Associations between different measurements of sarcopenic obesity and health outcomes among non-frail community-dwelling older adults in Taiwan. Issue 11 (14th December 2021)

• Record Type: Journal Article
• Authors: Peng, Tao-Chun; Chen, Wei-Liang; Chen, Yuan-Yuei; Chao, Yuan-Ping; Wu, Li-Wei; Kao, Tung-Wei
• Is Part Of: British journal of nutrition. Volume 126:Issue 11(2021)
• Page Start: 1749
• Page End: 1757
• Publication Date: 2021-12-14
• Subjects: Sarcopenia -- Obesity -- Fall -- Metabolic syndrome; Nutrition -- Periodicals; 572.4
• DOI: 10.1017/S0007114521001288
• Languages: English
• ISSNs: 0007-1145

Abstract

Abstract: The most important issue for the clinical application of sarcopenic obesity (SO) is the lack of a consensus definition. The aim of the present study was to determine the best measurement for SO by estimating the association between various definitions and the risk of falls and metabolic syndrome (MS). We studied a community of 765 adults aged 65 years and older in 2015–2017. Sarcopenia obesity was measured by sarcopenia (defined by low muscle mass with either low handgrip strength or low gait speed or both) plus obesity (defined by waist circumference, body fat percentage and BMI). The MS was defined according to the National Cholesterol Education Program ATP III. Logistic regression models were constructed to examine the relationships between sarcopenia obesity and risk of fall and MS. In the analysis of the fall risk with SO defined by waist circumference, the participants with non-sarcopenia/non-obesity were treated as the reference group. The OR to fall in participants with SO was 10·16 (95 % CI 2·71, 38·13) after adjusting for confounding covariates. In the analysis of the risk of the MS between participants with individual components of sarcopenia coupled with obesity defined by waist circumference, the risk was statistically significant for low gait speed (OR: 7·19; 95 % CI 3·61, 14·30) and low grip strength (OR: 9·19; 95 % CI 5·00, 16·91). A combination of low grip strength and abdominal obesity for identifying SO may be a more precise and practical method for predicting target populations with unfavourable health risks, such as falls risk and MS.