Gestational diabetes & diabetes in pregnancy
Identification and validation of gestational diabetes subgroups by data-driven cluster analysis
Authors' conclusion
Our findings confirm the heterogeneity of GDM. The identification of subgroups (clusters) has the potential to help clinicians define more tailored treatment approaches for improved maternal and neonatal outcomes.
Study context
- Population
- Women with gestational diabetes mellitus (GDM) treated at two central European hospitals (Charité Berlin, n=1865; Medical University of Vienna, n=817), 2015–2022
- Participants
- 2,682 women2 hospitals
- Exposure
- Data-driven GDM clusters
- Outcome
- Distinct GDM subtypes identified through cluster analysis using routine clinical variables, and treatment needs and pregnancy outcomes across these subgroups
Methodological detailsdata-driven cluster analysis, two centres, internal and external validation
- Data-driven cluster analysis
- Two centres
- Internal and external validation
Main finding
As reported in the abstract
GDM subgroups identified
Optimal model identified three clusters from routinely available variables
3 clusters
Observational data: associations, not effects.
Further findings
Need for glucose-lowering medications · across clusters 1, 2 and 3
P < 0.0001
Large-for-gestational-age infants
- Cluster 1 participants at higher risk of delivering large-for-gestational-age infants
Type of insulin-based treatment, cluster 2 vs cluster 3 (external validation cohort)
- Differences in type of insulin-based treatment between cluster 2 and cluster 3 in the external validation cohort
Original publication
Identification and validation of gestational diabetes subgroups by data-driven cluster analysis. Diabetologia. Published 27 May 2024. doi:10.
Composed by AI from the published abstract and checked against it. How Briefs are made →