Random survival forest
T2DM, diabetes
REGRESSION, TYPE 2 DIABETES, TYPE 2 DIABETES MELLITUS, DIABETES
A stepwise RSF was carried out in the Training Set (60% of patients) to select the lipids with greater predictive power for T2DM development. Thus, the 15 lipids that produced the lowest prediction error, out of a total of 440 lipids variables originally tested, were included in the final model (Fig. Selection of the best model by Random Survival Forest. Selection in the Training set of lipid species with a higher predictive power for type 2 diabetes, by applying an Random Survival Forest in combination with a backward selection procedureSelection of lipids included in the model with the lowest prediction error in the Training SetPE: phosphatidylethanolamine; PG: phosphatidylglycerol; PI, phosphatidyl inositol; PC: phosphatidylcholine; TG: triacylglycerols; LPC: lysophosphatidylcholine; PS: phosphatidylserineTo evaluate the relationship of these lipids with the development of diabetes, an individual Cox proportional regression model was made for each of the 15 selected lipids. In total, 8 of the 15 lipids were directly associated with T2DM development, while 7 of them were inversely associated with T2DM development. It is important to note that different members of the PC, PE, phosphatidyl glycerol (PG), and phosphatidyl inositol (PI) families were associated with both the development and non-development of T2DM (Table Association between lipids selected and type 2 diabetes mellitus development in the Training Set, per standard deviation increaseModel 1 was unadjusted and Model 2 was adjusted by age, gender, diet, body mass index, high density lipoproteins-cholesterol, plasma triacylglycerols, and statin intensity treatmentHR: hazard ratio; CI: confidence interval. LPC: lysophosphatidylcholine; PC: phosphatidylcholine; PE: phosphatidylethanolamine; PG: phosphatidylglycerol; PI, phosphatidyl inositol; PS: phosphatidylserine; TG: triacylglycerolsMoreover, the model with the 15 lipids produced a C Index of 0.714; but when the clinical variables (i.e., diet, age, gender, BMI, HDL, TGs, and statin intensity treatment) were added into the model, the C Index increased to 0.757, while the clinical variables, taken separately, showed a C Index of only 0.618 (Table Parameters of the different modelsReceiver operating characteristic curves of the model including the clinical variables separately and the model including the 15 lipids and the clinical variables. The clinical variables included: age, gender, diet, body mass index, high density lipoproteins-cholesterol, plasma triacylglycerols, and statin intensity treatment. AUC: area under the curveWe then tested the predictive power of the RSF model with the selected lipids in the Validation Set. The C Index was 0.703 for lipids, which rose to 0.755 when the clinical parameters were included, which had previously yielded 0.653 when they were tested separately. ROC curves of the selected lipids yielded an AUC of 0.742, which increased to 0.799 when the clinical variables were included, while the clinical variables taken separately yielded an AUC of only 0.659 (Fig. 