Fig. 4: Machine learning-based identification of blood pressure-associated lipid biomarkers.
From: Multiomics insights into eating time patterns and cardiovascular risk among Chinese children
a Down-regulated Lipid Features Selected by 4 Models. Venn diagram showing overlap of down-regulated lipid features (from Extended Eating Window (EW) versus Early EW) that were selected by four machine learning algorithms as associated with elevated blood pressure (HBP) (Random Forest (RF), Support Vector Machine (SVM), XGBoost, Elastic Net). The intersection of all four methods identifies the most robust down-regulated biomarkers with consensus support across all analytical approaches. b Up-regulated Lipid Features Selected by 4 Models. Venn diagram showing overlap of up-regulated lipid features (from Early EW versus Extended EW comparison) that were selected by four machine learning algorithms as associated with elevated blood pressure. c Heatmap of high-confidence hub lipid metabolites selected by at least three algorithms. d Core Hub Metabolites Expression by HBP Status. Box plots comparing the expression levels of the six core hub metabolites between normotensive controls (n = 25) and children with elevated blood pressure (n = 26). Box plots show the median (center line), interquartile range (box bounds: 25th–75th percentile), whiskers extend to 1.5× interquartile range from box bounds, and outliers are shown as individual points. Statistical analysis was performed using a two-sided Wilcoxon rank-sum test. Exact P values are displayed on the figure. *P < 0.05, **P < 0.01, ***P < 0.001. TAG triacylglycerol, FA fatty acid, PC phosphatidylcholine, PE phosphatidylethanolamine, Hex2Cer dihexosylceramide, HexCer hexosylceramide.
