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Huang G, Sun Y, Li R, Mo L, Liang Q, Yu X. Functional genetic variants and susceptibility and prediction of gestational diabetes mellitus. Sci Rep 2024; 14:18123. [PMID: 39103437 DOI: 10.1038/s41598-024-69079-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2024] [Accepted: 07/31/2024] [Indexed: 08/07/2024] Open
Abstract
The aetiological mechanism of gestational diabetes mellitus (GDM) has still not been fully understood. The aim of this study was to explore the associations between functional genetic variants screened from a genome-wide association study (GWAS) and GDM risk among 554 GDM patients and 641 healthy controls in China. Functional analysis of single nucleotide polymorphisms (SNPs) positively associated with GDM was further performed. Univariate regression and multivariate logistic regression analyses were used to screen clinical risk factors, and a predictive nomogram model was established. After adjusting for age and prepregnancy BMI, rs9283638 was significantly associated with GDM susceptibility (P < 0.05). Moreover, an obvious interaction between rs9283638 and clinical variables was detected (Pinteraction < 0.05). Functional analysis confirmed that rs9283638 can regulate not only target gene transcription factor binding, but it also regulates the mRNA levels of SAMD7 (P < 0.05). The nomogram model constructed with the factors of age, FPG, 1hPG, 2hPG, HbA1c, TG and rs9283638 revealed an area under the ROC curve of 0.920 (95% CI 0.902-0.939). Decision curve analysis (DCA) suggested that the model had greater net clinical benefit. Conclusively, genetic variants can alter women's susceptibility to GDM by affecting the transcription of target genes. The predictive nomogram model constructed based on genetic and clinical variables can effectively distinguish individuals with different GDM risk factors.
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Affiliation(s)
- Gongchen Huang
- The Guangxi Key Laboratory of Environmental Exposomics and Entire Lifecycle Health, The School of Public Health, Guilin Medical University, Guilin, 541000, China
| | - Yan Sun
- The Guangxi Key Laboratory of Environmental Exposomics and Entire Lifecycle Health, The School of Public Health, Guilin Medical University, Guilin, 541000, China
| | - Ruiqi Li
- The Guangxi Key Laboratory of Environmental Exposomics and Entire Lifecycle Health, The School of Public Health, Guilin Medical University, Guilin, 541000, China
| | - Lei Mo
- Department of Obstetrics and Gynecology, The Second Affiliated Hospital of Guilin Medical University, Guilin, 541000, China
| | - Qiulian Liang
- The Guangxi Key Laboratory of Environmental Exposomics and Entire Lifecycle Health, The School of Public Health, Guilin Medical University, Guilin, 541000, China.
| | - Xiangyuan Yu
- The Guangxi Key Laboratory of Environmental Exposomics and Entire Lifecycle Health, The School of Public Health, Guilin Medical University, Guilin, 541000, China.
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2
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Elliott A, Walters RK, Pirinen M, Kurki M, Junna N, Goldstein JI, Reeve MP, Siirtola H, Lemmelä SM, Turley P, Lahtela E, Mehtonen J, Reis K, Elnahas AG, Reigo A, Palta P, Esko T, Mägi R, Palotie A, Daly MJ, Widén E. Distinct and shared genetic architectures of gestational diabetes mellitus and type 2 diabetes. Nat Genet 2024; 56:377-382. [PMID: 38182742 PMCID: PMC10937370 DOI: 10.1038/s41588-023-01607-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2023] [Accepted: 11/07/2023] [Indexed: 01/07/2024]
Abstract
Gestational diabetes mellitus (GDM) is a common metabolic disorder affecting more than 16 million pregnancies annually worldwide1,2. GDM is related to an increased lifetime risk of type 2 diabetes (T2D)1-3, with over a third of women developing T2D within 15 years of their GDM diagnosis. The diseases are hypothesized to share a genetic predisposition1-7, but few studies have sought to uncover the genetic underpinnings of GDM. Most studies have evaluated the impact of T2D loci only8-10, and the three prior genome-wide association studies of GDM11-13 have identified only five loci, limiting the power to assess to what extent variants or biological pathways are specific to GDM. We conducted the largest genome-wide association study of GDM to date in 12,332 cases and 131,109 parous female controls in the FinnGen study and identified 13 GDM-associated loci, including nine new loci. Genetic features distinct from T2D were identified both at the locus and genomic scale. Our results suggest that the genetics of GDM risk falls into the following two distinct categories: one part conventional T2D polygenic risk and one part predominantly influencing mechanisms disrupted in pregnancy. Loci with GDM-predominant effects map to genes related to islet cells, central glucose homeostasis, steroidogenesis and placental expression.
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Grants
- R00 AG062787 NIA NIH HHS
- R01 MH101244 NIMH NIH HHS
- A.E. was a research Scholar supported by Sarnoff Cardiovascular Research Foundation
- U.S. Department of Health & Human Services | National Institutes of Health (NIH)
- Academy of Finland (Suomen Akatemia)
- U.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging)
- The FinnGen project is funded by two grants from Business Finland (HUS 4685/31/2016 and UH 4386/31/2016) and by eleven industry partners (AbbVie Inc, AstraZeneca UK Ltd, Biogen MA Inc, Celgene Corporation, Celgene International II Sàrl, Genentech Inc, Merck Sharp & Dohme Corp, Pfizer Inc., GlaxoSmithKline, Sanofi, Maze Therapeutics Inc., Janssen Biotech Inc).
- EstBB GWAS analysis is supported by research funding from the Estonian Research Council: Team grant PRG1291 and PRG1911.
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Affiliation(s)
- Amanda Elliott
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Raymond K Walters
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Matti Pirinen
- Institute for Molecular Medicine Finland, Helsinki Institute of Life Sciences, University of Helsinki, Helsinki, Finland
- Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland
- Department of Public Health, University of Helsinki, Helsinki, Finland
| | - Mitja Kurki
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Nella Junna
- Institute for Molecular Medicine Finland, Helsinki Institute of Life Sciences, University of Helsinki, Helsinki, Finland
| | - Jacqueline I Goldstein
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Mary Pat Reeve
- Institute for Molecular Medicine Finland, Helsinki Institute of Life Sciences, University of Helsinki, Helsinki, Finland
| | - Harri Siirtola
- TAUCHI Research Center, Faculty of Information Technology and Communication Sciences (ITC), Tampere University, Tampere, Finland
| | - Susanna M Lemmelä
- Institute for Molecular Medicine Finland, Helsinki Institute of Life Sciences, University of Helsinki, Helsinki, Finland
- Finnish Institute for Health and Welfare (THL), Helsinki, Finland
| | - Patrick Turley
- Center for Economic and Social Research, University of Southern California, Los Angeles, CA, USA
- Department of Economics, University of Southern California, Los Angeles, CA, USA
| | - Elisa Lahtela
- Institute for Molecular Medicine Finland, Helsinki Institute of Life Sciences, University of Helsinki, Helsinki, Finland
| | - Juha Mehtonen
- Institute for Molecular Medicine Finland, Helsinki Institute of Life Sciences, University of Helsinki, Helsinki, Finland
| | - Kadri Reis
- Institute of Genomics, University of Tartu, Tartu, Estonia
| | | | - Anu Reigo
- Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Priit Palta
- Institute for Molecular Medicine Finland, Helsinki Institute of Life Sciences, University of Helsinki, Helsinki, Finland
- Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Tõnu Esko
- Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Reedik Mägi
- Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Aarno Palotie
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Harvard Medical School, Boston, MA, USA
- Institute for Molecular Medicine Finland, Helsinki Institute of Life Sciences, University of Helsinki, Helsinki, Finland
| | - Mark J Daly
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA.
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, USA.
- Harvard Medical School, Boston, MA, USA.
- Institute for Molecular Medicine Finland, Helsinki Institute of Life Sciences, University of Helsinki, Helsinki, Finland.
| | - Elisabeth Widén
- Institute for Molecular Medicine Finland, Helsinki Institute of Life Sciences, University of Helsinki, Helsinki, Finland.
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Hughes ZH, Hughes LM, Khan SS. Genetic contributions to risk of adverse pregnancy outcomes. CURRENT CARDIOVASCULAR RISK REPORTS 2023; 17:185-193. [PMID: 38186860 PMCID: PMC10768680 DOI: 10.1007/s12170-023-00729-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/01/2023] [Indexed: 01/09/2024]
Abstract
Purpose of Review Adverse pregnancy outcomes (APOs), including hypertensive disorders of pregnancy (HDP), low birthweight (LBW), and preterm birth (PTB), along with peripartum cardiomyopathy (PPCM) are associated with short- and long-term maternal and fetal cardiovascular risks. This review focuses on the genetic contributions to the risk of APOs and PPCM. Recent Findings The expansion of genome-wide association studies (GWAS) has led to better understanding of the biologic mechanisms underpinning APO, PPCM, and the predisposition to cardiovascular disease across the life course. Genetic loci known to be involved with the risk of hypertension (FTO, ZNF831) have been associated with the development of overall HDP and preeclampsia. Additionally, four loci significantly associated with type 2 diabetes have been associated with GDM (CDKAL1, MTNR1B, TCF7L2, CDK2NA-CDKN2B). Variants in loci known to affect genes coding for proteins involved in immune cell function and placental health (EBF1, EEFSEC, AGTR2, 2q13) have been implicated in the development of PTB and future cardiovascular risks for both the mother and the offspring. Genetic similarities in rare variants between PPCM and dilated cardiomyopathy have been described suggesting shared pathophysiologic origins as well as predisposition for future risk of heart failure, highlighting the need for the development PPCM genetic counseling guidelines. Summary Genetics may inform mechanisms, risk, and counseling for individuals after an APO or PPCM. Through recent advances in genetic techniques and analytic approaches, new insights into the underlying biologic mechanisms and genetic variants leading to these risks have been discovered.
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Affiliation(s)
- Zachary H. Hughes
- Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, UA
| | - Lydia M. Hughes
- Department of Obstetrics & Gynecology, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, UA
| | - Sadiya S. Khan
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
- Division of Cardiology, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
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Belsti Y, Moran L, Handiso DW, Versace V, Goldstein R, Mousa A, Teede H, Enticott J. Models Predicting Postpartum Glucose Intolerance Among Women with a History of Gestational Diabetes Mellitus: a Systematic Review. Curr Diab Rep 2023; 23:231-243. [PMID: 37294513 PMCID: PMC10435618 DOI: 10.1007/s11892-023-01516-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 05/22/2023] [Indexed: 06/10/2023]
Abstract
PURPOSE OF REVIEW Despite the crucial role that prediction models play in guiding early risk stratification and timely intervention to prevent type 2 diabetes after gestational diabetes mellitus (GDM), their use is not widespread in clinical practice. The purpose of this review is to examine the methodological characteristics and quality of existing prognostic models predicting postpartum glucose intolerance following GDM. RECENT FINDINGS A systematic review was conducted on relevant risk prediction models, resulting in 15 eligible publications from research groups in various countries. Our review found that traditional statistical models were more common than machine learning models, and only two were assessed to have a low risk of bias. Seven were internally validated, but none were externally validated. Model discrimination and calibration were done in 13 and four studies, respectively. Various predictors were identified, including body mass index, fasting glucose concentration during pregnancy, maternal age, family history of diabetes, biochemical variables, oral glucose tolerance test, use of insulin in pregnancy, postnatal fasting glucose level, genetic risk factors, hemoglobin A1c, and weight. The existing prognostic models for glucose intolerance following GDM have various methodological shortcomings, with only a few models being assessed to have low risk of bias and validated internally. Future research should prioritize the development of robust, high-quality risk prediction models that follow appropriate guidelines, in order to advance this area and improve early risk stratification and intervention for glucose intolerance and type 2 diabetes among women who have had GDM.
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Affiliation(s)
- Yitayeh Belsti
- Monash Centre for Health Research and Implementation, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Australia
| | - Lisa Moran
- Monash Centre for Health Research and Implementation, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Australia
| | - Demelash Woldeyohannes Handiso
- Monash Centre for Health Research and Implementation, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Australia
| | - Vincent Versace
- Deakin Rural Health, School of Medicine, Deakin University, Warrnambool, Australia
| | - Rebecca Goldstein
- Monash Centre for Health Research and Implementation, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Australia
- Monash Health, Clayton, Melbourne, Australia
| | - Aya Mousa
- Monash Centre for Health Research and Implementation, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Australia
| | - Helena Teede
- Monash Centre for Health Research and Implementation, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Australia
- Monash Health, Clayton, Melbourne, Australia
| | - Joanne Enticott
- Monash Centre for Health Research and Implementation, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Australia.
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5
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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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Lowe WL. Genetics and Epigenetics: Implications for the Life Course of Gestational Diabetes. Int J Mol Sci 2023; 24:6047. [PMID: 37047019 PMCID: PMC10094577 DOI: 10.3390/ijms24076047] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Revised: 03/19/2023] [Accepted: 03/21/2023] [Indexed: 04/14/2023] Open
Abstract
Gestational diabetes (GDM) is one of the most common complications of pregnancy, affecting as many as one in six pregnancies. It is associated with both short- and long-term adverse outcomes for the mother and fetus and has important implications for the life course of affected women. Advances in genetics and epigenetics have not only provided new insight into the pathophysiology of GDM but have also provided new approaches to identify women at high risk for progression to postpartum cardiometabolic disease. GDM and type 2 diabetes share similarities in their pathophysiology, suggesting that they also share similarities in their genetic architecture. Candidate gene and genome-wide association studies have identified susceptibility genes that are shared between GDM and type 2 diabetes. Despite these similarities, a much greater effect size for MTNR1B in GDM compared to type 2 diabetes and association of HKDC1, which encodes a hexokinase, with GDM but not type 2 diabetes suggest some differences in the genetic architecture of GDM. Genetic risk scores have shown some efficacy in identifying women with a history of GDM who will progress to type 2 diabetes. The association of epigenetic changes, including DNA methylation and circulating microRNAs, with GDM has also been examined. Targeted and epigenome-wide approaches have been used to identify DNA methylation in circulating blood cells collected during early, mid-, and late pregnancy that is associated with GDM. DNA methylation in early pregnancy had some ability to identify women who progressed to GDM, while DNA methylation in blood collected at 26-30 weeks gestation improved upon the ability of clinical factors alone to identify women at risk for progression to abnormal glucose tolerance post-partum. Finally, circulating microRNAs and long non-coding RNAs that are present in early or mid-pregnancy and associated with GDM have been identified. MicroRNAs have also proven efficacious in predicting both the development of GDM as well as its long-term cardiometabolic complications. Studies performed to date have demonstrated the potential for genetic and epigenetic technologies to impact clinical care, although much remains to be done.
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Affiliation(s)
- William L Lowe
- Department of Medicine, Division of Endocrinology, Metabolism and Molecular Medicine, Northwestern University Feinberg School of Medicine, Rubloff 12, 420 E. Superior Street, Chicago, IL 60611, USA
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7
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Elliott A, Walters RK, Pirinen M, Kurki M, Junna N, Goldstein J, Reeve M, Siirtola H, Lemmelä S, Turley P, Palotie A, Daly M, Widén E. Distinct and shared genetic architectures of Gestational diabetes mellitus and Type 2 Diabetes Mellitus. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.02.16.23286014. [PMID: 36865330 PMCID: PMC9980250 DOI: 10.1101/2023.02.16.23286014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/25/2023]
Abstract
Gestational diabetes mellitus (GDM) affects more than 16 million pregnancies annually worldwide and is related to an increased lifetime risk of Type 2 diabetes (T2D). The diseases are hypothesized to share a genetic predisposition, but there are few GWAS studies of GDM and none of them is sufficiently powered to assess whether any variants or biological pathways are specific to GDM. We conducted the largest genome-wide association study of GDM to date in 12,332 cases and 131,109 parous female controls in the FinnGen Study and identified 13 GDM-associated loci including 8 novel loci. Genetic features distinct from T2D were identified both at the locus and genomic scale. Our results suggest that the genetics of GDM risk falls into two distinct categories - one part conventional T2D polygenic risk and one part predominantly influencing mechanisms disrupted in pregnancy. Loci with GDM-predominant effects map to genes related to islet cells, central glucose homeostasis, steroidogenesis, and placental expression. These results pave the way for an improved biological understanding of GDM pathophysiology and its role in the development and course of T2D.
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Affiliation(s)
- A. Elliott
- Analytic and Translational Genetics Unit, Massachusetts Gen. Hosp., Boston, MA
- Stanley Ctr. for Psychiatric Res., Broad Inst. of Harvard and MIT, Cambridge, MA
- Harvard Med. Sch., Boston, MA
| | - R. K. Walters
- Analytic and Translational Genetics Unit, Massachusetts Gen. Hosp., Boston, MA
- Stanley Ctr. for Psychiatric Res., Broad Inst. of Harvard and MIT, Cambridge, MA
- Harvard Med. Sch., Boston, MA
| | - M. Pirinen
- Institute for Molecular Med. Finland, Helsinki Institute of Life Sciences., University of Helsinki, Helsinki, Finland
- Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland
- Department of Public Health, University of Helsinki, Helsinki, Finland
| | - M. Kurki
- Analytic and Translational Genetics Unit, Massachusetts Gen. Hosp., Boston, MA
- Stanley Ctr. for Psychiatric Res., Broad Inst. of Harvard and MIT, Cambridge, MA
| | - N. Junna
- Institute for Molecular Med. Finland, Helsinki Institute of Life Sciences., University of Helsinki, Helsinki, Finland
| | - J. Goldstein
- Stanley Ctr. for Psychiatric Res., Broad Inst. of Harvard and MIT, Cambridge, MA
| | - M.P. Reeve
- Institute for Molecular Med. Finland, Helsinki Institute of Life Sciences., University of Helsinki, Helsinki, Finland
| | - H. Siirtola
- TAUCHI Research Center, Faculty of Information Technology and Communication Sciences (ITC), Tampere University, Tampere, Finland
| | - S. Lemmelä
- Institute for Molecular Med. Finland, Helsinki Institute of Life Sciences., University of Helsinki, Helsinki, Finland
- Finnish Institute for Health and Welfare (THL), Helsinki, Finland
| | - P. Turley
- Center for Economic and Social Research, University of Southern California, Los Angeles, CA, USA
- Department of Economics, University of Southern California, Los Angeles, CA, USA
| | | | - A. Palotie
- Analytic and Translational Genetics Unit, Massachusetts Gen. Hosp., Boston, MA
- Stanley Ctr. for Psychiatric Res., Broad Inst. of Harvard and MIT, Cambridge, MA
- Harvard Med. Sch., Boston, MA
- Institute for Molecular Med. Finland, Helsinki Institute of Life Sciences., University of Helsinki, Helsinki, Finland
| | - M. Daly
- Analytic and Translational Genetics Unit, Massachusetts Gen. Hosp., Boston, MA
- Stanley Ctr. for Psychiatric Res., Broad Inst. of Harvard and MIT, Cambridge, MA
- Harvard Med. Sch., Boston, MA
- Institute for Molecular Med. Finland, Helsinki Institute of Life Sciences., University of Helsinki, Helsinki, Finland
| | - E. Widén
- Institute for Molecular Med. Finland, Helsinki Institute of Life Sciences., University of Helsinki, Helsinki, Finland
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8
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Zhang M, Li Q, Wang KL, Dong Y, Mu YT, Cao YM, Liu J, Li ZH, Cui HL, Liu HY, Hu AQ, Zheng YJ. Lipolysis and gestational diabetes mellitus onset: a case-cohort genome-wide association study in Chinese. J Transl Med 2023; 21:47. [PMID: 36698149 PMCID: PMC9875546 DOI: 10.1186/s12967-023-03902-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Accepted: 01/17/2023] [Indexed: 01/26/2023] Open
Abstract
BACKGROUND Genetic knowledge of gestational diabetes mellitus (GDM) in Chinese women is quite limited. This study aimed to identify the risk factors and mechanism of GDM at the genetic level in a Chinese population. METHODS We conducted a genome-wide association study (GWAS) based on single nucleotide polymorphism (SNP) array genotyping (ASA-CHIA Bead chip, Illumina) and a case-cohort study design. Variants including SNPs, copy number variants (CNVs), and insertions-deletions (InDels) were called from genotyping data. A total of 2232 pregnant women were enrolled in their first/second trimester between February 2018 and December 2020 from Anqing Municipal Hospital in Anhui Province, China. The GWAS included 193 GDM patients and 819 subjects without a diabetes diagnosis, and risk ratios (RRs) and their 95% confidence intervals (CIs) were estimated by a regression-based method conditional on the population structure. The calling and quality control of genotyping data were performed following published guidelines. CNVs were merged into CNV regions (CNVR) to simplify analyses. To interpret the GWAS results, gene mapping and overexpression analyses (ORAs) were further performed to prioritize the candidate genes and related biological mechanisms. RESULTS We identified 14 CNVRs (false discovery rate corrected P values < 0.05) and two suggestively significant SNPs (P value < 0.00001) associated with GDM, and a total of 19 candidate genes were mapped. Ten genes were significantly enriched in gene sets related to lipase (triglyceride lipase and lipoprotein lipase) activity (LIPF, LIPK, LIPN, and LIPJ genes), oxidoreductase activity (TPH1 and TPH2 genes), and cellular components beta-catenin destruction complex (APC and GSK3B genes), Wnt signalosome (APC and GSK3B genes), and lateral element in the Gene Ontology resource (BRCA1 and SYCP2 genes) by two ORA methods (adjusted P values < 0.05). CONCLUSIONS Genes related to lipolysis, redox reaction, and proliferation of islet β-cells are associated with GDM in Chinese women. Energy metabolism, particularly lipolysis, may play an important role in GDM aetiology and pathology, which needs further molecular studies to verify.
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Affiliation(s)
- Miao Zhang
- grid.8547.e0000 0001 0125 2443Department of Epidemiology, School of Public Health, Fudan University, Shanghai, 200032 China ,grid.8547.e0000 0001 0125 2443Key Laboratory for Health Technology Assessment, National Commission of Health and Family Planning, Fudan University, Shanghai, 200032 China ,grid.8547.e0000 0001 0125 2443Key Laboratory of Public Health Safety, Ministry of Education, Fudan University, Shanghai, 200032 China
| | - Qing Li
- Department of Obstetrics and Gynecology, Anqing Municipal Hospital, Anqing, 246003 China
| | - Kai-Lin Wang
- grid.8547.e0000 0001 0125 2443Department of Epidemiology, School of Public Health, Fudan University, Shanghai, 200032 China ,grid.8547.e0000 0001 0125 2443Key Laboratory for Health Technology Assessment, National Commission of Health and Family Planning, Fudan University, Shanghai, 200032 China ,grid.8547.e0000 0001 0125 2443Key Laboratory of Public Health Safety, Ministry of Education, Fudan University, Shanghai, 200032 China
| | - Yao Dong
- grid.8547.e0000 0001 0125 2443Department of Epidemiology, School of Public Health, Fudan University, Shanghai, 200032 China ,grid.8547.e0000 0001 0125 2443Key Laboratory for Health Technology Assessment, National Commission of Health and Family Planning, Fudan University, Shanghai, 200032 China ,grid.8547.e0000 0001 0125 2443Key Laboratory of Public Health Safety, Ministry of Education, Fudan University, Shanghai, 200032 China
| | - Yu-Tong Mu
- grid.8547.e0000 0001 0125 2443Department of Epidemiology, School of Public Health, Fudan University, Shanghai, 200032 China ,grid.8547.e0000 0001 0125 2443Key Laboratory for Health Technology Assessment, National Commission of Health and Family Planning, Fudan University, Shanghai, 200032 China ,grid.8547.e0000 0001 0125 2443Key Laboratory of Public Health Safety, Ministry of Education, Fudan University, Shanghai, 200032 China
| | - Yan-Min Cao
- grid.8547.e0000 0001 0125 2443Department of Epidemiology, School of Public Health, Fudan University, Shanghai, 200032 China ,grid.8547.e0000 0001 0125 2443Key Laboratory for Health Technology Assessment, National Commission of Health and Family Planning, Fudan University, Shanghai, 200032 China ,grid.8547.e0000 0001 0125 2443Key Laboratory of Public Health Safety, Ministry of Education, Fudan University, Shanghai, 200032 China
| | - Jin Liu
- grid.8547.e0000 0001 0125 2443Department of Epidemiology, School of Public Health, Fudan University, Shanghai, 200032 China ,grid.8547.e0000 0001 0125 2443Key Laboratory for Health Technology Assessment, National Commission of Health and Family Planning, Fudan University, Shanghai, 200032 China ,grid.8547.e0000 0001 0125 2443Key Laboratory of Public Health Safety, Ministry of Education, Fudan University, Shanghai, 200032 China
| | - Zi-Heng Li
- grid.8547.e0000 0001 0125 2443Department of Epidemiology, School of Public Health, Fudan University, Shanghai, 200032 China ,grid.8547.e0000 0001 0125 2443Key Laboratory for Health Technology Assessment, National Commission of Health and Family Planning, Fudan University, Shanghai, 200032 China ,grid.8547.e0000 0001 0125 2443Key Laboratory of Public Health Safety, Ministry of Education, Fudan University, Shanghai, 200032 China
| | - Hui-Lu Cui
- grid.8547.e0000 0001 0125 2443Department of Epidemiology, School of Public Health, Fudan University, Shanghai, 200032 China ,grid.8547.e0000 0001 0125 2443Key Laboratory for Health Technology Assessment, National Commission of Health and Family Planning, Fudan University, Shanghai, 200032 China ,grid.8547.e0000 0001 0125 2443Key Laboratory of Public Health Safety, Ministry of Education, Fudan University, Shanghai, 200032 China
| | - Hai-Yan Liu
- Department of Clinical Laboratory, Anqing Municipal Hospital, Anqing, 246003 China
| | - An-Qun Hu
- Department of Clinical Laboratory, Anqing Municipal Hospital, Anqing, 246003 China
| | - Ying-Jie Zheng
- grid.8547.e0000 0001 0125 2443Department of Epidemiology, School of Public Health, Fudan University, Shanghai, 200032 China ,grid.8547.e0000 0001 0125 2443Key Laboratory for Health Technology Assessment, National Commission of Health and Family Planning, Fudan University, Shanghai, 200032 China ,grid.8547.e0000 0001 0125 2443Key Laboratory of Public Health Safety, Ministry of Education, Fudan University, Shanghai, 200032 China
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9
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Lamri A, De Paoli M, De Souza R, Werstuck G, Anand S, Pigeyre M. Insight into genetic, biological, and environmental determinants of sexual-dimorphism in type 2 diabetes and glucose-related traits. Front Cardiovasc Med 2022; 9:964743. [PMID: 36505380 PMCID: PMC9729955 DOI: 10.3389/fcvm.2022.964743] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Accepted: 10/21/2022] [Indexed: 11/25/2022] Open
Abstract
There is growing evidence that sex and gender differences play an important role in risk and pathophysiology of type 2 diabetes (T2D). Men develop T2D earlier than women, even though there is more obesity in young women than men. This difference in T2D prevalence is attenuated after the menopause. However, not all women are equally protected against T2D before the menopause, and gestational diabetes represents an important risk factor for future T2D. Biological mechanisms underlying sex and gender differences on T2D physiopathology are not yet fully understood. Sex hormones affect behavior and biological changes, and can have implications on lifestyle; thus, both sex-specific environmental and biological risk factors interact within a complex network to explain the differences in T2D risk and physiopathology in men and women. In addition, lifetime hormone fluctuations and body changes due to reproductive factors are generally more dramatic in women than men (ovarian cycle, pregnancy, and menopause). Progress in genetic studies and rodent models have significantly advanced our understanding of the biological pathways involved in the physiopathology of T2D. However, evidence of the sex-specific effects on genetic factors involved in T2D is still limited, and this gap of knowledge is even more important when investigating sex-specific differences during the life course. In this narrative review, we will focus on the current state of knowledge on the sex-specific effects of genetic factors associated with T2D over a lifetime, as well as the biological effects of these different hormonal stages on T2D risk. We will also discuss how biological insights from rodent models complement the genetic insights into the sex-dimorphism effects on T2D. Finally, we will suggest future directions to cover the knowledge gaps.
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Affiliation(s)
- Amel Lamri
- Department of Medicine, McMaster University, Hamilton, ON, Canada,Population Health Research Institute (PHRI), Hamilton, ON, Canada
| | - Monica De Paoli
- Department of Medicine, McMaster University, Hamilton, ON, Canada,Thrombosis and Atherosclerosis Research Institute (TaARI), Hamilton, ON, Canada
| | - Russell De Souza
- Population Health Research Institute (PHRI), Hamilton, ON, Canada,Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada
| | - Geoff Werstuck
- Department of Medicine, McMaster University, Hamilton, ON, Canada,Thrombosis and Atherosclerosis Research Institute (TaARI), Hamilton, ON, Canada
| | - Sonia Anand
- Department of Medicine, McMaster University, Hamilton, ON, Canada,Population Health Research Institute (PHRI), Hamilton, ON, Canada,Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada
| | - Marie Pigeyre
- Department of Medicine, McMaster University, Hamilton, ON, Canada,Population Health Research Institute (PHRI), Hamilton, ON, Canada,*Correspondence: Marie Pigeyre
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10
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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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11
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Genomics and Epigenomics of Gestational Diabetes Mellitus: Understanding the Molecular Pathways of the Disease Pathogenesis. Int J Mol Sci 2022; 23:ijms23073514. [PMID: 35408874 PMCID: PMC8998752 DOI: 10.3390/ijms23073514] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Revised: 03/01/2022] [Accepted: 03/04/2022] [Indexed: 11/16/2022] Open
Abstract
One of the most common complications during pregnancy is gestational diabetes mellitus (GDM), hyperglycemia that occurs for the first time during pregnancy. The condition is multifactorial, caused by an interaction between genetic, epigenetic, and environmental factors. However, the underlying mechanisms responsible for its pathogenesis remain elusive. Moreover, in contrast to several common metabolic disorders, molecular research in GDM is lagging. It is important to recognize that GDM is still commonly diagnosed during the second trimester of pregnancy using the oral glucose tolerance test (OGGT), at a time when both a fetal and maternal pathophysiology is already present, demonstrating the increased blood glucose levels associated with exacerbated insulin resistance. Therefore, early detection of metabolic changes and associated epigenetic and genetic factors that can lead to an improved prediction of adverse pregnancy outcomes and future cardio-metabolic pathologies in GDM women and their children is imperative. Several genomic and epigenetic approaches have been used to identify the genes, genetic variants, metabolic pathways, and epigenetic modifications involved in GDM to determine its etiology. In this article, we explore these factors as well as how their functional effects may contribute to immediate and future pathologies in women with GDM and their offspring from birth to adulthood. We also discuss how these approaches contribute to the changes in different molecular pathways that contribute to the GDM pathogenesis, with a special focus on the development of insulin resistance.
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12
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Pace NP, Vella B, Craus J, Caruana R, Savona-Ventura C, Vassallo J. Screening for monogenic subtypes of gestational diabetes in a high prevalence island population - A whole exome sequencing study. Diabetes Metab Res Rev 2022; 38:e3486. [PMID: 34278679 DOI: 10.1002/dmrr.3486] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Revised: 07/01/2021] [Accepted: 07/13/2021] [Indexed: 12/12/2022]
Abstract
AIMS The reported frequency of monogenic defects of beta cell function in gestational diabetes (GDM) varies extensively. This study aimed to evaluate the frequency and molecular spectrum of variants in genes associated with monogenic/atypical diabetes in non-obese females of Maltese ethnicity with GDM. METHODS 50 non-obese females who met the International Association of the Diabetes and Pregnancy Study Groups (IADPSG) criteria for diagnosis of GDM and with a first-degree relative with non-autoimmune diabetes were included in this study. Whole exome capture and high throughput sequencing was carried out. Rare sequence variants were filtered, annotated, and prioritised according to the American College for Medical Genetics guidelines. For selected missense variants we explored effects on protein stability and structure through in-silico tools. RESULTS We identified three pathogenic variants in GCK, ABCC8 and HNF1A and several variants of uncertain significance in the cohort. Genotype-phenotype correlations and post-pregnancy follow-up data are described. CONCLUSIONS This study provides the first insight into an underlying monogenic aetiology in non-obese females with GDM from an island population having a high prevalence of diabetes. It suggests that monogenic variants constitute an underestimated cause of diabetes detected in pregnancy, and that careful evaluation of GDM probands to identify monogenic disease subtypes is indicated.
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Affiliation(s)
- Nikolai Paul Pace
- Centre for Molecular Medicine and Biobanking, Faculty of Medicine and Surgery, University of Malta, Msida, Malta
| | - Barbara Vella
- Centre for Molecular Medicine and Biobanking, Faculty of Medicine and Surgery, University of Malta, Msida, Malta
| | - Johann Craus
- Department of Obstetrics and Gynaecology, Faculty of Medicine and Surgery, University of Malta, Msida, Malta
| | - Ruth Caruana
- Department of Medicine, Faculty of Medicine and Surgery, University of Malta, Msida, Malta
| | - Charles Savona-Ventura
- Department of Obstetrics and Gynaecology, Faculty of Medicine and Surgery, University of Malta, Msida, Malta
| | - Josanne Vassallo
- Centre for Molecular Medicine and Biobanking, Faculty of Medicine and Surgery, University of Malta, Msida, Malta
- Department of Medicine, Faculty of Medicine and Surgery, University of Malta, Msida, Malta
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13
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Chen X, Jiang Y, Chen R, Qi Q, Zhang X, Zhao S, Liu C, Wang W, Li Y, Sun G, Song J, Huang H, Cheng C, Zhang J, Cheng L, Liu J. Clinical efficiency of simultaneous CNV-seq and whole-exome sequencing for testing fetal structural anomalies. J Transl Med 2022; 20:10. [PMID: 34980134 PMCID: PMC8722033 DOI: 10.1186/s12967-021-03202-9] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Accepted: 12/16/2021] [Indexed: 12/27/2022] Open
Abstract
Background Birth defects are responsible for approximately 7% of neonatal deaths worldwide by World Health Organization in 2004. Many methods have been utilized for examining the congenital anomalies in fetuses. This study aims to investigate the efficiency of simultaneous CNV-seq and whole-exome sequencing (WES) in the diagnosis of fetal anomaly based on a large Chinese cohort. Methods In this cohort study, 1800 pregnant women with singleton fetus in Hubei Province were recruited from 2018 to 2020 for prenatal ultrasonic screening. Those with fetal structural anomalies were transferred to the Maternal and Child Health Hospital of Hubei Province through a referral network in Hubei, China. After multidisciplinary consultation and decision on fetal outcome, products of conception (POC) samples were obtained. Simultaneous CNV-seq and WES was conducted to identify the fetal anomalies that can compress initial DNA and turnaround time of reports. Results In total, 959 couples were finally eligible for the enrollment. A total of 227 trios were identified with a causative alteration (CNV or variant), among which 191 (84.14%) were de novo. Double diagnosis of pathogenic CNVs and variants have been identified in 10 fetuses. The diagnostic yield of multisystem anomalies was significantly higher than single system anomalies (32.28% vs. 22.36%, P = 0.0183). The diagnostic rate of fetuses with consistent intra- and extra-uterine phenotypes (172/684) was significantly higher than the rate of these with inconsistent phenotypes (17/116, P = 0.0130). Conclusions Simultaneous CNV-seq and WES analysis contributed to fetal anomaly diagnosis and played a vital role in elucidating complex anomalies with compound causes. Supplementary Information The online version contains supplementary material available at 10.1186/s12967-021-03202-9.
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Affiliation(s)
- Xinlin Chen
- Department of Ultrasound Diagnosis, Maternal and Child Health Hospital of Hubei Province, Wuhan, 430070, Hubei, China
| | - Yulin Jiang
- Department of Obstetrics and Gynecology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China
| | - Ruiguo Chen
- Berry Genomics Corporation, Beijing, 102200, China
| | - Qingwei Qi
- Department of Obstetrics and Gynecology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China
| | | | - Sheng Zhao
- Department of Ultrasound Diagnosis, Maternal and Child Health Hospital of Hubei Province, Wuhan, 430070, Hubei, China
| | - Chaoshi Liu
- Berry Genomics Corporation, Beijing, 102200, China
| | - Weiyun Wang
- Department of Ultrasound Diagnosis, Maternal and Child Health Hospital of Hubei Province, Wuhan, 430070, Hubei, China
| | - Yuezhen Li
- Berry Genomics Corporation, Beijing, 102200, China
| | - Guoqiang Sun
- Department of Obstetrics, Maternal and Child Health Hospital of Hubei Province, Wuhan, 430070, Hubei, China
| | - Jieping Song
- Department of Genetic Laboratory, Maternal and Child Health Hospital of Hubei Province, Wuhan, 430070, Hubei, China
| | - Hui Huang
- Department of Ultrasound Diagnosis, Maternal and Child Health Hospital of Hubei Province, Wuhan, 430070, Hubei, China
| | - Chen Cheng
- Department of Ultrasound Diagnosis, Maternal and Child Health Hospital of Hubei Province, Wuhan, 430070, Hubei, China
| | | | - Longxian Cheng
- Department of Ultrasound Diagnosis, Hubei Maternity and Child Health Hospital, No. 745, Wuluo Road, Hongshan District, Wuhan, 430030, Hubei, China.
| | - Juntao Liu
- Department of Obstetrics and Gynecology, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, No. 1, Shuaifu Garden, Dongcheng District, Beijing, 100730, China.
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14
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Wang H, Yang W, Liu J, Leng J, Li W, Yu Z, Li J, Ma RCW, Hu G, Fang Z, Wang Y, Yang X. Serum concentrations of SFAs and CDKAL1 single-nucleotide polymorphism rs7747752 are related to an increased risk of gestational diabetes mellitus. Am J Clin Nutr 2021; 114:1698-1707. [PMID: 34192303 DOI: 10.1093/ajcn/nqab225] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Accepted: 06/10/2021] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Interactions between genetic and nutritional factors can contribute to the risk of gestational diabetes mellitus (GDM). OBJECTIVES We aimed to explore the associations of cyclin-dependent kinase 5 regulatory subunit associated protein 1-like 1 (CDKAL1) single-nucleotide polymorphism (SNP) rs7747752 and serum concentrations of SFAs with the risk of GDM in Chinese women. METHODS We conducted a 1:1 case-control study in a prospective cohort of pregnant women in Tianjin, China. Serum SFA data were collected from a total of 243 women with GDM and their controls matched by maternal age (±1 y). Among them, 207 case-control pairs had high-quality sequencing data. P/L and S/P ratios were defined as palmitic acid (16:0)/lauric acid (12:0) and stearic acid (18:0)/palmitic acid, respectively. Conditional logistic regression analysis was performed to estimate associations of CDKAL1 SNP rs7747752 and serum concentrations of SFAs with the risk of GDM. An additive interaction between rs7747752 and palmitic acid was analyzed to test the contribution of their interaction to the risk of GDM. RESULTS Among the 5 tested SFAs, palmitic acid was positively whereas lauric acid was negatively associated with the risk of GDM. A P/L ratio ≥12.2 and an S/P ratio ≤0.71 were independently and synergistically associated with an increased risk of GDM. The CDKAL1 rs7747752 G > C variant was significantly associated with an increased risk of GDM (P < 0.05). Furthermore, the presence of the rs7747752 G > C variant increased the OR (95% CI) of high palmitic acid concentration from 1.55 (0.61, 3.97) to 4.34 (2.04, 9.23), with a significant additive interaction. CONCLUSIONS The interaction between high serum palmitic acid concentration and the CDKAL1 rs7747752 G > C variant played a critical role in GDM. Given that a hypocaloric low-carbohydrate diet can lower palmitic acid concentrations, it is worthwhile to test whether such a diet is effective in reducing the risk of GDM, especially among women who have both risk factors.
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Affiliation(s)
- Hui Wang
- Department of Epidemiology and Biostatistics, School of Public Health, Tianjin Medical University, Tianjin, China
| | - Wen Yang
- Department of Epidemiology and Biostatistics, School of Public Health, Tianjin Medical University, Tianjin, China
| | - Jinnan Liu
- Department of Epidemiology and Biostatistics, School of Public Health, Tianjin Medical University, Tianjin, China
| | - Junhong Leng
- Project Office, Tianjin Women and Children's Health Center, Tianjin, China
| | - Weiqin Li
- Project Office, Tianjin Women and Children's Health Center, Tianjin, China
| | - Zhijie Yu
- Population Cancer Research Program, Dalhousie University, Halifax, Nova Scotia, Canada.,Department of Pediatrics, Dalhousie University, Halifax, Nova Scotia, Canada
| | - Jing Li
- Department of Epidemiology and Biostatistics, School of Public Health, Tianjin Medical University, Tianjin, China.,Tianjin Key Laboratory of Environment, Nutrition and Public Health, Tianjin, China.,Tianjin Center for International Collaborative Research on Environment, Nutrition and Public Health, Tianjin, China
| | - Ronald C W Ma
- Department of Medicine and Therapeutics, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong, China.,Li Ka Shing Institute of Health Sciences, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong, China
| | - Gang Hu
- Chronic Disease Epidemiology Laboratory, Pennington Biomedical Research Center, Baton Rouge, LA, USA
| | - Zhongze Fang
- Tianjin Key Laboratory of Environment, Nutrition and Public Health, Tianjin, China.,Tianjin Center for International Collaborative Research on Environment, Nutrition and Public Health, Tianjin, China.,Department of Toxicology and Sanitary Chemistry, School of Public Health, Tianjin Medical University, Tianjin, China
| | - Ying Wang
- Scientific Research Platform of the Second School of Clinical Medicine, Guangdong Medical University, Dongguan, Guangdong, China.,Key Laboratory of 3D Printing Technology in Stomatology, Guangdong Medical University, Dongguan, Guangdong, China
| | - Xilin Yang
- Department of Epidemiology and Biostatistics, School of Public Health, Tianjin Medical University, Tianjin, China.,Tianjin Key Laboratory of Environment, Nutrition and Public Health, Tianjin, China.,Tianjin Center for International Collaborative Research on Environment, Nutrition and Public Health, Tianjin, China
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15
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Association between functional genetic variants in retinoid X receptor-α/γ and the risk of gestational diabetes mellitus in a southern Chinese population. Biosci Rep 2021; 41:229913. [PMID: 34633445 PMCID: PMC8529336 DOI: 10.1042/bsr20211338] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Revised: 09/23/2021] [Accepted: 10/07/2021] [Indexed: 12/28/2022] Open
Abstract
To clarify the effect of retinoid X receptor-α/γ (RXR-α/γ) genes functional genetic variants (RXR-α rs4842194 G>A, RXR-γ rs100537 A>G and rs2134095 T>C) on the risk of gestational diabetes mellitus (GDM), a case–control study with 573 GDM patients and 740 pregnant women with normal glucose tolerance was performed in Guangxi area of China. An odds ratio (OR) with its corresponding 95% confidence interval (CI) was used to assess the strengths of the association between genetic variation and GDM. After adjustment of age and pre-BMI, the logistic regression analysis showed that the rs2134095 was significantly associated with GDM risk (CC vs. TT/TC: adjusted OR = 0.71, 95% CI = 0.56–0.90) in all subjects, and this result remained highly significant after Bonferroni’s correction for multiple testing (P=0.004). The stratified analysis showed that rs2134095 was significantly associated with the risk of GDM among age > 30 years (adjusted OR = 0.61, 95% CI = 0.39–0.97), BMI > 22 kg/m2 (adjusted OR = 0.46, 95% CI = 0.30–0.70), systolic blood pressure (SBP) > 120 mmHg (adjusted OR = 1.96, 95% CI = 1.14–3.36), glycosylated hemoglobin A1c (HbA1c) < 6.5% (adjusted OR = 1.41, 95% CI = 1.11–1.78), TG ≤ 1.7 mmol/l (adjusted OR = 2.57, 95% CI = 1.45–4.53), TC ≤ 5.18 mmol/l (adjusted OR = 1.58, 95% CI = 1.13–2.22), high-density lipoprotein cholesterol (HDL-c) ≤ 1.5 mmol/l (adjusted OR = 1.70, 95% CI = 1.16–2.49) and low-density lipoprotein cholesterol (LDL-c) > 3.12 mmol/l (adjusted OR = 1.47, 95% CI = 1.08–2.00) subjects, under the recessive genetic model. We also found that rs2134095 interacted with age (Pinteraction=0.039), pre-BMI (Pinteraction=0.040) and TG (Pinteraction=0.025) influencing individual’s genetic susceptibility to GDM. The rs2134095 T>C is significantly associated with the risk of GDM by effect of a single locus and/or complex joint gene–gene and gene–environment interactions. Larger sample-size and different population studies are required to confirm the findings.
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16
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Zhang T, Zhao L, Wang S, Liu J, Chang Y, Ma L, Feng J, Niu Y. Common Variants in NUS1 and GP2 Genes Contributed to the Risk of Gestational Diabetes Mellitus. Front Endocrinol (Lausanne) 2021; 12:685524. [PMID: 34326813 PMCID: PMC8315097 DOI: 10.3389/fendo.2021.685524] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Accepted: 06/18/2021] [Indexed: 01/12/2023] Open
Abstract
BACKGROUND Recently, NUS1 and GP2 genes were reported to be associated with the risk of type 2 diabetes (T2D) in a Japanese population. Given the sharing of pathogenic contribution from genetic factors between T2D and gestational diabetes mellitus (GDM), we conducted the study to systematically examine the relationship of NUS1 and GP2 genes with the susceptibility to GDM in Chinese Han population. METHODS A total of 4,250 subjects comprised of 1,282 patients with GDM and 2,968 controls were recruited, and 20 tag single nucleotide polymorphisms (SNPs) (10 from NUS1 and 10 from GP2) were selected for genotyping. Association analyses were conducted for GDM and its related biomedical indexes including fasting glucose and HbA1c levels. RESULTS Two SNPs, rs80196932 from NUS1 (P=2.93×10-5) and rs117267808 from GP2 (P=5.68×10-5), were identified to be significantly associated with the risk of GDM. Additionally, SNP rs80196932 was significantly associated with HbA1c level in both patients with GDM (P=0.0009) and controls (P=0.0003), while SNP rs117267808 was significantly associated with fasting glucose level in both patients with GDM (P=0.0008) and controls (P=0.0007). Serum levels of protein NUS1 and GP2 were measured for the study subjects, and significant differences were identified among groups with different genotypes of SNP rs80196932 and rs117267808, respectively. CONCLUSIONS Our findings indicate that NUS1 and GP2 genes contribute to the risk of GDM, which would help to offer the potential to improve our understanding of the etiology of GDM and, in turn, could facilitate the development of novel medicines and treatments for GDM.
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Affiliation(s)
- Tianxiao Zhang
- Department of Epidemiology and Biostatistics, School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an, China
- Department of Endocrinology and Metabolism, Ninth Hospital of Xi’an, Xi’an, China
| | - Longrui Zhao
- Department of Forensic Medicine, School of Medicine & Forensics, Xi’an Jiaotong University Health Science Center, Xi’an, China
| | - Shujin Wang
- Department of Endocrinology and Metabolism, Ninth Hospital of Xi’an, Xi’an, China
| | - Juan Liu
- Department of Obstetrics, Northwest Women and Children’s Hospital, Xi’an, China
| | - Ying Chang
- Department of Pharmacy, Northwest Women and Children’s Hospital, Xi’an, China
| | - Louyan Ma
- Department of General Practice, Ninth Hospital of Xi’an, Xi’an, China
| | - Jia Feng
- Department of Endocrinology and Metabolism, Ninth Hospital of Xi’an, Xi’an, China
| | - Yu Niu
- Department of Endocrinology and Metabolism, Ninth Hospital of Xi’an, Xi’an, China
- *Correspondence: Yu Niu,
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17
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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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18
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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: 30] [Impact Index Per Article: 7.5] [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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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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Lamri A, Mao S, Desai D, Gupta M, Paré G, Anand SS. Fine-tuning of Genome-Wide Polygenic Risk Scores and Prediction of Gestational Diabetes in South Asian Women. Sci Rep 2020; 10:8941. [PMID: 32488059 PMCID: PMC7265287 DOI: 10.1038/s41598-020-65360-y] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2019] [Accepted: 04/23/2020] [Indexed: 12/11/2022] Open
Abstract
Gestational diabetes Mellitus (GDM) affects 1 in 7 births and is associated with numerous adverse health outcomes for both mother and child. GDM is suspected to share a large common genetic background with type 2 diabetes (T2D). The aim of our study was to characterize different GDM polygenic risk scores (PRSs) and test their association with GDM using data from the South Asian Birth Cohort (START). PRSs were derived for 832 South Asian women from START using the pruning and thresholding (P + T), LDpred, and GraBLD methods. Weights were derived from a multi-ethnic and a white Caucasian study of the DIAGRAM consortium. GDM status was defined using South Asian-specific glucose values in response to an oral glucose tolerance test. Association with GDM was tested using logistic regression. Results were replicated in South Asian women from the UK Biobank (UKB) study. The top ranking P + T, LDpred and GraBLD PRSs were all based on DIAGRAM's multi-ethnic study. The best PRS was highly associated with GDM in START (AUC = 0.62, OR = 1.60 [95% CI = 1.44-1.69]), and in South Asian women from UKB (AUC = 0.65, OR = 1.69 [95% CI = 1.28-2.24]). Our results highlight the importance of combining genome-wide genotypes and summary statistics from large multi-ethnic studies to optimize PRSs in South Asians.
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Affiliation(s)
- Amel Lamri
- Department of Medicine, McMaster University Hamilton, Ontario, Canada
- Population Health Research Institute (PHRI), Hamilton, Ontario, Canada
| | - Shihong Mao
- Population Health Research Institute (PHRI), Hamilton, Ontario, Canada
| | - Dipika Desai
- Population Health Research Institute (PHRI), Hamilton, Ontario, Canada
| | - Milan Gupta
- Department of Medicine, McMaster University Hamilton, Ontario, Canada
- Canadian Collaborative Research Network (CCRN), Brampton, ON, Canada
| | - Guillaume Paré
- Population Health Research Institute (PHRI), Hamilton, Ontario, Canada
- Department of Pathology and Molecular Medicine, McMaster University, Hamilton, Ontario, Canada
| | - Sonia S Anand
- Department of Medicine, McMaster University Hamilton, Ontario, Canada.
- Population Health Research Institute (PHRI), Hamilton, Ontario, Canada.
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada.
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Elkin ER, Bridges D, Loch-Caruso R. The trichloroethylene metabolite S-(1,2-dichlorovinyl)-L-cysteine induces progressive mitochondrial dysfunction in HTR-8/SVneo trophoblasts. Toxicology 2019; 427:152283. [PMID: 31476333 DOI: 10.1016/j.tox.2019.152283] [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: 07/06/2019] [Revised: 08/24/2019] [Accepted: 08/28/2019] [Indexed: 02/08/2023]
Abstract
Trichloroethylene is an industrial solvent and common environmental pollutant. Despite efforts to ban trichloroethylene, its availability and usage persist globally, constituting a hazard to human health. Recent studies reported associations between maternal trichloroethylene exposure and increased risk for low birth weight. Despite these associations, the toxicological mechanism underlying trichloroethylene adverse effects on pregnancy remains largely unknown. The trichloroethylene metabolite S-(1,2-dichlorovinyl)-L-cysteine (DCVC) induces mitochondrial-mediated apoptosis in a trophoblast cell line. To gain further understanding of mitochondrial-mediated DCVC placental toxicity, this study investigated the effects of DCVC exposure on mitochondrial function using non-cytolethal concentrations in placental cells. Human trophoblasts, HTR-8/SVneo, were exposed in vitro to a maximum of 20 μM DCVC for up to 12 h. Cell-based oxygen consumption and extracellular acidification assays were used to evaluate key aspects of mitochondrial function. Following 6 h of exposure to 20 μM DCVC, elevated oxygen consumption, mitochondrial proton leak and sustained energy coupling deficiency were observed. Similarly, 12 h of exposure to 20 μM DCVC decreased mitochondrial-dependent basal, ATP-linked and maximum oxygen consumption rates. Using the fluorochrome TMRE, dissipation of mitochondrial membrane potential was detected after a 12-h exposure to 20 μM DCVC, and (±)-α-tocopherol, a known suppressor of lipid peroxidation, attenuated DCVC-stimulated mitochondrial membrane depolarization but failed to rescue oxygen consumption perturbations. Together, these results suggest that DCVC caused progressive mitochondrial dysfunction, resulting in lipid peroxidation-associated mitochondrial membrane depolarization. Our findings contribute to the biological plausibility of DCVC-induced placental impairment and provide new insights into the role of the mitochondria in DCVC-induced toxicity.
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Affiliation(s)
- Elana R Elkin
- Department of Environmental Health Sciences, University of Michigan, 1415 Washington Heights, Ann Arbor, MI, 48109-2029, USA.
| | - Dave Bridges
- Department of Nutritional Sciences, University of Michigan, 1415 Washington Heights, Ann Arbor, MI, 48109-2029, USA.
| | - Rita Loch-Caruso
- Department of Environmental Health Sciences, University of Michigan, 1415 Washington Heights, Ann Arbor, MI, 48109-2029, USA.
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Shah BR, Xu W, Mraz J. Cytochrome P450 1B1: role in health and disease and effect of nutrition on its expression. RSC Adv 2019; 9:21050-21062. [PMID: 35515562 PMCID: PMC9065998 DOI: 10.1039/c9ra03674a] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2019] [Accepted: 06/23/2019] [Indexed: 01/06/2023] Open
Abstract
This review summarizes the available literature stating CYP1B1 to provide the readers with a comprehensive understanding of its role in different diseases, as well as the importance of nutrition in their control in terms of the influence of different nutrients on its expression. CYP1B1, a member of the cytochrome P450 enzyme family is expressed in different human tissues and is known to contribute to different life alarming pathologies. Particularly, till now much attention has been paid to its involvement in the development of primary congenital glaucoma (PCG) and cancer. However, recently there are some reports highlighting CYP1B1 as a potential regulator in energy homeostasis and adipogenesis thus promoting obesity and hypertension as well. Therefore, seeking out effective strategies to modulate the expression of CYP1B1 is a challenging task. In this context, nutrients based strategies will be the best choice as they are mostly harmless and are easily available in one's diet. In conclusion, this article will be helpful in providing a base for further research that is needed to identify the role of CYP1B1 in progression of different diseases, hypertension and obesity in particular, and then to present the effectiveness, mechanisms, and biologic plausibility of nutrients against its expression.
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Affiliation(s)
- Bakht Ramin Shah
- University of South Bohemia in Ceske Budejovice, Faculty of Fisheries and Protection of Waters, South Bohemian Research Center of Aquaculture and Biodiversity of Hydrocenoses, Institute of Aquaculture and Protection of Waters Na Sádkách 1780 370 05 České Budějovice Czech Republic +420 775022640
| | - Wei Xu
- College of Life Science, Xinyang Normal University Xinyang 464000 People's Republic of China
| | - Jan Mraz
- University of South Bohemia in Ceske Budejovice, Faculty of Fisheries and Protection of Waters, South Bohemian Research Center of Aquaculture and Biodiversity of Hydrocenoses, Institute of Aquaculture and Protection of Waters Na Sádkách 1780 370 05 České Budějovice Czech Republic +420 775022640
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Zhao L, Zheng X, Liu J, Zheng R, Yang R, Wang Y, Sun L. The placental transcriptome of the first-trimester placenta is affected by in vitro fertilization and embryo transfer. Reprod Biol Endocrinol 2019; 17:50. [PMID: 31262321 PMCID: PMC6604150 DOI: 10.1186/s12958-019-0494-7] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/26/2019] [Accepted: 06/17/2019] [Indexed: 02/08/2023] Open
Abstract
BACKGROUND The placenta is a highly specialized temporary organ that is related to fetal development and pregnancy outcomes, and epidemiological data demonstrate an increased risk of placental abnormality after in vitro fertilization and embryo transfer (IVF-ET). METHODS This study examines alterations in the transcriptome profile of first-trimester placentas from IVF-ET pregnancies and analyzes the potential mechanisms that play a role in the adverse perinatal outcomes associated with IVF-ET procedures. Four human placental villi from first-trimester samples were obtained through fetal bud aspiration from patients subjected to IVF-ET due to oviductal factors. An additional four control human placental villi were derived from a group of subjects who spontaneously conceived a twin pregnancy. We analyzed their transcriptomes by microarray. Then, RT-qPCR and immunohistochemistry were utilized to analyze several dysregulated genes to validate the microarray results. Biological functions and pathways were analyzed with bioinformatics tools. RESULTS A total of 3405 differentially regulated genes were identified as significantly dysregulated (> 2-fold change; P < 0.05) in the IVF-ET placenta in the first trimester: 1910 upregulated and 1495 downregulated genes. Functional enrichment analysis of the differentially regulated genes demonstrated that the genes were involved in more than 50 biological processes and pathways that have been shown to play important roles in the first trimester in vivo. These pathways can be clustered into coagulation cascades, immune response, transmembrane signaling, metabolism, cell cycle, stress control, invasion and vascularization. Nearly the same number of up- and downregulated genes participate in the same biological processes related to placental development and maintenance. Procedures utilized in IVF-ET altered the expression of first-trimester placental genes that are critical to these biological processes and triggered a compensatory mechanism during early implantation in vivo. CONCLUSION These data provide a potential basis for further analysis of the higher frequency of adverse perinatal outcomes following IVF-ET, with the ultimate goal of developing safer IVF-ET protocols.
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Affiliation(s)
- Liang Zhao
- Department of Obstetrics and Gynecology, Beijing Jishuitan, Hospital, No. 31, Xinjiekou East Street, Xicheng District, Beijing, 100035, People's Republic of China
| | - Xiuli Zheng
- Department of Obstetrics and Gynecology, Beijing Jishuitan, Hospital, No. 31, Xinjiekou East Street, Xicheng District, Beijing, 100035, People's Republic of China
| | - Jingfang Liu
- Department of Obstetrics and Gynecology, Beijing Jishuitan, Hospital, No. 31, Xinjiekou East Street, Xicheng District, Beijing, 100035, People's Republic of China
| | - Rong Zheng
- Department of Obstetrics and Gynecology, Beijing Jishuitan, Hospital, No. 31, Xinjiekou East Street, Xicheng District, Beijing, 100035, People's Republic of China
| | - Rui Yang
- Reproductive Medical Center, Department of Obstetrics and Gynecology, Peking University Third Hospital, No. 49, Huayuan North Road, Haidian District, Beijing, 100191, People's Republic of China
| | - Ying Wang
- Reproductive Medical Center, Department of Obstetrics and Gynecology, Peking University Third Hospital, No. 49, Huayuan North Road, Haidian District, Beijing, 100191, People's Republic of China
| | - Lifang Sun
- Department of Obstetrics and Gynecology, Beijing Jishuitan, Hospital, No. 31, Xinjiekou East Street, Xicheng District, Beijing, 100035, People's Republic of China.
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