Desarrollo de un modelo de predicción para identificar adultos mayores con baja ingesta de proteínas mediante un...

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.