Diabetes/metabolism research and reviews · 2025
Early Postpartum Metabolic Heterogeneity Among Women Who Progressed to Type 2 Diabetes After Gestational Diabetes: A Prospective Cohort.
СтатьяПоддержка правительства СШАПоддержка NIH: внешняя программаПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
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Khan Saifur R, Van Julie A D, Xiangyu Zhang, Alexeeff Stacey E, Razani Babak, Wheeler Michael B, Gunderson Erica P
Аннотация
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.
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.