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.

Elevated low-density lipoprotein (LDL) cholesterol is a major risk factor for cardiovascular disease. Statins are the cornerstone of preventing and treating cardiovascular disease and can reduce LDL cholesterol by more than 60%. Although statins have high tolerability and safety, as the number of users increases, their adverse reactions in the liver, kidneys, skeletal muscles, and their potential to induce diabetes have also received widespread attention. How to maximize the lipid-lowering effect of statins, reduce the incidence of adverse reactions, promote the rational application of statins in the clinic, and improve the risk-benefit level, in order to benefit more cardiovascular patients and provide reference for the related basic research of statins. Key scientific concepts of review: This article provides a comprehensive review of the clinical manifestations of statin-related adverse reactions (associated myopathy, hepatotoxicity, nephrotoxicity, glycemic effects, central nervous system, hemorrhagic stroke, etc.), risk factors for triggering adverse reactions, statin interactions with other drugs (food), potential etiopathological mechanisms and common interventions in the clinic. Genetic diversity is strongly associated with statin adverse effects, and thus, in the future genetic testing may also be key to mitigating statin adverse effects.

Background: The gut microbial ecosystem is an important factor that regulates host health and the onset of chronic diseases, such as inflammatory bowel diseases, obesity, hyperlipidemia, and diabetes mellitus, which are important risk factors for atherosclerosis. However, the links among diet, microbiota composition, and atherosclerotic progression are unclear. Methods and results: Four-week-old mice (^-/- mice, C57Bl/6) were randomly divided into two groups, namely, supplementation with culture medium (control, CTR) and Bacteroides fragilis (BFS), and were fed a high-fat diet. The gut microbiota abundance in feces was evaluated using the 16S rDNA cloning library construction, sequencing, and bioinformatics analysis. The atherosclerotic lesion was estimated using Oil Red O staining. Levels of CD36, a scavenger receptor implicated in atherosclerosis, and F4/80, a macrophage marker in small intestine, were quantified by quantitative real-time PCR. Compared with the CTR group, the BFS group showed increased food intake, fasting blood glucose level, body weight, low-density lipoprotein level, and aortic atherosclerotic lesions. BFS dramatically reduced Lactobacillaceae (LAC) abundance and increased Desulfovibrionaceae (DSV) abundance. The mRNA expression levels of CD36 and F4/80 in small intestine and aorta tissue in the BFS group were significantly higher than those in the CTR group. Conclusions: gut microbiota dysbiosis was induced by BFS. It was characterized by reduced LAC and increased DSV abundance and led to the deterioration of glucose/lipid metabolic dysfunction and inflammatory response, which likely promoted aorta plaque formation and the progression of atherosclerosis.

High serum level of palmitic acid(PA) is implicated in pathogenesis of cardiovascular diseases. PA serves as the substrate for protein palmitoylation. However, it is still unknown whether palmitoylation is involved in PA‐induced cardiovascular dysfunction. Here, in clinical cohort studies of 1040 patients with coronary heart disease, high level of PA is associated with risk of major adverse cardiovascular events (MACE) and death. In ApoE^−/−mice, 10 mg/kg⁻¹ PA treatment induces blood pressure elevation, cardiac contractile dysfunction, endothelial dysfunction and atherosclerotic plaqueformation. In endothelial cells, inhibition of palmitoylation bypalmitoyl‐transferase inhibitor 2‐BP eliminates PA‐induced endothelial injury, whereas promotion of palmitoylation by depalmitoylase inhibitor ML349 exacerbates the harmful effect of PA. Palmitoyl‐proteomics analysis identifies pyruvate kinase isozyme type M2 (PKM2) as the palmitoylated protein responsible for PA‐induced endothelial injury, and Cys31 as the predominant palmitoylated site. PKM2‐C31S mutants (cysteine replaced by serine) prevents PA‐induced endothelial injury. Endothelial‐specific AAV‐C31S PKM2^endo ameliorates cardiovascular dysfunction caused by PA in ApoE^−/− mice. Mechanistically, PKM2‐C31 palmitoylation impairs PKM2 tetramerization to inhibit its pyruvate kinase activity and endothelial glycolysis. Finally, zDHHC13 is identified as the palmitoyl acyltransferase of PKM2. In conclusion, these findings suggest that PKM2‐C31 palmitoylation contributes to PA‐induced endothelial injury and cardiovascular dysfunction.

Background: Coronary stenosis severity determines ischemic symptoms and adverse outcomes. The metabolomic analysis of human fluids can provide an insight into the pathogenesis of complex disease. Thus, this study aims to investigate the metabolomic and lipidomic biomarkers of coronary artery disease (CAD) severity and to develop diagnostic models for distinguishing individuals at an increased risk of atherosclerotic burden and plaque instability.Methods: Widely targeted metabolomic and lipidomic analyses of plasma in 1,435 CAD patients from three independent centers were performed. These patients were classified as stable coronary artery disease (SCAD), unstable angina (UA), and myocardial infarction (MI). Associations between CAD stages and metabolic conditions were assessed by multivariable-adjusted logistic regression. Furthermore, the least absolute shrinkage and selection operator logistic-based classifiers were used to identify biomarkers and to develop prediagnostic models for discriminating the diverse CAD stages.Results: On the basis of weighted correlation network analysis, 10 co-clustering metabolite modules significantly (p < 0.05) changed at different CAD stages and showed apparent correlation with CAD severity indicators. Moreover, cross-comparisons within CAD patients characterized that a total of 72 and 88 metabolites/lipid species significantly associated with UA (vs. SCAD) and MI (vs. UA), respectively. The disturbed pathways included glycerophospholipid metabolism, and cysteine and methionine metabolism. Furthermore, models incorporating metabolic and lipidomic profiles with traditional risk factors were constructed. The combined model that incorporated 11 metabolites/lipid species and four traditional risk factors represented better discrimination of UA and MI (C-statistic = 0.823, 95% CI, 0.783–0.863) compared with the model involving risk factors alone (C-statistic = 0.758, 95% CI, 0.712–0.810). The combined model was successfully used in discriminating UA and MI patients (p < 0.001) in a three-center validation cohort.Conclusion: Differences in metabolic profiles of diverse CAD subtypes provided a new approach for the risk stratification of unstable plaque and the pathogenesis decipherment of CAD progression.

Yibin Liu, Hongkun Ma, Qian Zhu, Bin Zhang, Hong Yan, Hanping Li, Jinxiu Meng, Weihua Lai, Liwen Li, Danqing Yu, Shilong Zhong