Literature DB >> 29974585

Effects of the interaction between glycated haemoglobin genetic risk score and postpartum weight reduction on glycaemic changes: A gene-weight interaction analysis.

Liyuan Han1,2, Donghui Duan1,2,3, Shuang Zhang4, Weiqin Li4, Leishen Wang4, Huikun Liu4, Junhong Leng4, Nan Li4, Xiaoyun Shang5, Gang Hu6, Lu Qi2,7,8,9.   

Abstract

AIM: To investigate the effects of the interaction between glycated haemoglobin (HbA1c) genetic risk score and weight changes during and after pregnancy (postpartum weight reduction and gestational weight gain) on long-term glycaemic changes in the largest cohort of women with a history of gestational diabetes mellitus (GDM).
METHODS: This was a retrospective cohort using the baseline data from the Tianjin Gestational Diabetes Mellitus Prevention Programme. A genetic risk score was established by combining 10 HbA1c-related single-nucleotide polymorphisms, which were identified by genome-wide association studies. General linear regression models were applied to evaluate the effect of interaction between HbA1c genetic risk score and weight changes during and after pregnancy (postpartum weight reduction and gestational weight gain) on glycaemic changes.
RESULTS: 'A total of 1156 women with a history of GDM were included in this respective cohort study. Statistical differences in pre-pregnancy weight, pre-delivery weight and postpartum weight were evidenced across different groups of postpartum weight reduction. After adjusting for covariates, statistical significance for changes in HbA1c level was only observed in the postpartum weight reduction <5 kg/y group (P = 0.002), and a significant effect of interaction between HbA1c genetic risk score and postpartum weight reduction on long-term changes in HbA1c was evidenced (P interaction = 0.01). In women with postpartum weight reduction ≥8 kg/y, those with a lower HbA1c genetic risk score had a greater decrease in HbA1c level.
CONCLUSIONS: HbA1c genetic risk score interacts with postpartum weight reduction to affect long-term changes in HbA1c levels among women with a history of GDM.
© 2018 John Wiley & Sons Ltd.

Entities:  

Keywords:  genetic risk score; gestational diabetes mellitus; glycaemic changes; postpartum weight reduction

Mesh:

Substances:

Year:  2018        PMID: 29974585      PMCID: PMC6231972          DOI: 10.1111/dom.13452

Source DB:  PubMed          Journal:  Diabetes Obes Metab        ISSN: 1462-8902            Impact factor:   6.577


  28 in total

1.  Increasing prevalence of gestational diabetes mellitus in Chinese women from 1999 to 2008.

Authors:  F Zhang; L Dong; C P Zhang; B Li; J Wen; W Gao; S Sun; F Lv; H Tian; J Tuomilehto; L Qi; C L Zhang; Z Yu; X Yang; G Hu
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2.  Parity and incidence of non-insulin-dependent diabetes mellitus.

Authors:  J E Manson; E B Rimm; G A Colditz; M J Stampfer; W C Willett; R A Arky; B Rosner; C H Hennekens; F E Speizer
Journal:  Am J Med       Date:  1992-07       Impact factor: 4.965

3.  Obesity index and the risk of diabetes among Chinese women with prior gestational diabetes.

Authors:  L Wang; H Liu; S Zhang; J Leng; G Liu; C Zhang; W Q Li; N Li; W Li; Y Li; S Sun; Z Yu; X Yang; G Hu
Journal:  Diabet Med       Date:  2014-07-09       Impact factor: 4.359

Review 4.  Risk factors for type 2 diabetes among women with gestational diabetes: a systematic review.

Authors:  Kesha Baptiste-Roberts; Bethany B Barone; Tiffany L Gary; Sherita H Golden; Lisa M Wilson; Eric B Bass; Wanda K Nicholson
Journal:  Am J Med       Date:  2009-03       Impact factor: 4.965

5.  Geographical variation in the major risk factors of coronary heart disease in men and women aged 35-64 years. The WHO MONICA Project.

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Journal:  World Health Stat Q       Date:  1988

6.  Cardiometabolic implications of postpartum weight changes in the first year after delivery.

Authors:  Simone Kew; Chang Ye; Anthony J Hanley; Philip W Connelly; Mathew Sermer; Bernard Zinman; Ravi Retnakaran
Journal:  Diabetes Care       Date:  2014-03-25       Impact factor: 19.112

7.  Common variants at 10 genomic loci influence hemoglobin A₁(C) levels via glycemic and nonglycemic pathways.

Authors:  Nicole Soranzo; Serena Sanna; Eleanor Wheeler; Christian Gieger; Dörte Radke; Josée Dupuis; Nabila Bouatia-Naji; Claudia Langenberg; Inga Prokopenko; Elliot Stolerman; Manjinder S Sandhu; Matthew M Heeney; Joseph M Devaney; Muredach P Reilly; Sally L Ricketts; Alexandre F R Stewart; Benjamin F Voight; Christina Willenborg; Benjamin Wright; David Altshuler; Dan Arking; Beverley Balkau; Daniel Barnes; Eric Boerwinkle; Bernhard Böhm; Amélie Bonnefond; Lori L Bonnycastle; Dorret I Boomsma; Stefan R Bornstein; Yvonne Böttcher; Suzannah Bumpstead; Mary Susan Burnett-Miller; Harry Campbell; Antonio Cao; John Chambers; Robert Clark; Francis S Collins; Josef Coresh; Eco J C de Geus; Mariano Dei; Panos Deloukas; Angela Döring; Josephine M Egan; Roberto Elosua; Luigi Ferrucci; Nita Forouhi; Caroline S Fox; Christopher Franklin; Maria Grazia Franzosi; Sophie Gallina; Anuj Goel; Jürgen Graessler; Harald Grallert; Andreas Greinacher; David Hadley; Alistair Hall; Anders Hamsten; Caroline Hayward; Simon Heath; Christian Herder; Georg Homuth; Jouke-Jan Hottenga; Rachel Hunter-Merrill; Thomas Illig; Anne U Jackson; Antti Jula; Marcus Kleber; Christopher W Knouff; Augustine Kong; Jaspal Kooner; Anna Köttgen; Peter Kovacs; Knut Krohn; Brigitte Kühnel; Johanna Kuusisto; Markku Laakso; Mark Lathrop; Cécile Lecoeur; Man Li; Mingyao Li; Ruth J F Loos; Jian'an Luan; Valeriya Lyssenko; Reedik Mägi; Patrik K E Magnusson; Anders Mälarstig; Massimo Mangino; María Teresa Martínez-Larrad; Winfried März; Wendy L McArdle; Ruth McPherson; Christa Meisinger; Thomas Meitinger; Olle Melander; Karen L Mohlke; Vincent E Mooser; Mario A Morken; Narisu Narisu; David M Nathan; Matthias Nauck; Chris O'Donnell; Konrad Oexle; Nazario Olla; James S Pankow; Felicity Payne; John F Peden; Nancy L Pedersen; Leena Peltonen; Markus Perola; Ozren Polasek; Eleonora Porcu; Daniel J Rader; Wolfgang Rathmann; Samuli Ripatti; Ghislain Rocheleau; Michael Roden; Igor Rudan; Veikko Salomaa; Richa Saxena; David Schlessinger; Heribert Schunkert; Peter Schwarz; Udo Seedorf; Elizabeth Selvin; Manuel Serrano-Ríos; Peter Shrader; Angela Silveira; David Siscovick; Kjioung Song; Timothy D Spector; Kari Stefansson; Valgerdur Steinthorsdottir; David P Strachan; Rona Strawbridge; Michael Stumvoll; Ida Surakka; Amy J Swift; Toshiko Tanaka; Alexander Teumer; Gudmar Thorleifsson; Unnur Thorsteinsdottir; Anke Tönjes; Gianluca Usala; Veronique Vitart; Henry Völzke; Henri Wallaschofski; Dawn M Waterworth; Hugh Watkins; H-Erich Wichmann; Sarah H Wild; Gonneke Willemsen; Gordon H Williams; James F Wilson; Juliane Winkelmann; Alan F Wright; Carina Zabena; Jing Hua Zhao; Stephen E Epstein; Jeanette Erdmann; Hakon H Hakonarson; Sekar Kathiresan; Kay-Tee Khaw; Robert Roberts; Nilesh J Samani; Mark D Fleming; Robert Sladek; Gonçalo Abecasis; Michael Boehnke; Philippe Froguel; Leif Groop; Mark I McCarthy; W H Linda Kao; Jose C Florez; Manuela Uda; Nicholas J Wareham; Inês Barroso; James B Meigs
Journal:  Diabetes       Date:  2010-09-21       Impact factor: 9.461

8.  Fried food consumption, genetic risk, and body mass index: gene-diet interaction analysis in three US cohort studies.

Authors:  Qibin Qi; Audrey Y Chu; Jae H Kang; Jinyan Huang; Lynda M Rose; Majken K Jensen; Liming Liang; Gary C Curhan; Louis R Pasquale; Janey L Wiggs; Immaculata De Vivo; Andrew T Chan; Hyon K Choi; Rulla M Tamimi; Paul M Ridker; David J Hunter; Walter C Willett; Eric B Rimm; Daniel I Chasman; Frank B Hu; Lu Qi
Journal:  BMJ       Date:  2014-03-19

9.  Long-term changes in glucose metabolism after gestational diabetes: a double cohort study.

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Journal:  BMC Pregnancy Childbirth       Date:  2014-08-30       Impact factor: 3.007

10.  Power and predictive accuracy of polygenic risk scores.

Authors:  Frank Dudbridge
Journal:  PLoS Genet       Date:  2013-03-21       Impact factor: 5.917

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