Statistical modelling to characterise the dietary determinants of health outcomes in mothers and their children.

目前，当科学家分析饮食数据时，他们寻找一个时间点的饮食摄入量与当时的健康或晚年健康之间的关系。虽然这可能是有用的，并且是一种为许多与营养有关的公共卫生战略提供信息的方法，但最近统计科学的发展应该有助于提供更深入的见解，通过观察长期以来饮食数据的重复测量。这个奖项的目的是为这种饮食和健康的创新方法开发方法。目前我在伦敦国王学院（KCL）工作，我将与南安普顿大学的专家一起工作一年，他们将提供完成这项工作所需的不同统计方法的培训，我也将参加外部培训课程。我特别感兴趣的是母亲在怀孕期间的饮食之间的关系，以及这如何影响发育中的婴儿，从而影响婴儿在童年时期患肥胖症的风险。在这段培训期间，我将使用已经从英国妇女及其子女的两项研究中获得的数据来测试所学到的方法。这些信息包括孕期、婴儿期和儿童期的饮食信息。他们还测量了婴儿和儿童时期的“肥胖”程度。这些数据集是独一无二的，因为它们包含了关于食物摄入和身体脂肪测量的非常详细的信息，并提供了一个很好的训练资源。这项工作的一个独特之处在于，我们还对其中一项研究中妇女的血液进行了营养健康的生物学测量，这些研究是在怀孕的不同时期进行的。这将增加额外的细节，并帮助我弄清楚母亲的饮食如何直接影响发育中的婴儿，以增加或减少孩子肥胖的风险。该项目是营养科学领域的首个此类项目，有可能为该领域的新型创新分析技术铺平道路。此外，它将提供的结果可转化为侧重于孕产妇饮食和儿童肥胖负担的公共卫生政策。

Currently when scientists analyse dietary data, they look for relationships between dietary intake at one time point and either the health of a person at that time, or their health in later life. Although this can be useful, and is an approach that has informed many nutrition-related public health strategies, recent developments in statistical sciences are available that should help provide greater insights, by looking at repeated measurements of dietary data over time. The aim of this award is to develop methodologies for this innovative approach to diet and health.Currently based at King's College London (KCL), I will work for one year with experts at the University of Southampton who will be providing training in the different statistical methods needed to do this work and I will also attend external training courses. My particular interest is in the relationship between mothers' diet in pregnancy and how this may affect the developing baby in a way that influences the risk of the baby developing obesity in childhood. During this period of training, I will test out the methods learnt using data that are already available from two studies of women and their children in the UK. These have information on diet across pregnancy, infancy and into childhood. They also have measures of 'fatness' in infancy and childhood. The datasets are unique, as they comprise very detailed information about food intake and measures of body fat, and provide an excellent training resource. A unique element of this work is that we also have biological measures of nutritional health evaluated in the blood of the women from one of the studies, taken at different times in pregnancy. This will add an extra level of detail and help me to work out how diet in the mother may directly affect the developing baby, to increase or reduce the risk of obesity in the child. This project is the first of its kind within nutritional sciences and has potential to pave the way for new and innovative analytical techniques within the field. In addition, it will provide results that could translate into public health policies focused on maternal diet and the burden of childhood obesity.