Predictive Power of Anthropometric Measures and Indices in Assessing Nutrition-Related Risk Using the Geriatric Nutritional Risk Index in Elderly Patients: A Cross-Sectional Study
Year: 2024
Type: article
Study design: Observational Study
Abstract: BACKGROUND Effective assessment and diagnosis using simple nutritional screening tools are crucial for identifying malnutrition in older adults. The aim of the study was to evaluate how effectively different anthropometric parameters, indices, and body composition metrics can assess nutrition-related risks, using the Geriatric Nutritional Risk Index (GNRI) in a cohort of 185 patients >60 years. MATERIAL AND METHODS This study included 185 patients over 60 years old. Anthropometric measurements, indices, and body composition were examined. Nutritional status based on GNRI was categorized as major risk (1.1%), moderate risk (9.7%), low risk (15.1%), and no risk (74.1%). RESULTS The strongest correlations with the GNRI were observed for body mass index (BMI) (ρ=0.8628) and body fat in kilograms (ρ=0.8269), P<0.001. A unit increase in BMI decreased the odds of being in the risk group by 52.1% (OR 0.479; 95% CI 0.377-0.609; P<0.001). ROC analysis showed BMI ≤25.0 had the highest predictive value (AUC 0.93, 95% CI 0.89-0.97) in assessing nutrition-related risk in the elderly. Body fat (AUC 0.89, 95% CI 0.85-0.94), abdominal volume index (AUC 0.86, 95% CI 0.80-0.91), hip circumference (AUC 0.85, 95% CI 0.79-0.91), and waist circumference (AUC 0.85, 95% CI 0.80-0.91) also demonstrated significant predictive power (P<0.001). CONCLUSIONS Our study underscores the importance of using BMI and other related anthropometric measures and indices as part of routine assessments to identify and manage nutrition-related risks among elderly individuals in hospitals, care facilities, and dietetic clinics, particularly in situations where standardized tools for assessing malnutrition are not available or are impossible to use.
Source: Medical Science Monitor
DOI: 10.12659/msm.946316
Institution: Medical University of Silesia
Language: English
Subfield: Physiology
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Funder: Uniwersytet Śląski w Katowicach
OpenAlex ID: W4404194502