Evidence map›Paper›PMID 40201617›Full record

ArticleAJOG global reports2025

Understanding the potential contribution of polygenic risk scores to the prediction of gestational and type 2 diabetes in women from British Pakistani and Bangladeshi groups: a cohort study in Genes and Health.

Julia Zöllner, Binur Orazumbekova, Sam Hodgson, David A van Heel, Genes and Health Research Team, Stamatina Iliodromiti, Moneeza Siddiqui, Rohini Mathur, Sarah Finer, Jennifer Jardine

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In one paragraph

Article in AJOG global reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Review
  2. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Julia ZöllnerInstitute for Women's Health, Population Health Sciences, University College London, London, UK (Zöllner).
Binur OrazumbekovaWolfson Institute of Population Health, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK (Zöllner, Orazumbekova, Hodgson, Iliodromiti, Siddiqui, Mathur, Finer, and Jardine).
Sam HodgsonWolfson Institute of Population Health, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK (Zöllner, Orazumbekova, Hodgson, Iliodromiti, Siddiqui, Mathur, Finer, and Jardine).
David A van HeelCentre for Cell Biology and Cutaneous Research, Blizard Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK (van Heel).
Genes and Health Research Team
Stamatina IliodromitiWolfson Institute of Population Health, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK (Zöllner, Orazumbekova, Hodgson, Iliodromiti, Siddiqui, Mathur, Finer, and Jardine).
Moneeza SiddiquiWolfson Institute of Population Health, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK (Zöllner, Orazumbekova, Hodgson, Iliodromiti, Siddiqui, Mathur, Finer, and Jardine).
Rohini MathurWolfson Institute of Population Health, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK (Zöllner, Orazumbekova, Hodgson, Iliodromiti, Siddiqui, Mathur, Finer, and Jardine).
Sarah FinerWolfson Institute of Population Health, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK (Zöllner, Orazumbekova, Hodgson, Iliodromiti, Siddiqui, Mathur, Finer, and Jardine).
Jennifer JardineWolfson Institute of Population Health, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK (Zöllner, Orazumbekova, Hodgson, Iliodromiti, Siddiqui, Mathur, Finer, and Jardine).

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: British Pakistani and Bangladeshi (BPB) women have disproportionately high rates of gestational diabetes mellitus (GDM), with prevalence estimates up to three times higher than in the general population. They are also at increased risk of progressing to type 2 diabetes, leading to significant health complications. Despite this, predictive models tailored to this high-risk, yet understudied group are lacking. Objective: To investigate whether combining genetic and traditional clinical data improves risk prediction of GDM and progression to type 2 diabetes among BPB women. We hypothesized that incorporating polygenic risk scores (PRS) would enhance the predictive accuracy of existing models. Study Design: An observational cohort study utilizing the Genes & Health dataset, which includes comprehensive electronic health records. Women who gave birth between 2000 and 2023, both with and without a history of GDM, were included. Controls were defined as women without a GDM diagnosis during this period but who had a birth record. A total of 117 type 2 diabetes or GDM PRS were tested to determine the optimal PRS based on predictive performance metrics. The best-performing PRS was integrated with clinical variables for statistical analyses, including descriptive statistics, chi-square tests, logistic regression, and receiver operating characteristic curve analysis. Results: Of 13,489 women with birth records, 10,931 were included in the analysis, with 29.3% developing GDM. Women with GDM were older (mean age 31.7 years, Conclusion: The integration of genetic assessment with traditional clinical factors significantly enhances risk prediction for BPB women at high risk of developing type 2 diabetes after GDM. These findings support the implementation of targeted interventions and personalized monitoring strategies in this high-risk population. Future research should focus on validating these predictive models in external cohorts and exploring their integration into clinical practice to improve health outcomes.

Indexed as

gestational diabetespolygenic riskprediction modelpregnancy complicationsprognosisrisk stratificationSouth Asian

Identifiers

PMID40201617
PMCPMC11976246

What Socratic holds

Textmetadata
LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.