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Deriving Convergent and Divergent Metabolomic Correlates of Pulmonary Arterial Hypertension

Deriving Convergent and Divergent Metabolomic Correlates of Pulmonary Arterial Hypertension

Authors :
Cheng, Mona Alotaibi
Yunxian Liu
Gino A. Magalang
Alan C. Kwan
Joseph E. Ebinger
William C. Nichols
Michael W. Pauciulo
Mohit Jain
Susan
Source :
Metabolites; Volume 13; Issue 7; Pages: 802
Publication Year :
2023
Publisher :
Multidisciplinary Digital Publishing Institute, 2023.

Abstract

High-dimensional metabolomics analyses may identify convergent and divergent markers, potentially representing aligned or orthogonal disease pathways that underly conditions such as pulmonary arterial hypertension (PAH). Using a comprehensive PAH metabolomics dataset, we applied six different conventional and statistical learning techniques to identify analytes associated with key outcomes and compared the results. We found that certain conventional techniques, such as Bonferroni/FDR correction, prioritized metabolites that tended to be highly intercorrelated. Statistical learning techniques generally agreed with conventional techniques on the top-ranked metabolites, but were also more inclusive of different metabolite groups. In particular, conventional methods prioritized sterol and oxylipin metabolites in relation to idiopathic versus non-idiopathic PAH, whereas statistical learning methods tended to prioritize eicosanoid, bile acid, fatty acid, and fatty acyl ester metabolites. Our findings demonstrate how conventional and statistical learning techniques can offer both concordant or discordant results. In the case of a rare yet morbid condition, such as PAH, convergent metabolites may reflect common pathways to shared disease outcomes whereas divergent metabolites could signal either distinct etiologic mechanisms, different sub-phenotypes, or varying stages of disease progression. Notwithstanding the need to investigate the mechanisms underlying the observed results, our main findings suggest that a multi-method approach to statistical analyses of high-dimensional human metabolomics datasets could effectively broaden the scientific yield from a given study design.

Details

Language :
English
ISSN :
22181989
Database :
OpenAIRE
Journal :
Metabolites; Volume 13; Issue 7; Pages: 802
Accession number :
edsair.multidiscipl..0c8c2b34023ed7e384539cab8c8bf6ba
Full Text :
https://doi.org/10.3390/metabo13070802