Zhang Xin, Mo Jipeng, Yang Kaiyu, Tan Tiewu, Zhao Cuiping, Qin Hui
Department of Emergency Medicine, The Affiliated Changzhou No.2 People's Hospital of Nanjing Medical University, The Third Affiliated Hospital of Nanjing Medical University, Nanjing Medical University, Changzhou, China.
Department of Intensive Care Medicine, The Affiliated Changzhou No.2 People's Hospital of Nanjing Medical University, The Third Affiliated Hospital of Nanjing Medical University, Nanjing Medical University, Changzhou, China.
Front Nutr. 2024 Dec 18;11:1519782. doi: 10.3389/fnut.2024.1519782. eCollection 2024.
Recent research has identified the Low-Carbohydrate Diet (LCD) score as a novel biomarker, with studies showing that LCDs can reduce carbon dioxide retention, potentially improving lung function. While the link between the LCD score and chronic obstructive pulmonary disease (COPD) has been explored, its relevance in the US population remains uncertain. This study aims to explore the association between the LCD score and the likelihood of COPD prevalence in this population.
Data from 16,030 participants in the National Health and Nutrition Examination Survey (NHANES) collected between 2007 and 2023 were analyzed to examine the relationship between LCD score and COPD. Propensity score matching (PSM) was employed to reduce baseline bias. Weighted multivariable logistic regression models were applied, and restricted cubic spline (RCS) regression was used to explore possible nonlinear relationships. Subgroup analyses were performed to evaluate the robustness of the results. Additionally, we employed eight machine learning methods-Boost Tree, Decision Tree, Logistic Regression, MLP, Naive Bayes, KNN, Random Forest, and SVM RBF-to build predictive models and evaluate their performance. Based on the best-performing model, we further examined variable importance and model accuracy.
Upon controlling for variables, the LCD score demonstrated a strong correlation with the odds of COPD prevalence. In compared to the lowest quartile, the adjusted odds ratios (ORs) for the high quartile were 0.77 (95% CI: 0.63, 0.95), 0.74 (95% CI: 0.59, 0.93), and 0.61 (95% CI: 0.48, 0.78). RCS analysis demonstrated a linear inverse relationship between the LCD score and the odds of COPD prevalence. Furthermore, the random forest model exhibited robust predictive efficacy, with an area under the curve (AUC) of 71.6%.
Our study of American adults indicates that adherence to the LCD may be linked to lower odds of COPD prevalence. These findings underscore the important role of the LCD score as a tool for enhancing COPD prevention efforts within the general population. Nonetheless, additional prospective cohort studies are required to assess and validate these results.
最近的研究已将低碳水化合物饮食（LCD）评分确定为一种新的生物标志物，研究表明低碳水化合物饮食可减少二氧化碳潴留，有可能改善肺功能。虽然已经探讨了LCD评分与慢性阻塞性肺疾病（COPD）之间的联系，但其在美国人群中的相关性仍不确定。本研究旨在探讨LCD评分与该人群中COPD患病率可能性之间的关联。
分析了2007年至2023年期间从美国国家健康与营养检查调查（NHANES）的16,030名参与者收集的数据，以研究LCD评分与COPD之间的关系。采用倾向评分匹配（PSM）来减少基线偏差。应用加权多变量逻辑回归模型，并使用受限立方样条（RCS）回归来探索可能的非线性关系。进行亚组分析以评估结果的稳健性。此外，我们采用了八种机器学习方法——提升树、决策树、逻辑回归、多层感知器、朴素贝叶斯、K近邻、随机森林和支持向量机径向基函数——来建立预测模型并评估其性能。基于表现最佳的模型，我们进一步检查了变量重要性和模型准确性。
在控制变量后，LCD评分与COPD患病率的几率显示出很强的相关性。与最低四分位数相比，高四分位数的调整优势比（OR）分别为0.77（95%可信区间：0.63，0.95）、0.74（95%可信区间：0.59，0.93）和0.61（95%可信区间：0.48，0.78）。RCS分析表明LCD评分与COPD患病率几率之间存在线性反比关系。此外，随机森林模型表现出强大的预测效力，曲线下面积（AUC）为71.6%。
我们对美国成年人的研究表明，坚持低碳水化合物饮食可能与较低的COPD患病率几率相关。这些发现强调了LCD评分作为加强普通人群中COPD预防工作工具的重要作用。尽管如此，仍需要额外的前瞻性队列研究来评估和验证这些结果。
