Predictive value of the combined DTI-ALPS index and serum creatinine levels in mild cognitive impairment in Parkinson's disease.

OBJECTIVE

To identify independent risk factors for Parkinson disease mild cognitive impairment (PD-MCI) and develop a prediction model integrating clinical indicators, blood biomarker, and neuroimaging data, aiding in detection and intervention.

METHODS

A retrospective study was conducted with 150 PD patients. The PD-MCI group (n = 64) and PD with normal cognition (PD-NC, n = 86) were identified using the Montreal Cognitive Assessment scale. Data on demographics, motor symptoms, cognitive function, quality of life, blood markers, and diffusion tensor imaging along perivascular spaces (DTI-ALPS) were collected. Univariate analysis identified significant variables, and multivariate logistic regression identified independent risk factors. A nomogram prediction model was developed using R software. Model performance was evaluated using Receiver Operating Characteristic (ROC) curves, bootstrap resampling calibration curves, and decision curve analysis (DCA).

RESULTS

Significant differences between the groups were found in levodopa equivalent daily dose (LEDD), PD Quality of Life Questionnaire, creatinine, cystatin C, and ALPS index. Multivariate regression identified higher LEDD (OR = 1.01, 95%CI 1.00-1.03, p = 0.005) and creatinine levels (OR = 1.34, 95%CI 1.10-1.66, p = 0.005) as independent risk factors. The nomogram model demonstrated strong discriminatory ability (AUC = 0.864, 95%CI 0.807-0.922) and good calibration. DCA showed a significant net benefit within clinical threshold ranges.

CONCLUSION

This study developed a PD-MCI prediction model incorporating DTI-ALPS and clinical blood biomarkers. It confirmed that LEDD and creatinine levels are independent risk factors, with high clinical value for early screening and individualized treatment.