CRP-Albumin-Lymphocyte index (CALLYI) as a risk-predicting biomarker in association with osteoarthritis

As a novel biomarker, the C-reactive protein-Albumin-Lymphocyte Index (CALLYI) offers a comprehensive evaluation of the human body from three perspectives. However, the association between CALLYI and the incidence of osteoarthritis (OA) remains unclear. This cross-sectional study investigates the potential relationship between CALLYI and OA in US adults, develops a clinical prediction model, and validates its effectiveness. The study cohort consisted of 18,624 U.S. adults who participated in the National Health and Nutrition Examination Survey (NHANES) from 1999 to 2010. The CALLYI was calculated using the formula: albumin * lymphocytes / CRP * 10. Three weighted multiple regression models were constructed to investigate the correlation between CALLYI and OA. Restricted cubic splines (RCS) were employed to evaluate the nonlinear relationship between these two variables. Subgroup analyses were conducted to examine interactions. Univariate logistic regression, binary logistic regression, and least absolute shrinkage and selection operator (LASSO) were utilized for variable selection in the prediction model. Decision curve analysis (DCA) and receiver operating characteristic (ROC) curve analysis were applied to assess the predictive performance of the models. The total sample size analyzed in this study was 18,624, of which 1,977 (10.62%) were diagnosed with OA. And the mean value of CALLYI was 5.13 (2.12,12.86). The multivariate logistic regression model revealed a negative correlation between elevated CALLYI and OA. The fully adjusted Model 3 demonstrated a significant 28% reduction in OA risk in the Q4 compared to the Q1 of CALLYI (OR = 0.72 95% CI: 0.59–0.88, p = 0.001). Subgroup analyses did not reveal any significant interactions (p > 0.05). Additionally, a significant non-linear relationship between CALLYI and OA using RCS (p < 0.0001). After variable screening, we constructed an OA prediction model incorporating CALLYI, and the results were visualized using a nomogram. The area under the curve (AUC) was 0.825 (95% CI: 0.817–0.834), and DCA indicated that the model holds clinical significance. This study, utilizing NHANES statistics, is the first to establish a nonlinear negative relationship between CALLYI and OA, with no significant interaction observed in subgroup analyses. In the OA prediction model incorporating CALLYI, we validated the effectiveness and clinical utility of this model, providing evidence that CALLYI can serve as a biomarker for OA risk prediction. Nevertheless, larger multicenter prospective cohort studies are necessary to mitigate the limitations inherent in cross-sectional designs and self-reported OA diagnoses.