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Record W4411792555 · doi:10.1101/2025.06.26.25330189

Examining the impact of gestational diabetes genetic susceptibility variants on maternal glucose levels during and post-pregnancy

2025· preprint· en· W4411792555 on OpenAlexaff
Aminata Hallimat Cissé, Alan Kuang, Catherine Allard, Justiina Ronkainen, Robin N. Beaumont, Sylvain Sebért, Denise Scholtens, Andrew T. Hattersley, Marja Vääräsmäki, Eero Kajantie, Luigi Bouchard, Patrice Perron, Elina Keikkala, Marie‐France Hivert, William L. Lowe, Alice E. Hughes, Rachel M. Freathy

Bibliographic record

VenuemedRxiv · 2025
Typepreprint
Languageen
FieldMedicine
TopicGestational Diabetes Research and Management
Canadian institutionsCentre Hospitalier Universitaire de Sherbrooke
FundersLastentautien TutkimussäätiöSydäntutkimussäätiöPäivikki ja Sakari Sohlbergin SäätiöJuho Vainion SäätiöYrjö Jahnssonin SäätiöSigrid Juséliuksen SäätiöSuomen Lääketieteen SäätiöNovo NordiskSigne ja Ane Gyllenbergin SäätiöFoundation for Cardiovascular Research
KeywordsGestational diabetesPregnancyDiabetes mellitusMedicineObstetricsGestationGeneticsEndocrinologyBiology

Abstract

fetched live from OpenAlex

Abstract Aim Gestational diabetes (GDM) has important environmental and genetic components. Genetic variants associated with GDM (n=14 SNPs) were recently classified into 2 groups: those with stronger effects on type 2 diabetes than GDM (Class-T, 3 SNPs) and those with stronger effects on GDM than on type 2 diabetes (Class-G, 8 SNPs), leaving 3 SNPs unclassified. It was suggested that the Class-G variants contribute to hyperglycaemia predominantly during gestation, but it is not known whether the effects of the two variant classes on maternal glucose levels vary with pregnancy status. We aimed to compare the effects of GDM-associated variants on glucose levels (fasting glucose and 2-hour post-OGTT) measured during vs. after pregnancy in longitudinal cohorts. Methods We calculated genetic scores (GS) by class (T_GS and G_GS) and overall (All_GS) in 10,225 pregnant women and 4,763 women post-pregnancy (mean 10.5 years post pregnancy) from 8 datasets representing 4 ancestrally-diverse cohorts: EFSOCH, Gen3G, HAPO and FinnGeDi. We used linear regression models adjusted for ancestry principal components to investigate associations between standardised GS and glucose levels during or after pregnancy. Analyses were performed separately in each dataset and then combined using inverse-variance weighted random-effects meta-analyses. Results In the meta-analysis, All_GS was associated with fasting glucose both during and after pregnancy (β[95%CI], in mmol/L per 1SD higher GS = 0.06 [0.04;0.08] during vs. 0.06 [0.04;0.07] post-pregnancy). All_GS was also associated with 2-hour post-OGTT glucose levels during pregnancy but not after (0.10 [0.04; 0.15] during vs. 0.01 [-0.04; 0.07] post-pregnancy). Both G_GS and T_GS showed consistent associations with fasting glucose during and post pregnancy (0.06 [0.04; 0.08] during and 0.05 [0.03; 0.07] post pregnancy for G_GS; 0.02 [0.01; 0.02] during and 0.02 [-0.001; 0.05] post pregnancy for T_GS). G_GS showed weak evidence of association with 2-hour glucose levels during pregnancy (0.06 [-0.002; 0.11]) and no association with 2-hour glucose levels post pregnancy (-0.03 [-0.08; 0.03]). However, T_GS was associated with 2-hour glucose during pregnancy and post pregnancy (0.10 [0.04; 0.16] and 0.06 [0.01; 0.12]). Conclusion Genetic scores for GDM have consistent associations with fasting glucose levels during and after pregnancy. This finding suggests that biological pathways underlying GDM genetic susceptibility to fasting hyperglycaemia are not pregnancy specific. However, the results for All_GS and 2-hour glucose provide evidence that some genetic associations with postprandial glucose may be stronger in pregnancy and should be followed up in larger samples.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.014
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.013
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.038
GPT teacher head0.318
Teacher spread0.280 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2025
Admission routes1
Has abstractyes

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