A study on predicting malnutrition risk in Parkinson's disease patients using a nomogram model

Area of Science:

• Neurology
• Nutritional Science
• Gerontology

Background:

• Parkinson's disease (PD) significantly impacts patient quality of life.
• Malnutrition is a critical complication in PD, leading to adverse outcomes.
• Current malnutrition assessments lack precision for early PD detection.

Purpose of the Study:

• Develop and validate a predictive nomogram for early malnutrition risk identification in PD patients.
• Identify critical risk factors associated with malnutrition in PD.

Main Methods:

• Cross-sectional study (February 2022 - December 2023) with 163 PD patients.
• Nutritional status assessed using the Mini Nutritional Assessment (MNA).
• Univariate and multivariate logistic regression analyses were performed.

Main Results:

• Significant risk factors identified: gender, BMI, GCSI, MoCA, and Barthel Index scores.
• The nomogram achieved an AUC of 0.92, sensitivity of 77.5%, and specificity of 88%.
• Internal validation (bootstrap) showed a C-index of 0.92 with strong calibration.

Conclusions:

• High prevalence of malnutrition in PD necessitates effective screening tools.
• The validated nomogram offers a promising method for predicting malnutrition risk in PD.
• Future integration into clinical practice can enhance patient management and outcomes.