基于出生队列研究代谢产物对妊娠期糖尿病发生风险的预测和预警

Gestational Diabetes Mellitus (GDM) is a common complication during pregnancy, and significantly affects the health of the pregnant women and their offspring. Therefore, early screening is important for physicians to identify high-risk women and implement targeted regimens to prevent the occurrence of GDM and decrease the adverse pregnancy outcomes. The current prediction models mostly include traditional risk factors and do not perform well in certain populations. Furthermore, metabolomics, as an important component of systems biology, has been widely used to identify novel metabolites for the development of type 2 diabetes in different populations. But few studies have used metabolomics in GDM research area. Thus, we propose to utilize both targeted and untargeted metabolomics in a nested case-control study within two birth cohort studies in Wuhan and Zhuhai. We will randomly select 400 GDM cases from two cohorts and 400 matched controls, and.measure serum metabolites in the first trimester to predict the occurrence of GDM in the late second trimester. We will also integrate data from genetics (selected diabetes risk SNPs), environmental factors and traditional biomarkers to predict GDM risk. Metabolomics study will also enable us to explore the potential biological mechanisms underlying the development of GDM. Our study will be the largest so far to utilize metabolomics approach in the investigation of GDM in Chinese women, and replication in the two cohorts will reduce the false positive signals and enhance the reliability of our results. In conclusion, using novel metabolomics study in two birth cohorts in Chinese women will be helpful for us to identify high-risk pregnant women of GDM and implement personal prevention and interventions to reduce the risk of GDM and its consequences.

妊娠期糖尿病（Gestational Diabetes Mellitus，GDM）是妊娠期主要合并症，既严重影响到孕妇和胎儿的健康，也显著增加不良妊娠结局的发生风险。因此，及早筛查GDM高危人群并采取一级预防和个体化干预措施，对于降低GDM发病率和改善妊娠结局至关重要。但目前GDM的发病机制仍不清楚，且基于传统危险因素构建的GDM风险模型预测能力有限。代谢组学通过对机体代谢产物的全面定性定量分析，为疾病的机制研究和风险预测提供新的思路。因此，本项目基于武汉和珠海地区的两个出生队列，采用前瞻性的巢式病例对照研究，选取400个GDM病例和400个对照，利用定向和非定向的代谢组学检测，重点阐明孕早期血液代谢小分子对GDM发生的作用。在此基础上，纳入新发现的代谢小分子与传统的环境和遗传危险因素，构建适合中国人群的GDM风险预测模型，为GDM高危人群筛检和防治措施的制订提供科学依据。

妊娠期糖尿病（Gestational Diabetes Mellitus，GDM）是妊娠期主要合并症之一，增加不良妊娠结局及孕妇和其后代远期罹患2型糖尿病、心血管病等发生风险。因此，及早筛查GDM高危人群，并采取一级预防和个体化干预措施，对于降低GDM发病率和改善妊娠结局至关重要。但目前GDM的发病机制仍不清楚，且基于传统危险因素构建的GDM风险模型预测能力有限，亟需探索GDM发病机制及纳入新的风险因素以提高模型预测能力。代谢组学通过对机体代谢产物的全面定性定量分析，为疾病的机制研究和风险预测提供新的思路。因此，本项目基于同济-双流出生队列采用1:2配对的巢式病例对照研究，选取336名GDM孕妇与672名健康对照，利用代谢组学检测技术，重点探讨孕早期血液代谢小分子在GDM发生发展中的作用。在此基础上，将新发现的代谢小分子与传统的GDM风险因素相结合，构建适合中国人群的GDM风险预测模型。研究发现了孕早期10种脂质代谢物水平与GDM的发生风险显著相关，将这10种脂质生物标志物加入到包含GDM传统危险因素的预测模型中可显著提高模型的预测性能，曲线下面积可由0.70显著增至0.80。通路分析发现差异脂质代谢物主要富集在甘油磷脂代谢通路，提示该通路的代谢改变可能是疾病发病的潜在机制。中介分析提示甲状腺激素可能通过影响特定脂质代谢从而增加GDM的发病风险。此外，研究还发现孕早期饱和脂肪酸14:0和16:0与GDM风险增加相关，而较高水平的n-6多不饱和脂肪酸（polyunsaturated fatty acid, PUFA）18:2n-6与较低的GDM发生率密切相关。另外，研究也鉴定出部分肠道菌群种属和结构与GDM发生风险显著相关。综上所述，本项目研究结果发现了孕早期的一些代谢小分子与孕中期GDM的发病风险显著相关，并显著提高了传统GDM风险预测模型的预测能力，为GDM高危人群筛检和防治措施的制订提供了科学依据。