Background: Sarcopenia, a multifactorial disorder involving metabolic disturbance, suggests potential for metabolite biomarkers. Carnitine (CN), essential for skeletal muscle energy metabolism, may be a candidate biomarker. We investigated whether CN metabolites are biomarkers for sarcopenia. Methods: Associations between the CN metabolites identified from an animal model of sarcopenia and muscle cells and sarcopenia status were evaluated in men from an age-matched discovery (72 cases, 72 controls) and a validation (21 cases, 47 controls) cohort. Results: An association between CN metabolites and sarcopenia showed in mouse and cell studies. In the discovery cohort, plasma C5-CN levels were lower in sarcopenic men (P=0.005). C5-CN levels in men tended to be associated with handgrip strength (HGS) (P=0.098) and were significantly associated with skeletal muscle mass (P=0.003). Each standard deviation increase in C5-CN levels reduced the odds of low muscle mass (odd ratio, 0.61; 95% confidence interval [CI], 0.42 to 0.89). The area under the receiver operating characteristic curve (AUROC) of CN score using a regression equation of C5-CN levels, for sarcopenia was 0.635 (95% CI, 0.544 to 0.726). In the discovery cohort, addition of CN score to HGS significantly improved AUROC from 0.646 (95% CI, 0.575 to 0.717; HGS only) to 0.727 (95% CI, 0.643 to 0.810; P=0.006; HGS+CN score). The improvement was confirmed in the validation cohort (AUROC=0.563; 95% CI, 0.470 to 0.656 for HGS; and AUROC=0.712; 95% CI, 0.569 to 0.855 for HGS+CN score; P=0.027). Conclusion C5-CN, indicative of low muscle mass, is a potential circulating biomarker for sarcopenia in men. Further studies are required to confirm these results and explore sarcopenia-related metabolomic changes.

Background: Sarcopenia, a multifactorial disorder involving metabolic disturbance, suggests potential for metabolite biomarkers. Carnitine (CN), essential for skeletal muscle energy metabolism, may be a candidate biomarker. We investigated whether CN metabolites are biomarkers for sarcopenia. Methods: Associations between the CN metabolites identified from an animal model of sarcopenia and muscle cells and sarcopenia status were evaluated in men from an age-matched discovery (72 cases, 72 controls) and a validation (21 cases, 47 controls) cohort. Results: An association between CN metabolites and sarcopenia showed in mouse and cell studies. In the discovery cohort, plasma C5-CN levels were lower in sarcopenic men (P=0.005). C5-CN levels in men tended to be associated with handgrip strength (HGS) (P=0.098) and were significantly associated with skeletal muscle mass (P=0.003). Each standard deviation increase in C5-CN levels reduced the odds of low muscle mass (odd ratio, 0.61; 95% confidence interval [CI], 0.42 to 0.89). The area under the receiver operating characteristic curve (AUROC) of CN score using a regression equation of C5-CN levels, for sarcopenia was 0.635 (95% CI, 0.544 to 0.726). In the discovery cohort, addition of CN score to HGS significantly improved AUROC from 0.646 (95% CI, 0.575 to 0.717; HGS only) to 0.727 (95% CI, 0.643 to 0.810; P=0.006; HGS+CN score). The improvement was confirmed in the validation cohort (AUROC=0.563; 95% CI, 0.470 to 0.656 for HGS; and AUROC=0.712; 95% CI, 0.569 to 0.855 for HGS+CN score; P=0.027). Conclusion C5-CN, indicative of low muscle mass, is a potential circulating biomarker for sarcopenia in men. Further studies are required to confirm these results and explore sarcopenia-related metabolomic changes.

Background: Sarcopenia, a multifactorial disorder involving metabolic disturbance, suggests potential for metabolite biomarkers. Carnitine (CN), essential for skeletal muscle energy metabolism, may be a candidate biomarker. We investigated whether CN metabolites are biomarkers for sarcopenia. Methods: Associations between the CN metabolites identified from an animal model of sarcopenia and muscle cells and sarcopenia status were evaluated in men from an age-matched discovery (72 cases, 72 controls) and a validation (21 cases, 47 controls) cohort. Results: An association between CN metabolites and sarcopenia showed in mouse and cell studies. In the discovery cohort, plasma C5-CN levels were lower in sarcopenic men (P=0.005). C5-CN levels in men tended to be associated with handgrip strength (HGS) (P=0.098) and were significantly associated with skeletal muscle mass (P=0.003). Each standard deviation increase in C5-CN levels reduced the odds of low muscle mass (odd ratio, 0.61; 95% confidence interval [CI], 0.42 to 0.89). The area under the receiver operating characteristic curve (AUROC) of CN score using a regression equation of C5-CN levels, for sarcopenia was 0.635 (95% CI, 0.544 to 0.726). In the discovery cohort, addition of CN score to HGS significantly improved AUROC from 0.646 (95% CI, 0.575 to 0.717; HGS only) to 0.727 (95% CI, 0.643 to 0.810; P=0.006; HGS+CN score). The improvement was confirmed in the validation cohort (AUROC=0.563; 95% CI, 0.470 to 0.656 for HGS; and AUROC=0.712; 95% CI, 0.569 to 0.855 for HGS+CN score; P=0.027). Conclusion C5-CN, indicative of low muscle mass, is a potential circulating biomarker for sarcopenia in men. Further studies are required to confirm these results and explore sarcopenia-related metabolomic changes.

Background: Sarcopenia, a multifactorial disorder involving metabolic disturbance, suggests potential for metabolite biomarkers. Carnitine (CN), essential for skeletal muscle energy metabolism, may be a candidate biomarker. We investigated whether CN metabolites are biomarkers for sarcopenia. Methods: Associations between the CN metabolites identified from an animal model of sarcopenia and muscle cells and sarcopenia status were evaluated in men from an age-matched discovery (72 cases, 72 controls) and a validation (21 cases, 47 controls) cohort. Results: An association between CN metabolites and sarcopenia showed in mouse and cell studies. In the discovery cohort, plasma C5-CN levels were lower in sarcopenic men (P=0.005). C5-CN levels in men tended to be associated with handgrip strength (HGS) (P=0.098) and were significantly associated with skeletal muscle mass (P=0.003). Each standard deviation increase in C5-CN levels reduced the odds of low muscle mass (odd ratio, 0.61; 95% confidence interval [CI], 0.42 to 0.89). The area under the receiver operating characteristic curve (AUROC) of CN score using a regression equation of C5-CN levels, for sarcopenia was 0.635 (95% CI, 0.544 to 0.726). In the discovery cohort, addition of CN score to HGS significantly improved AUROC from 0.646 (95% CI, 0.575 to 0.717; HGS only) to 0.727 (95% CI, 0.643 to 0.810; P=0.006; HGS+CN score). The improvement was confirmed in the validation cohort (AUROC=0.563; 95% CI, 0.470 to 0.656 for HGS; and AUROC=0.712; 95% CI, 0.569 to 0.855 for HGS+CN score; P=0.027). Conclusion C5-CN, indicative of low muscle mass, is a potential circulating biomarker for sarcopenia in men. Further studies are required to confirm these results and explore sarcopenia-related metabolomic changes.

Purpose: Although numerous studies have identified potential risk factors for ipsilateral lymphedema development in patients with breast cancer following axillary node dissection, the risk factors for lymphedema in patients undergoing sentinel node biopsy without axillary dissection remain unclear. In this study, we aimed to determine the real-world incidence and risk factors for lymphedema in such patients. Methods: We conducted a single-center, retrospective review of medical records of patients with breast cancer who underwent sentinel node biopsy alone. The development cohort (5,051 patients, January 2017–December 2020) was analyzed to identify predictors of lymphedema, and a predictive model was subsequently created. A validation cohort (1,627 patients, January 2014–December 2016) was used to validate the model. Results: In the development cohort, 49 patients (0.9%) developed lymphedema over a median follow-up of 56 months, with most cases occurring within the first three years post-operation.Multivariate analysis revealed that a body mass index (BMI) of 30 kg/m2 or above, radiation therapy (RTx), chemotherapy, and more than three harvested lymph nodes significantly predicted lymphedema. The predictive model showed an area under the curve of 0.824 for systemic chemotherapy, with the number of harvested lymph nodes being the most significant factor. Patients were stratified into four risk groups, showing lymphedema incidences of 3.3% in the highest-risk group and 0.1% in the lowest-risk group. In the validation cohort, the incidences were 1.7% and 0.2% for the highest and lowest risk groups, respectively. Conclusion The lymphedema prediction model identifies RTx, chemotherapy, BMI ≥ 30 kg/m2 , and more than three harvested lymph nodes as significant risk factors. Although the overall incidence is low, the risk is notably influenced by the extent of lymph node removal and systemic therapies. The model’s high negative predictive value supports its application in designing tailored lymphedema surveillance programs for early intervention.

Purpose: Although numerous studies have identified potential risk factors for ipsilateral lymphedema development in patients with breast cancer following axillary node dissection, the risk factors for lymphedema in patients undergoing sentinel node biopsy without axillary dissection remain unclear. In this study, we aimed to determine the real-world incidence and risk factors for lymphedema in such patients. Methods: We conducted a single-center, retrospective review of medical records of patients with breast cancer who underwent sentinel node biopsy alone. The development cohort (5,051 patients, January 2017–December 2020) was analyzed to identify predictors of lymphedema, and a predictive model was subsequently created. A validation cohort (1,627 patients, January 2014–December 2016) was used to validate the model. Results: In the development cohort, 49 patients (0.9%) developed lymphedema over a median follow-up of 56 months, with most cases occurring within the first three years post-operation.Multivariate analysis revealed that a body mass index (BMI) of 30 kg/m2 or above, radiation therapy (RTx), chemotherapy, and more than three harvested lymph nodes significantly predicted lymphedema. The predictive model showed an area under the curve of 0.824 for systemic chemotherapy, with the number of harvested lymph nodes being the most significant factor. Patients were stratified into four risk groups, showing lymphedema incidences of 3.3% in the highest-risk group and 0.1% in the lowest-risk group. In the validation cohort, the incidences were 1.7% and 0.2% for the highest and lowest risk groups, respectively. Conclusion The lymphedema prediction model identifies RTx, chemotherapy, BMI ≥ 30 kg/m2 , and more than three harvested lymph nodes as significant risk factors. Although the overall incidence is low, the risk is notably influenced by the extent of lymph node removal and systemic therapies. The model’s high negative predictive value supports its application in designing tailored lymphedema surveillance programs for early intervention.

Purpose: Although numerous studies have identified potential risk factors for ipsilateral lymphedema development in patients with breast cancer following axillary node dissection, the risk factors for lymphedema in patients undergoing sentinel node biopsy without axillary dissection remain unclear. In this study, we aimed to determine the real-world incidence and risk factors for lymphedema in such patients. Methods: We conducted a single-center, retrospective review of medical records of patients with breast cancer who underwent sentinel node biopsy alone. The development cohort (5,051 patients, January 2017–December 2020) was analyzed to identify predictors of lymphedema, and a predictive model was subsequently created. A validation cohort (1,627 patients, January 2014–December 2016) was used to validate the model. Results: In the development cohort, 49 patients (0.9%) developed lymphedema over a median follow-up of 56 months, with most cases occurring within the first three years post-operation.Multivariate analysis revealed that a body mass index (BMI) of 30 kg/m2 or above, radiation therapy (RTx), chemotherapy, and more than three harvested lymph nodes significantly predicted lymphedema. The predictive model showed an area under the curve of 0.824 for systemic chemotherapy, with the number of harvested lymph nodes being the most significant factor. Patients were stratified into four risk groups, showing lymphedema incidences of 3.3% in the highest-risk group and 0.1% in the lowest-risk group. In the validation cohort, the incidences were 1.7% and 0.2% for the highest and lowest risk groups, respectively. Conclusion The lymphedema prediction model identifies RTx, chemotherapy, BMI ≥ 30 kg/m2 , and more than three harvested lymph nodes as significant risk factors. Although the overall incidence is low, the risk is notably influenced by the extent of lymph node removal and systemic therapies. The model’s high negative predictive value supports its application in designing tailored lymphedema surveillance programs for early intervention.