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Jiang L, Shen J, Darst BF, Haiman CA, Mancuso N, Conti DV. Hierarchical joint analysis of marginal summary statistics-Part II: High-dimensional instrumental analysis of omics data. Genet Epidemiol 2024. [PMID: 38887957 DOI: 10.1002/gepi.22577] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Revised: 04/04/2024] [Accepted: 05/15/2024] [Indexed: 06/20/2024]
Abstract
Instrumental variable (IV) analysis has been widely applied in epidemiology to infer causal relationships using observational data. Genetic variants can also be viewed as valid IVs in Mendelian randomization and transcriptome-wide association studies. However, most multivariate IV approaches cannot scale to high-throughput experimental data. Here, we leverage the flexibility of our previous work, a hierarchical model that jointly analyzes marginal summary statistics (hJAM), to a scalable framework (SHA-JAM) that can be applied to a large number of intermediates and a large number of correlated genetic variants-situations often encountered in modern experiments leveraging omic technologies. SHA-JAM aims to estimate the conditional effect for high-dimensional risk factors on an outcome by incorporating estimates from association analyses of single-nucleotide polymorphism (SNP)-intermediate or SNP-gene expression as prior information in a hierarchical model. Results from extensive simulation studies demonstrate that SHA-JAM yields a higher area under the receiver operating characteristics curve (AUC), a lower mean-squared error of the estimates, and a much faster computation speed, compared to an existing approach for similar analyses. In two applied examples for prostate cancer, we investigated metabolite and transcriptome associations, respectively, using summary statistics from a GWAS for prostate cancer with more than 140,000 men and high dimensional publicly available summary data for metabolites and transcriptomes.
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Affiliation(s)
- Lai Jiang
- Department of Population and Public Health Sciences, Division of Biostatistics, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
| | - Jiayi Shen
- Department of Population and Public Health Sciences, Division of Biostatistics, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
| | - Burcu F Darst
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
- Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, Washington, USA
| | - Christopher A Haiman
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
- Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, California, USA
| | - Nicholas Mancuso
- Department of Population and Public Health Sciences, Division of Biostatistics, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
- Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, California, USA
| | - David V Conti
- Department of Population and Public Health Sciences, Division of Biostatistics, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
- Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, California, USA
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Pei M, Yang Y, Zhang C, Huang Q, Fang Y, Xu L, Lin S, He H. Role of serum neuron-specific enolase levels in the early diagnosis and prognosis of sepsis-associated encephalopathy: a systematic review and meta-analysis. Front Neurol 2024; 15:1353063. [PMID: 38685952 PMCID: PMC11057363 DOI: 10.3389/fneur.2024.1353063] [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: 12/12/2023] [Accepted: 02/12/2024] [Indexed: 05/02/2024] Open
Abstract
Background Sepsis-associated encephalopathy (SAE) is one of the most ubiquitous complications of sepsis and is characterized by cognitive impairment, poor prognosis, and a lack of uniform clinical diagnostic criteria. Therefore, this study investigated the early diagnostic and prognostic value of serum neuron-specific enolase (NSE) in SAE. Methods This systematic review and meta-analysis systematically searched for clinical trials with serum NSE information in patients with sepsis in the PubMed, Web of Science, Embase, and Cochrane databases from their inception to April 10, 2023. Included studies were assessed for quality and risk of bias using The Quality Assessment of Diagnostic Accuracy-2 tool. The meta-analysis of the included studies was performed using Stata 17.0 and Review Manager version 5.4. Findings Eleven studies were included in this meta-analysis involving 1259 serum samples from 947 patients with sepsis. Our results showed that the serum NSE levels of patients with SAE were higher than those of the non-encephalopathy sepsis group (mean deviation, MD,12.39[95% CI 8.27-16.50, Z = 5.9, p < 0.00001]), and the serum NSE levels of patients with sepsis who died were higher than those of survivors (MD,4.17[95% CI 2.66-5.68, Z = 5.41, p < 0.00001]). Conclusion Elevated serum NSE levels in patients with sepsis are associated with the early diagnosis of SAE and mortality; therefore, serum NSE probably is a valid biomarker for the early diagnosis and prognosis of patients with SAE. Systematic review registration This study was registered in PROSPERO, CRD42023433111.
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Affiliation(s)
- MengQin Pei
- Department of Anesthesiology, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, China
| | - YuShen Yang
- Department of Anesthesiology, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, China
| | - ChunYan Zhang
- Department of Anesthesiology, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, China
| | - QiaoMei Huang
- Department of Anesthesiology, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, China
| | - YuMing Fang
- Department of Anesthesiology, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, China
| | - LiMing Xu
- Department of Anesthesiology, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, China
| | - Shu Lin
- Centre of Neurological and Metabolic Research, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, China
- Diabetes and Metabolism Division, Garvan Institute of Medical Research, Sydney, NSW, Australia
| | - HeFan He
- Department of Anesthesiology, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, China
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Hernandez Torres LD, Rezende F, Peschke E, Will O, Hövener JB, Spiecker F, Özorhan Ü, Lampe J, Stölting I, Aherrahrou Z, Künne C, Kusche-Vihrog K, Matschl U, Hille S, Brandes RP, Schwaninger M, Müller OJ, Raasch W. Incidence of microvascular dysfunction is increased in hyperlipidemic mice, reducing cerebral blood flow and impairing remote memory. Front Endocrinol (Lausanne) 2024; 15:1338458. [PMID: 38469142 PMCID: PMC10925718 DOI: 10.3389/fendo.2024.1338458] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Accepted: 01/24/2024] [Indexed: 03/13/2024] Open
Abstract
Introduction The development of cognitive dysfunction is not necessarily associated with diet-induced obesity. We hypothesized that cognitive dysfunction might require additional vascular damage, for example, in atherosclerotic mice. Methods We induced atherosclerosis in male C57BL/6N mice by injecting AAV-PCSK9DY (2x1011 VG) and feeding them a cholesterol-rich Western diet. After 3 months, mice were examined for cognition using Barnes maze procedure and for cerebral blood flow. Cerebral vascular morphology was examined by immunehistology. Results In AAV-PCSK9DY-treated mice, plaque burden, plasma cholesterol, and triglycerides are elevated. RNAseq analyses followed by KEGG annotation show increased expression of genes linked to inflammatory processes in the aortas of these mice. In AAV-PCSK9DY-treated mice learning was delayed and long-term memory impaired. Blood flow was reduced in the cingulate cortex (-17%), caudate putamen (-15%), and hippocampus (-10%). Immunohistological studies also show an increased incidence of string vessels and pericytes (CD31/Col IV staining) in the hippocampus accompanied by patchy blood-brain barrier leaks (IgG staining) and increased macrophage infiltrations (CD68 staining). Discussion We conclude that the hyperlipidemic PCSK9DY mouse model can serve as an appropriate approach to induce microvascular dysfunction that leads to reduced blood flow in the hippocampus, which could explain the cognitive dysfunction in these mice.
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Affiliation(s)
| | - Flavia Rezende
- Institute for Cardiovascular Physiology, Faculty of Medicine, Goethe-University Frankfurt, Frankfurt, Germany
- DZHK (German Center for Cardiovascular Research) Partner Site Rhine-Main, Germany
| | - Eva Peschke
- Section Biomedical Imaging, Molecular Imaging North Competence Center (MOIN CC), Department of Radiology and Neuroradiology, Universitätsklinikum Schleswig-Holstein (UKSH), Kiel University, Kiel, Germany
| | - Olga Will
- Section Biomedical Imaging, Molecular Imaging North Competence Center (MOIN CC), Department of Radiology and Neuroradiology, Universitätsklinikum Schleswig-Holstein (UKSH), Kiel University, Kiel, Germany
| | - Jan-Bernd Hövener
- Section Biomedical Imaging, Molecular Imaging North Competence Center (MOIN CC), Department of Radiology and Neuroradiology, Universitätsklinikum Schleswig-Holstein (UKSH), Kiel University, Kiel, Germany
| | - Frauke Spiecker
- Institute for Experimental and Clinical Pharmacology and Toxicology, University of Lübeck, Lübeck, Germany
| | - Ümit Özorhan
- Institute for Experimental and Clinical Pharmacology and Toxicology, University of Lübeck, Lübeck, Germany
| | - Josephine Lampe
- Institute for Experimental and Clinical Pharmacology and Toxicology, University of Lübeck, Lübeck, Germany
| | - Ines Stölting
- Institute for Experimental and Clinical Pharmacology and Toxicology, University of Lübeck, Lübeck, Germany
| | - Zouhair Aherrahrou
- Institute for Cardiogenetics, University Lübeck; University of Lübeck, Lübeck, Germany
- DZHK (German Centre for Cardiovascular Research), Partner Site Hamburg/Kiel/Lübeck, Germany
| | - Carsten Künne
- Department of Cardiac Development and Remodeling, Max Planck Institute for Heart and Lung Research, Bad Nauheim, Germany
| | - Kristina Kusche-Vihrog
- DZHK (German Centre for Cardiovascular Research), Partner Site Hamburg/Kiel/Lübeck, Germany
- Institute for Physiology, University Lübeck, Lübeck, Germany
| | - Urte Matschl
- Department Virus Immunology, Heinrich Pette Institute, Leibniz Institute for Experimental Virology, Hamburg, Germany
| | - Susanne Hille
- DZHK (German Centre for Cardiovascular Research), Partner Site Hamburg/Kiel/Lübeck, Germany
- Department of Internal Medicine III, University Hospital Schleswig-Holstein, Kiel, Germany
| | - Ralf P. Brandes
- Institute for Cardiovascular Physiology, Faculty of Medicine, Goethe-University Frankfurt, Frankfurt, Germany
- DZHK (German Center for Cardiovascular Research) Partner Site Rhine-Main, Germany
| | - Markus Schwaninger
- Institute for Experimental and Clinical Pharmacology and Toxicology, University of Lübeck, Lübeck, Germany
- DZHK (German Centre for Cardiovascular Research), Partner Site Hamburg/Kiel/Lübeck, Germany
- CBBM (Centre for Brain, Behavior and Metabolism), University of Lübeck, Lübeck, Germany
| | - Oliver J. Müller
- DZHK (German Centre for Cardiovascular Research), Partner Site Hamburg/Kiel/Lübeck, Germany
- Department of Internal Medicine III, University Hospital Schleswig-Holstein, Kiel, Germany
| | - Walter Raasch
- Institute for Experimental and Clinical Pharmacology and Toxicology, University of Lübeck, Lübeck, Germany
- DZHK (German Centre for Cardiovascular Research), Partner Site Hamburg/Kiel/Lübeck, Germany
- CBBM (Centre for Brain, Behavior and Metabolism), University of Lübeck, Lübeck, Germany
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Martin SS, Aday AW, Almarzooq ZI, Anderson CAM, Arora P, Avery CL, Baker-Smith CM, Barone Gibbs B, Beaton AZ, Boehme AK, Commodore-Mensah Y, Currie ME, Elkind MSV, Evenson KR, Generoso G, Heard DG, Hiremath S, Johansen MC, Kalani R, Kazi DS, Ko D, Liu J, Magnani JW, Michos ED, Mussolino ME, Navaneethan SD, Parikh NI, Perman SM, Poudel R, Rezk-Hanna M, Roth GA, Shah NS, St-Onge MP, Thacker EL, Tsao CW, Urbut SM, Van Spall HGC, Voeks JH, Wang NY, Wong ND, Wong SS, Yaffe K, Palaniappan LP. 2024 Heart Disease and Stroke Statistics: A Report of US and Global Data From the American Heart Association. Circulation 2024; 149:e347-e913. [PMID: 38264914 DOI: 10.1161/cir.0000000000001209] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/25/2024]
Abstract
BACKGROUND The American Heart Association (AHA), in conjunction with the National Institutes of Health, annually reports the most up-to-date statistics related to heart disease, stroke, and cardiovascular risk factors, including core health behaviors (smoking, physical activity, nutrition, sleep, and obesity) and health factors (cholesterol, blood pressure, glucose control, and metabolic syndrome) that contribute to cardiovascular health. The AHA Heart Disease and Stroke Statistical Update presents the latest data on a range of major clinical heart and circulatory disease conditions (including stroke, brain health, complications of pregnancy, kidney disease, congenital heart disease, rhythm disorders, sudden cardiac arrest, subclinical atherosclerosis, coronary heart disease, cardiomyopathy, heart failure, valvular disease, venous thromboembolism, and peripheral artery disease) and the associated outcomes (including quality of care, procedures, and economic costs). METHODS The AHA, through its Epidemiology and Prevention Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States and globally to provide the most current information available in the annual Statistical Update with review of published literature through the year before writing. The 2024 AHA Statistical Update is the product of a full year's worth of effort in 2023 by dedicated volunteer clinicians and scientists, committed government professionals, and AHA staff members. The AHA strives to further understand and help heal health problems inflicted by structural racism, a public health crisis that can significantly damage physical and mental health and perpetuate disparities in access to health care, education, income, housing, and several other factors vital to healthy lives. This year's edition includes additional global data, as well as data on the monitoring and benefits of cardiovascular health in the population, with an enhanced focus on health equity across several key domains. RESULTS Each of the chapters in the Statistical Update focuses on a different topic related to heart disease and stroke statistics. CONCLUSIONS The Statistical Update represents a critical resource for the lay public, policymakers, media professionals, clinicians, health care administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions.
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Shahjahan, Dey JK, Dey SK. Translational bioinformatics approach to combat cardiovascular disease and cancers. ADVANCES IN PROTEIN CHEMISTRY AND STRUCTURAL BIOLOGY 2024; 139:221-261. [PMID: 38448136 DOI: 10.1016/bs.apcsb.2023.11.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/08/2024]
Abstract
Bioinformatics is an interconnected subject of science dealing with diverse fields including biology, chemistry, physics, statistics, mathematics, and computer science as the key fields to answer complicated physiological problems. Key intention of bioinformatics is to store, analyze, organize, and retrieve essential information about genome, proteome, transcriptome, metabolome, as well as organisms to investigate the biological system along with its dynamics, if any. The outcome of bioinformatics depends on the type, quantity, and quality of the raw data provided and the algorithm employed to analyze the same. Despite several approved medicines available, cardiovascular disorders (CVDs) and cancers comprises of the two leading causes of human deaths. Understanding the unknown facts of both these non-communicable disorders is inevitable to discover new pathways, find new drug targets, and eventually newer drugs to combat them successfully. Since, all these goals involve complex investigation and handling of various types of macro- and small- molecules of the human body, bioinformatics plays a key role in such processes. Results from such investigation has direct human application and thus we call this filed as translational bioinformatics. Current book chapter thus deals with diverse scope and applications of this translational bioinformatics to find cure, diagnosis, and understanding the mechanisms of CVDs and cancers. Developing complex yet small or long algorithms to address such problems is very common in translational bioinformatics. Structure-based drug discovery or AI-guided invention of novel antibodies that too with super-high accuracy, speed, and involvement of considerably low amount of investment are some of the astonishing features of the translational bioinformatics and its applications in the fields of CVDs and cancers.
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Affiliation(s)
- Shahjahan
- Laboratory for Structural Biology of Membrane Proteins, Dr. B.R. Ambedkar Center for Biomedical Research, University of Delhi, Delhi, India
| | - Joy Kumar Dey
- Central Council for Research in Homoeopathy, Ministry of Ayush, Govt. of India, New Delhi, Delhi, India
| | - Sanjay Kumar Dey
- Laboratory for Structural Biology of Membrane Proteins, Dr. B.R. Ambedkar Center for Biomedical Research, University of Delhi, Delhi, India.
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Davogustto G, Zhao S, Li Y, Farber-Eger E, Lowery BD, Shaffer LL, Mosley JD, Shoemaker MB, Xu Y, Roden DM, Wells QS. Unbiased characterization of atrial fibrillation phenotypic architecture provides insight to genetic liability and clinically relevant outcomes. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.02.13.24302788. [PMID: 38405916 PMCID: PMC10888988 DOI: 10.1101/2024.02.13.24302788] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/27/2024]
Abstract
Background Atrial Fibrillation (AF) is a common and clinically heterogeneous arrythmia. Machine learning (ML) algorithms can define data-driven disease subtypes in an unbiased fashion, but whether the AF subgroups defined in this way align with underlying mechanisms, such as high polygenic liability to AF or inflammation, and associate with clinical outcomes is unclear. Methods We identified individuals with AF in a large biobank linked to electronic health records (EHR) and genome-wide genotyping. The phenotypic architecture in the AF cohort was defined using principal component analysis of 35 expertly curated and uncorrelated clinical features. We applied an unsupervised co-clustering machine learning algorithm to the 35 features to identify distinct phenotypic AF clusters. The clinical inflammatory status of the clusters was defined using measured biomarkers (CRP, ESR, WBC, Neutrophil %, Platelet count, RDW) within 6 months of first AF mention in the EHR. Polygenic risk scores (PRS) for AF and cytokine levels were used to assess genetic liability of clusters to AF and inflammation, respectively. Clinical outcomes were collected from EHR up to the last medical contact. Results The analysis included 23,271 subjects with AF, of which 6,023 had available genome-wide genotyping. The machine learning algorithm identified 3 phenotypic clusters that were distinguished by increasing prevalence of comorbidities, particularly renal dysfunction, and coronary artery disease. Polygenic liability to AF across clusters was highest in the low comorbidity cluster. Clinically measured inflammatory biomarkers were highest in the high comorbid cluster, while there was no difference between groups in genetically predicted levels of inflammatory biomarkers. Subgroup assignment was associated with multiple clinical outcomes including mortality, stroke, bleeding, and use of cardiac implantable electronic devices after AF diagnosis. Conclusion Patient subgroups identified by unsupervised clustering were distinguished by comorbidity burden and associated with risk of clinically important outcomes. Polygenic liability to AF across clusters was greatest in the low comorbidity subgroup. Clinical inflammation, as reflected by measured biomarkers, was lowest in the subgroup with lowest comorbidities. However, there were no differences in genetically predicted levels of inflammatory biomarkers, suggesting associations between AF and inflammation is driven by acquired comorbidities rather than genetic predisposition.
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Di Mauro V, Lauta FC, Modica J, Appleton SL, De Franciscis V, Catalucci D. Diagnostic and Therapeutic Aptamers: A Promising Pathway to Improved Cardiovascular Disease Management. JACC Basic Transl Sci 2024; 9:260-277. [PMID: 38510714 PMCID: PMC10950404 DOI: 10.1016/j.jacbts.2023.06.013] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Accepted: 06/29/2023] [Indexed: 03/22/2024]
Abstract
Despite advances in care, cardiovascular diseases remain the leading cause of death worldwide. As a result, identifying suitable biomarkers for early diagnosis and improving therapeutic and diagnostic strategies is crucial. Because of their significant advantages over other therapeutic approaches, nucleic-based therapies, particularly aptamers, are gaining increased attention. Aptamers are innovative synthetic polymers or oligomers of single-stranded DNA (ssDNA) or RNA molecules that can form 3-dimensional structures and thus interact with their targets with high specificity and affinity. Furthermore, they outperform classical protein-based antibodies in terms of in vitro selection, production, ease of modification and conjugation, high stability, low immunogenicity, and suitability for nanoparticle functionalization for targeted drug delivery. This work aims to review the advances made in the aptamers' field in biomarker detection, diagnosis, imaging, and targeted therapy, which highlight their huge potential in the management of cardiovascular diseases.
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Affiliation(s)
- Vittoria Di Mauro
- Veneto Institute of Molecular Medicine, Padua, Italy
- Institute of Genetic and Biomedical Research, Milan, Milan Italy
- Humanitas Cardio Center, IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy
| | | | - Jessica Modica
- Institute of Genetic and Biomedical Research, Milan, Milan Italy
- Humanitas Cardio Center, IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy
| | - Silvia Lucia Appleton
- Institute of Genetic and Biomedical Research, Milan, Milan Italy
- Humanitas Cardio Center, IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy
| | | | - Daniele Catalucci
- Institute of Genetic and Biomedical Research, Milan, Milan Italy
- Humanitas Cardio Center, IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy
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Plaza-Florido A, Rodriguez-Ayllon M, Altmäe S, Ortega FB, Esteban-Cornejo I. Cardiorespiratory fitness and targeted proteomics involved in brain and cardiovascular health in children with overweight/obesity. Eur J Sport Sci 2023; 23:2076-2085. [PMID: 36622372 DOI: 10.1080/17461391.2023.2167237] [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] [Indexed: 01/10/2023]
Abstract
Cardiorespiratory fitness (CRF) is inversely associated with cardiovascular disease (CVD) risk factors and brain health impairments. However, the molecular mechanisms linking CRF to health in children are poorly understood. We aimed to examine protein levels related to brain health and CVD in plasma of fit compared to unfit children with overweight/obesity (OW/OB). Eighty-seven children with OW/OB (10.08 ± 1.1 years, 59% boys) from the ActiveBrains project were included. CRF was measured by performing a treadmill test, and children were categorized into fit or unfit. Targeted proteomics in plasma was performed using Olink's proximity extension assay technology of Neurology panel in the whole sample and of Cardiovascular panel in a subsample. Sixteen proteins (PLXNB3, sFRP3, CLEC1B, RSPO1, Gal8, CLEC10A, GCP5, MDGA1, CTSC, LAT, IL4RA, PRSS27, CXCL1, Gal9, MERTK, and GT) were differentially expressed between fit and unfit children with OW/OB after adjusting for sex, maturational status, and body mass index. However, statistically significant differences disappeared after applying FDR correction. Potential candidate proteins related to CRF levels in children with OW/OB were detected, being involved in several biological processes such as neurogenesis, immune/inflammatory response, signal transduction, platelet activation. Nevertheless, these preliminary findings should be confirmed or contrasted in future studies using larger sample sizes, longitudinal and experimental designs.HighlightsThe molecular mechanisms underlying the link of cardiorespiratory fitness (CRF) with cardiovascular and brain health in children with overweight/obesity (OW/OB) are poorly understood.Targeted proteomic analysis revealed differentially expressed proteins (PLXNB3, sFRP3, CLEC1B, RSPO1, Gal8, CLEC10A, GCP5, MDGA1, CTSC, LAT, IL4RA, PRSS27, CXCL1, Gal9, MERTK, and GT) in plasma of "Fit" compared to "Unfit" children with OW/OB. These proteins are involved in several biological processes such as immune/inflammatory response, neurogenesis, signal transduction, and cellular metabolic process.Longitudinal and experimental studies are warranted to reveal how improvements in CRF are related to changes in circulating levels of the abovementioned proteins and how they might reduce cardiovascular diseases risk factors and brain health impairments later in life.
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Affiliation(s)
- Abel Plaza-Florido
- Faculty of Sport Sciences, University of Granada, Department of Physical and Sports Education; Sport and Health University Research Institute (iMUDS), Granada, Spain
- Pediatric Exercise and Genomics Research Center, Department of Pediatrics, School of Medicine, University of California at Irvine, Irvine, USA
| | - Maria Rodriguez-Ayllon
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, Netherlands
| | - Signe Altmäe
- Department of Biochemistry and Molecular Biology, Faculty of Sciences, University of Granada, Granada, Spain
- Instituto de Investigación Biosanitaria ibs.GRANADA, Granada, Spain
- Department of Clinical Science, Intervention and Technology (CLINTEC), Karolinska Institutet, and Division of Obstetrics and Gynecology, Karolinska Universitetssjukhuset, Stockholm, Sweden
| | - Francisco B Ortega
- Faculty of Sport Sciences, University of Granada, Department of Physical and Sports Education; Sport and Health University Research Institute (iMUDS), Granada, Spain
- Faculty of Sport and Health Sciences, University of Jyväskylä, Jyväskylä, Finland
- CIBERobn Physiopathology of Obesity and Nutrition, Granada, Spain
| | - Irene Esteban-Cornejo
- Faculty of Sport Sciences, University of Granada, Department of Physical and Sports Education; Sport and Health University Research Institute (iMUDS), Granada, Spain
- CIBERobn Physiopathology of Obesity and Nutrition, Granada, Spain
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Xu Y, Ritchie SC, Liang Y, Timmers PRHJ, Pietzner M, Lannelongue L, Lambert SA, Tahir UA, May-Wilson S, Foguet C, Johansson Å, Surendran P, Nath AP, Persyn E, Peters JE, Oliver-Williams C, Deng S, Prins B, Luan J, Bomba L, Soranzo N, Di Angelantonio E, Pirastu N, Tai ES, van Dam RM, Parkinson H, Davenport EE, Paul DS, Yau C, Gerszten RE, Mälarstig A, Danesh J, Sim X, Langenberg C, Wilson JF, Butterworth AS, Inouye M. An atlas of genetic scores to predict multi-omic traits. Nature 2023; 616:123-131. [PMID: 36991119 PMCID: PMC10323211 DOI: 10.1038/s41586-023-05844-9] [Citation(s) in RCA: 28] [Impact Index Per Article: 28.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Accepted: 02/15/2023] [Indexed: 03/30/2023]
Abstract
The use of omic modalities to dissect the molecular underpinnings of common diseases and traits is becoming increasingly common. But multi-omic traits can be genetically predicted, which enables highly cost-effective and powerful analyses for studies that do not have multi-omics1. Here we examine a large cohort (the INTERVAL study2; n = 50,000 participants) with extensive multi-omic data for plasma proteomics (SomaScan, n = 3,175; Olink, n = 4,822), plasma metabolomics (Metabolon HD4, n = 8,153), serum metabolomics (Nightingale, n = 37,359) and whole-blood Illumina RNA sequencing (n = 4,136), and use machine learning to train genetic scores for 17,227 molecular traits, including 10,521 that reach Bonferroni-adjusted significance. We evaluate the performance of genetic scores through external validation across cohorts of individuals of European, Asian and African American ancestries. In addition, we show the utility of these multi-omic genetic scores by quantifying the genetic control of biological pathways and by generating a synthetic multi-omic dataset of the UK Biobank3 to identify disease associations using a phenome-wide scan. We highlight a series of biological insights with regard to genetic mechanisms in metabolism and canonical pathway associations with disease; for example, JAK-STAT signalling and coronary atherosclerosis. Finally, we develop a portal ( https://www.omicspred.org/ ) to facilitate public access to all genetic scores and validation results, as well as to serve as a platform for future extensions and enhancements of multi-omic genetic scores.
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Affiliation(s)
- Yu Xu
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK.
| | - Scott C Ritchie
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | - Yujian Liang
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
| | - Paul R H J Timmers
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Maik Pietzner
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
- Computational Medicine, Berlin Institute of Health (BIH) at Charité - Universitätsmedizin Berlin, Berlin, Germany
- Precision Healthcare University Research Institute, Queen Mary University of London, London, UK
| | - Loïc Lannelongue
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
| | - Samuel A Lambert
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, School of Clinical Medicine, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, UK
| | - Usman A Tahir
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Sebastian May-Wilson
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Carles Foguet
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
| | - Åsa Johansson
- Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Praveen Surendran
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Artika P Nath
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
| | - Elodie Persyn
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
| | - James E Peters
- Department of Immunology and Inflammation, Faculty of Medicine, Imperial College London, London, UK
| | - Clare Oliver-Williams
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Shuliang Deng
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Bram Prins
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Jian'an Luan
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Lorenzo Bomba
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
- BioMarin Pharmaceutical, Novato, CA, USA
| | - Nicole Soranzo
- British Heart Foundation Centre of Research Excellence, School of Clinical Medicine, University of Cambridge, Cambridge, UK
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
- Department of Haematology, University of Cambridge, Cambridge, UK
- NIHR Blood and Transplant Research Unit in Donor Health and Behaviour, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Genomics Research Centre, Human Technopole, Milan, Italy
| | - Emanuele Di Angelantonio
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, School of Clinical Medicine, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
- NIHR Blood and Transplant Research Unit in Donor Health and Behaviour, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Health Data Science Research Centre, Human Technopole, Milan, Italy
| | - Nicola Pirastu
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, UK
- Genomics Research Centre, Human Technopole, Milan, Italy
| | - E Shyong Tai
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
- Department of Medicine, National University of Singapore and National University Health System, Singapore, Singapore
| | - Rob M van Dam
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
- Departments of Exercise and Nutrition Sciences and Epidemiology, Milken Institute School of Public Health, The George Washington University, Washington, DC, USA
| | - Helen Parkinson
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, UK
| | | | - Dirk S Paul
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | - Christopher Yau
- Nuffield Department of Women's and Reproductive Health, University of Oxford, Oxford, UK
- Division of Informatics, Imaging and Data Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
- Health Data Research UK, London, UK
| | - Robert E Gerszten
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Broad Institute of Harvard University and Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Anders Mälarstig
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Pfizer Worldwide Research, Development and Medical, Stockholm, Sweden
| | - John Danesh
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, School of Clinical Medicine, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
- NIHR Blood and Transplant Research Unit in Donor Health and Behaviour, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Xueling Sim
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
| | - Claudia Langenberg
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
- Computational Medicine, Berlin Institute of Health (BIH) at Charité - Universitätsmedizin Berlin, Berlin, Germany
- Precision Healthcare University Research Institute, Queen Mary University of London, London, UK
| | - James F Wilson
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, UK
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Adam S Butterworth
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, School of Clinical Medicine, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
- NIHR Blood and Transplant Research Unit in Donor Health and Behaviour, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Michael Inouye
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK.
- British Heart Foundation Centre of Research Excellence, School of Clinical Medicine, University of Cambridge, Cambridge, UK.
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK.
- Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia.
- The Alan Turing Institute, London, UK.
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10
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Guo Z, Valenzuela Ripoll C, Picataggi A, Rawnsley DR, Ozcan M, Chirinos JA, Chendamarai E, Girardi A, Riehl T, Evie H, Diab A, Kovacs A, Hyrc K, Ma X, Asnani A, Shewale SV, Scherrer-Crosbie M, Cowart LA, Parks JS, Zhao L, Gordon D, Ramirez-Valle F, Margulies KB, Cappola TP, Desai AA, Pedersen LN, Bergom C, Stitziel NO, Rettig MP, DiPersio JF, Hajny S, Christoffersen C, Diwan A, Javaheri A. Apolipoprotein M Attenuates Anthracycline Cardiotoxicity and Lysosomal Injury. JACC Basic Transl Sci 2023; 8:340-355. [PMID: 37034289 PMCID: PMC10077122 DOI: 10.1016/j.jacbts.2022.09.010] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Revised: 09/26/2022] [Accepted: 09/26/2022] [Indexed: 01/06/2023]
Abstract
Apolipoprotein M (ApoM) binds sphingosine-1-phosphate (S1P) and is inversely associated with mortality in human heart failure (HF). Here, we show that anthracyclines such as doxorubicin (Dox) reduce circulating ApoM in mice and humans, that ApoM is inversely associated with mortality in patients with anthracycline-induced heart failure, and ApoM heterozygosity in mice increases Dox-induced mortality. In the setting of Dox stress, our studies suggest ApoM can help sustain myocardial autophagic flux in a post-transcriptional manner, attenuate Dox cardiotoxicity, and prevent lysosomal injury.
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Affiliation(s)
- Zhen Guo
- Washington University School of Medicine, St Louis, Missouri, USA
| | | | | | | | - Mualla Ozcan
- Washington University School of Medicine, St Louis, Missouri, USA
| | - Julio A. Chirinos
- Perelman School of Medicine, University of Pennsylvania School of Medicine/Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | | | - Amanda Girardi
- Washington University School of Medicine, St Louis, Missouri, USA
| | - Terrence Riehl
- Washington University School of Medicine, St Louis, Missouri, USA
| | - Hosannah Evie
- Washington University School of Medicine, St Louis, Missouri, USA
| | - Ahmed Diab
- Washington University School of Medicine, St Louis, Missouri, USA
| | - Attila Kovacs
- Washington University School of Medicine, St Louis, Missouri, USA
| | - Krzysztof Hyrc
- Hope Center, Washington University School of Medicine, St Louis, Missouri, USA
| | - Xiucui Ma
- Washington University School of Medicine, St Louis, Missouri, USA
- John Cochran Veterans Affairs Medical Center, St Louis, Missouri, USA
| | - Aarti Asnani
- Beth Israel Deaconess, Harvard Medical School, Boston, Massachusetts, USA
| | - Swapnil V. Shewale
- Perelman School of Medicine, University of Pennsylvania School of Medicine/Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Marielle Scherrer-Crosbie
- Perelman School of Medicine, University of Pennsylvania School of Medicine/Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Lauren Ashley Cowart
- Virginia Commonwealth University, Richmond, Virginia, USA
- Hunter Holmes McGuire Veterans Affairs Medical Center, Richmond, Virginia, USA
| | - John S. Parks
- Wake Forest School of Medicine, Winston-Salem, North Carolina, USA
| | - Lei Zhao
- Bristol Myers Squibb, Princeton, New Jersey, USA
| | - David Gordon
- Bristol Myers Squibb, Princeton, New Jersey, USA
| | | | - Kenneth B. Margulies
- Perelman School of Medicine, University of Pennsylvania School of Medicine/Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Thomas P. Cappola
- Perelman School of Medicine, University of Pennsylvania School of Medicine/Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | | | | | - Carmen Bergom
- Washington University School of Medicine, St Louis, Missouri, USA
| | | | | | - John F. DiPersio
- Washington University School of Medicine, St Louis, Missouri, USA
| | - Stefan Hajny
- Department of Clinical Biochemistry, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
- Department of Biomedical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Christina Christoffersen
- Department of Clinical Biochemistry, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
- Department of Biomedical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Abhinav Diwan
- Washington University School of Medicine, St Louis, Missouri, USA
- John Cochran Veterans Affairs Medical Center, St Louis, Missouri, USA
| | - Ali Javaheri
- Washington University School of Medicine, St Louis, Missouri, USA
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11
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Tsao CW, Aday AW, Almarzooq ZI, Anderson CAM, Arora P, Avery CL, Baker-Smith CM, Beaton AZ, Boehme AK, Buxton AE, Commodore-Mensah Y, Elkind MSV, Evenson KR, Eze-Nliam C, Fugar S, Generoso G, Heard DG, Hiremath S, Ho JE, Kalani R, Kazi DS, Ko D, Levine DA, Liu J, Ma J, Magnani JW, Michos ED, Mussolino ME, Navaneethan SD, Parikh NI, Poudel R, Rezk-Hanna M, Roth GA, Shah NS, St-Onge MP, Thacker EL, Virani SS, Voeks JH, Wang NY, Wong ND, Wong SS, Yaffe K, Martin SS. Heart Disease and Stroke Statistics-2023 Update: A Report From the American Heart Association. Circulation 2023; 147:e93-e621. [PMID: 36695182 DOI: 10.1161/cir.0000000000001123] [Citation(s) in RCA: 1190] [Impact Index Per Article: 1190.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
BACKGROUND The American Heart Association, in conjunction with the National Institutes of Health, annually reports the most up-to-date statistics related to heart disease, stroke, and cardiovascular risk factors, including core health behaviors (smoking, physical activity, diet, and weight) and health factors (cholesterol, blood pressure, and glucose control) that contribute to cardiovascular health. The Statistical Update presents the latest data on a range of major clinical heart and circulatory disease conditions (including stroke, congenital heart disease, rhythm disorders, subclinical atherosclerosis, coronary heart disease, heart failure, valvular disease, venous disease, and peripheral artery disease) and the associated outcomes (including quality of care, procedures, and economic costs). METHODS The American Heart Association, through its Epidemiology and Prevention Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States to provide the most current information available in the annual Statistical Update with review of published literature through the year before writing. The 2023 Statistical Update is the product of a full year's worth of effort in 2022 by dedicated volunteer clinicians and scientists, committed government professionals, and American Heart Association staff members. The American Heart Association strives to further understand and help heal health problems inflicted by structural racism, a public health crisis that can significantly damage physical and mental health and perpetuate disparities in access to health care, education, income, housing, and several other factors vital to healthy lives. This year's edition includes additional COVID-19 (coronavirus disease 2019) publications, as well as data on the monitoring and benefits of cardiovascular health in the population, with an enhanced focus on health equity across several key domains. RESULTS Each of the chapters in the Statistical Update focuses on a different topic related to heart disease and stroke statistics. CONCLUSIONS The Statistical Update represents a critical resource for the lay public, policymakers, media professionals, clinicians, health care administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions.
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12
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Mohammadi-Shemirani P, Sood T, Paré G. From 'Omics to Multi-omics Technologies: the Discovery of Novel Causal Mediators. Curr Atheroscler Rep 2023; 25:55-65. [PMID: 36595202 PMCID: PMC9807989 DOI: 10.1007/s11883-022-01078-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/31/2022] [Indexed: 01/04/2023]
Abstract
PURPOSE OF REVIEW 'Omics studies provide a comprehensive characterisation of a biological entity, such as the genome, epigenome, transcriptome, proteome, metabolome, or microbiome. This review covers the unique properties of these types of 'omics and their roles as causal mediators in cardiovascular disease. Moreover, applications and challenges of integrating multiple types of 'omics data to increase predictive power, improve causal inference, and elucidate biological mechanisms are discussed. RECENT FINDINGS Multi-omics approaches are growing in adoption as they provide orthogonal evidence and overcome the limitations of individual types of 'omics data. Studies with multiple types of 'omics data have improved the diagnosis and prediction of disease states and afforded a deeper understanding of underlying pathophysiological mechanisms, beyond any single type of 'omics data. For instance, disease-associated loci in the genome can be supplemented with other 'omics to prioritise causal genes and understand the function of non-coding variants. Alternatively, techniques, such as Mendelian randomisation, can leverage genetics to provide evidence supporting a causal role for disease-associated molecules, and elucidate their role in disease pathogenesis. As technologies improve, costs for 'omics studies will continue to fall and datasets will become increasingly accessible to researchers. The intrinsically unbiased nature of 'omics data is well-suited to exploratory analyses that discover causal mediators of disease, and multi-omics is an emerging discipline that leverages the strengths of each type of 'omics data to provide insights greater than the sum of its parts.
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Affiliation(s)
- Pedrum Mohammadi-Shemirani
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton, ON Canada
- Thrombosis and Atherosclerosis Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton, ON Canada
| | - Tushar Sood
- Temerty Faculty of Medicine, University of Toronto, Toronto, Canada
| | - Guillaume Paré
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton, ON Canada
- Thrombosis and Atherosclerosis Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton, ON Canada
- Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, ON Canada
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON Canada
- Department of Pathology and Molecular Medicine, Michael G. DeGroote School of Medicine, McMaster University, Hamilton, ON Canada
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13
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Rani S, Chandna P. Multiomics Analysis-Based Biomarkers in Diagnosis of Polycystic Ovary Syndrome. Reprod Sci 2023; 30:1-27. [PMID: 35084716 PMCID: PMC10010205 DOI: 10.1007/s43032-022-00863-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2021] [Accepted: 01/20/2022] [Indexed: 01/06/2023]
Abstract
Polycystic ovarian syndrome is an utmost communal endocrine, psychological, reproductive, and metabolic disorder that occurs in women of reproductive age with extensive range of clinical manifestations. This may even lead to long-term multiple morbidities including obesity, diabetes mellitus, insulin resistance, cardiovascular disease, infertility, cerebrovascular diseases, and ovarian and endometrial cancer. Women affliction from PCOS in midst assemblage of manifestations allied with menstrual dysfunction and androgen exorbitance, which considerably affects eminence of life. PCOS is recognized as a multifactorial disorder and systemic syndrome in first-degree family members; therefore, the etiology of PCOS syndrome has not been copiously interpreted. The disorder of PCOS comprehends numerous allied health conditions and has influenced various metabolic processes. Due to multifaceted pathophysiology engaging several pathways and proteins, single genetic diagnostic tests cannot be supportive to determine in straight way. Clarification of cellular and biochemical pathways and various genetic players underlying PCOS could upsurge our consideration of pathophysiology of this syndrome. It is requisite to know pathophysiological relationship between biomarker and their reflection towards PCOS disease. Biomarkers deliver vibrantly and potent ways to apprehend the spectrum of PCOS with applications in screening, diagnosis, characterization, and monitoring. This paper relies on the endeavor to point out many candidates as potential biomarkers based on omics technologies, thus highlighting correlation between PCOS disease with innovative technologies. Therefore, the objective of existing review is to encapsulate more findings towards cutting-edge advances in prospective use of biomarkers for PCOS disease. Discussed biomarkers may be fruitful in guiding therapies, addressing disease risk, and predicting clinical outcomes in future.
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Affiliation(s)
- Shikha Rani
- Department of Biophysics, University of Delhi, South Campus, Benito Juarez Road, New Delhi , 110021, India.
| | - Piyush Chandna
- Natdynamics Biosciences Confederation, Gurgaon, Haryana, 122001, India
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14
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Liu J, Lane S, Lall R, Russo M, Farrell L, Debreli Coskun M, Curtin C, Araujo-Gutierrez R, Scherrer-Crosbie M, Trachtenberg BH, Kim J, Tolosano E, Ghigo A, Gerszten RE, Asnani A. Circulating hemopexin modulates anthracycline cardiac toxicity in patients and in mice. SCIENCE ADVANCES 2022; 8:eadc9245. [PMID: 36563141 PMCID: PMC9788780 DOI: 10.1126/sciadv.adc9245] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Accepted: 11/29/2022] [Indexed: 05/28/2023]
Abstract
Anthracyclines such as doxorubicin (Dox) are effective chemotherapies, but their use is limited by cardiac toxicity. We hypothesized that plasma proteomics in women with breast cancer could identify new mechanisms of anthracycline cardiac toxicity. We measured changes in 1317 proteins in anthracycline-treated patients (n = 30) and replicated key findings in a second cohort (n = 31). An increase in the heme-binding protein hemopexin (Hpx) 3 months after anthracycline initiation was associated with cardiac toxicity by echocardiography. To assess the functional role of Hpx, we administered Hpx to wild-type (WT) mice treated with Dox and observed improved cardiac function. Conversely, Hpx-/- mice demonstrated increased Dox cardiac toxicity compared to WT mice. Initial mechanistic studies indicate that Hpx is likely transported to the heart by circulating monocytes/macrophages and that Hpx may mitigate Dox-induced ferroptosis to confer cardioprotection. Together, these observations suggest that Hpx induction represents a compensatory response during Dox treatment.
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Affiliation(s)
- Jing Liu
- Division of Cardiovascular Medicine, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Sarah Lane
- Division of Cardiovascular Medicine, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Rahul Lall
- Division of Cardiovascular Medicine, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Michele Russo
- Department of Molecular Biotechnology and Health Sciences, Molecular Biotechnology Center, "Guido Tarone," University of Torino, Torino, Italy
| | - Laurie Farrell
- Division of Cardiovascular Medicine, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Melis Debreli Coskun
- Department of Biomedical and Nutritional Sciences, University of Massachusetts Lowell, Lowell, MA, USA
| | - Casie Curtin
- Division of Cardiovascular Medicine, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Raquel Araujo-Gutierrez
- Division of Advanced Heart Failure and Transplantation, Houston Methodist Heart and Vascular Center, Houston, TX, USA
| | - Marielle Scherrer-Crosbie
- Division of Cardiovascular Diseases, Department of Medicine, Hospital of the University of Pennsylvania, Philadelphia, PA, USA
| | - Barry H. Trachtenberg
- Division of Advanced Heart Failure and Transplantation, Houston Methodist Heart and Vascular Center, Houston, TX, USA
| | - Jonghan Kim
- Department of Biomedical and Nutritional Sciences, University of Massachusetts Lowell, Lowell, MA, USA
| | - Emanuela Tolosano
- Department of Molecular Biotechnology and Health Sciences, Molecular Biotechnology Center, "Guido Tarone," University of Torino, Torino, Italy
| | - Alessandra Ghigo
- Department of Molecular Biotechnology and Health Sciences, Molecular Biotechnology Center, "Guido Tarone," University of Torino, Torino, Italy
| | - Robert E. Gerszten
- Division of Cardiovascular Medicine, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Aarti Asnani
- Division of Cardiovascular Medicine, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
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15
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Vorn R, Mithani S, Devoto C, Meier TB, Lai C, Yun S, Broglio SP, McAllister TW, Giza CC, Kim HS, Huber D, Harezlak J, Cameron KL, McGinty G, Jackson J, Guskiewicz KM, Mihalik JP, Brooks A, Duma S, Rowson S, Nelson LD, Pasquina P, McCrea MA, Gill JM. Proteomic Profiling of Plasma Biomarkers Associated With Return to Sport Following Concussion: Findings From the NCAA and Department of Defense CARE Consortium. Front Neurol 2022; 13:901238. [PMID: 35928129 PMCID: PMC9343581 DOI: 10.3389/fneur.2022.901238] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Accepted: 06/08/2022] [Indexed: 11/30/2022] Open
Abstract
Objective To investigate the plasma proteomic profiling in identifying biomarkers related to return to sport (RTS) following a sport-related concussion (SRC). Methods This multicenter, prospective, case-control study was part of a larger cohort study conducted by the NCAA-DoD Concussion Assessment, Research, and Education (CARE) Consortium, athletes (n = 140) with blood collected within 48 h of injury and reported day to asymptomatic were included in this study, divided into two groups: (1) recovery <14-days (n = 99) and (2) recovery ≥14-days (n = 41). We applied a highly multiplexed proteomic technique that uses DNA aptamers assay to target 1,305 proteins in plasma samples from concussed athletes with <14-days and ≥14-days. Results We identified 87 plasma proteins significantly dysregulated (32 upregulated and 55 downregulated) in concussed athletes with recovery ≥14-days relative to recovery <14-days groups. The significantly dysregulated proteins were uploaded to Ingenuity Pathway Analysis (IPA) software for analysis. Pathway analysis showed that significantly dysregulated proteins were associated with STAT3 pathway, regulation of the epithelial mesenchymal transition by growth factors pathway, and acute phase response signaling. Conclusion Our data showed the feasibility of large-scale plasma proteomic profiling in concussed athletes with a <14-days and ≥ 14-days recovery. These findings provide a possible understanding of the pathophysiological mechanism in neurobiological recovery. Further study is required to determine whether these proteins can aid clinicians in RTS decisions.
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Affiliation(s)
- Rany Vorn
- School of Nursing, Johns Hopkins University, Baltimore, MD, United States
- National Institute of Nursing Research, National Institutes of Health, Bethesda, MD, United States
| | - Sara Mithani
- National Institute of Nursing Research, National Institutes of Health, Bethesda, MD, United States
- School of Nursing, University of Texas Health Science Center San Antonio, San Antonio, TX, United States
| | - Christina Devoto
- National Institute of Nursing Research, National Institutes of Health, Bethesda, MD, United States
- Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD, United States
| | - Timothy B. Meier
- Department of Neurosurgery, Medical College of Wisconsin, Milwaukee, WI, United States
| | - Chen Lai
- National Institute of Nursing Research, National Institutes of Health, Bethesda, MD, United States
- Center for Neuroscience and Regenerative Medicine, Uniformed Services University of the Health Sciences, Bethesda, MD, United States
| | - Sijung Yun
- Predictiv Care, Mountain View, CA, United States
| | - Steven P. Broglio
- Michigan Concussion Center, University of Michigan, Ann Arbor, MI, United States
| | - Thomas W. McAllister
- Department of Psychiatry, Indiana University School of Medicine, Indianapolis, IN, United States
| | - Christopher C. Giza
- Departments of Pediatrics and Neurosurgery, University of California, Los Angeles, Los Angeles, CA, United States
- UCLA Steve Tisch BrainSPORT Program, University of California, Los Angeles, Los Angeles, CA, United States
| | - Hyung-Suk Kim
- National Institute of Nursing Research, National Institutes of Health, Bethesda, MD, United States
| | - Daniel Huber
- Department of Neurosurgery, Medical College of Wisconsin, Milwaukee, WI, United States
| | - Jaroslaw Harezlak
- Department of Epidemiology and Biostatistics School of Public Health-Bloomington, Indiana University, Bloomington, IN, United States
| | - Kenneth L. Cameron
- John A. Feagin Sports Medicine Fellowship, Keller Army Hospital, West Point, NY, United States
| | - Gerald McGinty
- United States Air Force Academy, Colorado Springs, CO, United States
| | - Jonathan Jackson
- United States Air Force Academy, Colorado Springs, CO, United States
| | - Kevin M. Guskiewicz
- Matthew Gfeller Center, Department of Exercise and Sport Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Jason P. Mihalik
- Matthew Gfeller Center, Department of Exercise and Sport Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Alison Brooks
- Department of Orthopedics, Division of Sports Medicine, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, United States
| | - Stefan Duma
- Department of Biomedical Engineering and Mechanics, Virginia Tech, Blacksburg, VA, United States
| | - Steven Rowson
- Department of Biomedical Engineering and Mechanics, Virginia Tech, Blacksburg, VA, United States
| | - Lindsay D. Nelson
- Department of Neurosurgery, Medical College of Wisconsin, Milwaukee, WI, United States
| | - Paul Pasquina
- Center for Neuroscience and Regenerative Medicine, Uniformed Services University of the Health Sciences, Bethesda, MD, United States
| | - Michael A. McCrea
- Department of Neurosurgery, Medical College of Wisconsin, Milwaukee, WI, United States
| | - Jessica M. Gill
- School of Nursing, Johns Hopkins University, Baltimore, MD, United States
- Center for Neuroscience and Regenerative Medicine, Uniformed Services University of the Health Sciences, Bethesda, MD, United States
- Department of Neurology, Johns Hopkins University, Baltimore, MD, United States
- *Correspondence: Jessica M. Gill
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Harbaum L, Rhodes CJ, Wharton J, Lawrie A, Karnes JH, Desai AA, Nichols WC, Humbert M, Montani D, Girerd B, Sitbon O, Boehm M, Novoyatleva T, Schermuly RT, Ghofrani HA, Toshner M, Kiely DG, Howard LS, Swietlik EM, Gräf S, Pietzner M, Morrell NW, Wilkins MR. Mining the Plasma Proteome for Insights into the Molecular Pathology of Pulmonary Arterial Hypertension. Am J Respir Crit Care Med 2022; 205:1449-1460. [PMID: 35394406 PMCID: PMC9875902 DOI: 10.1164/rccm.202109-2106oc] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Accepted: 04/07/2022] [Indexed: 01/29/2023] Open
Abstract
Rationale: Pulmonary arterial hypertension (PAH) is characterized by structural remodeling of pulmonary arteries and arterioles. Underlying biological processes are likely reflected in a perturbation of circulating proteins. Objectives: To quantify and analyze the plasma proteome of patients with PAH using inherited genetic variation to inform on underlying molecular drivers. Methods: An aptamer-based assay was used to measure plasma proteins in 357 patients with idiopathic or heritable PAH, 103 healthy volunteers, and 23 relatives of patients with PAH. In discovery and replication subgroups, the plasma proteomes of PAH and healthy individuals were compared, and the relationship to transplantation-free survival in PAH was determined. To examine causal relationships to PAH, protein quantitative trait loci (pQTL) that influenced protein levels in the patient population were used as instruments for Mendelian randomization (MR) analysis. Measurements and Main Results: From 4,152 annotated plasma proteins, levels of 208 differed between patients with PAH and healthy subjects, and 49 predicted long-term survival. MR based on cis-pQTL located in proximity to the encoding gene for proteins that were prognostic and distinguished PAH from health estimated an adverse effect for higher levels of netrin-4 (odds ratio [OR], 1.55; 95% confidence interval [CI], 1.16-2.08) and a protective effect for higher levels of thrombospondin-2 (OR, 0.83; 95% CI, 0.74-0.94) on PAH. Both proteins tracked the development of PAH in previously healthy relatives and changes in thrombospondin-2 associated with pulmonary arterial pressure at disease onset. Conclusions: Integrated analysis of the plasma proteome and genome implicates two secreted matrix-binding proteins, netrin-4 and thrombospondin-2, in the pathobiology of PAH.
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Affiliation(s)
- Lars Harbaum
- National Heart and Lung Institute, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - Christopher J. Rhodes
- National Heart and Lung Institute, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - John Wharton
- National Heart and Lung Institute, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - Allan Lawrie
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Medical School, Sheffield, United Kingdom
| | - Jason H. Karnes
- Department of Pharmacy Practice and Science, University of Arizona College of Pharmacy, Tucson, Arizona
| | - Ankit A. Desai
- Department of Medical and Molecular Genetics, and Krannert Institute of Cardiology, Department of Medicine, Indiana University, Indianapolis, Indiana
| | - William C. Nichols
- Division of Human Genetics, Cincinnati Children’s Hospital Medical Center, University of Cincinnati College of Medicine, Cincinnati, Ohio
| | - Marc Humbert
- Université Paris–Saclay, AP-HP, INSERM UMR_S 999, Department of Respiratory and Intensive Care Medicine, Pulmonary Hypertension National Referral Centre, Hôpital de Bicêtre, Le Kremlin Bicêtre, France
| | - David Montani
- Université Paris–Saclay, AP-HP, INSERM UMR_S 999, Department of Respiratory and Intensive Care Medicine, Pulmonary Hypertension National Referral Centre, Hôpital de Bicêtre, Le Kremlin Bicêtre, France
| | - Barbara Girerd
- Université Paris–Saclay, AP-HP, INSERM UMR_S 999, Department of Respiratory and Intensive Care Medicine, Pulmonary Hypertension National Referral Centre, Hôpital de Bicêtre, Le Kremlin Bicêtre, France
| | - Olivier Sitbon
- Université Paris–Saclay, AP-HP, INSERM UMR_S 999, Department of Respiratory and Intensive Care Medicine, Pulmonary Hypertension National Referral Centre, Hôpital de Bicêtre, Le Kremlin Bicêtre, France
| | - Mario Boehm
- Department of Internal Medicine, Justus-Liebig-University Giessen, Giessen, Germany
| | - Tatyana Novoyatleva
- Department of Internal Medicine, Justus-Liebig-University Giessen, Giessen, Germany
| | - Ralph T. Schermuly
- Department of Internal Medicine, Justus-Liebig-University Giessen, Giessen, Germany
| | | | - Mark Toshner
- Department of Medicine, University of Cambridge School of Clinical Medicine, Cambridge, United Kingdom
| | - David G. Kiely
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Medical School, Sheffield, United Kingdom
| | - Luke S. Howard
- National Heart and Lung Institute, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - Emilia M. Swietlik
- Department of Medicine, University of Cambridge School of Clinical Medicine, Cambridge, United Kingdom
| | - Stefan Gräf
- Department of Medicine, University of Cambridge School of Clinical Medicine, Cambridge, United Kingdom
- National Institute for Health Research BioResource for Translational Research, University of Cambridge, Cambridge, United Kingdom
- Department of Haematology, University of Cambridge, Cambridge, United Kingdom
| | - Maik Pietzner
- Computational Medicine, Berlin Institute of Health (BIH) at Charité–Universitätsmedizin Berlin, Germany; and
- Medical Research Council Epidemiology Unit, University of Cambridge, Cambridge, United Kingdom
| | - Nicholas W. Morrell
- Department of Medicine, University of Cambridge School of Clinical Medicine, Cambridge, United Kingdom
| | - Martin R. Wilkins
- National Heart and Lung Institute, Faculty of Medicine, Imperial College London, London, United Kingdom
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17
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Shi M, Wang C, Mei H, Temprosa M, Florez JC, Tripputi M, Merino J, Lipworth L, Shu X, Gerszten RE, Wang TJ, Beckman JA, Gamboa JL, Mosley JD, Ferguson JF. Genetic Architecture of Plasma Alpha-Aminoadipic Acid Reveals a Relationship With High-Density Lipoprotein Cholesterol. J Am Heart Assoc 2022; 11:e024388. [PMID: 35621206 PMCID: PMC9238724 DOI: 10.1161/jaha.121.024388] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Accepted: 04/13/2022] [Indexed: 11/16/2022]
Abstract
Background Elevated plasma levels of alpha-aminoadipic acid (2-AAA) have been associated with the development of type 2 diabetes and atherosclerosis. However, the nature of the association remains unknown. Methods and Results We identified genetic determinants of plasma 2-AAA through meta-analysis of genome-wide association study data in 5456 individuals of European, African, and Asian ancestry from the Framingham Heart Study, Diabetes Prevention Program, Jackson Heart Study, and Shanghai Women's and Men's Health Studies. No single nucleotide polymorphisms reached genome-wide significance across all samples. However, the top associations from the meta-analysis included single-nucleotide polymorphisms in the known 2-AAA pathway gene DHTKD1, and single-nucleotide polymorphisms in genes involved in mitochondrial respiration (NDUFS4) and macrophage function (MSR1). We used a Mendelian randomization instrumental variable approach to evaluate relationships between 2-AAA and cardiometabolic phenotypes in large disease genome-wide association studies. Mendelian randomization identified a suggestive inverse association between increased 2-AAA and lower high-density lipoprotein cholesterol (P=0.005). We further characterized the genetically predicted relationship through measurement of plasma 2-AAA and high-density lipoprotein cholesterol in 2 separate samples of individuals with and without cardiometabolic disease (N=98), and confirmed a significant negative correlation between 2-AAA and high-density lipoprotein (rs=-0.53, P<0.0001). Conclusions 2-AAA levels in plasma may be regulated, in part, by common variants in genes involved in mitochondrial and macrophage function. Elevated plasma 2-AAA associates with reduced levels of high-density lipoprotein cholesterol. Further mechanistic studies are required to probe this as a possible mechanism linking 2-AAA to future cardiometabolic risk.
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Affiliation(s)
- Mingjian Shi
- Department of Biomedical InformaticsVanderbilt University Medical CenterNashvilleTN
| | - Chuan Wang
- Division of Cardiovascular MedicineDepartment of MedicineVanderbilt University Medical CenterNashvilleTN
| | - Hao Mei
- Department of Data ScienceSchool of Population HealthUniversity of Mississippi Medical CenterJacksonMS
| | - Marinella Temprosa
- Department of Biostatistics and BioinformaticsMilken Institute School of Public HealthGeorge Washington UniversityRockvilleMD
| | - Jose C. Florez
- Center for Genomic Medicine and Diabetes UnitMassachusetts General HospitalBostonMA
- Programs in Metabolism and Medical & Population GeneticsBroad InstituteCambridgeMA
- Department of MedicineHarvard Medical SchoolBostonMA
| | - Mark Tripputi
- Department of Biostatistics and BioinformaticsMilken Institute School of Public HealthGeorge Washington UniversityRockvilleMD
| | - Jordi Merino
- Center for Genomic Medicine and Diabetes UnitMassachusetts General HospitalBostonMA
- Programs in Metabolism and Medical & Population GeneticsBroad InstituteCambridgeMA
- Department of MedicineHarvard Medical SchoolBostonMA
| | - Loren Lipworth
- Division of EpidemiologyDepartment of MedicineVanderbilt University Medical CenterNashvilleTN
| | - Xiao‐Ou Shu
- Division of EpidemiologyDepartment of MedicineVanderbilt University Medical CenterNashvilleTN
| | - Robert E. Gerszten
- Division of Cardiovascular MedicineBeth Israel Deaconess Medical CenterBostonMA
- Broad Institute of Harvard and MITCambridgeMA
| | - Thomas J. Wang
- Department of MedicineUT Southwestern Medical CenterDallasTX
| | - Joshua A. Beckman
- Division of Cardiovascular MedicineDepartment of MedicineVanderbilt University Medical CenterNashvilleTN
| | - Jorge L. Gamboa
- Division of Clinical PharmacologyDepartment of MedicineVanderbilt University Medical CenterNashvilleTN
| | - Jonathan D. Mosley
- Department of Biomedical InformaticsVanderbilt University Medical CenterNashvilleTN
- Division of Clinical PharmacologyDepartment of MedicineVanderbilt University Medical CenterNashvilleTN
| | - Jane F. Ferguson
- Division of Cardiovascular MedicineDepartment of MedicineVanderbilt University Medical CenterNashvilleTN
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18
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Jiang W, Jones JC, Shankavaram U, Sproull M, Camphausen K, Krauze AV. Analytical Considerations of Large-Scale Aptamer-Based Datasets for Translational Applications. Cancers (Basel) 2022; 14:2227. [PMID: 35565358 PMCID: PMC9105298 DOI: 10.3390/cancers14092227] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Revised: 04/15/2022] [Accepted: 04/18/2022] [Indexed: 11/17/2022] Open
Abstract
The development and advancement of aptamer technology has opened a new realm of possibilities for unlocking the biocomplexity available within proteomics. With ultra-high-throughput and multiplexing, alongside remarkable specificity and sensitivity, aptamers could represent a powerful tool in disease-specific research, such as supporting the discovery and validation of clinically relevant biomarkers. One of the fundamental challenges underlying past and current proteomic technology has been the difficulty of translating proteomic datasets into standards of practice. Aptamers provide the capacity to generate single panels that span over 7000 different proteins from a singular sample. However, as a recent technology, they also present unique challenges, as the field of translational aptamer-based proteomics still lacks a standardizing methodology for analyzing these large datasets and the novel considerations that must be made in response to the differentiation amongst current proteomic platforms and aptamers. We address these analytical considerations with respect to surveying initial data, deploying proper statistical methodologies to identify differential protein expressions, and applying datasets to discover multimarker and pathway-level findings. Additionally, we present aptamer datasets within the multi-omics landscape by exploring the intersectionality of aptamer-based proteomics amongst genomics, transcriptomics, and metabolomics, alongside pre-existing proteomic platforms. Understanding the broader applications of aptamer datasets will substantially enhance current efforts to generate translatable findings for the clinic.
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Affiliation(s)
- Will Jiang
- Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute, NIH, 9000 Rockville Pike, Building 10, CRC, Bethesda, MD 20892, USA; (W.J.); (U.S.); (M.S.); (K.C.)
| | - Jennifer C. Jones
- Translational Nanobiology Section, Laboratory of Pathology, NIH/NCI/CCR, Bethesda, MD 20892, USA;
| | - Uma Shankavaram
- Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute, NIH, 9000 Rockville Pike, Building 10, CRC, Bethesda, MD 20892, USA; (W.J.); (U.S.); (M.S.); (K.C.)
| | - Mary Sproull
- Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute, NIH, 9000 Rockville Pike, Building 10, CRC, Bethesda, MD 20892, USA; (W.J.); (U.S.); (M.S.); (K.C.)
| | - Kevin Camphausen
- Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute, NIH, 9000 Rockville Pike, Building 10, CRC, Bethesda, MD 20892, USA; (W.J.); (U.S.); (M.S.); (K.C.)
| | - Andra V. Krauze
- Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute, NIH, 9000 Rockville Pike, Building 10, CRC, Bethesda, MD 20892, USA; (W.J.); (U.S.); (M.S.); (K.C.)
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19
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Schubert R, Geoffroy E, Gregga I, Mulford AJ, Aguet F, Ardlie K, Gerszten R, Clish C, Van Den Berg D, Taylor KD, Durda P, Johnson WC, Cornell E, Guo X, Liu Y, Tracy R, Conomos M, Blackwell T, Papanicolaou G, Lappalainen T, Mikhaylova AV, Thornton TA, Cho MH, Gignoux CR, Lange L, Lange E, Rich SS, Rotter JI, Manichaikul A, Im HK, Wheeler HE. Protein prediction for trait mapping in diverse populations. PLoS One 2022; 17:e0264341. [PMID: 35202437 PMCID: PMC8870552 DOI: 10.1371/journal.pone.0264341] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Accepted: 02/08/2022] [Indexed: 11/18/2022] Open
Abstract
Genetically regulated gene expression has helped elucidate the biological mechanisms underlying complex traits. Improved high-throughput technology allows similar interrogation of the genetically regulated proteome for understanding complex trait mechanisms. Here, we used the Trans-omics for Precision Medicine (TOPMed) Multi-omics pilot study, which comprises data from Multi-Ethnic Study of Atherosclerosis (MESA), to optimize genetic predictors of the plasma proteome for genetically regulated proteome-wide association studies (PWAS) in diverse populations. We built predictive models for protein abundances using data collected in TOPMed MESA, for which we have measured 1,305 proteins by a SOMAscan assay. We compared predictive models built via elastic net regression to models integrating posterior inclusion probabilities estimated by fine-mapping SNPs prior to elastic net. In order to investigate the transferability of predictive models across ancestries, we built protein prediction models in all four of the TOPMed MESA populations, African American (n = 183), Chinese (n = 71), European (n = 416), and Hispanic/Latino (n = 301), as well as in all populations combined. As expected, fine-mapping produced more significant protein prediction models, especially in African ancestries populations, potentially increasing opportunity for discovery. When we tested our TOPMed MESA models in the independent European INTERVAL study, fine-mapping improved cross-ancestries prediction for some proteins. Using GWAS summary statistics from the Population Architecture using Genomics and Epidemiology (PAGE) study, which comprises ∼50,000 Hispanic/Latinos, African Americans, Asians, Native Hawaiians, and Native Americans, we applied S-PrediXcan to perform PWAS for 28 complex traits. The most protein-trait associations were discovered, colocalized, and replicated in large independent GWAS using proteome prediction model training populations with similar ancestries to PAGE. At current training population sample sizes, performance between baseline and fine-mapped protein prediction models in PWAS was similar, highlighting the utility of elastic net. Our predictive models in diverse populations are publicly available for use in proteome mapping methods at https://doi.org/10.5281/zenodo.4837327.
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Affiliation(s)
- Ryan Schubert
- Department of Mathematics and Statistics, Loyola University Chicago, Chicago, IL, United States of America
- Department of Biology, Loyola University Chicago, Chicago, IL, United States of America
- Program in Bioinformatics, Loyola University Chicago, Chicago, IL, United States of America
| | - Elyse Geoffroy
- Program in Bioinformatics, Loyola University Chicago, Chicago, IL, United States of America
| | - Isabelle Gregga
- Department of Biology, Loyola University Chicago, Chicago, IL, United States of America
| | - Ashley J. Mulford
- Department of Biology, Loyola University Chicago, Chicago, IL, United States of America
- Program in Bioinformatics, Loyola University Chicago, Chicago, IL, United States of America
| | - Francois Aguet
- Broad Institute, Cambridge, MA, United States of America
| | - Kristin Ardlie
- Broad Institute, Cambridge, MA, United States of America
| | - Robert Gerszten
- Beth Israel Deaconess Medical Center, Boston, MA, United States of America
| | - Clary Clish
- Broad Institute, Cambridge, MA, United States of America
| | - David Van Den Berg
- University of Southern California, Los Angeles, CA, United States of America
| | - Kent D. Taylor
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, United States of America
| | - Peter Durda
- Laboratory for Clinical Biochemistry Research, University of Vermont, Burlington, VT, United States of America
| | - W. Craig Johnson
- Collaborative Health Studies Coordinating Center, University of Washington, Seattle, WA, United States of America
| | - Elaine Cornell
- Laboratory for Clinical Biochemistry Research, University of Vermont, Burlington, VT, United States of America
| | - Xiuqing Guo
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, United States of America
| | - Yongmei Liu
- Department of Medicine, Duke University School of Medicine, Durham, NC, United States of America
| | - Russell Tracy
- Laboratory for Clinical Biochemistry Research, University of Vermont, Burlington, VT, United States of America
| | - Matthew Conomos
- Department of Biostatistics, University of Washington, Seattle, WA, United States of America
| | - Tom Blackwell
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, United States of America
| | - George Papanicolaou
- Epidemiology Branch, National Heart, Lung and Blood Institute, Bethesda, MD, United States of America
| | - Tuuli Lappalainen
- New York Genome Center and Department of Systems Biology, Columbia University, New York, NY United States of America
| | - Anna V. Mikhaylova
- Department of Biostatistics, University of Washington, Seattle, WA, United States of America
| | - Timothy A. Thornton
- Department of Biostatistics, University of Washington, Seattle, WA, United States of America
| | - Michael H. Cho
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA, United States of America
| | - Christopher R. Gignoux
- Division of Biomedical Informatics and Personalized Medicine, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, United States of America
| | - Leslie Lange
- Division of Biomedical Informatics and Personalized Medicine, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, United States of America
| | - Ethan Lange
- Division of Biomedical Informatics and Personalized Medicine, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, United States of America
| | - Stephen S. Rich
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, United States of America
| | - Jerome I. Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, United States of America
| | | | - Ani Manichaikul
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, United States of America
| | - Hae Kyung Im
- Section of Genetic Medicine, The University of Chicago, Chicago, IL, United States of America
| | - Heather E. Wheeler
- Department of Biology, Loyola University Chicago, Chicago, IL, United States of America
- Program in Bioinformatics, Loyola University Chicago, Chicago, IL, United States of America
- * E-mail:
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20
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Tsao CW, Aday AW, Almarzooq ZI, Alonso A, Beaton AZ, Bittencourt MS, Boehme AK, Buxton AE, Carson AP, Commodore-Mensah Y, Elkind MSV, Evenson KR, Eze-Nliam C, Ferguson JF, Generoso G, Ho JE, Kalani R, Khan SS, Kissela BM, Knutson KL, Levine DA, Lewis TT, Liu J, Loop MS, Ma J, Mussolino ME, Navaneethan SD, Perak AM, Poudel R, Rezk-Hanna M, Roth GA, Schroeder EB, Shah SH, Thacker EL, VanWagner LB, Virani SS, Voecks JH, Wang NY, Yaffe K, Martin SS. Heart Disease and Stroke Statistics-2022 Update: A Report From the American Heart Association. Circulation 2022; 145:e153-e639. [PMID: 35078371 DOI: 10.1161/cir.0000000000001052] [Citation(s) in RCA: 2413] [Impact Index Per Article: 1206.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
BACKGROUND The American Heart Association, in conjunction with the National Institutes of Health, annually reports the most up-to-date statistics related to heart disease, stroke, and cardiovascular risk factors, including core health behaviors (smoking, physical activity, diet, and weight) and health factors (cholesterol, blood pressure, and glucose control) that contribute to cardiovascular health. The Statistical Update presents the latest data on a range of major clinical heart and circulatory disease conditions (including stroke, congenital heart disease, rhythm disorders, subclinical atherosclerosis, coronary heart disease, heart failure, valvular disease, venous disease, and peripheral artery disease) and the associated outcomes (including quality of care, procedures, and economic costs). METHODS The American Heart Association, through its Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States to provide the most current information available in the annual Statistical Update. The 2022 Statistical Update is the product of a full year's worth of effort by dedicated volunteer clinicians and scientists, committed government professionals, and American Heart Association staff members. This year's edition includes data on the monitoring and benefits of cardiovascular health in the population and an enhanced focus on social determinants of health, adverse pregnancy outcomes, vascular contributions to brain health, and the global burden of cardiovascular disease and healthy life expectancy. RESULTS Each of the chapters in the Statistical Update focuses on a different topic related to heart disease and stroke statistics. CONCLUSIONS The Statistical Update represents a critical resource for the lay public, policymakers, media professionals, clinicians, health care administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions.
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21
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Leopold JA. Personalizing treatments for patients based on cardiovascular phenotyping. EXPERT REVIEW OF PRECISION MEDICINE AND DRUG DEVELOPMENT 2022; 7:4-16. [PMID: 36778892 PMCID: PMC9913616 DOI: 10.1080/23808993.2022.2028548] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
Introduction Cardiovascular disease persists as the leading cause of death worldwide despite continued advances in diagnostics and therapeutics. Our current approach to patients with cardiovascular disease is rooted in reductionism, which presupposes that all patients share a similar phenotype and will respond the same to therapy; however, this is unlikely as cardiovascular diseases exhibit complex heterogeneous phenotypes. Areas covered With the advent of high-throughput platforms for omics testing, phenotyping cardiovascular diseases has advanced to incorporate large-scale molecular data with classical history, physical examination, and laboratory results. Findings from genomics, proteomics, and metabolomics profiling have been used to define more precise cardiovascular phenotypes and predict adverse outcomes in population-based and disease-specific patient cohorts. These molecular data have also been utilized to inform drug efficacy based on a patient's unique phenotype. Expert opinion Multiscale phenotyping of cardiovascular disease has revealed diversity among patients that can be used to personalize pharmacotherapies and predict outcomes. Nonetheless, precision phenotyping for cardiovascular disease remains a nascent field that has not yet translated into widespread clinical practice despite its many potential advantages for patient care. Future endeavors that demonstrate improved pharmacotherapeutic responses and associated reduction in adverse events will facilitate mainstream adoption of precision cardiovascular phenotyping.
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Affiliation(s)
- Jane A. Leopold
- Division of Cardiovascular Medicine, Brigham and Women’s Hospital, 77 Ave Louis Pasteur, NRB0630K, Boston, Massachusetts, USA
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22
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Nayor M, Shen L, Hunninghake GM, Kochunov P, Barr RG, Bluemke DA, Broeckel U, Caravan P, Cheng S, de Vries PS, Hoffmann U, Kolossváry M, Li H, Luo J, McNally EM, Thanassoulis G, Arnett DK, Vasan RS. Progress and Research Priorities in Imaging Genomics for Heart and Lung Disease: Summary of an NHLBI Workshop. Circ Cardiovasc Imaging 2021; 14:e012943. [PMID: 34387095 PMCID: PMC8486340 DOI: 10.1161/circimaging.121.012943] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Imaging genomics is a rapidly evolving field that combines state-of-the-art bioimaging with genomic information to resolve phenotypic heterogeneity associated with genomic variation, improve risk prediction, discover prevention approaches, and enable precision diagnosis and treatment. Contemporary bioimaging methods provide exceptional resolution generating discrete and quantitative high-dimensional phenotypes for genomics investigation. Despite substantial progress in combining high-dimensional bioimaging and genomic data, methods for imaging genomics are evolving. Recognizing the potential impact of imaging genomics on the study of heart and lung disease, the National Heart, Lung, and Blood Institute convened a workshop to review cutting-edge approaches and methodologies in imaging genomics studies, and to establish research priorities for future investigation. This report summarizes the presentations and discussions at the workshop. In particular, we highlight the need for increased availability of imaging genomics data in diverse populations, dedicated focus on less common conditions, and centralization of efforts around specific disease areas.
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Affiliation(s)
- Matthew Nayor
- Cardiology Division, Department of Medicine, Massachusetts
General Hospital, Harvard Medical School, Boston, MA
| | - Li Shen
- Department of Biostatistics, Epidemiology and Informatics,
Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Gary M. Hunninghake
- Division of Pulmonary and Critical Care Medicine, Harvard
Medical School, Brigham and Women’s Hospital, Boston, MA
| | - Peter Kochunov
- Maryland Psychiatric Research Center, Department of
Psychiatry, University of Maryland School of Medicine, Baltimore, MD
| | - R. Graham Barr
- Department of Medicine and Department of Epidemiology,
Mailman School of Public Health, Columbia University Irving Medical Center, New
York, NY
| | - David A. Bluemke
- Department of Radiology, University of Wisconsin-Madison
School of Medicine and Public Health, Madison, WI
| | - Ulrich Broeckel
- Section of Genomic Pediatrics, Department of Pediatrics,
Medicine and Physiology, Children’s Research Institute and Genomic Sciences
and Precision Medicine Center, Medical College of Wisconsin, Milwaukee, WI
| | - Peter Caravan
- Institute for Innovation in Imaging, Athinoula A. Martinos
Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical
School, Charlestown, MA
| | - Susan Cheng
- Department of Cardiology, Smidt Heart Institute,
Cedars-Sinai Medical Center, Los Angeles, CA
| | - Paul S. de Vries
- Human Genetics Center, Department of Epidemiology, Human
Genetics, and Environmental Sciences, School of Public Health, The University of
Texas Health Science Center at Houston, Houston, TX
| | - Udo Hoffmann
- Department of Radiology, Harvard Medical School,
Massachusetts General Hospital, Boston, Massachusetts
| | - Márton Kolossváry
- Department of Radiology, Harvard Medical School,
Massachusetts General Hospital, Boston, Massachusetts
| | - Huiqing Li
- Division of Cardiovascular Sciences, National Heart,
Lung, and Blood Institute, Bethesda, MD
| | - James Luo
- Division of Cardiovascular Sciences, National Heart,
Lung, and Blood Institute, Bethesda, MD
| | - Elizabeth M. McNally
- Center for Genetic Medicine, Northwestern University
Feinberg School of Medicine, Chicago, IL
| | - George Thanassoulis
- Preventive and Genomic Cardiology, McGill University
Health Center and Research Institute, Montreal, Quebec, Canada
| | - Donna K. Arnett
- College of Public Health, University of Kentucky,
Lexington KY
| | - Ramachandran S. Vasan
- Sections of Preventive Medicine and Epidemiology, and
Cardiology, Department of Medicine, Department of Epidemiology, Boston University
Schools of Medicine and Public Health, and Center for Computing and Data Sciences,
Boston University, Boston, MA
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23
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Kaur D, Arora C, Raghava GPS. Prognostic Biomarker-Based Identification of Drugs for Managing the Treatment of Endometrial Cancer. Mol Diagn Ther 2021; 25:629-646. [PMID: 34155607 DOI: 10.1007/s40291-021-00539-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/27/2021] [Indexed: 12/26/2022]
Abstract
INTRODUCTION Uterine corpus endometrial carcinoma (UCEC) causes thousands of deaths per year. To improve the overall survival of patients with UCEC, there is a need to identify prognostic biomarkers and potential drugs. OBJECTIVES The aim of this study was twofold: the identification of prognostic gene signatures from expression profiles of pattern recognition receptor (PRR) genes and identification of the most effective existing drugs using the prognostic gene signature. METHODS This study was based on the expression profile of PRR genes of 541 patients with UCEC obtained from The Cancer Genome Atlas. Key prognostic signatures were identified using various approaches, including survival analysis, network, and clustering. Hub genes were identified by constructing a co-expression network. Representative genes were identified using k-means and k-medoids-based clustering. Univariate Cox proportional hazard (PH) analysis was used to identify survival-associated genes. 'cmap2' was used to identify potential drugs that can suppress/enhance the expression of prognostic genes. RESULTS Models were developed using hub genes and achieved a maximum hazard ratio (HR) of 1.37 (p = 0.294). Then, a clustering-based model was developed using seven genes (HR 9.14; p = 1.49 × 10-12). Finally, a nine gene-based risk stratification model was developed (CLEC1B, CLEC3A, IRF7, CTSB, FCN1, RIPK2, NLRP10, NLRP9, and SARM1) and achieved HR 10.70; p = 1.1 × 10-12. The performance of this model improved significantly in combination with the clinical stage and achieved HR 15.23; p = 2.21 × 10-7. We also developed a model for predicting high-risk patients (survival ≤ 4.3 years) and achieved an area under the receiver operating characteristic curve (AUROC) of 0.86. CONCLUSION We identified potential immunotherapeutic agents based on prognostic gene signature: hexamethonium bromide and isoflupredone. Several novel candidate drugs were suggested, including human interferon-α-2b, paclitaxel, imiquimod, MESO-DAP1, and mifamurtide. These biomolecules and repurposed drugs may be utilised for prognosis and treatment for better survival.
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Affiliation(s)
- Dilraj Kaur
- Department of Computational Biology, Indraprastha Institute of Information Technology-Delhi, Okhla Industrial Estate, New Delhi, 110020, India
| | - Chakit Arora
- Department of Computational Biology, Indraprastha Institute of Information Technology-Delhi, Okhla Industrial Estate, New Delhi, 110020, India
| | - Gajendra Pal Singh Raghava
- Department of Computational Biology, Indraprastha Institute of Information Technology-Delhi, Okhla Industrial Estate, New Delhi, 110020, India.
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Du H, Rao Y, Liu R, Deng K, Guan Y, Luo D, Mao Q, Yu J, Bo T, Fan Z, Ouyang H, Feng Y, Zhu W. Proteomics and metabolomics analyses reveal the full spectrum of inflammatory and lipid metabolic abnormalities in dyslipidemia. Biomed Chromatogr 2021; 35:e5183. [PMID: 34058018 DOI: 10.1002/bmc.5183] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Revised: 05/18/2021] [Accepted: 05/26/2021] [Indexed: 01/21/2023]
Abstract
Dyslipidemia is a common, chronic metabolic disease associated with cardiovascular complications. Due to the multiplicity of etiological factors, the pathogenesis of dyslipidemia is still unclear. In this study, we combined proteomics and metabolomics methods to analyze the plasma of patients with dyslipidemia and healthy subjects. isobaric tags for relative and absolute quantification (iTRAQ) markers, combined with LC-MS/MS proteomics technology and the UHPLC/Orbitfast-X Tribrid system, were used to establish the metabolite profile in clinical dyslipidemia. A total of 137 differentially expressed proteins, mainly related to biological processes such as protein activation cascades, adaptive immune responses, complement activation, acute inflammatory responses, and regulation of acute inflammatory responses, were identified. These proteins are involved in the regulation of important metabolic pathways, such as immunity and inflammation, coagulation and hemostasis, lipid metabolism, and oxidation and antioxidant defenses. The analysis of clinical metabolites showed there were 69 different metabolites in plasma, mainly related to glycerolipid, sphingolipid, porphyrin, α-linolenic acid, linoleic acid, and arachidonic acid metabolism, suggesting that the regulation of inflammation and lipid metabolism may be disturbed in patients with dyslipidemia. Among these, significant changes were observed in indole-3-propionic acid (IPA), which is considered as a potential biomarker of dyslipidemia. The combined analysis of proteins and metabolites showed that arachidonic acid, linoleic acid, and lipid metabolic pathways were closely related to dyslipidemia. IPA may be a potential biomarker. The information provided in this study may provide new insights into the pathogenesis of animal models of dyslipidemia and related disease models, as well as potential intervention targets.
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Affiliation(s)
- Hui Du
- Jiangxi University of Traditional Chinese Medicine, Nanchang, China
| | - Yifei Rao
- Jiangxi University of Traditional Chinese Medicine, Nanchang, China
| | - Ronghua Liu
- Jiangxi University of Traditional Chinese Medicine, Nanchang, China
| | - Kesui Deng
- The Affiliated Hospital of Jiangxi University of Traditional Chinese Medicine, Nanchang, China
| | - Yongmei Guan
- Jiangxi University of Traditional Chinese Medicine, Nanchang, China
| | - Dewei Luo
- Jiangxi University of Traditional Chinese Medicine, Nanchang, China
| | - Qiping Mao
- Jiangxi University of Traditional Chinese Medicine, Nanchang, China
| | - Jianwei Yu
- The Affiliated Hospital of Jiangxi University of Traditional Chinese Medicine, Nanchang, China
| | - Tao Bo
- Thermo Fisher Scientific-CN, Shanghai, China
| | - Ziquan Fan
- Thermo Fisher Scientific-CN, Shanghai, China
| | - Hui Ouyang
- Jiangxi University of Traditional Chinese Medicine, Nanchang, China
| | - Yulin Feng
- State Key Laboratory of Innovative Drug and Efficient Energy-Saving Pharmaceutical Equipment, Nanchang, China
| | - Weifeng Zhu
- Key Laboratory of Ministry of Education of Jiangxi University of Traditional Chinese Medicine, Nanchang, China
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25
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Liu G, Shi M, Mosley JD, Weng C, Zhang Y, Lee MTM, Jarvik GP, Hakonarson H, Namjou-Khales B, Sleiman P, Luo Y, Mentch F, Denny JC, Linton MF, Wei WQ, Stein CM, Feng Q. A Mendelian Randomization Approach Using 3-HMG-Coenzyme-A Reductase Gene Variation to Evaluate the Association of Statin-Induced Low-Density Lipoprotein Cholesterol Lowering With Noncardiovascular Disease Phenotypes. JAMA Netw Open 2021; 4:e2112820. [PMID: 34097045 PMCID: PMC8185593 DOI: 10.1001/jamanetworkopen.2021.12820] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/26/2022] Open
Abstract
IMPORTANCE Observational studies suggest that statins, which inhibit 3-hydroxy-3-methylglutaryl coenzyme A (HMG-CoA) reductase, may be associated with beneficial effects in many noncardiovascular diseases. OBJECTIVE To construct a weighted HMG-CoA reductase (HMGCR) gene genetic risk score (GRS) using variants in the HMGCR gene affecting low-density lipoprotein cholesterol as an instrumental variable for mendelian randomization analyses to test associations with candidate noncardiovascular phenotypes previously associated with statin use in observational studies. DESIGN, SETTING, AND PARTICIPANTS This cohort study included 53 385 unrelated adults of European ancestry with genome-wide genotypes available from BioVU (a practice-based biobank, used for discovery) and 30 444 unrelated adults with European ancestry available in the Electronic Medical Records and Genomics (eMERGE; a research consortium that conducts genetic research using electronic medical records, used for replication). The study was conducted from February 6, 2015, through April 31, 2019; data analysis was performed from August 26, 2019, through December 22, 2020. INTERVENTIONS An HMGCR GRS was calculated. MAIN OUTCOMES AND MEASURES The association between the HMGCR GRS and the presence or absence of 22 noncardiovascular phenotypes previously associated with statin use in clinical studies. RESULTS Of the 53 385 individuals in BioVU, 29 958 (56.1%) were women; mean (SD) age was 59.9 (15.6) years. The finding between the HMGCR GRS and the noncardiovascular phenotypes of interest in this cohort was significant only for type 2 diabetes. An HMGCR GRS equivalent to a 10-mg/dL decrease in the low-density lipoprotein cholesterol level was associated with an increased risk of type 2 diabetes (odds ratio [OR], 1.09; 95% CI, 1.04-1.15; P = 5.58 × 10-4). The HMGCR GRS was not associated with other phenotypes; the closest were increased risk of Parkinson disease (OR, 1.30; 95% CI, 1.07-1.58; P = .007) and kidney failure (OR, 1.18; 95% CI, 1.05-1.34; P = .008). Of the 30 444 individuals in eMERGE, 16 736 (55.0%) were women; mean (SD) age was 68.7 (15.4) years. The association between the HMGCR GRS and type 2 diabetes was replicated in this cohort (OR, 1.09; 95% CI, 1.01-1.17; P = .02); however, the HMGCR GRS was not associated with Parkinson disease (OR, 0.93; 95% CI, 0.75-1.16; P = .53) and kidney failure (OR, 1.18; 95% CI, 0.98-1.41; P = .08) in the eMERGE cohort. CONCLUSIONS AND RELEVANCE A mendelian randomization approach using variants in the HMGCR gene replicated the association between statin use and increased type 2 diabetes risk but provided no strong evidence for pleiotropic effects of statin-induced decrease of the low-density lipoprotein cholesterol level on other diseases.
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Affiliation(s)
- Ge Liu
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Mingjian Shi
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Jonathan D. Mosley
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee
- Division of Clinical Pharmacology, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Chunhua Weng
- Department of Biomedical Informatics, Columbia University Medical Center, New York, New York
| | - Yanfei Zhang
- Genomic Medicine Institute, Geisinger Health System, Danville, Pennsylvania
- Musculoskeletal Institute, Geisinger, Danville, Pennsylvania
| | - Ming Ta Michael Lee
- Genomic Medicine Institute, Geisinger Health System, Danville, Pennsylvania
- Musculoskeletal Institute, Geisinger, Danville, Pennsylvania
| | - Gail P. Jarvik
- Department of Medicine, Division of Medical Genetics, University of Washington Medical Center, Seattle
- Department of Genome Sciences, University of Washington, Seattle
| | - Hakon Hakonarson
- The Center for Applied Genomics, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania
- Department of Pediatrics, The Perelman School of Medicine, University of Pennsylvania, Philadelphia
- Division of Human Genetics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
- Division of Pulmonary Medicine, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - Bahram Namjou-Khales
- UC Department of Pediatrics, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio
| | - Patrick Sleiman
- The Center for Applied Genomics, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania
- Department of Pediatrics, The Perelman School of Medicine, University of Pennsylvania, Philadelphia
- Division of Human Genetics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - Yuan Luo
- Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, Illinois
| | - Frank Mentch
- The Center for Applied Genomics, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - Joshua C. Denny
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee
- at the time of the study, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
- All of Us Research Program, National Institutes of Health, Bethesda, Maryland
- now, National Institutes of Health, Bethesda, Maryland
| | - MacRae F. Linton
- Division of Cardiovascular Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
- Department of Pharmacology, Vanderbilt University, Nashville, Tennessee
| | - Wei-Qi Wei
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee
| | - C. Michael Stein
- Division of Clinical Pharmacology, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
- Department of Pharmacology, Vanderbilt University, Nashville, Tennessee
| | - QiPing Feng
- Division of Clinical Pharmacology, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
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Framingham Heart Study: JACC Focus Seminar, 1/8. J Am Coll Cardiol 2021; 77:2680-2692. [PMID: 34045026 DOI: 10.1016/j.jacc.2021.01.059] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Revised: 01/04/2021] [Accepted: 01/20/2021] [Indexed: 01/12/2023]
Abstract
The Framingham Heart Study is the longest-running cardiovascular epidemiological study, starting in 1948. This paper gives an overview of the various cohorts, collected data, and most important research findings to date. In brief, the Framingham Heart Study, funded by the National Institutes of Health and managed by Boston University, spans 3 generations of well phenotyped White persons and 2 cohorts comprised of racial and ethnic minority groups. These cohorts are densely phenotyped, with extensive longitudinal follow-up, and they continue to provide us with important information on human cardiovascular and noncardiovascular physiology over the lifespan, as well as to identify major risk factors for cardiovascular disease. This paper also summarizes some of the more recent progress in molecular epidemiology and discusses the future of the study.
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Joshi A, Rienks M, Theofilatos K, Mayr M. Systems biology in cardiovascular disease: a multiomics approach. Nat Rev Cardiol 2021; 18:313-330. [PMID: 33340009 DOI: 10.1038/s41569-020-00477-1] [Citation(s) in RCA: 108] [Impact Index Per Article: 36.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 11/02/2020] [Indexed: 12/13/2022]
Abstract
Omics techniques generate large, multidimensional data that are amenable to analysis by new informatics approaches alongside conventional statistical methods. Systems theories, including network analysis and machine learning, are well placed for analysing these data but must be applied with an understanding of the relevant biological and computational theories. Through applying these techniques to omics data, systems biology addresses the problems posed by the complex organization of biological processes. In this Review, we describe the techniques and sources of omics data, outline network theory, and highlight exemplars of novel approaches that combine gene regulatory and co-expression networks, proteomics, metabolomics, lipidomics and phenomics with informatics techniques to provide new insights into cardiovascular disease. The use of systems approaches will become necessary to integrate data from more than one omic technique. Although understanding the interactions between different omics data requires increasingly complex concepts and methods, we argue that hypothesis-driven investigations and independent validation must still accompany these novel systems biology approaches to realize their full potential.
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Affiliation(s)
- Abhishek Joshi
- King's British Heart Foundation Centre, King's College London, London, UK
- Bart's Heart Centre, St. Bartholomew's Hospital, London, UK
| | - Marieke Rienks
- King's British Heart Foundation Centre, King's College London, London, UK
| | | | - Manuel Mayr
- King's British Heart Foundation Centre, King's College London, London, UK.
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Taha K, Davuluri R, Yoo P, Spencer J. Personizing the prediction of future susceptibility to a specific disease. PLoS One 2021; 16:e0243127. [PMID: 33406077 PMCID: PMC7787538 DOI: 10.1371/journal.pone.0243127] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2019] [Accepted: 11/17/2020] [Indexed: 01/22/2023] Open
Abstract
A traceable biomarker is a member of a disease's molecular pathway. A disease may be associated with several molecular pathways. Each different combination of these molecular pathways, to which detected traceable biomarkers belong, may serve as an indicative of the elicitation of the disease at a different time frame in the future. Based on this notion, we introduce a novel methodology for personalizing an individual's degree of future susceptibility to a specific disease. We implemented the methodology in a working system called Susceptibility Degree to a Disease Predictor (SDDP). For a specific disease d, let S be the set of molecular pathways, to which traceable biomarkers detected from most patients of d belong. For the same disease d, let S' be the set of molecular pathways, to which traceable biomarkers detected from a certain individual belong. SDDP is able to infer the subset S'' ⊆{S-S'} of undetected molecular pathways for the individual. Thus, SDDP can infer undetected molecular pathways of a disease for an individual based on few molecular pathways detected from the individual. SDDP can also help in inferring the combination of molecular pathways in the set {S'+S''}, whose traceable biomarkers collectively is an indicative of the disease. SDDP is composed of the following four components: information extractor, interrelationship between molecular pathways modeler, logic inferencer, and risk indicator. The information extractor takes advantage of the exponential increase of biomedical literature to automatically extract the common traceable biomarkers for a specific disease. The interrelationship between molecular pathways modeler models the hierarchical interrelationships between the molecular pathways of the traceable biomarkers. The logic inferencer transforms the hierarchical interrelationships between the molecular pathways into rule-based specifications. It employs the specification rules and the inference rules for predicate logic to infer as many as possible undetected molecular pathways of a disease for an individual. The risk indicator outputs a risk indicator value that reflects the individual's degree of future susceptibility to the disease. We evaluated SDDP by comparing it experimentally with other methods. Results revealed marked improvement.
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Affiliation(s)
- Kamal Taha
- Department of Electrical and Computer Science, Khalifa University, Abu Dhabi, UAE
- * E-mail:
| | - Ramana Davuluri
- Department of Biomedical Informatics, School of Medicine and College of Engineering and Applied Sciences, Stony Brook University, Stony Brook, New York, United States of America
| | - Paul Yoo
- Department of Computer Science & Information Systems, University of London, Birkbeck College, London, United Kingdom
| | - Jesse Spencer
- Department of Pathology, University of Utah, Salt Lake City, Utah, United States of America
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Abstract
In addition to their fundamental role in clearance, the kidneys release select molecules into the circulation, but whether any of these anabolic functions provides insight on kidney health is unknown. Using aptamer-based proteomics, we characterized arterial (A)-to-renal venous (V) gradients for >1,300 proteins in 22 individuals who underwent invasive sampling. Although most of the proteins that changed significantly decreased from A to V, consistent with renal clearance, several were found to increase, the most significant of which was testican-2. To assess the clinical implications of these physiologic findings, we examined proteomic data in the Jackson Heart Study (JHS), an African-American cohort (n = 1,928), with replication in the Framingham Heart Study (FHS), a White cohort (n = 1,621). In both populations, testican-2 had a strong, positive correlation with estimated glomerular filtration rate (eGFR). In addition, higher baseline testican-2 levels were associated with a lower rate of eGFR decline in models adjusted for age, gender, hypertension, type 2 diabetes, body mass index, baseline eGFR, and albuminuria. Glomerular expression of testican-2 in human kidneys was demonstrated by immunohistochemistry, immunofluorescence, and electron microscopy, while single-cell RNA sequencing of human kidneys showed expression of the cognate gene, SPOCK2, exclusively in podocytes. In vitro, testican-2 increased glomerular endothelial tube formation and motility, raising the possibility that its secretion has a functional role within the glomerulus. Taken together, our findings identify testican-2 as a podocyte-derived biomarker of kidney health and prognosis.
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30
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Genetics meets proteomics: perspectives for large population-based studies. Nat Rev Genet 2020; 22:19-37. [PMID: 32860016 DOI: 10.1038/s41576-020-0268-2] [Citation(s) in RCA: 163] [Impact Index Per Article: 40.8] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/14/2020] [Indexed: 12/22/2022]
Abstract
Proteomic analysis of cells, tissues and body fluids has generated valuable insights into the complex processes influencing human biology. Proteins represent intermediate phenotypes for disease and provide insight into how genetic and non-genetic risk factors are mechanistically linked to clinical outcomes. Associations between protein levels and DNA sequence variants that colocalize with risk alleles for common diseases can expose disease-associated pathways, revealing novel drug targets and translational biomarkers. However, genome-wide, population-scale analyses of proteomic data are only now emerging. Here, we review current findings from studies of the plasma proteome and discuss their potential for advancing biomedical translation through the interpretation of genome-wide association analyses. We highlight the challenges faced by currently available technologies and provide perspectives relevant to their future application in large-scale biobank studies.
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31
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Bretherick AD, Canela-Xandri O, Joshi PK, Clark DW, Rawlik K, Boutin TS, Zeng Y, Amador C, Navarro P, Rudan I, Wright AF, Campbell H, Vitart V, Hayward C, Wilson JF, Tenesa A, Ponting CP, Baillie JK, Haley C. Linking protein to phenotype with Mendelian Randomization detects 38 proteins with causal roles in human diseases and traits. PLoS Genet 2020; 16:e1008785. [PMID: 32628676 PMCID: PMC7337286 DOI: 10.1371/journal.pgen.1008785] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2019] [Accepted: 04/21/2020] [Indexed: 01/25/2023] Open
Abstract
To efficiently transform genetic associations into drug targets requires evidence that a particular gene, and its encoded protein, contribute causally to a disease. To achieve this, we employ a three-step proteome-by-phenome Mendelian Randomization (MR) approach. In step one, 154 protein quantitative trait loci (pQTLs) were identified and independently replicated. From these pQTLs, 64 replicated locally-acting variants were used as instrumental variables for proteome-by-phenome MR across 846 traits (step two). When its assumptions are met, proteome-by-phenome MR, is equivalent to simultaneously running many randomized controlled trials. Step 2 yielded 38 proteins that significantly predicted variation in traits and diseases in 509 instances. Step 3 revealed that amongst the 271 instances from GeneAtlas (UK Biobank), 77 showed little evidence of pleiotropy (HEIDI), and 92 evidence of colocalization (eCAVIAR). Results were wide ranging: including, for example, new evidence for a causal role of tyrosine-protein phosphatase non-receptor type substrate 1 (SHPS1; SIRPA) in schizophrenia, and a new finding that intestinal fatty acid binding protein (FABP2) abundance contributes to the pathogenesis of cardiovascular disease. We also demonstrated confirmatory evidence for the causal role of four further proteins (FGF5, IL6R, LPL, LTA) in cardiovascular disease risk.
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Affiliation(s)
- Andrew D. Bretherick
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh, Scotland, United Kingdom
| | - Oriol Canela-Xandri
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh, Scotland, United Kingdom
- The Roslin Institute, University of Edinburgh, Easter Bush, Edinburgh, Scotland, United Kingdom
| | - Peter K. Joshi
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Teviot Place, Edinburgh, Scotland, United Kingdom
| | - David W. Clark
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Teviot Place, Edinburgh, Scotland, United Kingdom
| | - Konrad Rawlik
- The Roslin Institute, University of Edinburgh, Easter Bush, Edinburgh, Scotland, United Kingdom
| | - Thibaud S. Boutin
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh, Scotland, United Kingdom
| | - Yanni Zeng
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh, Scotland, United Kingdom
- Faculty of Forensic Medicine, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, China
- Guangdong Province Translational Forensic Medicine Engineering Technology Research Center, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, China
- Guangdong Province Key Laboratory of Brain Function and Disease, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, China
| | - Carmen Amador
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh, Scotland, United Kingdom
| | - Pau Navarro
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh, Scotland, United Kingdom
| | - Igor Rudan
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Teviot Place, Edinburgh, Scotland, United Kingdom
| | - Alan F. Wright
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh, Scotland, United Kingdom
| | - Harry Campbell
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Teviot Place, Edinburgh, Scotland, United Kingdom
| | - Veronique Vitart
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh, Scotland, United Kingdom
| | - Caroline Hayward
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh, Scotland, United Kingdom
| | - James F. Wilson
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh, Scotland, United Kingdom
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Teviot Place, Edinburgh, Scotland, United Kingdom
| | - Albert Tenesa
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh, Scotland, United Kingdom
- The Roslin Institute, University of Edinburgh, Easter Bush, Edinburgh, Scotland, United Kingdom
| | - Chris P. Ponting
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh, Scotland, United Kingdom
| | - J. Kenneth Baillie
- The Roslin Institute, University of Edinburgh, Easter Bush, Edinburgh, Scotland, United Kingdom
| | - Chris Haley
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh, Scotland, United Kingdom
- The Roslin Institute, University of Edinburgh, Easter Bush, Edinburgh, Scotland, United Kingdom
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Abstract
Susceptibility to atrial fibrillation (AF) is determined by well-recognized risk factors such as diabetes mellitus or hypertension, emerging risk factors such as sleep apnea or inflammation, and increasingly well-defined genetic variants. As discussed in detail in a companion article in this series, studies in families and in large populations have identified multiple genetic loci, specific genes, and specific variants increasing susceptibility to AF. Since it is becoming increasingly inexpensive to obtain genotype data and indeed whole genome sequence data, the question then becomes to define whether using emerging new genetics knowledge can improve care for patients both before and after development of AF. Examples of improvements in care could include identifying patients at increased risk for AF (and thus deploying increased surveillance or even low-risk preventive therapies should these be available), identifying patient subsets in whom specific therapies are likely to be effective or ineffective or in whom the driving biology could motivate the development of new mechanism-based therapies or identifying an underlying susceptibility to comorbid cardiovascular disease. While current guidelines for the care of patients with AF do not recommend routine genetic testing, this rapidly increasing knowledge base suggests that testing may now or soon have a place in the management of select patients. The opportunity is to generate, validate, and deploy clinical predictors (including family history) of AF risk, to assess the utility of incorporating genomic variants into those predictors, and to identify and validate interventions such as wearable or implantable device-based monitoring ultimately to intervene in patients with AF before they present with catastrophic complications like heart failure or stroke.
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Affiliation(s)
- M. Benjamin Shoemaker
- Department of Medicine (Cardiovascular Medicine), Vanderbilt University Medical Center, Nashville, TN
| | - Rajan L. Shah
- Department of Medicine (Cardiovascular Medicine), Stanford University Medical Center, Palo Alto, CA
| | - Dan M. Roden
- Departments of Medicine (Cardiovascular Medicine and Clinical Pharmacology), Pharmacology, and Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN
| | - Marco V. Perez
- Stanford Center for Inherited Cardiovascular Diseases, Stanford University, Palo Alto, CA
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Gutmann C, Joshi A, Mayr M. Platelet "-omics" in health and cardiovascular disease. Atherosclerosis 2020; 307:87-96. [PMID: 32646580 DOI: 10.1016/j.atherosclerosis.2020.05.022] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/08/2020] [Revised: 04/28/2020] [Accepted: 05/27/2020] [Indexed: 12/21/2022]
Abstract
The importance of platelets for cardiovascular disease was established as early as the 19th century. Their therapeutic inhibition stands alongside the biggest achievements in medicine. Still, certain aspects of platelet pathophysiology remain unclear. This includes platelet resistance to antiplatelet therapy and the contribution of platelets to vascular remodelling and extends beyond cardiovascular disease to haematological disorders and cancer. To address these gaps in our knowledge, a better understanding of the underlying molecular processes is needed. This will be enabled by technologies that capture dysregulated molecular processes and can integrate them into a broader network of biological systems. The advent of -omics technologies, such as mass spectrometry proteomics, metabolomics and lipidomics; highly multiplexed affinity-based proteomics; microarray- or RNA-sequencing-(RNA-seq)-based transcriptomics, and most recently ribosome footprint-based translatomics, has enabled a more holistic understanding of platelet biology. Most of these methods have already been applied to platelets, and this review will summarise this information and discuss future developments in this area of research.
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Affiliation(s)
- Clemens Gutmann
- King's British Heart Foundation Centre, King's College London, 125 Coldharbour Lane, London, SE5 9NU, United Kingdom
| | - Abhishek Joshi
- King's British Heart Foundation Centre, King's College London, 125 Coldharbour Lane, London, SE5 9NU, United Kingdom
| | - Manuel Mayr
- King's British Heart Foundation Centre, King's College London, 125 Coldharbour Lane, London, SE5 9NU, United Kingdom.
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Hippocampal complement C3 might contribute to cognitive impairment induced by anesthesia and surgery. Neuroreport 2020; 31:507-514. [PMID: 32168099 DOI: 10.1097/wnr.0000000000001422] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
Postoperative cognitive dysfunction is a well-recognized complication after major surgery in the elderly, but its pathophysiological mechanism is not fully understood. In the present study, we used liquid chromatography-tandem mass spectrometry combined with tandem mass tags to identify differentially expressed proteins and perform further functional studies on protein of interest. Here, we showed that hippocampal complement C3 was significantly upregulated after surgery, which was accompanied by marked decreases in synaptic related proteins and density. In aged patients undergoing gastrointestinal surgery, we also found significantly increased plasma level of C3b postoperatively and were negatively associated with cognitive performance. Notably, selective inhibition of complement C3 by compstatin was able to rescue synaptic and cognitive impairments induced by surgery in aged mice. Collectively, our study confirms that surgery can induce cognitive impairments, and the possible mechanisms might be related to abnormal complement signaling and synaptic disruption.
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Nayor M, Short MI, Rasheed H, Lin H, Jonasson C, Yang Q, Hveem K, Felix JF, Morrison AC, Wild PS, Morley MP, Cappola TP, Benson MD, Ngo D, Sinha S, Keyes MJ, Shen D, Wang TJ, Larson MG, Brumpton BM, Gerszten RE, Omland T, Vasan RS. Aptamer-Based Proteomic Platform Identifies Novel Protein Predictors of Incident Heart Failure and Echocardiographic Traits. Circ Heart Fail 2020; 13:e006749. [PMID: 32408813 PMCID: PMC7236427 DOI: 10.1161/circheartfailure.119.006749] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND We used a large-scale, high-throughput DNA aptamer-based discovery proteomic platform to identify circulating biomarkers of cardiac remodeling and incident heart failure (HF) in community-dwelling individuals. METHODS We evaluated 1895 FHS (Framingham Heart Study) participants (age 55±10 years, 54% women) who underwent proteomic profiling and echocardiography. Plasma levels of 1305 proteins were related to echocardiographic traits and to incident HF using multivariable regression. Statistically significant protein-HF associations were replicated in the HUNT (Nord-Trøndelag Health) study (n=2497, age 63±10 years, 43% women), and results were meta-analyzed. Genetic variants associated with circulating protein levels (pQTLs) were related to echocardiographic traits in the EchoGen (n=30 201) and to incident HF in the CHARGE (n=20 926) consortia. RESULTS Seventeen proteins associated with echocardiographic traits in cross-sectional analyses (false discovery rate <0.10), and 8 of these proteins had pQTLs associated with echocardiographic traits in EchoGen (P<0.0007). In Cox models adjusted for clinical risk factors, 29 proteins demonstrated associations with incident HF in FHS (174 HF events, mean follow-up 19 [limits, 0.2-23.7] years). In meta-analyses of FHS and HUNT, 6 of these proteins were associated with incident HF (P<3.8×10-5; 3 with higher risk: NT-proBNP [N-terminal proB-type natriuretic peptide], TSP2 [thrombospondin-2], MBL [mannose-binding lectin]; and 3 with lower risk: ErbB1 [epidermal growth factor receptor], GDF-11/8 [growth differentiation factor-11/8], and RGMC [hemojuvelin]). For 5 of the 6 proteins, pQTLs were associated with echocardiographic traits (P<0.0006) in EchoGen, and for RGMC, a protein quantitative trait loci was associated with incident HF (P=0.001). CONCLUSIONS A large-scale proteomics approach identified new predictors of cardiac remodeling and incident HF. Future studies are warranted to elucidate how biological pathways represented by these proteins may mediate cardiac remodeling and HF risk and to assess if these proteins can improve HF risk prediction.
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Affiliation(s)
- Matthew Nayor
- Framingham Heart Study, Framingham, MA
- Cardiology Division, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA
| | - Meghan I. Short
- Department of Biostatistics, Boston University School of Public Health, Boston, MA
- Glenn Biggs Institute for Alzheimer’s and Neurodegenerative Diseases, University of Texas Health Science Center, San Antonio, TX
| | - Humaira Rasheed
- K.G. Jebsen Centre for Genetic Epidemiology, Department of Public Health and Nursing, Norwegian University of Science and Technology, Norway
- MRC Integrative Epidemiology Unit, University of Bristol, UK
| | - Honghuang Lin
- Section of Computational Biomedicine, Department of Medicine, Boston University School of Medicine, Boston, MA
| | - Christian Jonasson
- K.G. Jebsen Centre for Genetic Epidemiology, Department of Public Health and Nursing, Norwegian University of Science and Technology, Norway
| | - Qiong Yang
- Department of Biostatistics, Boston University School of Public Health, Boston, MA
| | - Kristian Hveem
- K.G. Jebsen Centre for Genetic Epidemiology, Department of Public Health and Nursing, Norwegian University of Science and Technology, Norway
| | - Janine F. Felix
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Alanna C. Morrison
- Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, Texas
| | - Philipp S. Wild
- Preventive Cardiology and Preventive Medicine, Center for Cardiology, and Center for Thrombosis and Hemostasis, University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany
- DZHK (German Center for Cardiovascular Research), partner site RhineMain, Mainz, Germany
| | - Michael P. Morley
- The Cardiovascular Institute, Perelman School of Medicine, University of Pennsylvania, PA
| | - Thomas P. Cappola
- The Cardiovascular Institute, Perelman School of Medicine, University of Pennsylvania, PA
- Division of Cardiovascular Medicine, Perelman School of Medicine, University of Pennsylvania, PA
| | - Mark D. Benson
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA
| | | | | | - Debby Ngo
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA
| | - Sumita Sinha
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA
| | - Michelle J. Keyes
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA
| | - Dongxiao Shen
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA
| | - Thomas J. Wang
- Division of Cardiovascular Medicine, Vanderbilt University, Nashville, TN
| | - Martin G. Larson
- Framingham Heart Study, Framingham, MA
- Department of Biostatistics, Boston University School of Public Health, Boston, MA
| | - Ben M. Brumpton
- Glenn Biggs Institute for Alzheimer’s and Neurodegenerative Diseases, University of Texas Health Science Center, San Antonio, TX
- K.G. Jebsen Centre for Genetic Epidemiology, Department of Public Health and Nursing, Norwegian University of Science and Technology, Norway
- Clinic of Thoracic and Occupational Medicine, St. Olavs Hospital, Trondheim University Hospital, Norway
| | - Robert E. Gerszten
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA
| | - Torbjørn Omland
- Department of Cardiology, Akershus University Hospital, Lørenskog, and Center for Heart Failure Research, Institute of Clinical Medicine, University of Oslo, Norway
| | - Ramachandran S. Vasan
- Framingham Heart Study, Framingham, MA
- Sections of Preventive Medicine & Epidemiology, and Cardiology, Department of Medicine, Boston University School of Medicine, Boston, MA
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Abstract
PURPOSE OF REVIEW Comprehensive analyses of the genome, transcriptome, proteome and metabolome are instrumental in identifying biomarkers of disease, to gain insight into mechanisms underlying the development of cardiovascular disease, and show promise for better stratifying patients according to disease subtypes. This review highlights recent 'omics' studies, including integration of multiple 'omics' that have advanced mechanistic understanding and diagnosis in humans and animal models. RECENT FINDINGS Transcriptome-based discovery continues to be a primary method to obtain data for hypothesis generation and the understanding of disease pathogenesis has been enhanced by single cell-based methods capable of revealing heterogeneity in cellular responses. Advances in proteome coverage and quantitation of individual protein species, together with enhanced methods for detecting posttranslational modifications, have improved discovery of protein-based biomarkers. SUMMARY High-throughput assays capable of quantitating the vast majority of any particular type of biomolecule within a tissue sample, isolated cells or plasma are now available. In order to make best use of the large amount of data that can be generated on given molecule types, as well as their interrelationships in disease, continued development of pattern-recognition algorithms ('machine learning') will be required and the subclassification of disease that is made possible by such algorithms will be likely to inform clinical practice, and vice versa.
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Affiliation(s)
- Abhishek Joshi
- King's College London British Heart Foundation Centre, School of Cardiovascular Medicine and Sciences, London, United Kingdom
- Bart’s Heart Centre, St. Bartholomew’s Hospital, London, United Kingdom
| | - Manuel Mayr
- King's College London British Heart Foundation Centre, School of Cardiovascular Medicine and Sciences, London, United Kingdom
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