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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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Liu R, Song L, Zhang C, Jiang L, Tian J, Xu L, Feng X, Wan L, Zhao X, Xu O, Li C, Gao R, Hui R, Zhao W, Yuan J. Implications of left atrial volume index in patients with three-vessel coronary disease: A 6.6-year follow-up cohort study. Chin Med J (Engl) 2024; 137:441-449. [PMID: 37262047 PMCID: PMC10876251 DOI: 10.1097/cm9.0000000000002723] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Indexed: 06/03/2023] Open
Abstract
BACKGROUND Risk assessment and treatment stratification for three-vessel coronary disease (TVD) remain challenging. This study aimed to investigate the prognostic value of left atrial volume index (LAVI) with the Synergy Between Percutaneous Coronary Intervention with Taxus and Cardiac Surgery (SYNTAX) score II, and its association with the long-term prognosis after three strategies (percutaneous coronary intervention [PCI], coronary artery bypass grafting [CABG], and medical therapy [MT]) in patients with TVD. METHODS This study was a post hoc analysis of a large, prospective cohort of patients with TVD in China, that aimed to determine the long-term outcomes after PCI, CABG, or optimal MT alone. A total of 8943 patients with TVD were consecutively enrolled between 2004 and 2011 at Fuwai Hospital. A total of 7818 patients with available baseline LAVI data were included in the study. Baseline, procedural, and follow-up data were collected. The primary endpoint was major adverse cardiac and cerebrovascular events (MACCE), which was a composite of all-cause death, myocardial infarction (MI), and stroke. Secondary endpoints included all-cause death, cardiac death, MI, revascularization, and stroke. Long-term outcomes were evaluated among LAVI quartile groups. RESULTS During a median follow-up of 6.6 years, a higher LAVI was strongly associated with increased risk of MACCE (Q3: hazard ratio [HR] 1.20, 95% confidence interval [CI] 1.06-1.37, P = 0.005; Q4: HR 1.85, 95%CI 1.64-2.09, P <0.001), all-cause death (Q3: HR 1.41, 95% CI 1.17-1.69, P <0.001; Q4: HR 2.54, 95%CI 2.16-3.00, P <0.001), and cardiac death (Q3: HR 1.81, 95% CI 1.39-2.37, P <0.001; Q4: HR 3.47, 95%CI 2.71-4.43, P <0.001). Moreover, LAVI significantly improved discrimination and reclassification of the SYNTAX score II. Notably, there was a significant interaction between LAVI quartiles and treatment strategies for MACCE. CABG was associated with lower risk of MACCE than MT alone, regardless of LAVI quartiles. Among patients in the fourth quartile, PCI was associated with significantly increased risk of cardiac death compared with CABG (HR: 5.25, 95% CI: 1.97-14.03, P = 0.001). CONCLUSIONS LAVI is a potential index for risk stratification and therapeutic decision-making in patients with three-vessel coronary disease. CABG is associated with improved long-term outcomes compared with MT alone, regardless of LAVI quartiles. When LAVI is severely elevated, PCI is associated with higher risk of cardiac death than CABG.
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Affiliation(s)
- Ru Liu
- Department of Cardiology, Fuwai Hospital, Chinese Academy of Medical Sciences, Beijing 100037, China
- Department of Respiratory and Pulmonary Vascular Disease, Fuwai Yunnan Cardiovascular Hospital, Kunming, Yunnan 650102, China
| | - Lei Song
- Department of Cardiology, Fuwai Hospital, Chinese Academy of Medical Sciences, Beijing 100037, China
| | - Ce Zhang
- Department of Cardiology, Fuwai Hospital, Chinese Academy of Medical Sciences, Beijing 100037, China
| | - Lin Jiang
- Department of Cardiology, Fuwai Hospital, Chinese Academy of Medical Sciences, Beijing 100037, China
| | - Jian Tian
- Department of Cardiology, Fuwai Hospital, Chinese Academy of Medical Sciences, Beijing 100037, China
| | - Lianjun Xu
- Department of Cardiology, Fuwai Hospital, Chinese Academy of Medical Sciences, Beijing 100037, China
| | - Xinxing Feng
- Department of Endocrinology, Fuwai Hospital, Chinese Academy of Medical Sciences, Beijing 100037, China
| | - Linyuan Wan
- Department of Echocardiography, Fuwai Hospital, Chinese Academy of Medical Sciences, Beijing 100037, China
| | - Xueyan Zhao
- Department of Cardiology, Fuwai Hospital, Chinese Academy of Medical Sciences, Beijing 100037, China
| | - Ou Xu
- Department of Respiratory and Pulmonary Vascular Disease, Fuwai Yunnan Cardiovascular Hospital, Kunming, Yunnan 650102, China
| | - Chongjian Li
- Department of Cardiology, Fuwai Hospital, Chinese Academy of Medical Sciences, Beijing 100037, China
| | - Runlin Gao
- Department of Cardiology, Fuwai Hospital, Chinese Academy of Medical Sciences, Beijing 100037, China
| | - Rutai Hui
- Department of Cardiology, Fuwai Hospital, Chinese Academy of Medical Sciences, Beijing 100037, China
| | - Wei Zhao
- Information Center, Fuwai Hospital, Chinese Academy of Medical Sciences, Beijing 100037, China
| | - Jinqing Yuan
- Department of Cardiology, Fuwai Hospital, Chinese Academy of Medical Sciences, Beijing 100037, China
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Li J, Xin Y, Li J, Meng M, Zhou L, Qiu H, Chen H, Li H. The predictive effect of direct-indirect bilirubin ratio on clinical events in acute coronary syndrome: results from an observational cohort study in north China. BMC Cardiovasc Disord 2022; 22:478. [DOI: 10.1186/s12872-022-02894-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2022] [Accepted: 10/11/2022] [Indexed: 11/12/2022] Open
Abstract
Abstract
Background:
Patients with extremely high-risk ASCVD usually suffered poor prognosis, bilirubin is considered closely related to cardiovascular outcomes. However, there is controversy over the relationship between bilirubin and coronary artery disease. This study aimed to evaluate the predictive value of the DIBIL ratio in patients with extremely high-risk ASCVD.
Methods:
10,260 consecutive patients with extremely high-risk ASCVD were enrolled in this study. All patients were divided into three groups according to their DIBIL ratio. The incidence of MACCEs was recorded, and in a competing risk regression, the incidence of MACCEs and their subgroups were recorded. The direct-indirect bilirubin ratio (DIBIL ratio) was calculated by the direct bilirubin (umol/L)/indirect bilirubin (umol/L) ratio, all laboratory values were obtained from the first fasting blood samples during hospitalization.
Results:
The area under the ROC curve of the DIBIL ratio to predict the occurrence of all-cause death was 0.668, the cut-off value of which is 0.275. Competing risk regression indicated that DIBIL ratio was positively correlated with all-cause death [1.829 (1.405–2.381), p < 0.001], CV death [1.600 (1.103, 2.321), p = 0.013]. The addition of DIBIL ratio to a baseline risk model had an incremental effect on the predictive value for all-cause death [IDI 0.004(0, 0.010), p < 0.001; C-index 0.805(0.783–0.827), p < 0.001].
Conclusion:
The DIBIL ratio was an excellent tool to predict poor prognosis, suggesting that this index may be developed as a biomarker for risk stratification and prognosis in extremely ASCVD patients.
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