Comprehensive Metabolic Profiling of Inflammation Indicated Key Roles of Glycerophospholipid and Arginine Metabolism in Coronary Artery Disease

Abstract

Background: Systemic immune inflammation is a key mediator in the progression of coronary artery disease (CAD), concerning various metabolic and lipid changes. In this study, the relationship between the inflammatory index and metabolic profile in patients with CAD was investigated to provide deep insights into metabolic disturbances related to inflammation.

Methods: Widely targeted plasma metabolomic and lipidomic profiling was performed in 1,234 patients with CAD. Laboratory circulating inflammatory markers were mainly used to define general systemic immune and low-grade inflammatory states. Multivariable-adjusted linear regression was adopted to assess the associations between 860 metabolites and 7 inflammatory markers. Least absolute shrinkage and selection operator (LASSO) logistic-based classifiers and multivariable logistic regression were applied to identify biomarkers of inflammatory states and develop models for discriminating an advanced inflammatory state.

Results: Multiple metabolites and lipid species were linearly associated with the seven inflammatory markers [false discovery rate (FDR) <0.05]. LASSO and multivariable-adjusted logistic regression analysis identified significant associations between 45 metabolites and systemic immune-inflammation index, 46 metabolites and neutrophil-lymphocyte ratio states, 32 metabolites and low-grade inflammation score, and 26 metabolites and high-sensitivity C-reactive protein states (P < 0.05). Glycerophospholipid metabolism and arginine and proline metabolism were determined as key altered metabolic pathways for systemic immune and low-grade inflammatory states. Predictive models based solely on metabolite combinations showed feasibility (area under the curve: 0.81 to 0.88) for discriminating the four parameters that represent inflammatory states and were successfully validated using a validation cohort. The inflammation-associated metabolite, namely, β-pseudouridine, was related to carotid and coronary arteriosclerosis indicators (P < 0.05).

Conclusions: This study provides further information on the relationship between plasma metabolite profiles and inflammatory states represented by various inflammatory markers in CAD. These metabolic markers provide potential insights into pathological changes during CAD progression and may aid in the development of therapeutic targets.

Keywords: arteriosclerosis; coronary artery disease; glycerophospholipid metabolism; inflammation; lipidome; metabolome.

Figure 1

Overview of the flow chart for study design and data analysis. CAD, coronary artery disease; SII, systemic immune-inflammation index; NLR, neutrophil–lymphocyte ratio; PLR, platelet–lymphocyte ratio; hs-CRP, high-sensitivity C-reactive protein; WBC, white blood cell count; FIB, fibrinogen; INFLA score, low-grade inflammation score; LASSO, least absolute shrinkage and selection operator; UPLC–MS/MS, ultra-performance liquid chromatography–tandem mass spectrometry.

Figure 2

Assessment of metabolite associated with each inflammatory marker. (A) Heat map of lipid species [false discovery rate (FDR) <0.01] and metabolites (FDR < 0.05) and systemic immune-inflammatory markers in the SII cohort. (B) Heat map of lipid species (FDR < 0.05) and metabolites (FDR < 0.1) and low-grade inflammatory markers in the INFLA cohort. The blue- and red-color-coded FDR indicate positive and negative associations, respectively. Significance (FDR < 0.05) by linear regression analyses is marked with an asterisk, adjusted for age, sex, smoking, hypertension, diabetes mellitus, arrhythmia, LDLC, HDLC, TRIG, AST, eGFR, and medications. CE, cholesteryl esters; Cer, ceramide; HexCer, hexosylceramide; CerP, ceramide phosphate; LPC; lysophosphatidylcholine; LPE, lysophosphatidylethanolamine; PC, phosphatidylcholine; PE, phosphatidylethanolamine; TCA, tricarboxylic acid cycle; TG, triacylglycerol; DG, diacylglycerol; LPA, lysophosphatidic acid; LPS, lysophosphatidylserine. The other abbreviations are as listed in Table 1. The corresponding beta estimates and FDR values are given in Supplementary Tables S3, S5.

Figure 3

Relationship between metabolic signatures and different inflammatory states. Forest plot of odds ratios (OR) and 95% confidence intervals (95% CI) for the logistic regression of individual metabolites/lipid species associated with inflammatory states of high vs. low (A) SII and (B) NLR in the SII cohort and high vs. low (C) INFLA and (D) hs-CRP in the INFLA cohort, adjusted for age, sex, smoking, hypertension, diabetes mellitus, arrhythmia, LDLC, HDLC, TRIG, AST, eGFR, and medications (P < 0.05). The abbreviations are as in Figure 2.

Figure 4

Metaboanalyst pathway analysis showing disturbed pathways for different inflammatory states. High vs. low (A) SII and (B) NLR in the SII cohort and high vs. low (C) INFLA and (D) hs-CRP in the INFLA cohort. The abbreviations are as in Figure 1.

Figure 5

The discriminating performance in the discovery phase is shown by ROC curves between high vs. low (A) SII and (B) NLR in SII cohort 1 and high vs. low (C) INFLA and (D) hs-CRP in INFLA cohort 1. The combined discriminative score in the validation phase was compared between high vs. low (E) SII and (F) NLR in SII cohort 2 and high vs. low (G) INFLA and (H) hs-CRP in INFLA cohort 2. AUC, area under curve; ROC, receiver operating characteristic. The other abbreviations are as in Figure 1.

Figure 6

Relationship between inflammatory state-associated metabolites and atherosclerosis indicators. The blue- and red-color-coded P-values indicate positive and negative associations, respectively. Significance (P < 0.05) by linear regression analyses is marked with an asterisk. All analyses were adjusted for 12 main confounders. The corresponding beta estimates and P-values are given in Supplementary Table S9. IMT, intima-media thickness.