OBJECTIVE: To investigate the effects of sarcopenia on therapeutic response and prognosis of newly diagnosed acute myeloid leukemia (AML) patients, and reveal its predictive value for the clinical outcomes of AML patients. METHODS: A total of 122 AML patients who were initially diagnosed and treated with induction chemotherapy at the Department of Hematology in the Affiliated Hospital of Nantong University from January 2017 to December 2020 were included in this study. The sarcopenia was diagnosed by measuring body composition parameters with multifrequency bioelectrical impedance analyzer, and all AML patients were divided into sarcopenia and non-sarcopenia groups. Kaplan-Meier curves and log-rank test were used to compare the survival difference between the two groups. The relationship between sarcopenia and overall survival (OS) of AML patients was further determined by the univariate and multivariate Cox regression analysis. RESULTS: Among 122 AML patients, 46 (37.7%) were diagnosed with sarcopenia before induction chemotherapy. The body mass index (BMI) of patients with sarcopenia was significantly lower than that of non-sarcopenia patients ( t =4.258, P <0.001), and the complete response (CR) and partial response (PR) rates of sarcopenia patients after induction chemotherapy were significantly lower than those of nonsarcopenia patients (χ²=6.348, P =0.042). Kaplan-Meier curves showed that sarcopenic patients had a shorter OS than non-sarcopenic patients, and the median OS of the two groups were 20.7 (95%CI : 12.6-27.8) months and 27.8 (95%CI : 22.3-31.9) months, respectively (χ²= 5.659, P =0.017). Subgroup analysis indicated that the median OS of sarcopenic and non-sarcopenic AML patients who received standard induction chemotherapy were 12.2 (95%CI : 5.4-24.7) months and 26.1 (95%CI : 16.7-35.4) months, respectively (χ²=3.949, P =0.047). The multivariate Cox regression analysis revealed that sarcopenia (HR=1.671, 95%CI : 1.034-2.701, P =0.036) was an independent predictor for poor prognosis in AML patients. CONCLUSION Sarcopenia is significantly associated with low response rate of induction chemotherapy and poor prognosis in AML patients, and it might be an useful tool for predicting the clinical outcome of AML patients.
Humans ; Sarcopenia/complications* ; Leukemia, Myeloid, Acute/diagnosis* ; Prognosis ; Male ; Female ; Induction Chemotherapy ; Middle Aged ; Body Mass Index ; Kaplan-Meier Estimate

OBJECTIVE: To investigate the clinical characteristics of overlapping syndromes of low muscle mass in Chinese patients with rheumatoid arthritis (RA) and their impact on physical function. METHODS: Consecutive patients with RA were recruited from September 2019 to April 2024 at Department of Rheumatology and Immunology, Sun Yat-Sen Memorial Hospital. Clinical data including disease acti-vity, physical function and radiographic assessment were collected. All patients also finished measurement of body composition, grip strength, and gait speed, and overlapping syndromes of low muscle mass as well as malnutrition, sarcopenia, sarcopenic obesity, and cachexia were evaluated. The Stanford health assessment questionnaire- disability index (HAQ-DI) was used to evaluate physical function. Logistic regression was used to analyze the related factors of physical dysfunction. RESULTS: A total of 1 016 RA patients were recruited. Their mean age was (52.4±12.5) years, and 82.5% were female. There were 557 cases (54.8%) with overlapping syndromes of low muscle mass and all of them were malnutrition. On this basis, 326 cases (32.1%) exhibited sarcopenia, 124 (12.2%) sarcopenic obesity, and 33 (3.2%) cachexia. There were 584 (57.4%) of RA patients having physical dysfunction, with varying degrees of severity 421 (41.4%) mild, 124 (12.2%) moderate, and 39 (3.8%) severe. Compared with patients without overlapping syndromes of low muscle mass (n=459) or with malnutrition only (n=231), RA patients with both malnutrition and sarcopenia (n=326) had significantly higher core disease activity indicators and higher rate of physical dysfunction (69.6% vs. 42.0% vs. 56.6%). However, compared with patients without overlapping syndromes of low muscle mass, patients with malnutrition only had lower HAQ-DI score (median 0.0 vs. 0.1) and lower rate of physical dysfunction (42.0% vs. 56.6%). Multivariate Logistic regression analysis showed that simultaneously overlapping malnutrition and sarcopenia were associated factors of physical dysfunction (OR=2.021, 95%CI: 1.067-3.828), but malnutrition only was not. CONCLUSION Simultaneously overlapping malnutrition and sarcopenia can deteriorate disease activity and physical dysfunction in RA patients. The screening and evaluation of overlapping syndromes of low muscle mass, especially sarcopenia should be emphasized in patients with RA.
Humans ; Arthritis, Rheumatoid/physiopathology* ; Female ; Male ; Sarcopenia/complications* ; Middle Aged ; Cachexia/diagnosis* ; Malnutrition/etiology* ; Obesity/physiopathology* ; Body Composition ; Syndrome ; Hand Strength ; Adult ; Muscle, Skeletal/physiopathology* ; Surveys and Questionnaires

BACKGROUND: Sarcopenia is an age-related progressive skeletal muscle disorder involving the loss of muscle mass or strength and physiological function. Efficient and precise AI algorithms may play a significant role in the diagnosis of sarcopenia. In this study, we aimed to develop a machine learning model for sarcopenia diagnosis using clinical characteristics and laboratory indicators of aging cohorts. METHODS: We developed models of sarcopenia using the baseline data from the West China Health and Aging Trend (WCHAT) study. For external validation, we used the Xiamen Aging Trend (XMAT) cohort. We compared the support vector machine (SVM), random forest (RF), eXtreme Gradient Boosting (XGB), and Wide and Deep (W&D) models. The area under the receiver operating curve (AUC) and accuracy (ACC) were used to evaluate the diagnostic efficiency of the models. RESULTS: The WCHAT cohort, which included a total of 4057 participants for the training and testing datasets, and the XMAT cohort, which consisted of 553 participants for the external validation dataset, were enrolled in this study. Among the four models, W&D had the best performance (AUC = 0.916 ± 0.006, ACC = 0.882 ± 0.006), followed by SVM (AUC =0.907 ± 0.004, ACC = 0.877 ± 0.006), XGB (AUC = 0.877 ± 0.005, ACC = 0.868 ± 0.005), and RF (AUC = 0.843 ± 0.031, ACC = 0.836 ± 0.024) in the training dataset. Meanwhile, in the testing dataset, the diagnostic efficiency of the models from large to small was W&D (AUC = 0.881, ACC = 0.862), XGB (AUC = 0.858, ACC = 0.861), RF (AUC = 0.843, ACC = 0.836), and SVM (AUC = 0.829, ACC = 0.857). In the external validation dataset, the performance of W&D (AUC = 0.970, ACC = 0.911) was the best among the four models, followed by RF (AUC = 0.830, ACC = 0.769), SVM (AUC = 0.766, ACC = 0.738), and XGB (AUC = 0.722, ACC = 0.749). CONCLUSIONS: The W&D model not only had excellent diagnostic performance for sarcopenia but also showed good economic efficiency and timeliness. It could be widely used in primary health care institutions or developing areas with an aging population. TRIAL REGISTRATION Chictr.org, ChiCTR 1800018895.
Humans ; Aged ; Sarcopenia/diagnosis* ; Deep Learning ; Aging ; Algorithms ; Biomarkers

OBJECTIVES: Patients with head and neck cancer (HNC) have a high risk of sarcopenia, which is associated with poor prognosis. Skeletal-muscle area and index at the third lumbar (L3) vertebra level (L3MA and L3MI) are recommended for the detection of sarcopenia. However, L3 level is not included in many imaging protocols and there are no data for optimal levels and cutoffs for the diagnosis of sarcopenia in head and neck computed tomography (HNCT) scans. Our aim was to assess the relationship between cervical paravertebral muscle values and L3MI and to investigate optimal level to diagnose sarcopenia on HNCTs. METHODS: Patients with HNC (n=159) who underwent positron emission tomography-CT for tumor staging were retrospectively analyzed. On CT images, paravertebral and sternocleidomastoid muscle areas at second (C2), third (C3), and fourth (C4) cervical vertebrae levels (C2MA, C3MA, C4MA, SCMA) and L3MA were measured. Cross-sectional areas were normalized for stature (muscle area/height square) and muscle index (C2MI, C3MI, C4MI, SCMI, L3MI) values were obtained. Spearman correlation and linear regression analyses were used for assessing correlations. To calculate the diagnostic performance of SCMI, C2MI, C3MI, and C4MI for the diagnosis of sarcopenia with respect to the cutoffs of L3MI, receiver operating characteristic (ROC) analysis was used. RESULTS: Males had significantly higher muscle areas than females. Although C2MI, C3MI, C4MI, and SCMI values all showed very strong and significant correlation with L3MI (P<0.001). According to the ROC analysis, the best discriminative for sarcopenia was C3MI in males (area under curve [AUC], 0.967) and SCMI in females (AUC, 0.898). CONCLUSION: C2MI, C3MI, C4MI, and SCMI values can be used as alternatives for the diagnosis of sarcopenia in routine HNCT examinations.
Body Mass Index ; Cervical Vertebrae ; Diagnosis ; Electrons ; Female ; Head and Neck Neoplasms ; Head ; Humans ; Image Processing, Computer-Assisted ; Linear Models ; Male ; Neck ; Neoplasm Staging ; Prognosis ; Retrospective Studies ; ROC Curve ; Sarcopenia ; Spine

BACKGROUND: We determined the prevalence of sarcopenia according to fracture site and evaluated the associated risk factors in female patients with osteoporotic fractures. METHODS: A total of 108 patients aged 50 years or older with an osteoporotic fracture (hip, spine, or wrist) were enrolled in this retrospective observational study. A diagnosis of sarcopenia was confirmed using whole-body densitometry for skeletal muscle mass measurement. Logistic regression analysis was used to analyze the risk factors for sarcopenia. RESULTS: Of 108 female patients treated for osteoporotic fractures between January 2016 and June 2017, sarcopenia was diagnosed in 39 (36.1%). Of these, 41.5% (17/41) had hip fractures, 35% (14/40) had spine fractures, and 29.6% (8/27) had distal radius fractures. Body mass index (BMI; P=0.036) and prevalence of chronic kidney disease (CKD; P=0.046) and rheumatoid arthritis (P=0.051) were significantly different between the groups. In multivariable analysis, BMI (odds ratio [OR], 0.76; 95% confidence interval [CI], 0.55–1.05, P=0.098) and CKD (OR 2.51; 95% CI, 0.38–16.2; P=0.233) were associated with an increased risk of sarcopenia; however, this was not statistically significant. CONCLUSIONS: This study evaluated the prevalence of sarcopenia according to the fracture site and identified associated risk factors in patients with osteoporotic fractures. A longterm, observational study with a larger population is needed to validate our results.
Arthritis, Rheumatoid ; Body Mass Index ; Densitometry ; Diagnosis ; Female ; Hip Fractures ; Humans ; Logistic Models ; Muscle, Skeletal ; Observational Study ; Osteoporosis ; Osteoporotic Fractures ; Prevalence ; Radius Fractures ; Renal Insufficiency, Chronic ; Retrospective Studies ; Risk Factors ; Sarcopenia ; Spine

BACKGROUND: We sought to develop a novel index based on the skeletal muscle mass that reflects the change of quality of life (QOL), and is the most appropriate index for the body composition of the elderly in Korea. Whether lower extremity skeletal muscle mass index (LESMI) is an appropriate novel new index to diagnose patients with sarcopenia was also evaluated. A cut-off value for each index was reported to facilitate the diagnosis of patients with sarcopenia in a Korean population. METHODS: We used the 5th Korean National Health and Nutrition Examination Survey data from 2010. We analyzed 409 elderly patients, including 231 men and 178 women, aged ≥65 years. Patients were diagnosed by calculating their skeletal muscle index based on the skeletal muscle mass measured using dual energy X-ray absorptiometry. Obesity and osteoporosis were used to screen data and EuroQOL-5 dimension as a health questionnaire. RESULTS: The prevalence of sarcopenia in each index was obtained based on its cut-off value for diagnosing sarcopenia. There was a significant difference between the obesity rate of elderly patients diagnosed with sarcopenia and those who were not based on each index. There was no significant difference in the prevalence of osteoporosis between the groups. Sarcopenia diagnosis based on the LESMI was significantly correlated with QOL. CONCLUSIONS: LESMI, a novel index based on skeletal muscle mass, reflects changes in QOL and is appropriate for the body composition of elderly people in Korea.
Absorptiometry, Photon ; Aged ; Body Composition ; Diagnosis ; Female ; Humans ; Korea ; Lower Extremity ; Male ; Muscle, Skeletal ; Nutrition Surveys ; Obesity ; Osteoporosis ; Prevalence ; Quality of Life ; Sarcopenia

Sarcopenia was listed in the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) as M62.84, on October 1, 2016. Sarcopenia is primarily associated with metabolic diseases, such as diabetes, obesity, and cachexia, as well as chronic renal failure, congestive heart failure, and chronic obstructive pulmonary disease. Sarcopenia is also significantly associated with osteoporosis in elderly populations and the combined disease is defined as osteosarcopenia. Several studies have confirmed that sarcopenia and osteoporosis (osteosarcopenia) share common risk factors and biological pathways. Osteosarcopenia is associated with significant physical disability, representing a significant threat to the loss of independence in later life. However, the pathophysiology and diagnosis of osteosarcopenia are not fully defined. Additionally, pharmacologic and hormonal treatments for sarcopenia are undergoing clinical trials. This review summarizes the epidemiology, pathophysiology, diagnosis, and treatment of osteosarcopenia, and includes Korean data.
Aged ; Cachexia ; Diagnosis ; Epidemiology ; Heart Failure ; Humans ; International Classification of Diseases ; Kidney Failure, Chronic ; Korea ; Metabolic Diseases ; Obesity ; Osteoporosis ; Pulmonary Disease, Chronic Obstructive ; Risk Factors ; Sarcopenia

OBJECTIVES: This study evaluated the relationship between the skeletal muscle mass (SMM), obtained by predictive equations, and the body composition, nutritional aspects, functionality and physical performance in elderly people. METHODS: The sample consisted of adults aged 65 years or over from the cross-sectional study of the Brazilian Elderly Frailty Study Network, in Cuiabá, Mato Grosso State, Brazil. The anthropometric parameters, instrumental activities of daily living (IADL), Short Physical Performance Battery (SPPB), and handgrip strength (HGS) were evaluated. The SMM was estimated by 2 predictive anthropometric equations. RESULTS: Both SMM equations correlated with age, anthropometric indices, SPPB, IADL, and HGS. However, only HGS and neck circumference strongly correlated in both equations, being higher in SMM II. CONCLUSIONS: It seems that both equations are sensitive to obtain the SMM, contributing to the diagnosis of sarcopenia, nutritional status, and a physical performance condition.
Activities of Daily Living ; Adult ; Aged ; Body Composition ; Brazil ; Cross-Sectional Studies ; Diagnosis ; Humans ; Muscle, Skeletal ; Neck ; Nutritional Status ; Sarcopenia

BACKGROUND/AIMS: The most widely used method for diagnosing sarcopenia is the skeletal muscle index (SMI). Several studies have suggested that psoas muscle thickness per height (PMTH) is also effective for detecting sarcopenia and predicting prognosis in patients with cirrhosis. The aim of this study was to evaluate the optimal cutoff values of PMTH for detecting sarcopenia in cirrhotic patients. METHODS: All cirrhotic patients who underwent abdominal computed tomography (CT) scan including L3 and umbilical levels for measuring SMI and transverse psoas muscle thickness, respectively, were included. Two definitions of sarcopenia were used: (1) sex-specific cutoffs of SMI (≤52.4 cm² /m² in men and ≤38.5 cm² /m² in women) for SMI-sarcopenia and (2) cutoff of PMTH ( < 16.8 mm/m) for PMTH-sarcopenia. RESULTS: Six hundred fifty-three patients were included. The average age was 53.6 ± 10.2 years, and 499 patients (76.4%) were men. PMTH correlated well with SMI in both men and women (P < 0.001). Two hundred forty-one (36.9%) patients met the criteria for SMI-sarcopenia. The best PMTH cutoff values for predicting SMI-sarcopenia were 17.3 mm/m in men and 10.4 mm/m in women, and these were defined as sex-specific cutoffs of PMTH (SsPMTH). The previously published cutoff of PMTH was defined as sex-nonspecific cutoff of PMTH (SnPMTH). Two hundred thirty (35.2%) patients were diagnosed with SsPMTH-sarcopenia, and 280 (44.4%) patients were diagnosed with SnPMTH-sarcopenia. On a multivariate Cox regression analysis, SsPMTH-sarcopenia (hazard ratio [HR], 1.944; 95% confidence interval [CI], 1.144–3.304; P=0.014) was significantly associated with mortality, while SnPMTH-sarcopenia was not (HR, 1.446; 95% CI, 0.861–2.431; P=0.164). CONCLUSIONS: PMTH was well correlated with SMI in cirrhotic patients. SsPMTH-sarcopenia was an independent predictor of mortality in these patients and more accurately predicted mortality compared to SnPMTH-sarcopenia.
Diagnosis* ; Female ; Fibrosis ; Humans ; Liver Cirrhosis* ; Liver* ; Male ; Methods ; Mortality ; Muscle, Skeletal ; Prognosis ; Psoas Muscles* ; Sarcopenia*

OBJECTIVE: Body composition is measured using bioelectrical impedance analysis (BIA), and correlation between the result of BIA and cognitive function is analyzed. METHODS: A total of 118 elderly (46 male, 72 female) were recruited. They were divided into three groups; normal (n=33), mild cognitive impairment (n=42), and Alzheimer's dementia (n=43) according to the diagnostic criteria. Skeletal muscle mass, body fat mass, and fat-free mass were measured using a BIA device, and were converted to the ratio of body weight. All participants underwent Korean version of Mini-Mental State Examination (MMSE-K). RESULTS: In pearson correlation analysis, skeletal muscle percentage (SMP) and fat-free mass percentage (FFMP) were positively correlated with MMSE-K score (r=0.309, p=0.001; r=0.245, p=0.008), and body fat percentage was negatively correlated (r=−0.258, p=0.005). In multiple regression analysis, SMP (β=2.012, t=4.457, p < 0.001) and FFMP (β=−1.733, t=−3.838, p < 0.001) were selected as the best predictors of changes in MMSE-K score (R2=0.198). CONCLUSION: Reduced skeletal muscle and increased body fat correlate with decreased cognitive function, suggesting the need for prevention of frailty and early diagnosis of cognitive impairment.
Adipose Tissue ; Aged ; Body Composition* ; Body Weight ; Cognition Disorders ; Cognition* ; Dementia ; Early Diagnosis ; Electric Impedance* ; Humans ; Male ; Mild Cognitive Impairment ; Muscle, Skeletal ; Sarcopenia