PDF(Pubmed)
Abstract：
UNASSIGNED: The co-occurrence of kidney disease in patients with type 2 diabetes (T2D) is a major public health challenge. Although early detection and intervention can prevent or slow down the progression, the commonly used estimated glomerular filtration rate (eGFR) based on serum creatinine may be influenced by factors unrelated to kidney function. Therefore, there is a need to identify novel biomarkers that can more accurately assess renal function in T2D patients. In this study, we employed an interpretable machine-learning framework to identify plasma metabolomic features associated with GFR in T2D patients.
UNASSIGNED: We retrieved 1626 patients with type 2 diabetes (T2D) in Liaoning Medical University First Affiliated Hospital (LMUFAH) as a development cohort and 716 T2D patients in Second Affiliated Hospital of Dalian Medical University (SAHDMU) as an external validation cohort. The metabolite features were screened by the orthogonal partial least squares discriminant analysis (OPLS-DA). We compared machine learning prediction methods, including logistic regression (LR), support vector machine (SVM), random forest (RF), and eXtreme Gradient Boosting (XGBoost). The Shapley Additive exPlanations (SHAP) were used to explain the optimal model.
UNASSIGNED: For T2D patients, compared with the normal or elevated eGFR group, glutarylcarnitine (C5DC) and decanoylcarnitine (C10) were significantly elevated in GFR mild reduction group, and citrulline and 9 acylcarnitines were also elevated significantly (FDR<0.05, FC > 1.2 and VIP > 1) in moderate or severe reduction group. The XGBoost model with metabolites had the best performance: in the internal validate dataset (AUROC=0.90, AUPRC=0.65, BS=0.064) and external validate cohort (AUROC=0.970, AUPRC=0.857, BS=0.046). Through the SHAP method, we found that C5DC higher than 0.1μmol/L, Cit higher than 26 μmol/L, triglyceride higher than 2 mmol/L, age greater than 65 years old, and duration of T2D more than 10 years were associated with reduced GFR.
UNASSIGNED: Elevated plasma levels of citrulline and a panel of acylcarnitines were associated with reduced GFR in T2D patients, independent of other conventional risk factors.
UNASSIGNED: We retrieved 1626 patients with type 2 diabetes (T2D) in Liaoning Medical University First Affiliated Hospital (LMUFAH) as a development cohort and 716 T2D patients in Second Affiliated Hospital of Dalian Medical University (SAHDMU) as an external validation cohort. The metabolite features were screened by the orthogonal partial least squares discriminant analysis (OPLS-DA). We compared machine learning prediction methods, including logistic regression (LR), support vector machine (SVM), random forest (RF), and eXtreme Gradient Boosting (XGBoost). The Shapley Additive exPlanations (SHAP) were used to explain the optimal model.
UNASSIGNED: For T2D patients, compared with the normal or elevated eGFR group, glutarylcarnitine (C5DC) and decanoylcarnitine (C10) were significantly elevated in GFR mild reduction group, and citrulline and 9 acylcarnitines were also elevated significantly (FDR<0.05, FC > 1.2 and VIP > 1) in moderate or severe reduction group. The XGBoost model with metabolites had the best performance: in the internal validate dataset (AUROC=0.90, AUPRC=0.65, BS=0.064) and external validate cohort (AUROC=0.970, AUPRC=0.857, BS=0.046). Through the SHAP method, we found that C5DC higher than 0.1μmol/L, Cit higher than 26 μmol/L, triglyceride higher than 2 mmol/L, age greater than 65 years old, and duration of T2D more than 10 years were associated with reduced GFR.
UNASSIGNED: Elevated plasma levels of citrulline and a panel of acylcarnitines were associated with reduced GFR in T2D patients, independent of other conventional risk factors.
摘要：
■2型糖尿病（T2D）患者并发肾脏疾病是一项重大的公共卫生挑战。尽管早期发现和干预可以预防或减缓进展，基于血清肌酐的常用估算肾小球滤过率(eGFR)可能受到与肾功能无关的因素的影响.因此,有必要鉴定能够更准确评估T2D患者肾功能的新型生物标志物.在这项研究中,我们采用可解释的机器学习框架来识别与T2D患者GFR相关的血浆代谢组学特征.
■我们检索了辽宁医科大学附属第一医院（LMUFAH）的1626例2型糖尿病（T2D）患者作为发展队列和大连医科大学附属第二医院（SAHDMU）的716例T2D患者作为外部验证队列。通过正交偏最小二乘判别分析（OPLS-DA）筛选代谢物特征。我们比较了机器学习预测方法，包括逻辑回归(LR)，支持向量机(SVM)，随机森林(RF)，和极限梯度提升（XGBoost）。使用Shapley加法扩张（SHAP）来解释最佳模型。
■对于T2D患者，与正常或升高的eGFR组相比，GFR轻度降低组谷氨酰肉碱(C5DC)和癸酸酰肉碱(C10)显著升高,瓜氨酸和9种酰基肉碱在中度或重度减量组中也显着升高（FDR<0.05，FC>1.2和VIP>1）。具有代谢物的XGBoost模型具有最佳性能：在内部验证数据集（AUROC=0.90，AUPRC=0.65，BS=0.064）和外部验证队列（AUROC=0.970，AUPRC=0.857，BS=0.046）中。通过SHAP方法,我们发现C5DC高于0.1μmol/L,Cit高于26μmol/L,甘油三酯高于2mmol/L，年龄大于65岁，T2D持续时间超过10年与GFR降低相关。
■T2D患者血浆瓜氨酸和一组酰基肉碱水平升高与GFR降低相关，独立于其他常规风险因素。
■我们检索了辽宁医科大学附属第一医院（LMUFAH）的1626例2型糖尿病（T2D）患者作为发展队列和大连医科大学附属第二医院（SAHDMU）的716例T2D患者作为外部验证队列。通过正交偏最小二乘判别分析（OPLS-DA）筛选代谢物特征。我们比较了机器学习预测方法，包括逻辑回归(LR)，支持向量机(SVM)，随机森林(RF)，和极限梯度提升（XGBoost）。使用Shapley加法扩张（SHAP）来解释最佳模型。
■对于T2D患者，与正常或升高的eGFR组相比，GFR轻度降低组谷氨酰肉碱(C5DC)和癸酸酰肉碱(C10)显著升高,瓜氨酸和9种酰基肉碱在中度或重度减量组中也显着升高（FDR<0.05，FC>1.2和VIP>1）。具有代谢物的XGBoost模型具有最佳性能：在内部验证数据集（AUROC=0.90，AUPRC=0.65，BS=0.064）和外部验证队列（AUROC=0.970，AUPRC=0.857，BS=0.046）中。通过SHAP方法,我们发现C5DC高于0.1μmol/L,Cit高于26μmol/L,甘油三酯高于2mmol/L，年龄大于65岁，T2D持续时间超过10年与GFR降低相关。
■T2D患者血浆瓜氨酸和一组酰基肉碱水平升高与GFR降低相关，独立于其他常规风险因素。
