Prediction of Chronic Low Back Pain Risk Based on Dietary Trace Elements Using Multiple Machine Learning Models and Dual Interpretability Frameworks

Summary

This study investigates associations between dietary trace elements and chronic low back pain using NHANES data. Interpretable machine learning models are applied to evaluate predictive performance and identify nutrients associated with reduced risk, providing insights into potential dietary factors linked to low back pain.

Abstract

The relationship between dietary trace elements and the risk of chronic low back pain (LBP) remains unclear. Data from the National Health and Nutrition Examination Survey (NHANES) 2001–2004 and 2009–2010 cycles were analyzed. Multicollinearity was assessed using Spearman’s correlation and variance inflation factors (VIF), and feature selection was performed using the Boruta algorithm. Six machine learning models were subsequently developed, with SHAP and LIME applied to enhance interpretability and identify key associations between dietary trace elements and LBP risk. Among the evaluated models, Random Forest demonstrated the best predictive performance, both when incorporating demographic variables and when using dietary trace elements alone. Interpretability analyses consistently identified several dietary components—including moisture, theobromine, calcium, caffeine, sodium, and vitamin C—as inversely associated with LBP risk. Although model discrimination was moderate (AUC ≈ 0.60–0.72), these findings reveal clinically relevant patterns that may support population-level risk stratification and highlight potentially modifiable dietary factors for future investigation.

Introduction

Chronic low back pain (LBP) is among the most prevalent musculoskeletal disorders globally and has significant socioeconomic costs associated with disability, healthcare, and loss of quality of life. Epidemiological data indicate that 7–10% of adults report LBP globally, with lifetime prevalence sometimes reaching 84%¹. It is most common in low/middle-income countries, where work-related factors, poor access to health services, and socio-economic inequity increase risk, with prevalence often exceeding 20% among working-age adults². Adolescents and the elderly, particularly, are more vulnerable to structural and deg....

Protocol

This investigation was approved by the National Center for Health Statistics (NCHS) Ethics Review Board, and informed consent was obtained from all participants. Publicly available NHANES data were used in compliance with relevant guidelines.

Study population

The National Health and Nutrition Examination Survey (NHANES), conducted by the NCHS, provides nationally representative data on the health and nutritional status of the non-institutionalized U.S. population. Data from the 2001–2004 and 2009–2010 cycles were retrieved. Participants aged ≥18 years were included, while individu....

Results

Baseline characteristics by chronic LBP status

Baseline characteristics stratified by chronic LBP status are described in Table 1. The final analytic cohort included 8,710 participants from NHANES 2001–2004 and 2009–2010, with a mean age of 49.13 years (SD = 18.43). Of these, 4,528 (51.99%) were women and 4,182 (48.01%) were men. Overall, 3,838 participants reported having LBP (mean age 48.62 years; SD = 17.69). Compared to participants without LBP, those with.......

Discussion

This study used NHANES data and applied several algorithms, finding an association between dietary micronutrients and chronic LBP. Importantly, given the cross-sectional observational design, these findings reflect predictive associations rather than causal relationships, and the machine learning models cannot establish causality between micronutrient intake and LBP risk. After feature selection and collinearity testing, the Random Forest appeared to give the best predictions, both with demographics included and just die.......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
Automated Multiple-Pass Method (AMPM)	National Center for Health Statistics (NCHS), CDC	N/A	Dietary assessment system used in NHANES 24-hour dietary recall interviews to improve reporting accuracy
Boruta Algorithm	Open-source (R package: Boruta)	N/A	Feature selection method based on random forests; used to identify relevant variables through shadow feature comparison
Computer-Assisted Personal Interviewing (CAPI)	National Center for Health Statistics (NCHS), CDC	N/A	Interview system used by trained personnel to collect questionnaire data
National Health and Nutrition Examination Survey (NHANES)	National Center for Health Statistics (NCHS), CDC	N/A	Nationwide survey providing demographic, dietary, and health-related data
Prostate Conditions Questionnaire (KIQ)	National Center for Health Statistics (NCHS), CDC	N/A	Questionnaire source used to assess back pain information
Random Forest Algorithm	Open-source (e.g., R / Python implementations)	N/A	Machine learning model used in feature selection and classification
Synthetic Minority Over-sampling Technique (SMOTE)	Open-source (e.g., DMwR / imbalanced-learn)	N/A	Oversampling technique used to address class imbalance by generating synthetic minority samples