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

在过去的5年里，临床、遗传和生物数据的数量呈指数级增长。然而，伴随这种数据爆炸而来的并不是分析这些数据和了解如何将这些数据转化为治疗疾病的解决方案的并行能力。因此，我们需要探索统计模型，使我们能够在这些“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。最近的新闻标题已经确定需要一场“全球医学革命”来重新思考我们理解疾病的方式，以便我们走向更加个性化的医疗策略，其中包括来自患者遗传和生物学概况的知识。这是一个令人兴奋的医学研究时代，我们正在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学的更好理解，从而改善全球社区中个人的健康。要实现这一目标，需要一种真正的多学科方法，将临床知识和数学计算专业知识结合起来，以便我们能够了解不同疾病的潜在原因，从而使我们能够改进我们的策略，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，有必要对我们今天所理解的疾病进行重新分类。哮喘就是这样一种疾病。哮喘作为儿童最常见的慢性疾病在最近的新闻中占有重要地位，每年导致许多人住院，许多可预防的死亡。在过去的30年里，患哮喘的儿童人数急剧增加。目前还不清楚为什么有些人会得哮喘，而有些人不会。环境中的许多因素可能导致哮喘的发展（例如饮食、免疫接种、抗生素、宠物和吸烟），但我们不知道如何改变环境来降低风险。难以理解哮喘病因的一个原因是，我们曾经认为的单一病症可能是引起类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前途的方法是使用统计机器学习，它使我们能够创建识别疾病随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是从出生开始的各种临床和生物学测量数据。这些儿童分布在英国的不同地区，大约每2-3年收集一次数据，看看他们的模式是如何随着时间的推移而变化的，以及通常被归类为“哮喘患者”的儿童的模式差异是否允许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，包括分子和生物学数据，这些数据告诉我们这些儿童如何随着时间的推移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。如果我们能够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。希望更精细的内型发现将导致更好地了解疾病的原因和性质，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，结合长期收集的大量数据，来告知我们对疾病的理解的方法，不仅适用于哮喘和过敏，也适用于其他疾病。

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.