Figure 3.
Diagnostic models for pre-MetS and MetS by 26 hub PMFs adjusted for age and gender using ML
(A) Typical mass spectrometry spectra within an m/z range of 100–300 obtained by ferric particle-assisted LDI-MS of plasma samples from the HC, pre-MetS, and MetS groups.
(B) The final 26 hub PMFs were filtered out by comparison of differential PMFs among HC vs. pre-MetS, pre-MetS vs. MetS, and HC vs. MetS groups using Kruskal-Wallis rank-sum test and Bonferroni/Dunnett correction in the discovery cohorts.
(C) Power analysis of the diagnosis of pre-MetS and MetS using a two-sided Z test. AUC0 and AUC1 are the areas under the receiver operating characteristic (ROC) curves (AUCs) for the null and alternative hypotheses, respectively. N+ and N− are the numbers of items sampled from cases and controls, respectively. The stars indicate the numbers in our datasets (n = 4,548, 4,548, and 7,008) for classification among HC vs. pre-MetS, HC vs. MetS, and pre-MetS vs. MetS, respectively.
(D) Distribution of the AUC using generalized linear models via least absolute shrinkage and selection operator and elastic-net regularization (GLMNET), support vector machine (SVM), multivariate adaptive regression splines (MARS), random forest (RF), and adaptive boosting (ADABOOST) to distinguish between HC and MetS groups in the validation cohort (n = 1,364).
(E) Distribution of the AUCs of HC vs. pre-MetS and pre-MetS vs. MetS in both the discovery and validation sets (HC vs. pre-MetS in pink and pre-MetS vs. MetS in purple).
(F) ROC curves for the PMF-based MetS diagnostic model using the GLMNET algorithm to distinguish between MetS and HC in the discovery (n = 3,184) and validation sets (n = 1,364).
(G) Calibration curves for our model showed good correlation between predicted and observed outcomes. The calibration curve was close to the 45° perfectly calibrated line.
(H) DCA plot depicting the standardized net benefit of our model.