# Identifying Malnutrition Risk in the Elderly: A Single- and Multi-Parameter Approach

## TL;DR

This study compares single and multi-parameter methods to assess malnutrition risk in elderly individuals, finding that a multi-parameter model is more accurate.

## Contribution

The study introduces a multi-parameter model that outperforms single-parameter approaches in predicting malnutrition risk in the elderly.

## Key findings

- The MNA identified malnutrition risk in 36.8% of elderly participants.
- A multi-parameter logistic regression model achieved an AUC of 0.84 for predicting malnutrition risk.
- Parameters like handgrip strength, skeletal muscle mass, and depression were significant predictors in the model.

## Abstract

Malnutrition is a significant concern affecting the elderly, necessitating a complex assessment. This study aims to deepen the understanding of factors associated with the assessment of malnutrition in the elderly by comparing single- and multi-parameter approaches. In this cross-sectional study, 154 individuals underwent a comprehensive geriatric assessment (CGA). Malnutrition risk was determined using the mini nutritional assessment (MNA). Additional factors assessed included sarcopenia, polypharmacy, depression, appetite, handgrip strength, and gait speed. Phase angle (PA) and body composition were measured using bioelectrical impedance analysis (BIA). The MNA identified a malnutrition risk in 36.8% of individuals. The geriatric depression scale (GDS) and PA demonstrated moderate effectiveness in assessing malnutrition risk, with AUC values of 0.69 (95% CI: 0.60–0.78) and 0.62 (95% CI: 0.54–0.72), respectively. A logistic regression model incorporating handgrip strength, skeletal muscle mass, sarcopenia, osteoporosis, depression, specific antidepressant use, mobility, appetite, and smoking achieved superior performance in predicting malnutrition risk, with an AUC of 0.84 (95% CI: 0.77–0.91). In conclusion, this study demonstrates that integrating multiple parameters into a composite model provides a more accurate and comprehensive assessment of malnutrition risk in elderly adults.