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Brito Nunes C, Borges MC, Freathy RM, Lawlor DA, Qvigstad E, Evans DM, Moen GH. Understanding the Genetic Landscape of Gestational Diabetes: Insights into the Causes and Consequences of Elevated Glucose Levels in Pregnancy. Metabolites 2024; 14:508. [PMID: 39330515 PMCID: PMC11434570 DOI: 10.3390/metabo14090508] [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] [Received: 08/26/2024] [Revised: 09/16/2024] [Accepted: 09/17/2024] [Indexed: 09/28/2024] Open
Abstract
Background/Objectives: During pregnancy, physiological changes in maternal circulating glucose levels and its metabolism are essential to meet maternal and fetal energy demands. Major changes in glucose metabolism occur throughout pregnancy and consist of higher insulin resistance and a compensatory increase in insulin secretion to maintain glucose homeostasis. For some women, this change is insufficient to maintain normoglycemia, leading to gestational diabetes mellitus (GDM), a condition characterized by maternal glucose intolerance and hyperglycaemia first diagnosed during the second or third trimester of pregnancy. GDM is diagnosed in approximately 14.0% of pregnancies globally, and it is often associated with short- and long-term adverse health outcomes in both mothers and offspring. Although recent studies have highlighted the role of genetic determinants in the development of GDM, research in this area is still lacking, hindering the development of prevention and treatment strategies. Methods: In this paper, we review recent advances in the understanding of genetic determinants of GDM and glycaemic traits during pregnancy. Results/Conclusions: Our review highlights the need for further collaborative efforts as well as larger and more diverse genotyped pregnancy cohorts to deepen our understanding of the genetic aetiology of GDM, address research gaps, and further improve diagnostic and treatment strategies.
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Affiliation(s)
- Caroline Brito Nunes
- Institute for Molecular Bioscience, The University of Queensland, Brisbane 4067, Australia
| | - Maria Carolina Borges
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol BS8 1QU, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol BS8 2PS, UK
| | - Rachel M. Freathy
- Department of Clinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter, Exeter EX4 4PY, UK;
| | - Deborah A. Lawlor
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol BS8 1QU, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol BS8 2PS, UK
| | - Elisabeth Qvigstad
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, 0372 Oslo, Norway
- Department of Endocrinology, Morbid Obesity and Preventive Medicine, Oslo University Hospital, 0424 Oslo, Norway
| | - David M. Evans
- Institute for Molecular Bioscience, The University of Queensland, Brisbane 4067, Australia
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol BS8 1QU, UK
- Frazer Institute, University of Queensland, Brisbane 4102, Australia
| | - Gunn-Helen Moen
- Institute for Molecular Bioscience, The University of Queensland, Brisbane 4067, Australia
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, 0372 Oslo, Norway
- Frazer Institute, University of Queensland, Brisbane 4102, Australia
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, 7491 Trondheim, Norway
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Aliyu U, Umlai UKI, Toor SM, Elashi AA, Al-Sarraj YA, Abou−Samra AB, Suhre K, Albagha OME. Genome-wide association study and polygenic score assessment of insulin resistance. Front Endocrinol (Lausanne) 2024; 15:1384103. [PMID: 38938516 PMCID: PMC11208314 DOI: 10.3389/fendo.2024.1384103] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Accepted: 05/14/2024] [Indexed: 06/29/2024] Open
Abstract
Insulin resistance (IR) and beta cell dysfunction are the major drivers of type 2 diabetes (T2D). Genome-Wide Association Studies (GWAS) on IR have been predominantly conducted in European populations, while Middle Eastern populations remain largely underrepresented. We conducted a GWAS on the indices of IR (HOMA2-IR) and beta cell function (HOMA2-%B) in 6,217 non-diabetic individuals from the Qatar Biobank (QBB; Discovery cohort; n = 2170, Replication cohort; n = 4047) with and without body mass index (BMI) adjustment. We also developed polygenic scores (PGS) for HOMA2-IR and compared their performance with a previously derived PGS for HOMA-IR (PGS003470). We replicated 11 loci that have been previously associated with HOMA-IR and 24 loci that have been associated with HOMA-%B, at nominal statistical significance. We also identified a novel locus associated with beta cell function near VEGFC gene, tagged by rs61552983 (P = 4.38 × 10-8). Moreover, our best performing PGS (Q-PGS4; Adj R2 = 0.233 ± 0.014; P = 1.55 x 10-3) performed better than PGS003470 (Adj R2 = 0.194 ± 0.014; P = 5.45 x 10-2) in predicting HOMA2-IR in our dataset. This is the first GWAS on HOMA2 and the first GWAS conducted in the Middle East focusing on IR and beta cell function. Herein, we report a novel locus in VEGFC that is implicated in beta cell dysfunction. Inclusion of under-represented populations in GWAS has potentials to provide important insights into the genetic architecture of IR and beta cell function.
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Affiliation(s)
- Usama Aliyu
- College of Health and Life Sciences (CHLS), Hamad Bin Khalifa University (HBKU), Qatar Foundation (QF), Doha, Qatar
| | - Umm-Kulthum Ismail Umlai
- College of Health and Life Sciences (CHLS), Hamad Bin Khalifa University (HBKU), Qatar Foundation (QF), Doha, Qatar
| | - Salman M. Toor
- College of Health and Life Sciences (CHLS), Hamad Bin Khalifa University (HBKU), Qatar Foundation (QF), Doha, Qatar
| | - Asma A. Elashi
- College of Health and Life Sciences (CHLS), Hamad Bin Khalifa University (HBKU), Qatar Foundation (QF), Doha, Qatar
| | - Yasser A. Al-Sarraj
- Qatar Genome Program (QGP), Qatar Foundation Research, Development and Innovation, Qatar Foundation (QF), Doha, Qatar
| | | | - Karsten Suhre
- Bioinformatics Core, Weill Cornell Medicine-Qatar, Doha, Qatar
- Department of Biophysics and Physiology, Weill Cornell Medicine, New York, NY, United States
| | - Omar M. E. Albagha
- College of Health and Life Sciences (CHLS), Hamad Bin Khalifa University (HBKU), Qatar Foundation (QF), Doha, Qatar
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Development of pharmacogenomic algorithm to optimize nateglinide dose for the treatment of type 2 diabetes mellitus. Pharmacol Rep 2022; 74:1083-1091. [PMID: 35932448 DOI: 10.1007/s43440-022-00400-0] [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: 05/12/2022] [Revised: 07/16/2022] [Accepted: 07/26/2022] [Indexed: 10/16/2022]
Abstract
BACKGROUND Nateglinide is a meglitinide used for the treatment of type 2 diabetes mellitus. Individual studies demonstrated the association of CYP2C9, SLCO1B1, and MTNR1B variants with the safety and efficacy of nateglinide. The current study aimed to develop a pharmacogenomic algorithm to optimize nateglinide therapy. METHODS Multiple linear regression (MLR) and classification and regression tree (CART) were used to develop a pharmacogenomic algorithm for nateglinide dosing based on the published nateglinide pharmacokinetic data on the area under the curve data (AUC) and Cmax (n = 143). CYP2C9 metabolizer phenotype, SLCO1B1, MTNR1B genotypes, and CYP2C9 inhibitor usage were used as the input variables. The results and associations were further confirmed by meta-analysis and in silico studies. RESULTS The MLR models of AUC and Cmax explain 87.4% and 59% variability in nateglinide pharmacokinetics. The Bland and Altman analysis of the nateglinide dose predicted by these two MLR models showed a bias of ± 26.28 mg/meal. The CART algorithm was proposed based on these findings. This model is further justified by the meta-analysis showing increased AUCs in CYP2C9 intermediate metabolizers and SLCOB1 TC and CC genotypes compared to the wild genotypes. The increased AUC in SLCO1B1 mutants is due to decreased binding affinity of nateglinide to the mutant affecting the influx of nateglinide into hepatocytes. MTNR1B rs10830963 G-allele-mediated poor response to nateglinide is attributed to increased transcriptional factor binding causing decreased insulin secretion. CONCLUSION CYP2C9, SLCO1B1, and MTNR1B genotyping help in optimizing nateglinide therapy based on this algorithm and ensuring safety and efficacy.
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