Development and validation of a multimodal nomogram predicting anxiety and depression in Parkinson's disease: integrating plasma biomarkers and clinical phenotypes.

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IntroductionAnxiety and depression are prevalent, disabling, yet frequently underdiagnosed non-motor symptoms in Parkinson's disease (PD). This study aimed to develop and validate a non-invasive model predicting these affective disorders by integrating peripheral blood biomarkers with standardized clinical scales to facilitate early screening.MethodsWe retrospectively analyzed data from 290 patients with PD, who were randomly allocated into a training cohort (n = 203) and a validation cohort (n = 87). Baseline plasma neurofilament light chain (NfL) levels and clinical phenotypes were assessed. Independent risk factors were determined via multivariate logistic regression analysis to construct the clinical prediction nomogram. Model performance was comprehensively evaluated using the area under the receiver operating characteristic curve (AUC) for discrimination, calibration curves for risk consistency, and decision curve analysis (DCA) for clinical utility.ResultsMultivariate logistic regression identified plasma NfL, Hoehn and Yahr stage, MMSE score, and MDS-UPDRS Part III score as independent predictors for anxiety and depression in PD patients (all p DiscussionIn conclusion, combining peripheral plasma NfL levels with standard clinical phenotypes provides an objective, quantifiable, and non-invasive tool for early risk stratification of affective disorders in PD. This multimodal nomogram effectively expands the therapeutic window for timely personalized psychiatric interventions, potentially improving long-term quality of life and clinical outcomes for PD patients.

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