Decrypting the genetic architecture of heart failure imaging signatures through machine learning in large-scale genome-wide association studies

在大规模全基因组关联研究中通过机器学习解密心力衰竭成像特征的遗传结构

• 批准号: MR/X020924/1
• 负责人: Nay Aung
• 金额: $ 196.52万
• 依托单位: Queen Mary University of London
• 依托单位国家: 英国
• 项目类别: Fellowship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FX020924%2F1
• 关键词: Decrypting; genetic; architecture; heart; failure

Heart failure is a common medical condition in which the heart does not efficiently pump blood around the body. It frequently leads to disabling symptoms and early death. Detection of abnormal changes in the heart structure and function by imaging aids heart failure diagnosis and can predict poor outcomes. Genetics determine variation in the heart structure and function as supported by the heritability assessment in family studies. Prior research into the genetic influence of heart structure and function focused on conventional heart measurements such as the heart size or overall pump function in a relatively modest number of mostly European (White) individuals. These research studies found a limited number of genetic loci (regions in the genome) explaining a small proportion of observed heritability in a population. This study will assemble one of the largest datasets containing heart imaging, electrocardiogram (ECG, a simple test to check heart rhythm), detailed lifestyle information and health outcomes in up to 888,000 individuals with diverse racial backgrounds. The large dataset will permit the development of reliable artificial intelligence (AI) techniques to automatically extract or estimate heart measurements from imaging and ECG. Conventional imaging metrics such as the volume of the heart chambers and the pump function as well as more advanced measures such as the pressure changes in the heart, which could be a more sensitive marker of a failing heart, will be developed. Using these imaging measurements along with individual genetic data, computational experiments will be carried out to discover the genetic markers which could provide additional knowledge on the origin and causes of HF and assist in the development of new medications which are urgently needed for certain types of heart failure. The results will also allow evaluation of the cause-and-effect relationships between cardiovascular risk factors such as obesity and high blood pressure and abnormal changes in the heart and eventual development of heart failure using a technique called "Mendelian Randomisation". This information will help in crafting targeted public health and preventive strategies. Lastly, with the data on genetic associations, we will build the genetic risk scores for each individual and test if they can predict heart failure development and perform well across different ethnic groups. If successful, this approach will transform the future management of heart failure by allowing doctors to provide a personalised assessment of each person's lifetime susceptibility to heart failure which will in turn permit early diagnosis, tailored lifestyle advice and pre-emptive medical treatment.

心力衰竭是一种常见的医学病症，心脏不能有效地将血液泵入全身。它经常导致致残症状和早期死亡。通过影像学检测心脏结构和功能的异常变化有助于心衰诊断，并可预测不良预后。遗传决定心脏结构和功能的变异，这在家庭研究中的遗传力评估中得到了支持。先前对心脏结构和功能的遗传影响的研究主要集中在传统的心脏测量上，如心脏大小或整体泵功能，主要针对相对较少的欧洲（白人）个体。这些研究发现了数量有限的遗传位点（基因组中的区域），解释了一个群体中观察到的遗传率的一小部分。这项研究将汇集最大的数据集之一，其中包括多达888,000名不同种族背景的人的心脏成像、心电图（ECG，一种检查心律的简单测试）、详细的生活方式信息和健康结果。大型数据集将允许开发可靠的人工智能（AI）技术，从成像和心电图中自动提取或估计心脏测量值。传统的成像指标，如心室容积和泵功能，以及更先进的措施，如心脏压力变化，这可能是一个更敏感的标志，心脏衰竭，将被开发出来。利用这些成像测量和个体遗传数据，将进行计算实验，以发现遗传标记，这些标记可以提供有关HF起源和原因的额外知识，并有助于开发某些类型心力衰竭急需的新药物。研究结果还将利用一种名为“孟德尔随机化”的技术，评估心血管风险因素（如肥胖和高血压）与心脏异常变化以及最终发展为心力衰竭之间的因果关系。这些信息将有助于制定有针对性的公共卫生和预防战略。最后，根据遗传关联的数据，我们将为每个人建立遗传风险评分，并测试他们是否可以预测心力衰竭的发展，并在不同的种族群体中表现良好。如果成功，这种方法将改变未来对心力衰竭的管理，允许医生对每个人一生中对心力衰竭的易感性进行个性化评估，从而实现早期诊断、量身定制的生活方式建议和先发制人的医疗治疗。