Additional file 1 of A novel 6-metabolite signature for prediction of clinical outcomes in type 2 diabetic patients undergoing percutaneous coronary intervention
Creators: name: Wang, Xue-bin
name: Cui, Ning-hua
name: Liu, Xia’nan
Publisher: name: figshare
Year: 2022
Subjects: subject: Biochemistry
subject: Medicine
subject: Cell Biology
subject: Genetics
subject: FOS: Biological sciences
subject: Physiology
subject: 39999 Chemical Sciences not elsewhere classified
subject: FOS: Chemical sciences
subject: 69999 Biological Sciences not elsewhere classified
subject: 80699 Information Systems not elsewhere classified
subject: FOS: Computer and information sciences
subject: 19999 Mathematical Sciences not elsewhere classified
subject: FOS: Mathematics
subject: Marine Biology
subject: Cancer
subject: 60506 Virology
subject: Computational Biology

description: Additional file 1: Supplementary methods. Figure S1. The Z distribution of methionine abundance in both discovery and internal validation sets. The Z scores of methionine across all samples were greater than -3 (-3SD), indicating that the serum samples were properly stored for metabolite detection. Figure S2. The Pearson correlation between metabolic data of quality control samples assessing the reliability of the LC-MS analysis in the discovery (A) and internal validation sets (B). Figure S3. The OPLS-DA analysis assessing the performance of 35 differential metabolites for discrimination of MACEs from matched controls in the discovery (A) and internal validation sets (B). Figure S4. The differences in cumulative rates of MACEs between participants with high-risk and low risk scores of the 6-metabolite signatures. P values were derived from Cox regression with adjustment for age, sex, smoking status, obesity (BMI &gt; 25 kg/m2), hypertension, HbA1c, LVEF &lt; 50%, clinical presentations, multivessel CAD, SYNTAX score, and stent types. Figure S5. Scatter plots for comparing the predictive performance of the random forest (A) and XGBoost (B) models to the logistic regression model of the 6-metabolite panel. The models are generated using the discovery dataset and presented here in the internal validation set. Red lines indicate the cut-offs of random forest and XGBoost models; Black lines indicate the cut-off of logistic regression model. Black circles label MACEs that would be identified using the random forest and XGBoost models but would be missed when the logistic regression model is applied. Figure S6. The importance of each predictor of the 6-metabolite classifier constructed by random forest (A) and XGBoost (B). Abbreviations: NAM, nicotinamide; ADPR, adenosine diphosphate ribose; 1-MNAM, 1-methylnicotinamide; PC, phosphatidylcholine. Figure S7. Calibration plots for the logistic regression and 4 machine learning models of the 6-metabolite classifier in the discovery (A) and internal validation sets (B). Abbreviations: RF, random forest; XGBoost, extreme gradient boosting; SVM, Support Vector Machines; DNN, deep neural network. Figure S8. Backward stepwise Cox regression analyses of the association between the 6-metabolite classifier and MACEs in the external validation set. (A) The log-minus-log plot for graphically testing the proportional hazards assumption. (B) Kaplan-Meier curve for assessing the performance of the 6-metabolite classifier to predict MACEs. In the backward stepwise Cox regression analyses, variables including age, sex, smoking status, obesity (BMI &gt; 25 kg/m2), hypertension, HbA1c, LVEF &lt; 50%, clinical presentations, multivessel CAD, SYNTAX score, stent types, and the 6-metabolite classifier were first entered one at a time. Then, 4 variables with P&lt; 0.10 (i.e. HbA1c, LVEF &lt; 50%, multivessel CAD, and the 6-metabolite classifier) in the stepwise procedure were retained to fit the final model. The HR and P value were calculated accordingly. Table S1. Calibration data for 6 metabolites detected by targeted metabolite analyses. Table S2. List of primers used in RT-qPCR. Table S3. Ontology classes of metabolites in the discovery and internal validation sets. Table S4. 69 differential metabolites in the discovery set. Table S5. 89 differential metabolites in the internal validation set. Table S6. The additional values of the RF-based 6-metabolite model beyond the FREEDOM clinical risk score in the external validation set.