Abstract
BACKGROUND: The CRP-Albumin-Lymphocyte (CALLY) index, a novel inflammatory biomarker combining serum albumin, lymphocyte count, and C-reactive protein (CRP), has been proposed for clinical use. This study aimed to investigate the association between CALLY index and Asthma-COPD Overlap (ACO) in the general US population. METHODS: We analyzed data from 6,797 participants aged ≥ 40 years from the 2015-2018 National Health and Nutrition Examination Survey (NHANES). Participants were categorized into quartiles based on natural logarithmic transformed (ln) CALLY index. ACO was defined as self-reported physician-diagnosed asthma and COPD. Logistic regression models were used to examine the association between ln CALLY and ACO, adjusting for potential confounders across three models. Generalized additive models, subgroup analyses, and receiver operating characteristic (ROC) curve analysis were also performed. RESULTS: The prevalence of ACO across the four CALLY quartiles was 5.56%, 1.89%, 1.54%, and 0.66%. In the fully adjusted model, for each 1-unit increase in ln CALLY, the risk of ACO decreased by 43% (OR = 0.57, 95% CI: 0.44-0.73, P = 0.001). Compared with Q1, the risk of ACO in Q2, Q3, and Q4 was reduced by 63% (OR = 0.37), 66% (OR = 0.34), and 87% (OR = 0.13), respectively (P for trend = 0.003). Generalized additive models showed a non-linear negative relationship (P < 0.001). Subgroup analysis revealed that the association remained consistent across different sexes, age groups, races, smoking status, and disease statuses (arthritis, DM, and hypertension). ROC curve analysis indicated moderate predictive ability of ln CALLY for ACO (AUC = 0.675, 95% CI: 0.636-0.714), with an optimal cutoff value of 8.007 (sensitivity 0.669, specificity 0.598). CONCLUSION: Higher CALLY index is independently associated with lower risk of ACO, suggesting its potential value as a biomarker for ACO risk assessment in clinical practice. By integrating inflammation, immune, and nutritional status evaluation, the CALLY index offers a novel perspective for early identification of high-risk individuals in clinical practice.