AUTHOR=Zhang Weiye , Li Yan , Shao Pengwei , Du Yuxuan , Zhao Ke , Zhan Jiawen , Tan Lee A. 

TITLE=Association of weight-adjusted waist index and body mass index with chronic low back pain in American adults: a retrospective cohort study and predictive model development based on machine learning algorithms (NHANES 2009–2010)

JOURNAL=Frontiers in Public Health

VOLUME=Volume 13 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1617732

DOI=10.3389/fpubh.2025.1617732

ISSN=2296-2565

ABSTRACT=ObjectiveThis study utilized National Health and Nutrition Examination Survey (NHANES) data to investigate the associations between weight-adjusted waist index (WWI), body mass index (BMI), and chronic low back pain (CLBP) risk, and to develop machine learning models to assess the predictive capacity of WWI for CLBP.MethodsThis cross-sectional analysis was based on NHANES 2009–2010 data. Weighted logistic regression models were employed to evaluate associations between WWI, BMI, and CLBP, with subgroup analyses, smooth curve fitting, and threshold effect analyses conducted to enhance result robustness. Receiver operating characteristic (ROC) curves were plotted to determine which indicator demonstrated stronger association with CLBP. Subsequently, permutation feature importance was applied for machine learning feature selection, random undersampling was utilized to address data imbalance, and the dataset was randomly divided into training and testing sets at a 7:3 ratio. Six machine learning algorithms were employed to predict CLBP occurrence and identify the optimal algorithm.ResultsA total of 4,687 participants were included. Significant differences were observed between CLBP and non-CLBP groups in age, diabetes prevalence, smoking status, BMI, WWI, and education level. Both WWI and BMI showed significant associations with CLBP; after covariate adjustment, WWI demonstrated stronger and more consistent associations across quartiles. Subgroup analyses, nonlinear analyses, and ROC analyses further supported these findings. Machine learning feature selection identified 19 variables, with the Random Forest model demonstrating optimal performance.ConclusionBoth WWI and BMI were associated with increased CLBP risk, with WWI potentially serving as a more sensitive predictive indicator. Prospective studies are needed to validate causal relationships. The Random Forest machine learning model demonstrated high accuracy in CLBP prediction.