Association of Genetic Predisposition and Physical Activity With Risk of Gestational Diabetes in Nulliparous Women.


Journal

JAMA network open
ISSN: 2574-3805
Titre abrégé: JAMA Netw Open
Pays: United States
ID NLM: 101729235

Informations de publication

Date de publication:
01 08 2022
Historique:
entrez: 30 8 2022
pubmed: 31 8 2022
medline: 3 9 2022
Statut: epublish

Résumé

Polygenic risk scores (PRS) for type 2 diabetes (T2D) can improve risk prediction for gestational diabetes (GD), yet the strength of the association between genetic and lifestyle risk factors has not been quantified. To assess the association of PRS and physical activity in existing GD risk models and identify patient subgroups who may receive the most benefits from a PRS or physical activity intervention. The Nulliparous Pregnancy Outcomes Study: Monitoring Mothers-to-Be cohort was established to study individuals without previous pregnancy lasting at least 20 weeks (nulliparous) and to elucidate factors associated with adverse pregnancy outcomes. A subcohort of 3533 participants with European ancestry was used for risk assessment and performance evaluation. Participants were enrolled from October 5, 2010, to December 3, 2013, and underwent genotyping between February 19, 2019, and February 28, 2020. Data were analyzed from September 15, 2020, to November 10, 2021. Self-reported total physical activity in early pregnancy was quantified as metabolic equivalents of task (METs). Polygenic risk scores were calculated for T2D using contributions of 84 single nucleotide variants, weighted by their association in the Diabetes Genetics Replication and Meta-analysis Consortium data. Estimation of the development of GD from clinical, genetic, and environmental variables collected in early pregnancy, assessed using measures of model discrimination. Odds ratios and positive likelihood ratios were used to evaluate the association of PRS and physical activity with GD risk. A total of 3533 women were included in this analysis (mean [SD] age, 28.6 [4.9] years). In high-risk population subgroups (body mass index ≥25 or aged ≥35 years), individuals with high PRS (top 25th percentile) or low activity levels (METs <450) had increased odds of a GD diagnosis of 25% to 75%. Compared with the general population, participants with both high PRS and low activity levels had higher odds of a GD diagnosis (odds ratio, 3.4 [95% CI, 2.3-5.3]), whereas participants with low PRS and high METs had significantly reduced risk of a GD diagnosis (odds ratio, 0.5 [95% CI, 0.3-0.9]; P = .01). In this cohort study, the addition of PRS was associated with the stratified risk of GD diagnosis among high-risk patient subgroups, suggesting the benefits of targeted PRS ascertainment to encourage early intervention.

Identifiants

pubmed: 36040739
pii: 2795756
doi: 10.1001/jamanetworkopen.2022.29158
pmc: PMC9428742
doi:

Types de publication

Journal Article Meta-Analysis Research Support, N.I.H., Extramural

Langues

eng

Sous-ensembles de citation

IM

Pagination

e2229158

Subventions

Organisme : NCATS NIH HHS
ID : UL1 TR001863
Pays : United States

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Auteurs

Kymberleigh A Pagel (KA)

Department of Computer Science, Indiana University, Bloomington.
Institute of Computational Medicine, Johns Hopkins University, Baltimore, Maryland.

Hoyin Chu (H)

Khoury College of Computer Sciences, Northeastern University, Boston, Massachusetts.
Dana-Farber Cancer Institute, Boston, Massachusetts.

Rashika Ramola (R)

Khoury College of Computer Sciences, Northeastern University, Boston, Massachusetts.

Rafael F Guerrero (RF)

Department of Biological Sciences, North Carolina State University, Raleigh.

Judith H Chung (JH)

Department of Obstetrics and Gynecology, University of California, Irvine.

Samuel Parry (S)

Department of Obstetrics and Gynecology, University of Pennsylvania School of Medicine, Philadelphia.

Uma M Reddy (UM)

Department of Obstetrics, Gynecology, and Reproductive Sciences, Yale School of Medicine, Yale University, New Haven, Connecticut.

Robert M Silver (RM)

Department of Obstetrics and Gynecology, University of Utah School of Medicine, Salt Lake City.

Jonathan G Steller (JG)

Department of Obstetrics and Gynecology, University of California, Irvine.

Lynn M Yee (LM)

Department of Obstetrics and Gynecology, Northwestern University Feinberg School of Medicine, Chicago, Illinois.

Ronald J Wapner (RJ)

College of Physicians and Surgeons, Columbia University, New York, New York.

Matthew W Hahn (MW)

Department of Computer Science, Indiana University, Bloomington.
Department of Biology, Indiana University, Bloomington.

Sriraam Natarajan (S)

Department of Computer Science, The University of Texas at Dallas.

David M Haas (DM)

Department of Obstetrics and Gynecology, Indiana University School of Medicine, Indianapolis.

Predrag Radivojac (P)

Khoury College of Computer Sciences, Northeastern University, Boston, Massachusetts.

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