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Background: Chronic pain and obesity, together with their corresponding characteristics, are concerning health issues with high socioeconomic burden. The objective of this study is to ascertain the prevalence of chronic pain among individuals residing in the community and examine its association with obesity. Methods: The present study employed a cross-sectional design and analyzed data from three cycles of the National Health and Nutrition Examination Survey (NHANES). Univariate and multivariate logistic regression analysis were performed to examine the relationship between chronic pain and obesity. To evaluate the potential nonlinear association of chronic with body mass index (BMI), the restricted cubic spline (RCS) analysis was performed in multivariable-adjusted models. The researchers conducted subgroup analyses in order to investigate the potential influence of different confounding factors on the relationship between chronic pain and obesity. Results: Our final analysis included a sample size of 13,700 participants with higher prevalence of chronic pain with higher BMI, older age, female sex, lower educational level, smoking, and other pathologies. The prevalence of chronic pain in different BMI groups was 17.0% (underweight), 11.8% (normal weight), 12.9% (overweight), and 17.9% (obesity), respectively. In the fully adjusted model, obesity was associated with a 45% increase in the risk of chronic pain compared with the normal weight. The RCS analyses revealed a nonlinear and J-shaped positive association between BMI and chronic pain (OR 1.45, 95% CI 1.27-1.66, all P for nonlinearity < 0.05). The results of the subgroup analyses indicate that the presence of osteoporosis significantly influenced the relationship between obesity and chronic pain, as evidenced by a statistically significant interaction effect (OR 2.25, 95% CI 1.38-3.68, P for interaction = 0.019). Conclusion: The presence of obesity was found to be significantly correlated with an increased likelihood of experiencing chronic pain among adults living in the United States.
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BACKGROUND: The Corona Virus Disease 2019(COVID-19) pandemic has strained healthcare systems worldwide, necessitating the early prediction of patients requiring critical care. This study aimed to analyze the laboratory examination indicators, CT features, and prognostic risk factors in COVID-19 patients. METHODS: A retrospective study was conducted on 90 COVID-19 patients at the First Affiliated Hospital of Gannan Medical University between December 17, 2022, and March 17, 2023. Clinical data, laboratory examination results, and computed tomography (CT) imaging data were collected. Logistic multivariate regression analysis was performed to identify independent risk factors, and the predictive ability of each risk factor was assessed using the area under the receiver operating characteristic (ROC) curve. RESULTS: Multivariate logistic regression analysis revealed that comorbid diabetes (odds ratio [OR] = 526.875, 95%CI = 1.384-1960.84, P = 0.053), lymphocyte count reduction (OR = 8.773, 95%CI = 1.432-53.584, P = 0.064), elevated D-dimer level (OR = 362.426, 95%CI = 1.228-984.995, P = 0.023), and involvement of five lung lobes (OR = 0.926, 95%CI = 0.026-0.686, P = 0.025) were risk factors for progression to severe COVID-19. ROC curve analysis showed the highest predictive value for 5 lung lobes (AUC = 0.782). Oxygen saturation was positively correlated with normally aerated lung volume and the proportion of normally aerated lung volume (P < 0.05). CONCLUSIONS: The study demonstrated that comorbid diabetes, lymphocyte count reduction, elevated D-dimer levels, and involvement of the five lung lobes are significant risk factors for severe COVID-19. In CT lung volume quantification, normal aerated lung volume and the proportion of normal aerated lung volume correlated with blood oxygen saturation.