Early Postpartum Metabolic Heterogeneity Among Women Who Progressed to Type 2 Diabetes After Gestational Diabetes: A Prospective Cohort

IF 4.6 2区医学 Q1 ENDOCRINOLOGY & METABOLISM Diabetes/Metabolism Research and Reviews Pub Date : 2025-01-15 DOI:10.1002/dmrr.70027

Saifur R. Khan, Julie A. D. Van, Zhang Xiangyu, Stacey E. Alexeeff, Babak Razani, Michael B. Wheeler, Erica P. Gunderson

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Abstract

Aims

Gestational diabetes mellitus (GDM) poses a significant risk for developing type 2 diabetes mellitus (T2D) and exhibits heterogeneity. However, understanding the link between different types of post-GDM individuals without diabetes and their progression to T2D is crucial to advance personalised medicine approaches.

Materials and Methods

We employed a discovery-based unsupervised machine learning clustering method to generate clustering models for analysing metabolomics, clinical, and biochemical datasets. For this analysis, we selected 225 women who later developed T2D during the 12-year follow-up period from the cohort of 1010 women who returned to a non-diabetic state at 6–9 weeks (study baseline) after a GDM pregnancy based on 2-h 75 g research OGTTs. The optimal model was selected by assessing Bayesian Information Criterion values, class separation performance, and the potential for clinically distinguishable clusters, accounting for participant prenatal and early postpartum characteristics.

Results

The selected model comprises three clusters: pancreatic beta cell dysfunction (cluster-β: median HOMA-B 161.3 and median HOMA-IR 3.8), insulin-resistance (cluster-IR: median HOMA-B 630.5 and median HOMA-IR 16.8), and a mixed cluster (cluster-mixed: median HOMA-B 307.2 and median HOMA-IR 8.6). These clusters are distinguishable based on postpartum blood test parameters such as glucose tolerance, HOMA indices, and fasting lipid profiles including triglycerides, leptin, HDL-c, and adiponectin, as well as participant age and BMI. Metabolomic analysis identified unique molecular signatures for each cluster. However, the time to T2D onset was not statistically significant among the three clusters (p = 0.22).

Conclusion

This study enhances our understanding of the heterogeneity of early postpartum metabolic profiles that characterise the future onset of T2D diabetes in a diverse cohort of women with GDM, revealing insights into distinct mechanisms and personalised intervention strategies for the prevention of T2D.

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妊娠糖尿病后发展为2型糖尿病的妇女早期产后代谢异质性：一项前瞻性队列研究

目的：妊娠期糖尿病（GDM）是发生2型糖尿病（T2D）的重要风险因素，且具有异质性。然而，了解没有糖尿病的不同类型的gdm后个体与其进展为T2D之间的联系对于推进个性化医疗方法至关重要。材料和方法：我们采用一种基于发现的无监督机器学习聚类方法来生成聚类模型，用于分析代谢组学、临床和生化数据集。为了进行这项分析，我们从1010名妊娠GDM后6-9周（研究基线）恢复为非糖尿病状态的妇女中选择了225名在12年随访期间后来发展为T2D的妇女，这些妇女基于2小时75克研究ogtt。通过评估贝叶斯信息准则值、类分离性能和临床可区分聚类的潜力，考虑参与者产前和产后早期特征，选择最优模型。结果：选择的模型包括三个簇：胰腺β细胞功能障碍（簇-β：中位HOMA-B 161.3和中位HOMA-IR 3.8），胰岛素抵抗（簇- ir：中位HOMA-B 630.5和中位HOMA-IR 16.8）和混合簇（簇-混合：中位HOMA-B 307.2和中位HOMA-IR 8.6）。根据产后血液测试参数，如葡萄糖耐量、HOMA指数、空腹脂质谱（包括甘油三酯、瘦素、HDL-c和脂联素）以及参与者的年龄和BMI，可以区分这些群集。代谢组学分析确定了每个簇的独特分子特征。然而，三组患者发生T2D的时间差异无统计学意义（p = 0.22）。结论：本研究增强了我们对产后早期代谢谱异质性的理解，这些代谢谱是GDM女性不同队列中T2D糖尿病未来发病的特征，揭示了预防T2D的独特机制和个性化干预策略。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Diabetes/Metabolism Research and Reviews 医学-内分泌学与代谢

CiteScore

17.20

自引率

2.50%

发文量

审稿时长

4-8 weeks

期刊介绍： Diabetes/Metabolism Research and Reviews is a premier endocrinology and metabolism journal esteemed by clinicians and researchers alike. Encompassing a wide spectrum of topics including diabetes, endocrinology, metabolism, and obesity, the journal eagerly accepts submissions ranging from clinical studies to basic and translational research, as well as reviews exploring historical progress, controversial issues, and prominent opinions in the field. Join us in advancing knowledge and understanding in the realm of diabetes and metabolism.