What question did this study set out to answer?

The aim is to create a classification model that uses substantia nigra hyperechogenicity and inflammation biomarkers to stage cognitive impairment in Parkinson’s disease.

April 1, 2026Open Access

Integrating Substantia Nigra Hyperechogenicity and Inflammation-Associated Biomarkers: A Classification Model for Staging Cognitive Impairment in Parkinson’s Disease

JZJiahang ZhaoBeijing Tian Tan Hospital CHChao HouAffiliated Hospital of Southwest Medical University SWSen WangBeijing Tian Tan Hospital

Key Points

The aim is to create a classification model that uses substantia nigra hyperechogenicity and inflammation biomarkers to stage cognitive impairment in Parkinson’s disease.
Enrolled 184 patients with Parkinson’s disease who underwent transcranial sonography.
Categorized participants into PD-CI, PD-MCI, and PD-dementia based on Montreal Cognitive Assessment scores.
Used multivariate logistic regression to identify diagnostic factors.
Applied receiver operating characteristic curve analysis to evaluate model performance.
Identified significant factors for cognitive impairment: age <50 years and more years of education.
Found that SNH and elevated levels of CRP and homocysteine were independent risk factors.
Achieved AUCs of 0.783, 0.729, and 0.823 for predicting PD-CI, PD-MCI, and PD-dementia respectively, outperforming single or dual marker models.

Abstract

Background: Cognitive impairment (CI) is recognized as a debilitating complication of Parkinson’s disease (PD). This study was designed to develop a diagnostic classification model by integrating substantia nigra hyperechogenicity (SNH) and inflammationassociated biomarkers to evaluate its diagnostic performance in distinguishing PD CI stages. Methods: Between January, 2023 and May, 2024, 184 patients with PD who underwent transcranial sonography were prospectively enrolled. Based on Montreal Cognitive Assessment (MoCA) scores, participants were categorized into three groups: cognitive impairment (PD-CI, MoCA <26), mild cognitive impairment (PD-MCI, MoCA 22–25), and dementia (PD-dementia, MoCA ≤21). Ultrasound features and inflammationassociated biomarkers were screened with univariate analyses. Multivariate logistic regression was used to identify independent diagnostic factors, and receiver operating characteristic (ROC) curve analysis was used to assess model discrimination. Results: Multivariate regression analysis indicated that age <50 years and more years of education were significantly associated factors for CI (OR = 0.170, p = 0.0350; OR = 0.8780, p = 0.0020, respectively), whereas Unified Parkinson’s Disease Rating Scale Part III (UPDRSIII) score (OR = 1.024, p = 0.0270), SNH (OR = 2.550, p = 0.0030), elevated C-reactive protein (CRP) (OR = 2.038, p = 0.0350), and elevated homocysteine (Hcy) (OR = 2.830, p = 0.0020) were independent risk factors. The area uinder the curves (AUCs) for the combined SNH+CRP+Hcy model in predicting PD-CI, PD-MCI, and PD-dementia were 0.783, 0.729, and 0.823, respectively; these values were significantly superior to those for single or dual marker combinations (p < 0.05), with the strongest performance for distinguishing PD-dementia. Conclusion: An SNH and inflammationassociated biomarkerbased model was developed for predicting the stage of cognitive impairment in PD. Clinical targets for individualized intervention can be provided, and clinical risk stratification and care pathways can be optimized. Furthermore, the model supports the iron deposition-neuroinflammation-CI pathway hypothesis, providing a mechanistic rationale for ultrasoundbased PD-CI diagnosis.

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