Chung Heaseung Sophia, Middleton Lawrence, Garg Manik, Hristova Ventzislava A, Vega Rick B, Baker David, Challis Benjamin G, Vitsios Dimitrios, Hess Sonja, Wallenius Kristina, Holmäng Agneta, Andersson-Hall Ulrika
Dynamic Omics, Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Gaithersburg, Maryland, USA.
Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, United Kingdom.
JCI Insight. 2024 Nov 26;10(2):e183213. doi: 10.1172/jci.insight.183213.
We characterized the longitudinal serum protein signatures of women 6 and 10 years after having gestational diabetes mellitus (GDM) to identify factors associated with the development of type 2 diabetes mellitus (T2D) and prediabetes in this at-risk post-GDM population, aiming to discover potential biomarkers for early diagnosis and prevention of T2D. Our study identified 75 T2D-associated serum proteins and 23 prediabetes-associated proteins, some of which were validated in an independent T2D cohort. Machine learning (ML) performed on the longitudinal proteomics highlighted protein signatures associated with progression to post-GDM diabetes. We also proposed prognostic biomarker candidates that were differentially regulated in healthy participants at 6 years postpartum who later progressed to having T2D. Our longitudinal study revealed T2D risk factors for post-GDM populations who are relatively young and healthy, providing insights for clinical decisions and early lifestyle interventions.
我们对患有妊娠期糖尿病(GDM)6年和10年后女性的纵向血清蛋白质特征进行了表征,以确定在这个GDM后高危人群中与2型糖尿病(T2D)和糖尿病前期发展相关的因素,旨在发现用于T2D早期诊断和预防的潜在生物标志物。我们的研究确定了75种与T2D相关的血清蛋白和23种与糖尿病前期相关的蛋白,其中一些在独立的T2D队列中得到了验证。对纵向蛋白质组学进行的机器学习(ML)突出了与进展为GDM后糖尿病相关的蛋白质特征。我们还提出了预后生物标志物候选物,这些候选物在产后6年后来进展为T2D的健康参与者中受到差异调节。我们的纵向研究揭示了相对年轻和健康的GDM后人群的T2D风险因素,为临床决策和早期生活方式干预提供了见解。