Metabolic systems approaches update molecular insights of clinical phenotypes and cardiovascular risk in patients with homozygous familial hypercholesterolemia

Figures

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  Fig. 1. Study overview. a Experimental procedure of cross-sectional study. Two independent multi-center cohorts were included in the global metabolomic/lipidomic analyses. Multivariate statistical analysis was used to discover and validate the specific metabolite patterns of HoFH. Regression analysis was employed to investigate the associations of HoFH-related metabolites with typical clinical phenotypes of HoFH patients. b Experimental procedure of follow-up study; 84 HoFH patients with available follow-up information were enrolled from the discovery cohort. Targeted metabolomics and deep proteomics were performed to investigate the ASCVD-associated metabolic network at multi-omic levels. Random forest algorithm was used to select the important metabolic/clinical markers in predicting ASCVD events

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  Fig. 2. Pattern analysis of the dataset from the serum metabolome and lipidome profiling. a Treemap overview for the metabolic category distribution of annotated metabolites based on untargeted metabolomic/lipidomic analyses. b PCA score plot of the metabolic profiling data from serum samples of HoFH patients, HeFH patients, and non-FH individuals in the discovery and validation cohorts; each point represents an individual serum sample. c Chord diagram of the major differentiated metabolic classes across the HoFH, HeFH, and non-FH groups from the discovery and validation cohorts; the width of the curves indicates the mean percentage of each metabolic class between the study groups. Each color represents a unique metabolite class. d Heatmap of the differentiated metabolites that distinguished HoFH patients from HeFH and non-FH individuals in discovery cohort. HoFH homozygous familial hypercholesterolemia, HeFH heterozygous familial hypercholesterolemia, non-FH non-familial hypercholesterolemia, FA fatty acids, CE cholesterol esters, Cer ceramide, CAR acyl carnitines, LPA lysophosphatidic acid, LPI lysophosphatidylinositol, PC diacylglycerophosphocholines, LPC lyso PC, LPC-O alkyl-LPC, LPE…

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  Fig. 3. Pathway enrichment and clinical phenotype-correlation analyses of metabolic signatures. a Expression level-based quantitative pathway enrichment analysis of the differentiated metabolites that distinguished the HoFH group from the non-FH and HeFH groups. The bubble size refers to the enrichment ratio of the pathway and the color represents the natural log of the FDR-adjusted P value. b IPA-based functional network between the HoFH-associated metabolites and enriched biological pathways/diseases. Rectangles, nodes, and edges represent metabolites, functional pathways/diseases, and associations, respectively. c Correlation plot depicting the correlation of clinical lipids with corneal arcus/xanthomas, supravalvular aortic stenosis, and valvular aortic stenosis, using regression analyses (FDR-adjusted *P < 0.05; FDR-adjusted **P < 0.01). d–f Correlation plot depicting the relation of metabolomic features with corneal arcus/xanthomas, supravalvular aortic stenosis, or valvular aortic stenosis (FDR-adjusted *P < 0.05; FDR-adjusted **P < 0.01). LDL-C low-density lipoprotein cholesterol, HDL-C high-density lipoprotein cholesterol, Lp(a) lipoprotein (a), ApoA1 apolipoprotein A1, ApoB…

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  Fig. 4. ASCVD-associated metabolite and protein alterations in the sera of HoFH patients. a Dot histogram of metabolite levels in HoFH patients with and without ASCVD events (concentration, nmol/mL). Mann–Whitney U test were used for each comparison. b Functionally grouped network of enriched pathways of differentiated proteins in patients with ASCVD events. c Enriched GO categories of differentiated proteins. BP biological process, CC cellular component, MP molecular function. d Topology analysis of pathway enrichment and impact from the joint pathway of differentially expressed proteins and metabolites. e Metabolite-protein interaction network. f Topology analysis of pathway enrichment and impact from the joint-pathway of differentially expressed proteins and metabolites. LPA 16:0 lysophosphatidic acid 16:0, LPC-O 18:0 lysophosphocholine alkyl − 18:0, LPC 20:4 lysophosphocholine 20:4, 12,13-EpOME 12,13-epoxyoctadecenoic acid, Cer (d18:1_16:0) Ceramide (d18:1_16:0). Other abbreviations are seen in Figs. 1 and 2

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  Fig. 5. Selection of the optimal ASCVD-related metabolic/clinical variable panel by using Random Forest algorithm. a Top important ASCVD-associated variable selection by using the values of mean decrease accuracy in Random Forest analysis. b ROC curves generated from MCCV-based multivariate Random Forest models. The AUC-ROC value and its 95% CI are shown. c Predictive accuracies of multivariable Random Forest models with different numbers of top important variables. d Kaplan–Meier survival analysis of eight top important metabolites in HoFH patients with and without ASCVD events during the follow-up. Log-rank test P values are shown. LDLR status: at least one null mutation. Other abbreviations are shown in Table 2 and Figs. 2 and 3