R version 4 2

First, a description of the included variables was provided, estimating percentages for categorical variables and averages, as well as standard deviations for numerical variables. Subsequently, differences between men and women were assessed by applying a Pearson’s chi-square test for categorical variables and Student’s t-test for numerical variables. Finally, linear regression models were used to analyze the relationship between the dependent variables (i.e., ALSTI, handgrip strength, and gait speed) and each of the covariates while controlling for sex, mean energy intake (kcal/day), and the interaction between the exposure variable and sex. The estimated coefficients (Est), standard errors (SE), and p-values for each exposure variable were reported.
To measure the size of the effect of the studied associations, we utilized the Spearman’s Rho coefficient, assuming the following cutoff points: at least 0.8 (very strong), 0.6 up to 0.8 (moderately strong), 0.3–0.5 (fair), and <0.3 (poor) [20 ,21 ].
In addition, correlation graphs were constructed to visualize the association between the ALSTI and the statistically significant covariates. The statistical significance level was set at 0.05, and the Benjamini and Hochberg method was used to adjust for multiple comparisons [22 ]. The analyses were performed using R version 4.2.1 and STATA 17.0 software.