Association between life’s crucial 9 and lung health: a population-based study
Abstract Background As a cardiovascular health (CVH) assessment tool, Life’s Crucial 9 (LC9) is often associated with diverse chronic health indicators. However, no study has yet explored the association of LC9 with multifactorial components of lung health. Thus, this study aimed to investigate the...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2025-05-01
|
| Series: | BMC Pulmonary Medicine |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12890-025-03684-z |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Abstract Background As a cardiovascular health (CVH) assessment tool, Life’s Crucial 9 (LC9) is often associated with diverse chronic health indicators. However, no study has yet explored the association of LC9 with multifactorial components of lung health. Thus, this study aimed to investigate the correlation of LC9 with lung health. Methods This cross-sectional study used data from the National Health and Nutrition Examination Survey (NHANES), which covers individuals aged 40 years and older with complete LC9 and lung health data. Multiple regression was employed in linear relationships investigation, while Restricted Cubic Spline (RCS) was used to explore nonlinear relationships. Subgroup analyses and interaction tests demonstrated the stability of associations. Combining LC9 to build a Light Gradient Boosting Machine (LightGBM) machine learning (ML) model to predict lung health, Shapley Additive Explanations (SHAP) sorted the contribution of LC9 components to the model. Results From a total of 10,461 study participants, 1725 had low CVH, 7476 had moderate CVH, and 1260 had high CVH. There was a strong positive correlation between LC9 score and lung health. This association remained consistent across subcomponent strata. RCS analysis revealed non-linear associations between LC9 and respiratory outcomes, including cough, asthma, and COPD. The LightGBM model incorporating LC9 demonstrated excellent predictive performance for lung health, with favorable metrics in Area Under the Curve (AUC), accuracy, and specificity. SHAP analysis identified depression, nicotine exposure, and BMI scores as the predominant contributors among LC9 components to the model’s predictive capability. Conclusion Individuals with better CVH as assessed by LC9 tended to have better lung health. The combination of the LightGBM model could achieve better prediction results. |
|---|---|
| ISSN: | 1471-2466 |