Evaluating the Relationship Between Early Non-Motor Symptoms and Late Diagnosis of Parkinson's Disease Based on the PPMI Database From 2012 to 2018: A Retrospective Cross-Sectional Study.

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Background and aimsParkinson's (PD) diagnosis is frequently delayed, particularly when patients present with non-motor symptoms (NMS). This study evaluates the association between NMS, socio-demographic factors, and diagnostic timing in PD using PPMI data. We aimed to determine how clinical and demographic profiles influence the interval between NMS onset and clinical diagnosis, acknowledging that this interval reflects both biological disease progression and healthcare-related factors.MethodsA cross-sectional analysis was conducted on de-identified data from 1024 PD patients in the PPMI cohort (2012-2018). Multivariable logistic regression was utilized to identify independent factors associated with diagnostic timing (early vs. late). Predictive performance was assessed using the Area Under the ROC Curve (AUC) and calibration analysis. Statistical significance was defined as p  1 was considered indicative of later diagnostic timing.ResultsAmong the 1024 examined patients, multivariable logistic regression indicated that individual early NMS were not independently associated with diagnostic timing (p > 0.05 for all). In contrast, specific socio-demographic factors exhibited significant independent associations: male gender was associated with higher odds of later diagnosis (aOR = 1.48, p = 0.0114), while primary education (aOR = 0.29, p p = 0.0025) were significantly associated with earlier diagnosis. The overall model showed fair-to-good discriminative ability with an AUC of 0.692, and no issues with multicollinearity (GVIF ≤ 1.15) were detected.ConclusionSocio-demographic variables, rather than individual non-motor symptoms, demonstrate the strongest independent associations with diagnostic timing in this cohort. The results suggest that diagnostic delays may be influenced more by gender-based healthcare-seeking behaviors and socioeconomic factors than by specific clinical NMS profiles. These findings highlight the need for socio-demographically tailored screening strategies in the prodromal phase of PD.

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