Multidimensional dietary assessment and interpretable machine learning models predict the risk of prediabetes/diabetes and osteoporosis comorbidity in older adults.

ABSTRACT:

Background

The health burden of diabetes mellitus and osteoporosis (DM-OP) comorbidity in the aging population is increasing, and dietary factors are modifiable risk determinants. This study developed and validated a machine learning model to predict DM-OP comorbidity using multidimensional dietary assessment.

Methods

This study utilized data from NHANES cycles 2005-2010, 2013-2014, and 2017-2020, ultimately including 4,678 participants aged ≥65 years. Dietary data were collected through 24-h dietary recalls, encompassing macronutrients, micronutrients, food processing classification (NOVA), and five dietary quality scores. Missing data were handled using random forest algorithm, feature selection was performed using Boruta algorithm, and SMOTE technique addressed class imba

SUBMITTER: ShangGuan Y 

PROVIDER: S-EPMC12667436 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature