The risk of secondary spontaneous pneumothorax in patients with chronic obstructive pulmonary disease in Taiwan

Relationships between vitamin C intake and COPD assessed by machine learning approaches from the NHANES (2017-2023)

Background

This research aims to explore the possible link between Vitamin C Intake (VCI) and the incidence of Chronic Obstructive Pulmonary Disease (COPD) in Americans aged over 20.

Methods

This study analyzed data from 10,757 participants with or without COPD from NHANES (2017-2023). The primary exposure variable, VCI, was grouped by quartiles. Missing data were handled via multiple imputations. A Directed Acyclic Graph (DAG) was used to pre-identify VCI -and COPD-related covariates. Variance Inflation Factor (VIF) eliminated highly collinear variables. Machine learning methods (LASSO, Random Forest, and XGBoost) screened variables. A weighted multivariate logistic regression model explored the VCI-COPD relationship. Restricted Cubic Spline (RCS) and threshold analysis examined non-linear relationships. Subgroup analysis and interaction tests ensured reliability. A nomogram showed the predictive factors' importance for COPD. Model performance was reported using the Area Under the Receiver Operating Characteristic Curve (AUC).

Results

In all models, we found that there was a negative correlation between VCI (≥50.1 mg/day) and the prevalence of COPD. The RCS and threshold analysis results show a negative correlation between COPD and VCI (≤135.6 mg/day). Subgroup analysis shows a negative association between VCI and the prevalence of COPD, specifically among females and individuals with dietary fiber intake in the second quartile (Q2). The AUC results show that our model has good diagnostic performance.

Limitations

The cross-sectional design limits causal inference and lacks external validation.

Conclusion

An elevated VCI within 50.1-135.6 is linked to a decreased risk for COPD.