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
Aug 2026· Health Science Reports· Vol 9· 0 citations· 34 references
Medicine
TL;DR
Socio‐demographic variables, rather than individual non‐motor symptoms, demonstrate the strongest independent associations with diagnostic timing in this cohort, highlighting the need for socio‐demographically tailored screening strategies in the prodromal phase of PD.
Abstract
ABSTRACT Background and Aims Parkinson'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. Methods A 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 < 0.05, and an Odds Ratio (OR) > 1 was considered indicative of later diagnostic timing. Results Among 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 < 0.001) and unemployment (aOR = 0.53, 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. Conclusion Socio‐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.
Introduction Anxiety 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. Methods We 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. Results Multivariate 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 < 0.05). The established model exhibited high discriminative power, achieving an AUC of 0.94 (95% CI: 0.873–0.964) in the training cohort and 0.84 (95% CI: 0.782–0.879) in the validation cohort. Calibration curves demonstrated excellent consistency between predicted and actual probabilities, and DCA confirmed strong clinical net benefits. Discussion In 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.
Guidong Liu, Yan-Qin Geng, Hanwen Zhang et al.· Frontiers in Aging Neuroscie...· 0 citations
Background Impulse control disorders (ICDs) are common non-motor symptoms in Parkinson's disease (PD) and associated with significant psychiatric morbidity and reduced quality of life. The prognostic nutritional index (PNI) reflects both nutritional and immune status. However, the relationship between PNI and ICDs in early-stage PD remains unknown. Methods This cross-sectional study included 1,302 early-stage PD patients (Hoehn and Yahr stages 1–3) from the Parkinson's Progression Markers Initiative (PPMI) database. Logistic regression models were constructed with sequential adjustment for demographic, clinical, neuropsychiatric, metabolic, and laboratory covariates. Sensitivity analyses included complete-case analysis, exclusion of participants with abnormal liver or renal function, and propensity score-based methods (IPTW, SMRW, PA). Receiver operating characteristic (ROC) curves, net reclassification improvement (NRI), and integrated discrimination improvement (IDI) were calculated to assess incremental predictive value. Results The overall prevalence of ICDs was 22.6% (294/1,302). In the fully adjusted model, each 1-unit increase in PNI was associated with 5% lower odds of ICDs (OR = 0.95, 95% CI: 0.92–0.98, P = 0.003). Compared with the lowest PNI tertile (T1), the highest tertile (T3) showed 34% reduced odds of ICDs (OR = 0.66, 95% CI: 0.46–0.94, P = 0.023), with a significant dose-response trend (P for trend = 0.025). Subgroup analyses revealed no significant interactions in any strata. Adding PNI to the baseline model significantly improved category-free reclassification (continuous NRI = 0.14, P = 0.03404; IDI = 0.0069, P = 0.00497), although the increase in discrimination was modest and non-significant (C-statistic from 0.714 to 0.720, P = 0.223). All sensitivity analyses confirmed the robustness of these association. Conclusions Higher PNI is independently associated with lower odds of ICDs in early-stage PD patients. PNI, a simple and readily available composite marker of nutritional and immune status, may serve as a valuable adjunctive tool for discriminating PD patients with higher odds of prevalent ICDs. Prospective longitudinal studies are warranted to establish causality and explore potential nutritional or immunomodulatory interventions.
Wen Zhou, Jiaojiao Shi, Duan Liu et al.· Frontiers in Nutrition· 0 citations
AD and/or LBD biomarker‐positive VLOSLP may be associated with greater dementia progression and cognitive decline, although findings should be interpreted cautiously given the small sample size and retrospective design.
Y. Satake, H. Kanemoto, Daiki Taomoto et al.· Psychogeriatrics· 0 citations
It is established that disease duration–specific percentiles for prospective application of the DM/IM/MMP subtyping model is established, supporting patient stratification and enrichment in disease‐modifying trials.
Ahmed Negida, Nitai D. Mukhopadhyay, Brian D Berman et al.· Movement Disorders Clinical...· 0 citations
Background Parkinson’s disease (PD) is a progressive neurodegenerative disorder causing a variety of motor and non-motor symptoms. To date, no disease modifying treatment exists, and diagnosis is based on the manifestation of the clinical motor symptoms, which occurs when the neurodegenerative process is already advanced. Studies designed to find diagnostic/prognostic biomarkers and pathophysiological pathways involved in disease onset/progression are needed. Objective To describe the study protocol of the Vall d’Hebron Initiative for Parkinson (VHIP) Cohort, a prospective and longitudinal observational cohort study aimed at deeply phenotyping de novo PD patients, including carriers of PD-linked mutations. We anticipate this initiative will contribute to identifying biomarkers for PD risk, diagnosis, and prognosis, as well as to stratify disease subtypes and increase our understanding of pathophysiology at early disease stages. Methods/Design Subjects are prospectively recruited by movement disorder specialists at the Vall d’Hebron University Hospital (VHUH). To date, the study cohort includes 174 subjects—consisting of 117 patients with de novo Parkinson’s disease, 17 individuals with REM sleep behavior disorder (RBD), and 33 healthy controls—and recruitment is still ongoing. After informed consent, subjects are evaluated in detail to primarily assess objectives within four major domains of PD: motor, cognitive-affective, autonomic function, and vision. This includes first an extensive clinical evaluation (i) recording demographic, personal history, lifestyle, and dietary habits and (ii) performing clinical scales covering motor and non-motor symptoms. Second, data from autonomic function and visual function tests, together with neuroimaging data, are acquired. And finally, a wide range of biospecimens are collected for biochemical and molecular assessments including the genotyping of all participants. Participants receive follow-up assessments at 2.5 and 5-year intervals. Results/Discussion VHIP is the first study to establish a Spanish cohort combining clinical, imaging, biochemical and molecular data over time in de novo patients, including PD-linked mutation carriers. VHIP provides a unique opportunity to link pre-diagnosis disturbances to PD development, as well as to identify risk, diagnosis, and prognosis biomarkers. Clinical trial registration https://vhir.vallhebron.com/es/investigacion/proyecto-vall-dhebron-iniciativa-para-el-parkinson-vhip.
Daniela Samaniego, Silvia Enríquez-Calzada, L. Domingo et al.· Frontiers in Aging Neuroscie...· 0 citations
Background: Stroke survivors often present vascular risk factors (VRFs) that are increasingly recognized as contributing to neurodegenerative diseases such as Parkinson’s disease (PD). We examined the influence of VRFs on the risk of PD in stroke survivors. Methods: Using the Korean National Health Insurance Service database, we identified 284,799 individuals with new-onset stroke and followed them for the occurrence of PD. The diagnosis of PD was confirmed using the International Classification of Diseases (ICD-10) code G20 and the rare disease registration code (V124). Results: Cox proportional hazard analyses revealed that smoking (former: Hazard ratio [HR] = 0.80; current: HR = 0.66) and alcohol consumption (moderate: HR = 0.83; heavy: HR = 0.74) were associated with a lower risk of PD. Conversely, diabetes mellitus, especially with a duration ≥ 5 years, was associated with increased PD risk (HR = 1.38). Conclusions: These findings suggest that several VRFs are associated with subsequent PD risk in stroke survivors. Further studies are warranted to determine whether modification of these risk factors influences the development of PD.
S. Yoon, H. Moon, Jin-Hyung Jung et al.· Journal of Clinical Medicine· 0 citations
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