Insights into the prognosis of lipidomic dysregulation for death risk in patients with coronary artery disease

IL-1β-driven NF-κB transcription of ACE2 as a Mechanism of Macrophage Infection by SARS-CoV-2

Coronavirus disease 2019 (COVID-19), caused by infection with the enveloped RNA betacoronavirus, SARS-CoV-2, led to a global pandemic involving over 7 million deaths. Macrophage inflammatory responses impact COVID-19 severity; however, it is unclear whether macrophages are infected by SARS-CoV-2. We sought to identify mechanisms regulating macrophage expression of ACE2, the primary receptor for SARS-CoV-2, and to determine if macrophages are susceptible to productive infection. We developed a humanized ACE2 (hACE2) mouse whereby hACE2 cDNA was cloned into the mouse ACE2 locus under control of the native promoter. We validated susceptibility of hACE2 mice to SARS-CoV-2 infection relative to wild-type mice and an established K18-hACE2 model of acute fulminating disease. Intranasal exposure to SARS-CoV-2 led to pulmonary consolidations with cellular infiltrate, edema, and hemorrhage, consistent with pneumonia, yet unlike the K18-hACE2 model, hACE2 mice survived and maintained stable weight. Infected hACE2 mice also exhibited a unique plasma chemokine, cytokine, and growth factor inflammatory signature relative to K18-hACE2 mice. Infected hACE2 mice demonstrated evidence of viral replication in infiltrating lung macrophages, and infection of macrophages in vitro revealed a transcriptional profile indicative of altered RNA and ribosomal processing machinery as well as activated cellular antiviral defense. Macrophage IL-1β-driven NF-κB transcription of ACE2 was an important mechanism of dynamic ACE2 upregulation, promoting macrophage susceptibility to infection. Experimental models of COVID-19 that make use of native hACE2 expression will allow for mechanistic insight into factors that can either promote host resilience or increase susceptibility to worsening severity of infection.

Serum Lipidomic Signatures Mediate the Association Between Coarse Grain Preference and Central Obesity in Adults With Normal Weight and High Wheat Intake

Little is known about the association between grain preference andabdominal fat accumulation, and mediating roles of circulating lipidomicsignatures. We quantified 1245 circulating lipids in 150 normal-weight centralobesity (NWCO) cases and 150 controls using targeted lipidomics. Grainpreference was determined by the highest intake frequency of grains (whiterice, wheat, or coarse grain). In our participants with high wheat intakefrequency, preferring coarse grain over rice was associated with a 60% lowerrisk of NWCO. Of the 585 lipids showing opposing associations with white riceand coarse grains, 46 were significantly linked to either (p < 0.05), predominantly alkylacyl phospholipids (PE-Os; n < 9) and alkenylacylphospholipids (PE-Ps; nx = 7). Network analysis identified a module primarilycomposed of PE-Os and PE-Ps, which was positively associated with coarse grain (p = 0.014). Another module, mainly consisting of triacylglycerols (TGs), was associatedwith white rice (p = 0.003) and mediated the association between white rice(mediation proportion: 20.30%; p = 0.027) or coarse grain preference (11.43%; p = 0.040) and NWCO. Specific lipids, such as TG(8:0_16:0_16:0) and TG(8:0_14:0_18:0), exhibited notable mediation effects. In normal-weight individuals with highwheat intake frequency, preferring coarse grain was inversely associated with NWCO, mediated by specific lipidomic signatures.

Lipidomic perturbations of normal-weight adiposity phenotypes and their mediations on diet-adiposity associations

BACKGROUND & AIMS

Normal-weight obesity (NWO) and normal-weight central obesity (NWCO) have been linked to higher cardiometabolic risks, but their etiological bases and attributable dietary factors remain unclear. In this study we therefore aimed to identify lipidomic signatures and dietary factors related to NWO and NWCO and to explore the mediation associations of lipids in diet-adiposity associations.

METHODS

Using a high-coverage targeted lipidomic approach, we quantified 1245 serum lipids in participants with NWO (n = 150), NWCO (n = 150), or propensity-score-matched normal-weight controls (n = 150) based on the Regional Ethnic Cohort Study in Northwest China. Consumption frequency of 28 major food items was recorded using a food frequency questionnaire.

RESULTS

Profound lipidomic perturbations of NWCO relative to NWO were observed, and 249 (dominantly glycerolipids) as well as 48 (dominantly glycerophospholipids) lipids were exclusively associated with NWCO or NWO. Based on strong lipidomic signatures identified by a LASSO model, phospholipid biosynthesis was the top enriched pathway of NWCO, and sphingolipid metabolism was the top pathway of NWO. Remarkably, sphingolipids were positively associated with NWO and NWCO, but lyso-phosphatidylcholines were negatively associated with them. Rice, fruit juice, and carbonated drink intakes were positively associated with the risk of NWCO. Both global and individual lipidomic signatures, including SE(28:1_22:6) and HexCer(d18:1/20:1), mediated these diet-NWCO associations (mediation proportion: 15.92%-26.10%).

CONCLUSIONS

Differential lipidomic signatures were identified for overall and abdominal adiposity accumulation in normal-weight individuals, underlining their core mediation roles in dietary contributions to adiposity deposition.

Lipidomic analyses reveal potential biomarkers for predicting death and heart failure after acute myocardial infarction

Background Acute myocardial infarction (AMI) and postmyocardial infarction heart failure (pMIHF) have high mortality rates worldwide. This study aimed to explore lipidomic profiles and identify potential biomarkers for the prediction of death and heart failure (HF) after AMI. Methods All serum samples were collected at Xuanwu Hospital, Capital Medical University, and their clinical characteristics and lipidomic profiles were analyzed in different groups. LC-MS/MS was used for lipidomic analyses, and underlying biomarkers were screened by receiver operating characteristic (ROC) curve analysis. Results Lipidomic analyses of the survival and nonsurvival groups revealed that the decrease of the content of SM (d18:1/22:0), PE (P-20:1/18:0), PC (18:2), LPE (18:2), PE (P-20:0/18:0), LPC (18:0) and PC (20:0/20:3) while increase of the content of PG (18:1/18:1) could increase the risk of death after AMI. In parallel, the lipidomic analysis of the HF and non-HF groups revealed that the decrease of the content of PC (20:3/20:4), LPC (20:3), LPC (18:0), LPC (18:2), LPC (20:0), LPC (18:3), LPE (16:1) and PC (18:2/20:3) could increase the risk of HF after AMI. Conclusion Several lipids could be potential biomarkers for the prediction of death and HF after AMI.

Plasma metabolomics reveals the shared and distinct metabolic disturbances associated with cardiovascular events in coronary artery disease

Risk prediction for subsequent cardiovascular events remains an unmet clinical issue in patients with coronary artery disease. We aimed to investigate prognostic metabolic biomarkers by considering both shared and distinct metabolic disturbance associated with the composite and individual cardiovascular events. Here, we conducted an untargeted metabolomics analysis for 333 incident cardiovascular events and 333 matched controls. The cardiovascular events were designated as cardiovascular death, myocardial infarction/stroke and heart failure. A total of 23 shared differential metabolites were associated with the composite of cardiovascular events. The majority were middle and long chain acylcarnitines. Distinct metabolic patterns for individual events were revealed, and glycerophospholipids alteration was specific to heart failure. Notably, the addition of metabolites to clinical markers significantly improved heart failure risk prediction. This study highlights the potential significance of plasma metabolites on tailed risk assessment of cardiovascular events, and strengthens the understanding of the heterogenic mechanisms across different events.

Combinatory data-independent acquisition and parallel reaction monitoring method for revealing the lipid metabolism biomarkers of coronary heart disease and its comorbidities

Disorders of lipid metabolism are a common cause of coronary heart disease (CHD) and its comorbidities. In this study, ultra-performance liquid chromatography-high-resolution mass spectrometry in data-independent acquisition (DIA) mode was applied to collect abundant tandem mass spectrometry data, which provided valuable information for lipid annotation. For the lipid isomers that could not be completely separated by chromatography, parallel reaction monitoring (PRM) mode was used for quantification. A total of 223 plasma lipid metabolites were annotated, and 116 of them were identified for their fatty acyl chain composition and location. In addition, 152 plasma lipids in patients with CHD and its comorbidities were quantitatively analyzed. Multivariate statistical analysis and metabolic pathway analysis demonstrated that glycerophospholipid and sphingolipid metabolism deserved more attention for CHD. This study proposed a method combining DIA and PRM for high-throughput characterization of plasma lipids. The results also improved our understanding of metabolic disorders of CHD and its comorbidities, which can provide valuable suggestions for medical intervention.

Multinomial machine learning identifies independent biomarkers by integrated metabolic analysis of acute coronary syndrome

A multi-class classification model for acute coronary syndrome (ACS) remains to be constructed based on multi-fluid metabolomics. Major confounders may exert spurious effects on the relationship between metabolism and ACS. The study aims to identify an independent biomarker panel for the multiclassification of HC, UA, and AMI by integrating serum and urinary metabolomics. We performed a liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based metabolomics study on 300 serum and urine samples from 44 patients with unstable angina (UA), 77 with acute myocardial infarction (AMI), and 29 healthy controls (HC). Multinomial machine learning approaches, including multinomial adaptive least absolute shrinkage and selection operator (LASSO) regression and random forest (RF), and assessment of the confounders were applied to integrate a multi-class classification biomarker panel for HC, UA and AMI. Different metabolic landscapes were portrayed during the transition from HC to UA and then to AMI. Glycerophospholipid metabolism and arginine biosynthesis were predominant during the progression from HC to UA and then to AMI. The multiclass metabolic diagnostic model (MDM) dependent on ACS, including 2-ketobutyric acid, LysoPC(18:2(9Z,12Z)), argininosuccinic acid, and cyclic GMP, demarcated HC, UA, and AMI, providing a C-index of 0.84 (HC vs. UA), 0.98 (HC vs. AMI), and 0.89 (UA vs. AMI). The diagnostic value of MDM largely derives from the contribution of 2-ketobutyric acid, and LysoPC(18:2(9Z,12Z)) in serum. Higher 2-ketobutyric acid and cyclic GMP levels were positively correlated with ACS risk and atherosclerosis plaque burden, while LysoPC(18:2(9Z,12Z)) and argininosuccinic acid showed the reverse relationship. An independent multiclass biomarker panel for HC, UA, and AMI was constructed using the multinomial machine learning methods based on serum and urinary metabolite signatures.

Integrating Widely Targeted Lipidomics and Transcriptomics Unravels Aberrant Lipid Metabolism and Identifies Potential Biomarkers of Food Allergies in Rats

SCOPE

Oral food challenges (OFCs) are currently the gold standard for determining the clinical reactivity of food allergy (FA) but are time-consuming, expensive, and risky. To screen novel peripheral biomarkers of FA and characterize the aberrant lipid metabolism in serum, 24 rats are divided into four groups: peanut, milk, and shrimp allergy (PA, MA, and SA, respectively) and control groups, with six rats in each group, and used for widely targeted lipidomics and transcriptomics analysis.

METHODS AND RESULTS

Widely targeted lipidomics reveal 144, 162, and 206 differentially accumulated lipids in PA, MA, and SA groups, respectively. The study integrates widely targeted lipidomics and transcriptomics and identifies abnormal lipid metabolism correlated with widespread differential accumulation of diverse lipids (including triacylglycerol, diacylglycerol, sphingolipid, and glycerophospholipid) in PA, MA, and SA. Simplified random forest classifier is constructed through five repetitions of 10-fold cross-validation to distinguish allergy from control. A subset of 15 lipids as potential biomarkers allows for more reliable and more accurate prediction of FA. Independent replication validates the reproducibility of potential biomarkers.

CONCLUSION

The results reveal the major abnormalities in lipid metabolism and suggest the potential role of lipids as novel molecular signatures for FA.

Longitudinal lipidomic signatures of all-cause and CVD mortality in American Indians: findings from the Strong Heart Study

Dyslipidemia is an independent and modifiable risk factor for aging and age-related disorders. Routine lipid panel cannot capture all individual lipid species in blood (i.e., blood lipidome). To date, a comprehensive assessment of the blood lipidome associated with mortality is lacking in large-scale community-dwelling individuals, especially in a longitudinal setting. Using liquid chromatograph-mass spectrometry, we repeatedly measured individual lipid species in 3,821 plasma samples collected at two visits (~ 5.5 years apart) from 1,930 unique American Indians in the Strong Heart Family Study. We first identified baseline lipids associated with risks for all-cause mortality and CVD mortality (mean follow-up period: 17.8 years) in American Indians, followed by replication of top hits in European Caucasians in the Malmö Diet and Cancer-Cardiovascular Cohort (n = 3,943, mean follow-up period: 23.7 years). The model adjusted age, sex, BMI, smoking, hypertension, diabetes, and LDL-c at baseline. We then examined the associations between changes in lipid species and risk of mortality. Multiple testing was controlled by false discovery rate (FDR). We found that baseline levels and longitudinal changes of multiple lipid species, e.g., cholesterol esters, glycerophospholipids, sphingomyelins, and triacylglycerols, were significantly associated with risks of all-cause or CVD mortality. Many lipids identified in American Indians could be replicated in European Caucasians. Network analysis identified differential lipid networks associated with risk of mortality. Our findings provide novel insight into the role of dyslipidemia in disease mortality and offer potential biomarkers for early prediction and risk reduction in American Indians and other ethnic groups.

Widely targeted quantitative lipidomics and prognostic model reveal plasma lipid predictors for nasopharyngeal carcinoma

BACKGROUND

Dysregulation of lipid metabolism is closely associated with cancer progression. The study aimed to establish a prognostic model to predict distant metastasis-free survival (DMFS) in patients with nasopharyngeal carcinoma (NPC), based on lipidomics.

METHODS

The plasma lipid profiles of 179 patients with locoregionally advanced NPC (LANPC) were measured and quantified using widely targeted quantitative lipidomics. Then, patients were randomly split into the training (125 patients, 69.8%) and validation (54 patients, 30.2%) sets. To identify distant metastasis-associated lipids, univariate Cox regression was applied to the training set (P < 0.05). A deep survival method called DeepSurv was employed to develop a proposed model based on significant lipid species (P < 0.01) and clinical biomarkers to predict DMFS. Concordance index and receiver operating curve analyses were performed to assess model effectiveness. The study also explored the potential role of lipid alterations in the prognosis of NPC.

RESULTS

Forty lipids were recognized as distant metastasis-associated (P < 0.05) by univariate Cox regression. The concordance indices of the proposed model were 0.764 (95% confidence interval (CI), 0.682-0.846) and 0.760 (95% CI, 0.649-0.871) in the training and validation sets, respectively. High-risk patients had poorer 5-year DMFS compared with low-risk patients (Hazard ratio, 26.18; 95% CI, 3.52-194.80; P < 0.0001). Moreover, the six lipids were significantly correlated with immunity- and inflammation-associated biomarkers and were mainly enriched in metabolic pathways.

CONCLUSIONS

Widely targeted quantitative lipidomics reveals plasma lipid predictors for LANPC, the prognostic model based on that demonstrated superior performance in predicting metastasis in LANPC patients.

Coronary Artery Disease with Elevated Levels of HDL Cholesterol Is Associated with Distinct Lipid Signatures

Levels of high-density lipoprotein cholesterol (HDL-C) are inversely associated with the incidence of coronary artery disease (CAD). However, the underlying mechanism of CAD in the context of elevated HDL-C levels is unclear. Our study aimed to explore the lipid signatures in patients with CAD and elevated HDL-C levels and to identify potential diagnostic biomarkers for these conditions. We measured the plasma lipidomes of forty participants with elevated HDL-C levels (men with >50 mg/dL and women with >60 mg/dL), with or without CAD, using liquid chromatography-tandem mass spectrometry. We analyzed four hundred fifty-eight lipid species and identified an altered lipidomic profile in subjects with CAD and high HDL-C levels. In addition, we identified eighteen distinct lipid species, including eight sphingolipids and ten glycerophospholipids; all of these, except sphingosine-1-phosphate (d20:1), were higher in the CAD group. Pathways for sphingolipid and glycerophospholipid metabolism were the most significantly altered. Moreover, our data led to a diagnostic model with an area under the curve of 0.935, in which monosialo-dihexosyl ganglioside (GM3) (d18:1/22:0), GM3 (d18:0/22:0), and phosphatidylserine (38:4) were combined. We found that a characteristic lipidome signature is associated with CAD in individuals with elevated HDL-C levels. Additionally, the disorders of sphingolipid as well as glycerophospholipid metabolism may underlie CAD.

Plasma Lipidomics Profiles Highlight the Associations of the Dual Antioxidant/Pro-oxidant Molecules Sphingomyelin and Phosphatidylcholine with Subclinical Atherosclerosis in Patients with Type 1 Diabetes

Here, we report on our study of plasma lipidomics profiles of patients with type 1 diabetes (T1DM) and explore potential associations. One hundred and seven patients with T1DM were consecutively recruited. Ultrasound imaging of peripheral arteries was performed using a high image resolution B-mode ultrasound system. Untargeted lipidomics analysis was performed using UHPLC coupled to qTOF/MS. The associations were evaluated using machine learning algorithms. SM(32:2) and ether lipid species (PC(O-30:1)/PC(P-30:0)) were significantly and positively associated with subclinical atherosclerosis (SA). This association was further confirmed in patients with overweight/obesity (specifically with SM(40:2)). A negative association between SA and lysophosphatidylcholine species was found among lean subjects. Phosphatidylcholines (PC(40:6) and PC(36:6)) and cholesterol esters (ChoE(20:5)) were associated positively with intima-media thickness both in subjects with and without overweight/obesity. In summary, the plasma antioxidant molecules SM and PC differed according to the presence of SA and/or overweight status in patients with T1DM. This is the first study showing the associations in T1DM, and the findings may be useful in the targeting of a personalized approach aimed at preventing cardiovascular disease in these patients.

Exploration of Metabolic Biomarkers Linking Red Meat Consumption to Ischemic Heart Disease Mortality in the UK Biobank

Growing evidence suggests that red meat consumption is a risk factor for cardiovascular health, with potential sex disparity. The metabolic mechanisms have not been fully understood. Using the UK Biobank, first we examined the associations of unprocessed red meat and processed meat with ischemic heart disease (IHD) mortality overall and by sex using logistic regression. Then, we examined the overall and sex-specific associations of red meat consumption with metabolites using multivariable regression, as well as the associations of selected metabolites with IHD mortality using logistic regression. We further selected metabolic biomarkers that are linked to both red meat consumption and IHD, with concordant directions. Unprocessed red meat and processed meat consumption was associated with higher IHD mortality overall and in men. Thirteen metabolites were associated with both unprocessed red meat and IHD mortality overall and showed a consistent direction, including triglycerides in different lipoproteins, phospholipids in very small very-low-density lipoprotein (VLDL), docosahexaenoic acid, tyrosine, creatinine, glucose, and glycoprotein acetyls. Ten metabolites related to triglycerides and VLDL were positively associated with both unprocessed red meat consumption and IHD mortality in men, but not in women. Processed meat consumption showed similar results with unprocessed red meat. Triglycerides in lipoproteins, fatty acids, and some nonlipid metabolites may play a role linking meat consumption to IHD. Triglycerides and VLDL-related lipid metabolism may contribute to the sex-specific associations. Sex differences should be considered in dietary recommendations.

Plasma metabolomics provides new insights into the relationship between metabolites and outcomes and left ventricular remodeling of coronary artery disease

BACKGROUND

Coronary artery disease (CAD) is a metabolically perturbed pathological condition. However, the knowledge of metabolic signatures on outcomes of CAD and their potential causal effects and impacts on left ventricular remodeling remains limited. We aim to assess the contribution of plasma metabolites to the risk of death and major adverse cardiovascular events (MACE) as well as left ventricular remodeling.

RESULTS

In a prospective study with 1606 Chinese patients with CAD, we have identified and validated several independent metabolic signatures through widely-targeted metabolomics. The predictive model respectively integrating four metabolic signatures (dulcitol, β-pseudouridine, 3,3',5-Triiodo-L-thyronine, and kynurenine) for death (AUC of 83.7% vs. 76.6%, positive IDI of 0.096) and metabolic signatures (kynurenine, lysoPC 20:2, 5-methyluridine, and L-tryptophan) for MACE (AUC of 67.4% vs. 59.8%, IDI of 0.068) yielded better predictive value than trimethylamine N-oxide plus clinical model, which were successfully applied to predict patients with high risks of death (P = 0.0014) and MACE (P = 0.0008) in the multicenter validation cohort. Mendelian randomisation analysis showed that 11 genetically inferred metabolic signatures were significantly associated with risks of death or MACE, such as 4-acetamidobutyric acid, phenylacetyl-L-glutamine, tryptophan metabolites (kynurenine, kynurenic acid), and modified nucleosides (β-pseudouridine, 2-(dimethylamino) guanosine). Mediation analyses show that the association of these metabolites with the outcomes could be partly explained by their roles in promoting left ventricular dysfunction.

CONCLUSIONS

This study provided new insights into the relationship between plasma metabolites and clinical outcomes and its intermediate pathological process left ventricular dysfunction in CAD. The predictive model integrating metabolites can help to improve the risk stratification for death and MACE in CAD. The metabolic signatures appear to increase death or MACE risks partly by promoting adverse left ventricular dysfunction, supporting potential therapeutic targets of CAD for further investigation.

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

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.

Comprehensive Metabolomics Identified the Prominent Role of Glycerophospholipid Metabolism in Coronary Artery Disease Progression

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.

Associations of Mitochondrial Variants With Lipidomic Traits in a Chinese Cohort With Coronary Artery Disease

Plasma lipids have been at the center stage of the prediction and prevention strategies for cardiovascular diseases (CVDs), and novel lipidomic traits have been recognized as reliable biomarkers for CVD risk prediction. The mitochondria serve as energy supply sites for cells and can synthesize a variety of lipids autonomously. Therefore, investigating the relationships between mitochondrial single nucleotide polymorphism (SNPs) and plasma lipidomic traits is meaningful. Here, we enrolled a total of 1,409 Han Chinese patients with coronary artery disease from three centers and performed linear regression analyses on the SNPs of mitochondrial DNA (mtDNA) and lipidomic traits in two independent groups. Sex, age, aspartate aminotransferase, estimated glomerular filtration rate, antihypertensive drugs, hypertension, and diabetes were adjusted. We identified three associations, namely, D-loop_m.16089T>C with TG(50:4) NL-16:0, D-loop_m.16145G>A with TG(54:5) NL-18:0, and D-loop_m.16089T>C with PC(16:0_16:1) at the statistically significant threshold of FDR < 0.05. Then, we explored the relationships between mitochondrial genetic variants and traditional lipids, including triglyceride, total cholesterol (TC), low-density lipoprotein cholesterol (LDLC), and high-density lipoprotein cholesterol. Two significant associations were found, namely MT-ND6_m.14178T>C with TC and D-loop_m.215A>G with LDLC. Furthermore, we performed linear regression analysis to determine on the SNPs of mtDNA and left ventricular ejection fraction (LVEF) and found that the SNP D-loop_m.16145G>A was nominally significantly associated with LVEF (P = 0.047). Our findings provide insights into the lipidomic context of mtDNA variations and highlight the importance of studying mitochondrial genetic variants related to lipid species.

Htd2 deficiency-associated suppression of α-lipoic acid production provokes mitochondrial dysfunction and insulin resistance in adipocytes

Mitochondria harbor a unique fatty acid synthesis pathway (mtFAS) with mysterious functions gaining increasing interest, while its involvement in metabolic regulation is essentially unknown. Here we show that 3-Hydroxyacyl-ACP dehydratase (HTD2), a key enzyme in mtFAS pathway was primarily downregulated in adipocytes of mice under metabolic disorders, accompanied by decreased de novo production of lipoic acid, which is the byproduct of mtFAS pathway. Knockdown of Htd2 in 3T3-L1 preadipocytes or differentiated 3T3-L1 mature adipocytes impaired mitochondrial function via suppression of complex I activity, resulting in enhanced oxidative stress and impaired insulin sensitivity, which were all attenuated by supplement of lipoic acid. Moreover, lipidomic study revealed limited lipid alterations in mtFAS deficient cells which primarily presenting accumulation of triglycerides, attributed to mitochondrial dysfunction. Collectively, the present study highlighted the pivotal role of mtFAS pathway in regulating mitochondrial function and adipocytes insulin sensitivity, demonstrating supportive evidence for lipoic acid being potential effective nutrient for improving insulin resistance and related metabolic disorders.

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3-Hydroxyacyl-ACP dehydratase is decreased in adipocytes under diabetic condition.

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Deficient of 3-Hydroxyacyl-ACP dehydratase (HTD2) triggers mitochondrial dysfunction.

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Deficient of HTD2 promotes insulin resistance in adipocytes.

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Supplement of lipoic acid ameliorates deleterious effects of HTD2 deficiency.