Are sociodemographic and anthropometric variables effective in screening probable and confirmed sarcopenia in community-dwelling older adults? A cross-sectional study

Souza, Larissa Franciny de; Fontanela, Laís Coan; Leopoldino, Amanda Aparecida Oliveira; Mendonça, Vanessa Amaral; Danielewicz, Ana Lúcia; Lacerda, Ana Cristina Rodrigues; Avelar, Núbia Carelli Pereira de

doi:10.1590/1516-3180.2022.0141.r1.17082022

Cited by 4 publications

(1 citation statement)

References 39 publications

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“…Thus, divergences in the prevalence of probable sarcopenia across studies may also be related to the socioeconomic conditions of the populations studied. Notably, previous research by our group identified cutoff points for sociodemographic and anthropometric variables in screening for probable and confirmed sarcopenia in community-dwelling older adults [34]. In addition, Kim and Won (2019) found a high prevalence of confirmed sarcopenia in older Korean women (14.4%) when using 5XSST + ASM, which is in line with the present results (27.5%) [35].…”

Section: Discussionsupporting

confidence: 92%

Prevalence of sarcopenia in older women and level of agreement between the diagnostic instruments proposed by the European Working Group on Sarcopenia in Older People 2 (EWGSOP2)

Sutil

Parentoni

Teixeira

et al. 2023

BMC Musculoskelet Disord

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Background The European Working Group on Sarcopenia in Older People 2 (EWGSOP2) proposed the use of different diagnostic tools to assess sarcopenia. This study aimed to determine prevalence rates of sarcopenia according to the diagnostic instruments proposed by EWGSOP2 and to assess their level of agreement in older Brazilian women. Methods A cross-sectional study with 161 community-dwelling older Brazilian women. Probable sarcopenia was assessed through Handgrip Strength (HGS) and the 5-times sit-to-stand test (5XSST). In addition to reduced strength, Appendicular Skeletal Muscle Mass (ASM) (obtained by Dual-energy X-ray absorptiometry) and ASM/height² were considered for diagnosis confirmation. Sarcopenia severity was determined by reduced muscle strength and mass and poor functional performance assessed by Gait Speed (GS), Short Physical Performance Battery (SPPB), and Timed Up and Go test (TUG). McNemar’s test and Cochran’s Q-test were used to compare sarcopenia prevalence. Cohen’s Kappa and Fleiss’s Kappa tests were used to assess the level of agreement. Results The prevalence of probable sarcopenia was significantly different (p < 0.05) when using HGS (12.8%) and 5XSST (40.6%). Regarding confirmed sarcopenia, the prevalence was lower when using ASM/height² than with ASM. Regarding severity, the use of SPPB resulted in a higher prevalence in relation to GS and TUG. Conclusion There were differences in the prevalence rates of sarcopenia and low agreement between the diagnostic instruments proposed by the EWGSOP2. The findings suggest that these issues must be considered in the discussion on the concept and assessment of sarcopenia, which could ultimately help to better identify patients with this disease in different populations.

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Section: Discussionsupporting

confidence: 92%

Prevalence of sarcopenia in older women and level of agreement between the diagnostic instruments proposed by the European Working Group on Sarcopenia in Older People 2 (EWGSOP2)

Sutil

Parentoni

Teixeira

et al. 2023

BMC Musculoskelet Disord

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Association between Auricularia auricula-judae consumption and sarcopenia: A Cross-Sectional Study

Niu,

Li,

Wang

et al. 2023

Preprint

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Background: Several studies have shown that Auricularia auricula-judae (AAJ) can reduce inflammation and oxidative stress. Although these factors are closely involved in the pathogenesis of sarcopenia, limited research has focused on the association between AAJconsumption and sarcopenia in the general population. We aimed to investigate the potential association between AAJ consumption and sarcopenia in the elderly general population. Methods: We conducted a cross-sectional analysis using data from the Tianjin Chronic Low-grade Systemic Inflammation and Health (TCLSIH) study, which included 4,034 participants aged 60 years or older. The diagnosis of sarcopenia was based on the Asian Working Group for Sarcopenia 2019 (AWGS 2019) criteria. AAJ consumption was assessed using a validated self-administered 100-item food frequency questionnaire. Multiple logistic regression model and Analysis of covariance (ANCOVA) was employed to examine the effect of AAJ consumption on sarcopenia. Results: There were 548 (15.1%) sarcopenia cases. In males, after adjusting for confounding factors, the odds ratios (ORs) (95% confidence interval [CI]) for sarcopenia decreased with increasing frequency of AAJ intake: 1.00 (reference) for almost never, 0.79 (0.53, 1.18) for ≤1 time/week, 0.62 (0.41, 0.93) for ≥2-3 times/week (P for trend=0.02). However, in females, no significant association between AAJ consumption and sarcopenia was identified. Similar results were also found in sarcopenia components. Conclusions: Our findings suggested that AAJ consumption was inversely associated with the prevalence of sarcopenia in Chinese elderly males but not in elderly females. The exact mechanism underlying this association warrants further investigations.

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Deep-learning model for predicting physical fitness in possible sarcopenia: analysis of the Korean physical fitness award from 2010 to 2023

Bae

Seo

Kim

2023

Front. Public Health

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IntroductionPhysical fitness is regarded as a significant indicator of sarcopenia. This study aimed to develop and evaluate a deep-learning model for predicting the decline in physical fitness due to sarcopenia in individuals with potential sarcopenia.MethodsThis study used the 2010–2023 Korean National Physical Fitness Award data. The data comprised exercise- and health-related measurements in Koreans aged >65 years and included body composition and physical fitness variables. Appendicular muscle mass (ASM) was calculated as ASM/height2 to define normal and possible sarcopenia. The deep-learning model was created with EarlyStopping and ModelCheckpoint to prevent overfitting and was evaluated using stratified k-fold cross-validation (k = 5). The model was trained and tested using training data and validation data from each fold. The model’s performance was assessed using a confusion matrix, receiver operating characteristic curve, and area under the curve. The average performance metrics obtained from each cross-validation were determined. For the analysis of feature importance, SHAP, permutation feature importance, and LIME were employed as model-agnostic explanation methods.ResultsThe deep-learning model proved effective in distinguishing from sarcopenia, with an accuracy of 87.55%, precision of 85.57%, recall of 90.34%, and F1 score of 87.89%. Waist circumference (WC, cm), absolute grip strength (kg), and body fat (BF, %) had an influence on the model output. SHAP, LIME, and permutation feature importance analyses revealed that WC and absolute grip strength were the most important variables. WC, figure-of-8 walk, BF, timed up-and-go, and sit-and-reach emerged as key factors for predicting possible sarcopenia.ConclusionThe deep-learning model showed high accuracy and recall with respect to possible sarcopenia prediction. Considering the need for the development of a more detailed and accurate sarcopenia prediction model, the study findings hold promise for enhancing sarcopenia prediction using deep learning.

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Are sociodemographic and anthropometric variables effective in screening probable and confirmed sarcopenia in community-dwelling older adults? A cross-sectional study

Cited by 4 publications

References 39 publications

Prevalence of sarcopenia in older women and level of agreement between the diagnostic instruments proposed by the European Working Group on Sarcopenia in Older People 2 (EWGSOP2)

Prevalence of sarcopenia in older women and level of agreement between the diagnostic instruments proposed by the European Working Group on Sarcopenia in Older People 2 (EWGSOP2)

Association between Auricularia auricula-judae consumption and sarcopenia: A Cross-Sectional Study

Deep-learning model for predicting physical fitness in possible sarcopenia: analysis of the Korean physical fitness award from 2010 to 2023

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