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Vignoli A, Gori AM, Berteotti M, Cesari F, Giusti B, Bertelli A, Kura A, Sticchi E, Salvadori E, Barbato C, Formelli B, Pescini F, Marcucci R, Tenori L, Poggesi A. The serum metabolomic profiles of atrial fibrillation patients treated with direct oral anticoagulants or vitamin K antagonists. Life Sci 2024; 351:122796. [PMID: 38852797 DOI: 10.1016/j.lfs.2024.122796] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2024] [Revised: 05/03/2024] [Accepted: 06/04/2024] [Indexed: 06/11/2024]
Abstract
AIMS Long-term oral anticoagulation is the primary therapy for preventing ischemic stroke in patients with atrial fibrillation (AF). Different types of oral anticoagulant drugs can have specific effects on the metabolism of patients. Here we characterize, for the first time, the serum metabolomic and lipoproteomic profiles of AF patients treated with anticoagulants: vitamin K antagonists (VKAs) or direct oral anticoagulants (DOACs). MATERIALS AND METHODS Serum samples of 167 AF patients (median age 78 years, 62 % males, 70 % on DOACs treatment) were analyzed via high resolution 1H nuclear magnetic resonance (NMR) spectroscopy. Data on 25 metabolites and 112 lipoprotein-related fractions were quantified and analyzed with multivariate and univariate statistical approaches. KEY FINDINGS Our data provide evidence that patients treated with VKAs and DOACs present significant differences in their profiles: lower levels of alanine and lactate (odds ratio: 1.72 and 1.84), free cholesterol VLDL-4 subfraction (OR: 1.75), triglycerides LDL-1 subfraction (OR: 1.80) and 4 IDL cholesterol fractions (ORs ∼ 1.80), as well as higher levels of HDL cholesterol (OR: 0.48), apolipoprotein A1 (OR: 0.42) and 7 HDL cholesterol fractions/subfractions (ORs: 0.40-0.51) are characteristic of serum profile of patients on DOACs' therapy. SIGNIFICANCE Our results support the usefulness of NMR-based metabolomics for the description of the effects of oral anticoagulants on AF patient circulating metabolites and lipoproteins. The higher serum levels of HDL cholesterol observed in patients on DOACs could contribute to explaining their reduced cardiovascular risk, suggesting the need of further studies in this direction to fully understand possible clinical implications.
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Affiliation(s)
- Alessia Vignoli
- Department of Chemistry "Ugo Schiff", University of Florence, 50019 Sesto Fiorentino, Italy; Magnetic Resonance Center (CERM), University of Florence, 50019 Sesto Fiorentino, Italy
| | - Anna Maria Gori
- Atherothrombotic Center, Department of Experimental and Clinical Medicine, University of Florence, AOU Careggi, 50134 Florence, Italy
| | - Martina Berteotti
- Atherothrombotic Center, Department of Experimental and Clinical Medicine, University of Florence, AOU Careggi, 50134 Florence, Italy
| | - Francesca Cesari
- Atherothrombotic Center, Department of Experimental and Clinical Medicine, University of Florence, AOU Careggi, 50134 Florence, Italy
| | - Betti Giusti
- Atherothrombotic Center, Department of Experimental and Clinical Medicine, University of Florence, AOU Careggi, 50134 Florence, Italy
| | - Alessia Bertelli
- Atherothrombotic Center, Department of Experimental and Clinical Medicine, University of Florence, AOU Careggi, 50134 Florence, Italy
| | - Ada Kura
- Atherothrombotic Center, Department of Experimental and Clinical Medicine, University of Florence, AOU Careggi, 50134 Florence, Italy
| | - Elena Sticchi
- Atherothrombotic Center, Department of Experimental and Clinical Medicine, University of Florence, AOU Careggi, 50134 Florence, Italy
| | - Emilia Salvadori
- NEUROFARBA Department, University of Florence, 50139 Florence, Italy
| | - Carmen Barbato
- NEUROFARBA Department, University of Florence, 50139 Florence, Italy
| | | | | | - Rossella Marcucci
- Atherothrombotic Center, Department of Experimental and Clinical Medicine, University of Florence, AOU Careggi, 50134 Florence, Italy
| | - Leonardo Tenori
- Department of Chemistry "Ugo Schiff", University of Florence, 50019 Sesto Fiorentino, Italy; Magnetic Resonance Center (CERM), University of Florence, 50019 Sesto Fiorentino, Italy; Consorzio Interuniversitario Risonanze Magnetiche MetalloProteine (CIRMMP), 50019 Sesto Fiorentino, Italy.
| | - Anna Poggesi
- NEUROFARBA Department, University of Florence, 50139 Florence, Italy; Stroke Unit, AOU Careggi, 50134, Florence, Italy.
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Lteif C, Huang Y, Guerra LA, Gawronski BE, Duarte JD. Using Omics to Identify Novel Therapeutic Targets in Heart Failure. CIRCULATION. GENOMIC AND PRECISION MEDICINE 2024; 17:e004398. [PMID: 38766848 PMCID: PMC11187651 DOI: 10.1161/circgen.123.004398] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
Omics refers to the measurement and analysis of the totality of molecules or biological processes involved within an organism. Examples of omics data include genomics, transcriptomics, epigenomics, proteomics, metabolomics, and more. In this review, we present the available literature reporting omics data on heart failure that can inform the development of novel treatments or innovative treatment strategies for this disease. This includes polygenic risk scores to improve prediction of genomic data and the potential of multiomics to more efficiently identify potential treatment targets for further study. We also discuss the limitations of omic analyses and the barriers that must be overcome to maximize the utility of these types of studies. Finally, we address the current state of the field and future opportunities for using multiomics to better personalize heart failure treatment strategies.
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Affiliation(s)
- Christelle Lteif
- Center for Pharmacogenomics and Precision Medicine, Department of Pharmacotherapy and Translational Research, University of Florida College of Pharmacy, Gainesville, FL
| | - Yimei Huang
- Center for Pharmacogenomics and Precision Medicine, Department of Pharmacotherapy and Translational Research, University of Florida College of Pharmacy, Gainesville, FL
| | - Leonardo A Guerra
- Center for Pharmacogenomics and Precision Medicine, Department of Pharmacotherapy and Translational Research, University of Florida College of Pharmacy, Gainesville, FL
| | - Brian E Gawronski
- Center for Pharmacogenomics and Precision Medicine, Department of Pharmacotherapy and Translational Research, University of Florida College of Pharmacy, Gainesville, FL
| | - Julio D Duarte
- Center for Pharmacogenomics and Precision Medicine, Department of Pharmacotherapy and Translational Research, University of Florida College of Pharmacy, Gainesville, FL
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Joo J, Shearer JJ, Wolska A, Remaley AT, Otvos JD, Connelly MA, Sampson M, Bielinski SJ, Larson NB, Park H, Conners KM, Turecamo S, Roger VL. Incremental Value of a Metabolic Risk Score for Heart Failure Mortality: A Population-Based Study. CIRCULATION. GENOMIC AND PRECISION MEDICINE 2024; 17:e004312. [PMID: 38516784 PMCID: PMC11021175 DOI: 10.1161/circgen.123.004312] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Accepted: 03/10/2024] [Indexed: 03/23/2024]
Abstract
BACKGROUND Heart failure is heterogeneous syndrome with persistently high mortality. Nuclear magnetic resonance spectroscopy enables high-throughput metabolomics, suitable for precision phenotyping. We aimed to use targeted metabolomics to derive a metabolic risk score (MRS) that improved mortality risk stratification in heart failure. METHODS Nuclear magnetic resonance was used to measure 21 metabolites (lipoprotein subspecies, branched-chain amino acids, alanine, GlycA (glycoprotein acetylation), ketone bodies, glucose, and citrate) in plasma collected from a heart failure community cohort. The MRS was derived using least absolute shrinkage and selection operator penalized Cox regression and temporal validation. The association between the MRS and mortality and whether risk stratification was improved over the Meta-Analysis Global Group in Chronic Heart Failure clinical risk score and NT-proBNP (N-terminal pro-B-type natriuretic peptide) levels were assessed. RESULTS The study included 1382 patients (median age, 78 years, 52% men, 43% reduced ejection fraction) with a 5-year survival rate of 48% (95% CI, 46%-51%). The MRS included 9 metabolites measured. In the validation data set, a 1 standard deviation increase in the MRS was associated with a large increased rate of death (hazard ratio, 2.2 [95% CI, 1.9-2.5]) that remained after adjustment for Meta-Analysis Global Group in Chronic Heart Failure score and NT-proBNP (hazard ratio, 1.6 [95% CI, 1.3-1.9]). These associations did not differ by ejection fraction. The integrated discrimination and net reclassification indices, and Uno's C statistic, indicated that the addition of the MRS improved discrimination over Meta-Analysis Global Group in Chronic Heart Failure and NT-proBNP. CONCLUSIONS This MRS developed in a heart failure community cohort was associated with a large excess risk of death and improved risk stratification beyond an established risk score and clinical markers.
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Affiliation(s)
- Jungnam Joo
- Office of Biostatistics Research, National Heart, Lung, and Blood Inst
| | - Joseph J. Shearer
- Heart Disease Phenomics Laboratory, Epidemiology & Community Health Branch, National Heart, Lung, and Blood Inst
| | - Anna Wolska
- Lipoprotein Metabolism Laboratory, Translational Vascular Medicine Branch, National Heart, Lung, and Blood Inst
| | - Alan T. Remaley
- Lipoprotein Metabolism Laboratory, Translational Vascular Medicine Branch, National Heart, Lung, and Blood Inst
| | - James D. Otvos
- Lipoprotein Metabolism Laboratory, Translational Vascular Medicine Branch, National Heart, Lung, and Blood Inst
| | | | - Maureen Sampson
- Dept of Laboratory Medicine, Clinical Ctr, National Institutes of Health, Bethesda, MD
| | | | - Nicholas B. Larson
- Division of Clinical Trials & Biostatistics, Dept of Quantitative Health Sciences, Mayo Clinic College of Medicine & Science, Rochester, MN
| | - Hoyoung Park
- Dept of Statistics, Sookmyung Women’s University, Seoul, Korea
| | - Katherine M. Conners
- Heart Disease Phenomics Laboratory, Epidemiology & Community Health Branch, National Heart, Lung, and Blood Inst
| | - Sarah Turecamo
- Heart Disease Phenomics Laboratory, Epidemiology & Community Health Branch, National Heart, Lung, and Blood Inst
| | - Véronique L. Roger
- Heart Disease Phenomics Laboratory, Epidemiology & Community Health Branch, National Heart, Lung, and Blood Inst
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Wang H, Cai P, Yu X, Li S, Zhu W, Liu Y, Wang D. Bioinformatics identifies key genes and potential drugs for energy metabolism disorders in heart failure with dilated cardiomyopathy. Front Pharmacol 2024; 15:1367848. [PMID: 38510644 PMCID: PMC10952830 DOI: 10.3389/fphar.2024.1367848] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Accepted: 02/21/2024] [Indexed: 03/22/2024] Open
Abstract
Background: Dysfunction in myocardial energy metabolism plays a vital role in the pathological process of Dilated Cardiomyopathy (DCM). However, the precise mechanisms remain unclear. This study aims to investigate the key molecular mechanisms of energy metabolism and potential therapeutic agents in the progression of dilated cardiomyopathy with heart failure. Methods: Gene expression profiles and clinical data for patients with dilated cardiomyopathy complicated by heart failure, as well as healthy controls, were sourced from the Gene Expression Omnibus (GEO) database. Gene sets associated with energy metabolism were downloaded from the Molecular Signatures Database (MSigDB) for subsequent analysis. Weighted Gene Co-expression Network Analysis (WGCNA) and differential expression analysis were employed to identify key modules and genes related to heart failure. Potential biological mechanisms were investigated through Gene Set Enrichment Analysis (GSEA), Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and the construction of a competing endogenous RNA (ceRNA) network. Molecular docking simulations were then conducted to explore the binding affinity and conformation of potential therapeutic drugs with hub genes. Results: Analysis of the left ventricular tissue expression profiles revealed that, compared to healthy controls, patients with dilated cardiomyopathy exhibited 234 differentially expressed genes and 2 genes related to myocardial energy metabolism. Additionally, Benzoylaconine may serve as a potential therapeutic agent for the treatment of dilated cardiomyopathy. Conclusion: The study findings highlight the crucial role of myocardial energy metabolism in the progression of Dilated Cardiomyopathy. Notably, Benzoylaconine emerges as a potential candidate for treating Dilated Cardiomyopathy, potentially exerting its therapeutic effects by targeted modulation of myocardial energy metabolism through NRK and NT5.
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Affiliation(s)
- Haixia Wang
- Guangzhou University of Traditional Chinese Medicine ShunDe Traditional Chinese Medicine Hospital, Guangzhou, China
| | - Peifeng Cai
- Guangzhou University of Traditional Chinese Medicine ShunDe Traditional Chinese Medicine Hospital, Guangzhou, China
| | - Xiaohan Yu
- Guangzhou University of Traditional Chinese Medicine ShunDe Traditional Chinese Medicine Hospital, Guangzhou, China
| | - Shiqi Li
- Guangzhou University of Traditional Chinese Medicine ShunDe Traditional Chinese Medicine Hospital, Guangzhou, China
| | - Wei Zhu
- The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
- Guangdong Provincial Key Laboratory of Clinical Research on Traditional Chinese Medicine Syndrome, China
| | - Yuntao Liu
- The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
- State Key Laboratory of Traditional Chinese Medicine Syndrome/Departments of Gynecologic Oncology, Guangzhou, China
| | - Dawei Wang
- State Key Laboratory of Traditional Chinese Medicine Syndrome/Departments of Gynecologic Oncology, Guangzhou, China
- The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
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Ghini V, Meoni G, Vignoli A, Di Cesare F, Tenori L, Turano P, Luchinat C. Fingerprinting and profiling in metabolomics of biosamples. PROGRESS IN NUCLEAR MAGNETIC RESONANCE SPECTROSCOPY 2023; 138-139:105-135. [PMID: 38065666 DOI: 10.1016/j.pnmrs.2023.10.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Revised: 10/13/2023] [Accepted: 10/15/2023] [Indexed: 12/18/2023]
Abstract
This review focuses on metabolomics from an NMR point of view. It attempts to cover the broad scope of metabolomics and describes the NMR experiments that are most suitable for each sample type. It is addressed not only to NMR specialists, but to all researchers who wish to approach metabolomics with a clear idea of what they wish to achieve but not necessarily with a deep knowledge of NMR. For this reason, some technical parts may seem a bit naïve to the experts. The review starts by describing standard metabolomics procedures, which imply the use of a dedicated 600 MHz instrument and of four properly standardized 1D experiments. Standardization is a must if one wants to directly compare NMR results obtained in different labs. A brief mention is also made of standardized pre-analytical procedures, which are even more essential. Attention is paid to the distinction between fingerprinting and profiling, and the advantages and disadvantages of fingerprinting are clarified. This aspect is often not fully appreciated. Then profiling, and the associated problems of signal assignment and quantitation, are discussed. We also describe less conventional approaches, such as the use of different magnetic fields, the use of signal enhancement techniques to increase sensitivity, and the potential of field-shuttling NMR. A few examples of biomedical applications are also given, again with the focus on NMR techniques that are most suitable to achieve each particular goal, including a description of the most common heteronuclear experiments. Finally, the growing applications of metabolomics to foodstuffs are described.
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Affiliation(s)
- Veronica Ghini
- Magnetic Resonance Center (CERM), University of Florence, Sesto Fiorentino, Italy; Department of Chemistry "Ugo Schiff", University of Florence, Sesto Fiorentino, Italy
| | - Gaia Meoni
- Magnetic Resonance Center (CERM), University of Florence, Sesto Fiorentino, Italy; Department of Chemistry "Ugo Schiff", University of Florence, Sesto Fiorentino, Italy
| | - Alessia Vignoli
- Magnetic Resonance Center (CERM), University of Florence, Sesto Fiorentino, Italy; Department of Chemistry "Ugo Schiff", University of Florence, Sesto Fiorentino, Italy
| | - Francesca Di Cesare
- Magnetic Resonance Center (CERM), University of Florence, Sesto Fiorentino, Italy; Department of Chemistry "Ugo Schiff", University of Florence, Sesto Fiorentino, Italy
| | - Leonardo Tenori
- Magnetic Resonance Center (CERM), University of Florence, Sesto Fiorentino, Italy; Department of Chemistry "Ugo Schiff", University of Florence, Sesto Fiorentino, Italy; Consorzio Interuniversitario Risonanze Magnetiche Metallo Proteine (CIRMMP), Sesto Fiorentino, Italy
| | - Paola Turano
- Magnetic Resonance Center (CERM), University of Florence, Sesto Fiorentino, Italy; Department of Chemistry "Ugo Schiff", University of Florence, Sesto Fiorentino, Italy; Consorzio Interuniversitario Risonanze Magnetiche Metallo Proteine (CIRMMP), Sesto Fiorentino, Italy.
| | - Claudio Luchinat
- Consorzio Interuniversitario Risonanze Magnetiche Metallo Proteine (CIRMMP), Sesto Fiorentino, Italy; Giotto Biotech S.r.l., Sesto Fiorentino, Italy.
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Zinga MM, Abdel-Shafy E, Melak T, Vignoli A, Piazza S, Zerbini LF, Tenori L, Cacciatore S. KODAMA exploratory analysis in metabolic phenotyping. Front Mol Biosci 2022; 9:1070394. [PMID: 36733493 PMCID: PMC9887019 DOI: 10.3389/fmolb.2022.1070394] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Accepted: 12/28/2022] [Indexed: 01/18/2023] Open
Abstract
KODAMA is a valuable tool in metabolomics research to perform exploratory analysis. The advanced analytical technologies commonly used for metabolic phenotyping, mass spectrometry, and nuclear magnetic resonance spectroscopy push out a bunch of high-dimensional data. These complex datasets necessitate tailored statistical analysis able to highlight potentially interesting patterns from a noisy background. Hence, the visualization of metabolomics data for exploratory analysis revolves around dimensionality reduction. KODAMA excels at revealing local structures in high-dimensional data, such as metabolomics data. KODAMA has a high capacity to detect different underlying relationships in experimental datasets and correlate extracted features with accompanying metadata. Here, we describe the main application of KODAMA exploratory analysis in metabolomics research.
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Affiliation(s)
- Maria Mgella Zinga
- Bioinformatics Unit, International Centre for Genetic Engineering and Biotechnology, Cape Town, South Africa
- Department of Medical Parasitology and Entomology, Catholic University of Health and Allied Sciences, Mwanza, Tanzania
| | - Ebtesam Abdel-Shafy
- Bioinformatics Unit, International Centre for Genetic Engineering and Biotechnology, Cape Town, South Africa
- National Research Centre, Cairo, Egypt
| | - Tadele Melak
- Computation Biology, International Centre for Genetic Engineering and Biotechnology, Trieste, Italy
- Department of clinical chemistry, University of Gondar, Gondar, Ethiopia
| | - Alessia Vignoli
- Magnetic Resonance Center (CERM) and Department of Chemistry “Ugo Schiff”, University of Florence, Sesto Fiorentino, Italy
- Consorzio Interuniversitario Risonanze Magnetiche Metallo Proteine (CIRMMP), Sesto Fiorentino, Italy
| | - Silvano Piazza
- Computation Biology, International Centre for Genetic Engineering and Biotechnology, Trieste, Italy
| | - Luiz Fernando Zerbini
- Cancer Genomics, International Centre for Genetic Engineering and Biotechnology, Cape Town, South Africa
| | - Leonardo Tenori
- Magnetic Resonance Center (CERM) and Department of Chemistry “Ugo Schiff”, University of Florence, Sesto Fiorentino, Italy
- Consorzio Interuniversitario Risonanze Magnetiche Metallo Proteine (CIRMMP), Sesto Fiorentino, Italy
| | - Stefano Cacciatore
- Bioinformatics Unit, International Centre for Genetic Engineering and Biotechnology, Cape Town, South Africa
- Institute of Reproductive and Developmental Biology, Imperial College London, London, United Kingdom
- *Correspondence: Stefano Cacciatore,
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