Development of a prediction model for identifying older adults with low protein using a simple food intake questionnaire.

Abstract

Adequate protein intake is crucial for preventing frailty and sarcopenia in older adults, but conventional dietary assessments are time-consuming. Developing a rapid screening tool for identifying those at risk of low protein intake is essential; however, no such tool exists for Asian populations. This study developed a prediction model to identify older adults in Japan at risk of low protein intake using a simple food intake questionnaire. The model was developed using data from 4,085 older adults aged ≥65 years from the 2013 and 2017 National Health and Nutrition Surveys. Protein intake was assessed using a one-day dietary record with a semi-weighted method. A multivariable logistic regression model was developed to predict low protein intake (<1.0 g/kg adjusted body weight/day), incorporating sex, age, adjusted body weight, and food intake frequency of nine food groups. Model performance was evaluated using the area under the receiver operator characteristic (ROC) curve and a calibration plot, both adjusted for optimism through bootstrap resampling. Participants had a mean age of 74.1 years (standard deviation = 6.6), and 55.5% of all participants were female. The prevalence of low protein intake was 21.8%. Internal validation showed a bootstrap optimism-corrected ROC area of 0.695 and a calibration slope of 0.921. We developed a 12-item prediction model for identifying older adults at risk of low protein intake. The model provides a practical tool to identify older adults at high risk of low protein intake and is expected to be used in public health settings.