Abstract
BACKGROUND: Chronic obstructive pulmonary disease (COPD), the third leading cause of global mortality, imposes substantial socioeconomic burdens. Existing therapies, such as smoking cessation and non-invasive ventilation, primarily alleviate symptoms without arresting disease progression. Comorbidities, including cardiovascular disease and metabolic syndrome, exacerbate functional decline, yet the causal role of dyslipidemia in COPD pathogenesis remains unclear. This study seeks to establish a causal link between hypercholesterolemia and COPD while identifying potential biomarkers and therapeutic targets. METHODS: Leveraging cross-sectional data from the National Health and Nutrition Examination Survey (NHANES), we employed Mendelian randomization (MR) analysis using 71 single-nucleotide polymorphisms (SNPs) associated with hypercholesterolemia, integrated with bioinformatics tools for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Demographic variables (age, sex, body mass index [BMI]) and dietary factors were compared between COPD patients and controls. RESULTS: Univariate analyses identified significant differences in demographics and dietary patterns between COPD and non-COPD groups (P < 0.05). Multivariate logistic regression revealed: (1) a reduced risk of COPD with elevated total cholesterol levels (odds ratio [OR]: 0.815, 95% confidence interval [CI]: 0.721-0.923, P = 0.001); and (2) increased COPD risk associated with higher age and BMI. Bioinformatics analyses pinpointed atorvastatin, fenofibrate, and pravastatin as candidate therapeutics. Gene interaction networks and pathway enrichment highlighted roles for lipid homeostasis and cholesterol metabolism. CONCLUSION: Analysis of NHANES data demonstrates an inverse association between cholesterol levels and COPD prevalence, with MR confirming a causal relationship. These findings underscore targetable pathways and suggest repurposing statins and fibrates, meriting further mechanistic studies and clinical trials for validation.