Prognostic value of computed tomography derived skeletal muscle mass index in lung cancer:A meta-analysis

BACKGROUND The prognostic role of the skeletal muscle mass index(SMI)derived from computed tomography(CT)imaging been well verified in several types of cancers.However,whether the SMI could serve as a reliable and valuable predictor of long-term survival in lung cancer patients remains unclear.AIM To identify the prognostic value of the CT-derived SMI in lung cancer patients.METHODS The PubMed,Web of Science,and Embase electronic databases were searched up to November 5,2021 for relevant studies.The Reference Citation Analysis databases were used during the literature searching and selection.Hazard ratios(HRs)and 95%confidence intervals(CIs)were calculated to assess the association of the SMI with the overall survival(OS)of lung cancer patients.All statistical analyses were performed with STATA 12.0 software.RESULTS A total of 12 studies involving 3002 patients were included.The pooled results demonstrated that a lower SMI was significantly related to poorer OS(HR=1.23,95%CI:1.11-1.37,P<0.001).In addition,the subgroup analyses stratified by treatment(nonsurgery vs surgery),tumor stage(advanced stage vs early stage),and tumor type(non-small cell lung cancer vs lung cancer)showed similar results.CONCLUSION The CT-derived SMI is a novel and valuable prognostic indicator in lung cancer and might contribute to the clinical management and treatment of lung cancer patients.

Comparison of the Two Algorithms of Skeletal Muscle Mass Index: An Observational Study in a Large Cohort of Chinese Adults

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/m2) = 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 β = −0.9849, P 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 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.