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 val...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.展开更多
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<sup>2</sup>...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<sup>2</sup>) = total skeletal muscle mass (kg)/height (m)<sup>2</sup>]. 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.展开更多
BACKGROUND Type 2 diabetes mellitus(T2DM),a fast-growing issue in public health,is one of the most common chronic metabolic disorders in older individuals.Osteoporosis and sarcopenia are highly prevalent in T2DM patie...BACKGROUND Type 2 diabetes mellitus(T2DM),a fast-growing issue in public health,is one of the most common chronic metabolic disorders in older individuals.Osteoporosis and sarcopenia are highly prevalent in T2DM patients and may result in fractures and disabilities.In people with T2DM,the association between nutrition,sarcopenia,and osteoporosis has rarely been explored.AIM To evaluate the connections among nutrition,bone mineral density(BMD)and body composition in patients with T2DM.METHODS We enrolled 689 patients with T2DM for this cross-sectional study.All patients underwent dual energy X-ray absorptiometry(DXA)examination and were categorized according to baseline Geriatric Nutritional Risk Index(GNRI)values calculated from serum albumin levels and body weight.The GNRI was used to evaluate nutritional status,and DXA was used to investigate BMD and body composition.Multivariate forward linear regression analysis was used to identify the factors associated with BMD and skeletal muscle mass index.RESULTS Of the total patients,394 were men and 295 were women.Compared with patients in tertile 1,those in tertile 3 who had a high GNRI tended to be younger and had lower HbA1c,higher BMD at all bone sites,and higher appendicular skeletal muscle index(ASMI).These important trends persisted even when the patients were divided into younger and older subgroups.The GNRI was positively related to ASMI(men:r=0.644,P<0.001;women:r=0.649,P<0.001),total body fat(men:r=0.453,P<0.001;women:r=0.557,P<0.001),BMD at all bone sites,lumbar spine(L1-L4)BMD(men:r=0.110,P=0.029;women:r=0.256,P<0.001),FN-BMD(men:r=0.293,P<0.001;women:r=0.273,P<0.001),and hip BMD(men:r=0.358,P<0.001;women:r=0.377,P<0.001).After adjustment for other clinical parameters,the GNRI was still significantly associated with BMD at the lumbar spine and femoral neck.Additionally,a low lean mass index and higherβ-collagen special sequence were associated with low BMD at all bone sites.Age was negatively correlated with ASMI,whereas weight was positively correlated with ASMI.CONCLUSION Poor nutrition,as indicated by a low GNRI,was associated with low levels of ASMI and BMD at all bone sites in T2DM patients.Using the GNRI to evaluate nutritional status and using DXA to investigate body composition in patients with T2DM is of value in assessing bone health and physical performance.展开更多
目的探讨骨骼肌质量指数(Skeletal muscle mass index,SMI)、肌少症指数(Sarcope-nia index,SI)与呼吸危重症患者营养状态及预后的关系。方法选取2021年9月至2023年8月南通市第一人民医院收治的呼吸危重患者132例。采用改良危重症营养...目的探讨骨骼肌质量指数(Skeletal muscle mass index,SMI)、肌少症指数(Sarcope-nia index,SI)与呼吸危重症患者营养状态及预后的关系。方法选取2021年9月至2023年8月南通市第一人民医院收治的呼吸危重患者132例。采用改良危重症营养风险评分表(The modi-fied nutrition risk in critically ill,mNUTRIC)评估患者营养状况,根据评分结果分为低风险患者(n=83)、高风险患者(n=49),比较低风险患者与高风险患者的SMI、SI。以离开ICU为时间终点评估预后,分为死亡组(n=37)和存活组(n=95),比较死亡组与存活组患者的一般资料,采用多因素Logistic回归模型分析影响呼吸危重症患者死亡的因素,受试者工作特征(Receiver operator characteristic,ROC)曲线分析SMI、SI预测呼吸危重症患者死亡的风险价值。结果与低风险患者相比,高风险患者的SMI、SI降低(P<0.05)。以离开重症监护室(Intensive care unit,ICU)为评估时间终点,与存活组相比,死亡组年龄、入院24 h内的急性生理评分、年龄评分及慢性健康评分(Acute physiology and chronic health evaluation scoring system,APACHEⅡ)评分及降钙素原(Procalcitonin,PCT)水平较高,白蛋白(Albumin,ALB)、SI和SMI则较低(P<0.05)。多因素Logistic回归模型显示,高APACHEⅡ评分及低SMI、SI值是影响呼吸危重症死亡的独立危险因素。ROC曲线显示,SMI、SI单独预测呼吸危重症患者死亡的曲线下面积(Area under curve,AUC)为0.784(0.720~0.839)、0.726(0.657~0.788),采用SMI、SI联合预测AUC为0.890(0.835~0.938),联合预测效能较单独预测效能更好(P<0.05)。结论SMI、SI与呼吸危重症患者营养状态关系密切,两者联合预测呼吸危重症患者死亡的临床价值高于单独预测。展开更多
基金Supported by 1·3·5 Project for Disciplines of Excellence,West China Hospital,Sichuan University,No.ZYGD18019.
文摘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.
文摘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<sup>2</sup>) = total skeletal muscle mass (kg)/height (m)<sup>2</sup>]. 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.
基金Supported by Social Development Projects of Nantong,No.MS22021008 and No.QNZ2022005.
文摘BACKGROUND Type 2 diabetes mellitus(T2DM),a fast-growing issue in public health,is one of the most common chronic metabolic disorders in older individuals.Osteoporosis and sarcopenia are highly prevalent in T2DM patients and may result in fractures and disabilities.In people with T2DM,the association between nutrition,sarcopenia,and osteoporosis has rarely been explored.AIM To evaluate the connections among nutrition,bone mineral density(BMD)and body composition in patients with T2DM.METHODS We enrolled 689 patients with T2DM for this cross-sectional study.All patients underwent dual energy X-ray absorptiometry(DXA)examination and were categorized according to baseline Geriatric Nutritional Risk Index(GNRI)values calculated from serum albumin levels and body weight.The GNRI was used to evaluate nutritional status,and DXA was used to investigate BMD and body composition.Multivariate forward linear regression analysis was used to identify the factors associated with BMD and skeletal muscle mass index.RESULTS Of the total patients,394 were men and 295 were women.Compared with patients in tertile 1,those in tertile 3 who had a high GNRI tended to be younger and had lower HbA1c,higher BMD at all bone sites,and higher appendicular skeletal muscle index(ASMI).These important trends persisted even when the patients were divided into younger and older subgroups.The GNRI was positively related to ASMI(men:r=0.644,P<0.001;women:r=0.649,P<0.001),total body fat(men:r=0.453,P<0.001;women:r=0.557,P<0.001),BMD at all bone sites,lumbar spine(L1-L4)BMD(men:r=0.110,P=0.029;women:r=0.256,P<0.001),FN-BMD(men:r=0.293,P<0.001;women:r=0.273,P<0.001),and hip BMD(men:r=0.358,P<0.001;women:r=0.377,P<0.001).After adjustment for other clinical parameters,the GNRI was still significantly associated with BMD at the lumbar spine and femoral neck.Additionally,a low lean mass index and higherβ-collagen special sequence were associated with low BMD at all bone sites.Age was negatively correlated with ASMI,whereas weight was positively correlated with ASMI.CONCLUSION Poor nutrition,as indicated by a low GNRI,was associated with low levels of ASMI and BMD at all bone sites in T2DM patients.Using the GNRI to evaluate nutritional status and using DXA to investigate body composition in patients with T2DM is of value in assessing bone health and physical performance.
文摘目的探讨骨骼肌质量指数(Skeletal muscle mass index,SMI)、肌少症指数(Sarcope-nia index,SI)与呼吸危重症患者营养状态及预后的关系。方法选取2021年9月至2023年8月南通市第一人民医院收治的呼吸危重患者132例。采用改良危重症营养风险评分表(The modi-fied nutrition risk in critically ill,mNUTRIC)评估患者营养状况,根据评分结果分为低风险患者(n=83)、高风险患者(n=49),比较低风险患者与高风险患者的SMI、SI。以离开ICU为时间终点评估预后,分为死亡组(n=37)和存活组(n=95),比较死亡组与存活组患者的一般资料,采用多因素Logistic回归模型分析影响呼吸危重症患者死亡的因素,受试者工作特征(Receiver operator characteristic,ROC)曲线分析SMI、SI预测呼吸危重症患者死亡的风险价值。结果与低风险患者相比,高风险患者的SMI、SI降低(P<0.05)。以离开重症监护室(Intensive care unit,ICU)为评估时间终点,与存活组相比,死亡组年龄、入院24 h内的急性生理评分、年龄评分及慢性健康评分(Acute physiology and chronic health evaluation scoring system,APACHEⅡ)评分及降钙素原(Procalcitonin,PCT)水平较高,白蛋白(Albumin,ALB)、SI和SMI则较低(P<0.05)。多因素Logistic回归模型显示,高APACHEⅡ评分及低SMI、SI值是影响呼吸危重症死亡的独立危险因素。ROC曲线显示,SMI、SI单独预测呼吸危重症患者死亡的曲线下面积(Area under curve,AUC)为0.784(0.720~0.839)、0.726(0.657~0.788),采用SMI、SI联合预测AUC为0.890(0.835~0.938),联合预测效能较单独预测效能更好(P<0.05)。结论SMI、SI与呼吸危重症患者营养状态关系密切,两者联合预测呼吸危重症患者死亡的临床价值高于单独预测。
