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Article

Predictive Modeling of Alzheimer’s and Parkinson’s Disease Using Metabolomic and Lipidomic Profiles from Cerebrospinal Fluid

1
Department of Statistics and Applied Probability, University of California, Santa Barbara, CA 93106, USA
2
Northwest Metabolomics Research Center, Department of Anesthesiology and Pain Medicine, University of Washington School of Medicine, Seattle, WA 98195, USA
3
Veterans Affairs Puget Sound Health Care System, Seattle, WA 98108, USA
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Department of Neurology, University of Washington School of Medicine, Seattle, WA 98195, USA
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Department of Pathology, Stanford University School of Medicine, Palo Alto, CA 94304, USA
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Portland Veterans Affairs Medical Center, Portland, OR 97239, USA
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Department of Neurology, Oregon Health and Science University, Portland, OR 97239, USA
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Department of Psychiatry and Behavioral Sciences, University of Washington School of Medicine, Seattle, WA 98102, USA
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Department of Biology, University of Washington, Seattle, WA 98105, USA
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Department of Laboratory Medicine & Pathology, University of Washington School of Medicine, Seattle, WA 98195, USA
*
Author to whom correspondence should be addressed.
Current address: The First Affiliated Hospital of Chongqing Medical University, Yuzhong District, Chongqing 400016, China.
Academic Editor: Markus R. Meyer
Metabolites 2022, 12(4), 277; https://doi.org/10.3390/metabo12040277
Received: 21 January 2022 / Revised: 8 March 2022 / Accepted: 17 March 2022 / Published: 22 March 2022
In recent years, metabolomics has been used as a powerful tool to better understand the physiology of neurodegenerative diseases and identify potential biomarkers for progression. We used targeted and untargeted aqueous, and lipidomic profiles of the metabolome from human cerebrospinal fluid to build multivariate predictive models distinguishing patients with Alzheimer’s disease (AD), Parkinson’s disease (PD), and healthy age-matched controls. We emphasize several statistical challenges associated with metabolomic studies where the number of measured metabolites far exceeds sample size. We found strong separation in the metabolome between PD and controls, as well as between PD and AD, with weaker separation between AD and controls. Consistent with existing literature, we found alanine, kynurenine, tryptophan, and serine to be associated with PD classification against controls, while alanine, creatine, and long chain ceramides were associated with AD classification against controls. We conducted a univariate pathway analysis of untargeted and targeted metabolite profiles and find that vitamin E and urea cycle metabolism pathways are associated with PD, while the aspartate/asparagine and c21-steroid hormone biosynthesis pathways are associated with AD. We also found that the amount of metabolite missingness varied by phenotype, highlighting the importance of examining missing data in future metabolomic studies. View Full-Text
Keywords: predictive modeling; biomarker; cerebrospinal fluid; cross-sectional study; neurodegenerative disease predictive modeling; biomarker; cerebrospinal fluid; cross-sectional study; neurodegenerative disease
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MDPI and ACS Style

Hwangbo, N.; Zhang, X.; Raftery, D.; Gu, H.; Hu, S.-C.; Montine, T.J.; Quinn, J.F.; Chung, K.A.; Hiller, A.L.; Wang, D.; Fei, Q.; Bettcher, L.; Zabetian, C.P.; Peskind, E.R.; Li, G.; Promislow, D.E.L.; Davis, M.Y.; Franks, A. Predictive Modeling of Alzheimer’s and Parkinson’s Disease Using Metabolomic and Lipidomic Profiles from Cerebrospinal Fluid. Metabolites 2022, 12, 277. https://doi.org/10.3390/metabo12040277

AMA Style

Hwangbo N, Zhang X, Raftery D, Gu H, Hu S-C, Montine TJ, Quinn JF, Chung KA, Hiller AL, Wang D, Fei Q, Bettcher L, Zabetian CP, Peskind ER, Li G, Promislow DEL, Davis MY, Franks A. Predictive Modeling of Alzheimer’s and Parkinson’s Disease Using Metabolomic and Lipidomic Profiles from Cerebrospinal Fluid. Metabolites. 2022; 12(4):277. https://doi.org/10.3390/metabo12040277

Chicago/Turabian Style

Hwangbo, Nathan, Xinyu Zhang, Daniel Raftery, Haiwei Gu, Shu-Ching Hu, Thomas J. Montine, Joseph F. Quinn, Kathryn A. Chung, Amie L. Hiller, Dongfang Wang, Qiang Fei, Lisa Bettcher, Cyrus P. Zabetian, Elaine R. Peskind, Ge Li, Daniel E. L. Promislow, Marie Y. Davis, and Alexander Franks. 2022. "Predictive Modeling of Alzheimer’s and Parkinson’s Disease Using Metabolomic and Lipidomic Profiles from Cerebrospinal Fluid" Metabolites 12, no. 4: 277. https://doi.org/10.3390/metabo12040277

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