Cardiac and vascular disease prevention after hypertensive pregnancy: insights from AI-derived, multi-organ, hypertensive disease progression models

怀孕期间出现血压问题的女性在以后的生活中更有可能患高血压、心脏病或中风。孕妇所生的孩子也往往有较高的血压，并且更有可能在自己怀孕期间出现问题。我们的工作表明，儿童和母亲的血管、心脏和大脑发生了变化，这些变化可以在他们患上高血压或遭受严重并发症之前很久就被发现。我们认为这些身体上的变化在他们的一生中是缓慢发展的，这些变化的进展会增加他们以后患病的风险。通过了解一生中身体多个部位的变化模式，我们认为我们可以确定一个人的潜在疾病有多严重，以及他们的疾病在未来几年可能会如何发展。此外，由于身体不同部位的变化速度可能不同，身体某个部位的变化可能会导致其他部位的疾病发展，我们也可以利用这些信息来决定最佳治疗方法。为了减缓某一特定领域的疾病发展，可能需要采取某些治疗措施，而干预措施的选择可能需要在生命和疾病的不同阶段有所改变。为了验证这些想法，我们将使用我们多年来获得的大型数据集，这些数据集是基于对高血压妊娠后妇女和儿童的成像研究。这些数据包括心脏、大脑和血管等几个重要器官的结构和功能信息。我们还将通过对老年妇女、在不同环境中长大的妇女和年轻人进行额外的研究来扩展这些数据集。在所有这些研究中，同样的方法被用于成像，我们将协调所有的数据集，这样我们就可以研究不同器官的早期潜在疾病进展如何随着她们的怀孕史、年龄和目前的健康状况而变化。最初的分析将集中于评估特定器官的变化，但我们也想知道这种模式是如何在全身出现的。要做到这一点，我们需要同时结合来自许多不同措施的信息，并使用人工智能（AI）的一些最新进展来分析我们研究中收集的数据，以及英国境内其他大型研究。当一个人从“健康”状态发展到“疾病”状态时，计算机将学习器官变化的多维模式。从这些信息中，我们将发现高血压疾病发展的独特模式，并希望为每个人量身定制更好的干预和治疗打开大门。例如，可以根据复杂成像标记的特定组合来确定疾病的特定阶段。由此，我们可以使用计算机来学习最接近更复杂模式的简单测量方法的组合，以生成可用于任何医院或社区的工具，以管理和跟踪单个患者的疾病发展。在本工作方案结束时，我们将了解患有高血压妊娠的母亲及其子女在其疾病的整个生命过程中身体是如何变化的。通过人工智能技术增强的这一知识有可能导致在临床中使用的简单测试，以确定个人的疾病阶段。然后，这些测试的结果可以就最佳预防干预（例如生活方式的选择）或保护妇女或儿童免受疾病发展的治疗策略提供建议。

Women who develop blood pressure problems during pregnancy are more likely to have high blood pressure in later life as well as heart attacks or strokes. The children born to the pregnancy also tend to have higher blood pressure and are more likely to have problems during their own pregnancies. Our work has shown that the children and mothers have changes in their blood vessels, heart and brain that can be identified long before they develop high blood pressure or suffer the severe complications. We think these changes in the body develop slowly throughout their life and the progression of these changes is establishing their risk for later disease. By understanding the pattern of changes across multiple parts of the body, over a lifetime, we think we can identify how advanced the underlying disease is for an individual and how their disease is likely to develop over the next few years. Furthermore, as the rate of change is likely to differ between parts of the body, and a change in one area of the body could drive development of disease elsewhere, we can also use this information to decide on optimal treatments. Certain treatments may be required to slow down the development of disease in a particular area and the selection of interventions may need to change at different stages of life and disease.To test these ideas, we are going use large datasets we have acquired over the years based on imaging studies of women and children after a hypertensive pregnancy. The data includes information on the structure and function of several important organs such as the heart, brain and blood vessels. We will also expand these datasets by undertaking additional studies in older women, women who have grown up in different environments and in young adults. In all these studies, the same approaches have been used for the imaging and we will harmonise all the datasets so that we can study how this early underlying disease progression in different organs varies with their pregnancy history, age and their current health. The initial analysis will focus on assessing changes in specific organs, but we also want to know how the patterns emerge across the whole body. To do this we will need to combine information from many different measures at the same time and use some of the latest advances in artificial intelligence (AI) to analyse the data collected in our studies, as well as other large studies of people within the UK. The computer will learn the multi-dimensional patterns of changes to the organs that occur as someone progresses from 'health' to a 'disease' state. From this information we will discover the unique patterns of hypertensive disease development, and with that hope to open the door to better interventions and therapies tailored to each person. For example, specific stages of disease could be identified based on a particular combination of complex imaging markers. From this, we could then use the computer to learn the combination of simple measures that best approximate to the more complex pattern to generate tools that can be used in any hospital or community to manage and track disease development for an individual patient. At the end of this programme of work we will understand how the body changes in mothers who have a hypertensive pregnancy, and their children, over the life course of their disease. This knowledge, enhanced by AI technology, has the potential to lead to simple tests that can be used in the clinic to determine the stage of disease for an individual. The results of these tests could then advise on the best preventive intervention (e.g. lifestyle choice) or treatment strategy that protect the woman or child from developing disease.