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)

Bibliographic Data

ID22086729
AuthorsWeiye Zhang (0000-0002-1137-1750, University of California, San Francisco, corresponding author), Yan Li (0000-0001-6650-7726, Chinese Academy of Medical Sciences & Peking Union Medical College), Pengwei Shao (Beijing University of Chinese Medicine), Yuxuan Du (0009-0008-8304-5058, Chinese Academy of Medical Sciences & Peking Union Medical College), Ke Zhao (0000-0002-9974-4901, Chinese Academy of Medical Sciences & Peking Union Medical College), Jiawen Zhan (Chinese Academy of Medical Sciences & Peking Union Medical College, corresponding author), Lee A Tan (0000-0003-3497-3321, University of California, San Francisco)
Year2025
Volume13
Pages1617732-1617732
Publication date2025-07-11
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueFrontiers in Public Health (JOURNAL)
Journal identifiersISSN: 2296-2565 • E-ISSN: 2296-2565
PublisherFrontiers Media SA (PUBLISHER • CH)
DOI10.3389/fpubh.2025.1617732
PMID40717955
OpenAlexW4412185140
LanguageEN
References cited49

Objective: This 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. Methods: This 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. Results: A 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. Conclusion: Both 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

Body mass index · Confidence interval · Environmental health · Feature selection · Logistic regression · Machine learning · National Health and Nutrition Examination Survey · Population · Quartile · Random forest · Receiver operating characteristic · Waist · Computer Science · Fibromyalgia and Chronic Fatigue Syndrome Research · Medicine · Musculoskeletal pain and rehabilitation · Occupational Health and Performance · Artificial Intelligence · Internal Medicine

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