PULMONOLOGY / BASIC RESEARCH
Causal analysis of plasma fatty acids and pneumonia: identifying diagnostic biomarkers through transcriptome-wide association study
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1
Department of Rehabilitation Medicine, Lishui Hospital of Wenzhou Medical University, The First Affiliated Hospital of Lishui University, Lishui People’s Hospital, Lishui, Zhejiang 323000, China
2
Department of Neurosurgery, Lishui Hospital of Wenzhou Medical University, The First Affiliated Hospital of Lishui University, Lishui People’s Hospital, Lishui, Zhejiang 323000, China
3
Department of Hepatology and Infectious Diseases, Lishui Hospital of Wenzhou Medical University, The First Affiliated Hospital of Lishui University, Lishui People’s Hospital, Lishui, Zhejiang 323000, China
Submission date: 2025-03-14
Final revision date: 2025-05-19
Acceptance date: 2025-07-18
Online publication date: 2025-09-06
Corresponding author
Xiaomei Wang
Department of Hepatology
and Infectious Diseases
Lishui Hospital of Wenzhou
Medical University,
The First Affiliated Hospital
of Lishui University,
Lishui People’s Hospital,
No. 15 Dazhong Street
Liandu District, Lishui
323000, Zhejiang, China
Phone: +86-18957092292
KEYWORDS
TOPICS
ABSTRACT
Introduction:
Fatty acids mediate pulmonary inflammation through cytokine regulation, interactions with the tryptophan-kynurenine pathway, and mediators such as 18-HEPE that boost interferon-λ. To systematically dissect these mechanisms and their translational implications, we pioneered a novel framework integrating bidirectional two-sample Mendelian randomization (MR) with multi-algorithm machine learning. This approach not only quantifies causal relationships between fatty acids and inflammatory outcomes but also identifies clinically actionable diagnostic biomarkers.
Material and methods:
We obtained single nucleotide polymorphisms (SNPs) significantly associated with eight plasma fatty acids from genome-wide association study (GWAS) summary statistics and used them as instrumental variables in bidirectional two-sample MR across six pneumonia phenotypes (pneumonia, asthma-related pneumonia, bacterial pneumonia, critical pneumonia, viral pneumonia, and lobar pneumonia). Causal estimates were calculated using inverse variance weighting (IVW), which combines SNP-specific Wald ratios weighted by the inverse of their variance, with MR-Egger and weighted median approaches for sensitivity analysis. Transcriptomic data were then analyzed by LASSO regression, support vector machine recursive feature elimination and random forest to identify fatty acid metabolism–related biomarker candidates.
Results:
MR analysis suggested potential causal associations between omega-6 fatty acids and critical pneumonia (OR = 1.28, 95% CI: 1.01–1.61, p = 0.038), linoleic acids (LA) and bacterial pneumonia (OR = 0.85, 95% CI: 0.73–0.99, p = 0.047), and docosahexaenoic fatty acids (DHA) and pneumonia (OR = 0.83, 95% CI: 0.74–0.93, p = 0.002). Moreover, ACAA1 and OLAH, which are genes involved in fatty acid metabolism, were identified as potential candidate biomarkers for pneumonia.
Conclusions:
Our study employed MR analysis to establish a causal association of omega-6, LA, and DHA with pneumonia. Additionally, through transcriptomic analysis, we identified plasma fatty acid metabolism-associated biomarkers that may serve as diagnostic indicators for pneumonia.
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