Effects of Various Heavy Metal Exposures on Insulin Resistance in Non-diabetic Populations: Interpretability Analysis from Machine Learning Modeling Perspective

作者全名："Liu, Jun; Li, Xingyu; Zhu, Peng"

作者地址："[Liu, Jun; Zhu, Peng] Chongqing Med Univ, Affiliated Hosp 2, Dept Gastrointestinal Surg, 74 Linjiang Rd, Chongqing 400010, Peoples R China; [Li, Xingyu] Chongqing Med Univ, Affiliated Hosp 2, Cardiovasc Med, Chongqing, Peoples R China"

通信作者："Zhu, P (通讯作者)，Chongqing Med Univ, Affiliated Hosp 2, Dept Gastrointestinal Surg, 74 Linjiang Rd, Chongqing 400010, Peoples R China."

来源：BIOLOGICAL TRACE ELEMENT RESEARCH

ESI学科分类：BIOLOGY & BIOCHEMISTRY

WOS号：WOS:001171660700001

JCR分区：Q2

影响因子：3.4

年份：2024

卷号：　

期号：　

开始页：　

结束页：　

文献类型：Article; Early Access

关键词：Interpretable machine learning; Heavy metals; Insulin resistance; HOMA-IR; NHANES

摘要："Increasing and compelling evidence has been proved that heavy metal exposure is involved in the development of insulin resistance (IR). We trained an interpretable predictive machine learning (ML) model for IR in the non-diabetic populations based on levels of heavy metal exposure. A total of 4354 participants from the NHANES (2003-2020) with complete information were randomly divided into a training set and a test set. Twelve ML algorithms, including random forest (RF), XGBoost (XGB), logistic regression (LR), GaussianNB (GNB), ridge regression (RR), support vector machine (SVM), multilayer perceptron (MLP), decision tree (DT), AdaBoost (AB), Gradient Boosting Decision Tree (GBDT), Voting Classifier (VC), and K-Nearest Neighbour (KNN), were constructed for IR prediction using the training set. Among these models, the RF algorithm had the best predictive performance, showing an accuracy of 80.14%, an AUC of 0.856, and an F1 score of 0.74 in the test set. We embedded three interpretable methods, the permutation feature importance analysis, partial dependence plot (PDP), and Shapley additive explanations (SHAP) in RF model for model interpretation. Urinary Ba, urinary Mo, blood Pb, and blood Cd levels were identified as the main influencers of IR. Within a specific range, urinary Ba (0.56-3.56 mu g/L) and urinary Mo (1.06-20.25 mu g/L) levels exhibited the most pronounced upwards trend with the risk of IR, while blood Pb (0.05-2.81 mu g/dL) and blood Cd (0.24-0.65 mu g/L) levels showed a declining trend with IR. The findings on the synergistic effects demonstrated that controlling urinary Ba levels might be more crucial for the management of IR. The SHAP decision plot offered personalized care for IR based on heavy metal control. In conclusion, by utilizing interpretable ML approaches, we emphasize the predictive value of heavy metals for IR, especially Ba, Mo, Pb, and Cd."

基金机构：　

基金资助正文：