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Bline AP, Ellis LB, Pelch KE, Lam J, Sen S, Zlatnik M, Varshavsky J. The effect of per and polyfluoroalkyl substance (PFAS) exposure on gestational diabetes mellitus and its subclinical risk factors: A systematic review and meta-analysis protocol. ENVIRONMENT INTERNATIONAL 2024; 188:108711. [PMID: 38754246 DOI: 10.1016/j.envint.2024.108711] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 03/30/2024] [Accepted: 04/27/2024] [Indexed: 05/18/2024]
Abstract
BACKGROUND Multiple lines of evidence suggest that exposure to per- and polyfluoroalkyl substances (PFAS) may alter glucose homeostasis, particularly during pregnancy, and may affect risk for developing gestational diabetes mellitus (GDM). While previous systematic reviews have been conducted on this topic, they did not assess internal validity of the included studies and their search strategies were narrowly focused. OBJECTIVE The objective of this study is to assess the effect of higher PFAS exposure (defined by individual compounds or mixtures measured before or during pregnancy) on GDM and subclinical measures of impaired glucose homeostasis (measured during pregnancy) compared to lower PFAS exposure in pregnant. METHODS We developed our systematic review protocol in accordance with the Navigation Guide. Peer-reviewed journal and grey literature searches were piloted in to identify relevant studies and refine our search terms and strategy. We also piloted the study screening criteria and data extraction form in DistillerSR, and refined our protocol accordingly. The risk of bias assessment protocol was adapted from Navigation Guide guidance and will be piloted and performed in DistillerSR. Pending the identification of comparable studies, quantitative meta-analyses will be performed where possible. Study results that cannot be quantitatively synthesized will be included in a narrative synthesis. The quality and strength of the body of evidence will be evaluated using Navigation Guide methodology, which is informed by guidance from the Cochrane Collaboration and Grading of Recommendations Assessment, Development and Evaluation (GRADE). We also made refinements to the quality of evidence considerations based on guidance from the National Institute of Environmental Health Sciences (NIEHS) Office of Health Assessment and Translation (OHAT). FUNDING This work was supported by the Systematizing Data on Per- and Polyfluoroalkyl Substances and Health Northeastern University TIER 1 Award.
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Affiliation(s)
- Abigail P Bline
- Social Science Environmental Health Research Institute, Northeastern University, Boston, MA, United States; Silent Spring Institute, Newton, MA, United States.
| | - Lauren B Ellis
- Social Science Environmental Health Research Institute, Northeastern University, Boston, MA, United States; Department of Health Sciences, Northeastern University, Boston, MA, United States.
| | - Katherine E Pelch
- Natural Resources Defense Council, San Francisco, CA, United States.
| | - Juleen Lam
- Department of Public Health, California State University, East Bay, Hayward, CA, United States.
| | - Saunak Sen
- College of Medicine, The University of Tennessee Health Science Center, Memphis, TN, United States.
| | - Marya Zlatnik
- Department of Obstetrics, Gynecology and Reproductive Sciences, Program on Reproductive Health and the Environment, University of California San Francisco, San Francisco, CA, United States.
| | - Julia Varshavsky
- Social Science Environmental Health Research Institute, Northeastern University, Boston, MA, United States; Department of Health Sciences, Northeastern University, Boston, MA, United States; Department of Civil and Environmental Engineering, Northeastern University, Boston, MA, United States.
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2
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Goyal S, Rani J, Bhat MA, Vanita V. Genetics of diabetes. World J Diabetes 2023; 14:656-679. [PMID: 37383588 PMCID: PMC10294065 DOI: 10.4239/wjd.v14.i6.656] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Revised: 03/13/2023] [Accepted: 04/17/2023] [Indexed: 06/14/2023] Open
Abstract
Diabetes mellitus is a complicated disease characterized by a complex interplay of genetic, epigenetic, and environmental variables. It is one of the world's fastest-growing diseases, with 783 million adults expected to be affected by 2045. Devastating macrovascular consequences (cerebrovascular disease, cardiovascular disease, and peripheral vascular disease) and microvascular complications (like retinopathy, nephropathy, and neuropathy) increase mortality, blindness, kidney failure, and overall quality of life in individuals with diabetes. Clinical risk factors and glycemic management alone cannot predict the development of vascular problems; multiple genetic investigations have revealed a clear hereditary component to both diabetes and its related complications. In the twenty-first century, technological advancements (genome-wide association studies, next-generation sequencing, and exome-sequencing) have led to the identification of genetic variants associated with diabetes, however, these variants can only explain a small proportion of the total heritability of the condition. In this review, we address some of the likely explanations for this "missing heritability", for diabetes such as the significance of uncommon variants, gene-environment interactions, and epigenetics. Current discoveries clinical value, management of diabetes, and future research directions are also discussed.
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Affiliation(s)
- Shiwali Goyal
- Department of Ophthalmic Genetics and Visual Function Branch, National Eye Institute, Rockville, MD 20852, United States
| | - Jyoti Rani
- Department of Human Genetics, Guru Nanak Dev University, Amritsar 143005, Punjab, India
| | - Mohd Akbar Bhat
- Department of Ophthalmology, Georgetown University Medical Center, Washington DC, DC 20057, United States
| | - Vanita Vanita
- Department of Human Genetics, Guru Nanak Dev University, Amritsar 143005, Punjab, India
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3
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Zulueta M, Gallardo-Rincón H, Martinez-Juarez LA, Lomelin-Gascon J, Ortega-Montiel J, Montoya A, Mendizabal L, Arregi M, Martinez-Martinez MDLA, Camarillo Romero EDS, Mendieta Zerón H, Garduño García JDJ, Simón L, Tapia-Conyer R. Development and validation of a multivariable genotype-informed gestational diabetes prediction algorithm for clinical use in the Mexican population: insights into susceptibility mechanisms. BMJ Open Diabetes Res Care 2023; 11:11/2/e003046. [PMID: 37085278 PMCID: PMC10124192 DOI: 10.1136/bmjdrc-2022-003046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Accepted: 04/01/2023] [Indexed: 04/23/2023] Open
Abstract
INTRODUCTION Gestational diabetes mellitus (GDM) is underdiagnosed in Mexico. Early GDM risk stratification through prediction modeling is expected to improve preventative care. We developed a GDM risk assessment model that integrates both genetic and clinical variables. RESEARCH DESIGN AND METHODS Data from pregnant Mexican women enrolled in the 'Cuido mi Embarazo' (CME) cohort were used for development (107 cases, 469 controls) and data from the 'Mónica Pretelini Sáenz' Maternal Perinatal Hospital (HMPMPS) cohort were used for external validation (32 cases, 199 controls). A 2-hour oral glucose tolerance test (OGTT) with 75 g glucose performed at 24-28 gestational weeks was used to diagnose GDM. A total of 114 single-nucleotide polymorphisms (SNPs) with reported predictive power were selected for evaluation. Blood samples collected during the OGTT were used for SNP analysis. The CME cohort was randomly divided into training (70% of the cohort) and testing datasets (30% of the cohort). The training dataset was divided into 10 groups, 9 to build the predictive model and 1 for validation. The model was further validated using the testing dataset and the HMPMPS cohort. RESULTS Nineteen attributes (14 SNPs and 5 clinical variables) were significantly associated with the outcome; 11 SNPs and 4 clinical variables were included in the GDM prediction regression model and applied to the training dataset. The algorithm was highly predictive, with an area under the curve (AUC) of 0.7507, 79% sensitivity, and 71% specificity and adequately powered to discriminate between cases and controls. On further validation, the training dataset and HMPMPS cohort had AUCs of 0.8256 and 0.8001, respectively. CONCLUSIONS We developed a predictive model using both genetic and clinical factors to identify Mexican women at risk of developing GDM. These findings may contribute to a greater understanding of metabolic functions that underlie elevated GDM risk and support personalized patient recommendations.
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Affiliation(s)
- Mirella Zulueta
- Research and Development Department, Patia Europe, San Sebastian, Spain
| | - Héctor Gallardo-Rincón
- Health Sciences University Center, University of Guadalajara, Guadalajara, Mexico
- Operative Solutions, Carlos Slim Foundation, Mexico City, Mexico
| | | | | | | | | | - Leire Mendizabal
- Research and Development Department, Patia Europe, San Sebastian, Spain
| | - Maddi Arregi
- Research and Development Department, Patia Europe, San Sebastian, Spain
| | | | | | - Hugo Mendieta Zerón
- Faculty of Medicine, Autonomous University of the State of Mexico, Toluca, Mexico
| | | | - Laureano Simón
- Research and Development Department, Patia Europe, San Sebastian, Spain
| | - Roberto Tapia-Conyer
- Faculty of Medicine, National Autonomous University of Mexico, Mexico City, Mexico
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Role of melatonin receptor 1B gene polymorphism and its effect on the regulation of glucose transport in gestational diabetes mellitus. J Zhejiang Univ Sci B 2023; 24:78-88. [PMID: 36632752 PMCID: PMC9837374 DOI: 10.1631/jzus.b2200136] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
Melatonin receptor 1B (MT2, encoded by the MTNR1B gene), a high-affinity receptor for melatonin, is associated with glucose homeostasis including glucose uptake and transport. The rs10830963 variant in the MTNR1B gene is linked to glucose metabolism disorders including gestational diabetes mellitus (GDM); however, the relationship between MT2-mediated melatonin signaling and a high birth weight of GDM infants from maternal glucose abnormality remains poorly understood. This article aims to investigate the relationship between rs10830963 variants and GDM development, as well as the effects of MT2 receptor on glucose uptake and transport in trophoblasts. TaqMan-MGB (minor groove binder) probe quantitative real-time polymerase chain reaction (qPCR) assays were used for rs10930963 genotyping. MT2 expression in the placenta of GDM and normal pregnant women was detected by immunofluorescence, western blot, and qPCR. The relationship between MT2 and glucose transporters (GLUTs) or peroxisome proliferator-activated receptor γ (PPARγ) was established by western blot, and glucose consumption of trophoblasts was measured by a glucose assay kit. The results showed that the genotype and allele frequencies of rs10830963 were significantly different between GDM and normal pregnant women (P<0.05). The fasting, 1-h and 2-h plasma glucose levels of G-allele carriers were significantly higher than those of C-allele carriers (P<0.05). Besides, the protein and messenger RNA (mRNA) expression of MT2 in the placenta of GDM was significantly higher than that of normal pregnant women (P<0.05). Melatonin could stimulate glucose uptake and GLUT4 and PPARγ protein expression in trophoblasts, which could be attenuated by MT2 receptor knockdown. In conclusion, the rs10830963 variant was associated with an increased risk of GDM. The MT2 receptor is essential for melatonin to raise glucose uptake and transport, which may be mediated by PPARγ.
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Svyatova G, Berezina G, Danyarova L, Kuanyshbekova R, Urazbayeva G. Genetic predisposition to gestational diabetes mellitus in the Kazakh population. Diabetes Metab Syndr 2022; 16:102675. [PMID: 36427366 DOI: 10.1016/j.dsx.2022.102675] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Revised: 11/08/2022] [Accepted: 11/15/2022] [Indexed: 11/19/2022]
Abstract
BACKGROUND AND AIMS The purpose of the study was to conduct a comparative analysis of population frequencies of alleles and genotypes of polymorphic variants of genes for impaired insulin synthesis and associated with insulin signal transduction. METHODS This investigation uses a genomic database of 1800 conditionally healthy individuals of Kazakh ethnicity, who underwent full genome genotyping using OmniChip 2.5-8 Illumina chips of ∼2.5 million Single Nucleotide Polymorphism at deCODE Iceland Genomic Centre. RESULTS The highest frequency of carriage of minor A allele - 17.6% rs4607517 polymorphism of Glucokinase gene, unfavorable genotypes A/G - 29.5% and A/A - 3.0% in comparison with European and Asian populations, indicates a contribution of hereditary family forms of Maturity-onset diabetes of the young type 2 to gestational diabetes mellitus in Kazakh population. CONCLUSIONS The study of the associations of genetic markers of gestational diabetes mellitus will allow timely identification of high-risk groups before and at an early stage of pregnancy, carrying out the necessary effective preventive measures and, in the case of gestational diabetes mellitus development, optimizing the correction of carbohydrate metabolism disorders and predicting outcomes for the mother and the fetus.
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Affiliation(s)
- Gulnara Svyatova
- Republican Medical Genetic Consultation, Scientific Center of Obstetrics, Gynecology and Perinatology, Almaty, Kazakhstan
| | - Galina Berezina
- Republican Medical Genetic Consultation, Scientific Center of Obstetrics, Gynecology and Perinatology, Almaty, Kazakhstan
| | - Laura Danyarova
- Department of Scientific Research Management, Scientific-Research Institute of Cardiology and Internal Diseases, Almaty, Kazakhstan.
| | - Roza Kuanyshbekova
- Scientific-Research Institute of Cardiology and Internal Diseases, Almaty, Kazakhstan
| | - Gulfairuz Urazbayeva
- Scientific Center of Obstetrics, Gynecology and Perinatology, Almaty, Kazakhstan
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6
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Jääskeläinen T, Klemetti MM. Genetic Risk Factors and Gene-Lifestyle Interactions in Gestational Diabetes. Nutrients 2022; 14:nu14224799. [PMID: 36432486 PMCID: PMC9694797 DOI: 10.3390/nu14224799] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Revised: 11/08/2022] [Accepted: 11/10/2022] [Indexed: 11/16/2022] Open
Abstract
Paralleling the increasing trends of maternal obesity, gestational diabetes (GDM) has become a global health challenge with significant public health repercussions. In addition to short-term adverse outcomes, such as hypertensive pregnancy disorders and fetal macrosomia, in the long term, GDM results in excess cardiometabolic morbidity in both the mother and child. Recent data suggest that women with GDM are characterized by notable phenotypic and genotypic heterogeneity and that frequencies of adverse obstetric and perinatal outcomes are different between physiologic GDM subtypes. However, as of yet, GDM treatment protocols do not differentiate between these subtypes. Mapping the genetic architecture of GDM, as well as accurate phenotypic and genotypic definitions of GDM, could potentially help in the individualization of GDM treatment and assessment of long-term prognoses. In this narrative review, we outline recent studies exploring genetic risk factors of GDM and later type 2 diabetes (T2D) in women with prior GDM. Further, we discuss the current evidence on gene-lifestyle interactions in the development of these diseases. In addition, we point out specific research gaps that still need to be addressed to better understand the complex genetic and metabolic crosstalk within the mother-placenta-fetus triad that contributes to hyperglycemia in pregnancy.
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Affiliation(s)
- Tiina Jääskeläinen
- Department of Food and Nutrition, University of Helsinki, P.O. Box 66, 00014 Helsinki, Finland
- Department of Medical and Clinical Genetics, University of Helsinki, P.O. Box 63, 00014 Helsinki, Finland
- Correspondence:
| | - Miira M. Klemetti
- Department of Medical and Clinical Genetics, University of Helsinki, P.O. Box 63, 00014 Helsinki, Finland
- Department of Obstetrics and Gynecology, Helsinki University Hospital, University of Helsinki, P.O. Box 140, 00029 Helsinki, Finland
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7
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Pervjakova N, Moen GH, Borges MC, Ferreira T, Cook JP, Allard C, Beaumont RN, Canouil M, Hatem G, Heiskala A, Joensuu A, Karhunen V, Kwak SH, Lin FTJ, Liu J, Rifas-Shiman S, Tam CH, Tam WH, Thorleifsson G, Andrew T, Auvinen J, Bhowmik B, Bonnefond A, Delahaye F, Demirkan A, Froguel P, Haller-Kikkatalo K, Hardardottir H, Hummel S, Hussain A, Kajantie E, Keikkala E, Khamis A, Lahti J, Lekva T, Mustaniemi S, Sommer C, Tagoma A, Tzala E, Uibo R, Vääräsmäki M, Villa PM, Birkeland KI, Bouchard L, Duijn CM, Finer S, Groop L, Hämäläinen E, Hayes GM, Hitman GA, Jang HC, Järvelin MR, Jenum AK, Laivuori H, Ma RC, Melander O, Oken E, Park KS, Perron P, Prasad RB, Qvigstad E, Sebert S, Stefansson K, Steinthorsdottir V, Tuomi T, Hivert MF, Franks PW, McCarthy MI, Lindgren CM, Freathy RM, Lawlor DA, Morris AP, Mägi R. Multi-ancestry genome-wide association study of gestational diabetes mellitus highlights genetic links with type 2 diabetes. Hum Mol Genet 2022; 31:3377-3391. [PMID: 35220425 PMCID: PMC9523562 DOI: 10.1093/hmg/ddac050] [Citation(s) in RCA: 37] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Revised: 02/09/2022] [Accepted: 02/23/2022] [Indexed: 11/12/2022] Open
Abstract
Gestational diabetes mellitus (GDM) is associated with increased risk of pregnancy complications and adverse perinatal outcomes. GDM often reoccurs and is associated with increased risk of subsequent diagnosis of type 2 diabetes (T2D). To improve our understanding of the aetiological factors and molecular processes driving the occurrence of GDM, including the extent to which these overlap with T2D pathophysiology, the GENetics of Diabetes In Pregnancy Consortium assembled genome-wide association studies of diverse ancestry in a total of 5485 women with GDM and 347 856 without GDM. Through multi-ancestry meta-analysis, we identified five loci with genome-wide significant association (P < 5 × 10-8) with GDM, mapping to/near MTNR1B (P = 4.3 × 10-54), TCF7L2 (P = 4.0 × 10-16), CDKAL1 (P = 1.6 × 10-14), CDKN2A-CDKN2B (P = 4.1 × 10-9) and HKDC1 (P = 2.9 × 10-8). Multiple lines of evidence pointed to the shared pathophysiology of GDM and T2D: (i) four of the five GDM loci (not HKDC1) have been previously reported at genome-wide significance for T2D; (ii) significant enrichment for associations with GDM at previously reported T2D loci; (iii) strong genetic correlation between GDM and T2D and (iv) enrichment of GDM associations mapping to genomic annotations in diabetes-relevant tissues and transcription factor binding sites. Mendelian randomization analyses demonstrated significant causal association (5% false discovery rate) of higher body mass index on increased GDM risk. Our results provide support for the hypothesis that GDM and T2D are part of the same underlying pathology but that, as exemplified by the HKDC1 locus, there are genetic determinants of GDM that are specific to glucose regulation in pregnancy.
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Affiliation(s)
- Natalia Pervjakova
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu 51010, Estonia
| | - Gunn-Helen Moen
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
- Diamantina Institute, The University of Queensland, Woolloongabba QLD 4102, Australia
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, UK
| | - Maria-Carolina Borges
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, UK
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
| | - Teresa Ferreira
- Big Data Institute, Li Ka Shing Center for Health for Health Information and Discovery, Oxford University, Oxford, UK
| | - James P Cook
- Department of Health Data Science, University of Liverpool, Liverpool, UK
| | - Catherine Allard
- Centre de Recherche du Centre Hospitalier Universitaire de Sherbrooke (CRCHUS), Universite de Sherbrooke, Quebec, Canada
| | - Robin N Beaumont
- Institute of Biomedical and Clinical Science, College of Medicine and Health, University of Exeter, Exeter, UK
| | - Mickaël Canouil
- Inserm U1283, CNRS UMR 8199, European Genomic Institute for Diabetes, Institut Pasteur de Lille, Lille F-59000, France
- University of Lille, Lille University Hospital, Lille F-59000, France
| | - Gad Hatem
- Department of Clinical Sciences, Lund University, Skåne University Hospital, Lund University Diabetes Centre, Malmö SE-20502, Sweden
| | - Anni Heiskala
- Centre for Life-Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Anni Joensuu
- Department of Public Health Solutions, Finnish Institute for Health and Welfare, Helsinki, Finland
- Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Ville Karhunen
- Centre for Life-Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland
- School of Public Health, Department of Epidemiology and Biostatistics, Imperial College London, St Mary's Hospital, London, UK
| | - Soo Heon Kwak
- Department of Internal Medicine, Seoul National University Hospital, Seoul, Republic of Korea
| | - Frederick T J Lin
- Division of Endocrinology, Metabolism, and Molecular Medicine, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Jun Liu
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, the Netherlands
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Sheryl Rifas-Shiman
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, USA
| | - Claudia H Tam
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong SAR, The People's Republic of China
| | - Wing Hung Tam
- Department of Obstetrics and Gynaecology, The Chinese University of Hong Kong, Hong Kong SAR, The People's Republic of China
| | | | - Toby Andrew
- Inserm U1283, CNRS UMR 8199, European Genomic Institute for Diabetes, Institut Pasteur de Lille, Lille F-59000, France
- University of Lille, Lille University Hospital, Lille F-59000, France
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - Juha Auvinen
- Centre for Life-Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Bishwajit Bhowmik
- Centre of Global Health Research, Diabetic Association of Bangladesh, Dhaka, Bangladesh
| | - Amélie Bonnefond
- Inserm U1283, CNRS UMR 8199, European Genomic Institute for Diabetes, Institut Pasteur de Lille, Lille F-59000, France
- University of Lille, Lille University Hospital, Lille F-59000, France
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - Fabien Delahaye
- Inserm U1283, CNRS UMR 8199, European Genomic Institute for Diabetes, Institut Pasteur de Lille, Lille F-59000, France
- University of Lille, Lille University Hospital, Lille F-59000, France
| | - Ayse Demirkan
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, the Netherlands
- Section of Statistical Multi-omics, Department of Clinical and Experimental Research, University of Surrey, Surrey, UK
| | - Philippe Froguel
- Inserm U1283, CNRS UMR 8199, European Genomic Institute for Diabetes, Institut Pasteur de Lille, Lille F-59000, France
- University of Lille, Lille University Hospital, Lille F-59000, France
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - Kadri Haller-Kikkatalo
- Department of Immunology, Institute of Biomedicine and Translational Medicine, University of Tartu, Tartu, Estonia
| | - Hildur Hardardottir
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
- Livio Reykjavik, Reykjavik, Iceland
| | - Sandra Hummel
- Institute of Diabetes Research, Helmholtz Zentrum München, German Research Center for Environmental Health, Munich-Neuherberg, Germany
- Forschergruppe Diabetes, Technical University Munich, at Klinikum rechts der Isar, Munich, Germany
| | - Akhtar Hussain
- Centre of Global Health Research, Diabetic Association of Bangladesh, Dhaka, Bangladesh
- Faculty of Health Sciences, Nord University, Bodø, Norway
| | - Eero Kajantie
- Population Health Unit, Finnish Institute for Health and Welfare, Helsinki and Oulu, Finland
- PEDEGO Research Unit, MRC Oulu, Oulu University Hospital and University of Oulu, Oulu, Finland
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway
| | - Elina Keikkala
- Population Health Unit, Finnish Institute for Health and Welfare, Helsinki and Oulu, Finland
- PEDEGO Research Unit, MRC Oulu, Oulu University Hospital and University of Oulu, Oulu, Finland
| | - Amna Khamis
- Inserm U1283, CNRS UMR 8199, European Genomic Institute for Diabetes, Institut Pasteur de Lille, Lille F-59000, France
- University of Lille, Lille University Hospital, Lille F-59000, France
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - Jari Lahti
- Department of Psychology and Logopedics, University of Helsinki, Helsinki, Finland
| | - Tove Lekva
- Research Institute of Internal Medicine, Oslo University Hospital, Oslo, Norway
| | - Sanna Mustaniemi
- Population Health Unit, Finnish Institute for Health and Welfare, Helsinki and Oulu, Finland
- PEDEGO Research Unit, MRC Oulu, Oulu University Hospital and University of Oulu, Oulu, Finland
| | - Christine Sommer
- Department of Endocrinology, Morbid Obesity and Preventive Medicine, Oslo University Hospital, Oslo, Norway
| | - Aili Tagoma
- Department of Immunology, Institute of Biomedicine and Translational Medicine, University of Tartu, Tartu, Estonia
| | - Evangelia Tzala
- School of Public Health, Department of Epidemiology and Biostatistics, Imperial College London, St Mary's Hospital, London, UK
| | - Raivo Uibo
- Department of Immunology, Institute of Biomedicine and Translational Medicine, University of Tartu, Tartu, Estonia
| | - Marja Vääräsmäki
- PEDEGO Research Unit, MRC Oulu, Oulu University Hospital and University of Oulu, Oulu, Finland
- Population Health Unit, Finnish Institute for Health and Welfare, Helsinki and Oulu, Finland
| | - Pia M Villa
- Department of Obstetrics and Gynaecology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Hyvinkää Hospital, Helsinki and Uusimaa Hospital District, Hyvinkää, Finland
| | - Kåre I Birkeland
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
- Department of Transplantation Medicine, Oslo University Hospital, Oslo, Norway
| | - Luigi Bouchard
- Department of Biochemistry and Functional Genomics, Faculty of Medicine and Health Sciences, Universite de Sherbrooke, Quebec, Canada
- Department of Medical Biology, Centre Intégré Universitaire de Santé et de Services Sociaux du Saguenay–Lac-St-Jean – Hôpital de Chicoutimi, Québec, Canada
| | - Cornelia M Duijn
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, the Netherlands
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Sarah Finer
- Centre for Genomics and Child Health, Blizard Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Leif Groop
- Department of Clinical Sciences, Lund University, Skåne University Hospital, Lund University Diabetes Centre, Malmö SE-20502, Sweden
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
| | - Esa Hämäläinen
- Department of Clinical Chemistry, University of Eastern Finland, Kuopio, Finland
| | - Geoffrey M Hayes
- Division of Endocrinology, Metabolism, and Molecular Medicine, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
- Center for Genetic Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
- Department of Anthropology, Northwestern University, Evanston, IL 60208, USA
| | - Graham A Hitman
- Centre for Genomics and Child Health, Blizard Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Hak C Jang
- Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Marjo-Riitta Järvelin
- Centre for Life-Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland
- School of Public Health, Department of Epidemiology and Biostatistics, Imperial College London, St Mary's Hospital, London, UK
| | - Anne Karen Jenum
- General Practice Research Unit (AFE), Department of General Practice, Institute of Health and Society, Faculty of Medicine, University of Oslo, Post Box 1130 Blindern, Oslo 0318, Norway
| | - Hannele Laivuori
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
- Department of Obstetrics and Gynecology, Tampere University, Hospital and Faculty of Medicine and Health Technology, Center for Child, Adolescent, and Maternal Health, Tampere University, Tampere, Finland
- Medical and Clinical Genetics, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Ronald C Ma
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong SAR, The People's Republic of China
- Laboratory for Molecular Epidemiology in Diabetes, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong SAR, The People's Republic of China
- Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong SAR, The People's Republic of China
| | - Olle Melander
- Department of Clinical Sciences, Lund University, Skåne University Hospital, Lund University Diabetes Centre, Malmö SE-20502, Sweden
| | - Emily Oken
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, USA
| | - Kyong Soo Park
- Department of Internal Medicine, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
- Department of Molecular Medicine and Biopharmaceutical Sciences, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Republic of Korea
| | - Patrice Perron
- Centre de Recherche du Centre Hospitalier Universitaire de Sherbrooke (CRCHUS), Universite de Sherbrooke, Quebec, Canada
- Department of Medicine, Faculty of Medicine and Health Sciences, University of Sherbrook, Québec, Canada
| | - Rashmi B Prasad
- Department of Clinical Sciences, Lund University, Skåne University Hospital, Lund University Diabetes Centre, Malmö SE-20502, Sweden
| | - Elisabeth Qvigstad
- Department of Endocrinology, Morbid Obesity and Preventive Medicine, Oslo University Hospital, Oslo, Norway
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Sylvain Sebert
- Centre for Life-Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Kari Stefansson
- deCODE Genetics/Amgen, Inc., Reykjavik, Iceland
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | | | - Tiinamaija Tuomi
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
- Department of Clinical Sciences, Lund University, Skåne University Hospital, Lund University Diabetes Centre, Malmö SE-20502, Sweden
- Department of Endocrinology, Abdominal Centre, Helsinki University Hospital, Helsinki, Finland
- Folkhalsan Research Center, Helsinki, Finland
| | - Marie-France Hivert
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, USA
- Department of Medicine, Faculty of Medicine and Health Sciences, University of Sherbrook, Québec, Canada
- Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Paul W Franks
- Department of Clinical Sciences, Lund University, Malmö, Sweden
- Department of Nutrition, Harvard School of Public Health, Boston, MA, USA
| | - Mark I McCarthy
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Cecilia M Lindgren
- Big Data Institute, Li Ka Shing Center for Health for Health Information and Discovery, Oxford University, Oxford, UK
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Program in Medical and Population Genetics, Broad Institute, Boston, MA, USA
| | - Rachel M Freathy
- Institute of Biomedical and Clinical Science, College of Medicine and Health, University of Exeter, Exeter, UK
| | - Deborah A Lawlor
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, UK
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Bristol NIHR Biomedical Research Centre, Bristol, UK
| | - Andrew P Morris
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, Division of Musculoskeletal and Dermatological Sciences, University of Manchester, Manchester, UK
| | - Reedik Mägi
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu 51010, Estonia
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8
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Abstract
Gestational diabetes mellitus (GDM) traditionally refers to abnormal glucose tolerance with onset or first recognition during pregnancy. GDM has long been associated with obstetric and neonatal complications primarily relating to higher infant birthweight and is increasingly recognized as a risk factor for future maternal and offspring cardiometabolic disease. The prevalence of GDM continues to rise internationally due to epidemiological factors including the increase in background rates of obesity in women of reproductive age and rising maternal age and the implementation of the revised International Association of the Diabetes and Pregnancy Study Groups' criteria and diagnostic procedures for GDM. The current lack of international consensus for the diagnosis of GDM reflects its complex historical evolution and pragmatic antenatal resource considerations given GDM is now 1 of the most common complications of pregnancy. Regardless, the contemporary clinical approach to GDM should be informed not only by its short-term complications but also by its longer term prognosis. Recent data demonstrate the effect of early in utero exposure to maternal hyperglycemia, with evidence for fetal overgrowth present prior to the traditional diagnosis of GDM from 24 weeks' gestation, as well as the durable adverse impact of maternal hyperglycemia on child and adolescent metabolism. The major contribution of GDM to the global epidemic of intergenerational cardiometabolic disease highlights the importance of identifying GDM as an early risk factor for type 2 diabetes and cardiovascular disease, broadening the prevailing clinical approach to address longer term maternal and offspring complications following a diagnosis of GDM.
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Affiliation(s)
- Arianne Sweeting
- Department of Endocrinology, Royal Prince Alfred Hospital, Sydney, Australia.,Faculty of Medicine and Health, University of Sydney, Sydney, Australia
| | - Jencia Wong
- Department of Endocrinology, Royal Prince Alfred Hospital, Sydney, Australia.,Faculty of Medicine and Health, University of Sydney, Sydney, Australia
| | - Helen R Murphy
- Diabetes in Pregnancy Team, Cambridge University Hospitals, Cambridge, UK.,Norwich Medical School, Bob Champion Research and Education Building, University of East Anglia, Norwich, UK.,Division of Women's Health, Kings College London, London, UK
| | - Glynis P Ross
- Department of Endocrinology, Royal Prince Alfred Hospital, Sydney, Australia.,Faculty of Medicine and Health, University of Sydney, Sydney, Australia
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9
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Polygenic Risk Score and Risk Factors for Gestational Diabetes. J Pers Med 2022; 12:jpm12091381. [PMID: 36143166 PMCID: PMC9505112 DOI: 10.3390/jpm12091381] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Revised: 08/09/2022] [Accepted: 08/18/2022] [Indexed: 02/07/2023] Open
Abstract
Gestational diabetes mellitus (GDM) is a common complication of pregnancy that adversely affects maternal and offspring health. A variety of risk factors, such as BMI and age, have been associated with increased risks of gestational diabetes. However, in many cases, gestational diabetes occurs in healthy nulliparous women with no obvious risk factors. Emerging data suggest that the tendency to develop gestational diabetes has genetic and environmental components. Here we develop a polygenic risk score for GDM and investigate relationships between its genetic architecture and genetically constructed risk factors and biomarkers. Our results demonstrate that the polygenic risk score can be used as an early screening tool that identifies women at higher risk of GDM before its onset allowing comprehensive monitoring and preventative programs to mitigate the risks.
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10
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Pagel KA, Chu H, Ramola R, Guerrero RF, Chung JH, Parry S, Reddy UM, Silver RM, Steller JG, Yee LM, Wapner RJ, Hahn MW, Natarajan S, Haas DM, Radivojac P. Association of Genetic Predisposition and Physical Activity With Risk of Gestational Diabetes in Nulliparous Women. JAMA Netw Open 2022; 5:e2229158. [PMID: 36040739 PMCID: PMC9428742 DOI: 10.1001/jamanetworkopen.2022.29158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
IMPORTANCE 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. OBJECTIVE 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. DESIGN, SETTINGS, AND PARTICIPANTS 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. EXPOSURES 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. MAIN OUTCOMES AND MEASURES 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. RESULTS 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). CONCLUSIONS AND RELEVANCE 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.
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Affiliation(s)
- Kymberleigh A. Pagel
- Department of Computer Science, Indiana University, Bloomington
- Institute of Computational Medicine, Johns Hopkins University, Baltimore, Maryland
| | - Hoyin Chu
- Khoury College of Computer Sciences, Northeastern University, Boston, Massachusetts
- Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Rashika Ramola
- Khoury College of Computer Sciences, Northeastern University, Boston, Massachusetts
| | - Rafael F. Guerrero
- Department of Biological Sciences, North Carolina State University, Raleigh
| | - Judith H. Chung
- Department of Obstetrics and Gynecology, University of California, Irvine
| | - Samuel Parry
- Department of Obstetrics and Gynecology, University of Pennsylvania School of Medicine, Philadelphia
| | - Uma M. Reddy
- Department of Obstetrics, Gynecology, and Reproductive Sciences, Yale School of Medicine, Yale University, New Haven, Connecticut
| | - Robert M. Silver
- Department of Obstetrics and Gynecology, University of Utah School of Medicine, Salt Lake City
| | | | - Lynn M. Yee
- Department of Obstetrics and Gynecology, Northwestern University Feinberg School of Medicine, Chicago, Illinois
| | - Ronald J. Wapner
- College of Physicians and Surgeons, Columbia University, New York, New York
| | - Matthew W. Hahn
- Department of Computer Science, Indiana University, Bloomington
- Department of Biology, Indiana University, Bloomington
| | | | - David M. Haas
- Department of Obstetrics and Gynecology, Indiana University School of Medicine, Indianapolis
| | - Predrag Radivojac
- Khoury College of Computer Sciences, Northeastern University, Boston, Massachusetts
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11
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Fang X, Jin L, Tang M, Lu W, Lai S, Zhang R, Zhang H, Jiang F, Luo M, Hu C. Common single-nucleotide polymorphisms combined with a genetic risk score provide new insights regarding the etiology of gestational diabetes mellitus. Diabet Med 2022; 39:e14885. [PMID: 35587197 DOI: 10.1111/dme.14885] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Accepted: 05/17/2022] [Indexed: 11/29/2022]
Abstract
AIMS Few studies have constructed a genetic risk score (GRS) to predict the risk of gestaional diabetes mellitus (GDM). We tested the hypothesis that single-nucleotide polymorphisms (SNPs) confirmed for diabetes and obesity and the GRS are associated with GDM. METHODS We conducted a case-control study comprising 971 GDM cases and 1682 controls from the University of Hong Kong Shenzhen Hospital. A total of 1448 SNPs reported with type 2 diabetes (T2D), type 1 diabetes (T1D), and obesity were selected and the GRS based on SNPs associated with GDM was created. RESULTS We confirmed that rs10830963 (OR = 1.41,95% CI = 1.25, 1.59) in MTNR1B and rs2206734 (OR = 1.38, 95% CI = 1.22, 1.55) in CDKAL1 were strongly associated with the risk of GDM. Compared with participants with GRS based on T2D SNPs in the low tertile, the ORs of GDM across increasing GRS tertiles were 1.63 (95% CI 1.29, 2.06) and 2.72 (95% CI 2.18, 3.38) in the middle and high tertile, respectively. The positive associations between the GRS and the risk of GDM were also observed in GRS based on obesity/waist-to-hip ratio (WHR)/body mass index (BMI) SNPs. The resulting GRS for each allele increase was significantly associated with higher glycemic indices and lower HOMA-B values for GRS based on T2D SNPs, but not for GRS based on T1D SNPs and GRS based on obesity/WHR/BMI SNPs. CONCLUSION These findings indicate that GDM may share a common genetic background with T2D and obesity and that SNPs associated with insulin secretion defects have a vital role in the development of GDM.
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Affiliation(s)
- Xiangnan Fang
- The Third School of Clinical Medicine, Southern Medical University, Guangzhou, China
- Department of Endocrinology and Metabolism, Fengxian Central Hospital Affiliated to the Southern Medical University, Shanghai, China
- Department of Endocrinology, First Affiliated Hospital of Gannan Medical University, Ganzhou, China
| | - Li Jin
- Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Clinical Center for Diabetes, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China
| | - Mengyang Tang
- The Third School of Clinical Medicine, Southern Medical University, Guangzhou, China
- Department of Endocrinology and Metabolism, Fengxian Central Hospital Affiliated to the Southern Medical University, Shanghai, China
| | - Wenqian Lu
- The Third School of Clinical Medicine, Southern Medical University, Guangzhou, China
- Department of Endocrinology and Metabolism, Fengxian Central Hospital Affiliated to the Southern Medical University, Shanghai, China
| | - Siyu Lai
- The Third School of Clinical Medicine, Southern Medical University, Guangzhou, China
- Department of Endocrinology and Metabolism, Fengxian Central Hospital Affiliated to the Southern Medical University, Shanghai, China
| | - Rong Zhang
- Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Clinical Center for Diabetes, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China
| | - Hong Zhang
- Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Clinical Center for Diabetes, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China
| | - Feng Jiang
- Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Clinical Center for Diabetes, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China
| | - Mingjuan Luo
- The Third School of Clinical Medicine, Southern Medical University, Guangzhou, China
- Department of Endocrinology and Metabolism, Fengxian Central Hospital Affiliated to the Southern Medical University, Shanghai, China
- Department of Endocrinology and Metabolism, The University of Hong Kong-Shenzhen Hospital, Shenzhen, China
| | - Cheng Hu
- The Third School of Clinical Medicine, Southern Medical University, Guangzhou, China
- Department of Endocrinology and Metabolism, Fengxian Central Hospital Affiliated to the Southern Medical University, Shanghai, China
- Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Clinical Center for Diabetes, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China
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12
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Huvinen E, Lahti J, Klemetti MM, Bergman PH, Räikkönen K, Orho-Melander M, Laivuori H, Koivusalo SB. Genetic risk of type 2 diabetes modifies the effects of a lifestyle intervention aimed at the prevention of gestational and postpartum diabetes. Diabetologia 2022; 65:1291-1301. [PMID: 35501401 PMCID: PMC9283155 DOI: 10.1007/s00125-022-05712-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Accepted: 02/23/2022] [Indexed: 11/29/2022]
Abstract
AIMS/HYPOTHESIS The aim of this study was to assess the interaction between genetic risk and lifestyle intervention on the occurrence of gestational diabetes mellitus (GDM) and postpartum diabetes. METHODS The RADIEL study is an RCT aimed at prevention of GDM and postpartum diabetes through lifestyle intervention. Participants with a BMI ≥30 kg/m2 and/or prior GDM were allocated to intervention and control groups before pregnancy or in early pregnancy. The study visits took place every 3 months before pregnancy, once in each trimester, and at 6 weeks and 6 and 12 months postpartum. We calculated a polygenic risk score (PRS) based on 50 risk variants for type 2 diabetes. RESULTS Altogether, 516 participants provided genetic and GDM data. The PRS was associated with higher glycaemic levels (fasting glucose and/or HbA1c) and a lower insulin secretion index in the second and third trimesters and at 12 months postpartum, as well as with a higher occurrence of GDM and glycaemic abnormalities at 12 months postpartum (n = 356). There was an interaction between the PRS and lifestyle intervention (p=0.016 during pregnancy and p=0.024 postpartum) when analysing participants who did not have GDM at the first study visit during pregnancy (n = 386). When analysing women in tertiles according to the PRS, the intervention was effective in reducing the age-adjusted occurrence of GDM only among those with the highest genetic risk (OR 0.37; 95% CI 0.17, 0.82). The risk of glycaemic abnormalities at 12 months postpartum was reduced in the same group after adjusting additionally for BMI, parity, smoking and education (OR 0.35; 95% CI 0.13, 0.97). CONCLUSIONS/INTERPRETATION Genetic predisposition to diabetes modifies the response to a lifestyle intervention aimed at prevention of GDM and postpartum diabetes. This suggests that lifestyle intervention may benefit from being tailored according to genetic risk. CLINICAL TRIAL REGISTRATION ClinicalTrials.gov identifier: NCT01698385.
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Affiliation(s)
- Emilia Huvinen
- Teratology Information Service, Department of Emergency Medicine and Services, Helsinki University Hospital and University of Helsinki, Helsinki, Finland.
| | - Jari Lahti
- Department of Psychology and Logopedics, University of Helsinki, Helsinki, Finland
| | - Miira M Klemetti
- Department of Obstetrics and Gynecology, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
| | - Paula H Bergman
- Biostatistics Consulting, Department of Public Health, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Katri Räikkönen
- Department of Psychology and Logopedics, University of Helsinki, Helsinki, Finland
| | | | - Hannele Laivuori
- Department of Obstetrics and Gynecology, Tampere University Hospital, Tampere, Finland
- Center for Child, Adolescent, and Maternal Health Research, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- Institute for Molecular Medicine Finland, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
- Medical and Clinical Genetics, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Saila B Koivusalo
- Department of Obstetrics and Gynecology, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
- Department of Obstetrics and Gynecology, Turku University Hospital and University of Turku, Turku, Finland
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13
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van Poppel MNM, Corcoy R, Hill D, Simmons D, Mendizabal L, Zulueta M, Simon L, Desoye G. Interaction between rs10830962 polymorphism in MTNR1B and lifestyle intervention on maternal and neonatal outcomes: secondary analyses of the DALI lifestyle randomized controlled trial. Am J Clin Nutr 2022; 115:388-396. [PMID: 34669935 DOI: 10.1093/ajcn/nqab347] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Accepted: 10/12/2021] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Interactions between polymorphisms of the melatonin receptor 1B (MTNR1B) gene and lifestyle intervention for gestational diabetes have been described. Whether these are specific for physical activity or the healthy eating intervention is unknown. OBJECTIVES The aim was to assess the interaction between MTNR1B rs10830962 and rs10830963 polymorphisms and lifestyle interventions during pregnancy. METHODS Women with a BMI (in kg/m2) of ≥29 (n = 436) received counseling on healthy eating (HE), physical activity (PA), or both. The control group received usual care. This secondary analysis had a factorial design with comparison of HE compared with no HE and PA compared with no PA. Maternal outcomes at 24-28 wk were gestational weight gain (GWG), maternal fasting glucose, insulin, insulin resistance (HOMA-IR), disposition index, and development of GDM. Neonatal outcomes were cord blood leptin and C-peptide and estimated neonatal fat percentage. The interaction between receiving either the HE or PA intervention and genotypes of both rs10830962 and rs10830963 was assessed using multilevel regression analysis. RESULTS GDM risk was increased in women homozygous for the G allele of rs10830962 (OR: 2.60; 95% CI: 1.34, 5.06) or rs10830963 (OR: 2.83; 95% CI: 1.24, 6.47). Significant interactions between rs10830962 and interventions were found: in women homozygous for the G allele but not in the other genotypes, the PA intervention reduced maternal fasting insulin (β: -0.16; 95% CI: -0.33, 0.02; P = 0.08) and HOMA-IR (β: -0.17; 95% CI: -0.35, 0.01; P = 0.06), and reduced cord blood leptin (β: -0.84; 95% CI: -1.42, -0.25; P = 0.01) and C-peptide (β: -0.62; 95% CI: -1.07, -0.17; P = 0.01). In heterozygous women, the HE intervention had no effect, whereas in women homozygous for the C allele, HE intervention reduced GWG (β: -1.6 kg; 95% CI: -2.4, -0.8 kg). No interactions were found. CONCLUSIONS In women homozygous for the risk allele of MTNR1B rs10830962, GDM risk was increased and PA intervention might be more beneficial than HE intervention for reducing maternal insulin resistance, cord blood C-peptide, and cord blood leptin.
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Affiliation(s)
| | - Rosa Corcoy
- Institut de Recerca de l´Hospital de la Santa Creu i Sant Pau, Barcelona, Spain.,CIBER Bioengineering, Biomaterials and Nanotechnology, Instituto de Salud Carlos III, Madrid, Spain
| | - David Hill
- Recherche en Santé Lawson SA, Bronschhofen, Switzerland.,Lawson Health Research Institute, London, Ontario, Canada
| | - David Simmons
- Western Sydney University, Campbelltown, New South Wales, Australia.,Institute of Metabolic Science, Addenbrooke's Hospital, Cambridge, United Kingdom
| | | | | | - Laureano Simon
- Department of Obstetrics and Gynecology, Medical University Graz, Graz, Austria
| | - Gernot Desoye
- Department of Obstetrics and Gynecology, Medical University Graz, Graz, Austria
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14
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Ramos-Levi A, Barabash A, Valerio J, García de la Torre N, Mendizabal L, Zulueta M, de Miguel MP, Diaz A, Duran A, Familiar C, Jimenez I, del Valle L, Melero V, Moraga I, Herraiz MA, Torrejon MJ, Arregi M, Simón L, Rubio MA, Calle-Pascual AL. Genetic variants for prediction of gestational diabetes mellitus and modulation of susceptibility by a nutritional intervention based on a Mediterranean diet. Front Endocrinol (Lausanne) 2022; 13:1036088. [PMID: 36313769 PMCID: PMC9612917 DOI: 10.3389/fendo.2022.1036088] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/03/2022] [Accepted: 09/20/2022] [Indexed: 11/13/2022] Open
Abstract
HYPOTHESIS Gestational diabetes mellitus (GDM) entails a complex underlying pathogenesis, with a specific genetic background and the effect of environmental factors. This study examines the link between a set of single nucleotide polymorphisms (SNPs) associated with diabetes and the development of GDM in pregnant women with different ethnicities, and evaluates its potential modulation with a clinical intervention based on a Mediterranean diet. METHODS 2418 women from our hospital-based cohort of pregnant women screened for GDM from January 2015 to November 2017 (the San Carlos Cohort, randomized controlled trial for the prevention of GDM ISRCTN84389045 and real-world study ISRCTN13389832) were assessed for evaluation. Diagnosis of GDM was made according to the International Association of Diabetes and Pregnancy Study Groups (IADPSG) criteria. Genotyping was performed by IPLEX MassARRAY PCR using the Agena platform (Agena Bioscience, SanDiego, CA). 110 SNPs were selected for analysis based on selected literature references. Statistical analyses regarding patients' characteristics were performed in SPSS (Chicago, IL, USA) version 24.0. Genetic association tests were performed using PLINK v.1.9 and 2.0 software. Bioinformatics analysis, with mapping of SNPs was performed using STRING, version 11.5. RESULTS Quality controls retrieved a total 98 SNPs and 1573 samples, 272 (17.3%) with GDM and 1301 (82.7%) without GDM. 1104 (70.2%) were Caucasian (CAU) and 469 (29.8%) Hispanic (HIS). 415 (26.4%) were from the control group (CG), 418 (26.6%) from the nutritional intervention group (IG) and 740 (47.0%) from the real-world group (RW). 40 SNPs (40.8%) presented some kind of significant association with GDM in at least one of the genetic tests considered. The nutritional intervention presented a significant association with GDM, regardless of the variant considered. In CAU, variants rs4402960, rs7651090, IGF2BP2; rs1387153, rs10830963, MTNR1B; rs17676067, GLP2R; rs1371614, DPYSL5; rs5215, KCNJ1; and rs2293941, PDX1 were significantly associated with an increased risk of GDM, whilst rs780094, GCKR; rs7607980, COBLL1; rs3746750, SLC17A9; rs6048205, FOXA2; rs7041847, rs7034200, rs10814916, GLIS3; rs3783347, WARS; and rs1805087, MTR, were significantly associated with a decreased risk of GDM, In HIS, variants significantly associated with increased risk of GDM were rs9368222, CDKAL1; rs2302593, GIPR; rs10885122, ADRA2A; rs1387153, MTNR1B; rs737288, BACE2; rs1371614, DPYSL5; and rs2293941, PDX1, whilst rs340874, PROX1; rs2943634, IRS1; rs7041847, GLIS3; rs780094, GCKR; rs563694, G6PC2; and rs11605924, CRY2 were significantly associated with decreased risk for GDM. CONCLUSIONS We identify a core set of SNPs in their association with diabetes and GDM in a large cohort of patients from two main ethnicities from a single center. Identification of these genetic variants, even in the setting of a nutritional intervention, deems useful to design preventive and therapeutic strategies.
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Affiliation(s)
- Ana Ramos-Levi
- Endocrinology and Nutrition Department, Hospital Universitario de la Princesa, Instituto de Investigación Princesa, Universidad Autónoma de Madrid, Madrid, Spain
| | - Ana Barabash
- Endocrinology and Nutrition Department, Hospital Clínico Universitario San Carlos and Instituto de Investigación Sanitaria del Hospital Clínico San Carlos (IdISSC), Madrid, Spain
- Facultad de Medicina. Medicina II Department, Universidad Complutense de Madrid, Madrid, Spain
- Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), Madrid, Spain
| | - Johanna Valerio
- Endocrinology and Nutrition Department, Hospital Clínico Universitario San Carlos and Instituto de Investigación Sanitaria del Hospital Clínico San Carlos (IdISSC), Madrid, Spain
| | - Nuria García de la Torre
- Endocrinology and Nutrition Department, Hospital Clínico Universitario San Carlos and Instituto de Investigación Sanitaria del Hospital Clínico San Carlos (IdISSC), Madrid, Spain
- Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), Madrid, Spain
- *Correspondence: Alfonso L. Calle-Pascual, ; Nuria García de la Torre,
| | | | | | - Maria Paz de Miguel
- Endocrinology and Nutrition Department, Hospital Clínico Universitario San Carlos and Instituto de Investigación Sanitaria del Hospital Clínico San Carlos (IdISSC), Madrid, Spain
- Facultad de Medicina. Medicina II Department, Universidad Complutense de Madrid, Madrid, Spain
| | - Angel Diaz
- Endocrinology and Nutrition Department, Hospital Clínico Universitario San Carlos and Instituto de Investigación Sanitaria del Hospital Clínico San Carlos (IdISSC), Madrid, Spain
- Facultad de Medicina. Medicina II Department, Universidad Complutense de Madrid, Madrid, Spain
| | - Alejandra Duran
- Endocrinology and Nutrition Department, Hospital Clínico Universitario San Carlos and Instituto de Investigación Sanitaria del Hospital Clínico San Carlos (IdISSC), Madrid, Spain
- Facultad de Medicina. Medicina II Department, Universidad Complutense de Madrid, Madrid, Spain
| | - Cristina Familiar
- Endocrinology and Nutrition Department, Hospital Clínico Universitario San Carlos and Instituto de Investigación Sanitaria del Hospital Clínico San Carlos (IdISSC), Madrid, Spain
| | - Inés Jimenez
- Endocrinology and Nutrition Department, Hospital Clínico Universitario San Carlos and Instituto de Investigación Sanitaria del Hospital Clínico San Carlos (IdISSC), Madrid, Spain
| | - Laura del Valle
- Endocrinology and Nutrition Department, Hospital Clínico Universitario San Carlos and Instituto de Investigación Sanitaria del Hospital Clínico San Carlos (IdISSC), Madrid, Spain
| | - Veronica Melero
- Endocrinology and Nutrition Department, Hospital Clínico Universitario San Carlos and Instituto de Investigación Sanitaria del Hospital Clínico San Carlos (IdISSC), Madrid, Spain
| | - Inmaculada Moraga
- Endocrinology and Nutrition Department, Hospital Clínico Universitario San Carlos and Instituto de Investigación Sanitaria del Hospital Clínico San Carlos (IdISSC), Madrid, Spain
| | - Miguel A. Herraiz
- Gynecology and Obstetrics Department, Hospital Clínico Universitario San Carlos and Instituto de Investigación Sanitaria del Hospital Clínico San Carlos (IdISSC), Madrid, Spain
| | - María José Torrejon
- Clinical Laboratory Department Hospital Clínico Universitario San Carlos and Instituto de Investigación Sanitaria del Hospital Clínico San Carlos (IdISSC), Madrid, Spain
| | - Maddi Arregi
- Patia Europe, Clinical Laboratory, San Sebastián, Spain
| | | | - Miguel A. Rubio
- Endocrinology and Nutrition Department, Hospital Clínico Universitario San Carlos and Instituto de Investigación Sanitaria del Hospital Clínico San Carlos (IdISSC), Madrid, Spain
- Facultad de Medicina. Medicina II Department, Universidad Complutense de Madrid, Madrid, Spain
| | - Alfonso L. Calle-Pascual
- Endocrinology and Nutrition Department, Hospital Clínico Universitario San Carlos and Instituto de Investigación Sanitaria del Hospital Clínico San Carlos (IdISSC), Madrid, Spain
- Facultad de Medicina. Medicina II Department, Universidad Complutense de Madrid, Madrid, Spain
- Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), Madrid, Spain
- *Correspondence: Alfonso L. Calle-Pascual, ; Nuria García de la Torre,
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15
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Park S. Interaction of Polygenetic Variants for Gestational Diabetes Mellitus Risk with Breastfeeding and Korean Balanced Diet to Influence Type 2 Diabetes Risk in Later Life in a Large Hospital-Based Cohort. J Pers Med 2021; 11:1175. [PMID: 34834527 PMCID: PMC8619899 DOI: 10.3390/jpm11111175] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2021] [Revised: 11/02/2021] [Accepted: 11/09/2021] [Indexed: 11/25/2022] Open
Abstract
The etiologies of gestational diabetes mellitus (GDM) and type 2 diabetes mellitus (T2DM) are similar. Genetic and environmental factors interact to influence the risk of both types of diabetes. We aimed to determine if the polygenetic risk scores (PRS) for GDM risk interacted with lifestyles to influence type 2 diabetes risk in women aged >40 years in a large hospital-based city cohort. The participants with GDM diagnosis without T2DM before pregnancy were considered the case group (n = 384) and those without GDM and T2DM as the control (n = 33,956) to explore GDM-related genetic variants. The participants with T2DM were the case (n = 2550), and the control (n = 33,956) was the same as GDM genetic analysis for the interaction analysis of GDM genetic risk with lifestyles to influence T2DM risk. The genetic variants for the GDM risk were selected from a genome-wide association study (GWAS), and their PRS from the best model with gene-gene interactions were generated. GDM was positively associated with age at first pregnancy, body mass index (BMI) at age 20, and education level. A previous GDM diagnosis increased the likelihood of elevated fasting serum glucose concentrations and HbA1c contents by 8.42 and 9.23 times in middle-aged and older women. However, it was not associated with the risk of any other metabolic syndrome components. Breast-feeding (≥1 year) was inversely associated with the T2DM risk in later life. In the genetic variant-genetic variant interaction, the best model with 5-SNPs included PTPRD_rs916855529, GPC6_rs9589710, CDKAL1_rs7754840, PRKAG2_rs11975504, and PTPRM_rs80164908. The PRS calculated from the 5-SNP model was positively associated with the GDM risk by 3.259 (2.17-4.89) times after adjusting GDM-related covariates. The GDM experience interacted with PRS for the T2DM risk. Only in non-GDM women PRS was positively associated with T2DM risk by 1.36-times. However, long breastfeeding did not interact with the PRS for T2DM risk. Among dietary patterns, only a Korean-style balanced diet (KBD) showed an interaction with PRS for the T2DM risk. Participants with a low-PRS had the lowest serum glucose concentrations in the high KBD intake but not low KBD intake. In conclusion, participants with a high PRS for GDM risk are positively associated with T2DM risk, and breastfeeding for ≥1 year and consuming KBD offset the PRS for GDM risk to influence T2DM risk in middle-aged and older.
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Affiliation(s)
- Sunmin Park
- Obesity/Diabetes Research Center, Department of Food and Nutrition, Institute of Basic Science, Hoseo University, YejunBio, 165 Sechul-Ri, BaeBang-Yup Asan-Si, ChungNam-Do, Asan 336-795, Korea
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16
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Furse S, Fernandez-Twinn DS, Chiarugi D, Koulman A, Ozanne SE. Lipid Metabolism Is Dysregulated before, during and after Pregnancy in a Mouse Model of Gestational Diabetes. Int J Mol Sci 2021; 22:7452. [PMID: 34299070 PMCID: PMC8306994 DOI: 10.3390/ijms22147452] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2021] [Revised: 07/06/2021] [Accepted: 07/07/2021] [Indexed: 12/29/2022] Open
Abstract
The aim of the current study was to test the hypothesis that maternal lipid metabolism was modulated during normal pregnancy and that these modulations are altered in gestational diabetes mellitus (GDM). We tested this hypothesis using an established mouse model of diet-induced obesity with pregnancy-associated loss of glucose tolerance and a novel lipid analysis tool, Lipid Traffic Analysis, that uses the temporal distribution of lipids to identify differences in the control of lipid metabolism through a time course. Our results suggest that the start of pregnancy is associated with several changes in lipid metabolism, including fewer variables associated with de novo lipogenesis and fewer PUFA-containing lipids in the circulation. Several of the changes in lipid metabolism in healthy pregnancies were less apparent or occurred later in dams who developed GDM. Some changes in maternal lipid metabolism in the obese-GDM group were so late as to only occur as the control dams' systems began to switch back towards the non-pregnant state. These results demonstrate that lipid metabolism is modulated in healthy pregnancy and the timing of these changes is altered in GDM pregnancies. These findings raise important questions about how lipid metabolism contributes to changes in metabolism during healthy pregnancies. Furthermore, as alterations in the lipidome are present before the loss of glucose tolerance, they could contribute to the development of GDM mechanistically.
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Affiliation(s)
- Samuel Furse
- University of Cambridge Metabolic Research Laboratories and MRC Metabolic Diseases Unit, Wellcome-MRC Institute of Metabolic Science, University of Cambridge, Addenbrooke’s Treatment Centre, Keith Day Road, Cambridge CB2 0QQ, UK; (S.F.); (D.S.F.-T.)
- Core Metabolomics and Lipidomics Laboratory, Wellcome-MRC Institute of Metabolic Science, University of Cambridge, Addenbrooke’s Treatment Centre, Keith Day Road, Cambridge CB2 0QQ, UK
- Biological Chemistry Group, Jodrell Laboratory, Royal Botanic Gardens Kew, London TW9 3AD, UK
| | - Denise S. Fernandez-Twinn
- University of Cambridge Metabolic Research Laboratories and MRC Metabolic Diseases Unit, Wellcome-MRC Institute of Metabolic Science, University of Cambridge, Addenbrooke’s Treatment Centre, Keith Day Road, Cambridge CB2 0QQ, UK; (S.F.); (D.S.F.-T.)
| | - Davide Chiarugi
- Bioinformatics and Biostatistics Core, Wellcome Trust-MRC Institute of Metabolic Science, University of Cambridge, Addenbrooke’s Treatment Centre, Keith Day Road, Cambridge CB2 0QQ, UK;
| | - Albert Koulman
- University of Cambridge Metabolic Research Laboratories and MRC Metabolic Diseases Unit, Wellcome-MRC Institute of Metabolic Science, University of Cambridge, Addenbrooke’s Treatment Centre, Keith Day Road, Cambridge CB2 0QQ, UK; (S.F.); (D.S.F.-T.)
- Core Metabolomics and Lipidomics Laboratory, Wellcome-MRC Institute of Metabolic Science, University of Cambridge, Addenbrooke’s Treatment Centre, Keith Day Road, Cambridge CB2 0QQ, UK
| | - Susan E. Ozanne
- University of Cambridge Metabolic Research Laboratories and MRC Metabolic Diseases Unit, Wellcome-MRC Institute of Metabolic Science, University of Cambridge, Addenbrooke’s Treatment Centre, Keith Day Road, Cambridge CB2 0QQ, UK; (S.F.); (D.S.F.-T.)
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17
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Hughes AE, Hayes MG, Egan AM, Patel KA, Scholtens DM, Lowe LP, Lowe WL, Dunne FP, Hattersley AT, Freathy RM. All thresholds of maternal hyperglycaemia from the WHO 2013 criteria for gestational diabetes identify women with a higher genetic risk for type 2 diabetes. Wellcome Open Res 2021; 5:175. [PMID: 33869792 PMCID: PMC8030121 DOI: 10.12688/wellcomeopenres.16097.3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/09/2021] [Indexed: 11/20/2022] Open
Abstract
Background: Using genetic scores for fasting plasma glucose (FPG GS) and type 2 diabetes (T2D GS), we investigated whether the fasting, 1-hour and 2-hour glucose thresholds from the WHO 2013 criteria for gestational diabetes (GDM) have different implications for genetic susceptibility to raised fasting glucose and type 2 diabetes in women from the Hyperglycemia and Adverse Pregnancy Outcome (HAPO) and Atlantic Diabetes in Pregnancy (DIP) studies. Methods: Cases were divided into three subgroups: (i) FPG ≥5.1 mmol/L only, n=222; (ii) 1-hour glucose post 75 g oral glucose load ≥10 mmol/L only, n=154 (iii) 2-hour glucose ≥8.5 mmol/L only, n=73; and (iv) both FPG ≥5.1 mmol/L and either of a 1-hour glucose ≥10 mmol/L or 2-hour glucose ≥8.5 mmol/L, n=172. We compared the FPG and T2D GS of these groups with controls (n=3,091) in HAPO and DIP separately. Results: In HAPO and DIP, the mean FPG GS in women with a FPG ≥5.1 mmol/L, either on its own or with 1-hour glucose ≥10 mmol/L or 2-hour glucose ≥8.5 mmol/L, was higher than controls (all P <0.01). Mean T2D GS in women with a raised FPG alone or with either a raised 1-hour or 2-hour glucose was higher than controls (all P <0.05). GDM defined by 1-hour or 2-hour hyperglycaemia only was also associated with a higher T2D GS than controls (all P <0.05). Conclusions: The different diagnostic categories that are part of the WHO 2013 criteria for GDM identify women with a genetic predisposition to type 2 diabetes as well as a risk for adverse pregnancy outcomes.
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Affiliation(s)
- Alice E Hughes
- Institute of Biomedical and Clinical Science, University of Exeter, Exeter, UK
- Royal Devon and Exeter Hospitals NHS Foundation Trust, Exeter, UK
| | - M Geoffrey Hayes
- Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Aoife M Egan
- Division of Endocrinology, Diabetes and Metabolism, Mayo Clinic School of Medicine, Rochester, MN, USA
| | - Kashyap A Patel
- Institute of Biomedical and Clinical Science, University of Exeter, Exeter, UK
- Royal Devon and Exeter Hospitals NHS Foundation Trust, Exeter, UK
| | | | - Lynn P Lowe
- Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - William L Lowe
- Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Fidelma P Dunne
- Galway Diabetes Research Centre and Saolta Hospital Group, National University of Ireland, Galway, Galway, Ireland
| | - Andrew T Hattersley
- Institute of Biomedical and Clinical Science, University of Exeter, Exeter, UK
- Royal Devon and Exeter Hospitals NHS Foundation Trust, Exeter, UK
- National Institute for Health Research Exeter Clinical Research Facility, Exeter, UK
| | - Rachel M Freathy
- Institute of Biomedical and Clinical Science, University of Exeter, Exeter, UK
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18
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Powe CE, Udler MS, Hsu S, Allard C, Kuang A, Manning AK, Perron P, Bouchard L, Lowe WL, Scholtens D, Florez JC, Hivert MF. Genetic Loci and Physiologic Pathways Involved in Gestational Diabetes Mellitus Implicated Through Clustering. Diabetes 2021; 70:268-281. [PMID: 33051273 PMCID: PMC7876560 DOI: 10.2337/db20-0772] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/26/2020] [Accepted: 10/08/2020] [Indexed: 12/17/2022]
Abstract
Hundreds of common genetic variants acting through distinguishable physiologic pathways influence the risk of type 2 diabetes (T2D). It is unknown to what extent the physiology underlying gestational diabetes mellitus (GDM) is distinct from that underlying T2D. In this study of >5,000 pregnant women from three cohorts, we aimed to identify physiologically related groups of maternal variants associated with GDM using two complementary approaches that were based on Bayesian nonnegative matrix factorization (bNMF) clustering. First, we tested five bNMF clusters of maternal T2D-associated variants grouped on the basis of physiology outside of pregnancy for association with GDM. We found that cluster polygenic scores representing genetic determinants of reduced β-cell function and abnormal hepatic lipid metabolism were associated with GDM; these clusters were not associated with infant birth weight. Second, we derived bNMF clusters of maternal variants on the basis of pregnancy physiology and tested these clusters for association with GDM. We identified a cluster that was strongly associated with GDM as well as associated with higher infant birth weight. The effect size for this cluster's association with GDM appeared greater than that for T2D. Our findings imply that the genetic and physiologic pathways that lead to GDM differ, at least in part, from those that lead to T2D.
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Affiliation(s)
- Camille E Powe
- Diabetes Unit, Endocrine Division, Massachusetts General Hospital, Boston, MA
- Broad Institute, Cambridge, MA
- Harvard Medical School, Boston, MA
| | - Miriam S Udler
- Diabetes Unit, Endocrine Division, Massachusetts General Hospital, Boston, MA
- Broad Institute, Cambridge, MA
- Harvard Medical School, Boston, MA
| | - Sarah Hsu
- Diabetes Unit, Endocrine Division, Massachusetts General Hospital, Boston, MA
- Broad Institute, Cambridge, MA
| | - Catherine Allard
- Centre de Recherche du Centre Hospitalier, Université de Sherbrooke, Sherbrooke, Quebec, Canada
| | - Alan Kuang
- Division of Biostatistics, Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL
| | - Alisa K Manning
- Broad Institute, Cambridge, MA
- Harvard Medical School, Boston, MA
- Clinical and Translational Epidemiology Unit, Mongan Institute, Massachusetts General Hospital, Boston, MA
| | - Patrice Perron
- Department of Medicine, Université de Sherbrooke, Quebec, Canada
| | - Luigi Bouchard
- Centre de Recherche du Centre Hospitalier, Université de Sherbrooke, Sherbrooke, Quebec, Canada
- Department of Biochemistry and Functional Genomics, Université de Sherbrooke, Sherbrooke, Quebec, Canada
- Department of Medical Biology, CIUSSS Saguenay-Lac-Saint-Jean-Hôpital Universitaire de Chicoutimi, Saguenay, Quebec, Canada
| | - William L Lowe
- Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL
| | - Denise Scholtens
- Division of Biostatistics, Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL
| | - Jose C Florez
- Diabetes Unit, Endocrine Division, Massachusetts General Hospital, Boston, MA
- Broad Institute, Cambridge, MA
- Harvard Medical School, Boston, MA
| | - Marie-France Hivert
- Diabetes Unit, Endocrine Division, Massachusetts General Hospital, Boston, MA
- Harvard Medical School, Boston, MA
- Department of Medicine, Université de Sherbrooke, Quebec, Canada
- Department of Population Medicine, Harvard Pilgrim Health Care Institute, Boston, MA
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19
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Abstract
PURPOSE OF REVIEW In this review, we summarize studies investigating genetics of gestational diabetes mellitus (GDM) and glucose metabolism in pregnancy. We describe these studies in the context of the larger body of literature on type 2 diabetes (T2D) and glycemic trait genomics. RECENT FINDINGS We reviewed 23 genetic association studies for GDM and performed a meta-analysis, which revealed variants at eight T2D loci significantly associated with GDM after the Bonferroni correction. These studies suggest that GDM and T2D share a number of genetic risk loci. Only two unbiased genome-wide association studies (GWASs) have successfully revealed genetic associations for GDM and related glycemic traits in pregnancy. A GWAS for GDM in Korean women identified two loci (near CDKAL1 and MTNR1B) known to be associated with T2D, though the association of the MTNR1B locus with GDM appears to be stronger than that for T2D. A multi-ethnic GWAS for glycemic traits in pregnancy identified two novel loci (near HKDC1 and BACE2) which appear to be associated with post-load glucose and fasting c-peptide specifically in pregnant women. There are ongoing efforts to use this genetic information, in the form of polygenic scores, to predict risk of GDM and postpartum T2D. The body of literature examining genetic associations with GDM is limited, especially when compared to the available literature on T2D and glycemic trait genomics. Additional genetic discovery for glucose metabolism in pregnant women will require larger pregnancy cohorts and international collaborative efforts. Studies on the clinical implications of these findings are also warranted.
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Affiliation(s)
- Camille E Powe
- Diabetes Unit, Endocrine Division, Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Soo Heon Kwak
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Department of Internal Medicine, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea.
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20
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Hughes AE, Hayes MG, Egan AM, Patel KA, Scholtens DM, Lowe LP, Lowe WL, Dunne FP, Hattersley AT, Freathy RM. All thresholds of maternal hyperglycaemia from the WHO 2013 criteria for gestational diabetes identify women with a higher genetic risk for type 2 diabetes. Wellcome Open Res 2020; 5:175. [PMID: 33869792 PMCID: PMC8030121.2 DOI: 10.12688/wellcomeopenres.16097.2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/13/2020] [Indexed: 04/02/2024] Open
Abstract
Background: Using genetic scores for fasting plasma glucose (FPG GS) and type 2 diabetes (T2D GS), we investigated whether the fasting, 1-hour and 2-hour glucose thresholds from the WHO 2013 criteria for gestational diabetes (GDM) have different implications for genetic susceptibility to raised fasting glucose and type 2 diabetes in women from the Hyperglycemia and Adverse Pregnancy Outcome (HAPO) and Atlantic Diabetes in Pregnancy (DIP) studies. Methods: Cases were divided into three subgroups: (i) FPG ≥5.1 mmol/L only, n=222; (ii) 1-hour glucose post 75 g oral glucose load ≥10 mmol/L only, n=154 (iii) 2-hour glucose ≥8.5 mmol/L only, n=73; and (iv) both FPG ≥5.1 mmol/L and either of a 1-hour glucose ≥10 mmol/L or 2-hour glucose ≥8.5 mmol/L, n=172. We compared the FPG and T2D GS of these groups with controls (n=3,091) in HAPO and DIP separately. Results: In HAPO and DIP, the mean FPG GS in women with a FPG ≥5.1 mmol/L, either on its own or with 1-hour glucose ≥10 mmol/L or 2-hour glucose ≥8.5 mmol/L, was higher than controls (all P <0.01). Mean T2D GS in women with a raised FPG alone or with either a raised 1-hour or 2-hour glucose was higher than controls (all P <0.05). GDM defined by 1-hour or 2-hour hyperglycaemia only was also associated with a higher T2D GS than controls (all P <0.05). Conclusions: The different diagnostic categories that are part of the WHO 2013 criteria for GDM identify women with a genetic predisposition to type 2 diabetes as well as a risk for adverse pregnancy outcomes.
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Affiliation(s)
- Alice E Hughes
- Institute of Biomedical and Clinical Science, University of Exeter, Exeter, UK
- Royal Devon and Exeter Hospitals NHS Foundation Trust, Exeter, UK
| | - M Geoffrey Hayes
- Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Aoife M Egan
- Division of Endocrinology, Diabetes and Metabolism, Mayo Clinic School of Medicine, Rochester, MN, USA
| | - Kashyap A Patel
- Institute of Biomedical and Clinical Science, University of Exeter, Exeter, UK
- Royal Devon and Exeter Hospitals NHS Foundation Trust, Exeter, UK
| | | | - Lynn P Lowe
- Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - William L Lowe
- Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Fidelma P Dunne
- Galway Diabetes Research Centre and Saolta Hospital Group, National University of Ireland, Galway, Galway, Ireland
| | - Andrew T Hattersley
- Institute of Biomedical and Clinical Science, University of Exeter, Exeter, UK
- Royal Devon and Exeter Hospitals NHS Foundation Trust, Exeter, UK
- National Institute for Health Research Exeter Clinical Research Facility, Exeter, UK
| | - Rachel M Freathy
- Institute of Biomedical and Clinical Science, University of Exeter, Exeter, UK
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21
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Powe CE, Hivert MF, Udler MS. Defining Heterogeneity Among Women With Gestational Diabetes Mellitus. Diabetes 2020; 69:2064-2074. [PMID: 32843565 PMCID: PMC7506831 DOI: 10.2337/dbi20-0004] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/04/2020] [Accepted: 04/29/2020] [Indexed: 12/17/2022]
Abstract
Attention to precision medicine in type 2 diabetes (T2D) has provided two favored approaches to subclassifying affected individuals and parsing heterogeneity apparent in this condition: phenotype-based and genotype-based. Gestational diabetes mellitus (GDM) shares phenotypic characteristics with T2D. However, unlike T2D, GDM emerges in the setting of profound pregnancy-related physiologic changes in glucose metabolism. T2D and GDM also share common genetic architecture, but there are likely to be unique genetic influences on pregnancy glycemic regulation that contribute to GDM. In this Perspective, we describe efforts to decipher heterogeneity in T2D and detail how we and others are applying approaches developed for T2D to the study of heterogeneity in GDM. Emerging results reveal the potential of phenotype- and genotype-based subclassification of GDM to deliver the promise of precision medicine to the obstetric population.
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Affiliation(s)
- Camille E Powe
- Diabetes Unit, Massachusetts General Hospital, Boston, MA
- Harvard Medical School, Boston, MA
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA
| | - Marie-France Hivert
- Diabetes Unit, Massachusetts General Hospital, Boston, MA
- Harvard Medical School, Boston, MA
- Department of Population Medicine, Harvard Pilgrim Healthcare Institute, Boston, MA
| | - Miriam S Udler
- Diabetes Unit, Massachusetts General Hospital, Boston, MA
- Harvard Medical School, Boston, MA
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA
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22
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Hughes AE, Hayes MG, Egan AM, Patel KA, Scholtens DM, Lowe LP, Lowe Jr WL, Dunne FP, Hattersley AT, Freathy RM. All thresholds of maternal hyperglycaemia from the WHO 2013 criteria for gestational diabetes identify women with a higher genetic risk for type 2 diabetes. Wellcome Open Res 2020; 5:175. [PMID: 33869792 PMCID: PMC8030121 DOI: 10.12688/wellcomeopenres.16097.1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/09/2020] [Indexed: 04/02/2024] Open
Abstract
Background: Using genetic scores for fasting plasma glucose (FPG GS) and type 2 diabetes (T2D GS), we investigated whether the fasting, 1-hour and 2-hour glucose thresholds from the WHO 2013 criteria for gestational diabetes (GDM) have different implications for genetic susceptibility to raised fasting glucose and type 2 diabetes in women from the Hyperglycemia and Adverse Pregnancy Outcome (HAPO) and Atlantic Diabetes in Pregnancy (DIP) studies. Methods: Cases were divided into three subgroups: (i) FPG ≥5.1 mmol/L only, n=222; (ii) 1-hour glucose post 75 g oral glucose load ≥10 mmol/L only, n=154 (iii) 2-hour glucose ≥8.5 mmol/L only, n=73; and (iv) both FPG ≥5.1 mmol/L and either of a 1-hour glucose ≥10 mmol/L or 2-hour glucose ≥8.5 mmol/L, n=172. We compared the FPG and T2D GS of these groups with controls (n=3,091) in HAPO and DIP separately. Results: In HAPO and DIP, the mean FPG GS in women with a FPG ≥5.1 mmol/L, either on its own or with 1-hour glucose ≥10 mmol/L or 2-hour glucose ≥8.5 mmol/L, was higher than controls (all P <0.01). Mean T2D GS in women with a raised FPG alone or with either a raised 1-hour or 2-hour glucose was higher than controls (all P <0.05). GDM defined by 1-hour or 2-hour hyperglycaemia only was also associated with a higher T2D GS than controls (all P <0.05). Conclusions: The different diagnostic categories that are part of the WHO 2013 criteria for GDM identify women with a genetic predisposition to type 2 diabetes as well as a risk for adverse pregnancy outcomes.
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Affiliation(s)
- Alice E. Hughes
- Institute of Biomedical and Clinical Science, University of Exeter, Exeter, UK
- Royal Devon and Exeter Hospitals NHS Foundation Trust, Exeter, UK
| | - M. Geoffrey Hayes
- Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Aoife M. Egan
- Division of Endocrinology, Diabetes and Metabolism, Mayo Clinic School of Medicine, Rochester, MN, USA
| | - Kashyap A. Patel
- Institute of Biomedical and Clinical Science, University of Exeter, Exeter, UK
- Royal Devon and Exeter Hospitals NHS Foundation Trust, Exeter, UK
| | | | - Lynn P. Lowe
- Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | | | - Fidelma P. Dunne
- Galway Diabetes Research Centre and Saolta Hospital Group, National University of Ireland, Galway, Galway, Ireland
| | - Andrew T. Hattersley
- Institute of Biomedical and Clinical Science, University of Exeter, Exeter, UK
- Royal Devon and Exeter Hospitals NHS Foundation Trust, Exeter, UK
- National Institute for Health Research Exeter Clinical Research Facility, Exeter, UK
| | - Rachel M. Freathy
- Institute of Biomedical and Clinical Science, University of Exeter, Exeter, UK
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23
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Shen Y, Jia Y, Li Y, Gu X, Wan G, Zhang P, Zhang Y, Jiang L. Genetic determinants of gestational diabetes mellitus: a case-control study in two independent populations. Acta Diabetol 2020; 57:843-852. [PMID: 32114639 DOI: 10.1007/s00592-020-01485-w] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/02/2019] [Accepted: 01/17/2020] [Indexed: 01/25/2023]
Abstract
BACKGROUND Genetic risk score (GRS) is more informative to identify the complicated associations between variants of genes and disease. Considering similar pathogenesis and shared genetic predispositions between gestational diabetes mellitus (GDM) and type 2 diabetes/obesity, we conducted this study to explore whether the GRS model integrating variants related to type 2 diabetes/obesity is also associated with GDM risk. METHODS A population-based case-control study that included 1429 subjects was conducted to investigate the association between the GRS model and GDM risk, which were analyzed employing stratified logistic regression analysis with the adjustment for age, BMI, parity and family history of diabetes. RESULTS We have screened 23 SNPs and further filtered six SNPs that were significantly associated with the risk of GDM: four risk SNPs (MTNR1B: rs10830963, rs1387153, rs2166706; MC4R: rs2229616) and two protective SNPs (MTNR1B: rs1447352 and rs4753426). The GRS model with a higher score indicated a higher genetic predisposition to develop GDM, especially in the highest quartile of GRS (all P < 0.001) and the strata of advanced maternal age (all P < 0.001) and obesity (all P = 0.005). CONCLUSION In this study, six SNPs were explored and further identified to be associated with GDM risk, which suggested GRSs including these polymorphisms might participate in facilitating GDM risk. These findings offer the potential to improve our understanding of the etiology of GDM.
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Affiliation(s)
- Yi Shen
- Department of Epidemiology and Medical Statistics, School of Public Health, Nantong University, Nantong, Jiangsu Province, People's Republic of China
| | - Yulong Jia
- Department of Epidemiology and Medical Statistics, School of Public Health, Nantong University, Nantong, Jiangsu Province, People's Republic of China
| | - Yuandong Li
- School of Management, Xuzhou Medical University, Xuzhou, Jiangsu Province, People's Republic of China
| | - Xuefeng Gu
- Shanghai Key Laboratory for Molecular Imaging, University of Medicine and Health Sciences, Shanghai, People's Republic of China
| | - Guoqing Wan
- Shanghai Key Laboratory for Molecular Imaging, University of Medicine and Health Sciences, Shanghai, People's Republic of China
| | - Peng Zhang
- School of Clinical Medicine, University of Medicine and Health Sciences, Shanghai, People's Republic of China
| | - Yafeng Zhang
- Affiliated Hospital of Nantong University, Nantong University, Nantong, Jiangsu Province, People's Republic of China.
| | - Liying Jiang
- Shanghai Key Laboratory for Molecular Imaging, University of Medicine and Health Sciences, Shanghai, People's Republic of China.
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Hivert MF, Cardenas A, Allard C, Doyon M, Powe CE, Catalano PM, Perron P, Bouchard L. Interplay of Placental DNA Methylation and Maternal Insulin Sensitivity in Pregnancy. Diabetes 2020; 69:484-492. [PMID: 31882564 PMCID: PMC7213861 DOI: 10.2337/db19-0798] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/10/2019] [Accepted: 12/24/2019] [Indexed: 02/06/2023]
Abstract
The placenta participates in maternal insulin sensitivity changes during pregnancy; however, mechanisms remain unclear. We investigated associations between maternal insulin sensitivity and placental DNA methylation markers across the genome. We analyzed data from 430 mother-offspring dyads in the Gen3G cohort. All women underwent 75-g oral glucose tolerance tests at ∼26 weeks of gestation; we used glucose and insulin measures to estimate insulin sensitivity (Matsuda index). At delivery, we collected samples from placenta (fetal side) and measured DNA methylation using Illumina EPIC arrays. Using linear regression models to quantify associations at 720,077 cytosine-guanine dinucleotides (CpGs), with adjustment for maternal age, gravidity, smoking, BMI, child sex, and gestational age at delivery, we identified 188 CpG sites where placental DNA methylation was associated with Matsuda index (P < 6.94 × 10-8). Among genes annotated to these 188 CpGs, we found enrichment in targets for miRNAs, in histone modifications, and in parent-of-origin DNA methylation including the H19/MIR675 locus (paternally imprinted). We identified 12 known placenta imprinted genes, including KCNQ1 Mendelian randomization analyses revealed five loci where placenta DNA methylation may causally influence maternal insulin sensitivity, including the maternally imprinted gene DLGAP2. Our results suggest that placental DNA methylation is fundamentally linked to the regulation of maternal insulin sensitivity in pregnancy.
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Affiliation(s)
- Marie-France Hivert
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA
- Diabetes Unit, Massachusetts General Hospital, and Harvard Medical School, Boston, MA
- Department of Medicine, Faculté de médecine et des sciences de la santé, Université de Sherbrooke, Sherbrooke, Quebec, Canada
| | - Andres Cardenas
- Division of Environmental Health Sciences, School of Public Health, University of California, Berkeley, Berkeley, CA
| | - Catherine Allard
- Centre de Recherche du Centre Hospitalier Universitaire de Sherbrooke, Sherbrooke, Quebec, Canada
| | - Myriam Doyon
- Centre de Recherche du Centre Hospitalier Universitaire de Sherbrooke, Sherbrooke, Quebec, Canada
| | - Camille E Powe
- Diabetes Unit, Massachusetts General Hospital, and Harvard Medical School, Boston, MA
| | - Patrick M Catalano
- Mother Infant Research Institute, Department of Obstetrics and Gynecology, Tufts Medical Center, Tufts University School of Medicine and Friedman School of Nutrition and Science Policy, Boston, MA
| | - Patrice Perron
- Department of Medicine, Faculté de médecine et des sciences de la santé, Université de Sherbrooke, Sherbrooke, Quebec, Canada
- Centre de Recherche du Centre Hospitalier Universitaire de Sherbrooke, Sherbrooke, Quebec, Canada
| | - Luigi Bouchard
- Centre de Recherche du Centre Hospitalier Universitaire de Sherbrooke, Sherbrooke, Quebec, Canada
- Department of Biochemistry, Faculté de médecine et des sciences de la santé, Université de Sherbrooke, Sherbrooke, Quebec, Canada
- Department of Medical Biology, CIUSSS du Saguenay-Lac-Saint-Jean, Hôpital de Chicoutimi, Saguenay, Quebec, Canada
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25
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Abbade J, Klemetti MM, Farrell A, Ermini L, Gillmore T, Sallais J, Tagliaferro A, Post M, Caniggia I. Increased placental mitochondrial fusion in gestational diabetes mellitus: an adaptive mechanism to optimize feto-placental metabolic homeostasis? BMJ Open Diabetes Res Care 2020; 8:8/1/e000923. [PMID: 32144130 PMCID: PMC7059528 DOI: 10.1136/bmjdrc-2019-000923] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/25/2019] [Revised: 01/07/2020] [Accepted: 02/07/2020] [Indexed: 12/18/2022] Open
Abstract
INTRODUCTION Gestational diabetes mellitus (GDM), a common pregnancy disorder, increases the risk of fetal overgrowth and later metabolic morbidity in the offspring. The placenta likely mediates these sequelae, but the exact mechanisms remain elusive. Mitochondrial dynamics refers to the joining and division of these organelles, in attempts to maintain cellular homeostasis in stress conditions or alterations in oxygen and fuel availability. These remodeling processes are critical to optimize mitochondrial function, and their disturbances characterize diabetes and obesity. METHODS AND RESULTS Herein we show that placental mitochondrial dynamics are tilted toward fusion in GDM, as evidenced by transmission electron microscopy and changes in the expression of key mechanochemical enzymes such as OPA1 and active phosphorylated DRP1. In vitro experiments using choriocarcinoma JEG-3 cells demonstrated that increased exposure to insulin, which typifies GDM, promotes mitochondrial fusion. As placental ceramide induces mitochondrial fission in pre-eclampsia, we also examined ceramide content in GDM and control placentae and observed a reduction in placental ceramide enrichment in GDM, likely due to an insulin-dependent increase in ceramide-degrading ASAH1 expression. CONCLUSIONS Placental mitochondrial fusion is enhanced in GDM, possibly as a compensatory response to maternal and fetal metabolic derangements. Alterations in placental insulin exposure and sphingolipid metabolism are among potential contributing factors. Overall, our results suggest that GDM has profound impacts on placental mitochondrial dynamics and metabolism, with plausible implications for the short-term and long-term health of the offspring.
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Affiliation(s)
- Joelcio Abbade
- Lunenfeld-Tanenbaum Research Institute, Toronto, Ontario, Canada
- Departamento de Ginecologia e Obstetrícia Faculdade de Medicina de Botucatu, Sao Paulo, Brazil
| | - Miira Marjuska Klemetti
- Lunenfeld-Tanenbaum Research Institute, Toronto, Ontario, Canada
- Department of Obstetrics and Gynecology, Helsinki University Central Hospital, Helsinki, Finland
- Department of Obstetrics and Gynecology, University of Toronto, Toronto, Ontario, Canada
| | - Abby Farrell
- Lunenfeld-Tanenbaum Research Institute, Toronto, Ontario, Canada
| | - Leonardo Ermini
- Lunenfeld-Tanenbaum Research Institute, Toronto, Ontario, Canada
| | - Taylor Gillmore
- Lunenfeld-Tanenbaum Research Institute, Toronto, Ontario, Canada
- Department of Physiology and Institute of Medical Sciences, University of Toronto, Toronto, Ontario, Canada
| | - Julien Sallais
- Lunenfeld-Tanenbaum Research Institute, Toronto, Ontario, Canada
- Department of Physiology and Institute of Medical Sciences, University of Toronto, Toronto, Ontario, Canada
| | | | - Martin Post
- Department of Physiology and Institute of Medical Sciences, University of Toronto, Toronto, Ontario, Canada
- Hospital for Sick Children SickKids Learning Institute, Toronto, Ontario, Canada
| | - Isabella Caniggia
- Lunenfeld-Tanenbaum Research Institute, Toronto, Ontario, Canada
- Department of Obstetrics and Gynecology, University of Toronto, Toronto, Ontario, Canada
- Department of Physiology and Institute of Medical Sciences, University of Toronto, Toronto, Ontario, Canada
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26
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Furse S, White SL, Meek CL, Jenkins B, Petry CJ, Vieira MC, Ozanne SE, Dunger DB, Poston L, Koulman A. Altered triglyceride and phospholipid metabolism predates the diagnosis of gestational diabetes in obese pregnancy. Mol Omics 2019; 15:420-430. [PMID: 31599289 PMCID: PMC7100894 DOI: 10.1039/c9mo00117d] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Gestational diabetes (GDM), a common pregnancy complication associated with obesity and long-term health risks, is usually diagnosed at approximately 28 weeks of gestation. An understanding of lipid metabolism in women at risk of GDM could contribute to earlier diagnosis and treatment. We tested the hypothesis that altered lipid metabolism at the beginning of the second trimester in obese pregnant women is associated with a diagnosis of GDM. Plasma samples from 831 participants (16-45 years, 15-18 weeks gestation, BMI ≥ 30) from the UPBEAT study of obese pregnant women were used. The lipid, sterol and glyceride fraction was isolated and analysed in a semi-quantitative fashion using direct infusion mass spectrometry. A combination of uni-, multi-variate and multi-variable statistical analyses was used to identify candidate biomarkers in plasma associated with a diagnosis of GDM (early third trimester; IADPSG criteria). Multivariable adjusted analyses showed that participants who later developed GDM had a greater abundance of several triglycerides (48:0, 50:1, 50:2, 51:5, 53:4) and phosphatidylcholine (38:5). In contrast sphingomyelins (32:1, 41:2, 42:3), lyso-phosphatidylcholine (16:0, 18:1), phosphatidylcholines (35:2, 40:7, 40:10), two polyunsaturated triglycerides (46:5, 48:6) and several oxidised triglycerides (48:6, 54:4, 56:4, 58:6) were less abundant. We concluded that both lipid and triglyceride metabolism were altered at least 10 weeks before diagnosis of GDM. Further investigation is required to determine the functional consequences of these differences and the mechanisms by which they arise.
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Affiliation(s)
- Samuel Furse
- Metabolic Research Laboratories and MRC Metabolic Diseases Unit, Wellcome Trust-MRC Institute of Metabolic Science, University of Cambridge, Box 289, Cambridge Biomedical Campus, Hills Road, Cambridge CB2 0QQ, UK.
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Liu S, Liu Y, Liao S. Heterogeneous impact of type 2 diabetes mellitus-related genetic variants on gestational glycemic traits: review and future research needs. Mol Genet Genomics 2019; 294:811-847. [PMID: 30945019 DOI: 10.1007/s00438-019-01552-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2018] [Accepted: 03/25/2019] [Indexed: 02/07/2023]
Abstract
Gestational glucose homeostasis influences mother's metabolic health, pregnancy outcomes, fetal development and offspring growth. To understand the genetic roles in pregnant glucose metabolism and genetic predisposition for gestational diabetes (GDM), we reviewed the recent literature up to Jan, 2018 and evaluated the influence of T2DM-related genetic variants on gestational glycemic traits and glucose tolerance. A total of 140 variants of 89 genes were integrated. Their associations with glycemic traits in and outside pregnancy were compared. The genetic circumstances underlying glucose metabolism exhibit a similarity between pregnant and non-pregnant populations. While, not all of the T2DM-associated genetic variants are related to pregnant glucose tolerance, such as genes involved in fasting insulin/C-peptide regulation. Some genetic variants may have distinct effects on gestational glucose homeostasis. And certain genes may be particularly involved in this process via specific mechanisms, such as HKDC1, MTNR1B, BACE2, genes encoding cell cycle regulators, adipocyte regulators, inflammatory factors and hepatic factors related to gestational glucose sensing and insulin signaling. However, it is currently difficult to evaluate these associations with quantitative synthesis due to inadequate data, different analytical methods, varied measurements for glycemic traits, controversies in diagnosis of GDM, and unknown ethnicity- and/or sex-related influences on pregnant maternal metabolism. In conclusion, different genetic associations with glycemic traits may exist between pregnant and non-pregnant conditions. Comprehensive research on specific genetic regulation in gestation is necessary.
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Affiliation(s)
- Shasha Liu
- Diabetes Center and Transplantation Translational Medicine, Key Laboratory of Sichuan Province, Sichuan Academy of Medical Science and Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Yihuanlu Xierduan 32#, Chengdu, 610072, China
| | - Yunqiang Liu
- Department of Medical Genetics and Division of Morbid Genomics, State Key Laboratory of Biotherapy, West China Hospital, West China Medical School, Sichuan University, Chengdu, 610041, China
| | - Shunyao Liao
- Diabetes Center and Transplantation Translational Medicine, Key Laboratory of Sichuan Province, Sichuan Academy of Medical Science and Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Yihuanlu Xierduan 32#, Chengdu, 610072, China.
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