ABSTRACT
OBJECTIVE: this retrospective study aims to compare the prevalence and diagnostic agreement of sarcopenic obesity (SO) using different obesity diagnostic methods among Chinese community-dwelling older adults. METHODS: SO was diagnosed with sarcopenia and obesity diagnostic methods. Sarcopenia was defined using the Asian Working Group for Sarcopenia criteria 2019 (AWGS2019). Four widely used indicators were used to define obesity: body mass index (BMI), waist circumference (WC), percent of body fat (PBF) and visceral fat area (VFA). Cohen's kappa was used to analyse the diagnosis agreement of SO between different diagnostic methods. RESULTS: a total of 1,050 participants were included, including 347 men (71.3 ± 7.4 years) and 703 women (69.9 ± 7.5 years). The prevalence of sarcopenia was 25% in total participants, there was no difference between men (24.2%) and women (25.5%), (P = 0.705). With different obesity diagnostic methods, the obesity prevalence ranged from 4.1 to 42.2%, the SO prevalence was 0.1-7.9%. The diagnosis agreement of SO was poor-to-moderate (κ ranged from -0.002 to 0.682). Among the four diagnostic methods, AWGS combined with BMI had the poorest agreement (κ = -0.002 with other methods), AWGS combined with VFA had the best agreement (κ = 0.641 and 0.682 with AWGS combined with PBF and with AWGS combined with WC, respectively). CONCLUSION: the prevalence of SO vary considerably and the diagnostic agreement is poor-to-moderate with non-uniform diagnostic methods. BMI has the lowest sensitivity, whereas VFA has the highest sensitivity in diagnosis of SO, and VFA has a relatively good diagnostic agreement with other diagnostic methods.
Subject(s)
Sarcopenia , Aged , Body Mass Index , China/epidemiology , Cross-Sectional Studies , Female , Humans , Independent Living , Male , Obesity/diagnosis , Obesity/epidemiology , Prevalence , Retrospective Studies , Sarcopenia/diagnosis , Sarcopenia/epidemiologyABSTRACT
Background and Objectives: This study aims to detect the prevalence of sarcopenia in community-dwelling older adults in Hunan Province, discuss factors related to lifestyle, and provide a reliable basis for the prevention and treatment of sarcopenia. Materials and Methods: In this study, a total of 1040 community-dwelling adults ≥ 60 years were examined for sarcopenia using a cluster stratified random sampling method, which was defined using the diagnostic criteria recommended by the Asian Working Group for Sarcopenia (AWGS) from September 2019 to March 2020. Multivariate logistic regression analysis was applied to determine the correlation between sarcopenia and smoking, drinking, nutritional status, physical activity, and sleep quality. Results: A total of 27.1% of the older adults were diagnosed with sarcopenia, with rates of 26.2% in men and 25.2% in women. Multiple logistic regression showed that advanced age (OR = 2.480, 95% CI: 1.730, 3.553), the risk of malnutrition (OR = 2.085, 95% CI: 1.440, 3.019), and malnutrition (OR = 1.212, 95% CI: 0.304, 4.834) were risk factors for sarcopenia. No falls in the previous year (OR = 0.616, 95% CI: 1.885, 1.209), normal weight (OR = 0.228, 95% CI: 0.109, 0.475), overweight (OR = 0.030, 95% CI: 0.013, 0.069), moderate physical activity (OR = 0.593, 95% CI: 0.377, 0.933), or high physical activity (OR = 0.417, 95% CI: 0.230, 0.755) were identified as protective factors for sarcopenia. Conclusions: The prevalence of sarcopenia was high among older adults in the community in Hunan Province. In addition, we found that lifestyle is an important factor in sarcopenia.
Subject(s)
Malnutrition , Sarcopenia , Male , Humans , Female , Middle Aged , Aged , Sarcopenia/diagnosis , Sarcopenia/epidemiology , Sarcopenia/etiology , Independent Living , Prevalence , Life StyleABSTRACT
OBJECTIVE: To establish and validate a nomogram that predicts the risk of sarcopenia for community-dwelling older residents. DESIGN: Retrospective study. SETTING AND PARTICIPANTS: A total of 1050 community-dwelling older adults. METHODS: Data from a survey of community-dwelling older residents (≥60 years old) in Hunan, China, from June to September 2019 were retrospectively analyzed. The survey included general demographic information, diet, and exercise habits. Sarcopenia diagnosis was according to 2019 Asian Working Group for Sarcopenia criteria. Participants were randomly divided into the development group and validation groups. Independent risk factors were screened by multivariate logistic regression analysis. Based on the independent risk factors, a nomogram model was developed to predict the risk of sarcopenia for community-dwelling older adults. Both in the development and validation sets, the discrimination, calibration, and clinical practicability of the nomogram were verified using receiver operating characteristic curve analysis, Hosmer-Lemeshow test, and decision curve analysis, respectively. RESULTS: Sarcopenia was identified in 263 (25.0%) participants. Age, body mass index, marital status, regular physical activity habit, uninterrupted sedentary time, and dietary diversity score were significant contributors to sarcopenia risk. A nomogram for predicting sarcopenia in community-dwelling older adults was developed using these factors. Receiver operating characteristic curve analysis showed that the area under the curve was 0.827 (95% CI 0.792-0.860) and 0.755 (95% CI 0.680-0.837) in the development and validation sets, respectively. The Hosmer-Lemeshow test yielded P values of .609 and .565, respectively, for the 2 sets. The nomogram demonstrated a high net benefit in the clinical decision curve in both sets. CONCLUSIONS AND IMPLICATIONS: This study developed and validated a risk prediction nomogram for sarcopenia among community-dwelling older adults. Sarcopenia risk was classified as low (<11%), moderate (11%-70%), and high (>70%). This nomogram provides an accurate visual tool to medical staff, caregivers, and older adults for prediction, early intervention, and graded management of sarcopenia.
Subject(s)
Sarcopenia , Aged , Humans , Independent Living , Middle Aged , Nomograms , Retrospective Studies , Risk Factors , Sarcopenia/diagnosisABSTRACT
OBJECTIVE: The 2019 Asian Working Group on Sarcopenia in Older People (AWGS 2019) recommends using either calf circumference or the strength, assistance in walking, rising from a chair, climbing stairs, and falls (SARC-F) or SARC-F combined with calf circumference (SARC-CalF) questionnaires for sarcopenia screening. The aim of this study was to compare the ability and applicability of calf circumference, SARC-F, and SARC-CalF for screening sarcopenia among community-dwelling older adults. DESIGN: Cross-sectional study. SETTING AND PARTICIPANTS: A total of 1050 community-dwelling older people were enrolled. METHODS: Sarcopenia was diagnosed according to the AWGS 2019 criteria: bioimpedance analysis for appendicular skeletal muscle index, hand grip, and 6-m gait speed test. Participants also completed the SARC-F questionnaire and calf circumference measurement. The screening tools' performances were evaluated through receiver operating characteristic (ROC) curves, area under the ROC curves (AUC), and sensitivity/specificity analyses. RESULTS: Sarcopenia was identified in 263 (25.0%) participants by the AWGS 2019 criteria. Calf circumference had a sensitivity of 81.4% and a specificity of 77.0%. Sensitivity and specificity of SARC-F for screening sarcopenia were 17.9% and 93.7%, respectively. SARC-CalF improved the sensitivity of SARC-F (47.5%) while keeping similar specificity (92.0%). The AUCs of calf circumference, SARC-F, and SARC-CalF were 0.79 [95% confidence interval (CI), 0.77-0.82], 0.56 (95% CI, 0.52-0.59), and 0.70 (95% CI, 0.67-0.73), respectively. The differences across ROC curves were statistically significant among 3 screening tools (P < .001). CONCLUSIONS AND IMPLICATIONS: The overall screening ability of calf circumference was better than that of SARC-F and SARC-CalF for sarcopenia in community-dwelling older persons despite gender, age, and cognitive function. SARC-F and SARC-CalF have high specificity but are susceptible to the preceding factors.