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. 2023 Oct 5;3(1):135.
doi: 10.1038/s43856-023-00371-0.

Precision gestational diabetes treatment: a systematic review and meta-analyses

Jamie L Benham #  1 , Véronique Gingras #  2   3 , Niamh-Maire McLennan #  4   5 , Jasper Most #  6 , Jennifer M Yamamoto #  7 , Catherine E Aiken #  8   9 , Susan E Ozanne  10 , Rebecca M Reynolds  11   12 ; ADA/EASD PMDI
Collaborators, Affiliations

Precision gestational diabetes treatment: a systematic review and meta-analyses

Jamie L Benham et al. Commun Med (Lond). .

Abstract

Background: Gestational Diabetes Mellitus (GDM) affects approximately 1 in 7 pregnancies globally. It is associated with short- and long-term risks for both mother and baby. Therefore, optimizing treatment to effectively treat the condition has wide-ranging beneficial effects. However, despite the known heterogeneity in GDM, treatment guidelines and approaches are generally standardized. We hypothesized that a precision medicine approach could be a tool for risk-stratification of women to streamline successful GDM management. With the relatively short timeframe available to treat GDM, commencing effective therapy earlier, with more rapid normalization of hyperglycaemia, could have benefits for both mother and fetus.

Methods: We conducted two systematic reviews, to identify precision markers that may predict effective lifestyle and pharmacological interventions.

Results: There was a paucity of studies examining precision lifestyle-based interventions for GDM highlighting the pressing need for further research in this area. We found a number of precision markers identified from routine clinical measures that may enable earlier identification of those requiring escalation of pharmacological therapy (to metformin, sulphonylureas or insulin). This included previous history of GDM, Body Mass Index and blood glucose concentrations at diagnosis.

Conclusions: Clinical measurements at diagnosis could potentially be used as precision markers in the treatment of GDM. Whether there are other sensitive markers that could be identified using more complex individual-level data, such as omics, and if these can feasibly be implemented in clinical practice remains unknown. These will be important to consider in future studies.

Plain language summary

Gestational diabetes (GDM) is high blood sugar first detected during pregnancy. Normalizing blood sugar levels quickly is important to avoid pregnancy complications. Many women achieve this with lifestyle changes, such as to diet, but some need to inject insulin or take tablets. We did two thorough reviews of existing research to see if we could predict which women need medication. Firstly we looked for ways to identify the characteristics of women who benefit most from changing their lifestyles to treat GDM, but found very limited research on this topic. We secondly searched for characteristics that help identify women who need medication to treat GDM. We found some useful characteristics that are obtained during routine pregnancy care. Further studies are needed to test if additional information could provide even better information about how we could make GDM treatment more tailored for individuals during pregnancy.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1. Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flow diagrams for precision approaches to enhance behavioural (diet and lifestyle) interventions.
The PRISMA flow diagram details the search and selection process applied in the review.
Fig. 2
Fig. 2. Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flow diagrams for precision markers for escalation of pharmacological interventions.
The PRISMA flow diagram details the search and selection process applied in the review.
Fig. 3
Fig. 3. Risk of bias graph: review authors’ judgements about each risk of bias item presented as percentages across all studies included in the meta-analyses.
Green circle with + sign, Yes, Red circle with – sign, No, Blank – not described.
Fig. 4
Fig. 4. Risk of bias summary: review authors’ judgements about each risk of bias item for each study included in the meta-analyses.
Green – low risk of bias, Grey – unclear risk of bias, Red – high risk of bias.
See this image and copyright information in PMC

References

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