R2=59.1%). This was comparable to the best two-predictor model of original variables (age and GG, R2=56.0%). These analyses suggest (1) one and possibly two functions of all 8 skinfolds, and two or three functions overall, are probably sufficient for most statistical purposes; (2) skinfold constrasts between trunk sites and extremities seems to be independent of the sum of all 8 skinfolds. We conclude the PC method can improve prediction accuracy, enhance interpretability, and identify redundancy.

We compared lumbar and femoral bone density in 129 black (M age=61.0 +/- 12.7 yrs) and 130 white (M age 62.7 +/- 8.4 yrs) postmenopausal women volunteers. A total of 44/129 (34%) black Ss and 84/130 (65%) of white Ss were currently taking hormone replacement therapy (HRT) as prescribed by their personal physicians. Standing height and body weight were obtained on Detecto scales. We measured the bone tissue with a Hologic QDR-2000 densitometer, and compared the bone density for the L2-L4 lumbar spine (SP), and the total femur(HIP) sites, adjusting for years past menopause (YPM: yrs) and body mass index(BMI: wt(kg)/ht(m2)]. The following means and adjusted means were obtained: This table shows that, after adjusting for body mass index and years past menopause, the overall L2-L4 regional differences appear to be less clinically striking. The adjusted racial differences in the total hip, although a bit more pronounced, are not statistically significant. Additional analyses, however, did yield some significant differences at various subsites (data not shown). We believe that the consideration of weight-bearing hypotheses and other lifestyle and dietary factors could possibly erode the often apparently large racial differences, suggesting that blacks with these identified lifestyle factors (or their correlates) may have similar risks for osteoporotic fractures as their white counterparts.

We evaluated the relationship between muscle mass and bone mass(non-dominant forearm, spine, and hip) in 114 postrnenopausal (PM) women. These sites were selected because of their increased susceptibility to osteoporotic fractures in this population. Muscle mass was assessed in two ways: 1) limb circumference, from Gulick Tape, and 2) The Total Body scan on the Hologic QDR-2000 (DEXA). Bone variables were reported in BMC (g) and BMC/BA (g/cm2). Several sites were measured in the forearm, spine, and hip which included the 1/3 Radius (1/3 R), the lumbar spine (LS), and the Femoral Neck (FN). Zero-order correlation coefficients were calculated between bone mass at each site and muscle mass. These statistical analyses revealed that muscle mass was significantly related to bone mass. Correlations ranged from.187 to.673 with selected correlation coefficients (*p<.05) as follows: Table On the basis of these data, it was concluded that muscle mass and bone mass were significantly related in the forearms and thighs but the correlations were modest. Further research on muscle mass and bone mass in the elderly seems warranted since muscle mass may decrease at a faster rate than bone mass.

Davis, D.; Shepherd, R.; Cussen, P.; Holiday, D.; McKeown, B.; Ballard, J. FACSM Author Information

Willard, S.; Ballard, J. FACSM; Halbrook, M.; McKeown, B.; Holiday, D. Author Information

Riddle, K.; Ballard, J. FACSM; Iloliday, D.; McKeown, B.; Hanley, D. Author Information

Hogan, J.; Holiday, D.; Ballard, J. FACSM; McKeown, B.; Hugghins, A.; Hanley, D. Author Information

Holiday, D.; Ballard, J.; McKeown, B.; Heyward, V.; Bryars, S.; Womack, L. Author Information