Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

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

Nathan Hwangbo
* ,
Xinyu Zhang
,
Daniel Raftery
ORCID logo ,
Haiwei Gu
,
Shu-Ching Hu
,
Thomas J. Montine
,
Joseph F. Quinn
,
Kathryn A. Chung
,
Amie L. Hiller
,
Dongfang Wang
,
Qiang Fei
,
Lisa Bettcher
,
Cyrus Zabetian
,
Elaine R. Peskind
,
Ge Li
,
Daniel Promislow
,
Marie Y. Davis
ORCID logo ,
Alexander Franks
ORCID logo
Version 1 : Received: 21 January 2022 / Approved: 24 January 2022 / Online: 24 January 2022 (12:59:55 CET)

A peer-reviewed article of this Preprint also exists.

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. 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.

Abstract

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.

Keywords

predictive modeling; biomarker; cerebrospinal fluid; cross-sectional study; neurodegenerative disease

Subject

Biology and Life Sciences, Endocrinology and Metabolism

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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