Universität zu Köln

Glaab, Enrico ORCID: 0000-0003-3977-7469, Trezzi, Jean-Pierre, Greuel, Andrea, Jaeger, Christian, Hodak, Zdenka, Drzezga, Alexander, Timmermann, Lars, Tittgemeyer, Marc, Diederich, Nico Jean and Eggers, Carsten (2019). Integrative analysis of blood metabolomics and PET brain neuroimaging data for Parkinson's disease. Neurobiol. Dis., 124. S. 555 - 563. SAN DIEGO: ACADEMIC PRESS INC ELSEVIER SCIENCE. ISSN 1095-953X

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Abstract

Background: The diagnosis of Parkinson's disease (PD) often remains a clinical challenge. Molecular neuroimaging can facilitate the diagnostic process. The diagnostic potential of metabolomic signatures has recently been recognized. Methods: We investigated whether the joint data analysis of blood metabolomics and PET imaging by machine learning provides enhanced diagnostic discrimination and gives further pathophysiological insights. Blood plasma samples were collected from 60 PD patients and 15 age- and gender-matched healthy controls. We determined metabolomic profiles by gas chromatography coupled to mass spectrometry (GC-MS). In the same cohort and at the same time we performed FDOPA PET in 44 patients and 14 controls and FDG PET in 51 patients and 16 controls. 18 PD patients were available for a follow-up exam after one year. Both data sets were analysed by two machine learning approaches, applying either linear support vector machines or random forests within a leave-one-out cross-validation scheme and computing receiver operating characteristic (ROC) curves. Results: In the metabolomics data, the baseline comparison between cases and controls as well as the follow-up assessment of patients pointed to metabolite changes associated with oxidative stress and inflammation. For the FDOPA and FDG PET data, the diagnostic predictive performance (DPP) in the ROC analyses was highest when combining imaging features with metabolomics data (ROC AUC for best FDOPA + metabolomics model: 0.98; AUC for best FDG + metabolomics model: 0.91). DPP was lower when using only PET attributes or only metabolomics signatures. Conclusion: Integrating blood metabolomics data combined with PET data considerably enhances the diagnostic discrimination power. Metabolomic signatures also indicate interesting disease-inherent changes in cellular processes, including oxidative stress response and inflammation.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Glaab, Enrico UNSPECIFIED orcid.org/0000-0003-3977-7469 UNSPECIFIED Trezzi, Jean-Pierre UNSPECIFIED UNSPECIFIED UNSPECIFIED Greuel, Andrea UNSPECIFIED UNSPECIFIED UNSPECIFIED Jaeger, Christian UNSPECIFIED UNSPECIFIED UNSPECIFIED Hodak, Zdenka UNSPECIFIED UNSPECIFIED UNSPECIFIED Drzezga, Alexander UNSPECIFIED UNSPECIFIED UNSPECIFIED Timmermann, Lars UNSPECIFIED UNSPECIFIED UNSPECIFIED Tittgemeyer, Marc UNSPECIFIED UNSPECIFIED UNSPECIFIED Diederich, Nico Jean UNSPECIFIED UNSPECIFIED UNSPECIFIED Eggers, Carsten UNSPECIFIED UNSPECIFIED UNSPECIFIED
URN:	urn:nbn:de:hbz:38-153206
DOI:	10.1016/j.nbd.2019.01.003
Journal or Publication Title:	Neurobiol. Dis.
Volume:	124
Page Range:	S. 555 - 563
Date:	2019
Publisher:	ACADEMIC PRESS INC ELSEVIER SCIENCE
Place of Publication:	SAN DIEGO
ISSN:	1095-953X
Language:	English
Faculty:	Unspecified
Divisions:	Unspecified
Subjects:	no entry
Uncontrolled Keywords:	Keywords Language FP-CIT SPECT; CEREBROSPINAL-FLUID; OXIDATIVE STRESS; GENE-EXPRESSION; BIOMARKERS; ALZHEIMERS; SIGNATURE; DIAGNOSIS; VARIANTS; CRITERIA Multiple languages Neurosciences Multiple languages
Refereed:	Yes
URI:	http://kups.ub.uni-koeln.de/id/eprint/15320