1
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Sweeting A, Hannah W, Backman H, Catalano P, Feghali M, Herman WH, Hivert MF, Immanuel J, Meek C, Oppermann ML, Nolan CJ, Ram U, Schmidt MI, Simmons D, Chivese T, Benhalima K. Epidemiology and management of gestational diabetes. Lancet 2024:S0140-6736(24)00825-0. [PMID: 38909620 DOI: 10.1016/s0140-6736(24)00825-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Revised: 04/07/2024] [Accepted: 04/19/2024] [Indexed: 06/25/2024]
Abstract
Gestational diabetes is defined as hyperglycaemia first detected during pregnancy at glucose concentrations that are less than those of overt diabetes. Around 14% of pregnancies globally are affected by gestational diabetes; its prevalence varies with differences in risk factors and approaches to screening and diagnosis; and it is increasing in parallel with obesity and type 2 diabetes. Gestational diabetes direct costs are US$1·6 billion in the USA alone, largely due to complications including hypertensive disorders, preterm delivery, and neonatal metabolic and respiratory consequences. Between 30% and 70% of gestational diabetes is diagnosed in early pregnancy (ie, early gestational diabetes defined by hyperglycaemia before 20 weeks of gestation). Early gestational diabetes is associated with worse pregnancy outcomes compared with women diagnosed with late gestational diabetes (hyperglycaemia from 24 weeks to 28 weeks of gestation). Randomised controlled trials show benefits of treating gestational diabetes from 24 weeks to 28 weeks of gestation. The WHO 2013 recommendations for diagnosing gestational diabetes (one-step 75 gm 2-h oral glucose tolerance test at 24-28 weeks of gestation) are largely based on the Hyperglycemia and Adverse Pregnancy Outcomes Study, which confirmed the linear association between pregnancy complications and late-pregnancy maternal glycaemia: a phenomenon that has now also been shown in early pregnancy. Recently, the Treatment of Booking Gestational Diabetes Mellitus (TOBOGM) trial showed benefit in diagnosis and treatment of early gestational diabetes for women with risk factors. Given the diabesity epidemic, evidence for gestational diabetes heterogeneity by timing and subtype, and advances in technology, a life course precision medicine approach is urgently needed, using evidence-based prevention, diagnostic, and treatment strategies.
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Affiliation(s)
- Arianne Sweeting
- Department of Endocrinology, Royal Prince Alfred Hospital and University of Sydney, Sydney, NSW, Australia
| | - Wesley Hannah
- Department of Epidemiology, Madras Diabetes Research Foundation, Chennai, India
| | - Helena Backman
- Department of Obstetrics and Gynecology, Faculty of Medicine and Health, Örebro University, Örebro, Sweden
| | - Patrick Catalano
- Maternal Infant Research Institute, Obstetrics and Gynecology Research, Friedman School of Nutrition Science and Policy, Tufts University School of Medicine, Tufts University, Boston, MA, USA
| | - Maisa Feghali
- Department of Obstetrics, Gynecology and Reproductive Sciences, University of Pittsburgh, Pittsburgh, PA, USA
| | - Willliam H Herman
- Schools of Medicine and Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Marie-France Hivert
- Department of Population Medicine, Division of Chronic Disease Research Across the Lifecourse, Harvard Pilgrim Health Care Institute, Harvard Medical School, Harvard University, Boston, MA, USA; Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Jincy Immanuel
- School of Medicine, Western Sydney University, Sydney, NSW, Australia; Texas Woman's University, Denton, TX, USA
| | - Claire Meek
- Leicester Diabetes Centre, Leicester General Hospital, Leicester, UK
| | - Maria Lucia Oppermann
- Department of Obstetrics and Gynecology, School of Medicine of Federal University of Rio Grande do Sul, Porto Alegre, Brazil
| | - Christopher J Nolan
- School of Medicine and Psychology, College of Health and Medicine, Australian National University, Canberra, ACT, Australia; Department of Endocrinology, Canberra Health Services, Woden, ACT, Australia
| | - Uma Ram
- Seethapathy Clinic and Hospital, Chennai, India
| | - Maria Inês Schmidt
- Postgraduate Program in Epidemiology, School of Medicine of Federal University of Rio Grande do Sul, Porto Alegre, Brazil
| | - David Simmons
- School of Medicine, Western Sydney University, Sydney, NSW, Australia.
| | - Tawanda Chivese
- Department of Population Medicine, College of Medicine, QU Health, Qatar University, Doha, Qatar
| | - Katrien Benhalima
- Endocrinology, University Hospital Gasthuisberg, KU Leuven, Leuven, Belgium
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2
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Amitrano F, Krishnan M, Murphy R, Okesene-Gafa KAM, Ji M, Thompson JMD, Taylor RS, Merriman TR, Rush E, McCowan M, McCowan LME, McKinlay CJD. The impact of CREBRF rs373863828 Pacific-variant on infant body composition. Sci Rep 2024; 14:8825. [PMID: 38627436 PMCID: PMC11021527 DOI: 10.1038/s41598-024-59417-5] [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: 12/21/2023] [Accepted: 04/10/2024] [Indexed: 04/19/2024] Open
Abstract
In Māori and Pacific adults, the CREBRF rs373863828 minor (A) allele is associated with increased body mass index (BMI) but reduced incidence of type-2 and gestational diabetes mellitus. In this prospective cohort study of Māori and Pacific infants, nested within a nutritional intervention trial for pregnant women with obesity and without pregestational diabetes, we investigated whether the rs373863828 A allele is associated with differences in growth and body composition from birth to 12-18 months' corrected age. Infants with and without the variant allele were compared using generalised linear models adjusted for potential confounding by gestation length, sex, ethnicity and parity, and in a secondary analysis, additionally adjusted for gestational diabetes. Carriage of the rs373863828 A allele was not associated with altered growth and body composition from birth to 6 months. At 12-18 months, infants with the rs373863828 A allele had lower whole-body fat mass [FM 1.4 (0.7) vs. 1.7 (0.7) kg, aMD -0.4, 95% CI -0.7, 0.0, P = 0.05; FM index 2.2 (1.1) vs. 2.6 (1.0) kg/m2 aMD -0.6, 95% CI -1.2,0.0, P = 0.04]. However, this association was not significant after adjustment for gestational diabetes, suggesting that it may be mediated, at least in part, by the beneficial effect of CREBRF rs373863828 A allele on maternal glycemic status.
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Affiliation(s)
| | - Mohanraj Krishnan
- Department of Obstetrics and Gynaecology, University of Auckland, Auckland, New Zealand
- Department of Medicine, University of Auckland, Auckland, New Zealand
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Rinki Murphy
- Department of Medicine, University of Auckland, Auckland, New Zealand
- Te Whatu Ora, Counties Manukau, Auckland, New Zealand
- Maurice Wilkins Centre for Molecular Biodiscovery, University of Auckland, Auckland, New Zealand
| | - Karaponi A M Okesene-Gafa
- Department of Obstetrics and Gynaecology, University of Auckland, Auckland, New Zealand
- Te Whatu Ora, Counties Manukau, Auckland, New Zealand
| | - Maria Ji
- Department of Obstetrics and Gynaecology, University of Auckland, Auckland, New Zealand
| | - John M D Thompson
- Department of Obstetrics and Gynaecology, University of Auckland, Auckland, New Zealand
- Department of Paediatrics: Child and Youth Health, University of Auckland, Auckland, New Zealand
| | - Rennae S Taylor
- Department of Obstetrics and Gynaecology, University of Auckland, Auckland, New Zealand
| | - Tony R Merriman
- Department of Microbiology and Immunology, University of Otago, Dunedin, New Zealand
- Division of Clinical Immunology and Rheumatology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Elaine Rush
- Faculty of Health and Environmental Science, Auckland University of Technology, Auckland, New Zealand
| | - Megan McCowan
- Te Whatu Ora, Counties Manukau, Auckland, New Zealand
| | - Lesley M E McCowan
- Department of Obstetrics and Gynaecology, University of Auckland, Auckland, New Zealand
- Te Whatu Ora, Counties Manukau, Auckland, New Zealand
| | - Christopher J D McKinlay
- Te Whatu Ora, Counties Manukau, Auckland, New Zealand.
- Department of Paediatrics: Child and Youth Health, University of Auckland, Auckland, New Zealand.
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3
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Manerkar K, Crowther CA, Harding JE, Meyer MP, Conlon CA, Rush EC, Alsweiler JM, McCowan LME, Rowan JA, Edlin R, Amitrano F, McKinlay CJD. Impact of Gestational Diabetes Detection Thresholds on Infant Growth and Body Composition: A Prospective Cohort Study Within a Randomized Trial. Diabetes Care 2024; 47:56-65. [PMID: 37643291 DOI: 10.2337/dc23-0464] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Accepted: 08/01/2023] [Indexed: 08/31/2023]
Abstract
OBJECTIVE Gestational diabetes mellitus (GDM) is associated with offspring metabolic disease, including childhood obesity, but causal mediators remain to be established. We assessed the impact of lower versus higher thresholds for detection and treatment of GDM on infant risk factors for obesity, including body composition, growth, nutrition, and appetite. RESEARCH DESIGN AND METHODS In this prospective cohort study within the Gestational Diabetes Mellitus Trial of Diagnostic Detection Thresholds (GEMS), pregnant women were randomly allocated to detection of GDM using the lower criteria of the International Association of Diabetes and Pregnancy Study Groups or higher New Zealand criteria (ACTRN12615000290594). Randomly selected control infants of women without GDM were compared with infants exposed to A) GDM by lower but not higher criteria, with usual treatment for diabetes in pregnancy; B) GDM by lower but not higher criteria, untreated; or C) GDM by higher criteria, treated. The primary outcome was whole-body fat mass at 5-6 months. RESULTS There were 760 infants enrolled, and 432 were assessed for the primary outcome. Fat mass was not significantly different between control infants (2.05 kg) and exposure groups: A) GDM by lower but not higher criteria, treated (1.96 kg), adjusted mean difference (aMD) -0.09 (95% CI -0.29, 0.10); B) GDM by lower but not higher criteria, untreated (1.94 kg), aMD -0.15 (95% CI -0.35, 0.06); and C) GDM detected and treated using higher thresholds (1.87 kg), aMD -0.17 (95% CI -0.37, 0.03). CONCLUSIONS GDM detected using lower but not higher criteria, was not associated with increased infant fat mass at 5-6 months, regardless of maternal treatment. GDM detected and treated using higher thresholds was also not associated with increased fat mass at 5-6 months.
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Affiliation(s)
- Komal Manerkar
- Liggins Institute, University of Auckland, Auckland, New Zealand
| | | | - Jane E Harding
- Liggins Institute, University of Auckland, Auckland, New Zealand
| | - Michael P Meyer
- Te Whatu Ora, Counties Manukau, Auckland, New Zealand
- Department of Paediatrics: Child and Youth Health, University of Auckland, Auckland, New Zealand
| | - Cathryn A Conlon
- School of Sport, Exercise and Nutrition, Massey University, Auckland, New Zealand
| | - Elaine C Rush
- School of Sport and Recreation, Faculty of Health and Environmental Sciences, Auckland University of Technology, Auckland, New Zealand
| | - Jane M Alsweiler
- Department of Paediatrics: Child and Youth Health, University of Auckland, Auckland, New Zealand
- Te Whatu Ora, Te Toka Tumai Auckland, Auckland, New Zealand
| | - Lesley M E McCowan
- Department of Obstetrics and Gynaecology, University of Auckland, Auckland, New Zealand
| | - Janet A Rowan
- Te Whatu Ora, Te Toka Tumai Auckland, Auckland, New Zealand
| | - Richard Edlin
- Health Systems, University of Auckland, Auckland, New Zealand
| | | | - Christopher J D McKinlay
- Te Whatu Ora, Counties Manukau, Auckland, New Zealand
- Department of Paediatrics: Child and Youth Health, University of Auckland, Auckland, New Zealand
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4
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Zhu M, Lv Y, Peng Y, Wu Y, Feng Y, Jia T, Xu S, Li S, Wang W, Tian J, Sun L. GCKR and ADIPOQ gene polymorphisms in women with gestational diabetes mellitus. Acta Diabetol 2023; 60:1709-1718. [PMID: 37524927 PMCID: PMC10587232 DOI: 10.1007/s00592-023-02165-1] [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: 05/22/2023] [Accepted: 07/18/2023] [Indexed: 08/02/2023]
Abstract
AIMS To investigate the associations of GCKR and ADIPOQ variants with the risk of gestational diabetes mellitus (GDM) in Chinese women. METHODS GCKR rs1260326, ADIPOQ rs266729, and rs1501299 were selected and genotyped in 519 GDM patients and 498 controls. Candidate SNPs were genotyped using multiplex polymerase chain reaction (PCR) combined with next-generation sequencing methods, and the association of these SNPs with GDM was analyzed. RESULTS We found that GCKR rs1260326 was significantly associated with an increased risk of GDM in the allele model, the codominant model (CC vs. TT), the dominant model, the recessive model, and the genotypic model distributions (p = 0.0029, p = 0.0022, p = 0.0402, p = 0.0038, and p = 0.0028, respectively). The rs1260326 polymorphism was shown to be associated with 1 h-OGTT level and gravidity in GDM patients (CC vs. TT: p = 0.0475 and p = 0.0220, respectively). Diastolic blood pressure (DBP) was significantly higher in the GDM patients with the rs266729 GG genotype compared to those with the CC or CG genotype (p = 0.0444 and p = 0.0339, respectively). The DBP of the GDM patients with the rs1501299 GT genotype was lower than that of those with the GG genotype (p = 0.0197). There was a weak linkage disequilibrium value between the GCKR and ADIPOQ SNPs. CONCLUSIONS The genes GCKR and ADIPOQ may be involved in the pathophysiology of GDM.
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Affiliation(s)
- Manning Zhu
- Cancer Center, Department of Ultrasound Medicine, Zhejiang Provincial People's Hospital(Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Yaer Lv
- Cancer Center, Department of Ultrasound Medicine, Zhejiang Provincial People's Hospital(Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Yanqing Peng
- Cancer Center, Department of Ultrasound Medicine, Zhejiang Provincial People's Hospital(Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Yingnan Wu
- Cancer Center, Department of Ultrasound Medicine, Zhejiang Provincial People's Hospital(Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Yanan Feng
- Department of Ultrasound, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang Province, China
| | - Tianshuang Jia
- Cancer Center, Department of Ultrasound Medicine, Zhejiang Provincial People's Hospital(Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Songcheng Xu
- Cancer Center, Department of Ultrasound Medicine, Zhejiang Provincial People's Hospital(Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Songxue Li
- Cancer Center, Department of Ultrasound Medicine, Zhejiang Provincial People's Hospital(Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Wei Wang
- Cancer Center, Department of Ultrasound Medicine, Zhejiang Provincial People's Hospital(Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Jiawei Tian
- Department of Ultrasound, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang Province, China.
| | - Litao Sun
- Cancer Center, Department of Ultrasound Medicine, Zhejiang Provincial People's Hospital(Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China.
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5
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Li Y, Wang H, Chen H, Liao Y, Gou S, Yan Q, Zhuang Z, Li H, Wang J, Suo Y, Lan T, Liu Y, Zhao Y, Zou Q, Nie T, Hui X, Lai L, Wu D, Fan N. Generation of a genetically modified pig model with CREBRF R457Q variant. FASEB J 2022; 36:e22611. [PMID: 36250915 DOI: 10.1096/fj.202201117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Accepted: 10/03/2022] [Indexed: 11/11/2022]
Abstract
Obesity is among the strongest risk factors for type 2 diabetes (T2D). The CREBRF missense allele rs373863828 (p. Arg457Gln, p. R457Q) is associated with increased body mass index but reduced risk of T2D in people of Pacific ancestry. To investigate the functional consequences of the CREBRF variant, we introduced the corresponding human mutation R457Q into the porcine genome. The CREBRFR457Q pigs displayed dramatically increased fat deposition, which was mainly distributed in subcutaneous adipose tissue other than visceral adipose tissue. The CREBRFR457Q variant promoted preadipocyte differentiation. The increased differentiation capacity of precursor adipocytes conferred pigs the unique histological phenotype that adipocytes had a smaller size but a greater number in subcutaneous adipose tissue (SAT) of CREBRFR457Q variant pigs. In addition, in SAT of CREBRFR457Q pigs, the contents of the peroxidative metabolites 4-hydroxy-nonenal and malondialdehyde were significantly decreased, while the activity of antioxidant enzymes, such as glutathione peroxidase, superoxide dismutase, and catalase, was increased, which was in accordance with the declined level of the reactive oxygen species (ROS) in CREBRFR457Q pigs. Together, these data supported a causal role of the CREBRFR457Q variant in the pathogenesis of obesity, partly via adipocyte hyperplasia, and further suggested that reduced oxidative stress in adipose tissue may mediate the relative metabolic protection afforded by this variant despite the related obesity.
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Affiliation(s)
- Yingying Li
- CAS Key Laboratory of Regenerative Biology, GIBH-HKU Guangdong-Hong Kong Stem Cell and Regenerative Medicine Research Centre, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China.,University of Chinese Academy of Sciences, Beijing, China.,Sanya Institute of Swine Resource, Hainan Provincial Research Centre of Laboratory Animals, Sanya, China.,Research Unit of Generation of Large Animal Disease Models, Chinese Academy of Medical Sciences (2019RU015), Guangzhou, China.,Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou, China
| | - Hai Wang
- CAS Key Laboratory of Regenerative Biology, GIBH-HKU Guangdong-Hong Kong Stem Cell and Regenerative Medicine Research Centre, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China.,Sanya Institute of Swine Resource, Hainan Provincial Research Centre of Laboratory Animals, Sanya, China.,Research Unit of Generation of Large Animal Disease Models, Chinese Academy of Medical Sciences (2019RU015), Guangzhou, China.,Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou, China
| | - Huangyao Chen
- CAS Key Laboratory of Regenerative Biology, GIBH-HKU Guangdong-Hong Kong Stem Cell and Regenerative Medicine Research Centre, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China.,Sanya Institute of Swine Resource, Hainan Provincial Research Centre of Laboratory Animals, Sanya, China.,Research Unit of Generation of Large Animal Disease Models, Chinese Academy of Medical Sciences (2019RU015), Guangzhou, China.,Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou, China
| | - Yuan Liao
- CAS Key Laboratory of Regenerative Biology, GIBH-HKU Guangdong-Hong Kong Stem Cell and Regenerative Medicine Research Centre, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China.,Institute of Physical Science and Information Technology, Anhui University, Hefei, China
| | - Shixue Gou
- CAS Key Laboratory of Regenerative Biology, GIBH-HKU Guangdong-Hong Kong Stem Cell and Regenerative Medicine Research Centre, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China.,Sanya Institute of Swine Resource, Hainan Provincial Research Centre of Laboratory Animals, Sanya, China.,Research Unit of Generation of Large Animal Disease Models, Chinese Academy of Medical Sciences (2019RU015), Guangzhou, China.,Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou, China
| | - Quanmei Yan
- CAS Key Laboratory of Regenerative Biology, GIBH-HKU Guangdong-Hong Kong Stem Cell and Regenerative Medicine Research Centre, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China
| | - Zhenpeng Zhuang
- CAS Key Laboratory of Regenerative Biology, GIBH-HKU Guangdong-Hong Kong Stem Cell and Regenerative Medicine Research Centre, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China.,Sanya Institute of Swine Resource, Hainan Provincial Research Centre of Laboratory Animals, Sanya, China.,Research Unit of Generation of Large Animal Disease Models, Chinese Academy of Medical Sciences (2019RU015), Guangzhou, China.,Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou, China
| | - Hao Li
- CAS Key Laboratory of Regenerative Biology, GIBH-HKU Guangdong-Hong Kong Stem Cell and Regenerative Medicine Research Centre, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China.,Sanya Institute of Swine Resource, Hainan Provincial Research Centre of Laboratory Animals, Sanya, China.,Research Unit of Generation of Large Animal Disease Models, Chinese Academy of Medical Sciences (2019RU015), Guangzhou, China.,Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou, China
| | - Jiaowei Wang
- CAS Key Laboratory of Regenerative Biology, GIBH-HKU Guangdong-Hong Kong Stem Cell and Regenerative Medicine Research Centre, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China.,Sanya Institute of Swine Resource, Hainan Provincial Research Centre of Laboratory Animals, Sanya, China.,Research Unit of Generation of Large Animal Disease Models, Chinese Academy of Medical Sciences (2019RU015), Guangzhou, China.,Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou, China
| | - Yangyang Suo
- CAS Key Laboratory of Regenerative Biology, GIBH-HKU Guangdong-Hong Kong Stem Cell and Regenerative Medicine Research Centre, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China.,Sanya Institute of Swine Resource, Hainan Provincial Research Centre of Laboratory Animals, Sanya, China.,Research Unit of Generation of Large Animal Disease Models, Chinese Academy of Medical Sciences (2019RU015), Guangzhou, China.,Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou, China
| | - Ting Lan
- CAS Key Laboratory of Regenerative Biology, GIBH-HKU Guangdong-Hong Kong Stem Cell and Regenerative Medicine Research Centre, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China.,Sanya Institute of Swine Resource, Hainan Provincial Research Centre of Laboratory Animals, Sanya, China.,Research Unit of Generation of Large Animal Disease Models, Chinese Academy of Medical Sciences (2019RU015), Guangzhou, China.,Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou, China
| | - Yang Liu
- CAS Key Laboratory of Regenerative Biology, GIBH-HKU Guangdong-Hong Kong Stem Cell and Regenerative Medicine Research Centre, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China.,Sanya Institute of Swine Resource, Hainan Provincial Research Centre of Laboratory Animals, Sanya, China.,Research Unit of Generation of Large Animal Disease Models, Chinese Academy of Medical Sciences (2019RU015), Guangzhou, China.,Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou, China
| | - Yu Zhao
- CAS Key Laboratory of Regenerative Biology, GIBH-HKU Guangdong-Hong Kong Stem Cell and Regenerative Medicine Research Centre, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China.,Sanya Institute of Swine Resource, Hainan Provincial Research Centre of Laboratory Animals, Sanya, China.,Research Unit of Generation of Large Animal Disease Models, Chinese Academy of Medical Sciences (2019RU015), Guangzhou, China.,Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou, China
| | - Qingjian Zou
- Guangdong Provincial Key Laboratory of Large Animal Models for Biomedicine, Wuyi University, Jiangmen, China
| | - Tao Nie
- CAS Key Laboratory of Regenerative Biology, GIBH-HKU Guangdong-Hong Kong Stem Cell and Regenerative Medicine Research Centre, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China
| | - Xiaoyan Hui
- School of Biomedical Sciences, the Chinese University of Hong Kong, Hong Kong SAR
| | - Liangxue Lai
- CAS Key Laboratory of Regenerative Biology, GIBH-HKU Guangdong-Hong Kong Stem Cell and Regenerative Medicine Research Centre, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China.,Sanya Institute of Swine Resource, Hainan Provincial Research Centre of Laboratory Animals, Sanya, China.,Research Unit of Generation of Large Animal Disease Models, Chinese Academy of Medical Sciences (2019RU015), Guangzhou, China.,Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou, China.,Guangdong Provincial Key Laboratory of Large Animal Models for Biomedicine, Wuyi University, Jiangmen, China
| | - Donghai Wu
- CAS Key Laboratory of Regenerative Biology, GIBH-HKU Guangdong-Hong Kong Stem Cell and Regenerative Medicine Research Centre, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China.,Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou, China
| | - Nana Fan
- CAS Key Laboratory of Regenerative Biology, GIBH-HKU Guangdong-Hong Kong Stem Cell and Regenerative Medicine Research Centre, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China.,Sanya Institute of Swine Resource, Hainan Provincial Research Centre of Laboratory Animals, Sanya, China.,Research Unit of Generation of Large Animal Disease Models, Chinese Academy of Medical Sciences (2019RU015), Guangzhou, China.,Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou, China
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6
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Machine learning-based models for gestational diabetes mellitus prediction before 24–28 weeks of pregnancy: A review. Artif Intell Med 2022; 132:102378. [DOI: 10.1016/j.artmed.2022.102378] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Revised: 07/21/2022] [Accepted: 08/18/2022] [Indexed: 11/21/2022]
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7
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Qu X, Tao J, Xie J. Circ_0009035 regulates the progression of cervical cancer by targeting miR-1305/CREBRF axis. Anticancer Drugs 2022; 33:539-552. [PMID: 35389936 DOI: 10.1097/cad.0000000000001278] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
Circular RNAs (circRNAs) have a crucial role in the occurrence of many diseases, such as tumors. Yet the roles of circ_0009035 (circRACGAP1) in cervical cancer are not fully characterized. The expression levels of circRACGAP1, miR-1305 and cAMP-responsive element-binding protein 3 regulatory factor (CREBRF) were detected by using real-time quantitative PCR or western blot. Cell counting kit-8 assay, 5-ethynyl-2'-deoxyuridine, colony formation assay, transwell assay and tube formation assay were used to detect cell proliferation, migration and invasion and angiogenesis, respectively. Flow cytometry assay was used to analyze the cell apoptosis. Dual-luciferase reporter assay and RNA immunoprecipitation assay were performed to analyze the targeting about miR-1305 and circ_0009035 or CREBRF. Xenograft model was built to study the role of circ_0009035 in vivo. Immunohistochemistry was used to detect the expression of Ki67, epithelial cadherin and vimentin. First, we found that circ_0009035 expression was significantly upregulated in tumor cells and tissues; second, knockdown of circ_0009035 could inhibit cell proliferation, migration and invasion and promote cell apoptosis. Subsequently, circ_0009035 was found to be able to target miR-1305, and the expression of miR-1305 in tumor tissues and cells was significantly lower. MiR-1305 inhibitor could restore cell-related progression of cervical cancer inhibited by si-circ_0009035. Finally, miR-1305 could target CREBRF, and circ_0009035 could regulate CREBRF expression by targeting miR-1305, thereby affecting cervical cancer tumorigenesis. In summary, our study confirmed that circ_0009035 could influence the development of cervical cancer through the targeted regulation of miR-1305/CREBRF.
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Affiliation(s)
- Xiangdong Qu
- Department of Obstetrics and Gynecology, Taizhou Central Hospital (Taizhou University Hospital), Taizhou City, Zhejiang Province, China
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8
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Limited Metabolic Effect of the CREBRF R457Q Obesity Variant in Mice. Cells 2022; 11:cells11030497. [PMID: 35159305 PMCID: PMC8833978 DOI: 10.3390/cells11030497] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Revised: 01/21/2022] [Accepted: 01/25/2022] [Indexed: 11/17/2022] Open
Abstract
The Arg457Gln missense variant in the CREBRF gene has previously been identified as driving excess body weight in Pacific/Oceanic populations. Intriguingly, Arg457Gln variant carriers also demonstrate paradoxical reductions in diabetes risk, indicating that the gene has a critical role in whole-body metabolism. To study the function of this variant in more detail, we generated mice on an FVB/N background with the Crebrf Arg458Gln variant knocked in to replace the endogenous Crebrf. The whole-body metabolic phenotype was characterized for male and female mice on a regular chow diet or an 8-week high-fat challenge. Regular assessment of body composition found that the Crebrf variant had no influence on total body weight or fat mass at any time point. Glucose tolerance tests demonstrated no obvious genotype effect on glucose homeostasis, with indirect calorimetry measures of whole-body energy expenditure likewise unaffected. Male chow-fed variant carriers displayed a trend towards increased lean mass and significantly reduced sensitivity to insulin administration. Overall, this novel mouse model showed only limited phenotypic effects associated with the Crebrf missense variant. The inability to recapitulate results of human association studies may invite reconsideration of the precise mechanistic link between CREBRF function and the risks of obesity and diabetes in variant allele carriers.
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Juan J, Sun Y, Wei Y, Wang S, Song G, Yan J, Zhou P, Yang H. Progression to type 2 diabetes mellitus after gestational diabetes mellitus diagnosed by IADPSG criteria: Systematic review and meta-analysis. Front Endocrinol (Lausanne) 2022; 13:1012244. [PMID: 36277725 PMCID: PMC9582268 DOI: 10.3389/fendo.2022.1012244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Accepted: 09/21/2022] [Indexed: 11/26/2022] Open
Abstract
BACKGROUND To estimate the progression rates to type 2 diabetes mellitus (T2DM) in women with gestational diabetes mellitus (GDM) diagnosed by the International Association of Diabetes and Pregnancy Study Group (IADPSG) criteria. METHODS Systematic review and meta-analysis were conducted by searching Medline, Embase, and Cochrane between January 1, 2010 and December 31, 2021 for observational studies investigating progression to T2DM after GDM. Inclusion criteria were IADPSG-diagnosed GDM, studies with both GDM and controls, postpartum follow-up duration at least one year. Data were pooled by random effects meta-analysis models. Heterogeneity was assessed by I2 statistic. The pooled relative risk for incidence of T2DM and pre-diabetes between GDM participants and controls were estimated. Reasons for heterogeneity among studies were investigated by prespecified subgroup and meta-regression analysis. Publication bias was assessed by the Begg's and Egger's tests. RESULTS This meta-analysis of six studies assessed a total of 61932 individuals (21978 women with GDM and 39954 controls). Women with IADPSG-diagnosed GDM were 6.43 times (RR=6.43, 95% CI:3.45-11.96) more likely to develop T2DM in the future compared with controls. For GDM women, the cumulative incidence of T2DM was 12.1% (95% CI: 6.9%-17.3%), while the pooled cumulative incidence of T2DM was estimated to be 8% (95% CI: 5-11%) in studies with 1 to 5 years of follow-up and increased to 19% (95% CI: 3-34%) for studies with more than 5 years of follow-up. Women with IADPSG-diagnosed GDM had 3.69 times (RR=3.69, 95% CI:2.70-5.06) higher risk of developing pre-diabetes (including impaired fasting glucose and/or impaired glucose tolerance) than controls. Meta-regression analysis showed that the study effect size was not significantly associated with study design, race, length of follow-up, and maternal age (P>0.05). Overall, the studies had a relatively low risk of bias. CONCLUSIONS Women with IADPSG-diagnosed GDM have higher risk of developing T2DM and pre-diabetes. The risk of T2DM in GDM women are higher with longer follow-up duration. Our results highlight the importance of promoting postpartum screening and keeping health lifestyle as well as pharmacological interventions to delay/prevent the onset of T2DM/pre-diabetes in GDM women. SYSTEMATIC REVIEW REGISTRATION https://www.crd.york.ac.uk/prospero, identifier (CRD42022314776).
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Affiliation(s)
- Juan Juan
- Department of Obstetrics and Gynecology, Peking University First Hospital, Beijing, China
| | - Yiying Sun
- Department of Obstetrics and Gynecology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Yumei Wei
- Department of Obstetrics and Gynecology, Peking University First Hospital, Beijing, China
| | - Shuang Wang
- Department of Obstetrics and Gynecology, Peking University First Hospital, Beijing, China
| | - Geng Song
- Department of Obstetrics and Gynecology, Peking University First Hospital, Beijing, China
| | - Jie Yan
- Department of Obstetrics and Gynecology, Peking University First Hospital, Beijing, China
| | - Pengxiang Zhou
- Department of Pharmacy, Peking University Third Hospital, Beijing, China
- Institute for drug evaluation, Peking University Health Science Center, Beijing, China
| | - Huixia Yang
- Department of Obstetrics and Gynecology, Peking University First Hospital, Beijing, China
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Burden HJ, Adams S, Kulatea B, Wright-McNaughton M, Sword D, Ormsbee JJ, Watene-O'Sullivan C, Merriman TR, Knopp JL, Chase JG, Krebs JD, Hall RM, Plank LD, Murphy R, Shepherd PR, Merry TL. The CREBRF diabetes-protective rs373863828-A allele is associated with enhanced early insulin release in men of Māori and Pacific ancestry. Diabetologia 2021; 64:2779-2789. [PMID: 34417843 DOI: 10.1007/s00125-021-05552-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Accepted: 06/29/2021] [Indexed: 10/20/2022]
Abstract
AIMS/HYPOTHESIS The minor A allele of rs373863828 (CREBRF p.Arg457Gln) is associated with increased BMI, but reduced risk of type 2 and gestational diabetes in Polynesian (Pacific peoples and Aotearoa New Zealand Māori) populations. This study investigates the effect of the A allele on insulin release and sensitivity in overweight/obese men without diabetes. METHODS A mixed meal tolerance test was completed by 172 men (56 with the A allele) of Māori or Pacific ancestry, and 44 (24 with the A allele) had a frequently sampled IVGTT and hyperinsulinaemic-euglycaemic clamp. Mixed linear models with covariates age, ancestry and BMI were used to analyse the association between the A allele of rs373863828 and markers of insulin release and blood glucose regulation. RESULTS The A allele of rs373863828 is associated with a greater increase in plasma insulin 30 min following a meal challenge without affecting the elevation in plasma glucose or incretins glucagon-like polypeptide-1 or gastric inhibitory polypeptide. Consistent with this point, following an i.v. infusion of a glucose bolus, participants with an A allele had higher early (p < 0.05 at 2 and 4 min) plasma insulin and C-peptide concentrations for a similar elevation in blood glucose as those homozygous for the major (G) allele. Despite increased plasma insulin, rs373863828 genotype was not associated with a significant difference (p > 0.05) in insulin sensitivity index or glucose disposal during hyperinsulinaemic-euglycaemic clamp. CONCLUSIONS/INTERPRETATION rs373863828-A allele associates with increased glucose-stimulated insulin release without affecting insulin sensitivity, suggesting that CREBRF p.Arg457Gln may increase insulin release to reduce the risk of type 2 diabetes.
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Affiliation(s)
- Hannah J Burden
- Department of Molecular Medicine and Pathology, School of Medical Sciences, The University of Auckland, Auckland, New Zealand
- Maurice Wilkins Centre for Molecular Biodiscovery, The University of Auckland, Auckland, New Zealand
| | - Shannon Adams
- Discipline of Nutrition, School of Medical Sciences, The University of Auckland, Auckland, New Zealand
| | - Braydon Kulatea
- Discipline of Nutrition, School of Medical Sciences, The University of Auckland, Auckland, New Zealand
| | | | - Danielle Sword
- Department of Medicine, University of Otago Wellington, Wellington, New Zealand
| | - Jennifer J Ormsbee
- Centre for Bioengineering, Department of Mechanical Bioengineering, University of Canterbury, Christchurch, New Zealand
| | - Conor Watene-O'Sullivan
- Maurice Wilkins Centre for Molecular Biodiscovery, The University of Auckland, Auckland, New Zealand
- Moko Foundation, Kaitaia, New Zealand
- Waharoa Ki Te Toi Research Centre, Kaitaia, New Zealand
| | - Tony R Merriman
- Maurice Wilkins Centre for Molecular Biodiscovery, The University of Auckland, Auckland, New Zealand
- Department of Biochemistry, School of Biomedical Sciences, University of Otago, Dunedin, New Zealand
- Division of Clinical Immunology and Rheumatology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Jennifer L Knopp
- Centre for Bioengineering, Department of Mechanical Bioengineering, University of Canterbury, Christchurch, New Zealand
| | - J Geoffrey Chase
- Centre for Bioengineering, Department of Mechanical Bioengineering, University of Canterbury, Christchurch, New Zealand
| | - Jeremy D Krebs
- Maurice Wilkins Centre for Molecular Biodiscovery, The University of Auckland, Auckland, New Zealand
- Department of Medicine, University of Otago Wellington, Wellington, New Zealand
| | - Rosemary M Hall
- Maurice Wilkins Centre for Molecular Biodiscovery, The University of Auckland, Auckland, New Zealand
- Department of Medicine, University of Otago Wellington, Wellington, New Zealand
| | - Lindsay D Plank
- Department of Surgery, School of Medicine, The University of Auckland, Auckland, New Zealand
| | - Rinki Murphy
- Maurice Wilkins Centre for Molecular Biodiscovery, The University of Auckland, Auckland, New Zealand
- Department of Medicine, School of Medicine, The University of Auckland, Auckland, New Zealand
| | - Peter R Shepherd
- Department of Molecular Medicine and Pathology, School of Medical Sciences, The University of Auckland, Auckland, New Zealand
- Maurice Wilkins Centre for Molecular Biodiscovery, The University of Auckland, Auckland, New Zealand
| | - Troy L Merry
- Maurice Wilkins Centre for Molecular Biodiscovery, The University of Auckland, Auckland, New Zealand.
- Discipline of Nutrition, School of Medical Sciences, The University of Auckland, Auckland, New Zealand.
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Kanshana JS, Mattila PE, Ewing MC, Wood AN, Schoiswohl G, Meyer AC, Kowalski A, Rosenthal SL, Gingras S, Kaufman BA, Lu R, Weeks DE, McGarvey ST, Minster RL, Hawley NL, Kershaw EE. A murine model of the human CREBRFR457Q obesity-risk variant does not influence energy or glucose homeostasis in response to nutritional stress. PLoS One 2021; 16:e0251895. [PMID: 34520472 PMCID: PMC8439463 DOI: 10.1371/journal.pone.0251895] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Accepted: 08/09/2021] [Indexed: 01/02/2023] Open
Abstract
Obesity and diabetes have strong heritable components, yet the genetic contributions to these diseases remain largely unexplained. In humans, a missense variant in Creb3 regulatory factor (CREBRF) [rs373863828 (p.Arg457Gln); CREBRFR457Q] is strongly associated with increased odds of obesity but decreased odds of diabetes. Although virtually nothing is known about CREBRF's mechanism of action, emerging evidence implicates it in the adaptive transcriptional response to nutritional stress downstream of TORC1. The objectives of this study were to generate a murine model with knockin of the orthologous variant in mice (CREBRFR458Q) and to test the hypothesis that this CREBRF variant promotes obesity and protects against diabetes by regulating energy and glucose homeostasis downstream of TORC1. To test this hypothesis, we performed extensive phenotypic analysis of CREBRFR458Q knockin mice at baseline and in response to acute (fasting/refeeding), chronic (low- and high-fat diet feeding), and extreme (prolonged fasting) nutritional stress as well as with pharmacological TORC1 inhibition, and aging to 52 weeks. The results demonstrate that the murine CREBRFR458Q model of the human CREBRFR457Q variant does not influence energy/glucose homeostasis in response to these interventions, with the exception of possible greater loss of fat relative to lean mass with age. Alternative preclinical models and/or studies in humans will be required to decipher the mechanisms linking this variant to human health and disease.
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Affiliation(s)
- Jitendra S. Kanshana
- Division of Endocrinology and Metabolism, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, United States of America
| | - Polly E. Mattila
- Division of Endocrinology and Metabolism, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, United States of America
| | - Michael C. Ewing
- Division of Endocrinology and Metabolism, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, United States of America
| | - Ashlee N. Wood
- Division of Endocrinology and Metabolism, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, United States of America
| | - Gabriele Schoiswohl
- Department of Pharmacology and Toxicology, University of Graz, Graz, Austria
| | - Anna C. Meyer
- Division of Endocrinology and Metabolism, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, United States of America
| | - Aneta Kowalski
- Division of Endocrinology and Metabolism, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, United States of America
| | - Samantha L. Rosenthal
- Center for Craniofacial and Dental Genetics, Department of Oral and Craniofacial Sciences, University of Pittsburgh, Pittsburgh, PA, United States of America
| | - Sebastien Gingras
- Department of Immunology, University of Pittsburgh, Pittsburgh, PA, United States of America
| | - Brett A. Kaufman
- Division of Cardiology, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, United States of America
| | - Ray Lu
- Department of Molecular and Cellular Biology, College of Biological Science, University of Guelph, Guelph, ON, Canada
| | - Daniel E. Weeks
- Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
- Department of Biostatistics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Stephen T. McGarvey
- International Health Institute, Department of Epidemiology, Brown University School of Public Health, Providence, Rhode Island, United States of America
- Department of Anthropology, Brown University, Providence, Rhode Island, United States of America
| | - Ryan L. Minster
- Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Nicola L. Hawley
- Department of Chronic Disease Epidemiology, Yale School of Public Health, New Haven, Connecticut, United States of America
| | - Erin E. Kershaw
- Division of Endocrinology and Metabolism, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, United States of America
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Integrated bioinformatics analysis reveals novel key biomarkers and potential candidate small molecule drugs in gestational diabetes mellitus. Biosci Rep 2021; 41:228450. [PMID: 33890634 PMCID: PMC8145272 DOI: 10.1042/bsr20210617] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Revised: 04/21/2021] [Accepted: 04/22/2021] [Indexed: 02/08/2023] Open
Abstract
Gestational diabetes mellitus (GDM) is the metabolic disorder that appears during pregnancy. The current investigation aimed to identify central differentially expressed genes (DEGs) in GDM. The transcription profiling by array data (E-MTAB-6418) was obtained from the ArrayExpress database. The DEGs between GDM samples and non-GDM samples were analyzed. Functional enrichment analysis were performed using ToppGene. Then we constructed the protein–protein interaction (PPI) network of DEGs by the Search Tool for the Retrieval of Interacting Genes database (STRING) and module analysis was performed. Subsequently, we constructed the miRNA–hub gene network and TF–hub gene regulatory network. The validation of hub genes was performed through receiver operating characteristic curve (ROC). Finally, the candidate small molecules as potential drugs to treat GDM were predicted by using molecular docking. Through transcription profiling by array data, a total of 869 DEGs were detected including 439 up-regulated and 430 down-regulated genes. Functional enrichment analysis showed these DEGs were mainly enriched in reproduction, cell adhesion, cell surface interactions at the vascular wall and extracellular matrix organization. Ten genes, HSP90AA1, EGFR, RPS13, RBX1, PAK1, FYN, ABL1, SMAD3, STAT3 and PRKCA were associated with GDM, according to ROC analysis. Finally, the most significant small molecules were predicted based on molecular docking. This investigation identified hub genes, signal pathways and therapeutic agents, which might help us, enhance our understanding of the mechanisms of GDM and find some novel therapeutic agents for GDM.
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