PurposeThis study was designed to explore the relationship between bone mineral density (BMD) and body composition indicators in Chinese adults (≥50 years) in order to provide a scientific basis for optimal bone health management.MethodIndividuals ≥50 years old who received physical examinations and routine check-ups at the Health Management Research Institute of PLA General Hospital from September 2014 through March 2022 were included as research subjects in this study. Basic clinical and demographic information were recorded for all subjects, along with smoking and drinking status, height and body weight. A panel of routine blood chemistry and metabolite markers were measured, along with lean muscle mass and body fat mass using body composition bioelectrical impedance analysis (BIA). Body mass index (BMI), body fat percentage (BFP), skeletal muscle mass index (SMI), and bone mineral density (BMD) were calculated for all individuals. For comparative analysis, individuals were grouped based on their BMI, BFP, SMI and BMD T-score. Follow-up examinations were performed in a cohort of 1,608 individuals matched for age, sex, smoking and drinking history for ≥5 years,ResultsIn this large cross-sectional study, age, smoking, homocysteine (Hcy) and blood glucose levels were established as independent risk factors for osteoporosis. Multi-factor logistic regression analysis showed that age, sex, BMI, intact parathyroid hormone (iPTH), SMI, BFP, smoking, blood levels of inorganic phosphate (P) and K+ were all significantly associated with osteoporosis risk (P<0.05). A subset of these factors- BMI, SMI, BFP and K+, were determined to be protective. In the cohort followed for ≥5 years, SMI and BMD decreased while BFP and BMI increased significantly (P<0.001) over time.ConclusionRisk of osteoporosis may be reduced by increasing body weight, particularly lean muscle mass, while simultaneously controlling BFP.

Objective: To compare the two skeletal muscle mass index (SMI) algorithms. One is SMM [SMM(%) = total skeletal muscle mass (kg)/body weight mass (kg) × 100%]; and the other is SMH [SMH (kg/m 2 ) = total skeletal muscle mass (kg)/height (m) 2 ]. Methods: Body composition, body mass index (BMI) and body fat percentage (BFP) were estimated using a bioelectrical impedance analyzer. SMI was calculated by the two algorithms described above, and measurement parameters were stratified by age, BMI and levels of physical activity. Results: Levels of BMI, BFP, SMM and SMH differed significantly between the sexes. BMI and BFP were positively associated with age, while SMM was negatively associated with age (β = −0.2294, P < 0.001). Furthermore, SMM was determined to have a negative association with BMI (β = −0.5340, P < 0.001), while a positive association between SMH and BMI (β = 0.7930, P < 0.001) was observed. Both SMM (β = −0.9849, P < 0.001) and SMH (β = −0.0642, P < 0.001) were negatively associated with BFP. In both men and women, SMM maintained the analogous correlation with other indicators. In the general population, SMM showed a gradual downward trend from low body weight to grade III obesity (F = 9528.32, P < 0.001), but SMH (F = 34395.46, P < 0.001) and BFP (F = 9706.20, P < 0.001) had a reciprocal association. BMI, BFP and SMM differences were observed based on levels of physical activity (P < 0.001). However, there was no significant difference in SMH based on exercise (P > 0.05). Conclusions: SMM may be a more ideal and accurate clinical algorithm for SMI because it is more tightly associated with other body composition indices, as compared with SMH.