Plasma Metabolomic Profiles Differentiate Patients With Dilated Cardiomyopathy and Ischemic Cardiomyopathy

Zhao, Jiying; Yang, Shengwen; Jing, Ran; Jin, Han; Hu, Yiran; Wang, Jing; Gu, Min; Niu, Hongxia; Zhang, Shu; Chen, Liang; Hua, Wei

doi:10.3389/fcvm.2020.597546

Cited by 18 publications

(15 citation statements)

References 58 publications

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“…Orotic acid and N-methylproline were a distinct metabolic signature for differentiated individuals with HF with preserved ejection fraction versus HF with reduced ejection fraction [34]. Norvaline,1-pyrroline-2-carboxylate, lysophosphatidylinositol (16 : 0/0 : 0), phosphatidylglycerol (6 : 0/8 : 0), fatty acid esters of hydroxy fatty acid (24 : 1), and phosphatidylcholine (18 : 0/18 : 3) may have the potential to differentiate patients with dilated cardiomyopathy and ischemic cardiomyopathy, which were common causes of HF [35]. Studies have shown Functional prediction of gut microbiota showed amino acid metabolism, metabolism of cofactors and vitamins, energy metabolism, replication and repair, and metabolism of terpenoids and polyketides pathways were significantly enriched in the healthy group, and membrane transport and signal transduction pathways were significantly enriched in CHF group at L2 pathways.…”

Section: Discussionmentioning

confidence: 99%

The Correlation between Gut Microbiota and Serum Metabolomic in Elderly Patients with Chronic Heart Failure

Wang

Cai

Ferrari

et al. 2021

Mediators of Inflammation

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Objective. Chronic heart failure (CHF) refers to a state of persistent heart failure that can be stable, deteriorated, or decompensated. The mechanism and pathogenesis of myocardial remodeling remain unknown. Based on 16S rDNA sequencing and metabolomics technology, this study analyzed the gut microbiota and serum metabolome in elderly patients with CHF to provide new insights into the microbiota and metabolic phenotypes of CHF. Methods. Blood and fecal samples were collected from 25 elderly patients with CHF and 25 healthy subjects. The expression of inflammatory factors in blood was detected by ELISA. 16S rDNA sequencing was used to analyze the changes in microorganisms in the samples. The changes of small molecular metabolites in serum samples were analyzed by LC-MS/MS. Spearman correlation coefficients were used to analyze the correlation between gut microbiota and serum metabolites. Results. Our results showed that the IL-6, IL-8, and TNF-α levels were significantly increased, and the IL-10 level was significantly decreased in the elderly patients with CHF compared with the healthy subjects. The diversity of the gut microbiota was decreased in the elderly patients with CHF. Moreover, Escherichia Shigella was negatively correlated with biocytin and RIBOFLAVIN. Haemophilus was negatively correlated with alpha-lactose, cellobiose, isomaltose, lactose, melibiose, sucrose, trehalose, and turanose. Klebsiella was positively correlated with bilirubin and ethylsalicylate. Klebsiella was negatively correlated with citramalate, hexanoylcarnitine, inosine, isovalerylcarnitine, methylmalonate, and riboflavin. Conclusion. The gut microbiota is simplified by the disease, and serum small-molecule metabolites evidently change in elderly patients with CHF. Serum and fecal biomarkers could be used for elderly patients with CHF screening.

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Section: Discussionmentioning

confidence: 99%

The Correlation between Gut Microbiota and Serum Metabolomic in Elderly Patients with Chronic Heart Failure

Wang

Cai

Ferrari

et al. 2021

Mediators of Inflammation

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“…Here, the researchers reported that plasma levels of arginine, glutamine, and hydroxytetradecanoylcarnitine could effectively distinguish coronary heart disease and DCM patients [33]. Further, in a study utilizing a high-resolution liquid chromatography coupled to the Q-Exactive quadrupole-Orbitrap tandem mass spectrometry (QE LC-MS/MS) approach, Zhao et al [18] analyzed plasma samples of HF patients with different etiology (38 patients with DCM and 18 patients with ICM) and 20 healthy controls and revealed a combination panel of 6 metabolites including 1-pyrroline-2-carboxylate, norvaline, lysophosphatidylinositol (16:0/0:0), phosphatidylglycerol (6:0/8:0), fatty acid esters of hydroxy fatty acid (24:1), and PC (18:0/18:3) to differentiate patients with DCM and ICM with median areas under the curve (AUCs) of the ROC of 0.98 and 0.93 with accuracy of 95% and 87% in training and testing dataset, respectively, indicating an excellent discrimination of the combination model for the metabolites panel.…”

Section: Biomarkers Quest For Differential Diagnosis Progression and ...mentioning

confidence: 99%

“…Typically, two main technologies [17] have been applied in metabolic profiling of DCM: nuclear magnetic resonance (NMR) spectrometry and mass spectrometry (MS) [18, 19]. While NMR generally has a better resolution compared to MS since this instrument does not interact directly with samples; however, it suffers from low sensitivity issue and limited to only high-abundance metabolites [20].…”

Section: Introductionmentioning

confidence: 99%

Metabolic and Metabolomics Insights into Dilated Cardiomyopathy

Ampong¹

2022

Ann Nutr Metab

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Background: Dilated cardiomyopathy (DCM) is the most common form of heart muscle disease characterized by progressive dilatation and ventricular dysfunction. Metabolomics is an emerging and powerful discipline that provides a global information on the phenotype of mammalian systems via the study of endogenous and exogenous metabolites in cells, tissues and biofluids. These studies aid in the identification of biomarkers to prevent diseases in later life or help to early detect onset of diseases as well as aiding in the elucidation of disease mechanisms. Summary: Metabolomics provides a unique opportunity to discover biomarkers for DCM. This review demonstrates evidence of metabolite-based biomarkers useful for predicting, diagnosing and monitoring therapeutic interventions of DCM. Key metabolites identified as potential biomarkers for diagnosing DCM include acyl-carnitines, succinic acid, malate, methylhistidine, aspartate, methionine, phenylalanine. In terms of differentiating DCM from ICM, potential biomarkers including 1-pyrroline-2-carboxylate, norvaline, lysophosphatidylinositol (16:0/0:0), phosphatidylglycerol, fatty acid esters of hydroxy fatty acid, and phosphatidylcholine were identified. Acyl-carnitines, isoleucine and linoleic acid and tryptophan were the main biomarkers to monitor treatment response to DCM. Mapping metabolites to metabolic pathways revealed dysregulation of BCAA, glycolysis, tricarboxylic acid cycle and triacylglycerol and pentose phosphate metabolism which have therapeutic potential for DCM. This review shows several limitations including the use of small sample sizes, lack of interpretation of age and sex differences in most studies and the fact that studies have so far been limited to case-control study designs. Key messages: Metabolites have close proximity to disease phenotype. With recent advancements in metabolomics field, potential biomarkers for DCM have been identified based on studies using different biological and metabolomics technologies. However, multi-center studies with larger populations that will lead to validation of these identified biomarkers to enable their clinical translation and utilization are still needed.

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“…According to statistics, 26 million people in the world have been diagnosed with HF, and about 54.35% of them die within 4 years after the diagnosis [ 3 ]. Ischemic cardiomyopathy and idiopathic cardiomyopathy are the triggers of HF [ 4 ]. Moreover, among HF patients, idiopathic dilated cardiomyopathy (IDCM) has a high mortality rate, and many patients are under 10 years of age.…”

Section: Introductionmentioning

confidence: 99%

The Bioinformatical Identification of Potential Biomarkers in Heart Failure Diagnosis and Treatment

et al. 2022

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Background. Heart failure (HF) is defined as the inability of the heart’s systolic and diastolic function to properly discharge blood flow from the veins to the heart. The goal of our research is to look into the possible mechanism that causes HF. Methods. The GSE5406 database was used for screening the differentially expressed genes (DEGs). Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Protein-Protein Interaction (PPI) network were applied to analyze DEGs. Besides, cell counting Kit-8 (CCK-8) was conducted to observe the knockdown effect of hub genes on cell proliferation. Results. Finally, 377 upregulated and 461 downregulated DEGs came out, enriched in the extracellular matrix organization and gap junction. According to GSEA results, Hoft cd4 positive alpha beta memory t cell bcg vaccine age 18–45 yo id 7 dy top 100 deg ex vivo up, Sobolev t cell pandemrix age 18–64 yo 7 dy dn, and so on were significantly related to gene set GSE5406. 7 hub genes, such as COL1A1, UBB, COL3A1, HSP90AA1, MYC, STAT3 and MAPK1, were selected from PPI networks. CCK-8 indicated silencing of STAT3 promoted the proliferation of H9C2 cells and silencing of UBB inhibited the proliferation of H9C2 cells. Conclusion. Our analysis reveals that COL1A1, UBB, COL3A1, HSP90AA1, MYC, STAT3, and MAPK1 might promote the progression of HF and become the biomarkers for diagnosis and treatment of HF.

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Plasma Metabolomic Profiles Differentiate Patients With Dilated Cardiomyopathy and Ischemic Cardiomyopathy

Cited by 18 publications

References 58 publications

The Correlation between Gut Microbiota and Serum Metabolomic in Elderly Patients with Chronic Heart Failure

The Correlation between Gut Microbiota and Serum Metabolomic in Elderly Patients with Chronic Heart Failure

Metabolic and Metabolomics Insights into Dilated Cardiomyopathy

The Bioinformatical Identification of Potential Biomarkers in Heart Failure Diagnosis and Treatment

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