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Causal association between tobacco use, fibroblast growth factors, and pulmonary fibrosis: A two-sample Mendelian
Renzhi Yu1, Xiaoyue Han2, Minghuan Wang3
1Department of Respiratory MedicineMudanjiang Medical University, Affiliated Hongqi HospitalMudanjiangChina.
Introduction:
Cigarette smoking is a risk factor for pulmonary fibrosis, but its underlying mechanisms remain unclear. Fibroblast growth factors (FGFs) have been implicated in pulmonary fibrosis, yet their role in smoking-induced pulmonary fibrosis remains unclear. This study employed Mendelian randomization (MR) analysis to investigate the causal relationships among FGFs, smoking, and pulmonary fibrosis.
Methods:
FGFs, smoking behavior, and pulmonary fibrosis data were collected from the IEU Open GWAS Project and the GWAS Catalog. Single-nucleotide polymorphisms were used for instrumental variable selection. Inverse-variance weighted (IVW) was the primary MR method, supplemented by Weighted mode, Weighted median, and MR-Egger regression. Sensitivity analyses included MR-PRESSO, MR-Egger, Cochran's Q, and leave-one-out.
Results:
IVW analysis showed that FGF10 (OR=0.74; 95% CI: 0.57-0.96, p=0.02), FGFR1 (OR=1.38; 95% CI: 1.04-1.84, p=0.03), and FGF19 (OR=0.78; 95% CI: 0.64-0.94, p=0.01) were statistically associated with pulmonary fibrosis risk. MR analysis of smoking behaviors as exposures and FGF10/FGFR1/FGF19 as outcomes showed no significant associations across all MR approaches. MR-Egger regression showed that horizontal multiplicity did not impact analysis except for FGFR3. MR-PRESSO did not identify any outliers. Sensitivity analysis showed that the association results were robust.
Conclusions:
FGF10, FGF19, and FGFR1 may be causally associated with pulmonary fibrosis. However, the MR analysis did not support a causal relationship between smoking and FGFs, suggesting that smoking contributes to pulmonary fibrosis independently of FGFs. This highlights the need for well-designed prospective studies to minimize bias in observational studies and for larger GWAS datasets to enhance future MR analyses.
