The Relationship between Change of Weight and Chronic Low Back Pain in Population over 50 Years of Age: A Nationwide Cross-Sectional Study

Abstract

Introduction: There is increasing evidence supporting an association between obesity and low back pain (LBP). However, the association between weight change and LBP in the general population is poorly understood. We investigated the relationship between weight change and LBP in a representative sample of the Korean general population from a nationwide survey.

Methods: We analyzed data collected from the Korea National Health and Nutrition Examination Survey VI (2013-2015). Chronic LBP was defined as LBP lasting over 30 days in the last 3 months in the self-report health survey. Weight change was defined as the difference in weight from one year prior, and the amount of change was divided into no change, 3-6 kg, and ≥6 kg. Sampling weights were used to generate representative estimates for the general Korean population.

Results: Overall, 6629 (12.0%) and 1848 (11.5%) participants were in the non-LBP and LBP groups, respectively. On multiple regression analysis, weight gain was significantly associated with LBP (adjusted odds ratio (OR) 1.29, p = 0.011), compared with no weight change. Weight gain of ≥6 kg was particularly closely associated with LBP (adjusted OR 1.42, p = 0.037), compared with no weight change. No association was found between LBP and weight loss.

Conclusion: Weight gain is significantly associated with chronic LBP and, in particular, the greater the amount of weight gain, the stronger the association with an increased risk of chronic LBP. Clinicians should carefully monitor weight gain in LBP patients.

Keywords: body weight changes; cross-sectional study; low back pain; odds ratio.

Figure 1

Flow chart of study subjects from the 2013 to 2015 Korea National Health and Nutrition Examination Surveys (KNHANES VI-1, VI-2, and VI-3). LBP, low back pain.

Figure 2

Association between change of weight and low back pain. Model was adjusted by age, sex, and other environmental factors such as obesity, smoking, alcohol consumption, educational level, household income, occupation, physical activity, duration of sleep, and comorbidities.