‘Turning the tide’ on hyperglycemia in pregnancy: insights from multiscale dynamic simulation modeling

Louise Freebairn,Roland Dyck,Louise Maple-Brown,Alison L Kent,Ante Prodan,Paul M Kelly,Luke Penza,Geoff Mcdonnell,Christopher J Nolan,Nathaniel D Osgood,Jo-An Atkinson,Weicheng Qian,Allen Mclean,Yang Qin,Anahita Safarishahrbijari

doi:10.1136/bmjdrc-2019-000975

Abstract

IntroductionHyperglycemia in pregnancy (HIP, including gestational diabetes and pre-existing type 1 and type 2 diabetes) is increasing, with associated risks to the health of women and their babies. Strategies to...

Highlights

Hyperglycemia in pregnancy (HIP, including gestational diabetes and pre-existing type 1 and type 2 diabetes) is increasing, with associated risks to the health of women and their babies
The aim of this paper is to explore the impact of prevention interventions targeting weight status on HIP incidence and insulin sensitivity
The study presented in this paper is unique in that Dynamic simulation models (DSM) was used to explore the latent factors and metabolic dynamics underlying the development of HIP and compare the likely impact of population-level interventions with interventions targeting high-risk individuals

Summary

Introduction

Hyperglycemia in pregnancy (HIP, including gestational diabetes and pre-existing type 1 and type 2 diabetes) is increasing, with associated risks to the health of women and their babies. The available evidence for HIP policy and treatment planning is not definitive,[1] and current challenges include determining the timing and methods of screening, criteria for diagnosis, targets for treatment, resource allocation, identification and management of pre-e xisting diabetes during pregnancy, risk stratification, timing and type of prevention activities, and individual differential effects of treatment.[1 8,9,10] To address the increasing incidence of HIP, there have been increasing calls for upstream prevention activities to focus on lifestyle risk factors preconception rather than during or interpregnancy.[11,12,13] These contested intervention options cross the spectrum from population-based primary prevention approaches to highly specialized clinical management targeted at those at highest risk, which can be implemented independently or in combination and may be phased or implemented simultaneously. Sophisticated analytical tools are required to synthesize diverse evidence types across disciplines and support decision making

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