Avaliação da acurácia e desempenho diagnóstico da circunferência do braço, comparada a outros marcadores antropométricos e funcionais, para a detecção de sarcopenia em pessoas idosas

Abstract

In aging, the decline of muscle mass occurs progressively, accompanied by a reduction in muscle strength, favoring the development of sarcopenia and increasing the risks of falls, mobility limitations, and loss of independence. The diagnosis of sarcopenia is based on reduced muscle strength and mass. However, the methods for identifying this disease are costly and not commonly used in clinical practice, especially in primary healthcare. Anthropometry is a lowcost, non-invasive, and easily applicable method that can be a more accessible alternative for predicting sarcopenia. Thus, the objective of this study was to evaluate the accuracy of arm circumference (AC), compared to other anthropometric and functional markers, as a predictor of sarcopenia in community-dwelling older adults. This is a study derived from a populationbased, observational, cross-sectional, analytical, and descriptive household survey involving older adults, called "I Diagnóstico alagoano sobre saúde, nutrição e qualidade de vida da pessoa idosa," with data collection carried out between April 2022 and December 2023. Individuals aged 60 years or older with information in the main study's database on AC measurements and variables indicating sarcopenia, muscle strength, and muscle mass were included. Sociodemographic, economic, and anthropometric variables, as well as variables related to sarcopenia (muscle strength, muscle mass, physical performance), were collected. The analyses were performed using R software and included data description, predictive modeling, and discriminative analysis of the variables. After excluding those who were not eligible for the study, the final sample was composed of 708 older adults. The majority of the sample was composed of females aged between 60 and 94 years, with a mean age of 70 years (± 7.74). Sarcopenia was identified through low muscle mass and strength, and its prevalence was 14.7%, with 4.9% presenting the severe form of the disease. Linear regression analysis indicated a negative correlation between AC (β = -0.01, p < 0.01, 95% CI: 0.75-0.92), CC (β = -0.01, p < 0.01, 95% CI: 0.58-0.76), and sarcopenia. Logistic regression suggested that for each additional centimeter in AC, the odds of an individual having sarcopenia are reduced by 17% (OR: 0.83; 95% CI: 0.75 – 0.92; p = 0.001). CC and the time spent on the Timed Up And Go (TUG) test also proved to be significant predictors, with emphasis on CC (OR: 0.66; 95% CI: 0.58 – 0.76; p < 0.001). Gait speed and the Chair Stand Test did not show statistical significance in the logistic regression. AC demonstrated good discriminative ability for sarcopenia, suggesting satisfactory accuracy in identifying individuals with and without sarcopenia, regardless of sex (AUC: 0.85). After comparing the areas under the curve (AUC) between AC, CC, and TUG, the results indicate that AC and CC do not present significant differences in AUCs (z = 0.24; p = 0.81), highlighting that both AC and CC have the same relevance in predicting sarcopenia. The analysis of diagnostic precision showed a slight superiority of AC (27.1%, 95% CI: 20.3 - 33.9 and 43.0%, 95% CI: 33.6 - 52.4) in relation to CC (26.6%, 95% CI: 20.2 - 33.0 and 37.6%, 95% CI: 28.8 - 46.4) in diagnostic precision in men and women, respectively. Our results indicate that AC and CC are the most promising variables for the prediction of sarcopenia, presenting high discriminative ability and cutoff points that balance sensitivity and specificity, contributing to a more accurate and objective data-driven diagnosis.