Network Medicine: A Clinical Approach for Precision Medicine and Personalized Therapy in Coronary Heart Disease

Early identification of coronary atherosclerotic pathogenic mechanisms is useful for predicting the risk of coronary heart disease (CHD) and future cardiac events. Epigenome changes may clarify a significant fraction of this “missing hereditability”, thus offering novel potential biomarkers for prevention and care of CHD. The rapidly growing disciplines of systems biology and network science are now poised to meet the fields of precision medicine and personalized therapy. Network medicine integrates standard clinical recording and non-invasive, advanced cardiac imaging tools with epigenetics into deep learning for in-depth CHD molecular phenotyping. This approach could potentially explore developing novel drugs from natural compounds (i.e. polyphenols, folic acid) and repurposing current drugs, such as statins and metformin. Several clinical trials have exploited epigenetic tags and epigenetic sensitive drugs both in primary and secondary prevention. Due to their stability in plasma and easiness of detection, many ongoing clinical trials are focused on the evaluation of circulating miRNAs (e.g. miR-8059 and miR-320a) in blood, in association with imaging parameters such as coronary calcifications and stenosis degree detected by coronary computed tomography angiography (CCTA), or functional parameters provided by FFR/CT and PET/CT. Although epigenetic modifications have also been prioritized through network based approaches, the whole set of molecular interactions (interactome) in CHD is still under investigation for primary prevention strategies.

[1]  Yan Zhang,et al.  Integrated systems approach identifies risk regulatory pathways and key regulators in coronary artery disease , 2015, Journal of Molecular Medicine.

[2]  Mario J. Garcia,et al.  Prevalence, impact, and predictive value of detecting subclinical coronary and carotid atherosclerosis in asymptomatic adults: the BioImage study. , 2015, Journal of the American College of Cardiology.

[3]  C. Napoli,et al.  Age-Related Effects on Atherogenesis and Scavenger Enzymes of Intracranial and Extracranial Arteries in Men Without Classic Risk Factors for Atherosclerosis , 2001, Stroke.

[4]  J. Tijssen,et al.  Effect of metformin on left ventricular function after acute myocardial infarction in patients without diabetes: the GIPS-III randomized clinical trial. , 2014, JAMA.

[5]  A. Valsesia,et al.  Genome-wide identification of circulating-miRNA expression quantitative trait loci reveals the role of several miRNAs in the regulation of Cardiometabolic phenotypes. , 2019, Cardiovascular research.

[6]  A. Carass,et al.  Automatic Coronary Wall and Atherosclerotic Plaque Segmentation from 3D Coronary CT Angiography , 2019, Scientific Reports.

[7]  Jingyuan Fu,et al.  Inter-Tissue Gene Co-Expression Networks between Metabolically Healthy and Unhealthy Obese Individuals , 2016, PloS one.

[8]  Albert-László Barabási,et al.  Network-based approach to prediction and population-based validation of in silico drug repurposing , 2018, Nature Communications.

[9]  A. Barabasi,et al.  Uncovering disease-disease relationships through the incomplete interactome , 2015, Science.

[10]  A. Soricelli,et al.  Epigenetic Hallmarks of Fetal Early Atherosclerotic Lesions in Humans , 2018, JAMA cardiology.

[11]  P. Libby,et al.  Rosuvastatin to prevent vascular events in men and women with elevated C-reactive protein. , 2008, The New England journal of medicine.

[12]  C. Napoli,et al.  Epigenetic-related therapeutic challenges in cardiovascular disease. , 2015, Trends in pharmacological sciences.

[13]  Steve Horvath,et al.  A Systems Genetics Approach Implicates USF1, FADS3, and Other Causal Candidate Genes for Familial Combined Hyperlipidemia , 2009, PLoS genetics.

[14]  R. Giugliano,et al.  Reduction in Total Cardiovascular Events With Ezetimibe/Simvastatin Post-Acute Coronary Syndrome: The IMPROVE-IT Trial. , 2016, Journal of the American College of Cardiology.

[15]  Leon Axel,et al.  Recent Advances in Cardiovascular Magnetic Resonance: Techniques and Applications , 2017, Circulation. Cardiovascular imaging.

[16]  C. Napoli,et al.  The fetal origins of atherosclerosis: maternal hypercholesterolemia, and cholesterol‐lowering or antioxidant treatment during pregnancy influence in utero programming and postnatal susceptibility to atherogenesis , 2002, FASEB journal : official publication of the Federation of American Societies for Experimental Biology.

[17]  M. Triggiani,et al.  Oxidative structural modifications of low density lipoprotein in homozygous familial hypercholesterolemia. , 1995, Atherosclerosis.

[18]  Y. Jia,et al.  Reducing both radiation and contrast doses in coronary CT angiography in lean patients on a 16-cm wide-detector CT using 70 kVp and ASiR-V algorithm, in comparison with the conventional 100-kVp protocol , 2018, European Radiology.

[19]  F. Otsuka,et al.  Noninvasive Coronary Plaque Imaging , 2018, Journal of atherosclerosis and thrombosis.

[20]  Xiaoyu Zuo,et al.  Identifying functional modules for coronary artery disease by a prior knowledge-based approach. , 2014, Gene.

[21]  Andrew D. Johnson,et al.  Integrative network analysis reveals molecular mechanisms of blood pressure regulation , 2015, Molecular systems biology.

[22]  O. Schillaci,et al.  Myocardial-coronary fusion imaging with positron emission tomography and computed tomography: Benchmarking and slingshotting , 2018, Journal of Nuclear Cardiology.

[23]  A. Barabasi,et al.  Network medicine : a network-based approach to human disease , 2010 .

[24]  S. Duan,et al.  Identification of susceptibility modules for coronary artery disease using a genome wide integrated network analysis. , 2013, Gene.

[25]  L. Liang,et al.  Genome-Wide Analysis of DNA Methylation and Acute Coronary Syndrome , 2017, Circulation research.

[26]  B. Gersh,et al.  Angiographic Versus Functional Severity of Coronary Artery Stenoses in the FAME Study: Fractional Flow Reserve Versus Angiography in Multivessel Evaluation , 2011 .

[27]  M. Macek,et al.  ResearchFluvastatin in the first-line therapy of acute coronary syndrome : results of the multicenter , randomized , double-blind , placebo-controlled trial ( the FACS-trial ) , 2015 .

[28]  M. Coenen,et al.  Effect of metformin pretreatment on myocardial injury during coronary artery bypass surgery in patients without diabetes (MetCAB): a double-blind, randomised controlled trial. , 2015, The lancet. Diabetes & endocrinology.

[29]  N. Samani,et al.  Network analysis of coronary artery disease risk genes elucidates disease mechanisms and druggable targets , 2018, Scientific Reports.

[30]  N. Nanda,et al.  Ultrasound assessment of carotid arteries: Current concepts, methodologies, diagnostic criteria, and technological advancements , 2018, Echocardiography.

[31]  E. Silverman,et al.  Developing New Drug Treatments in the Era of Network Medicine , 2013, Clinical pharmacology and therapeutics.

[32]  W. Wongcharoen,et al.  Effects of curcuminoids on frequency of acute myocardial infarction after coronary artery bypass grafting. , 2012, The American journal of cardiology.

[33]  Ozlem Keskin,et al.  Methods for Discovering and Targeting Druggable Protein-Protein Interfaces and Their Application to Repurposing. , 2018, Methods in molecular biology.

[34]  A. Fuisz,et al.  Cardiac Magnetic Resonance for Diagnosis and Risk Stratification. , 2019, Cardiology clinics.

[35]  Kyung-Han Lee,et al.  Molecular Imaging in the Era of Personalized Medicine , 2015, Journal of pathology and translational medicine.

[36]  Zhong-xiang Yuan,et al.  Dysfunctional co-expression network analysis of familial hypercholesterolemia. , 2013, Journal of cardiology.

[37]  J. Loscalzo,et al.  The Emerging Paradigm of Network Medicine in the Study of Human Disease , 2012, Circulation research.

[38]  Andrea Soricelli,et al.  Contemporary Reviews in Cardiovascular Medicine Primary Prevention of Atherosclerosis A Clinical Challenge for the Reversal of Epigenetic Mechanisms , 2012 .

[39]  J. Leipsic,et al.  Coronary CT Angiography-Derived Fractional Flow Reserve , 2016, Current Radiology Reports.

[40]  C. Napoli,et al.  Novel epigenetic-sensitive clinical challenges both in type 1 and type 2 diabetes. , 2018, Journal of diabetes and its complications.

[41]  J. Loscalzo,et al.  Putting the Patient Back Together - Social Medicine, Network Medicine, and the Limits of Reductionism. , 2017, The New England journal of medicine.

[42]  A. Barabasi,et al.  Network medicine--from obesity to the "diseasome". , 2007, The New England journal of medicine.

[43]  V. Fuster,et al.  Vascular Inflammation in Subclinical Atherosclerosis Detected by Hybrid PET/MRI. , 2019, Journal of the American College of Cardiology.

[44]  Y. Jia,et al.  The Value of 16-cm Wide-Detector Computed Tomography in Coronary Computed Tomography Angiography for Patients With High Heart Rate Variability , 2018, Journal of computer assisted tomography.

[45]  A. Barabasi,et al.  Network-based in silico drug efficacy screening , 2016, Nature Communications.

[46]  E. Nagel,et al.  Dual-energy CT of the heart current and future status. , 2018, European journal of radiology.

[47]  J. Borén,et al.  Personalized Cardiovascular Disease Prediction and Treatment—A Review of Existing Strategies and Novel Systems Medicine Tools , 2016, Front. Physiol..

[48]  S. Yusuf,et al.  Cholesterol Lowering in Intermediate-Risk Persons without Cardiovascular Disease. , 2016, The New England journal of medicine.

[49]  Jing Liu,et al.  Weighted gene co-expression network analysis identifies specific modules and hub genes related to coronary artery disease , 2016, BMC Cardiovascular Disorders.

[50]  Jian-ying Li,et al.  Contrast dose reduction with shortened injection durations in coronary CT angiography on 16-cm Wide-detector CT scanner. , 2018, The British journal of radiology.

[51]  Daniel E Forman,et al.  2018 AHA/ACC/AACVPR/AAPA/ABC/ACPM/ADA/AGS/APhA/ASPC/NLA/PCNA Guideline on the Management of Blood Cholesterol: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. , 2019, Journal of the American College of Cardiology.

[52]  Roland Krug,et al.  Cardiac MR imaging: current status and future direction. , 2015, Cardiovascular diagnosis and therapy.

[53]  Joseph Loscalzo,et al.  Network-Based Disease Module Discovery by a Novel Seed Connector Algorithm with Pathobiological Implications. , 2018, Journal of molecular biology.

[54]  Venkatesh L. Murthy,et al.  Clinical Quantification of Myocardial Blood Flow Using PET: Joint Position Paper of the SNMMI Cardiovascular Council and the ASNC , 2017, Journal of Nuclear Cardiology.

[55]  Ruth McPherson,et al.  Genetics of Coronary Artery Disease. , 2016, Circulation research.

[56]  U. Hoffmann,et al.  Secondary cardiac risk stratifying tests after coronary computed tomography angiography in emergency department patients. , 2018, Journal of cardiovascular computed tomography.

[57]  Taylor M. Duguay,et al.  Coronary CT angiography radiation dose trends: A 10-year analysis to develop institutional diagnostic reference levels. , 2019, European journal of radiology.

[58]  Jeroen J. Bax,et al.  Machine learning of clinical variables and coronary artery calcium scoring for the prediction of obstructive coronary artery disease on coronary computed tomography angiography: analysis from the CONFIRM registry. , 2019, European heart journal.

[59]  V. Bettinardi,et al.  Carotid artery plaque uptake of 11C-PK11195 inversely correlates with circulating monocytes and classical CD14++CD16− monocytes expressing HLA-DR , 2018, International journal of cardiology. Heart & vasculature.

[60]  Oznur Tastan,et al.  Integromic Analysis of Genetic Variation and Gene Expression Identifies Networks for Cardiovascular Disease Phenotypes , 2015, Circulation.

[61]  C. Napoli,et al.  Maternal-foetal epigenetic interactions in the beginning of cardiovascular damage. , 2011, Cardiovascular Research.

[62]  T. Assimes,et al.  Genome-Wide Association Studies of Coronary Artery Disease: Recent Progress and Challenges Ahead , 2018, Current Atherosclerosis Reports.

[63]  M. Hacker,et al.  Sodium-fluoride PET-CT for the non-invasive evaluation of coronary plaques in symptomatic patients with coronary artery disease: a cross-correlation study with intravascular ultrasound , 2018, European Journal of Nuclear Medicine and Molecular Imaging.

[64]  Albert,et al.  Emergence of scaling in random networks , 1999, Science.

[65]  Stephanie C Allen,et al.  Pleiotropic and Adverse Effects of Statins—Do Epigenetics Play a Role? , 2017, The Journal of Pharmacology and Experimental Therapeutics.

[66]  S. Plein,et al.  Cardiovascular magnetic resonance and single-photon emission computed tomography for diagnosis of coronary heart disease (CE-MARC): a prospective trial , 2012, The Lancet.

[67]  Joseph Loscalzo,et al.  Precision medicine in cardiology , 2016, Nature Reviews Cardiology.

[68]  C. Catalano,et al.  T1 and extracellular volume fraction mapping in cardiac magnetic resonance: estimation of accuracy and precision of a novel algorithm , 2019, Physics in medicine and biology.

[69]  Z. Fayad,et al.  New methods to image unstable atherosclerotic plaques , 2018, Atherosclerosis.

[70]  Y. Sheikine,et al.  FDG-PET imaging of atherosclerosis: Do we know what we see? , 2010, Atherosclerosis.

[71]  K. Kumamaru,et al.  Submillisievert imaging protocol using full reconstruction and advanced patient motion correction in 320-row area detector coronary CT angiography , 2018, The International Journal of Cardiovascular Imaging.

[72]  Yao Lu,et al.  Maximization of the usage of coronary CTA derived plaque information using a machine learning based algorithm to improve risk stratification; insights from the CONFIRM registry. , 2018, Journal of cardiovascular computed tomography.

[73]  R. McPherson,et al.  Partitioning the heritability of coronary artery disease highlights the importance of immune-mediated processes and epigenetic sites associated with transcriptional activity , 2017, Cardiovascular research.

[74]  D. Berman,et al.  Clinical Quantification of Myocardial Blood Flow Using PET: Joint Position Paper of the SNMMI Cardiovascular Council and the ASNC , 2017, The Journal of Nuclear Medicine.

[75]  Adam R. Davis,et al.  Genotype-driven identification of a molecular network predictive of advanced coronary calcium in ClinSeq® and Framingham Heart Study cohorts , 2017, BMC Systems Biology.

[76]  N. Curzen,et al.  MicroRNA 8059 as a marker for the presence and extent of coronary artery calcification , 2018, Open Heart.

[77]  Andrea Soricelli,et al.  An integrated approach to coronary heart disease diagnosis and clinical management. , 2017, American journal of translational research.

[78]  K. Taylor,et al.  Genome-Wide Association , 2007, Diabetes.

[79]  J. Buscombe Exploring the nature of atheroma and cardiovascular inflammation in vivo using positron emission tomography (PET). , 2015, The British journal of radiology.

[80]  Joseph Loscalzo,et al.  Network Medicine in Pathobiology. , 2019, The American journal of pathology.

[81]  C. Krittanawong,et al.  Artificial Intelligence in Precision Cardiovascular Medicine. , 2017, Journal of the American College of Cardiology.

[82]  Joseph Loscalzo,et al.  Emerging Role of Precision Medicine in Cardiovascular Disease. , 2018, Circulation research.

[83]  F. Cheng,et al.  Quantitative and systems pharmacology 4. Network-based analysis of drug pleiotropy on coronary artery disease. , 2019, European journal of medicinal chemistry.

[84]  Giuditta Benincasa,et al.  Epigenetic Inheritance Underlying Pulmonary Arterial Hypertension: A New Challenge for Network Medicine , 2019, Arteriosclerosis, thrombosis, and vascular biology.

[85]  Claudio Napoli,et al.  Rethinking primary prevention of atherosclerosis-related diseases. , 2006, Circulation.

[86]  R. Abbott,et al.  Coronary Artery Calcium and Carotid Artery Intima Media Thickness and Plaque: Clinical Use in Need of Clarification , 2017, Journal of atherosclerosis and thrombosis.

[87]  G. Fakhri,et al.  Quantification of PET Myocardial Blood Flow , 2019, Current Cardiology Reports.

[88]  S. Yusuf,et al.  Reducing the Global Burden of Cardiovascular Disease, Part 1: The Epidemiology and Risk Factors. , 2017, Circulation research.

[89]  Y. Ohashi,et al.  Usefulness of Pravastatin in Primary Prevention of Cardiovascular Events in Women: Analysis of the Management of Elevated Cholesterol in the Primary Prevention Group of Adult Japanese (MEGA Study) , 2008, Circulation.

[90]  R. Valls,et al.  Cocoa Consumption Alters the Global DNA Methylation of Peripheral Leukocytes in Humans with Cardiovascular Disease Risk Factors: A Randomized Controlled Trial , 2013, PloS one.

[91]  Kipp W. Johnson,et al.  Deep learning for cardiovascular medicine: a practical primer. , 2019, European heart journal.

[92]  P. Kellman,et al.  Automated Pixel-Wise Quantitative Myocardial Perfusion Mapping by CMR to Detect Obstructive Coronary Artery Disease and Coronary Microvascular Dysfunction: Validation Against Invasive Coronary Physiology. , 2019, JACC. Cardiovascular imaging.

[93]  R. Holman,et al.  Metformin for non-diabetic patients with coronary heart disease (the CAMERA study): a randomised controlled trial. , 2014, The lancet. Diabetes & endocrinology.

[94]  Diagnostic accuracy of low-radiation coronary computed tomography angiography with low tube voltage and knowledge-based model reconstruction , 2019, Scientific Reports.

[95]  Haibin Chen,et al.  Transcriptome and miRNA network analysis of familial hypercholesterolemia. , 2014, International journal of molecular medicine.

[96]  Jan-Eric Litton,et al.  Launch of an Infrastructure for Health Research: BBMRI-ERIC. , 2018, Biopreservation and biobanking.

[97]  David M. Herrington,et al.  Multiple rare alleles at LDLR and APOA5 confer risk for early-onset myocardial infarction , 2014, Nature.

[98]  Cecilia M. Lindgren,et al.  Genetics and epigenetics of obesity , 2011, Maturitas.

[99]  Ayellet V. Segrè,et al.  Integrative Genomics Reveals Novel Molecular Pathways and Gene Networks for Coronary Artery Disease , 2014, PLoS genetics.

[100]  J. Loscalzo,et al.  Complexity and network dynamics in physiological adaptation: An integrated view , 2014, Physiology & Behavior.

[101]  Filippo Cademartiri,et al.  Evidence of association of circulating epigenetic-sensitive biomarkers with suspected coronary heart disease evaluated by Cardiac Computed Tomography , 2019, PloS one.

[102]  G. Lip,et al.  Novel Risk Markers and Risk Assessments for Cardiovascular Disease , 2017, Circulation research.

[103]  Honghuang Lin,et al.  Tissue-specific Network Analysis of Genetic Variants Associated with Coronary Artery Disease , 2018, Scientific Reports.

[104]  J. Danesh,et al.  A comprehensive 1000 Genomes-based genome-wide association meta-analysis of coronary artery disease , 2016 .

[105]  Yasuo Ohashi,et al.  Primary prevention of cardiovascular disease with pravastatin in Japan (MEGA Study): a prospective randomised controlled trial , 2006, The Lancet.

[106]  T. Ruddy,et al.  Molecular imaging of coronary inflammation. , 2019, Trends in cardiovascular medicine.

[107]  D. Berman,et al.  Validation of the appropriate use criteria for percutaneous coronary intervention in patients with stable coronary artery disease (from the COURAGE trial). , 2015, The American journal of cardiology.

[108]  M. Motwani,et al.  Machine learning for prediction of all-cause mortality in patients with suspected coronary artery disease: a 5-year multicentre prospective registry analysis , 2016, European heart journal.

[109]  Filippo Cademartiri,et al.  Correlation of Circulating miR-765, miR-93-5p, and miR-433-3p to Obstructive Coronary Heart Disease Evaluated by Cardiac Computed Tomography. , 2019, The American journal of cardiology.