Inflammatory Biomarkers in Newly Diagnosed Patients With Parkinson Disease and Related Neurodegenerative Disorders

Abstract

Background and objectives: Neuroinflammation contributes to Parkinson disease (PD) pathology, and inflammatory biomarkers may aid in PD diagnosis. Proximity extension assay (PEA) technology is a promising method for multiplex analysis of inflammatory markers. Neuroinflammation also plays a role in related neurodegenerative diseases, such as dementia with Lewy bodies (DLB) and Alzheimer disease (AD). The aim of this work was to assess the value of inflammatory biomarkers in newly diagnosed patients with PD and in patients with DLB and AD.

Methods: Patients from the Norwegian ParkWest and Dementia Study of Western Norway longitudinal cohorts (PD, n = 120; DLB, n = 15; AD, n = 27) and 44 normal controls were included in this study. A PEA inflammation panel of 92 biomarkers was measured in the CSF. Disease-associated biomarkers were identified using elastic net (EN) analysis. We assessed the discriminatory power of disease-associated biomarkers using receiver operating characteristic (ROC) curve analysis and estimated the optimism-adjusted area under the curve (AUC) using the bootstrapping method.

Results: EN analysis identified 9 PEA inflammatory biomarkers (ADA, CCL23, CD5, CD8A, CDCP1, FGF-19, IL-18R1, IL-6, and MCP-2) associated with PD. Seven of the 9 biomarkers were included in a diagnostic panel, which was able to discriminate between those with PD and controls (optimism-adjusted AUC 0.82). Our 7-biomarker PD panel was also able to distinguish PD from DLB and from AD. In addition, 4 inflammatory biomarkers were associated with AD and included in a panel, which could distinguish those with AD from controls (optimism-adjusted AUC 0.87). Our 4-biomarker AD panel was also able to distinguish AD from DLB and from PD.

Discussion: In our exploratory study, we identified a 7-biomarker panel for PD and a 4-biomarker panel for AD. Our findings indicate potential inflammation-related biomarker candidates that could contribute toward PD-specific and AD-specific diagnostic panels, which should be further explored in other larger cohorts.

Figure 1. Workflow Diagram Tracking the Number of Biomarkers Included at Different Stages of the Study

(A) Of 92 biomarkers, (B) only the 40 that were detected in more than 90% of all participants were included in the study. (C) Through the EN analysis, the authors identified 9 and 4 biomarkers that were associated with PD and AD, respectively. (D) Finally, through the ROC curve analysis, the authors assessed the diagnostic performance of biomarkers associated with either PD or AD that had low multicollinearity (VIF <5). Arrows pointing up indicate biomarkers that were found to be increased in either PD or AD in the EN analysis, and arrows pointing down indicate biomarkers that were decreased with disease. Biomarkers in bold were identified as candidates in both the PD and AD EN analyses. VIFs = variance inflation factors.

Figure 2. Regularized Regression With Elastic Net Penalization for α Values Between 0 and 1 to Identify Biomarkers Associated With PD or AD Compared With Controls

Variables are highlighted in red when increased values are associated with disease and they are highlighted in blue when decreased values are associated with disease. The intensity of color reflects the strength of association. (A) Nine PEA biomarkers were associated with PD across all levels of α, and (B) 4 PEA biomarkers were associated with AD across all levels of α. The names of biomarkers associated with disease status across all levels of α are indicated. For abbreviations, see eTable 5 (links.lww.com/NXI/A868). PEA = proximity extension assay.