Optimizing clinical strategies for nutritional and immune indices prediction of chronic lung diseases: a cross-sectional study from NHANES 2007-2012

优化营养和免疫指标预测慢性肺病的临床策略:一项基于2007-2012年NHANES数据的横断面研究

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Abstract

BACKGROUND: Composite nutritional and immune indices (NIIs) have been associated with chronic lung diseases (CLDs). However, systematic research on their associations with different CLD subtypes and potential threshold patterns remains limited. OBJECTIVES: To examine cross-sectional associations between multiple NII and CLD subtypes, and to explore potential linear/nonlinear relationships and threshold ranges using a representative dataset. DESIGN: Cross-sectional, population-based study using National Health and Nutrition Examination Survey (NHANES) data. METHODS: Data were obtained from the US NHANES from 2007 to 2012. Participants aged 18-79 years with complete blood and baseline data were included. Survey-weighted regression models were used to assess associations between NIIs and CLDs. Restricted cubic spline (RCS) regression models were employed to explore linear/nonlinear relationships and potential threshold ranges. RESULTS: The study included 5837 participants (mean age 49.82 ± 16.15). Regression analyses revealed significant associations between NIIs and CLDs across different ranges. RCS analysis identified thresholds for each NII: prognostic nutritional index (PNI) levels > 46.04 showed a significant inverse association with emphysema and chronic bronchitis. Elevated platelet-to-lymphocyte ratio (PLR) (>113.57) and MLR (>0.14) were positively associated with these conditions. systemic immune-inflammation index (SII) (>429.43) and neutrophil-to-lymphocyte ratio (NLR) (>0.99) were associated with a higher prevalence of asthma, emphysema, and chronic bronchitis. CONCLUSION: In this cross-sectional population-based study, NIIs were associated with different CLD subtypes, with evidence of potential threshold patterns. These findings may help inform future epidemiological studies and hypothesis generation, while causal inference and clinical application require further longitudinal validation.

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