An Anthropometric Prediction Equation for Appendicular Skeletal Muscle Mass in Combination With a Measure of Muscle Function to Screen for Sarcopenia in Primary and Aged Care

Yu, Solomon; Appleton, Sarah; Chapman, Ian; Adams, Robert; Wittert, Gary; Visvanathan, T; Visvanathan, Renuka

doi:10.1016/j.jamda.2014.06.018

Cited by 46 publications

(41 citation statements)

References 16 publications

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“…Therefore, this study assessed the average of muscle mass index based on body high from this study subjects was 6. The value of this muscle mass index is higher than elderly subjects in China (Wang et al, 2016) but lower than other research (Santana et al, 2015;Yu et al, 2015). Although the value of muscle mass index in this study was lower (Santana et al, 2015;Yu et al, 2015) and stated in the categorization of subjects with muscle weakness, but the muscle mass index value this study was above the cut-off value of low muscle used in the Yu study (5.35 kg/m2) (Yu et al, 2015).…”

Section: Resultscontrasting

confidence: 68%

“…The value of this muscle mass index is higher than elderly subjects in China (Wang et al, 2016) but lower than other research (Santana et al, 2015;Yu et al, 2015). Although the value of muscle mass index in this study was lower (Santana et al, 2015;Yu et al, 2015) and stated in the categorization of subjects with muscle weakness, but the muscle mass index value this study was above the cut-off value of low muscle used in the Yu study (5.35 kg/m2) (Yu et al, 2015). This result is supported by a study which states that the mean of muscle mass index of this study (6.69kg / m2) is not considered as sarcopenia diagnoses by Asian Working Group for Sarcopenia (AWGS) since the cut-off rate of muscle mass for sarcopenia diagnosis is above 5.7 kg / m (Chen et al, 2014).…”

Section: Resultscontrasting

confidence: 58%

“…This is related to studies which suggest that muscle mass is strongly correlated with calf circumference values (Kawakami et al, 2015). Low muscle mass in low boundaries might be due to racial differences, educational level, and lifestyle (nutrition and activity) which will influence nutritional status characteristic such as body height, body mass index, as well as due to differences in muscle mass measurement instruments used (Marcus et al, 2012;Legrand et al, 201;Auyeung et al 2014;Han et al, 2016;Yu et al, 2015). Studies showed that racial differences influence muscle mass.…”

Section: Resultsmentioning

confidence: 91%

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Anthropometric Prediction Equations for Estimating Muscle Mass of Elderly Women

Handayani

Sadewa

Farmawati

et al. 2018

Kemas

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Muscle Mass (MM) has an important role in health and physical performance. There are many MM prediction equations, but none is formulated in Indonesia. This study aimed to develop Anthropometric Equations (AE) prediction for MM. A cross sectional study was used to formulate AE prediction through multiple regression analysis. The significance of observed differences between predicted and actual MM was tested by t test while level of agreement was assessed by Bland Altman plot. A significant correlation was found between MM and height, body mass index, calf/arm/waist circumferences, and waist hip ratio (p<0.05). Regression anal¬ysis indicated that age, height, and Mid Arm Circumference (MAC) contributed significantly to MM. The resulting equation was MM (kg) = -10.22+(-.097x age)+(0.16xheight)+(0.30xMAC). There was no significant difference between actual and predicted MM results, and both had significant correlation. These results suggest that age, AP related to MM and AE provide valid prediction of MM for healthy elderly women in Jakarta.

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Section: Resultscontrasting

confidence: 68%

Section: Resultscontrasting

confidence: 58%

Section: Resultsmentioning

confidence: 91%

See 1 more Smart Citation

Anthropometric Prediction Equations for Estimating Muscle Mass of Elderly Women

Handayani

Sadewa

Farmawati

et al. 2018

Kemas

View full text Add to dashboard Cite

show abstract

“…9 With dual energy X-ray absorptiometric technique, muscle mass can be estimated by the formula "skeletal muscle mass index = appendicular skeletal muscle mass (kg)/height 2 (m 2 )", with sarcopenia defined as values lower than 7.36 kg/ m 2 for men and 5.81 kg/m 2 for women. 10 Based on the fact that sarcopenia and obesity act synergistically on metabolic and functional impairments in elderly, the formula "appendicular muscle mass/body weight" was used to define sarcopenic obesity. 11,12 However, Bret in 1997 proposed that upper body fat distribution heightens the risk for insulin resistance and metabolic syndrome.…”

Section: Discussionmentioning

confidence: 99%

Association between Muscle Mass and Body Mass Index in Elderly Diabetic Patients Attending Tertiary Care Center in Bangalore, India

Palanisami

Kulkarni

2016

Int J Med Students

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Background: Sarcopenia is a disorder causing age-related loss of muscle mass. Its multifaceted nature has been linked to an increased risk of disability and mortality. Equally, obesity is a well-known risk factor for a host of disorders. A combination of sarcopenia and obesity in elderly diabetics can synergistically lead to increased insulin resistance and risk of metabolic syndrome. This study aimed to identify the association between sarcopenia and obesity in elderly diabetic patients by a cost-effective anthropometric method. Methods: A case-control study was conducted from January 2016 to April 2016 at Dr. B. R. Ambedkar Medical College in Bangalore. Height, weight, mid-arm circumference, and triceps skin fold thickness of 112 diabetic patients and 131 healthy adults were measured. Descriptive statistical analysis and multiple linear regression analysis were carried out. Results: 26.8% of male and 76.8% of female diabetic patients were obese (body mass index ≥25 kg/m2). Incidence of sarcopenia (muscle mass one standard deviation smaller than healthy reference population, cut-off value for diabetic males being <9.79 kg/m2 and for diabetic females <8.53 kg/m2) were 12.5% in male diabetic patients and 5.4% in female diabetic patients. Conclusion: Sarcopenia and obesity are co-morbid illnesses which can cause functional and metabolic impairments in elderly diabetic patients. There exists a moderate association between muscle mass and body mass index. Loss of muscle strength (dynapenia), rather than loss of muscle mass (sarcopenia), is closely associated with disabilities in these patients.

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“…Goodman et al , utilizing the NHANES data, suggested that Body Mass Index (BMI) is a reasonable proxy as a skeletal muscle index. Yu et al . utilized equations using BMI, weight, and age and showed that these are excellent predictive equations for skeletal muscle mass.…”

Section: Sarc‐f Screen For Sarcopeniamentioning

confidence: 99%