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Author Notes:

Correspondence: malu.tansey@emory.edu

See publication for full list of author contributions.

We thank the patients and subject volunteers for participating in this study and staff at California Clinical Trials Medical Group for conducting this study (LE, MY).

We thank the Tansey Lab for useful discussions.

The content is solely the responsibility of the authors and does not necessarily reflect the official views of the National Institutes of Health.

All relevant study documentation was reviewed and approved by the responsible Institutional Review Board (IRB; Aspire IRB, 11,491 Woodside Avenue, Santee, California 92,071, USA).

All subjects were informed of the nature and purpose of the study and provided written informed consent during pre-study screening.

The authors declare that they have no competing interests.


Research Funding:

This study was supported in part by The Michael J. Fox Foundation for Parkinson’s Research 24-Hour Biofluids (MGT) and Assay Development (BM, PT) Programs, the National Institutes of Health/National Institute of Neurological Disorders and Stroke NRSA Fellowship F31 NS081830-02 (GTK) and the Emory Multiplexed Immunoassay Core (EMIC), which is subsidized by the Emory University School of Medicine and is one of the Emory Integrated Core Facilities.

Additional support was provided by the National Center for Advancing Translational Sciences of the National Institutes of Health under award number UL1TR000454.


  • Science & Technology
  • Life Sciences & Biomedicine
  • Immunology
  • Neurosciences
  • Neurosciences & Neurology
  • Parkinson's disease
  • Inflammation
  • Protein biomarkers
  • Daily rhythm
  • CSF
  • Serum

Candidate inflammatory biomarkers display unique relationships with alpha-synuclein and correlate with measures of disease severity in subjects with Parkinson's disease

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Journal Title:

Journal of Neuroinflammation


Volume 14, Number 1


, Pages 164-164

Type of Work:

Article | Final Publisher PDF


Background: Efforts to identify fluid biomarkers of Parkinson's disease (PD) have intensified in the last decade. As the role of inflammation in PD pathophysiology becomes increasingly recognized, investigators aim to define inflammatory signatures to help elucidate underlying mechanisms of disease pathogenesis and aid in identification of patients with inflammatory endophenotypes that could benefit from immunomodulatory interventions. However, discordant results in the literature and a lack of information regarding the stability of inflammatory factors over a 24-h period have hampered progress. Methods: Here, we measured inflammatory proteins in serum and CSF of a small cohort of PD (n=12) and age-matched healthy control (HC) subjects (n=6) at 11 time points across 24h to (1) identify potential diurnal variation, (2) reveal differences in PD vs HC, and (3) to correlate with CSF levels of amyloid β (Aβ) and α-synuclein in an effort to generate data-driven hypotheses regarding candidate biomarkers of PD. Results: Despite significant variability in other factors, a repeated measures two-way analysis of variance by time and disease state for each analyte revealed that serum IFNγ, TNF, and neutrophil gelatinase-associated lipocalin (NGAL) were stable across 24h and different between HC and PD. Regression analysis revealed that C-reactive protein (CRP) was the only factor with a strong linear relationship between CSF and serum. PD and HC subjects showed significantly different relationships between CSF Aβ proteins and α-synuclein and specific inflammatory factors, and CSF IFNγ and serum IL-8 positively correlated with clinical measures of PD. Finally, linear discriminant analysis revealed that serum TNF and CSF α-synuclein discriminated between PD and HC with a minimum of 82% sensitivity and 83% specificity. Conclusions: Our findings identify a panel of inflammatory factors in serum and CSF that can be reliably measured, distinguish between PD and HC, and monitor inflammation as disease progresses or in response to interventional therapies. This panel may aid in generating hypotheses and feasible experimental designs towards identifying biomarkers of neurodegenerative disease by focusing on analytes that remain stable regardless of time of sample collection.

Copyright information:

© 2017 The Author(s).

This is an Open Access work distributed under the terms of the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/).
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