基于膳食营养摄入的机器学习衍生风险评分，用于早期检测和预测保留率受损的肺活量
A Machine Learning-Derived Risk Score Based on Dietary Nutrient Intake for Early Detection and Prognostic Prediction of Preserved Ratio Impaired Spirometry.
摘要(中文译文)
保留比值受损肺功能（PRISm）是一种亚临床肺部表型，与慢性阻塞性肺疾病（COPD）、心血管疾病及全因死亡风险增加相关。PRISm的早期识别和分层预防仍是临床难题。利用美国国家健康与营养调查（NHANES）2007-2012年的数据，我们开发并验证了一个堆叠机器学习（ML）模型，该模型整合了膳食摄入和人口学特征，以生成连续的PRISm风险评分。数据集被划分为训练集、验证集和测试集。采用ROC曲线和校准评估模型性能。使用逻辑回归和Kaplan-Meier分析评估风险评分与不良健康结局之间的关联。进行亚组分析以评估生活方式在不同风险分层中的影响。堆叠ML模型表现出较强的预测能力，在测试集中AUC达到0.818。风险评分与多种慢性疾病显著相关，包括高血压、糖尿病、心血管疾病和COPD。高风险个体的死亡率较低风险组显著升高。在低风险组中，坚持健康生活方式与不良结局的发生几率显著降低相关，而在高风险组中未观察到这种关联。本研究提出了一种基于膳食和人口学特征的无创、数据驱动的PRISm风险预测和健康结局分层模型。PRISm风险评分可能有助于早期筛查并为个性化预防策略提供依据。
Abstract
Preserved Ratio Impaired Spirometry (PRISm) is a subclinical pulmonary phenotype associated with increased risk of chronic obstructive pulmonary disease (COPD), cardiovascular disease, and all-cause mortality. Early identification and stratified prevention of PRISm remain a clinical challenge. Using data from the US National Health and Nutrition Examination Survey (NHANES) 2007-2012, we developed and validated a stacked machine learning (ML) model integrating dietary intake and demographic features to generate a continuous PRISm risk score. The dataset was split into training, validation, and test sets. Model performance was evaluated using ROC curves and calibration. The associations between the risk score and adverse health outcomes were assessed using logistic regression and Kaplan-Meier analysis. Subgroup analysis was performed to assess the impact of lifestyle across risk strata. The stacked ML model demonstrated strong predictive ability, achieving an AUC of 0.818 in the test set. The risk score was significantly associated with multiple chronic conditions, including hypertension, diabetes, cardiovascular disease, and COPD. High-risk individuals had substantially increased mortality rates compared to the low-risk group. In the low-risk group, adherence to a healthy lifestyle was associated with significantly lower odds of adverse outcomes, while no such association was observed in the high-risk group. This study presents a non-invasive, data-driven model for PRISm risk prediction and health outcome stratification based on dietary and demographic features. The PRISm risk score may aid early screening and inform personalized prevention strategies.
如何引用
AMA
Qihang Xie, Haoran Qu, Siyu Xie, Rui Lan, Jianfeng Li. A Machine Learning-Derived Risk Score Based on Dietary Nutrient Intake for Early Detection and Prognostic Prediction of Preserved Ratio Impaired Spirometry.. International journal of chronic obstructive pulmonary disease. 2026; doi:10.2147/COPD.S562473.
APA
Qihang Xie, Haoran Qu, Siyu Xie, Rui Lan, Jianfeng Li (2026). A Machine Learning-Derived Risk Score Based on Dietary Nutrient Intake for Early Detection and Prognostic Prediction of Preserved Ratio Impaired Spirometry.. International journal of chronic obstructive pulmonary disease. https://doi.org/10.2147/COPD.S562473
GB/T 7714
Qihang Xie, Haoran Qu, Siyu Xie, Rui Lan, Jianfeng Li. A Machine Learning-Derived Risk Score Based on Dietary Nutrient Intake for Early Detection and Prognostic Prediction of Preserved Ratio Impaired Spirometry.[J]. International journal of chronic obstructive pulmonary disease, 2026 doi:10.2147/COPD.S562473.
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