Oral Presentation Australasian Diabetes in Pregnancy Society Conference 2026

Resolving heterogeneity in gestational diabetes based on insulin secretion and sensitivity and the association with pregnancy outcomes: a data-driven cluster analysis in a multi-ethnic birth cohort (138457)

Chuanyu Zhao 1 2 3 , Huishu Lin 1 2 3 , Gechang Yu 1 2 3 , Claudia HT Tam 1 2 3 , Kristen S. Gibbons 4 , Lene R. Madsen 5 , Cadmon KP Lim 1 2 3 , Julia Lowe 6 , David A. Sacks 7 , Patrick M. Catalano 8 , H. David McIntyre 4 , Wing Hung Tam 9 10 , Ronald CW Ma 1 2 3
  1. Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong
  2. Chinese University of Hong Kong-Shanghai Jiao Tong University Joint Research Centre in Diabetes Genomics and Precision Medicine, Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong
  3. Laboratory of Molecular Epidemiology in Diabetes, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong
  4. Children’s Intensive Care Research Program, Faculty of Health, Medicine, and Behavioural Sciences, The University of Queensland, South Brisbane, QLD, Australia
  5. Steno Diabetes Centre Aarhus, Aarhus University Hospital, Aarhus, Denmark
  6. Department of Medicine, University of Toronto, Toronto, Canada
  7. Department of Research and Evaluation, Kaiser Permanente, Pasadena, CA, USA
  8. Massachusetts General Hospital, Boston, MA, USA
  9. Department of Obstetrics and Gynaecology, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong
  10. CUHK Medical Centre, Shatin, N.T., Hong Kong

Aims:
Gestational diabetes mellitus (GDM) is recognized as a heterogeneous disorder with varying degrees of impairment in insulin secretion and sensitivity. We aimed to identify heterogeneous metabolic clusters using routine clinical measures and to evaluate their associations with obstetric and neonatal outcomes.

Methods:
We analysed 6,334 participants from five centres of the Hyperglycaemia and Adverse Pregnancy Outcome (HAPO) Study, including 1,084 (17.1%) women with GDM according to IADPSG criteria. Homeostatic model assessment 2 estimates of β-cell function (HOMA2-β) and insulin resistance (HOMA2-IR) were derived from blinded 75-g 2-h OGTT. K-means clustering was applied based on maternal age, ethnicity-adjusted BMI z-score, HOMA2-β, and HOMA2-IR, with the optimal number of clusters determined by gap statistics. Relative risks (RRs) for adverse pregnancy outcomes were estimated using robust Poisson regression, and predictive performance was assessed using the area under ROC curves.

Results:
Five reproducible clusters were identified, with 1,675 (26.4%), 1,587 (25.1%), 1,261 (19.9%), 1,177 (18.6%), and 634 (10.0%) women in Clusters 1-5. Clusters 1-3 mainly comprised women with normal BMI, with Cluster 1 representing the oldest group (mean [SD]: 34.0 [3.2] years) and Cluster 2 the youngest (24.7 [3.0] years). Cluster 1 showed the most favourable metabolic profile with lower fasting glucose (median [IQR]: 4.4 [4.2-4.7] mmol/L), whereas Cluster 5 had the highest glucose (4.8 [4.5-5.1] mmol/L) and BMI (36.1 [32.2-40.4] kg/m2). Compared with Cluster 1, women in Clusters 2-5 showed increasingly higher risks of adverse pregnancy outcomes, with the highest risk observed in Cluster 5. The risk of preeclampsia increased by 55% (RR 1.55, 95% CI 1.12-2.14), 81% (1.81, 1.30-2.52), and 262% (3.62, 2.61-5.01) in Clusters 3-5, respectively. The clustering approach demonstrated better predictive performance compared with conventional GDM classification (AUROC for preeclampsia: 0.668 vs 0.552; p<0.001).

Conclusion:
Data-driven clustering based on routine clinical variables revealed metabolic heterogeneity in GDM and improved risk stratification beyond conventional diagnosis, supporting more individualized pregnancy care.

Acknowledgement:
This study was partially supported by a grant from the RGC Area of Excellence Scheme (M-401/24-R). CZ acknowledge support from the Hong Kong PhD Fellowship Scheme.