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Trastulla L, Dolgalev G, Moser S, Jiménez-Barrón LT, Andlauer TFM, von Scheidt M, Budde M, Heilbronner U, Papiol S, Teumer A, Homuth G, Völzke H, Dörr M, Falkai P, Schulze TG, Gagneur J, Iorio F, Müller-Myhsok B, Schunkert H, Ziller MJ. Distinct genetic liability profiles define clinically relevant patient strata across common diseases. Nat Commun 2024; 15:5534. [PMID: 38951512 PMCID: PMC11217418 DOI: 10.1038/s41467-024-49338-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Accepted: 05/31/2024] [Indexed: 07/03/2024] Open
Abstract
Stratified medicine holds great promise to tailor treatment to the needs of individual patients. While genetics holds great potential to aid patient stratification, it remains a major challenge to operationalize complex genetic risk factor profiles to deconstruct clinical heterogeneity. Contemporary approaches to this problem rely on polygenic risk scores (PRS), which provide only limited clinical utility and lack a clear biological foundation. To overcome these limitations, we develop the CASTom-iGEx approach to stratify individuals based on the aggregated impact of their genetic risk factor profiles on tissue specific gene expression levels. The paradigmatic application of this approach to coronary artery disease or schizophrenia patient cohorts identified diverse strata or biotypes. These biotypes are characterized by distinct endophenotype profiles as well as clinical parameters and are fundamentally distinct from PRS based groupings. In stark contrast to the latter, the CASTom-iGEx strategy discovers biologically meaningful and clinically actionable patient subgroups, where complex genetic liabilities are not randomly distributed across individuals but rather converge onto distinct disease relevant biological processes. These results support the notion of different patient biotypes characterized by partially distinct pathomechanisms. Thus, the universally applicable approach presented here has the potential to constitute an important component of future personalized medicine paradigms.
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Affiliation(s)
- Lucia Trastulla
- Max Planck Institute of Psychiatry, Munich, Germany
- Technische Universität München Medical Graduate Center Experimental Medicine, Munich, Germany
- Human Technopole, Milan, Italy
- Department of Psychiatry, University of Münster, Münster, Germany
| | - Georgii Dolgalev
- Department of Psychiatry, University of Münster, Münster, Germany
| | - Sylvain Moser
- Max Planck Institute of Psychiatry, Munich, Germany
- Technische Universität München Medical Graduate Center Experimental Medicine, Munich, Germany
- International Max Planck Research School for Translational Psychiatry (IMPRS-TP), Munich, Germany
| | - Laura T Jiménez-Barrón
- Max Planck Institute of Psychiatry, Munich, Germany
- International Max Planck Research School for Translational Psychiatry (IMPRS-TP), Munich, Germany
| | - Till F M Andlauer
- Max Planck Institute of Psychiatry, Munich, Germany
- Department of Neurology, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Moritz von Scheidt
- Klinik für Herz-und Kreislauferkrankungen, Deutsches Herzzentrum München, Technical University Munich, Munich, Germany
- German Center for Cardiovascular Research (DZHK), Partner Site Munich Heart Alliance, Munich, Germany
| | - Monika Budde
- Institute of Psychiatric Phenomics and Genomics (IPPG), LMU University Hospital, LMU Munich, Munich, 80336, Germany
| | - Urs Heilbronner
- Institute of Psychiatric Phenomics and Genomics (IPPG), LMU University Hospital, LMU Munich, Munich, 80336, Germany
| | - Sergi Papiol
- Max Planck Institute of Psychiatry, Munich, Germany
- Institute of Psychiatric Phenomics and Genomics (IPPG), LMU University Hospital, LMU Munich, Munich, 80336, Germany
| | - Alexander Teumer
- German Center for Cardiovascular Research (DZHK), Partner Site Greifswald, Greifswald, Germany
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
| | - Georg Homuth
- Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, Greifswald, Germany
| | - Henry Völzke
- German Center for Cardiovascular Research (DZHK), Partner Site Greifswald, Greifswald, Germany
- Institute of Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Marcus Dörr
- German Center for Cardiovascular Research (DZHK), Partner Site Greifswald, Greifswald, Germany
- Department of Internal Medicine B, University Medicine Greifswald, Greifswald, Germany
| | - Peter Falkai
- Max Planck Institute of Psychiatry, Munich, Germany
- Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich, 80336, Germany
| | - Thomas G Schulze
- Institute of Psychiatric Phenomics and Genomics (IPPG), LMU University Hospital, LMU Munich, Munich, 80336, Germany
- Department of Psychiatry and Behavioral Sciences, SUNY Upstate Medical University, Syracuse, NY, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Julien Gagneur
- School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
- Institute of Human Genetics, School of Medicine and Health, Technical University of Munich, Munich, Germany
- Computational Health Center, Helmholtz Center Munich, Neuherberg, Germany
| | | | - Bertram Müller-Myhsok
- Max Planck Institute of Psychiatry, Munich, Germany
- Institute of Translational Medicine, University of Liverpool, Liverpool, UK
| | - Heribert Schunkert
- Klinik für Herz-und Kreislauferkrankungen, Deutsches Herzzentrum München, Technical University Munich, Munich, Germany
- German Center for Cardiovascular Research (DZHK), Partner Site Munich Heart Alliance, Munich, Germany
| | - Michael J Ziller
- Max Planck Institute of Psychiatry, Munich, Germany.
- Department of Psychiatry, University of Münster, Münster, Germany.
- Center for Soft Nanoscience, University of Münster, Münster, Germany.
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Esquivel Gaytan A, Bomer N, Grote Beverborg N, van der Meer P. 404-error "Disease not found": Unleashing the translational potential of -omics approaches beyond traditional disease classification in heart failure research. Eur J Heart Fail 2024; 26:1313-1323. [PMID: 38741225 DOI: 10.1002/ejhf.3268] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Revised: 03/15/2024] [Accepted: 04/14/2024] [Indexed: 05/16/2024] Open
Abstract
The emergence of personalized medicine, facilitated by the progress in -omics technologies, has initiated a new era in medical diagnostics and treatment. This review examines the potential of -omics approaches in heart failure, a condition that has not yet fully capitalized on personalized strategies compared to other medical fields like cancer therapy. Here, we argue that integrating multi-omics technology with systems medicine approaches could fundamentally transform heart failure management, moving away from the traditional paradigm of 'one size fits all'. Our review examines how omics can enhance understanding of heart failure's molecular foundations and contribute to a more comprehensive disease classification. We draw attention to the current state of medical practice that only relies on clinical evidence and a number of standard laboratory tests. At the same time, we propose a shift towards a universal approach that uses quantitative data from multi-omics to unravel complex molecular interactions. The discussion centres around the potential of the transition as a means to enhance individual risk assessment and emphasizes management within clinical settings. While the use of omics in cardiovascular research is not recent, many past studies have focused only on a single omics approach. In order to achieve a better understanding of disease mechanisms, we explore more holistic approaches using genomics, transcriptomics, epigenomics, and proteomics. This review concludes with a call to action to adopt multi-omics in clinical trials and practice to pave the way for more personalized disease management and more effective heart failure interventions.
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Affiliation(s)
- Antonio Esquivel Gaytan
- Department of Cardiology, University Medical Centre Groningen, University of Groningen, Groningen, The Netherlands
| | - Nils Bomer
- Department of Cardiology, University Medical Centre Groningen, University of Groningen, Groningen, The Netherlands
| | - Niels Grote Beverborg
- Department of Cardiology, University Medical Centre Groningen, University of Groningen, Groningen, The Netherlands
| | - Peter van der Meer
- Department of Cardiology, University Medical Centre Groningen, University of Groningen, Groningen, The Netherlands
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Sánchez-Valle J, Valencia A. Molecular bases of comorbidities: present and future perspectives. Trends Genet 2023; 39:773-786. [PMID: 37482451 DOI: 10.1016/j.tig.2023.06.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Revised: 06/12/2023] [Accepted: 06/12/2023] [Indexed: 07/25/2023]
Abstract
Co-occurrence of diseases decreases patient quality of life, complicates treatment choices, and increases mortality. Analyses of electronic health records present a complex scenario of comorbidity relationships that vary by age, sex, and cohort under study. The study of similarities between diseases using 'omics data, such as genes altered in diseases, gene expression, proteome, and microbiome, are fundamental to uncovering the origin of, and potential treatment for, comorbidities. Recent studies have produced a first generation of genetic interpretations for as much as 46% of the comorbidities described in large cohorts. Integrating different sources of molecular information and using artificial intelligence (AI) methods are promising approaches for the study of comorbidities. They may help to improve the treatment of comorbidities, including the potential repositioning of drugs.
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Affiliation(s)
- Jon Sánchez-Valle
- Life Sciences Department, Barcelona Supercomputing Center, Barcelona, 08034, Spain.
| | - Alfonso Valencia
- Life Sciences Department, Barcelona Supercomputing Center, Barcelona, 08034, Spain; ICREA, Barcelona, 08010, Spain.
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Sonawane AR, Aikawa E, Aikawa M. Connections for Matters of the Heart: Network Medicine in Cardiovascular Diseases. Front Cardiovasc Med 2022; 9:873582. [PMID: 35665246 PMCID: PMC9160390 DOI: 10.3389/fcvm.2022.873582] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Accepted: 04/19/2022] [Indexed: 01/18/2023] Open
Abstract
Cardiovascular diseases (CVD) are diverse disorders affecting the heart and vasculature in millions of people worldwide. Like other fields, CVD research has benefitted from the deluge of multiomics biomedical data. Current CVD research focuses on disease etiologies and mechanisms, identifying disease biomarkers, developing appropriate therapies and drugs, and stratifying patients into correct disease endotypes. Systems biology offers an alternative to traditional reductionist approaches and provides impetus for a comprehensive outlook toward diseases. As a focus area, network medicine specifically aids the translational aspect of in silico research. This review discusses the approach of network medicine and its application to CVD research.
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Affiliation(s)
- Abhijeet Rajendra Sonawane
- Center for Interdisciplinary Cardiovascular Sciences, Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
- Center for Excellence in Vascular Biology, Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Elena Aikawa
- Center for Interdisciplinary Cardiovascular Sciences, Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
- Center for Excellence in Vascular Biology, Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Masanori Aikawa
- Center for Interdisciplinary Cardiovascular Sciences, Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
- Center for Excellence in Vascular Biology, Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
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Azad TD, Shah PP, Kim HB, Stevens RD. Endotypes and the Path to Precision in Moderate and Severe Traumatic Brain Injury. Neurocrit Care 2022; 37:259-266. [PMID: 35314969 DOI: 10.1007/s12028-022-01475-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Accepted: 02/15/2022] [Indexed: 12/19/2022]
Abstract
Heterogeneity is recognized as a major barrier in efforts to improve the care and outcomes of patients with traumatic brain injury (TBI). Even within the narrower stratum of moderate and severe TBI, current management approaches do not capture the complexity of this condition characterized by manifold clinical, anatomical, and pathophysiologic features. One approach to heterogeneity may be to resolve undifferentiated TBI populations into endotypes, subclasses that are distinguished by shared biological characteristics. The endotype paradigm has been explored in a range of medical domains, including psychiatry, oncology, immunology, and pulmonology. In intensive care, endotypes are being investigated for syndromes such as sepsis and acute respiratory distress syndrome. This review provides an overview of the endotype paradigm as well as some of its methods and use cases. A conceptual framework is proposed for endotype research in moderate and severe TBI, together with a scientific road map for endotype discovery and validation in this population.
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Affiliation(s)
- Tej D Azad
- Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Pavan P Shah
- Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Han B Kim
- Department of Anesthesiology and Critical Care Medicine, Johns Hopkins University School of Medicine, Phipps Suite 455, Baltimore, MD, 21287, USA
| | - Robert D Stevens
- Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, MD, USA. .,Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA. .,Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD, USA. .,Department of Anesthesiology and Critical Care Medicine, Johns Hopkins University School of Medicine, Phipps Suite 455, Baltimore, MD, 21287, USA.
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Cheang I, Zhu X, Zhu Q, Li M, Liao S, Zuo Z, Yao W, Zhou Y, Zhang H, Li X. Inverse association between blood ethylene oxide levels and obesity in the general population: NHANES 2013-2016. Front Endocrinol (Lausanne) 2022; 13:926971. [PMID: 36171904 PMCID: PMC9510609 DOI: 10.3389/fendo.2022.926971] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/23/2022] [Accepted: 08/18/2022] [Indexed: 11/21/2022] Open
Abstract
BACKGROUND Ethylene oxide (EO) has been shown to associate with increased cardiovascular risk. This study aimed to explore the relationship and its meditating factors between EO exposure and the major cardiovascular risk factor of obesity among the general adult population. METHODS Cross-sectional data of 3,220 participants from National Health and Nutritional Examination Survey (NHANES) 2013-2016 were enrolled. Obesity was defined as body mass index (BMI) ≥30 kg/m2, and abdominal obesity was defined as waist circumference (WC) ≥102 cm in men and ≥88 cm in women. The association among hemoglobin adduct of EO (HbEO), inflammatory biomarkers, and obesity was evaluated using restricted cubic splines and the multivariable linear regression model. Mediation analysis was used to further assess their association. RESULTS The increased quartiles of HbEO were inversely associated with BMI and WC [Q1 vs. Q4, BMI: β = -2.98 (-3.74, -2.22), WC: β = -6.50 (-8.60, -4.39); all p for trend < 0.05], and were inversely associated with obesity after full adjustment [obesity: OR = 0.43 (0.31, 0.58), abdominal obesity: OR = 0.42 (0.27, 0.65); all p for trend < 0.05]. The levels of alkaline phosphatase, white blood cells, lymphocytes, and neutrophils were also positively associated with BMI and WC (all p < 0.05). Mediation analysis showed that exposure of EO not only had a negative direct effect on BMI and WC, but also generated an inverse indirect effect. CONCLUSIONS Current findings showed an inverse association between HbEO and obesity, and suggested that systemic inflammation may not be their only mediator. Additional research is required to explore the underlying link of EO and system metabolism.
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Affiliation(s)
- Iokfai Cheang
- Department of Cardiology, The First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing, China
| | - Xu Zhu
- Department of Cardiology, The First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing, China
| | - Qingqing Zhu
- Department of Cardiology, The First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing, China
| | - Menghuan Li
- Department of Cardiology, The First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing, China
| | - Shengen Liao
- Department of Cardiology, The First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing, China
| | - Zhi Zuo
- Department of Cardiology, The First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing, China
| | - Wenming Yao
- Department of Cardiology, The First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing, China
| | - Yanli Zhou
- Department of Cardiology, The First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing, China
| | - Haifeng Zhang
- Department of Cardiology, The First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing, China
- Department of Cardiology, Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Suzhou, China
| | - Xinli Li
- Department of Cardiology, The First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing, China
- *Correspondence: Xinli Li,
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Vaidyanathan K, Wang C, Krajnik A, Yu Y, Choi M, Lin B, Jang J, Heo SJ, Kolega J, Lee K, Bae Y. A machine learning pipeline revealing heterogeneous responses to drug perturbations on vascular smooth muscle cell spheroid morphology and formation. Sci Rep 2021; 11:23285. [PMID: 34857846 PMCID: PMC8640073 DOI: 10.1038/s41598-021-02683-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2020] [Accepted: 11/22/2021] [Indexed: 02/06/2023] Open
Abstract
Machine learning approaches have shown great promise in biology and medicine discovering hidden information to further understand complex biological and pathological processes. In this study, we developed a deep learning-based machine learning algorithm to meaningfully process image data and facilitate studies in vascular biology and pathology. Vascular injury and atherosclerosis are characterized by neointima formation caused by the aberrant accumulation and proliferation of vascular smooth muscle cells (VSMCs) within the vessel wall. Understanding how to control VSMC behaviors would promote the development of therapeutic targets to treat vascular diseases. However, the response to drug treatments among VSMCs with the same diseased vascular condition is often heterogeneous. Here, to identify the heterogeneous responses of drug treatments, we created an in vitro experimental model system using VSMC spheroids and developed a machine learning-based computational method called HETEROID (heterogeneous spheroid). First, we established a VSMC spheroid model that mimics neointima-like formation and the structure of arteries. Then, to identify the morphological subpopulations of drug-treated VSMC spheroids, we used a machine learning framework that combines deep learning-based spheroid segmentation and morphological clustering analysis. Our machine learning approach successfully showed that FAK, Rac, Rho, and Cdc42 inhibitors differentially affect spheroid morphology, suggesting that multiple drug responses of VSMC spheroid formation exist. Overall, our HETEROID pipeline enables detailed quantitative drug characterization of morphological changes in neointima formation, that occurs in vivo, by single-spheroid analysis.
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Affiliation(s)
- Kalyanaraman Vaidyanathan
- Department of Pathology and Anatomical Sciences, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, 14203, USA
| | - Chuangqi Wang
- Department of Biomedical Engineering, Worcester Polytechnic Institute, Worcester, MA, 01609, USA
| | - Amanda Krajnik
- Department of Pathology and Anatomical Sciences, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, 14203, USA
| | - Yudong Yu
- Department of Biomedical Engineering, Worcester Polytechnic Institute, Worcester, MA, 01609, USA
| | - Moses Choi
- Department of Biomedical Engineering, Worcester Polytechnic Institute, Worcester, MA, 01609, USA
| | - Bolun Lin
- Department of Computer Science, Worcester Polytechnic Institute, Worcester, MA, 01609, USA
| | - Junbong Jang
- Vascular Biology Program, Boston Children's Hospital, Boston, MA, 02115, USA
| | - Su-Jin Heo
- Department of Orthopedic Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - John Kolega
- Department of Pathology and Anatomical Sciences, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, 14203, USA
| | - Kwonmoo Lee
- Department of Biomedical Engineering, Worcester Polytechnic Institute, Worcester, MA, 01609, USA.
- Vascular Biology Program, Boston Children's Hospital, Boston, MA, 02115, USA.
- Department of Surgery, Harvard Medical School, Boston, MA, 02115, USA.
| | - Yongho Bae
- Department of Pathology and Anatomical Sciences, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, 14203, USA.
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