Unified probabilistic latent variable modelling strategies to accelerate endotype discovery in longitudinal studies

在过去的5年里，可以获得的临床、遗传和生物数据呈指数级增长。然而，这种数据爆炸并没有伴随着分析这些数据并理解如何将这些数据转化为治愈疾病的解决方案的平行能力。因此，我们需要探索统计模型，使我们能够在这一‘大数据’中识别有意义的模式，以便我们能够了解人类疾病的本质。最近的新闻头条指出，有必要进行一场“全球医学革命”，重新思考我们理解疾病的方式，以便我们走向更个性化的医学策略，将患者的基因和生物学特征知识纳入其中。这是一个令人振奋的医学研究时代，我们正在走向使用统计建模技术，将对人类遗传学和免疫学的科学理解转化为对疾病病因的更好理解，从而改善全球社区内个人的健康。要做到这一点，需要一种真正的多学科方法来结合临床知识和数学计算专业知识，以便我们能够了解不同疾病的根本原因，这将使我们能够改进我们的战略，以确定正确的治疗方法。丰富的遗传和生物信息使人们认识到，需要按照我们今天对疾病的理解对疾病进行重新分类。哮喘就是这样一种疾病。哮喘和儿童时期最常见的慢性疾病一样，在最近的新闻中占据了重要地位，这种疾病每年导致许多人入院治疗，以及许多可预防的死亡。在过去的30年里，患有哮喘的儿童数量急剧增加。目前还不清楚为什么一些人会患哮喘，而另一些人不会。环境中的许多因素可能会导致哮喘的发生(例如饮食、免疫、抗生素、宠物和烟草烟雾)，但我们不知道如何改变环境来降低风险。我们很难理解哮喘的原因之一是，我们曾经认为的单一疾病可能是导致相似症状的不同疾病的集合。研究这些不同的“哮喘”和过敏性疾病的一个有希望的方法是使用统计机器学习，它允许我们创建识别疾病如何随着时间变化的模式的模型。由于大多数哮喘是从童年开始的，我将在这个项目中使用的数据着眼于儿童从出生开始的各种临床和生物学测量。这些儿童分布在英国的不同地区，大约每2-3年从他们那里收集数据，以了解他们的模式如何随着时间的推移而变化，以及通常被归类为“哮喘患者”的儿童在模式上的差异是否可以让我们识别不同的哮喘亚型或“内型”。建立对内型发现适当复杂的模型，使我们能够纳入更复杂的数据集，包括分子和生物数据，这些数据告诉我们，随着时间的推移，这些儿童如何对病毒和细菌做出反应。数据和这些模型的复杂性需要反映疾病的复杂性。如果我们构建能够捕捉和预测随时间变化的最佳模型，这些技术也可以在全球范围内应用，可以用来更好地识别将从不同治疗方案中受益的儿童。希望更精确的内型发现将导致对疾病原因和本质的更好理解，从而导致更有针对性的治疗和管理策略。这种利用统计科学通过纳入随着时间推移收集的大量数据来了解疾病的方法不仅适用于哮喘和过敏，而且适用于其他疾病。

The past 5 years has seen an exponential increase in the amount of clinical, genetic and biological data that has become available. However, this data explosion has not been accompanied by a parallel capacity to analyse such data and understand how this data can be translated into solutions for curing diseases. We therefore need to explore statistical models which allow us to identify meaningful patterns in this 'big data' so that we can understand the nature of human diseases. Recent news headlines have identified the need for a "global medical revolution" to rethink the way we understand disease so that we move towards more personalised medicine strategies which incorporate knowledge from a patient's genetic and biological profile. This is an exciting era of medical research where we are moving towards using statistical modelling techniques to translate scientific understanding of human genetics and immunology into a better understanding of disease aetiology and as a consequence improve the health of individuals within the global community. To achieve this requires a truly multi-disciplinary approach to combining clinical knowledge and mathematical-computational expertise so that we can understand the underlying causes of different diseases which will allow us to improve our strategies for identifying the correct treatment. The abundance of genetic and biological information has led to the recognition that there needs to be a reclassification of diseases as we understand them today. One such disease is asthma. Asthma has featured in the recent news as is the most common chronic disease of childhood which causes many hospital admissions each year and many preventable deaths. The number of children suffering from asthma has increased dramatically over the past 30 years. It is unclear why some people get asthma and others do not. Many factors in the environment may contribute to the development of asthma (for example diet, immunizations, antibiotics, pets and tobacco smoke) but we don't know how to modify the environment to reduce the risks. One reason for the difficulty in understanding causes of asthma is that what we once thought to be a single condition may be a collection of different diseases which cause similar symptoms. A promising approach to investigating these different "asthmas" and allergic diseases is the use of statistical machine learning which allows us to create models which recognise patterns of how disease changes over time. Since most asthma is initiated from childhood, the data I would use for this project looks at various clinical and biological measurements from children starting at birth. These children are in different parts of the UK and data is collected from them approximately every 2-3 years to see how their patterns change over time and whether differences in patterns among children who would generally be classified as "asthmatics" allow us to identify different asthma subtypes or "endotypes". Building models which are appropriately complex for endotype discovery allows us to incorporate more complex datasets including molecular and biological data which tell us how these children respond to viruses and bacteria over time. The complexity of the data and these models needs to reflect the complexity of the diseases. If we construct optimal models which are able to capture and predict change over time, these techniques could also be applied on a global scale that can be used to better identify children that would benefit from different treatment regimens. It is hoped that more refined endotype discovery will lead to a better understanding of the causes and nature of disease and therefore lead to more targeted treatment and management strategies. This approach of using statistical science to inform our understanding of disease by incorporating a large scale of data which is collected over time is applicable not just to asthma and allergy, but is generalizable to other diseases.