Decomposing the genetic risk of autism spectrum disorder into discrete molecular subtypes underlie clinical heterogeneity based on transcriptome profile.

Continuous glucose monitoring (CGM), which has transformed glucose monitoring in type 1 diabetes mellitus (T1D) [], is increasingly proving valuable in supporting patients with type 2 diabetes mellitus (T2D) [, ]. CGM enables real‐time tracking of glucose levels, offering a more dynamic and less invasive alternative to traditional self‐monitoring of blood glucose (SMBG), which relies on frequent capillary finger‐prick tests. Using a small sensor worn on the body, CGM provides automated readings which support glucose management more effectively than SMBG [, , ]. Studies of pregnant women with T1D have shown beneficial effects of CGM on maternal glucose control and neonatal outcomes []. In particular, in a randomised controlled trial (RCT), CGM use by pregnant women with T1D was associated with significant improvements in maternal glycated haemoglobin (HbA1c), time in range (TIR), and time above range (TAR) as well as reductions in the rates of large‐for‐gestational‐age (LGA) births, neonatal intensive care unit (NICU) admissions, and neonatal hypoglycaemia compared with SMBG []. Data from this trial [] and an observational study of pregnant women with T1D using CGM [] showed that even a 5% increase in TIR was associated with improved neonatal outcomes []. In this article, we focus on hyperglycaemia first diagnosed during pregnancy (HIP). HIP is the most common medical condition affecting pregnancy [], with its incidence rising globally in parallel with increasing maternal age and the obesity epidemic []. Its growing burden highlights the need for improved diagnostic and management strategies []. We explore how CGM could be useful in the management of HIP.