基于机器学习方法识别和优化腹型肥胖患者慢性肾脏病的相关因素：来自 NHANES 2005-2018 的研究结果。

Identification and optimization of relevant factors for chronic kidney disease in abdominal obesity patients by machine learning methods: insights from NHANES 2005-2018.

机构信息

Senior Department of Pediatrics, The Seventh Medical Center of Chinese PLA General Hospital, No.5 Nanmen Cang Hutong, Dongcheng District, Beijing, People's Republic of China.

Outpatient Departmentof the 52nd Retired Cadre Center, Beijing, China.

出版信息

Lipids Health Dis. 2024 Nov 26;23(1):390. doi: 10.1186/s12944-024-02384-7.

DOI:10.1186/s12944-024-02384-7

PMID:39593076

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11590401/

Abstract

BACKGROUND

The intake of dietary antioxidants and glycolipid metabolism are closely related to chronic kidney disease (CKD), particularly among individuals with abdominal obesity. Nevertheless, the cumulative effect of multiple comorbid risk factors on the progression and complications of CKD remains inadequately characterized.

METHODS

This study analyzed data from the National Health and Nutrition Examination Survey (NHANES) dat abase (2005-2018), to examine potential factors related to CKD, including glycolipid metabolism, dietary antioxidant intake, and pertinent medical history. To explore the associations between these variables and CKD, the present study used a multivariable-adjusted least absolute shrinkage and selection operator (LASSO) regression model, along with a restricted cubic spline (RCS) model. Furthermore, an optimal predictive model was developed for CKD using ten machine learning algorithms and enhanced model interpretability with the Shapley Additive Explanations (SHAP) method.

RESULTS

A cohort comprising 8,764 eligible individuals (52% male, including 1,839 CKD patients) with abdominal obesity aged 20-85 years were included. The findings revealed significant positive correlations in patients with abdominal obesity between the presence of CKD and age, a history of heart failure, hypertension, diabetes, elevated lipid accumulation product (LAP) and triglyceride glucose-waist circumference (TyG-WC) levels. Conversely, negative correlations were identified between CKD and variables such as sex, high-density lipoprotein cholesterol (HDL-C) levels, and the composite dietary antioxidant index (CDAI). In parallel, RCS regression analysis revealed significant nonlinear associations between the CDAI, HDL-C, TyG-WC, and CKD among patients with abdominal obesity aged 60-80 years. The development of predictive models demonstrated that the CatBoost model surpassed other models, achieving an accuracy of 86.74% on the validation set. The model's area under the receiver operator curve (AUC) and F1 score were 0.938 and 0.889, respectively. The SHAP values revealed that age was the most significant predictor, followed by diabetes history, hypertension, HDL-C levels, CDAI index, TyG-WC, and LAP.

CONCLUSION

CatBoost models, along with glycolipid metabolism indexes and dietary antioxidant intake, are effective for early CKD detection in patients with abdominal obesity.

摘要

背景

饮食抗氧化剂的摄入和糖脂代谢与慢性肾脏病（CKD）密切相关，尤其是在腹部肥胖者中。然而，多种合并症风险因素对 CKD 进展和并发症的累积影响仍描述不足。

方法

本研究分析了国家健康和营养检查调查（NHANES）数据库（2005-2018 年）的数据，以检查与 CKD 相关的潜在因素，包括糖脂代谢、饮食抗氧化剂摄入和相关病史。为了探讨这些变量与 CKD 之间的关系，本研究使用了多变量调整的最小绝对收缩和选择算子（LASSO）回归模型，以及限制立方样条（RCS）模型。此外，使用十种机器学习算法为 CKD 开发了最佳预测模型，并使用 Shapley 加法解释（SHAP）方法增强了模型的可解释性。

结果

本研究纳入了一个 8764 名符合条件的（52%为男性，包括 1839 名 CKD 患者）年龄在 20-85 岁之间的腹部肥胖患者队列。研究结果表明，在腹部肥胖患者中，CKD 与年龄、心力衰竭史、高血压、糖尿病、升高的脂联素和甘油三酯葡萄糖腰围（TyG-WC）水平呈显著正相关。相反，CKD 与性别、高密度脂蛋白胆固醇（HDL-C）水平和复合饮食抗氧化指数（CDAI）等变量呈负相关。同时，RCS 回归分析显示，在年龄在 60-80 岁的腹部肥胖患者中，CDAI、HDL-C、TyG-WC 与 CKD 之间存在显著的非线性关系。预测模型的开发表明，CatBoost 模型优于其他模型，在验证集上的准确率为 86.74%。该模型的接收器操作特征曲线（AUC）和 F1 评分分别为 0.938 和 0.889。SHAP 值显示年龄是最重要的预测因子，其次是糖尿病史、高血压、HDL-C 水平、CDAI 指数、TyG-WC 和 LAP。