Mentoring and Research to ClassifyAtrial Cardiomyopathy

这项K24资助将为PI Lin Yee Chen博士提供一项职业中期NIH资助的以患者为导向的研究（POR） 调查员，有保护的时间和支持，需要（1）加快他目前的辅导轨迹 在心血管（CV）科学中进行POR的初级临床医生和研究者，（2）获得 在指导方法，机器学习和组学科学方面的额外培训，（3）促进他目前的 旨在确定异常心房基质-心房肌细胞关系的潜在机制的研究 心肌病和房颤（AF）到缺血性卒中和其他CV结局。在他的AF学员 临床研究小组将从美国国立卫生研究院资助的T32培训项目中招募， 明尼苏达州心脏病学分部、流行病学分部和生物统计学分部; 医学医师科学家培训计划; CTSI的KL 2和TL 1计划;以及研究生课程 (MS临床研究，公共卫生硕士和博士）在公共卫生学院。为了职业发展，博士。 Chen将磨练他的指导技能，并学习前沿领域（机器学习和组学）的新技能 科学）通过集中的研究，选定的课程，研讨会，并从高级合作者的指导， 专业领域。最后，这笔赠款将支持一个基于动脉粥样硬化风险的研究项目， 社区（ARIC）研究，该研究扩展了陈博士正在进行的工作，以阐明心房肌的作用。 心肌病在驱动AF相关结果中的作用。具体目的是：（1）鉴别心房性心肌病 通过使用机器学习方法分析ARIC访视5（2011-13）的大量数据， 临床、2D超声心动图、心率变异性、动脉硬度、ECG和多组学数据，（2）评价 亚型与缺血性卒中和其他CV结局的相关性，以及（3）发现以下疾病的特定风险因素： 通过分析访视1-4（1987-98）时收集的风险因素指标，确定亚型。至关重要的是，调查结果将是 在2个独立的基于社区的队列中验证：心血管健康研究（CHS）和多种族 动脉粥样硬化研究（梅萨）。该项目具有重大影响：（1）K24赠款将提供PI 有受保护的时间来加强他目前对参与POR的学员的指导。通过组建一个 高级合作者，包括指导，数据科学和分子流行病学专家，PI 提供了一个优秀的平台，让他的学员获得尖端技能的POR，（2）通过解决 心房心肌病的异质性，该团队将推进NIH的精准医学计划， 基于生物学基础为患者提供个性化的当前治疗，（3）研究结果将导致 AF患者卒中风险分类有临床意义的改善，这将改善患者 结果，（4）通过有效地利用深度表型NHLBI队列的现有资源，该项目将 填补预防和治疗方面的关键知识空白，从而对人类产生持续而强大的影响 简历公共卫生，临床实践和POR下一代研究人员的教育。

This K24 grant will provide the PI, Dr. Lin Yee Chen, a midcareer NIH-funded patient-oriented research (POR) investigator, with the protected time and support needed to (1) accelerate his current trajectory in mentoring junior clinicians and investigators who are conducting POR in cardiovascular (CV) science, (2) acquire additional training in mentoring methods, machine learning, and omics science, (3) promote his current research that aims to define the mechanisms underlying the relationship of the abnormal atrial substrate—atrial cardiomyopathy—and atrial fibrillation (AF) to ischemic stroke and other CV outcomes. Trainees in his AF Clinical Research Group will be recruited from the NIH-funded T32 training programs in the University of Minnesota's Division of Cardiology, Division of Epidemiology, and Division of Biostatistics; Department of Medicine Physician Scientist Training Program; KL2 and TL1 Programs of the CTSI; and graduate programs (MS in Clinical Research, MPH, and PhD) in the School of Public Health. For his career development, Dr. Chen will hone his mentoring skills and learn new skills in cutting-edge areas (machine learning and omics science) through focused study, selected coursework, seminars, and guidance from senior collaborators with domain expertise. Finally, this grant will support a research project that is based on the Atherosclerosis Risk in Communities (ARIC) Study, which extends Dr. Chen’s ongoing work to elucidate the role of atrial cardiomyopathy in driving AF-related outcomes. The specific aims are: (1) Identify atrial cardiomyopathy subtypes by using machine learning approaches to analyze the extensive data at ARIC Visit 5 (2011-13): clinical, 2D-echocardiographic, heart rate variability, arterial stiffness, ECG, and multi-omics data, (2) Evaluate association of subtypes with ischemic stroke and other CV outcomes, and (3) Discover specific risk factors for subtypes by analyzing risk factor measures collected at Visits 1-4 (1987-98). Crucially, findings will be validated in 2 independent community-based cohorts: Cardiovascular Health Study (CHS) and Multi-Ethnic Study of Atherosclerosis (MESA). This project has significant impact: (1) This K24 grant will provide the PI with the protected time to enhance his current mentoring of trainees involved in POR. By assembling a team of senior collaborators that comprise experts in mentoring, data science, and molecular epidemiology, the PI provides an outstanding platform for his mentees to acquire cutting-edge skills in POR, (2) By resolving heterogeneity in atrial cardiomyopathy, the team will advance the NIH’s Precision Medicine initiative by personalizing current treatment for patients based on biological underpinnings, (3) The findings will lead to a clinically meaningful improvement in classification of stroke risk in patients with AF, which will improve patient outcomes, (4) By efficiently leveraging existing resources of deeply phenotyped NHLBI cohorts, this project will fill critical knowledge gaps in prevention and treatment, thus achieving a sustained and powerful impact on CV public health, clinical practice, and education of the next generation of researchers in POR.