Diabetes and Islet Biology Group, School of Medicine, Western Sydney University, Campbelltown, NSW, Australia.
Diabetes and Islet Biology Group, Faculty of Medicine and Health, University of Sydney, Camperdown, NSW, Australia.
Diabetologia. 2021 Jul;64(7):1516-1526. doi: 10.1007/s00125-021-05429-z. Epub 2021 Mar 23.
AIMS/HYPOTHESIS: Type 2 diabetes mellitus is a major cause of morbidity and death worldwide. Women with gestational diabetes mellitus (GDM) have greater than a sevenfold higher risk of developing type 2 diabetes in later life. Accurate methods for postpartum type 2 diabetes risk stratification are lacking. Circulating microRNAs (miRNAs) are well recognised as biomarkers/mediators of metabolic disease. We aimed to determine whether postpartum circulating miRNAs can predict the development of type 2 diabetes in women with previous GDM.
In an observational study, plasma samples were collected at 12 weeks postpartum from 103 women following GDM pregnancy. Utilising a discovery approach, we measured 754 miRNAs in plasma from type 2 diabetes non-progressors (n = 11) and type 2 diabetes progressors (n = 10) using TaqMan-based real-time PCR on an OpenArray platform. Machine learning algorithms involving penalised logistic regression followed by bootstrapping were implemented.
Fifteen miRNAs were selected based on their importance in discriminating type 2 diabetes progressors from non-progressors in our discovery cohort. The levels of miRNA miR-369-3p remained significantly different (p < 0.05) between progressors and non-progressors in the validation sample set (n = 82; 71 non-progressors, 11 progressors) after adjusting for age and correcting for multiple comparisons. In a clinical model of prediction of type 2 diabetes that included six traditional risk factors (age, BMI, pregnancy fasting glucose, postpartum fasting glucose, cholesterol and triacylglycerols), the addition of the circulating miR-369-3p measured at 12 weeks postpartum improved the prediction of future type 2 diabetes from traditional AUC 0.83 (95% CI 0.68, 0.97) to an AUC 0.92 (95% CI 0.84, 1.00).
This is the first demonstration of miRNA-based type 2 diabetes prediction in women with previous GDM. Improved prediction will facilitate early lifestyle/drug intervention for type 2 diabetes prevention.
目的/假设：2 型糖尿病是全球发病率和死亡率的主要原因。患有妊娠期糖尿病（GDM）的女性在以后的生活中患 2 型糖尿病的风险高出七倍以上。目前缺乏产后 2 型糖尿病风险分层的准确方法。循环 microRNAs（miRNAs）是代谢疾病的生物标志物/介质。我们旨在确定产后循环 miRNA 是否可预测以前患有 GDM 的女性发生 2 型糖尿病。
在一项观察性研究中，我们从 103 名 GDM 妊娠后的女性中收集了产后 12 周的血浆样本。利用发现方法，我们使用 TaqMan 基于实时 PCR 在 OpenArray 平台上测量了来自 2 型糖尿病非进展者（n=11）和 2 型糖尿病进展者（n=10）的 754 种 miRNA。实施了涉及惩罚逻辑回归和引导的机器学习算法。
基于其在区分发现队列中 2 型糖尿病进展者和非进展者方面的重要性，选择了 15 种 miRNA。在经过年龄调整和多重比较校正后，miR-369-3p 在验证样本集中（n=82；71 名非进展者，11 名进展者），miR-369-3p 的水平在进展者和非进展者之间仍然存在显著差异（p<0.05）。在包含六个传统危险因素（年龄、BMI、妊娠空腹血糖、产后空腹血糖、胆固醇和三酰甘油）的 2 型糖尿病预测临床模型中，在产后 12 周测量的循环 miR-369-3p 的添加提高了传统 AUC 0.83（95%CI 0.68，0.97）至 AUC 0.92（95%CI 0.84，1.00）的未来 2 型糖尿病的预测。
这是首次在以前患有 GDM 的女性中证明基于 miRNA 的 2 型糖尿病预测。改善预测将有助于早期进行生活方式/药物干预以预防 2 型糖尿病。
