Study sample

Data were obtained from men enrolled in the Boston Area Community Health/Bone (BACH/Bone) Survey, which is a cross-sectional observational study of skeletal health and related outcomes in 1,219 (of 1,877 eligible, 65% response rate) randomly selected black, Hispanic, and white male Boston, MA residents aged 30 to 79 y [16]. Persons of other racial/ethnic backgrounds were not enrolled. BACH/Bone Survey subjects were a subset of 2,301 men previously enrolled in the parent Boston Area Community Health (BACH) Survey; full details of the BACH survey have been published previously [17]. Study protocols were approved by Institutional Review Boards at New England Research Institutes (NERI) and Boston University School of Medicine (BUSM). All subjects gave written informed consent separately for participation in each study.

Data collection

Trained staff at NERI and the BUSM's General Clinical Research Center (GCRC) conducted interviews and measurements for BACH and BACH/Bone, respectively. Data collection for BACH generally occurred in subjects' homes while data collection in BACH/Bone occurred at the BUSM GCRC. Age and self-rated health were obtained by self-report. Physical activity level was measured using the Physical Activity for the Elderly (PASE) scale [18]. Frequency and duration of leisure activities, paid or unpaid work (hours/week), and housework and similar duties (yes/no) over the past week were recorded for each subject. The PASE score was computed by multiplying the amount of time spent in each activity (hours/week) or participation (yes/no) in each activity by empirical item weights (derived from regressions of component scores developed from a 3-day physical activity monitor, 3-day physical activity diary, and a global self-report of physical activity on responses to the PASE in a community-dwelling sample of 277 older adults [18]) and summing over all activities. Measurements of subjects' height and weight were obtained using a stadiometer (Seca Corp., Hanover, MD) and digital scale (Tanita, Arlington Heights, IL), respectively. Body mass index (BMI) was calculated from by dividing measured weight (kg) by the square of measured height (m2).

Measures of body composition

Measurements of lean mass and fat mass were obtained from whole body dual energy x-ray absorptiometry (DXA) scans using a QDR 4500 W densitometer (Hologic, Inc., Waltham, MA) located at the BUSM GCRC. All mass quantities reported here exclude the head. Lean mass was calculated by subtracting bone mineral mass from non-fat mass. The DXA system was monitored weekly for drift.

Measures of strength

BACH/Bone has measures of subjects' upper and lower extremity strength/physical function. Upper extremity strength was assessed by hand grip strength. This was measured using a Jamar Hydraulic Hand Dynamometer (Sammons Preston, Bolingbrook, IL), which measures isometric grip force. Subjects were instructed to exert maximum effort for three seconds during two trials, each separated by a 1-min rest. The maximum result was used for analysis. Lower extremity strength was assessed by a chair stand test (time to stand up and sit down 5 times) and a walking test (time to walk 50 ft) [19]. Following a previous study, [19] we created a lower extremity composite physical function variable. Those who could not complete the test were assigned a score of 0. Those completing the walk and chair stand tests were assigned scores of 1-4, corresponding to the quartiles (derived from our population) of speeds in completing each task, with the fastest speeds scored 4. The cutpoints for walking speed were as follows: quartile 1, <1.19 m/s; quartile 2, 1.19-1.30 m/s; quartile 3, 1.31-1.40 m/s; quartile 4, ≥1.41 m/s. The cutpoints for chair stand speed were quartile 1, <0.314 stands/s; quartile 2, 0.314-0.360 stands/s; quartile 3, 0.361-0.430 stands/s; quartile 4, ≥0.431 stands/s. Only one subject was not able to complete the walk task, so we included that subject with those who were in the slowest quartile of walking speed and reassigned the walking speed quartiles to scores 0-3. The two items were summed to a final score with possible range of 0 to 7, with higher scores indicating better lower extremity function.

Indexed outcomes

Outcomes were indexed by either regional lean mass or the square of height as appropriate. Lean mass was divided by the square of height in meters to yield the lean mass index (LMI). Additional measures of relative strength in the upper and lower extremities was estimated by dividing upper and lower extremity measures of strength/physical function by their corresponding regional measures of lean mass [20]. Specifically, maximum grip strength was divided by arms lean mass and the lower extremity composite physical function score was divided by legs lean mass.

Analysis samples

Of the 1,219 men in BACH/Bone, 10 men did not have DXA scans performed. Of the remaining 1,209, we excluded 49 who were missing fat or lean mass and three men missing PASE. This left 1,157 men available as a base for analysis. From this base analysis sample, we used the maximum available data for each of the outcome measures: lean mass and lower extremity strength, N = 1,147; upper extremity strength, N = 970 (54 men were coded as missing due to dynamometer malfunction).

Statistical methodology

Sampling weights were used to produce estimates for means and percentages that are representative of the black, Hispanic, and white male population in Boston, MA between the ages of 30 and 79 y. Sampling weights account for the design effect of over-sampling of particular age and racial and ethnic groups [21].

Exploratory graphical analysis was conducted using locally weighted linear regression (LOESS) models where non-linear functions are fit to subsets of the data using weighted least squares [22]. Line graphs showing cross-sectional age differences in the main outcomes by race/ethnicity are presented.

Three multivariate linear regression models were constructed for each of the outcome variables: (1) Model 1: adjusted for age, race/ethnicity, and height. (2) Model 2: all variables in Model 1 plus potential confounding influences (fat mass, self-rated health status and physical activity). Smoking and a count of 6 major medical comorbidities were also examined but they made no significant contribution to any of the models. (3) Model 3: all variables in Model 2 plus grip strength, the composite physical function score, or lean mass (for non-indexed outcomes) or grip strength/arms lean mass, composite physical function score/legs lean mass, or lean mass index (for indexed outcomes). For instance, Model 3 with lean mass as the outcome would have included all factors in Model 2, plus grip strength and the composite physical function score. This third set of models was constructed in order to examine which variables differ most between the racial/ethnic groups in the presence of other muscle function variables of interest. Race/ethnicity was placed in each model as a categorical variable. Regression coefficients and 95% confidence intervals (CI) for black and Hispanic men (using white men as the reference category) were presented. Associations were considered statistically significant if null hypotheses could be rejected at the 0.05 level (two-sided). All statistical modeling was conducted using SUDAAN software (Research Triangle Institute, Research Triangle Park, NC).