27%. All scanning and analyses were conducted by certified radiologic technologists using a standardized protocol recommended by the International Society for Clinical Densitometry. The same

technologist scanned 78% of the subjects; two additional technologists scanned the remaining 19% and 3% of the subjects, respectively. To evaluate the reproducibility, the in vivo coefficient of variation was obtained by scanning 30 healthy women twice in the same learn more day by the same technologist as has been recommended [18, 19]. The site-specific coefficient of variation was 0.55% for the lumbar spine, 0.78% for the hip, 1.95% for the femoral neck, 4.83% for the spine bone mineral apparent density (BMAD), and 5.63% for the femoral neck BMAD. Densitometry measurements included BMD (g/cm2) measured at the lumbar spine (L1–L4) and total hip (Ward’s triangle, greater trochanter, intertrochanter, and femoral neck) of the left hip. Hip data are presented separately for the femoral neck, as this particular site is highly predictive of hip fracture [20]. Calculations for BMD (BMD = BMC

[g] / projected area of the bone [cm2]) have been shown to be influenced by bone MCC-950 size as they are based on two of three dimensions of bones (length and width without depth). To address this issue, we also calculated spine BMAD (g/cm3), which is an approximation of the volumetric density of bone estimated from the BMC and the projected area of the bone (A)

using the formula described by Carter et al. (spine BMAD = BMC / A 3/2) [21]. In this formula, the volume of the measured spine is approximated by A 3/2. We also calculated BMAD of the femoral neck by applying a formula developed by Katzman et al: femoral neck BMAD = BMC / A 2 [22]. Estimates of total fat mass (g), percent fat mass, and lean mass (g) were generated from DXA scans of the whole body. Statistical analysis One-way analysis of variance with Bonferroni corrections for continuous variables and chi-squared tests for Inositol monophosphatase 1 categorical variables were used to compare the three race/ethnic groups. We used multiple linear regression techniques to explore the relationship between the dependent variable (BMC, BMD, or BMAD) and the set of independent variables (age, age at menarche, race/ethnicity, weight, height, parity, months of DMPA/pill use, smoking, alcohol use, weight-bearing exercise, and calcium intake). The skewness-kurtosis test and ladder of powers were used to determine whether the dependent variable should be transformed and to identify the transformation. First, a model with all races/ethnicities was tried with main effects and interaction terms. If the interaction term between race/ethnicity and any of the two major variables (weight or height) was significant, three race-specific models were built.