Background: Changes in body composition, such as decreased muscle mass and increased adipose tissue, are significant in older adults, impacting health, functional capacity, and increasing the risk of metabolic diseases, functional decline, and frailty. Bioelectrical Impedance Analysis (BIA) is a non-invasive tool for assessing body composition, including fat-free mass (FFM), skeletal muscle mass (SMM), and fluid distribution (e.g., ECW/TBW ratio). Complementing BIA, the Mini Nutritional Assessment (MNA) serves as a validated tool for identifying malnutrition risk in the elderly. This study aimed to understand the correlation between BIA-derived parameters, MNA scores and clinical outcomes. Methods: This cross-sectional study involved 195 older adults (mean age 72.8 ± 5.4 years), divided into two groups based on body composition profiles determined by cluster analysis. Data collected included demographics, comprehensive BIA parameters (BMI, fat mass, FFM, SMM, ECW/TBW, phase angle), MNA scores, self-assessed health, chronic disease prevalence, frailty index (TFI), and functional limitations (EQ-5D). Statistical analyses included descriptive statistics, t-tests/ANOVA, chi-square tests, Pearson’s/Spearman’s correlations, point-biserial correlations, regression analyses, and ROC curve analysis to compare groups, explore variable relationships, and assess predictive abilities for malnutrition risk. Results: The first group had significantly higher BMI, AFM (AFM), FFM, and SMM, but a lower ECW/TBW ratio compared to Group 2 (N = 115), which was predominantly female and had higher frailty scores. MNA scores showed significant positive correlations with FFM (rho = 0.165, p = 0.021) and SMM (rho = 0.182, p = 0.011), and a negative correlation with ECW/TBW (rho = −0.188, p = 0.008). Higher adiposity (BMI, fat mass) correlated positively with arterial hypertension and obesity. Lower FFM and SMM were negatively correlated with gastroesophageal reflux disease. Skeletal muscle mass (AUC = 0.634, cut-off ≤ 17.3 kg) and ECW/TBW ratio (AUC = 0.626, cut-off ≥ 49.7%) showed modest discriminatory capacity to identify malnutrition risk. Individuals at risk of malnutrition reported greater functional limitations and lower self-assessed health. Numerous BIA parameters, including segmental muscle mass, total body water, phase angle, and impedance values, significantly correlated with MNA scores. Conclusions: The study highlights the importance of body composition analysis, particularly BIA, in correlation with MNA, for assessing nutritional status, functional limitations, and chronic disease associations in older adults. Integrating BIA and MNA into geriatric assessments provides a complementary profile of nutritional and functional vulnerability.
Tomasiewicz et al. (2026) studied this question.