BiomaRkers for AnorexIa NErvosa and autism spectrum Disorders- longitudinal study

确定不同因素对神经性厌食症的发展和持续性质的个体贡献存在独特的挑战。长期以来，人们一直认为自闭症合并症可能会使情况进一步复杂化，并加剧这种疾病的破坏性影响。因此，采取科学和理论驱动的方法，我们将随访患有厌食症的年轻女性，她们接受了厌食症治疗，以及从未患有饮食失调的年轻女性，她们以前参加过我们资助的MRC/MRF研究。第一次见到这些年轻女性时，我们进行了各种各样的测试、问卷调查、访谈和核磁共振扫描。这些不同形式的研究技术使我们能够检查他们如何思考和反应社会图像和情况（如看脸或讲故事），并看看他们的疾病和自闭症症状的水平是否会影响他们的反应。在这项拟议的进一步研究中，我们将在两年后再次见到这些年轻女性，并进行类似的测试和采访。这将使我们能够看到临床特征，他们的答案和反应如何随着时间的推移而变化。我们还将能够检查他们大脑和神经通路区域随时间的变化，这些区域用于在这些任务中执行不同类型的问题解决。然后，我们将使用机器学习，一套先进的计算和统计技术，开发一个模型，预测从厌食症恢复的可能性和时间。由于我们的目标是再次看到第一项研究的所有参与者，而不仅仅是患者和康复组，我们的研究结果将具有内在的鲁棒性，因为我们将能够比较健康对照组与患者和康复年轻女性的发展轨迹。这意味着我们将能够检查神经性厌食症、自闭症症状和衰老的独立但相互关联的影响，并能够控制疾病影响的正常发育变化。使用这种长期方法来观察参与者是开创性的，因为它将帮助我们将该领域推向对疾病途径和共病的终身视角的理解，以新颖的方式进行测试和分析，利用快速发展的大数据和机器学习世界的见解。这将使我们能够获得新的见解，有可能确定疾病发展和维持的潜在机制。这些知识，与领先的专家和决策者合作，有可能制定有针对性的干预措施，可以在国家和国际上实施，为减少神经性厌食症的耻辱和羞耻铺平道路，并使人们认识到它与自闭症谱系疾病的相互作用。

There are unique challenges to identify the individual contributions of different factors on the development and ongoing nature of anorexia nervosa. It has long been theorised that autism comorbidity may further complicate the picture and exacerbate the devastating impact of the illness. Therefore, taking a science- and theory-driven approach, we will follow up young women with anorexia, who were treated for anorexia, and young women who never had an eating disorder, who have previously participated in our funded study from MRC/MRF. The first time we saw these young women, we carried out a wide variety of tests, questionnaires, interviews, and MRI scans. These different forms of research techniques allowed us to examine how they think and react to social images and situations (such as looking at faces or storytelling), and to look at whether their illness and levels of autism symptoms had an impact on their responses.In this proposed further research, we will see these young women again two years later and carry out similar testing and interviews. This will allow us to look at clinical features, how their answers and reactions have changed over time. We will also be able to examine changes over time in the areas of their brains and neural pathways which are used to carry out different types of problem solving in these tasks. We are then going to use machine learning, a set of advanced computing and statistical techniques, to develop a model which predicts likelihood and length of recovery from anorexia. As we are aiming to see all participants from the first study again, rather than only the patient and recovered groups, our findings will be inherently robust because we will be able to compare between the developmental trajectories of the healthy controls and the patient and recovered young women. This means that we will be able to examine the independent, but interlinked, effects of anorexia nervosa, autism symptoms, and aging, with the ability to control for normal developmental changes alongside the impact of illness.Using this longer-term approach to seeing participants is groundbreaking, as it will help us to move the field towards an understanding of the lifelong perspective on illness pathways and co-morbidities, tested and analysed in novel ways which harness insights from the rapidly-advancing world of big data and machine learning. This will enable us to gain new insights, potentially identifying underlying mechanisms which underwrite illness development and maintenance. That knowledge, in partnership with leading experts and policymakers, has the potential to develop targeted interventions which can be implemented nationally and internationally paving the way to reducing stigma and shame around anorexia nervosa and creating recognition of its interaction with autism spectrum conditions.