Identification and validation of gestational diabetes subgroups by data-driven cluster analysis.

ABSTRACT:

Aims/hypothesis

Gestational diabetes mellitus (GDM) is a heterogeneous condition. Given such variability among patients, the ability to recognise distinct GDM subgroups using routine clinical variables may guide more personalised treatments. Our main aim was to identify distinct GDM subtypes through cluster analysis using routine clinical variables, and analyse treatment needs and pregnancy outcomes across these subgroups.

Methods

In this cohort study, we analysed datasets from a total of 2682 women with GDM treated at two central European hospitals (1865 participants from Charité University Hospital in Berlin and 817 participants from the Medical University of Vienna), collected between 2015 and 2022. We evaluated various clustering models, including k-means, k-medoids and

SUBMITTER: Salvatori B 

PROVIDER: S-EPMC11343786 | biostudies-literature | 2024 May

REPOSITORIES: biostudies-literature