Molecular Resolution of Diabesity in Children

儿童肥胖症在生命早期发展，并且已经伴随着新出现的合并症。这些临床观察结果在生物学上是由脂肪组织的过度积累和功能障碍引起的。这种脂肪组织功能障碍导致肥胖症的发展和随后的（前）糖尿病（“糖尿病”）已经在儿童中。解开这些关联背后的分子网络，不仅有希望确定导致糖尿病的分子和生物学机制，而且进一步发展基于病因学的策略，用于预测和干预肥胖和随后的合并症。为了实现这一目标，我们需要更好地了解疾病发病机制和进展的分子驱动因素。通过我们的儿童肥胖队列，我们有一个难得的机会直接访问和研究关键的相关组织之一：新出现疾病不同阶段的脂肪组织。我们项目的首要目标是促进我们对儿童肥胖和随之而来的糖尿病的分子机制的理解的一步变化。我们将部署一种综合方法来识别儿童脂肪组织的分子特征，并了解驱动代谢健康和疾病的生物过程，以加速发现潜在的干预目标。为此，我们将首次以这种规模和深度将多组学应用于来自儿童的相关组织，以确定效应基因及其作用机制，并评估分子性状与良好表征的临床和生物学表型的联系。通过整合多层分子谱分析（基因表达、甲基化、遗传变异）和表型，并将其与包括机器学习方法在内的大数据分析相结合，我们能够区分疾病相关过程的原因和后果。鉴定的候选基因将立即被进一步评估其在体外和体内模型中与脂肪组织（dys）功能的功能机制相关性，以及在我们的大型儿童队列中预测肥胖和糖尿病的临床相关性。因此，这种将脂肪组织中的多个互补分子标记与儿童脂肪组织功能障碍和糖尿病的生物学和临床表型联系起来的综合方法，将有助于确定脂肪组织中的分子机制，并了解驱动代谢健康和疾病的生物过程，对开发精确风险预测和干预工具具有潜在影响。

Childhood obesity develops early in life and is already accompanied by emerging comorbidities. These clinical observations are paralleled biologically by excess accumulation and dysfunction of adipose tissue. This adipose tissue dysfunction contributes to development of obesity and ensuing (pre)diabetes ("diabesity") already in children. Disentangling the molecular networks underlying these associations has the promise not only to identify the molecular and biological mechanisms that are causal in driving diabesity, but further on to develop etiology based strategies for prediction and intervention of obesity and ensuing comorbidities.To achieve this, we need a better understanding of the molecular drivers of disease pathogenesis and progression. With our childhood obesity cohorts, we have a rare opportunity to directly access and study one of the key relevant tissues: adipose tissue in different stages of emerging disease. The overarching aim of our project is to catalyse a step-change in our understanding of the molecular mechanisms underpinning obesity and ensuing diabetes in children. We will deploy an integrated approach to identify molecular signatures from adipose tissue of children and understand the biological processes driving metabolic health and disease, in order to accelerate the discovery of potential targets for intervention. For this, we will apply multi-omics for the first time to relevant tissue derived from children at this scale and depth, in order to identify effector genes and their mechanism of action and assess the link of molecular traits with well-characterized clinical and biological phenotypes. By integrating multiple layers of molecular profile assays (gene expression, methylome, genetic variation) and phenotypes, and coupling those to big data analytics including machine learning approaches, we are able to distinguish between cause and consequence of disease-relevant processes. Identified candidate genes will immediately be further assessed for their functional mechanistic relevance for adipose tissue (dys)function in in vitro and in vivo models as well as clinical relevance for predicting obesity and diabetes in our large childhood cohorts.Hence, this integrated approach linking multiple complementary molecular signatures in adipose tissue with biological and clinical phenotypes of adipose tissue dysfunction and diabesity in children, will help to identify molecular mechanisms in adipose tissue and understand the biological processes driving metabolic health and disease with potential implications for developing tools for precision risk prediction and intervention.