Cutoff points of adiposity anthropometric indices for low muscle mass screening in middle-aged and older healthy women

• Published in: BMC musculoskeletal disorders Vol. 22; no. 1; pp. 1 - 713
• Main Authors: do Nascimento, Rafaela Andrade, Vieira, Mariana Carmem Apolinario, dos Santos Aguiar Goncalves, Rafaella Silva, Moreira, Mayle Andrade, de Morais, Maria Socorro Medeiros, da Camara, Saionara Maria Aires, Maciel, Alvaro Campos Cavalcanti
• Format: Journal Article
• Language: English
• Published: London BioMed Central Ltd 20.08.2021; BioMed Central; BMC
• Subjects: Adipose tissue; Adiposity; Aged women; Aging; Analysis; Anthropometry; Body composition; Body mass index; Body measurements; Data collection; Diagnosis; Ethics; Medical examination; Methods; Middle age; Middle aged women; Mortality; Muscle contraction; Muscle mass; Muscle strength; Muscles; Musculoskeletal diseases; Musculoskeletal system; Obesity; Older people; Physical activity; Physical training; Physiological aspects; Reference values (Medicine); Risk factors; Sarcopenia; Sex hormones; Statistical analysis; Women; Womens health; Brazil
• ISSN: 1471-2474; 1471-2474
• DOI: 10.1186/s12891-021-04532-x

Abstract Background The reduction of female sex hormones causes changes in the contractile properties of muscles as well as infiltration of fat in the muscle tissue. This results in a consequent decline in muscle strength. These changes are related to higher levels of functional impairment and physical disability. In this sense, several anthropometric indices have been used to quantify body and visceral fat. Thus, the objective of this paper is to propose cutoff points for adiposity anthropometric indices in order to identify low muscle mass, as well as to analyze the relationship between these indices and low muscle mass in middle-aged and older women. Methods Cross-sectional analytical study carried out in the Northeast of Brazil. The sample was formed by 593 women between 40—80 years old. Data collection included anthropometric assessment (BMI: Body Mass Index – WC: Waist Circumference – WHR: Waist-to-hip Ratio – WHtR: Waist-to-height Ratio – CI: Conicity Index – BAI: Body Adiposity Index – VAI: Visceral Adiposity Index – LAP: Lipid Accumulation Product), bioimpedance test and biochemical dosage. Moreover, sociodemographic data and practice of physical activity were collected. Descriptive statistics, Student's t-test, ROC curves, chi-squared and logistic regression were performed. Results The participants had a mean age of 53.11 (8.89) years, BMI of 28.49 (5.17) kg/m 2 and WC of 95.35 (10.39). The prevalence of low muscle mass was 19.4%. Based on sensitivity and specificity of adiposity anthropometric indices, cutoff points were developed to identify the presence of low muscle mass ( p  < 0.05), except for VAI. After logistic regression, WC (OR = 6.2; CI 95%: 1.4—28.1), WHR (OR = 1.8; CI: 1.0—3.4), WHtR (OR = 5.0; CI 95%: 1.0—23.7) and BAI (OR = 14.5; CI 95%: 6.6—31.7) were associated with low muscle mass. Conclusions All anthropometric indices, except VAI, showed adequate accuracy in identifying low muscle mass in women, especially those that took into account WC. This suggests that they can become accessible and also be cost-effective strategies for assessing and managing health outcomes related to muscle mass analysis.