51
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Quantitative assessment of genetic testing for type 2 diabetes mellitus based on findings of genome-wide association studies. Ann Epidemiol 2016; 26:816-818.e6. [PMID: 27751632 DOI: 10.1016/j.annepidem.2016.09.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2016] [Revised: 08/26/2016] [Accepted: 09/16/2016] [Indexed: 12/29/2022]
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52
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Association of HLA-A, B, DRB1* and DQB1* alleles and haplotypes in south Indian T2DM patients. Gene 2016; 592:200-208. [DOI: 10.1016/j.gene.2016.08.001] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2016] [Revised: 07/02/2016] [Accepted: 08/02/2016] [Indexed: 12/17/2022]
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53
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Fan M, Li W, Wang L, Gu S, Dong S, Chen M, Yin H, Zheng J, Wu X, Jin J, Jiang X, Cai J, Liu P, Zheng C. Association of SLC30A8 gene polymorphism with type 2 diabetes, evidence from 46 studies: a meta-analysis. Endocrine 2016; 53:381-94. [PMID: 26832344 DOI: 10.1007/s12020-016-0870-4] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/16/2015] [Accepted: 01/13/2016] [Indexed: 11/30/2022]
Abstract
The solute carrier family 30 member 8 (SLC30A8) gene may be involved in the development of type 2 diabetes mellitus (T2DM) through disrupting β-cell function. The aim of this study was to assess the association between SLC30A8 rs13266634 polymorphism and susceptibility to T2DM. We searched all reports regarding the association between SLC30A8 rs13266634 polymorphism and T2DM risk through Pubmed, Embase, and the Cochrane Library for English language reports and Chongqing VIP database, Wanfang data, CBMDisc, and CNKI for Chinese language studies. Allelic and genotype comparisons between cases and controls were evaluated, and odds ratios with 95 % confidence intervals were used to assess the strength of their association. A random effects model was selected. Publication bias was estimated using Begg's test. Forty-six studies were included in the analysis with a total of 71,890 cases and 96,753 controls. This meta-analysis suggests that SLC30A8 (rs13266634) polymorphism was associated with T2DM risk. Although previous meta-analyses have shown that this association was only found in Asian and European groups, and not in African populations, our analysis revealed the deleterious effect of SLC30A8 rs13266634 on T2DM in an African population when stratified by ethnicity under additive model even with a small number of studies.
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Affiliation(s)
- Mengdi Fan
- Department of Pathology, The Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, Zhejiang, China
| | - Weimin Li
- Department of Ultrasonography, The Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, Zhejiang, China
| | - Lian Wang
- Diabetes Center and Department of Endocrinology, The Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, Zhejiang, China
| | - Suping Gu
- Diabetes Center and Department of Endocrinology, The Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, Zhejiang, China
| | - Sisi Dong
- Diabetes Center and Department of Endocrinology, The Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, Zhejiang, China
| | - Mengdie Chen
- Diabetes Center and Department of Endocrinology, The Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, Zhejiang, China
| | - Haimin Yin
- Diabetes Center and Department of Endocrinology, The Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, Zhejiang, China
| | - Jinjue Zheng
- Department of Ultrasonography, The Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, Zhejiang, China
| | - Xiaoying Wu
- Diabetes Center and Department of Endocrinology, The Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, Zhejiang, China
| | - Jian Jin
- Diabetes Center and Department of Endocrinology, The Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, Zhejiang, China
| | - Xuchao Jiang
- Department of Pathophysiology, Obesity and Diabetes Center, Second Military Medical University, Shanghai, 200433, China
| | - Jiao Cai
- Department of Pathophysiology, Obesity and Diabetes Center, Second Military Medical University, Shanghai, 200433, China
| | - Peining Liu
- Department of Child Health, The Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, Zhejiang, China.
| | - Chao Zheng
- Diabetes Center and Department of Endocrinology, The Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, Zhejiang, China.
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Kong Y, Sharma RB, Nwosu BU, Alonso LC. Islet biology, the CDKN2A/B locus and type 2 diabetes risk. Diabetologia 2016; 59:1579-93. [PMID: 27155872 PMCID: PMC4930689 DOI: 10.1007/s00125-016-3967-7] [Citation(s) in RCA: 71] [Impact Index Per Article: 7.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/21/2016] [Accepted: 03/29/2016] [Indexed: 02/06/2023]
Abstract
Type 2 diabetes, fuelled by the obesity epidemic, is an escalating worldwide cause of personal hardship and public cost. Diabetes incidence increases with age, and many studies link the classic senescence and ageing protein p16(INK4A) to diabetes pathophysiology via pancreatic islet biology. Genome-wide association studies (GWASs) have unequivocally linked the CDKN2A/B locus, which encodes p16 inhibitor of cyclin-dependent kinase (p16(INK4A)) and three other gene products, p14 alternate reading frame (p14(ARF)), p15(INK4B) and antisense non-coding RNA in the INK4 locus (ANRIL), with human diabetes risk. However, the mechanism by which the CDKN2A/B locus influences diabetes risk remains uncertain. Here, we weigh the evidence that CDKN2A/B polymorphisms impact metabolic health via islet biology vs effects in other tissues. Structured in a bedside-to-bench-to-bedside approach, we begin with a summary of the evidence that the CDKN2A/B locus impacts diabetes risk and a brief review of the basic biology of CDKN2A/B gene products. The main emphasis of this work is an in-depth look at the nuanced roles that CDKN2A/B gene products and related proteins play in the regulation of beta cell mass, proliferation and insulin secretory function, as well as roles in other metabolic tissues. We finish with a synthesis of basic biology and clinical observations, incorporating human physiology data. We conclude that it is likely that the CDKN2A/B locus influences diabetes risk through both islet and non-islet mechanisms.
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Affiliation(s)
- Yahui Kong
- AS7-2047, Division of Diabetes, Department of Medicine, University of Massachusetts Medical School, 368 Plantation Street, Worcester, MA, 01605, USA
| | - Rohit B Sharma
- AS7-2047, Division of Diabetes, Department of Medicine, University of Massachusetts Medical School, 368 Plantation Street, Worcester, MA, 01605, USA
| | - Benjamin U Nwosu
- Division of Endocrinology, Department of Pediatrics, University of Massachusetts Medical School, Worcester, MA, USA
| | - Laura C Alonso
- AS7-2047, Division of Diabetes, Department of Medicine, University of Massachusetts Medical School, 368 Plantation Street, Worcester, MA, 01605, USA.
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55
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Is the Mouse a Good Model of Human PPARγ-Related Metabolic Diseases? Int J Mol Sci 2016; 17:ijms17081236. [PMID: 27483259 PMCID: PMC5000634 DOI: 10.3390/ijms17081236] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2016] [Revised: 07/19/2016] [Accepted: 07/21/2016] [Indexed: 12/21/2022] Open
Abstract
With the increasing number of patients affected with metabolic diseases such as type 2 diabetes, obesity, atherosclerosis and insulin resistance, academic researchers and pharmaceutical companies are eager to better understand metabolic syndrome and develop new drugs for its treatment. Many studies have focused on the nuclear receptor peroxisome proliferator-activated receptor gamma (PPARγ), which plays a crucial role in adipogenesis and lipid metabolism. These studies have been able to connect this transcription factor to several human metabolic diseases. Due to obvious limitations concerning experimentation in humans, animal models—mainly mouse models—have been generated to investigate the role of PPARγ in different tissues. This review focuses on the metabolic features of human and mouse PPARγ-related diseases and the utility of the mouse as a model.
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56
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Lu J, Luo Y, Wang J, Hu C, Zhang R, Wang C, Jia W. Association of type 2 diabetes susceptibility loci with peripheral nerve function in a Chinese population with diabetes. J Diabetes Investig 2016; 8:115-120. [PMID: 27253191 PMCID: PMC5217885 DOI: 10.1111/jdi.12546] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/20/2016] [Revised: 04/18/2016] [Accepted: 06/01/2016] [Indexed: 12/17/2022] Open
Abstract
Aims/Introduction Previous studies have suggested a possible relationship between type 2 diabetes mellitus susceptibility loci and diabetic complications. The present study aimed to investigate the associations between type 2 diabetes mellitus loci with peripheral nerve function in a Chinese population with type 2 diabetes mellitus. Materials and Methods A total of 1,900 type 2 diabetes mellitus patients were recruited in the study. We selected ten single nucleotide polymorphisms (SNPs) from ten type 2 diabetes mellitus susceptibility genes previously confirmed in Chinese patients. Genotyping was carried out by using a MassARRAY Compact Analyzer. Peripheral nerve function was evaluated by nerve conduction studies in all participants. The composite Z‐scores for nerve conduction parameters including conduction velocity (CV), amplitude and latency were calculated, respectively. Results Rs5219 of KCNJ11 (E23K, G→A) was identified to be associated with all the parameters obtained from nerve conduction studies (Z‐score of CV: β = 0.113, P = 0.01; Z‐score of amplitude: β = 0.133, P = 0.01; Z‐score of latency: β = −0.116, P = 0.01) after adjustment for covariates including age, duration and glycated hemoglobin. Specifically, each copy of the A allele was related to better outcomes. CDKAL1 rs7756992 and TCF7L2 rs7903146 correlated with the composite Z‐score of amplitude (P = 0.028 and P = 0.016, respectively), but not CV (P = 0.393 and P = 0.281, respectively) or latency (P = 0.286 and P = 0.273, respectively). There were no significant associations between the other seven SNPs and peripheral nerve function. Conclusions Rs5219 at KCNJ11 (E23K) was associated with peripheral nerve function in a Chinese population with type 2 diabetes mellitus, suggesting shared genetic factors for type 2 diabetes mellitus and diabetic polyneuropathy in this population.
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Affiliation(s)
- Jingyi Lu
- Department of Endocrinology and Metabolism, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China.,Shanghai Diabetes Institute, Shanghai, China.,Shanghai Key Laboratory of Diabetes Mellitus, Shanghai, China.,Shanghai Clinical Center for Diabetes, Shanghai, China
| | - Yi Luo
- Department of Endocrinology and Metabolism, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China.,Shanghai Diabetes Institute, Shanghai, China.,Shanghai Key Laboratory of Diabetes Mellitus, Shanghai, China.,Shanghai Clinical Center for Diabetes, Shanghai, China
| | - Jie Wang
- Department of Endocrinology and Metabolism, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China.,Shanghai Diabetes Institute, Shanghai, China.,Shanghai Key Laboratory of Diabetes Mellitus, Shanghai, China.,Shanghai Clinical Center for Diabetes, Shanghai, China
| | - Cheng Hu
- Department of Endocrinology and Metabolism, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China.,Shanghai Diabetes Institute, Shanghai, China.,Shanghai Key Laboratory of Diabetes Mellitus, Shanghai, China.,Shanghai Clinical Center for Diabetes, Shanghai, China
| | - Rong Zhang
- Department of Endocrinology and Metabolism, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China.,Shanghai Diabetes Institute, Shanghai, China.,Shanghai Key Laboratory of Diabetes Mellitus, Shanghai, China.,Shanghai Clinical Center for Diabetes, Shanghai, China
| | - Congrong Wang
- Department of Endocrinology and Metabolism, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China.,Shanghai Diabetes Institute, Shanghai, China.,Shanghai Key Laboratory of Diabetes Mellitus, Shanghai, China.,Shanghai Clinical Center for Diabetes, Shanghai, China
| | - Weiping Jia
- Department of Endocrinology and Metabolism, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China.,Shanghai Diabetes Institute, Shanghai, China.,Shanghai Key Laboratory of Diabetes Mellitus, Shanghai, China.,Shanghai Clinical Center for Diabetes, Shanghai, China
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57
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Letronne F, Laumet G, Ayral AM, Chapuis J, Demiautte F, Laga M, Vandenberghe ME, Malmanche N, Leroux F, Eysert F, Sottejeau Y, Chami L, Flaig A, Bauer C, Dourlen P, Lesaffre M, Delay C, Huot L, Dumont J, Werkmeister E, Lafont F, Mendes T, Hansmannel F, Dermaut B, Deprez B, Hérard AS, Dhenain M, Souedet N, Pasquier F, Tulasne D, Berr C, Hauw JJ, Lemoine Y, Amouyel P, Mann D, Déprez R, Checler F, Hot D, Delzescaux T, Gevaert K, Lambert JC. ADAM30 Downregulates APP-Linked Defects Through Cathepsin D Activation in Alzheimer's Disease. EBioMedicine 2016; 9:278-292. [PMID: 27333034 PMCID: PMC4972530 DOI: 10.1016/j.ebiom.2016.06.002] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2016] [Revised: 05/31/2016] [Accepted: 06/01/2016] [Indexed: 01/12/2023] Open
Abstract
Although several ADAMs (A disintegrin-like and metalloproteases) have been shown to contribute to the amyloid precursor protein (APP) metabolism, the full spectrum of metalloproteases involved in this metabolism remains to be established. Transcriptomic analyses centred on metalloprotease genes unraveled a 50% decrease in ADAM30 expression that inversely correlates with amyloid load in Alzheimer's disease brains. Accordingly, in vitro down- or up-regulation of ADAM30 expression triggered an increase/decrease in Aβ peptides levels whereas expression of a biologically inactive ADAM30 (ADAM30(mut)) did not affect Aβ secretion. Proteomics/cell-based experiments showed that ADAM30-dependent regulation of APP metabolism required both cathepsin D (CTSD) activation and APP sorting to lysosomes. Accordingly, in Alzheimer-like transgenic mice, neuronal ADAM30 over-expression lowered Aβ42 secretion in neuron primary cultures, soluble Aβ42 and amyloid plaque load levels in the brain and concomitantly enhanced CTSD activity and finally rescued long term potentiation alterations. Our data thus indicate that lowering ADAM30 expression may favor Aβ production, thereby contributing to Alzheimer's disease development.
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Affiliation(s)
- Florent Letronne
- INSERM, U1167, Laboratoire d'Excellence Distalz, F59000 Lille, France; Institut Pasteur de Lille, F59000 Lille, France; Univ. Lille, F59000 Lille, France
| | - Geoffroy Laumet
- INSERM, U1167, Laboratoire d'Excellence Distalz, F59000 Lille, France; Institut Pasteur de Lille, F59000 Lille, France; Univ. Lille, F59000 Lille, France
| | - Anne-Marie Ayral
- INSERM, U1167, Laboratoire d'Excellence Distalz, F59000 Lille, France; Institut Pasteur de Lille, F59000 Lille, France; Univ. Lille, F59000 Lille, France
| | - Julien Chapuis
- INSERM, U1167, Laboratoire d'Excellence Distalz, F59000 Lille, France; Institut Pasteur de Lille, F59000 Lille, France; Univ. Lille, F59000 Lille, France
| | - Florie Demiautte
- INSERM, U1167, Laboratoire d'Excellence Distalz, F59000 Lille, France; Institut Pasteur de Lille, F59000 Lille, France; Univ. Lille, F59000 Lille, France
| | - Mathias Laga
- Department of Medical Protein Research, VIB, Ghent, Belgium; Department of Biochemistry, Ghent University, Ghent, Belgium
| | - Michel E Vandenberghe
- CEA, DSV, I2BM, MIRCen, Fontenay aux Roses, France; CNRS, UMR 9199, Fontenay aux Roses, France
| | - Nicolas Malmanche
- INSERM, U1167, Laboratoire d'Excellence Distalz, F59000 Lille, France; Institut Pasteur de Lille, F59000 Lille, France; Univ. Lille, F59000 Lille, France
| | - Florence Leroux
- Institut Pasteur de Lille, F59000 Lille, France; Univ. Lille, F59000 Lille, France; INSERM U1177, Drugs and Molecules for Living Systems, F5900 Lille, France
| | - Fanny Eysert
- INSERM, U1167, Laboratoire d'Excellence Distalz, F59000 Lille, France; Institut Pasteur de Lille, F59000 Lille, France; Univ. Lille, F59000 Lille, France
| | - Yoann Sottejeau
- INSERM, U1167, Laboratoire d'Excellence Distalz, F59000 Lille, France; Institut Pasteur de Lille, F59000 Lille, France; Univ. Lille, F59000 Lille, France
| | - Linda Chami
- Institut de Pharmacologie Moléculaire et Cellulaire, UMR 7275 CNRS, Laboratoire d'Excellence Distalz, Nice, France; Université de Nice-Sophia-Antipolis, Valbonne, France
| | - Amandine Flaig
- INSERM, U1167, Laboratoire d'Excellence Distalz, F59000 Lille, France; Institut Pasteur de Lille, F59000 Lille, France; Univ. Lille, F59000 Lille, France
| | - Charlotte Bauer
- Institut de Pharmacologie Moléculaire et Cellulaire, UMR 7275 CNRS, Laboratoire d'Excellence Distalz, Nice, France; Université de Nice-Sophia-Antipolis, Valbonne, France
| | - Pierre Dourlen
- INSERM, U1167, Laboratoire d'Excellence Distalz, F59000 Lille, France; Institut Pasteur de Lille, F59000 Lille, France; Univ. Lille, F59000 Lille, France
| | - Marie Lesaffre
- Univ. Lille, CNRS, Institut Pasteur de Lille, UMR 8161 - M3T - Mechanisms of Tumorigenesis and Targeted Therapies, F-59000 Lille, France
| | - Charlotte Delay
- INSERM, U1167, Laboratoire d'Excellence Distalz, F59000 Lille, France; Institut Pasteur de Lille, F59000 Lille, France; Univ. Lille, F59000 Lille, France
| | - Ludovic Huot
- Institut Pasteur de Lille, F59000 Lille, France; Univ. Lille, F59000 Lille, France; Center for Infection and Immunity of Lille, CNRS UMR 8204, INSERM 1019, Lille, France
| | - Julie Dumont
- Institut Pasteur de Lille, F59000 Lille, France; Univ. Lille, F59000 Lille, France; INSERM U1177, Drugs and Molecules for Living Systems, F5900 Lille, France
| | | | | | - Tiago Mendes
- INSERM, U1167, Laboratoire d'Excellence Distalz, F59000 Lille, France; Institut Pasteur de Lille, F59000 Lille, France; Univ. Lille, F59000 Lille, France
| | - Franck Hansmannel
- INSERM, U954, Vandoeuvre-lès-Nancy, France; Department of Hepato-Gastroenterology, University Hospital of Nancy, Université Henri Poincaré 1, Vandoeuvre-lès-Nancy, France
| | - Bart Dermaut
- INSERM, U1167, Laboratoire d'Excellence Distalz, F59000 Lille, France; Institut Pasteur de Lille, F59000 Lille, France; Univ. Lille, F59000 Lille, France
| | - Benoit Deprez
- Institut Pasteur de Lille, F59000 Lille, France; Univ. Lille, F59000 Lille, France; INSERM U1177, Drugs and Molecules for Living Systems, F5900 Lille, France
| | - Anne-Sophie Hérard
- CEA, DSV, I2BM, MIRCen, Fontenay aux Roses, France; CNRS, UMR 9199, Fontenay aux Roses, France
| | - Marc Dhenain
- CEA, DSV, I2BM, MIRCen, Fontenay aux Roses, France; CNRS, UMR 9199, Fontenay aux Roses, France
| | - Nicolas Souedet
- CEA, DSV, I2BM, MIRCen, Fontenay aux Roses, France; CNRS, UMR 9199, Fontenay aux Roses, France
| | - Florence Pasquier
- Univ. Lille, Inserm, U1171, - Degenerative & Vascular Cognitive Disorders, Laboratoire d'Excellence Distalz, F-59000 Lille, France; CHR&U, Lille, France
| | - David Tulasne
- Univ. Lille, CNRS, Institut Pasteur de Lille, UMR 8161 - M3T - Mechanisms of Tumorigenesis and Targeted Therapies, F-59000 Lille, France
| | - Claudine Berr
- INSERM, U1061, Université de Montpellier I, Hôpital La Colombière, Montpellier, France
| | - Jean-Jacques Hauw
- APHP-Raymond Escourolle Neuropathology Laboratory, la salpétrière Hospital, Paris, France
| | - Yves Lemoine
- Institut Pasteur de Lille, F59000 Lille, France; Univ. Lille, F59000 Lille, France; Center for Infection and Immunity of Lille, CNRS UMR 8204, INSERM 1019, Lille, France
| | - Philippe Amouyel
- INSERM, U1167, Laboratoire d'Excellence Distalz, F59000 Lille, France; Institut Pasteur de Lille, F59000 Lille, France; Univ. Lille, F59000 Lille, France; CHR&U, Lille, France
| | - David Mann
- Institute of Brain, Behaviour and Mental Health, University of Manchester, Salford Royal Hospital, Salford, UK
| | - Rebecca Déprez
- Institut Pasteur de Lille, F59000 Lille, France; Univ. Lille, F59000 Lille, France; INSERM U1177, Drugs and Molecules for Living Systems, F5900 Lille, France
| | - Frédéric Checler
- Institut de Pharmacologie Moléculaire et Cellulaire, UMR 7275 CNRS, Laboratoire d'Excellence Distalz, Nice, France; Université de Nice-Sophia-Antipolis, Valbonne, France
| | - David Hot
- Institut Pasteur de Lille, F59000 Lille, France; Univ. Lille, F59000 Lille, France; Center for Infection and Immunity of Lille, CNRS UMR 8204, INSERM 1019, Lille, France
| | - Thierry Delzescaux
- CEA, DSV, I2BM, MIRCen, Fontenay aux Roses, France; CNRS, UMR 9199, Fontenay aux Roses, France
| | - Kris Gevaert
- Department of Medical Protein Research, VIB, Ghent, Belgium; Department of Biochemistry, Ghent University, Ghent, Belgium
| | - Jean-Charles Lambert
- INSERM, U1167, Laboratoire d'Excellence Distalz, F59000 Lille, France; Institut Pasteur de Lille, F59000 Lille, France; Univ. Lille, F59000 Lille, France.
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Sun XF, Xiao XH, Zhang ZX, Liu Y, Xu T, Zhu XL, Zhang Y, Wu XP, Li WH, Zhang HB, Yu M. Positive Association Between Type 2 Diabetes Risk Alleles Near CDKAL1 and Reduced Birthweight in Chinese Han Individuals. Chin Med J (Engl) 2016; 128:1873-8. [PMID: 26168825 PMCID: PMC4717941 DOI: 10.4103/0366-6999.160489] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
Background: Fetal insulin hypothesis was proposed that the association between low birth weight and type 2 diabetes is principally genetically mediated. The aim of this study was to investigate whether common variants in genes CDKAL1, HHEX, ADCY5, SRR, PTPRD that predisposed to type 2 diabetes were also associated with reduced birthweight in Chinese Han population. Methods: Twelve single nucleotide polymorphisms (rs7756992/rs10946398 in CDKAL1, rs1111875 in HHEX, rs391300 in SRR, rs17584499 in PTPRD, rs1170806/rs9883204/rs4678017/rs9881942/rs7641344/rs6777397/rs6226243 in ADCY5) were genotyped in 1174 unrelated individuals born in Peking Union Medical College Hospital from 1921 to 1954 by TaqMan allelic discrimination assays, of which 645 had normal glucose tolerance, 181 had developed type 2 diabetes and 348 impaired glucose regulation. Associations of these 12 genetic variants with birthweight and glucose metabolism in later life were analyzed. Results: Birthweight was inversely associated with CDKAL1-rs10946398 (β = −41 g [95% confidence interval [CI]: −80, −3], P = 0.034), common variants both associated with increased risk of impaired glucose metabolism and decreased insulin secretion index later in life. After adjusting for sex, gestational weeks, parity and maternal age, the risk allele of CDKAL1-rs7756992 was associated with reduced birthweight (β = −36 g [95% CI: −72, −0.2], P = 0.048). The risk allele in SRR showed a trend toward a reduction of birthweight (P = 0.085). Conclusions: This study identified the association between type 2 diabetes risk variants in CDKAL1 and birthweight in Chinese Han individuals, and the carrier of risk allele within SRR had the trend of reduced birthweight. This demonstrates that there is a clear overlap between the genetics of type 2 diabetes and fetal growth, which proposes that lower birth weight and type 2 diabetes may be two phenotypes of one genotype.
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Affiliation(s)
| | - Xin-Hua Xiao
- Department of Endocrinology, Key Laboratory of Endocrinology, Ministry of Health, Peking Union Medical College Hospital, Diabetes Research Center of Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China
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59
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Dou HY, Wang YY, Yang N, Heng ML, Zhou X, Bu HE, Xu F, Zhao TN, Huang H, Wang HW. Association between genetic variants and characteristic symptoms of type 2 diabetes: A matched case-control study. Chin J Integr Med 2016; 23:415-424. [PMID: 26919830 DOI: 10.1007/s11655-015-2290-3] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2014] [Indexed: 12/15/2022]
Abstract
OBJECTIVE To examine the association of genetic variants with characteristic symptoms of type 2 diabetes mellitus (T2DM). METHODS A matched case-control study was performed to investigate the association between common variants in four genes (CDKAL1, GLIS3, GRK5, and TCF7L2) and symptoms of T2DM. Symptoms were examined with questionnaire for 710 subjects. Genomic DNA was extracted from peripheral blood mononuclear cell by salting-out procedure. Genotyping was carried out by direct sequencing of the unpurified polymerase chain reaction products. RESULT Most of the T2DM patients pressented characteristic symptoms, such as feeling weak in limbs (P =0.0057), hand tremor (P =0.0208), bradymasesis (P =0.0234), and polyuria (P =0.0051). Some of the T2DM patients shared characteristic symptoms, such as desire for cold drinks (P =0.0304), polyphagia (P =0.0051), and furred tongue (P =0.028). The impaired glucose regulation (IGR) cases took only one characteristic symptom of frequent micturition (P =0.0422). GLIS3 rs7034200 and GRK5 rs10886471 were significantly associated with increased T2DM risk (GLIS3 rs7034200 under dominant model: P=0.0307; GRK5 rs10886471 under recessive model: P=0.0092). However, only the rs10886471 polymorphism in GRK5 showed a significant effect on both differentiated symptoms and T2DM risk. The C-allele was involved in both dampness-heat encumbering Pi (Spleen) syndrome (P =0.047) and qi-yin deficiency syndrome (P =0.002) via increased GRK5 expression. CONCLUSIONS Both T2DM and IGR exhibited its corresponding characteristic symptoms. The variants of GRK5 were involved with both qi-yin deficiency syndrome and dampness-heat encumbering Pi syndrome.
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Affiliation(s)
- Hao-Ying Dou
- Department of Nursing, Tianjin University of Traditional Chinese Medicine, Tianjin, 300193, China
| | - Yuan-Yuan Wang
- Department of Nursing, Tianjin University of Traditional Chinese Medicine, Tianjin, 300193, China
| | - Nan Yang
- Department of Nursing, Tianjin University of Traditional Chinese Medicine, Tianjin, 300193, China
| | - Ming-Li Heng
- Department of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, 300193, China
| | - Xuan Zhou
- Department of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, 300193, China
| | - Huai-En Bu
- Department of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, 300193, China
| | - Fang Xu
- Department of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, 300193, China
| | - Tie-Niu Zhao
- Department of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, 300193, China
| | - He Huang
- Department of Biochemical Engineering, School of Chemical Engineering and Technology, Key Laboratory of Systems Bioengineering, Ministry of Education, Tianjin University, Tianjin, 300072, China
| | - Hong-Wu Wang
- Department of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, 300193, China.
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Xiao S, Zeng X, Fan Y, Su Y, Ma Q, Zhu J, Yao H. Gene Polymorphism Association with Type 2 Diabetes and Related Gene-Gene and Gene-Environment Interactions in a Uyghur Population. Med Sci Monit 2016; 22:474-87. [PMID: 26873362 PMCID: PMC4755665 DOI: 10.12659/msm.895347] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2015] [Accepted: 10/12/2015] [Indexed: 12/26/2022] Open
Abstract
BACKGROUND We investigated the association between 8 single-nucleotide polymorphisms (SNPs) at 3 genetic loci (CDKAL1, CDKN2A/2B and FTO) with type 2 diabetes (T2D) in a Uyghur population. MATERIAL AND METHODS A case-control study of 879 Uyghur patients with T2D and 895 non-diabetic Uyghur controls was conducted at the Hospital of Xinjiang Medical University between 2010 and 2013. Eight SNPs in CDKAL1, CDKN2A/2B and FTO were analyzed using Sequenom MassARRAY®SNP genotyping. Factors associated with T2D were assessed by logistic regression analyses. Gene-gene and gene-environment interactions were analyzed by generalized multifactor dimensionality reduction. RESULTS Genotype distributions of rs10811661 (CDKN2A/2B), rs7195539, rs8050136, and rs9939609 (FTO) and allele frequencies of rs8050136 and rs9939609 differed significantly between diabetes and control groups (all P<0.05). While rs10811661, rs8050136, and rs9939609 were eliminated after adjusting for covariates (P>0.05), rs7195539 distribution differed significantly in co-dominant and dominant models (P<0.05). In gene-gene interaction analysis, after adjusting for covariates the two-locus rs10811661-rs7195539 interaction model had a cross-validation consistency of 10/10 and the highest balanced accuracy of 0.5483 (P=0.014). In gene-environment interaction analysis, the 3-locus interaction model TG-HDL-family history of diabetes had a cross-validation consistency of 10/10 and the highest balanced accuracy of 0.7072 (P<0.001). The 4-locus interaction model, rs7195539-TG-HDL-family history of diabetes had a cross-validation consistency of 8/10 (P<0.001). CONCLUSIONS Polymorphisms in CDKN2A/2B and FTO, but not CDKAL1, may be associated with T2D, and alleles rs8050136 and rs9939609 are likely risk alleles for T2D in this population. There were potential interactions among CDKN2A/2B (rs10811661) - FTO (rs7195539) or FTO (rs7195539)-TG-HDL-family history of diabetes in the pathogenesis of T2D in a Uyghur population.
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Affiliation(s)
- Shan Xiao
- Center of Prevention, Diagnosis, and Treatment of Diabetes, Department of Endocrinology, The First Affiliated Hospital of Xinjiang Medical University, Urumchi, Xinjiang, P.R. China
| | - Xiaoyun Zeng
- Center of Prevention, Diagnosis, and Treatment of Diabetes, Department of Endocrinology, The First Affiliated Hospital of Xinjiang Medical University, Urumchi, Xinjiang, P.R. China
| | - Yong Fan
- Center of Prevention, Diagnosis, and Treatment of Diabetes, Department of Endocrinology, The First Affiliated Hospital of Xinjiang Medical University, Urumchi, Xinjiang, P.R. China
| | - Yinxia Su
- Center of Prevention, Diagnosis, and Treatment of Diabetes, Key Laboratory of Metabolic Disease in Xinjiang, The First Affiliated Hospital of Xinjiang Medical University, Urumchi, Xinjiang, P.R. China
| | - Qi Ma
- Center of Prevention, Diagnosis, and Treatment of Diabetes, Key Laboratory of Metabolic Disease in Xinjiang, The First Affiliated Hospital of Xinjiang Medical University, Urumchi, Xinjiang, P.R. China
| | - Jun Zhu
- Center of Prevention, Diagnosis, and Treatment of Diabetes, Department of Endocrinology, The First Affiliated Hospital of Xinjiang Medical University, Urumchi, Xinjiang, P.R. China
| | - Hua Yao
- Center of Prevention, Diagnosis, and Treatment of Diabetes, Key Laboratory of Metabolic Disease in Xinjiang, The First Affiliated Hospital of Xinjiang Medical University, Urumchi, Xinjiang, P.R. China
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Kodama S, Fujihara K, Ishiguro H, Horikawa C, Ohara N, Yachi Y, Tanaka S, Shimano H, Kato K, Hanyu O, Sone H. Meta-analytic research on the relationship between cumulative risk alleles and risk of type 2 diabetes mellitus. Diabetes Metab Res Rev 2016; 32:178-86. [PMID: 26265102 DOI: 10.1002/dmrr.2680] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/08/2015] [Accepted: 07/01/2015] [Indexed: 12/20/2022]
Abstract
BACKGROUND Our aim is to examine the dose-response association between cumulative genetic risk and actual risk of type 2 diabetes mellitus (T2DM) and the influence of adjustment for covariates on T2DM risk through a comprehensive meta-analysis of observational studies. METHODS Electronic literature search using EMBASE and MEDLINE (from 2003 to 2014) was conducted for cross-sectional or longitudinal studies that presented the odds ratio (OR) for T2DM in each group with categories based on the total number of risk alleles (RAs) carried (RAtotal ) using at least two single-nucleotide polymorphisms. Spline regression model was used to determine the shape of the relationship between the difference from the referent group of each study in RAtotal (ΔRAtotal ) and the natural logarithms of ORs (log OR) for T2DM. RESULTS Sixty-five eligible studies that included 68 267 cases among 182 603 participants were analysed. In both crude and adjusted ORs, defined by adjusting the risk for at least two confounders among age, gender and body mass index, the slope of the log OR for T2DM became less steep as the ΔRAtotal increased. In the analysis limited to 14 cross-sectional and four longitudinal studies presenting both crude and adjusted ORs, regression curves of both ORs in relation to ΔRAtotal were almost identical. CONCLUSION Using only single-nucleotide polymorphisms for T2DM screening was of limited value. However, when genotypic T2DM risk was considered independently from risk in relation to covariates, it was suggested that genetic profiles might have a supplementary role related to conventional T2DM risk factors in identifying individuals at high risk of T2DM. Copyright © 2015 John Wiley & Sons, Ltd.
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Affiliation(s)
- Satoru Kodama
- Department of Laboratory Medicine and Clinical Epidemiology for Prevention of Noncommunicable Diseases, Niigata University Faculty of Medicine, Niigata, Japan
| | - Kazuya Fujihara
- Department of Internal Medicine, University of Tsukuba Institute of Clinical Medicine, Ibaraki, Japan
| | - Hajime Ishiguro
- Department of Hematology, Endocrinology and Metabolism, Niigata University Faculty of Medicine, Niigata, Japan
| | - Chika Horikawa
- Department of Health and Nutrition, Faculty of Human Life Studies, University of Niigata Prefecture, Niigata, Japan
| | - Nobumasa Ohara
- Department of Laboratory Medicine and Clinical Epidemiology for Prevention of Noncommunicable Diseases, Niigata University Faculty of Medicine, Niigata, Japan
- Department of Hematology, Endocrinology and Metabolism, Niigata University Faculty of Medicine, Niigata, Japan
| | - Yoko Yachi
- Department of Administrative Dietetics, Faculty of Health and Nutrition, Yamanashi Gakuin University, Yamanashi, Japan
| | - Shiro Tanaka
- Department of Clinical Trial, Design and Management, Translational Research Center, Kyoto University Hospital, Kyoto, Japan
| | - Hitoshi Shimano
- Department of Internal Medicine, University of Tsukuba Institute of Clinical Medicine, Ibaraki, Japan
| | - Kiminori Kato
- Department of Laboratory Medicine and Clinical Epidemiology for Prevention of Noncommunicable Diseases, Niigata University Faculty of Medicine, Niigata, Japan
| | - Osamu Hanyu
- Department of Hematology, Endocrinology and Metabolism, Niigata University Faculty of Medicine, Niigata, Japan
| | - Hirohito Sone
- Department of Hematology, Endocrinology and Metabolism, Niigata University Faculty of Medicine, Niigata, Japan
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Wang X, Strizich G, Hu Y, Wang T, Kaplan RC, Qi Q. Genetic markers of type 2 diabetes: Progress in genome-wide association studies and clinical application for risk prediction. J Diabetes 2016; 8:24-35. [PMID: 26119161 DOI: 10.1111/1753-0407.12323] [Citation(s) in RCA: 49] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/01/2015] [Revised: 05/22/2015] [Accepted: 06/16/2015] [Indexed: 12/18/2022] Open
Abstract
Type 2 diabetes (T2D) has become a leading public health challenge worldwide. To date, a total of 83 susceptibility loci for T2D have been identified by genome-wide association studies (GWAS). Application of meta-analysis and modern genotype imputation approaches to GWAS data from diverse ethnic populations has been key in the effort to discover T2D loci. Genetic information is expected to play a vital role in the prediction of T2D, and many efforts have been made to develop T2D risk models that include both conventional and genetic risk factors. Yet, because most T2D genetic variants identified have small effect size individually (10%-20% increased risk of T2D per risk allele), their clinical utility remains unclear. Most studies report that a genetic risk score combining multiple T2D genetic variants does not substantially improve T2D risk prediction beyond conventional risk factors. In this article, we summarize the recent progress of T2D GWAS and further review the incremental predictive performance of genetic markers for T2D.
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Affiliation(s)
- Xueyin Wang
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, New York, USA
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China
| | - Garrett Strizich
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, New York, USA
| | - Yonghua Hu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China
| | - Tao Wang
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, New York, USA
| | - Robert C Kaplan
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, New York, USA
| | - Qibin Qi
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, New York, USA
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Sakai K, Imamura M, Tanaka Y, Iwata M, Hirose H, Kaku K, Maegawa H, Watada H, Tobe K, Kashiwagi A, Kawamori R, Maeda S. Replication study of the association of rs7578597 in THADA, rs10886471 in GRK5, and rs7403531 in RASGRP1 with susceptibility to type 2 diabetes among a Japanese population. Diabetol Int 2015. [DOI: 10.1007/s13340-015-0202-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Tuerxunyiming M, Mohemaiti P, Wufuer H, Tuheti A. Association of rs7754840 G/C polymorphisms in CDKAL1 with type 2 diabetes: a meta-analysis of 70141 subjects. Int J Clin Exp Med 2015; 8:17392-17405. [PMID: 26770330 PMCID: PMC4694230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2015] [Accepted: 09/17/2015] [Indexed: 06/05/2023]
Abstract
The reported association of the CDKAL1 rs7754840 G/C gene polymorphism with T2DM susceptibility remains controversial. In this study, this association was further investigated using a meta-analysis of 33,149 patients and 36,992 controls from 32 independent studies. The random-effect models were used in order to evaluate the pooled odds ratios (ORs) and their 95% confidence intervals (CIs). A significant relationship between the CDKAL1 rs7754840 G/C gene polymorphism and T2DM was observed under allelic (OR: 1.37, 95% CI: 1.22, 1.55, P < 0.001), recessive (OR: 1.58, 95% CI: 1.20-2.08, P < 0.001), dominant (OR: 1.13, 95% CI: 1.21-1.33, P = 0.01), and homozygous (OR: 1.27, 95% CI: 1.21-1.33, P < 0.001), and heterozygous (OR: 0.83, 95% CI: 0.75-0.93, P < 0.001). Overall, the CDKAL1 rs7754840 G/C gene polymorphism was found to be significantly associated with an increased T2DM risk; the C allele of the CDKAL1 rs7754840 G/C gene polymorphism may confer susceptibility to T2DM.
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Affiliation(s)
| | - Patamu Mohemaiti
- School of Public Health, Xinjiang Medical UniversityUrumqi 830011, China
| | | | - Awaguli Tuheti
- School of Public Health, Xinjiang Medical UniversityUrumqi 830011, China
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65
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Uma Jyothi K, Reddy BM. Gene-gene and gene-environment interactions in the etiology of type 2 diabetes mellitus in the population of Hyderabad, India. Meta Gene 2015; 5:9-20. [PMID: 26042206 PMCID: PMC4443428 DOI: 10.1016/j.mgene.2015.05.001] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2015] [Revised: 04/17/2015] [Accepted: 05/04/2015] [Indexed: 12/13/2022] Open
Abstract
Fifteen SNPs from nine different genes were genotyped on 1379 individuals, 758 T2DM patients and 621 controls, from the city of Hyderabad, India, using Sequenom Massarray platform. These data were analyzed to examine the role of gene-gene and gene-environment interactions in the manifestation of T2DM. The multivariate analysis suggests that TCF7L2, CDKAL1, IGF2BP2, HHEX and PPARG genes are significantly associated with T2DM, albeit only the first two of the above 5 were associated in the univariate analysis. Significant gene-gene and gene-environment interactions were also observed with reference to TCF7L2, CAPN10 and CDKAL1 genes, highlighting their importance in the pathophysiology of T2DM. In the analysis for cumulative effect of risk alleles, SLC30A8 steps in as significant contributor to the disease by its presence in all combinations of risk alleles. A striking difference between risk allele categories, 1-4 and 5-6, was evident in showing protective and susceptible roles, respectively, while the latter was characterized by the presence of TCF7L2 and CDKAL1 variants. Overall, these two genes TCF7L2 and CDKAL1 showed strong association with T2DM, either individually or in interaction with the other genes. However, we need further studies on gene-gene and gene-environment interactions among heterogeneous Indian populations to obtain unequivocal conclusions that are applicable for the Indian population as a whole.
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Affiliation(s)
- Kommoju Uma Jyothi
- Biological Anthropology Unit (Molecular Anthropology Group), Indian Statistical Institute, Hyderabad, India
| | - Battini Mohan Reddy
- Biological Anthropology Unit (Molecular Anthropology Group), Indian Statistical Institute, Hyderabad, India
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Mansoori Y, Daraei A, Naghizadeh MM, Salehi R. The HHEX rs1111875A/G gene polymorphism is associated with susceptibility to type 2 diabetes in the iranian population. Mol Biol 2015. [DOI: 10.1134/s0026893315040123] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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67
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Tallapragada DSP, Bhaskar S, Chandak GR. New insights from monogenic diabetes for "common" type 2 diabetes. Front Genet 2015; 6:251. [PMID: 26300908 PMCID: PMC4528293 DOI: 10.3389/fgene.2015.00251] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2015] [Accepted: 07/13/2015] [Indexed: 01/17/2023] Open
Abstract
Boundaries between monogenic and complex genetic diseases are becoming increasingly blurred, as a result of better understanding of phenotypes and their genetic determinants. This had a large impact on the way complex disease genetics is now being investigated. Starting with conventional approaches like familial linkage, positional cloning and candidate genes strategies, the scope of complex disease genetics has grown exponentially with scientific and technological advances in recent times. Despite identification of multiple loci harboring common and rare variants associated with complex diseases, interpreting and evaluating their functional role has proven to be difficult. Information from monogenic diseases, especially related to the intermediate traits associated with complex diseases comes handy. The significant overlap between traits and phenotypes of monogenic diseases with related complex diseases provides a platform to understand the disease biology better. In this review, we would discuss about one such complex disease, type 2 diabetes, which shares marked similarity of intermediate traits with different forms of monogenic diabetes.
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Affiliation(s)
| | | | - Giriraj R. Chandak
- Genomic Research on Complex Diseases Laboratory, Council of Scientific and Industrial Research-Centre for Cellular and Molecular BiologyHyderabad, India
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68
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Cheng L, Zhang D, Zhou L, Zhao J, Chen B. Association between SLC30A8 rs13266634 Polymorphism and Type 2 Diabetes Risk: A Meta-Analysis. Med Sci Monit 2015. [PMID: 26214053 PMCID: PMC4527121 DOI: 10.12659/msm.894052] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023] Open
Abstract
BACKGROUND Accumulating but inconsistent data about the role of rs13266634 variant of SLC30A8 in type 2 diabetes have been reported, partly due to small sample sizes and non-identical ethnicity. MATERIAL AND METHODS We searched PubMed and Cochrane Library to identify eligible studies and extract data of baseline characteristics, genotype count, odds ratio (OR), and 95% confidence interval (CI). Both adjusted OR with 95% CI and genotype counts were employed to assess the association. Genotype data were further pooled to provide estimates under different genetic models and the most appropriate model was determined. Sensitivity and cumulative analysis were conducted to assure the strength of results. RESULTS Fifty-five datasets of 39 studies (including 38 of 24 with genotype count) were included. Significant associations were found in allelic contrasts using adjusted ORs and raw genotype count, respectively, overall in Asian and European populations (overall: OR=1.147/1.157, 95% CI 1.114-1.181/1.135-1.180; Asian: OR=1.186/1.165, 95% CI 1.150-1.222/1.132-1.198; European: OR=1.100/1.151, 95% CI 1.049-1.153/1.120-1.183; All p=0.00), but not in African populations (African: OR=1.255/1.111, 95% CI 0.964-1.634/0.908-1.360, p=0.091/0.305). Further analysis with genotype count under different genetic models all showed that individuals with CC genotype had 33.0% and 16.5% higher risk of type 2 diabetes than those carrying TT and CT genotypes, respectively, under the most likely codominant model. Cumulative analysis indicated gradually improved precision of estimation after studies accumulated. CONCLUSIONS Our results suggest that rs13266634 may be an important genetic factor of type 2 diabetes risk among Asian and European but not African populations.
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Affiliation(s)
- Liqing Cheng
- Department of Endocrinology and Metabolism, Southwest Hospital, Third Military Medical University, Chongqing, China (mainland)
| | - Dongmei Zhang
- Department of Dermatology, Southwest Hospital, Third Military Medical University, Chongqing, China (mainland)
| | - Lina Zhou
- Department of Endocrinology and Metabolism, Southwest Hospital, Third Military Medical University, Chongqing, China (mainland)
| | - Jie Zhao
- Department of Endocrinology and Metabolism, Southwest Hospital, Third Military Medical University, Chongqing, China (mainland)
| | - Bing Chen
- Department of Endocrinology and Metabolism, Southwest Hospital, Third Military Medical University, Chongqing, China (mainland)
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Wei F, Cai C, Feng S, Lv J, Li S, Chang B, Zhang H, Shi W, Han H, Ling C, Yu P, Chen Y, Sun N, Tian J, Jiao H, Yang F, Li M, Wang Y, Zou L, Su L, Li J, Li R, Qiu H, Shi J, Liu S, Chang M, Lin J, Chen L, Li WD. TOX and CDKN2A/B Gene Polymorphisms Are Associated with Type 2 Diabetes in Han Chinese. Sci Rep 2015; 5:11900. [PMID: 26139146 PMCID: PMC4650661 DOI: 10.1038/srep11900] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2014] [Accepted: 06/08/2015] [Indexed: 12/27/2022] Open
Abstract
To study associations between type 2 diabetes (T2DM) candidate genes and microvascular complications of diabetes (MVCDs), we performed case-control association studies for both T2DM and MVCDs in Han Chinese subjects. We recruited 1,939 unrelated Han Chinese T2DM patients and 918 individuals with normal blood glucose levels as nondiabetic controls. Among T2DM patients, 1116 have MVCDs, 266 have a history of T2DM of >10 years but never developed MVCDs. Eighty-two single-nucleotide polymorphisms (SNPs) in 54 candidate genes were genotyped. Discrete association studies were performed by the PLINK program for T2DM and MVCDs. Significant associations were found among candidate gene SNPs and T2DM, including rs1526167 of the TOX gene (allele A, P = 2.85 × 10−9, OR = 1.44). The SNP rs10811661 of the CDKN2A/B gene was also associated with T2DM (allele T, P = 4.09 × 10−7, OR = 1.36). When we used control patients with >10 years of T2DM history without MVCD, we found that the G allele of SNP rs1526167 of the TOX gene was associated with MVCD (nominal P = 4.33 × 10−4). In our study, significant associations were found between TOX and CDKN2A/B gene SNPs and T2DM. The TOX polymorphism might account for the higher risk of T2DM and the lower risk of MVCDs in the Han Chinese population.
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Affiliation(s)
- Fengjiang Wei
- Research Center of Basic Medical Sciences, Tianjin Medical University, Tianjin, 300070, China
| | - Chunyou Cai
- Research Center of Basic Medical Sciences, Tianjin Medical University, Tianjin, 300070, China
| | - Shuzhi Feng
- Tianjin General Hospital, Tianjin Medical University, Tianjin, 300052, China
| | - Jia Lv
- Research Center of Basic Medical Sciences, Tianjin Medical University, Tianjin, 300070, China
| | - Shen Li
- Research Center of Basic Medical Sciences, Tianjin Medical University, Tianjin, 300070, China
| | - Baocheng Chang
- Metabolic Diseases Hospital, Tianjin Medical University, Tianjin, 300070, China
| | - Hong Zhang
- Eye Hospital, Tianjin Medical University, Tianjin, 300384, China
| | - Wentao Shi
- Research Center of Basic Medical Sciences, Tianjin Medical University, Tianjin, 300070, China
| | - Hongling Han
- Tianjin General Hospital, Tianjin Medical University, Tianjin, 300052, China
| | - Chao Ling
- Research Center of Basic Medical Sciences, Tianjin Medical University, Tianjin, 300070, China
| | - Ping Yu
- Research Center of Basic Medical Sciences, Tianjin Medical University, Tianjin, 300070, China
| | - Yongjun Chen
- Eye Hospital, Tianjin Medical University, Tianjin, 300384, China
| | - Ning Sun
- Tianjin General Hospital, Tianjin Medical University, Tianjin, 300052, China
| | - Jianli Tian
- Tianjin General Hospital, Tianjin Medical University, Tianjin, 300052, China
| | - Hongxiao Jiao
- Research Center of Basic Medical Sciences, Tianjin Medical University, Tianjin, 300070, China
| | - Fuhua Yang
- Research Center of Basic Medical Sciences, Tianjin Medical University, Tianjin, 300070, China
| | - Mingshan Li
- Research Center of Basic Medical Sciences, Tianjin Medical University, Tianjin, 300070, China
| | - Yuhua Wang
- Research Center of Basic Medical Sciences, Tianjin Medical University, Tianjin, 300070, China
| | - Lei Zou
- Research Center of Basic Medical Sciences, Tianjin Medical University, Tianjin, 300070, China
| | - Long Su
- Eye Hospital, Tianjin Medical University, Tianjin, 300384, China
| | - Jingbo Li
- Tianjin People's Hospital, Department of Endocrinology, Tianjin, 300191, China
| | - Ran Li
- Tianjin General Hospital, Tianjin Medical University, Tianjin, 300052, China
| | - Huina Qiu
- Tianjin People's Hospital, Department of Endocrinology, Tianjin, 300191, China
| | - Jingmin Shi
- Research Center of Basic Medical Sciences, Tianjin Medical University, Tianjin, 300070, China
| | - Shiying Liu
- Tianjin General Hospital, Tianjin Medical University, Tianjin, 300052, China
| | - Mingqin Chang
- Research Center of Basic Medical Sciences, Tianjin Medical University, Tianjin, 300070, China
| | - Jingna Lin
- Tianjin People's Hospital, Department of Endocrinology, Tianjin, 300191, China
| | - Liming Chen
- Metabolic Diseases Hospital, Tianjin Medical University, Tianjin, 300070, China
| | - Wei-Dong Li
- Research Center of Basic Medical Sciences, Tianjin Medical University, Tianjin, 300070, China
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Sokolova EA, Bondar IA, Shabelnikova OY, Pyankova OV, Filipenko ML. Replication of KCNJ11 (p.E23K) and ABCC8 (p.S1369A) Association in Russian Diabetes Mellitus 2 Type Cohort and Meta-Analysis. PLoS One 2015; 10:e0124662. [PMID: 25955821 PMCID: PMC4425644 DOI: 10.1371/journal.pone.0124662] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2014] [Accepted: 03/17/2015] [Indexed: 12/26/2022] Open
Abstract
The genes ABCC8 and KCNJ11 have received intense focus in type 2 diabetes mellitus (T2DM) research over the past two decades. It has been hypothesized that the p.E23K (KCNJ11) mutation in the 11p15.1 region may play an important role in the development of T2DM. In 2009, Hamming et al. found that the p.1369A (ABCC8) variant may be a causal factor in the disease; therefore, in this study we performed a meta-analysis to evaluate the association between these single nucleotide polymorphisms (SNPs), including our original data on the Siberian population (1384 T2DM and 414 controls). We found rs5219 and rs757110 were not associated with T2DM in this population, and that there was linkage disequilibrium in Siberians (D’=0.766, r2= 0.5633). In addition, the haplotype rs757110[T]-rs5219[C] (p.23K/p.S1369) was associated with T2DM (OR = 1.52, 95% CI: 1.04-2.24). We included 44 original studies published by June 2014 in a meta-analysis of the p.E23K association with T2DM. The total OR was 1.14 (95% CI: 1.11-1.17) for p.E23K for a total sample size of 137,298. For p.S1369A, a meta-analysis was conducted on a total of 10 studies with a total sample size of 14,136 and pooled OR of 1.14 [95% CI (1.08-1.19); p = 2 x 10-6]. Our calculations identified causal genetic variation within the ABCC8/KCNJ11 region for T2DM with an OR of approximately 1.15 in Caucasians and Asians. Moreover, the OR value was not dependent on the frequency of p.E23K or p.S1369A in the populations.
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Affiliation(s)
- Ekaterina Alekseevna Sokolova
- Laboratory of Pharmacogenomics, Institute of Chemical Biology and Fundamental Medicine, Siberian Division, Russian Academy of Sciences, Novosibirsk, Russia
- Novosibirsk State University, Novosibirsk, Russia
| | - Irina Arkadievna Bondar
- Novosibirsk State Regional Hospital, Regional Diabetes center, Novosibirsk, Russia
- Novosibirsk State Medical University, Novosibirsk, Russia
| | - Olesya Yurievna Shabelnikova
- Novosibirsk State Regional Hospital, Regional Diabetes center, Novosibirsk, Russia
- Novosibirsk State Medical University, Novosibirsk, Russia
| | - Olga Vladimirovna Pyankova
- Laboratory of Pharmacogenomics, Institute of Chemical Biology and Fundamental Medicine, Siberian Division, Russian Academy of Sciences, Novosibirsk, Russia
- Novosibirsk State University, Novosibirsk, Russia
| | - Maxim Leonidovich Filipenko
- Laboratory of Pharmacogenomics, Institute of Chemical Biology and Fundamental Medicine, Siberian Division, Russian Academy of Sciences, Novosibirsk, Russia
- Kazan Federal University, Kazan, Russia
- * E-mail:
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Davis JA, Burgoon LD. Can data science inform environmental justice and community risk screening for type 2 diabetes? PLoS One 2015; 10:e0121855. [PMID: 25875676 PMCID: PMC4396977 DOI: 10.1371/journal.pone.0121855] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2014] [Accepted: 02/16/2015] [Indexed: 11/24/2022] Open
Abstract
Background Having the ability to scan the entire country for potential “hotspots” with increased risk of developing chronic diseases due to various environmental, demographic, and genetic susceptibility factors may inform risk management decisions and enable better environmental public health policies. Objectives Develop an approach for community-level risk screening focused on identifying potential genetic susceptibility hotpots. Methods Our approach combines analyses of phenotype-genotype data, genetic prevalence of single nucleotide polymorphisms, and census/geographic information to estimate census tract-level population attributable risks among various ethnicities and total population for the state of California. Results We estimate that the rs13266634 single nucleotide polymorphism, a type 2 diabetes susceptibility genotype, has a genetic prevalence of 56.3%, 47.4% and 37.0% in Mexican Mestizo, Caucasian, and Asian populations. Looking at the top quintile for total population attributable risk, 16 California counties have greater than 25% of their population living in hotspots of genetic susceptibility for developing type 2 diabetes due to this single genotypic susceptibility factor. Conclusions This study identified counties in California where large portions of the population may bear additional type 2 diabetes risk due to increased genetic prevalence of a susceptibility genotype. This type of screening can easily be extended to include information on environmental contaminants of interest and other related diseases, and potentially enables the rapid identification of potential environmental justice communities. Other potential uses of this approach include problem formulation in support of risk assessments, land use planning, and prioritization of site cleanup and remediation actions.
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Affiliation(s)
- J. Allen Davis
- National Center for Environmental Assessment, Office of Research and Development, United States Environmental Protection Agency, Research Triangle Park, North Carolina, United States of America
- * E-mail:
| | - Lyle D. Burgoon
- National Center for Environmental Assessment, Office of Research and Development, United States Environmental Protection Agency, Research Triangle Park, North Carolina, United States of America
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Song M, Zhao F, Ran L, Dolikun M, Wu L, Ge S, Dong H, Gao Q, Zhai Y, Zhang L, Yan Y, Liu F, Yang X, Guo X, Wang Y, Wang W. The Uyghur population and genetic susceptibility to type 2 diabetes: potential role for variants in CDKAL1, JAZF1, and IGF1 genes. OMICS-A JOURNAL OF INTEGRATIVE BIOLOGY 2015; 19:230-7. [PMID: 25785549 DOI: 10.1089/omi.2014.0162] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Substantial evidence suggests that type 2 diabetes mellitus (T2DM) is a multi-factorial disease with a strong genetic component. A list of genetic susceptibility loci in populations of European and Asian ancestry has been established in the literature. Little is known on the inter-ethnic contribution of such established functional polymorphic variants. We performed a case-control study to explore the genetic susceptibility of 16 selected T2DM-related SNPs in a cohort of 102 Uyghur objects (51 cases and 51 controls). Three of the 16 SNPs showed significant association with T2DM in the Uyghur population. There were significant differences between the T2DM and control groups in frequencies of the risk allelic distributions of rs7754840 (CDKAL1) (p=0.014), rs864745 (JAZF1) (p=0.032), and rs35767 (IGF1) (p=0.044). Carriers of rs7754840-C, rs35767-A, and rs864745-C risk alleles had a 2.32-fold [OR (95% CI): 1.19-4.54], 2.06-fold [OR (95% CI): 1.02-4.17], 0.48-fold [OR (95% CI): 0.24-0.94] increased risk for T2DM, respectively. The cumulative risk allelic scores of these 16 SNPs differed significantly between the T2DM patients and the controls [17.1±8.1 vs. 15.4±7.3; OR (95%CI): 1.27(1.07-1.50), p=0.007]. This is the first study to evaluate genomic variation at 16 SNPs in respective T2DM candidate genes for the Uyghur population compared with other ethnic groups. The SNP rs7754840 in CDKAL1, rs864745 in JAZF1, and rs35767 in IGF1 might serve as potential susceptibility loci for T2DM in Uyghurs. We suggest a broader capture and study of the world populations, including who that are hitherto understudied, are essential for a comprehensive understanding of the genetic/genomic basis of T2DM.
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Affiliation(s)
- Manshu Song
- 1 School of Public Health, Capital Medical University , Beijing, China
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Nikitin AG, Potapov VA, Brovkin AN, Lavrikova EY, Khodyrev DS, Shamhalova MS, Smetanina SA, Suplotova LN, Shestakova MV, Nosikov VV, Averyanov AV. Association of FTO, KCNJ11, SLC30A8, and CDKN2B polymorphisms with type 2 diabetes mellitus. Mol Biol 2015. [DOI: 10.1134/s0026893315010112] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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74
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Cumulative effect and predictive value of genetic variants associated with type 2 diabetes in Han Chinese: a case-control study. PLoS One 2015; 10:e0116537. [PMID: 25587982 PMCID: PMC4294637 DOI: 10.1371/journal.pone.0116537] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2014] [Accepted: 12/09/2014] [Indexed: 11/19/2022] Open
Abstract
Background Genome-wide association studies (GWAS) have identified dozens of single nucleotide polymorphisms (SNPs) associated with type 2 diabetes risk. We have previously confirmed the associations of genetic variants in HHEX, CDKAL1, VEGFA and FTO with type 2 diabetes in Han Chinese. However, the cumulative effect and predictive value of these GWAS identified SNPs on the risk of type 2 diabetes in Han Chinese are largely unknown. Methodology/Principal Findings We conducted a two-stage case-control study consisting of 2,925 cases and 3,281controls to examine the association of 30 SNPs identified by GWAS with type 2 diabetes in Han Chinese. Significant associations were found for proxy SNPs at KCNQ1 [odds ratio (OR) = 1.41, P = 9.91 × 10–16 for rs2237897], CDKN2A/CDKN2B (OR = 1.30, P = 1.34 × 10–10 for rs10811661), CENTD2 (OR = 1.28, P = 9.88 × 10-4 for rs1552224) and SLC30A8 (OR = 1.19, P = 1.43 × 10-5 for rs13266634). We further evaluated the cumulative effect on type 2 diabetes of these 4 SNPs, in combination with 5 SNPs at HHEX, CDKAL1, VEGFA and FTO reported previously. Individuals carrying 12 or more risk alleles had a nearly 4-fold increased risk for developing type 2 diabetes compared with those carrying less than 6 risk alleles [adjusted OR = 3.68, 95% confidence interval (CI): 2.76–4.91]. Adding the genetic factors to clinical factors slightly improved the prediction of type 2 diabetes, with the area under the receiver operating characteristic curve increasing from 0.76 to 0.78. However, the difference was statistically significant (P < 0.0001). Conclusions/Significance We confirmed associations of SNPs in KCNQ1, CDKN2A/CDKN2B, CENTD2 and SLC30A8 with type 2 diabetes in Han Chinese. The utilization of genetic information may improve the accuracy of risk prediction in combination with clinical characteristics for type 2 diabetes.
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Haghvirdizadeh P, Mohamed Z, Abdullah NA, Haghvirdizadeh P, Haerian MS, Haerian BS. KCNJ11: Genetic Polymorphisms and Risk of Diabetes Mellitus. J Diabetes Res 2015; 2015:908152. [PMID: 26448950 PMCID: PMC4584059 DOI: 10.1155/2015/908152] [Citation(s) in RCA: 70] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/28/2014] [Revised: 11/18/2014] [Accepted: 11/27/2014] [Indexed: 01/12/2023] Open
Abstract
Diabetes mellitus (DM) is a major worldwide health problem and its prevalence has been rapidly increasing in the last century. It is caused by defects in insulin secretion or insulin action or both, leading to hyperglycemia. Of the various types of DM, type 2 occurs most frequently. Multiple genes and their interactions are involved in the insulin secretion pathway. Insulin secretion is mediated through the ATP-sensitive potassium (KATP) channel in pancreatic beta cells. This channel is a heteromeric protein, composed of four inward-rectifier potassium ion channel (Kir6.2) tetramers, which form the pore of the KATP channel, as well as sulfonylurea receptor 1 subunits surrounding the pore. Kir6.2 is encoded by the potassium inwardly rectifying channel, subfamily J, member 11 (KCNJ11) gene, a member of the potassium channel genes. Numerous studies have reported the involvement of single nucleotide polymorphisms of the KCNJ11 gene and their interactions in the susceptibility to DM. This review discusses the current evidence for the contribution of common KCNJ11 genetic variants to the development of DM. Future studies should concentrate on understanding the exact role played by these risk variants in the development of DM.
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Affiliation(s)
- Polin Haghvirdizadeh
- Pharmacogenomics Lab, Department of Pharmacology, Faculty of Medicine, University of Malaya, 50603 Kuala Lumpur, Malaysia
| | - Zahurin Mohamed
- Pharmacogenomics Lab, Department of Pharmacology, Faculty of Medicine, University of Malaya, 50603 Kuala Lumpur, Malaysia
| | - Nor Azizan Abdullah
- Department of Pharmacology, Faculty of Medicine, University of Malaya, 50603 Kuala Lumpur, Malaysia
| | | | - Monir Sadat Haerian
- Shahid Beheshti University of Medical Sciences, P.O. Box 19395-4763, Tehran, Iran
- Food and Drug Control Reference Labs Center (FDCRLC), Ministry of Health and Medical Education, Tehran 131456-8784, Iran
| | - Batoul Sadat Haerian
- Pharmacogenomics Lab, Department of Pharmacology, Faculty of Medicine, University of Malaya, 50603 Kuala Lumpur, Malaysia
- *Batoul Sadat Haerian:
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76
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Xi B, Takeuchi F, Meirhaeghe A, Kato N, Chambers JC, Morris AP, Cho YS, Zhang W, Mohlke KL, Kooner JS, Shu XO, Pan H, Tai ES, Pan H, Wu JY, Zhou D, Chandak GR, DIAGRAM Consortium, AGEN-T2D consortium, SAT2D Consortium. Associations of genetic variants in/near body mass index-associated genes with type 2 diabetes: a systematic meta-analysis. Clin Endocrinol (Oxf) 2014; 81:702-10. [PMID: 24528214 PMCID: PMC5568704 DOI: 10.1111/cen.12428] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/18/2013] [Revised: 12/07/2013] [Accepted: 01/25/2014] [Indexed: 01/09/2023]
Abstract
OBJECTIVE Genome-wide association studies have identified many obesity/body mass index (BMI)-associated loci in Europeans and East Asians. Since then, a large number of studies have investigated the role of BMI-associated loci in the development of type 2 diabetes (T2D). However, the results have been inconsistent. The objective of this study was to investigate the associations of eleven obesity/BMI loci with T2D risk and explore how BMI influences this risk. METHODS We retrieved published literature from PubMed and Embase. The pooled odds ratios (OR) with 95% confidence intervals (CI) were calculated using fixed- or random-effect models. RESULTS In the meta-analysis of 42 studies for 11 obesity/BMI-associated loci, we observed a statistically significant association of the FTO rs9939609 polymorphism (66 425 T2D cases/239 689 normoglycaemic subjects; P = 1·00 × 10(-41) ) and six other variants with T2D risk (17 915 T2D cases/27 531 normoglycaemic individuals: n = 40 629-130 001; all P < 0·001 for SH2B1 rs7498665, FAIM2 rs7138803, TMEM18 rs7561317, GNPDA2 rs10938397, BDNF rs925946 and NEGR1 rs2568958). After adjustment for BMI, the association remained statistically significant for four of the seven variants (all P < 0·05 for FTO rs9939609, SH2B1 rs7498665, FAIM2 rs7138803, GNPDA2 rs10938397). Subgroup analysis by ethnicity demonstrated similar results. CONCLUSIONS This meta-analysis indicates that several BMI-associated variants are significantly associated with T2D risk. Some variants increase the T2D risk independent of obesity, while others mediate this risk through obesity.
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Affiliation(s)
- Bo Xi
- Department of Maternal and Child Health Care, School of Public Health, Shandong University, Jinan, People’s Republic of China
| | - Fumihiko Takeuchi
- Department of Gene Diagnostics and Therapeutics, Research Institute, National Center for Global Health and Medicine, Tokyo, Japan
| | - Aline Meirhaeghe
- INSERM, U744, Lille; Institut Pasteur de Lille, Lille; Université de Lille 2, UMR-S744, Lille Cedex, France
| | - Norihiro Kato
- Department of Gene Diagnostics and Therapeutics, Research Institute, National Center for Global Health and Medicine, Tokyo, Japan
| | - John C Chambers
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK
- Ealing Hospital National Health Service (NHS) Trust, Middlesex, UK
| | - Andrew P Morris
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Yoon Shin Cho
- Department of Biomedical Science, Hallym University, Chuncheon, Gangwon-do, 200-702, Republic of Korea
| | - Weihua Zhang
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK
- Ealing Hospital National Health Service (NHS) Trust, Middlesex, UK
| | - Karen L Mohlke
- Department of Genetics, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Jaspal S Kooner
- Ealing Hospital National Health Service (NHS) Trust, Middlesex, UK
- National Heart and Lung Institute (NHLI), Imperial College London, Hammersmith Hospital, London, UK
| | - Xiao Ou Shu
- Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, Tennessee, USA
| | - Hongwei Pan
- Key Laboratory for Regenerative Medicine of Ministry of Education, Jinan University, Guangzhou, People’s Republic of China
- Department of Ophthalmology, Medical College, Jinan University, Guangzhou, People’s Republic of China
| | - E Shyong Tai
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Department of Medicine, National University Hospital, National University Health System, Singapore
- Duke-National University of Singapore Graduate Medical School, Singapore
| | - Haiyan Pan
- Department of Epidemiology and Biostatistics, Guangdong Medical College, Dongwan, People’s Republic of China
| | - Jer-Yuarn Wu
- Institute of Biomedical Sciences, Academia Sinica, Nankang, Taipei, Taiwan
- School of Chinese Medicine, China Medical University, Taichung, Taiwan
| | - Donghao Zhou
- Department of Endocrinology, Linyi People's Hospital, Linyi, People’s Republic of China
- Corresponding author: Donghaozhou, Department of Endocrinology, Linyi People's Hospital, 27 East Part of Jiefang Road, Linyi, People’s Republic of China. Tel: 86-539-8226999; Fax: 86-539-8226999; ; Giriraj R Chandak, Centre for Cellular and Molecular Biology, Council of Scientific and Industrial Research (CSIR), Uppal Road, Hyderabad 500 007, INDIA. Tel: 00-91-40-2719 2748; Fax: 00-91-40-2716 0591;
| | - Giriraj R Chandak
- Centre for Cellular and Molecular Biology, Council of Scientific and Industrial Research (CSIR), Hyderabad, India
- Corresponding author: Donghaozhou, Department of Endocrinology, Linyi People's Hospital, 27 East Part of Jiefang Road, Linyi, People’s Republic of China. Tel: 86-539-8226999; Fax: 86-539-8226999; ; Giriraj R Chandak, Centre for Cellular and Molecular Biology, Council of Scientific and Industrial Research (CSIR), Uppal Road, Hyderabad 500 007, INDIA. Tel: 00-91-40-2719 2748; Fax: 00-91-40-2716 0591;
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Kommoju UJ, Maruda J, Kadarkarai Samy S, Irgam K, Kotla JP, Reddy BM. Association of IRS1, CAPN10, and PPARG gene polymorphisms with type 2 diabetes mellitus in the high-risk population of Hyderabad, India. J Diabetes 2014; 6:564-73. [PMID: 24612564 DOI: 10.1111/1753-0407.12142] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/01/2013] [Revised: 02/17/2014] [Accepted: 02/19/2014] [Indexed: 11/30/2022] Open
Abstract
BACKGROUND We attempted to validate earlier findings on the nature of the association of the IRS1, CAPN10, and PPARG genes with type 2 diabetes mellitus (T2DM) in the high-risk population of Hyderabad, India. METHODS A sample of 1379 subjects (758 T2DM patients, 621 controls) was genotyped for single nucleotide polymorphisms (SNPs) of the IRS1 (rs1801278), CAPN10 (rs3792267, rs5030952), and PPARG (rs1801282) genes. RESULTS The allele and genotype frequencies of IRS1 (rs1801278) and CAPN10 (rs3792267) SNPs differed significantly between the patient and control groups. Logistic regression analysis suggested a significant association of these two SNPs (P ≤ 0.007) with T2DM and the strength of association did not alter when adjusted for age, gender, body mass index, and the waist : hip ratio as covariates. The same two SNPs showed significant association in multivariate logistic regression analyses, even after Bonferroni correction for multiple testing, suggesting an independent nature of the role of these genes in the manifestation of T2DM in our population. CONCLUSIONS We replicated the significant association of rs1801278 and rs3792267 SNPs of the IRS1 and CAPN10 genes with T2DM in the population of Hyderabad. Despite the known biological significance of the PPARG gene and a sufficient statistical power of the present study, we could not replicate the association of PPARG with T2DM in our high-risk population. Given the vast ethnic, geographic, and genetic heterogeneity of the Indian population, many more studies are needed covering the ethnic and geographic heterogeneity of India to enable identification of an Indian-specific profile of genes associated with T2DM.
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Affiliation(s)
- Uma Jyothi Kommoju
- Biological Anthropology Unit (Molecular Anthropology Group), Indian Statistical Institute, Hyderabad, India
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Li Q, Chen M, Zhang R, Jiang F, Wang J, Zhou J, Bao Y, Hu C, Jia W. KCNJ11E23K variant is associated with the therapeutic effect of sulphonylureas in Chinese type 2 diabetic patients. Clin Exp Pharmacol Physiol 2014; 41:748-54. [DOI: 10.1111/1440-1681.12280] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2014] [Revised: 06/06/2014] [Accepted: 06/16/2014] [Indexed: 11/28/2022]
Affiliation(s)
- Qing Li
- Department of Endocrinology and Metabolism; Shanghai Diabetes Institute; Shanghai Jiao Tong University Affiliated Sixth People's Hospital; Shanghai Clinical Center of Diabetes; Shanghai Diabetes Institute; Shanghai Key Laboratory of Diabetes Mellitus; Shanghai China
| | - Miao Chen
- Department of Endocrinology and Metabolism; Shanghai Diabetes Institute; Shanghai Jiao Tong University Affiliated Sixth People's Hospital; Shanghai Clinical Center of Diabetes; Shanghai Diabetes Institute; Shanghai Key Laboratory of Diabetes Mellitus; Shanghai China
| | - Rong Zhang
- Department of Endocrinology and Metabolism; Shanghai Diabetes Institute; Shanghai Jiao Tong University Affiliated Sixth People's Hospital; Shanghai Clinical Center of Diabetes; Shanghai Diabetes Institute; Shanghai Key Laboratory of Diabetes Mellitus; Shanghai China
| | - Feng Jiang
- Department of Endocrinology and Metabolism; Shanghai Diabetes Institute; Shanghai Jiao Tong University Affiliated Sixth People's Hospital; Shanghai Clinical Center of Diabetes; Shanghai Diabetes Institute; Shanghai Key Laboratory of Diabetes Mellitus; Shanghai China
| | - Jie Wang
- Department of Endocrinology and Metabolism; Shanghai Diabetes Institute; Shanghai Jiao Tong University Affiliated Sixth People's Hospital; Shanghai Clinical Center of Diabetes; Shanghai Diabetes Institute; Shanghai Key Laboratory of Diabetes Mellitus; Shanghai China
| | - Jian Zhou
- Department of Endocrinology and Metabolism; Shanghai Diabetes Institute; Shanghai Jiao Tong University Affiliated Sixth People's Hospital; Shanghai Clinical Center of Diabetes; Shanghai Diabetes Institute; Shanghai Key Laboratory of Diabetes Mellitus; Shanghai China
| | - Yuqian Bao
- Department of Endocrinology and Metabolism; Shanghai Diabetes Institute; Shanghai Jiao Tong University Affiliated Sixth People's Hospital; Shanghai Clinical Center of Diabetes; Shanghai Diabetes Institute; Shanghai Key Laboratory of Diabetes Mellitus; Shanghai China
| | - Cheng Hu
- Department of Endocrinology and Metabolism; Shanghai Diabetes Institute; Shanghai Jiao Tong University Affiliated Sixth People's Hospital; Shanghai Clinical Center of Diabetes; Shanghai Diabetes Institute; Shanghai Key Laboratory of Diabetes Mellitus; Shanghai China
| | - Weiping Jia
- Department of Endocrinology and Metabolism; Shanghai Diabetes Institute; Shanghai Jiao Tong University Affiliated Sixth People's Hospital; Shanghai Clinical Center of Diabetes; Shanghai Diabetes Institute; Shanghai Key Laboratory of Diabetes Mellitus; Shanghai China
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Bulut F, Erol D, Elyas H, Doğan H, Ozdemir FA, Keskin L. Protein Tyrosine Phosphatase Non-receptor 22 Gene C1858T Polymorphism in Patients with Coexistent Type 2 Diabetes and Hashimoto's Thyroiditis. Balkan Med J 2014; 31:37-42. [PMID: 25207165 DOI: 10.5152/balkanmedj.2014.9418] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2013] [Accepted: 12/04/2013] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND A protein tyrosine phosphatase non-receptor type 22 (PTPN22) C1858T gene polymorphism has been reported to be associated with both Type 2 diabetes mellitus (T2DM) and Hashimoto's thyroiditis (HT) separately. However, no study has been conducted to explore the C1858T polymorphism in T2DM and HT coexistent cases up to now. AIMS The study aimed to determine whether a relationship exists or not between the PTPN22 C1858T polymorphism and this coexistent patient group. STUDY DESIGN Case-control study. METHODS Peripheral blood samples from 135 T2DM patients, 102 patients with coexistent T2DM+HT, 71 HT patients and 135 healthy controls were collected into ethylenediaminetetraacetic acid (EDTA) anticoagulant tubes and genomic DNA was extracted. The PTPN22 C1858T polymorphism was analyzed using polymerase chain reaction (PCR) restriction fragment length polymorphism (RFLP) methods. RESULTS Statistically significant differences were not observed between the patient and control groups. This study demonstrated a statistically significant association between both the CT genotype and the T allele in the female patient group with coexistent T2DM+HT (CT genotype: p=0.04; T allele: p=0.045) with a statistically significant association between the CT genotype and the mean values of body mass index (BMI) and free T3 levels (FT3) (BMI: p=0.044 and FT3: p=0.021) that was detected in the patient group with coexistent T2DM+HT. The minor genotype TT was observed in none of the groups in this study. The CT genotype frequency was [number (frequency): 5 (3.8%), 7 (6.86%), 5 (7.04%), 3 (2.22%), while the T allele frequency was 5 (1.86%), 7 (3.44%), 5 (3.53%) and 3 (1.12%)] in the T2DM, T2DM+HT, HT and control groups, respectively. CONCLUSION Our data suggest that the PTPN22 1858T allele and the CT genotype are associated with increased risk in female patients for coexistent T2DM+HT. The CT genotype was associated with high mean BMI and free T3 values in the patient group with coexistent T2DM+HT. These results demonstrate that T allele carriers were more often in the T2DM+HT group than in the T2DM group. Therefore, the combination of T2DM and HT with female gender may have higher T allele carriage in comparison to the T2DM only and male groups.
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Affiliation(s)
- Funda Bulut
- Department of Medical Biology, Kırıkkale University Faculty of Medicine, Kırıkkale, Turkey
| | - Deniz Erol
- Department of Medical Biology, Fırat University Faculty of Medicine, Elazığ, Turkey
| | - Halit Elyas
- Department of Medical Biology, Fırat University Faculty of Medicine, Elazığ, Turkey
| | - Halil Doğan
- Department of Internal Medicine, Private Hayat Hospital, Elazığ, Turkey
| | - Fethi Ahmet Ozdemir
- Department of Medical Biology, Fırat University Faculty of Medicine, Elazığ, Turkey
| | - Lezan Keskin
- Department of Endocrinology, Elazığ Training and Research Hospital, Elazığ, Turkey
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Zheleznyakova AV, Lebedeva NO, Vikulova OK, Nosikov VV, Shamkhalova MS, Shestakova MV. Risk of chronic kidney disease in type 2 diabetes determined by polymorphisms in NOS3, APOB, KCNJ11, TCF7L2 genes as compound effect of risk genotypes combination. DIABETES MELLITUS 2014. [DOI: 10.14341/dm2014323-30] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Genetic susceptibility plays an important role in the risk of developing chronic complications in patients with type 2 diabetes mellitus (T2DM). Aims. In this study, we evaluated the possible association of the polymorphic variants that encode key renal damage mediators (endothelial dysfunction, lipid metabolism and insulin secretion/sensitivity) with the risk of chronic kidney disease (CKD) in patients with T2DM. Materials and Methods. We enrolled 435 patients with T2DM using case-control study design. In 253 patients, we used non-overlapping criteria to form groups with/without CKD (defined as GFR=10 years) (n=75 and 178, respectively) and analysed the following 4 polymorphic markers: I/D in ACE, ecNOS4a/4b in NOS3, I/D in APOB and e2/e3/e4 in APOE genes. We then divided 182 patients in groups with/without CKD (n=38 and 144, respectively) regardless of the duration of diabetes and studied pro12ala in PPARG2, rs5219 in KCNJ11, rs12255372 in TCF7L2 and rs13266634 in SLC30A8 genes. 2 test, and data were expressed as odds ratios (ORs) with 95% confidence intervals (CIs). Values of p
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Joint identification of genetic variants for physical activity in Korean population. Int J Mol Sci 2014; 15:12407-21. [PMID: 25026172 PMCID: PMC4139850 DOI: 10.3390/ijms150712407] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2014] [Accepted: 06/11/2014] [Indexed: 01/30/2023] Open
Abstract
There has been limited research on genome-wide association with physical activity (PA). This study ascertained genetic associations between PA and 344,893 single nucleotide polymorphism (SNP) markers in 8842 Korean samples. PA data were obtained from a validated questionnaire that included information on PA intensity and duration. Metabolic equivalent of tasks were calculated to estimate the total daily PA level for each individual. In addition to single- and multiple-SNP association tests, a pathway enrichment analysis was performed to identify the biological significance of SNP markers. Although no significant SNP was found at genome-wide significance level via single-SNP association tests, 59 genetic variants mapped to 76 genes were identified via a multiple SNP approach using a bootstrap selection stability measure. Pathway analysis for these 59 variants showed that maturity onset diabetes of the young (MODY) was enriched. Joint identification of SNPs could enable the identification of multiple SNPs with good predictive power for PA and a pathway enriched for PA.
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DeMenna J, Puppala S, Chittoor G, Schneider J, Kim JY, Shaibi GQ, Mandarino LJ, Duggirala R, Coletta DK. Association of common genetic variants with diabetes and metabolic syndrome related traits in the Arizona Insulin Resistance registry: a focus on Mexican American families in the Southwest. Hum Hered 2014; 78:47-58. [PMID: 25060389 DOI: 10.1159/000363411] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2013] [Accepted: 05/06/2014] [Indexed: 12/17/2022] Open
Abstract
BACKGROUND/AIMS The increased occurrence of type 2 diabetes and its clinical correlates is a global public health issue, and there are continued efforts to find its genetic determinant across ethnically diverse populations. The aims of this study were to determine the heritability of diabetes and metabolic syndrome phenotypes in the Arizona Insulin Resistance (AIR) registry and to perform an association analysis of common single nucleotide polymorphisms (SNPs) identified by GWAS with these traits. All study participants were Mexican Americans from the AIR registry. METHODS Metabolic, anthropometric, demographic and medical history information was obtained on the 667 individuals enrolled in the registry. RESULTS The heritability estimates were moderate to high in magnitude and significant, indicating that the AIR registry is well suited for the identification of genetic factors contributing to diabetes and the metabolic syndrome. From the 30 GWAS genes selected (some genes were represented by multiple SNPs), 20 SNPs exhibited associations with one or more of the diabetes related traits with nominal significance (p ≤ 0.05). In addition, 25 SNPs were nominally significantly associated with one or more of the metabolic phenotypes tested (p ≤ 0.05). Most notably, 5 SNPs from 5 genes [body mass index (BMI), hip circumference: rs3751812/FTO; fasting plasma glucose, hemoglobin A1c: rs4607517/GCK; very-low-density lipoprotein: rs10830963/MTNR1B; BMI: rs13266634/SLC30A8, and total cholesterol, low-density lipoprotein: rs7578597/THADA] were significantly associated with obesity, glycemic, and lipid phenotypes when using the multiple testing significance threshold of 0.0015. CONCLUSION These findings extend previous work on Mexican Americans to suggest that metabolic disease is strongly influenced by genetic background in this high-risk population.
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Affiliation(s)
- Jacob DeMenna
- School of Life Sciences, Arizona State University, Tempe, Ariz., USA
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Kundu S, Mihaescu R, Meijer CMC, Bakker R, Janssens ACJW. Estimating the predictive ability of genetic risk models in simulated data based on published results from genome-wide association studies. Front Genet 2014; 5:179. [PMID: 24982668 PMCID: PMC4056181 DOI: 10.3389/fgene.2014.00179] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2014] [Accepted: 05/27/2014] [Indexed: 01/18/2023] Open
Abstract
Background: There is increasing interest in investigating genetic risk models in empirical studies, but such studies are premature when the expected predictive ability of the risk model is low. We assessed how accurately the predictive ability of genetic risk models can be estimated in simulated data that are created based on the odds ratios (ORs) and frequencies of single-nucleotide polymorphisms (SNPs) obtained from genome-wide association studies (GWASs). Methods: We aimed to replicate published prediction studies that reported the area under the receiver operating characteristic curve (AUC) as a measure of predictive ability. We searched GWAS articles for all SNPs included in these models and extracted ORs and risk allele frequencies to construct genotypes and disease status for a hypothetical population. Using these hypothetical data, we reconstructed the published genetic risk models and compared their AUC values to those reported in the original articles. Results: The accuracy of the AUC values varied with the method used for the construction of the risk models. When logistic regression analysis was used to construct the genetic risk model, AUC values estimated by the simulation method were similar to the published values with a median absolute difference of 0.02 [range: 0.00, 0.04]. This difference was 0.03 [range: 0.01, 0.06] and 0.05 [range: 0.01, 0.08] for unweighted and weighted risk scores. Conclusions: The predictive ability of genetic risk models can be estimated using simulated data based on results from GWASs. Simulation methods can be useful to estimate the predictive ability in the absence of empirical data and to decide whether empirical investigation of genetic risk models is warranted.
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Affiliation(s)
- Suman Kundu
- Department of Epidemiology, Erasmus University Medical Center Rotterdam, Netherlands
| | - Raluca Mihaescu
- Department of Epidemiology, Erasmus University Medical Center Rotterdam, Netherlands
| | - Catherina M C Meijer
- Department of Epidemiology, Erasmus University Medical Center Rotterdam, Netherlands
| | - Rachel Bakker
- Department of Epidemiology, Erasmus University Medical Center Rotterdam, Netherlands
| | - A Cecile J W Janssens
- Department of Epidemiology, Erasmus University Medical Center Rotterdam, Netherlands ; Department of Epidemiology, Rollins School of Public Health, Emory University Atlanta, GA, USA
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Genetics of type 2 diabetes: insights into the pathogenesis and its clinical application. BIOMED RESEARCH INTERNATIONAL 2014; 2014:926713. [PMID: 24864266 PMCID: PMC4016836 DOI: 10.1155/2014/926713] [Citation(s) in RCA: 57] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 03/07/2014] [Accepted: 03/22/2014] [Indexed: 02/06/2023]
Abstract
With rapidly increasing prevalence, diabetes has become one of the major causes of mortality worldwide. According to the latest studies, genetic information makes substantial contributions towards the prediction of diabetes risk and individualized antidiabetic treatment. To date, approximately 70 susceptibility genes have been identified as being associated with type 2 diabetes (T2D) at a genome-wide significant level (P < 5 × 10−8). However, all the genetic loci identified so far account for only about 10% of the overall heritability of T2D. In addition, how these novel susceptibility loci correlate with the pathophysiology of the disease remains largely unknown. This review covers the major genetic studies on the risk of T2D based on ethnicity and briefly discusses the potential mechanisms and clinical utility of the genetic information underlying T2D.
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Chang YC, Liu PH, Yu YH, Kuo SS, Chang TJ, Jiang YD, Nong JY, Hwang JJ, Chuang LM. Validation of type 2 diabetes risk variants identified by genome-wide association studies in Han Chinese population: a replication study and meta-analysis. PLoS One 2014; 9:e95045. [PMID: 24736664 PMCID: PMC3988150 DOI: 10.1371/journal.pone.0095045] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2014] [Accepted: 03/23/2014] [Indexed: 01/16/2023] Open
Abstract
BACKGROUND Several genome-wide association studies (GWAS) involving European populations have successfully identified risk genetic variants associated with type 2 diabetes mellitus (T2DM). However, the effects conferred by these variants in Han Chinese population have not yet been fully elucidated. METHODS We analyzed the effects of 24 risk genetic variants with reported associations from European GWAS in 3,040 Han Chinese subjects in Taiwan (including 1,520 T2DM cases and 1,520 controls). The discriminative power of the prediction models with and without genotype scores was compared. We further meta-analyzed the association of these variants with T2DM by pooling all candidate-gene association studies conducted in Han Chinese. RESULTS Five risk variants in IGF2BP2 (rs4402960, rs1470579), CDKAL1 (rs10946398), SLC30A8 (rs13266634), and HHEX (rs1111875) genes were nominally associated with T2DM in our samples. The odds ratio was 2.22 (95% confidence interval, 1.81-2.73, P<0.0001) for subjects with the highest genetic score quartile (score>34) as compared with subjects with the lowest quartile (score<29). The incoporation of genotype score into the predictive model increased the C-statistics from 0.627 to 0.657 (P<0.0001). These estimates are very close to those observed in European populations. Gene-environment interaction analysis showed a significant interaction between rs13266634 in SLC30A8 gene and age on T2DM risk (P<0.0001). Further meta-analysis pooling 20 studies in Han Chinese confirmed the association of 10 genetic variants in IGF2BP2, CDKAL1, JAZF1, SCL30A8, HHEX, TCF7L2, EXT2, and FTO genes with T2DM. The effect sizes conferred by these risk variants in Han Chinese were similar to those observed in Europeans but the allele frequencies differ substantially between two populations. CONCLUSION We confirmed the association of 10 variants identified by European GWAS with T2DM in Han Chinese population. The incorporation of genotype scores into the prediction model led to a small but significant improvement in T2DM prediction.
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Affiliation(s)
- Yi-Cheng Chang
- Department of Internal Medicine, National Taiwan University Hospital HsinChu branch, Taipei, Taiwan
- Institute of Biomedical Science, Academia Sinica, Taipei, Taiwan
| | - Pi-Hua Liu
- Clinical Informatics and Medical Statistics Research Center, College of Medicine, Chang Gung University, Taoyuan, Taiwan
| | - Yu-Hsiang Yu
- Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
| | - Shan-Shan Kuo
- Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
| | - Tien-Jyun Chang
- Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
| | - Yi-Der Jiang
- Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
| | - Jiun-Yi Nong
- Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
| | - Juey-Jen Hwang
- Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
| | - Lee-Ming Chuang
- Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
- Institute of Clinical Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan
- * E-mail:
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Qiu L, Na R, Xu R, Wang S, Sheng H, Wu W, Qu Y. Quantitative assessment of the effect of KCNJ11 gene polymorphism on the risk of type 2 diabetes. PLoS One 2014; 9:e93961. [PMID: 24710510 PMCID: PMC3977990 DOI: 10.1371/journal.pone.0093961] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2013] [Accepted: 02/19/2014] [Indexed: 12/31/2022] Open
Abstract
To clarify the role of potassium inwardly-rectifying-channel, subfamily-J, member 11 (KCNJ11) variation in susceptibility to type 2 diabetes (T2D), we performed a systematic meta-analysis to investigate the association between the KCNJ11 E23K polymorphism (rs5219) and the T2D in different genetic models. Databases including PubMed, Medline, EMBASE, and ISI Web of Science were searched to identify relevant studies. A total of 48 published studies involving 56,349 T2D cases, 81,800 controls, and 483 family trios were included in this meta-analysis. Overall, the E23K polymorphism was significantly associated with increased T2D risk with per-allele odds ratio (OR) of 1.12 (95% CI: 1.09-1.16; P<10-5). The summary OR for T2D was 1.09 (95% CI: 1.03-1.14; P<10-5), and 1.26 (95% CI: 1.17-1.35; P<10-5), for heterozygous and homozygous, respectively. Similar results were also detected under dominant and recessive genetic models. When stratified by ethnicity, significantly increased risks were found for the polymorphism in Caucasians and East Asians. However, no such associations were detected among Indian and other ethnic populations. Significant associations were also observed in the stratified analyses according to different mean BMI of cases and sample size. Although significant between study heterogeneity was identified, meta-regression analysis suggested that the BMI of controls significantly correlated with the magnitude of the genetic effect. The current meta-analysis demonstrated that a modest but statistically significant effect of the 23K allele of rs5219 polymorphism in susceptibility to T2D. But the contribution of its genetic variants to the epidemic of T2D in Indian and other ethnic populations appears to be relatively low.
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Affiliation(s)
- Ling Qiu
- Department of Geriatrics, Shanghai Xuhui Central Hospital, Shanghai Clinical Center, Chinese Academy of Sciences, Shanghai, People's Republic of China
| | - Risu Na
- Department of Endocrinology, Shanghai Xuhui Central Hospital, Shanghai Clinical Center, Chinese Academy of Sciences, Shanghai, People's Republic of China
| | - Rong Xu
- Department of Geriatrics, Shanghai Xuhui Central Hospital, Shanghai Clinical Center, Chinese Academy of Sciences, Shanghai, People's Republic of China
| | - Siyang Wang
- Department of Geriatrics, Shanghai Xuhui Central Hospital, Shanghai Clinical Center, Chinese Academy of Sciences, Shanghai, People's Republic of China
| | - Hongguang Sheng
- Department of Endocrinology, Shanghai Xuhui Central Hospital, Shanghai Clinical Center, Chinese Academy of Sciences, Shanghai, People's Republic of China
| | - Wanling Wu
- Department of Endocrinology, The Ninth People's Hospital Attach to Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China
| | - Yi Qu
- Department of Geriatrics, Shanghai Xuhui Central Hospital, Shanghai Clinical Center, Chinese Academy of Sciences, Shanghai, People's Republic of China
- * E-mail:
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Salem SD, Saif-Ali R, Ismail IS, Al-Hamodi Z, Muniandy S. Contribution of SLC30A8 variants to the risk of type 2 diabetes in a multi-ethnic population: a case control study. BMC Endocr Disord 2014; 14:2. [PMID: 24393180 PMCID: PMC3893602 DOI: 10.1186/1472-6823-14-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/10/2013] [Accepted: 01/03/2014] [Indexed: 11/30/2022] Open
Abstract
BACKGROUND Several studies have shown the association of solute carrier family 30 (zinc transporter) member 8 (SLC30A8) rs13266634 with type 2 diabetes (T2D). However, the association of alternative variants and haplotypes of SLC30A8 with T2D have not been studied in different populations. The aim of this study is to assess the association of the alternative SLC30A8 variants, rs7002176 and rs1995222 as well as the most common variant, rs13266634 and haplotypes with glutamic acid decarboxylase antibodies (GADA) negative diabetes in Malaysian subjects. METHODS Single nucleotide polymorphisms (SNPs) of SLC30A8; rs7002176, rs1995222 and rs13266634 were genotyped in 1140 T2D and 973 non-diabetic control subjects. Of these, 33 GADA positive diabetic subjects and 353 metabolic syndrome (MetS) subjects were excluded from subsequent analysis. RESULTS The recessive genetic model controlled for age, race, gender and BMI shows that the alternative SLC30A8 variant, rs1995222 is associated with GADA negative diabetes (OR = 1.29, P = 0.02) in Malaysian subjects. The most common variant, rs13266634 is also associated with GADA negative diabetes (OR = 1.45, P = 0.001). This association is more pronounced among Malaysian Indians (OR = 1.93, P = 0.001). Moreover, the CG haplotype and CG-CG diplotype have been equally associated with increased diabetic risk (OR = 1.67, P = 8.6 × 10-5). CONCLUSIONS SLC30A8 SNPs and haplotypes are associated with GADA negative diabetes in Malaysian subjects, and this association is markedly higher among Malaysian Indian subjects.
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Affiliation(s)
- Sameer D Salem
- Department of Molecular Medicine, Faculty of Medicine, University of Malaya, 50603 Kuala Lumpur, Malaysia
- Department of Biochemistry, Faculty of Medicine, Sana’a University, Sana’a, Yemen
| | - Riyadh Saif-Ali
- Department of Biochemistry, Faculty of Medicine, Sana’a University, Sana’a, Yemen
| | - Ikram S Ismail
- Department of Medicine, Faculty of Medicine, University of Malaya Medical Centre, University of Malaya, 50603 Kuala Lumpur, Malaysia
| | - Zaid Al-Hamodi
- Department of Molecular Medicine, Faculty of Medicine, University of Malaya, 50603 Kuala Lumpur, Malaysia
- Department of Biochemistry, Faculty of Medicine, Sana’a University, Sana’a, Yemen
| | - Sekaran Muniandy
- Department of Molecular Medicine, Faculty of Medicine, University of Malaya, 50603 Kuala Lumpur, Malaysia
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Herder C, Kowall B, Tabak AG, Rathmann W. The potential of novel biomarkers to improve risk prediction of type 2 diabetes. Diabetologia 2014; 57:16-29. [PMID: 24078135 DOI: 10.1007/s00125-013-3061-3] [Citation(s) in RCA: 55] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/18/2013] [Accepted: 08/24/2013] [Indexed: 01/05/2023]
Abstract
The incidence of type 2 diabetes can be reduced substantially by implementing preventive measures in high-risk individuals, but this requires prior knowledge of disease risk in the individual. Various diabetes risk models have been designed, and these have all included a similar combination of factors, such as age, sex, obesity, hypertension, lifestyle factors, family history of diabetes and metabolic traits. The accuracy of prediction models is often assessed by the area under the receiver operating characteristic curve (AROC) as a measure of discrimination, but AROCs should be complemented by measures of calibration and reclassification to estimate the incremental value of novel biomarkers. This review discusses the potential of novel biomarkers to improve model accuracy. The range of molecules that serve as potential predictors of type 2 diabetes includes genetic variants, RNA transcripts, peptides and proteins, lipids and small metabolites. Some of these biomarkers lead to a statistically significant increase of model accuracy, but their incremental value currently seems too small for routine clinical use. However, only a fraction of potentially relevant biomarkers have been assessed with regard to their predictive value. Moreover, serial measurements of biomarkers may help determine individual risk. In conclusion, current risk models provide valuable tools of risk estimation, but perform suboptimally in the prediction of individual diabetes risk. Novel biomarkers still fail to have a clinically applicable impact. However, more efficient use of biomarker data and technological advances in their measurement in clinical settings may allow the development of more accurate predictive models in the future.
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Use of net reclassification improvement (NRI) method confirms the utility of combined genetic risk score to predict type 2 diabetes. PLoS One 2013; 8:e83093. [PMID: 24376643 PMCID: PMC3869744 DOI: 10.1371/journal.pone.0083093] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2013] [Accepted: 11/03/2013] [Indexed: 11/28/2022] Open
Abstract
Background Recent genome-wide association studies (GWAS) identified more than 70 novel loci for type 2 diabetes (T2D), some of which have been widely replicated in Asian populations. In this study, we investigated their individual and combined effects on T2D in a Chinese population. Methodology We selected 14 single nucleotide polymorphisms (SNPs) in T2D genes relating to beta-cell function validated in Asian populations and genotyped them in 5882 Chinese T2D patients and 2569 healthy controls. A combined genetic score (CGS) was calculated by summing up the number of risk alleles or weighted by the effect size for each SNP under an additive genetic model. We tested for associations by either logistic or linear regression analysis for T2D and quantitative traits, respectively. The contribution of the CGS for predicting T2D risk was evaluated by receiver operating characteristic (ROC) analysis and net reclassification improvement (NRI). Results We observed consistent and significant associations of IGF2BP2, WFS1, CDKAL1, SLC30A8, CDKN2A/B, HHEX, TCF7L2 and KCNQ1 (8.5×10−18<P<8.5×10−3), as well as nominal associations of NOTCH2, JAZF1, KCNJ11 and HNF1B (0.05<P<0.1) with T2D risk, which yielded odds ratios ranging from 1.07 to 2.09. The 8 significant SNPs exhibited joint effect on increasing T2D risk, fasting plasma glucose and use of insulin therapy as well as reducing HOMA-β, BMI, waist circumference and younger age of diagnosis of T2D. The addition of CGS marginally increased AUC (2%) but significantly improved the predictive ability on T2D risk by 11.2% and 11.3% for unweighted and weighted CGS, respectively using the NRI approach (P<0.001). Conclusion In a Chinese population, the use of a CGS of 8 SNPs modestly but significantly improved its discriminative ability to predict T2D above and beyond that attributed to clinical risk factors (sex, age and BMI).
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Kuo JZ, Sheu WHH, Assimes TL, Hung YJ, Absher D, Chiu YF, Mak J, Wang JS, Kwon S, Hsu CC, Goodarzi MO, Lee IT, Knowles JW, Miller BE, Lee WJ, Juang JMJ, Wang TD, Guo X, Taylor KD, Chuang LM, Hsiung CA, Quertermous T, Rotter JI, Chen YDI. Trans-ethnic fine mapping identifies a novel independent locus at the 3' end of CDKAL1 and novel variants of several susceptibility loci for type 2 diabetes in a Han Chinese population. Diabetologia 2013; 56:2619-28. [PMID: 24013783 PMCID: PMC3825282 DOI: 10.1007/s00125-013-3047-1] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/27/2013] [Accepted: 08/13/2013] [Indexed: 12/31/2022]
Abstract
AIMS/HYPOTHESIS Candidate gene and genome-wide association studies have identified ∼60 susceptibility loci for type 2 diabetes. A majority of these loci have been discovered and tested only in European populations. The aim of this study was to assess the presence and extent of trans-ethnic effects of these loci in an East Asian population. METHODS A total of 9,335 unrelated Chinese Han individuals, including 4,535 with type 2 diabetes and 4,800 non-diabetic ethnically matched controls, were genotyped using the Illumina 200K Metabochip. We tested 50 established loci for type 2 diabetes and related traits (fasting glucose, fasting insulin, 2 h glucose). Disease association with the additive model of inheritance was analysed with logistic regression. RESULTS We found that 14 loci significantly transferred to the Chinese population, with two loci (p = 5.7 × 10(-12) for KCNQ1; p = 5.0 × 10(-8) for CDKN2A/B-CDKN2BAS) reaching independent genome-wide statistical significance. Five of these 14 loci had similar lead single-nucleotide polymorphisms (SNPs) as were found in the European studies while the other nine were different. Further stepwise conditional analysis identified a total of seven secondary signals and an independent novel locus at the 3' end of CDKAL1. CONCLUSIONS/INTERPRETATION These results suggest that many loci associated with type 2 diabetes are commonly shared between European and Chinese populations. Identification of population-specific SNPs may increase our understanding of the genetic architecture underlying type 2 diabetes in different ethnic populations.
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Affiliation(s)
- Jane Z. Kuo
- Institute for Translational Genomics and Population Sciences, Los Angeles BioMedical Research Institute, Harbor-UCLA Medical Center, 1124 W. Carson Street, Torrance, CA 90502 USA
- Department of Ophthalmology, Shiley Eye Center, UC San Diego, La Jolla, CA USA
- Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA USA
| | - Wayne Huey-Herng Sheu
- Division of Endocrine and Metabolism, Department of Internal Medicine, Taichung Veterans General Hospital, Taichung, Taiwan
- School of Medicine, National Yang-Ming University, Taipei, Taiwan
- School of Medicine, National Defense Medical Center, Taipei, Taiwan
| | | | - Yi-Jen Hung
- Division of Endocrinology and Metabolism, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
| | - Devin Absher
- Hudson Alpha Institute for Biotechnology, Huntsville, AL USA
| | - Yen-Feng Chiu
- Institute of Population Health Sciences, National Health Research Institutes, Zhunan, Taiwan
| | - Jordan Mak
- Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA USA
| | - Jun-Sing Wang
- Division of Endocrine and Metabolism, Department of Internal Medicine, Taichung Veterans General Hospital, Taichung, Taiwan
| | - Soonil Kwon
- Institute for Translational Genomics and Population Sciences, Los Angeles BioMedical Research Institute, Harbor-UCLA Medical Center, 1124 W. Carson Street, Torrance, CA 90502 USA
| | - Chih-Cheng Hsu
- Division of Geriatrics and Gerontology, Institute of Population Health Sciences, National Health Research Institutes, Zhunan, Taiwan
| | - Mark O. Goodarzi
- Department of Endocrinology, Cedars-Sinai Medical Center, Los Angeles, CA USA
| | - I-Te Lee
- Division of Endocrine and Metabolism, Department of Internal Medicine, Taichung Veterans General Hospital, Taichung, Taiwan
- School of Medicine, National Yang-Ming University, Taipei, Taiwan
| | - Joshua W. Knowles
- Department of Medicine, Stanford University School of Medicine, Stanford, CA USA
| | - Brittany E. Miller
- Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA USA
| | - Wen-Jane Lee
- Department of Medical Research, Taichung Veterans General Hospital, Taichung, Taiwan
| | - Jyh-Ming J. Juang
- Cardiovascular Center and Division of Cardiology, Department of Internal Medicine, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan
| | - Tzung-Dau Wang
- Cardiovascular Center and Division of Cardiology, Department of Internal Medicine, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan
| | - Xiuqing Guo
- Institute for Translational Genomics and Population Sciences, Los Angeles BioMedical Research Institute, Harbor-UCLA Medical Center, 1124 W. Carson Street, Torrance, CA 90502 USA
| | - Kent D. Taylor
- Institute for Translational Genomics and Population Sciences, Los Angeles BioMedical Research Institute, Harbor-UCLA Medical Center, 1124 W. Carson Street, Torrance, CA 90502 USA
| | - Lee-Ming Chuang
- Division of Endocrinology and Metabolism, Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
| | - Chao A. Hsiung
- Institute of Population Health Sciences, National Health Research Institutes, Zhunan, Taiwan
| | - Thomas Quertermous
- Department of Medicine, Stanford University School of Medicine, Stanford, CA USA
| | - Jerome I. Rotter
- Institute for Translational Genomics and Population Sciences, Los Angeles BioMedical Research Institute, Harbor-UCLA Medical Center, 1124 W. Carson Street, Torrance, CA 90502 USA
| | - Yii-Der I. Chen
- Institute for Translational Genomics and Population Sciences, Los Angeles BioMedical Research Institute, Harbor-UCLA Medical Center, 1124 W. Carson Street, Torrance, CA 90502 USA
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Kommoju UJ, Maruda J, Kadarkarai S, Irgam K, Kotla JP, Velaga L, Mohan Reddy B. No detectable association of IGF2BP2 and SLC30A8 genes with type 2 diabetes in the population of Hyderabad, India. Meta Gene 2013; 1:15-23. [PMID: 25606370 PMCID: PMC4205031 DOI: 10.1016/j.mgene.2013.09.003] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
Genome-wide association studies identified novel genes associated with T2DM which have been replicated in different populations. We try to examine here if certain frequently replicated SNPs of Insulin growth factor 2 m-RNA binding protein 2 (IGF2BP2) (rs4402960, rs1470579) and Solute Carrier family 30 member 8 (SLC30A8) (rs13266634) genes, known to be implicated in insulin pathway, are associated with T2DM in the population of Hyderabad, which is considered to be a diabetic capital of India. Genotyping of the 1379 samples, 758 cases and 621 controls, for the SNPs was performed on sequenom massarray platform. The logistic regression analysis was done using SPSS software and the post-hoc power of the study was estimated using G power. The allele and genotype frequencies were similar between cases and controls, both for SNPs of IGF2BP2 and SLC30A8 genes. Logistic regression did not reveal significant allelic or genotypic association of any of the three SNPs with T2DM. Despite large sample size and adequate power, we could not replicate the association of IGF2BP2 and SLC30A8 SNPs with T2DM in our sample from Hyderabad (A.P.), India, albeit another study based on much larger sample but from heterogeneous populations from the northern parts of India showed significant association of two of the above 3 SNPs, suggesting variable nature of susceptibility of these genes in different ethnic groups. Although the IGF2BP2 and SLC30A8 genes are important in the functional pathway of Insulin secretion, it appears that these genes do not play a significant role in the susceptibility to T2DM in this population.
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Key Words
- ADA, American Diabetes Association
- Association
- BMI, Body Mass Index
- C.I., Confidence Interval.
- DBP, Diastolic Blood Pressure
- FPG, Fasting Plasma Glucose
- GWAS, Genome wide association studies
- IGF2BP2, Insulin growth factor 2 m-RNA binding protein 2
- IMP1, Insulin-like growth factor 2 mRNA-binding protein 1
- India
- LD, Linkage Disequilibrium
- O.R., Odds Ratio
- PCOS, Polycystic Ovarian Syndrome
- PPG, Post-prandial Plasma Glucose
- Population of Hyderabad
- PyPop, Python for Population Genomics
- RBG, Random Plasma Glucose
- SBP, Systolic Blood Pressure
- SLC30A8, Solute Carrier family 30 member 8
- SNP, Single Nucleotide Polymorphism
- SPSS, Statistical Package for Social Sciences
- Single nucleotide polymorphism (SNP)
- T2DM, Type 2 diabetes mellitus
- Type 2 diabetes mellitus (T2DM)
- WHR, Waist Hip Ratio
- ZnT8, Zinc Transporter 8
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Affiliation(s)
- Uma Jyothi Kommoju
- Molecular Anthropology Group, Biological Anthropology Unit, Indian Statistical Institute, Hyderabad, India
| | - Jayaraj Maruda
- Molecular Anthropology Group, Biological Anthropology Unit, Indian Statistical Institute, Hyderabad, India
| | - Subburaj Kadarkarai
- Molecular Anthropology Group, Biological Anthropology Unit, Indian Statistical Institute, Hyderabad, India
| | - Kumuda Irgam
- Molecular Anthropology Group, Biological Anthropology Unit, Indian Statistical Institute, Hyderabad, India
| | | | - Lakshmi Velaga
- Department of Human Genetics, Andhra University, Visakhapatnam, India
| | - Battini Mohan Reddy
- Molecular Anthropology Group, Biological Anthropology Unit, Indian Statistical Institute, Hyderabad, India
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Bao W, Hu FB, Rong S, Rong Y, Bowers K, Schisterman EF, Liu L, Zhang C. Predicting risk of type 2 diabetes mellitus with genetic risk models on the basis of established genome-wide association markers: a systematic review. Am J Epidemiol 2013; 178:1197-207. [PMID: 24008910 DOI: 10.1093/aje/kwt123] [Citation(s) in RCA: 47] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022] Open
Abstract
This study aimed to evaluate the predictive performance of genetic risk models based on risk loci identified and/or confirmed in genome-wide association studies for type 2 diabetes mellitus. A systematic literature search was conducted in the PubMed/MEDLINE and EMBASE databases through April 13, 2012, and published data relevant to the prediction of type 2 diabetes based on genome-wide association marker-based risk models (GRMs) were included. Of the 1,234 potentially relevant articles, 21 articles representing 23 studies were eligible for inclusion. The median area under the receiver operating characteristic curve (AUC) among eligible studies was 0.60 (range, 0.55-0.68), which did not differ appreciably by study design, sample size, participants' race/ethnicity, or the number of genetic markers included in the GRMs. In addition, the AUCs for type 2 diabetes did not improve appreciably with the addition of genetic markers into conventional risk factor-based models (median AUC, 0.79 (range, 0.63-0.91) vs. median AUC, 0.78 (range, 0.63-0.90), respectively). A limited number of included studies used reclassification measures and yielded inconsistent results. In conclusion, GRMs showed a low predictive performance for risk of type 2 diabetes, irrespective of study design, participants' race/ethnicity, and the number of genetic markers included. Moreover, the addition of genome-wide association markers into conventional risk models produced little improvement in predictive performance.
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93
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Qian Y, Liu S, Lu F, Li H, Dong M, Lin Y, Du J, Lin Y, Gong J, Jin G, Dai J, Hu Z, Shen H. Genetic variant in fat mass and obesity-associated gene associated with type 2 diabetes risk in Han Chinese. BMC Genet 2013; 14:86. [PMID: 24053193 PMCID: PMC3848839 DOI: 10.1186/1471-2156-14-86] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2013] [Accepted: 09/16/2013] [Indexed: 12/16/2022] Open
Abstract
Background Genome-wide association study (GWAS) has identified that rs8050136 C/A polymorphism in fat mass and obesity-associated gene (FTO) was associated with the risk of type 2 diabetes (T2D) in Europeans. But this association was abolished after adjustment for body mass index (BMI), suggesting that the effect of rs8050136 on T2D risk might be mediated by BMI in Europeans. However, the findings in subsequent studies were inconsistent among Asian populations. To determine whether rs8050136 polymorphism in FTO is independently associated with the risk of T2D in Chinese, we conducted a case–control study with 2,925 T2D patients and 3,281 controls in Han Chinese. Results Logistic regression revealed that the A allele of rs8050136 was significantly associated with an increased risk of T2D, independent of BMI (odds ratio (OR) = 1.17, 95% confidence interval (95% CI) = 1.03-1.32, p = 0.016). Meta-analysis containing 10 reported studies and our data with a total of 15,819 cases and 18,314 controls further confirmed the association between rs8050136 polymorphism and T2D risk in East Asians (OR = 1.13, 95% CI = 1.07-1.19). Conclusions Our findings indicate that the genetic variant in FTO may contribute to T2D risk in Han Chinese and rs8050136 polymorphism may be a genetic marker for T2D susceptibility.
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Affiliation(s)
- Yun Qian
- Department of Chronic Non-communicable Disease Control, Wuxi Center for Disease Control and Prevention, Wuxi, China.
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94
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Motavallian A, Andalib S, Vaseghi G, Mirmohammad-Sadeghi H, Amini M. Association between PRO12ALA polymorphism of the PPAR-γ2 gene and type 2 diabetes mellitus in Iranian patients. INDIAN JOURNAL OF HUMAN GENETICS 2013; 19:239-44. [PMID: 24019628 PMCID: PMC3758733 DOI: 10.4103/0971-6866.116126] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
BACKGROUND: Peroxisome proliferator-activated receptor (PPARs) have been identified as ligand-activated transcription factors that belong to the nuclear receptor superfamily. It has been shown that an association exists between Proline 12 alanine (Pro12Ala) polymorphism of PPAR-GAMMA2 (PPAR-γ2) gene and increased risk of type 2 diabetes mellitus (T2DM) in different populations. Therefore, the present study was designed to investigate the association between Pro12Ala polymorphism of PPAR-γ2 gene and T2DM in an Iranian population. MATERIALS AND METHODS: Two hundred unrelated people, including 100 healthy controls and 100 diabetic patients were recruited diagnosed based on American Diabetes Association criteria. Blood samples were used for isolation of genomic deoxyribonucleic acid (DNA). Having extracted the genomic DNA from human blood leukocytes by means of High Pure polymerase chain reaction (PCR) Template preparation kit, we carried out polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) on each blood sample. Then, Genomic DNA was digested by BstU-I restriction enzyme. Thereafter, restriction products were analyzed by means of Polyacrylamide gel electrophoresis and stained by Ethidium Bromide. RESULTS: We found that the frequency of Ala allele in healthy subjects was significantly higher than in diabetic subjects (P = 0003). Moreover, the genotype frequency of Ala/Ala in healthy subjects was significantly higher than in diabetic subjects (P < 0.001). However, the genotype frequency of Ala/Pro in diabetic subjects was significantly higher than in healthy subjects (P < 0.001). CONCLUSION: The present study suggests that polymorphism of PPAR-γ2 gene is associated with T2DM. Furthermore, Ala allele is significantly found in non-diabetic individual’s Iranian population.
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Affiliation(s)
- Azadeh Motavallian
- Department of Pharmacology, School of Medicine, Guilan University of Medical Sciences, Rasht, Iran
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95
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Peng F, Hu D, Gu C, Li X, Li Y, Jia N, Chu S, Lin J, Niu W. The relationship between five widely-evaluated variants in CDKN2A/B and CDKAL1 genes and the risk of type 2 diabetes: a meta-analysis. Gene 2013; 531:435-43. [PMID: 24012816 DOI: 10.1016/j.gene.2013.08.075] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2013] [Revised: 08/20/2013] [Accepted: 08/22/2013] [Indexed: 01/12/2023]
Abstract
The genes encoding two cyclin-dependent kinases-inhibitor-2A/B (CDKN2A/B) and 5 regulatory subunit-associated protein-like 1 (CDKAL1) have been investigated extensively in associations with type 2 diabetes; the results, however, are often irreproducible. We therefore sought to evaluate these associations by performing a meta-analysis on five widely-evaluated variants from the two genes. There were 38 studies (patients/controls: 51,940/52,234) for rs10811661, 16 studies (20,029/24,419) for rs564398 in CDKN2A/B gene, and 27 studies (28,383/47,635) for rs7756992, 26 studies (28,816/31,713) for rs7754840, 21 studies (29,260/38,400) for rs10946398 in CDKAL1 gene. Overall risk estimates for type 2 diabetes conferred by rs10811661-T, rs564398-A, rs7754840-C, rs7756992-G, and rs10946398-C alleles were 1.17 (95% CI: 1.10-1.23; P<0.0005; I(2)=83.9%), 1.1 (95% CI: 1.0-1.21; P=0.051; I(2)=88.3%), 1.24 (95% CI: 1.18-1.3; P<0.0005; I(2)=74.3%), 1.2 (95% CI: 1.11-1.3; P<0.0005; I(2)=92.0%), and 1.19 (95% CI: 1.1-1.29; P<0.0005; I(2)=90.8%), respectively. There was evident publication bias for rs564398 and rs7754840. Subgroup analyses by ethnicity showed remarkable divergences in risk estimate for rs564398 between Asians (odds ratio [OR]=1.01; 95% CI: 0.86-1.19; P=0.868) and Caucasians (OR=1.19; 95% CI: 1.03-1.35; P=0.012) (P<0.05). For all variants examined, the results of studies in retrospective design or with population-based controls were comparative with that of overall studies. In meta-regression analyses, age was found to exert a significant influence on the association between rs10811661 and type 2 diabetes (P=0.003), as well as between rs7754840 and gender (P=0.034). Taken together, our findings provide evidence for a significant contribution of CDKN2A/B gene rs10811661 and CDKAL1 gene rs7756992 and rs10946398 to type 2 diabetes.
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Affiliation(s)
- Feng Peng
- Department of Cardiology, the First Affiliated Hospital of Fujian Medical University, Fuzhou, China
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96
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Echouffo-Tcheugui JB, Dieffenbach SD, Kengne AP. Added value of novel circulating and genetic biomarkers in type 2 diabetes prediction: a systematic review. Diabetes Res Clin Pract 2013; 101:255-69. [PMID: 23647943 DOI: 10.1016/j.diabres.2013.03.023] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/19/2012] [Revised: 10/13/2012] [Accepted: 03/15/2013] [Indexed: 02/02/2023]
Abstract
AIMS To provide a systematic overview of the added value of novel circulating and genetic biomarkers in predicting type 2 diabetes (T2DM). METHODS We searched MEDLINE and EMBASE (January 2000 to September 2012) for studies that reported a measure of improvement in the performance of T2DM risk prediction models subsequent to adding novel biomarkers to traditional risk factors. We extracted data on study methods and metrics of incremental predictive value of novel biomarkers. RESULTS We included 34 publications from 30 studies. All studies reported a change in the area under the receiver-operating characteristic curve, which was modest, ranging from -0.004 to 0.1, with claims of statistically significant improvements in eleven studies. The net reclassification index was evaluated in 11 studies, and ranged from -2.2% to 10.2% after inclusion of genetic markers in six studies (statistically significant in two cases), and from -0.5% to 27.5% after inclusion of non-genetic markers in five studies (non-significant in two studies). The integrated discrimination index (0-2.04) was reported in eight studies, being statistically significant in five of these. CONCLUSIONS Currently known novel circulating and genetic biomarkers do not substantially improve T2DM risk prediction above and beyond the ability of traditional risk factors.
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Affiliation(s)
- Justin B Echouffo-Tcheugui
- Hubert Department of Global Health, Rollins School of Public Health, Emory University, 1518 Clifton Road, Northeast Atlanta, GA 30322, USA.
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97
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Kong X, Hong J, Chen Y, Chen L, Zhao Z, Li Q, Ge J, Chen G, Guo X, Lu J, Weng J, Jia W, Ji L, Xiao J, Shan Z, Liu J, Tian H, Ji Q, Zhu D, Zhou Z, Shan G, Yang W. Association of genetic variants with isolated fasting hyperglycaemia and isolated postprandial hyperglycaemia in a Han Chinese population. PLoS One 2013; 8:e71399. [PMID: 23990951 PMCID: PMC3747192 DOI: 10.1371/journal.pone.0071399] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2013] [Accepted: 06/28/2013] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND Though multiple single nucleotide polymorphisms (SNPs) associated with type 2 diabetes have been identified, the genetic bases of isolated fasting hyperglycaemia (IFH) and isolated postprandial hyperglycaemia (IPH) were still unclear. In present study, we aimed to investigate the association of genome-wide association study-validated genetic variants and IFH or IPH in Han Chinese. METHODS/PRINCIPAL FINDINGS We genotyped 27 validated SNPs in 6,663 unrelated individuals comprising 341 IFH, 865 IPH, 1,203 combined fasting hyperglycaemia and postprandial hyperglycaemia, and 4,254 normal glycaemic subjects of Han ancestry. The distributions of genotype frequencies of FTO, CDKAL1 and GCKR were significant different between individuals with IFH and those with IPH (SNP(ptrend ): rs8050136(0.0024), rs9939609(0.0049), rs7756992(0.0122), rs780094(0.0037)). Risk allele of FTO specifically increased the risk of IFH (rs8050136: OR 1.403 [95% CI 1.125-1.750], p = 0.0027; rs9939609: 1.398 [1.120-1.744], p = 0.0030). G allele of CDKAL1 specifically increased the risk of IPH (1.217 [1.092-1.355], p = 0.0004). G allele of GCKR increased the risk of IFH (1.167 [0.999-1.362], p = 0.0513), but decreased the risk of IPH (0.891 [0.801-0.991], p = 0.0331). In addition, TCF7L2 and KCNQ1 increased the risk of both IFH and IPH. When combined, each additional risk allele associated with IFH increased the risk for IFH by 1.246-fold (p<0.0001), while each additional risk allele associated with IPH increased the risk for IPH by 1.190-fold (p<0.0001). CONCLUSION/SIGNIFICANCE Our results indicate that genotype distributions of variants from FTO, GCKR, CDKAL1 were different between IPH and IFH in Han Chinese. Variants of genes modulating insulin sensitivity (FTO, GCKR) contributed to the risk of IFH, while variants of genes related to beta cell function (CDKAL1) increase the risk of IPH.
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Affiliation(s)
- Xiaomu Kong
- Department of Endocrinology, Key Laboratory of Diabetes Prevention and Control, China-Japan Friendship Hospital, Beijing, China
| | - Jing Hong
- Department of Endocrinology, Key Laboratory of Diabetes Prevention and Control, China-Japan Friendship Hospital, Beijing, China
| | - Ying Chen
- Department of Bioinformatics, Beijing Genetics Institute, Shenzhen, Guangdong, China
| | - Li Chen
- Department of Endocrinology, Qilu Hospital of Shandong University, Jinan, Shandong, China
| | - Zhigang Zhao
- Department of Endocrinology, Henan Provincial People's Hospital, Zhengzhou, Henan, China
| | - Qiang Li
- Department of Endocrinology, Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Jiapu Ge
- Department of Endocrinology, Xinjiang Uygur Autonomous Region's Hospital, Urmqi, Xinjiang, China
| | - Gang Chen
- Department of Endocrinology, Fujian Provincial Hospital, Fuzhou, Fujian, China
| | - Xiaohui Guo
- Department of Endocrinology, Peking University First Hospital, Beijing, China
| | - Juming Lu
- Department of Endocrinology, Chinese People's Liberation Army General Hospital, Beijing, China
| | - Jianping Weng
- Department of Endocrinology, Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Weiping Jia
- Department of Endocrinology, Shanghai Jiaotong University Affiliated Sixth People's Hospital, Shanghai, China
| | - Linong Ji
- Department of Endocrinology, Peking University People's Hospital, Beijing, China
| | - Jianzhong Xiao
- Department of Endocrinology, Key Laboratory of Diabetes Prevention and Control, China-Japan Friendship Hospital, Beijing, China
| | - Zhongyan Shan
- Department of Endocrinology, First Hospital of China Medical University, Shenyang, Liaoning, China
| | - Jie Liu
- Department of Endocrinology, Shanxi Provincial People's Hospital, Taiyuan, Shanxi, China
| | - Haoming Tian
- Department of Endocrinology, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Qiuhe Ji
- Department of Endocrinology, Xijing Hospital of Fourth Military Medical University, Xi'an, Shaanxi, China
| | - Dalong Zhu
- Department of Endocrinology, Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, Jiangsu, China
| | - Zhiguang Zhou
- Department of Endocrinology, Xiangya Second Hospital, Changsha, Hunan, China
| | - Guangliang Shan
- Department of Epidemiology, Peking Union Medical College, Beijing, China
| | - Wenying Yang
- Department of Endocrinology, Key Laboratory of Diabetes Prevention and Control, China-Japan Friendship Hospital, Beijing, China
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98
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Affiliation(s)
- Valeriya Lyssenko
- Department of Clinical Sciences, Diabetes and Endocrinology, Lund University, Malmö, Sweden.
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99
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Ma RCW, Hu C, Tam CH, Zhang R, Kwan P, Leung TF, Thomas GN, Go MJ, Hara K, Sim X, Ho JSK, Wang C, Li H, Lu L, Wang Y, Li JW, Wang Y, Lam VKL, Wang J, Yu W, Kim YJ, Ng DP, Fujita H, Panoutsopoulou K, Day-Williams AG, Lee HM, Ng ACW, Fang YJ, Kong APS, Jiang F, Ma X, Hou X, Tang S, Lu J, Yamauchi T, Tsui SKW, Woo J, Leung PC, Zhang X, Tang NLS, Sy HY, Liu J, Wong TY, Lee JY, Maeda S, Xu G, Cherny SS, Chan TF, Ng MCY, Xiang K, Morris AP, DIAGRAM Consortium, Keildson S, The MuTHER Consortium, Hu R, Ji L, Lin X, Cho YS, Kadowaki T, Tai ES, Zeggini E, McCarthy MI, Hon KL, Baum L, Tomlinson B, So WY, Bao Y, Chan JCN, Jia W. Genome-wide association study in a Chinese population identifies a susceptibility locus for type 2 diabetes at 7q32 near PAX4. Diabetologia 2013; 56:1291-305. [PMID: 23532257 PMCID: PMC3648687 DOI: 10.1007/s00125-013-2874-4] [Citation(s) in RCA: 71] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/21/2012] [Accepted: 01/31/2013] [Indexed: 12/18/2022]
Abstract
AIMS/HYPOTHESIS Most genetic variants identified for type 2 diabetes have been discovered in European populations. We performed genome-wide association studies (GWAS) in a Chinese population with the aim of identifying novel variants for type 2 diabetes in Asians. METHODS We performed a meta-analysis of three GWAS comprising 684 patients with type 2 diabetes and 955 controls of Southern Han Chinese descent. We followed up the top signals in two independent Southern Han Chinese cohorts (totalling 10,383 cases and 6,974 controls), and performed in silico replication in multiple populations. RESULTS We identified CDKN2A/B and four novel type 2 diabetes association signals with p < 1 × 10(-5) from the meta-analysis. Thirteen variants within these four loci were followed up in two independent Chinese cohorts, and rs10229583 at 7q32 was found to be associated with type 2 diabetes in a combined analysis of 11,067 cases and 7,929 controls (p meta = 2.6 × 10(-8); OR [95% CI] 1.18 [1.11, 1.25]). In silico replication revealed consistent associations across multiethnic groups, including five East Asian populations (p meta = 2.3 × 10(-10)) and a population of European descent (p = 8.6 × 10(-3)). The rs10229583 risk variant was associated with elevated fasting plasma glucose, impaired beta cell function in controls, and an earlier age at diagnosis for the cases. The novel variant lies within an islet-selective cluster of open regulatory elements. There was significant heterogeneity of effect between Han Chinese and individuals of European descent, Malaysians and Indians. CONCLUSIONS/INTERPRETATION Our study identifies rs10229583 near PAX4 as a novel locus for type 2 diabetes in Chinese and other populations and provides new insights into the pathogenesis of type 2 diabetes.
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Affiliation(s)
- R. C. W. Ma
- Department of Medicine and Therapeutics, Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, New Territories, Hong Kong, SAR People’s Republic of China
- Hong Kong Institute of Diabetes and Obesity, Chinese University of Hong Kong, Hong Kong, SAR People’s Republic of China
- Li Ka Shing Institute of Life Sciences, Chinese University of Hong Kong, Hong Kong, SAR People’s Republic of China
| | - C. Hu
- Department of Endocrinology and Metabolism, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Clinical Center for Diabetes, Shanghai Key Clinical Center for Metabolic Disease, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, 600 Yishan Road, Shanghai, 200233 People’s Republic of China
- Shanghai Jiao Tong University Affiliated Sixth People’s Hospital South Campus, Shanghai, People’s Republic of China
| | - C. H. Tam
- Department of Medicine and Therapeutics, Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, New Territories, Hong Kong, SAR People’s Republic of China
| | - R. Zhang
- Department of Endocrinology and Metabolism, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Clinical Center for Diabetes, Shanghai Key Clinical Center for Metabolic Disease, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, 600 Yishan Road, Shanghai, 200233 People’s Republic of China
| | - P. Kwan
- Department of Medicine and Therapeutics, Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, New Territories, Hong Kong, SAR People’s Republic of China
| | - T. F. Leung
- Department of Paediatrics, Chinese University of Hong Kong, Hong Kong, People’s Republic of China
| | - G. N. Thomas
- Department of Public Health, Epidemiology and Biostatistics, University of Birmingham, Birmingham, UK
| | - M. J. Go
- Center for Genome Science, National Institute of Health, Osong Health Technology Administration Complex, Gangoe-myeon, Yeonje-ri, Cheongwon-gun, Chungcheongbuk-do Republic of Korea
| | - K. Hara
- Department of Diabetes and Metabolic Diseases, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
- Department of Integrated Molecular Science on Metabolic Diseases, University of Tokyo Hospital, Tokyo, Japan
| | - X. Sim
- Centre for Molecular Epidemiology, Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Republic of Singapore
- Center for Statistical Genetics and Department of Biostatistics, University of Michigan, Ann Arbor, MI USA
| | - J. S. K. Ho
- Department of Medicine and Therapeutics, Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, New Territories, Hong Kong, SAR People’s Republic of China
| | - C. Wang
- Department of Endocrinology and Metabolism, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Clinical Center for Diabetes, Shanghai Key Clinical Center for Metabolic Disease, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, 600 Yishan Road, Shanghai, 200233 People’s Republic of China
| | - H. Li
- Key Laboratory of Nutrition and Metabolism, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences and Graduate School of the Chinese Academy of Sciences, Shanghai, People’s Republic of China
| | - L. Lu
- Key Laboratory of Nutrition and Metabolism, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences and Graduate School of the Chinese Academy of Sciences, Shanghai, People’s Republic of China
| | - Y. Wang
- Key Laboratory of Nutrition and Metabolism, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences and Graduate School of the Chinese Academy of Sciences, Shanghai, People’s Republic of China
| | - J. W. Li
- School of Life Sciences, Chinese University of Hong Kong, Hong Kong, SAR People’s Republic of China
| | - Y. Wang
- Department of Medicine and Therapeutics, Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, New Territories, Hong Kong, SAR People’s Republic of China
| | - V. K. L. Lam
- Department of Medicine and Therapeutics, Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, New Territories, Hong Kong, SAR People’s Republic of China
| | - J. Wang
- Department of Endocrinology and Metabolism, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Clinical Center for Diabetes, Shanghai Key Clinical Center for Metabolic Disease, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, 600 Yishan Road, Shanghai, 200233 People’s Republic of China
| | - W. Yu
- Department of Endocrinology and Metabolism, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Clinical Center for Diabetes, Shanghai Key Clinical Center for Metabolic Disease, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, 600 Yishan Road, Shanghai, 200233 People’s Republic of China
| | - Y. J. Kim
- Center for Genome Science, National Institute of Health, Osong Health Technology Administration Complex, Gangoe-myeon, Yeonje-ri, Cheongwon-gun, Chungcheongbuk-do Republic of Korea
| | - D. P. Ng
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Republic of Singapore
| | - H. Fujita
- Department of Diabetes and Metabolic Diseases, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
| | - K. Panoutsopoulou
- Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK
| | - A. G. Day-Williams
- Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK
| | - H. M. Lee
- Department of Medicine and Therapeutics, Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, New Territories, Hong Kong, SAR People’s Republic of China
| | - A. C. W. Ng
- Department of Medicine and Therapeutics, Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, New Territories, Hong Kong, SAR People’s Republic of China
| | - Y-J. Fang
- Department of Colorectal Surgery, State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Guangzhou, People’s Republic of China
| | - A. P. S. Kong
- Department of Medicine and Therapeutics, Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, New Territories, Hong Kong, SAR People’s Republic of China
| | - F. Jiang
- Department of Endocrinology and Metabolism, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Clinical Center for Diabetes, Shanghai Key Clinical Center for Metabolic Disease, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, 600 Yishan Road, Shanghai, 200233 People’s Republic of China
| | - X. Ma
- Department of Endocrinology and Metabolism, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Clinical Center for Diabetes, Shanghai Key Clinical Center for Metabolic Disease, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, 600 Yishan Road, Shanghai, 200233 People’s Republic of China
| | - X. Hou
- Department of Endocrinology and Metabolism, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Clinical Center for Diabetes, Shanghai Key Clinical Center for Metabolic Disease, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, 600 Yishan Road, Shanghai, 200233 People’s Republic of China
| | - S. Tang
- Department of Endocrinology and Metabolism, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Clinical Center for Diabetes, Shanghai Key Clinical Center for Metabolic Disease, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, 600 Yishan Road, Shanghai, 200233 People’s Republic of China
| | - J. Lu
- Department of Endocrinology and Metabolism, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Clinical Center for Diabetes, Shanghai Key Clinical Center for Metabolic Disease, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, 600 Yishan Road, Shanghai, 200233 People’s Republic of China
| | - T. Yamauchi
- Department of Diabetes and Metabolic Diseases, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
| | - S. K. W. Tsui
- School of Biomedical Sciences, Chinese University of Hong Kong, Hong Kong, SAR People’s Republic of China
| | - J. Woo
- Department of Medicine and Therapeutics, Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, New Territories, Hong Kong, SAR People’s Republic of China
| | - P. C. Leung
- Department of Orthopaedics, Chinese University of Hong Kong, Hong Kong, SAR People’s Republic of China
| | - X. Zhang
- Shanghai Jiao Tong University Affiliated Sixth People’s Hospital South Campus, Shanghai, People’s Republic of China
| | - N. L. S. Tang
- Department of Chemical Pathology, Chinese University of Hong Kong, Hong Kong, SAR People’s Republic of China
| | - H. Y. Sy
- Department of Paediatrics, Chinese University of Hong Kong, Hong Kong, People’s Republic of China
| | - J. Liu
- Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore, Republic of Singapore
| | - T. Y. Wong
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Republic of Singapore
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Republic of Singapore
- Centre for Eye Research Australia, University of Melbourne, East Melbourne, VIC Australia
| | - J. Y. Lee
- Center for Genome Science, National Institute of Health, Osong Health Technology Administration Complex, Gangoe-myeon, Yeonje-ri, Cheongwon-gun, Chungcheongbuk-do Republic of Korea
| | - S. Maeda
- Laboratory for Endocrinology and Metabolism, RIKEN Center for Genomic Medicine, Yokohama, Japan
| | - G. Xu
- Department of Medicine and Therapeutics, Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, New Territories, Hong Kong, SAR People’s Republic of China
| | - S. S. Cherny
- Department of Psychiatry and State Key Laboratory of Brain and Cognitive Sciences, University of Hong Kong, Hong Kong, SAR People’s Republic of China
| | - T. F. Chan
- School of Life Sciences, Chinese University of Hong Kong, Hong Kong, SAR People’s Republic of China
| | - M. C. Y. Ng
- Center for Genomics and Personalized Medicine Research, Center for Diabetes Research, Wake Forest School of Medicine, Winston-Salem, NC USA
| | - K. Xiang
- Department of Endocrinology and Metabolism, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Clinical Center for Diabetes, Shanghai Key Clinical Center for Metabolic Disease, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, 600 Yishan Road, Shanghai, 200233 People’s Republic of China
| | - A. P. Morris
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK
| | | | - S. Keildson
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK
| | | | - R. Hu
- Institute of Endocrinology and Diabetology, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, People’s Republic of China
| | - L. Ji
- Department of Endocrinology and Metabolism, Peking University People’s Hospital, Beijing, People’s Republic of China
| | - X. Lin
- Key Laboratory of Nutrition and Metabolism, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences and Graduate School of the Chinese Academy of Sciences, Shanghai, People’s Republic of China
| | - Y. S. Cho
- Department of Biomedical Science, Hallym University, Chuncheon, Gangwon-do Republic of Korea
| | - T. Kadowaki
- Department of Diabetes and Metabolic Diseases, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
| | - E. S. Tai
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Republic of Singapore
- Graduate Medical School, Duke-National University of Singapore, Singapore, Republic of Singapore
| | - E. Zeggini
- Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK
| | - M. I. McCarthy
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Churchill Hospital, Oxford, UK
| | - K. L. Hon
- Department of Paediatrics, Chinese University of Hong Kong, Hong Kong, People’s Republic of China
| | - L. Baum
- School of Pharmacy, Chinese University of Hong Kong, Hong Kong, SAR People’s Republic of China
| | - B. Tomlinson
- Department of Medicine and Therapeutics, Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, New Territories, Hong Kong, SAR People’s Republic of China
| | - W. Y. So
- Department of Medicine and Therapeutics, Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, New Territories, Hong Kong, SAR People’s Republic of China
| | - Y. Bao
- Department of Endocrinology and Metabolism, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Clinical Center for Diabetes, Shanghai Key Clinical Center for Metabolic Disease, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, 600 Yishan Road, Shanghai, 200233 People’s Republic of China
| | - J. C. N. Chan
- Department of Medicine and Therapeutics, Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, New Territories, Hong Kong, SAR People’s Republic of China
- Hong Kong Institute of Diabetes and Obesity, Chinese University of Hong Kong, Hong Kong, SAR People’s Republic of China
- Li Ka Shing Institute of Life Sciences, Chinese University of Hong Kong, Hong Kong, SAR People’s Republic of China
| | - W. Jia
- Department of Endocrinology and Metabolism, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Clinical Center for Diabetes, Shanghai Key Clinical Center for Metabolic Disease, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, 600 Yishan Road, Shanghai, 200233 People’s Republic of China
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100
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Saxena R, Saleheen D, Been LF, Garavito ML, Braun T, Bjonnes A, Young R, Ho WK, Rasheed A, Frossard P, Sim X, Hassanali N, Radha V, Chidambaram M, Liju S, Rees SD, Ng DPK, Wong TY, Yamauchi T, Hara K, Tanaka Y, Hirose H, McCarthy MI, Morris AP, DIAGRAM, MuTHER, AGEN, Basit A, Barnett AH, Katulanda P, Matthews D, Mohan V, Wander GS, Singh JR, Mehra NK, Ralhan S, Kamboh MI, Mulvihill JJ, Maegawa H, Tobe K, Maeda S, Cho YS, Tai ES, Kelly MA, Chambers JC, Kooner JS, Kadowaki T, Deloukas P, Rader DJ, Danesh J, Sanghera DK. Genome-wide association study identifies a novel locus contributing to type 2 diabetes susceptibility in Sikhs of Punjabi origin from India. Diabetes 2013; 62:1746-1755. [PMID: 23300278 PMCID: PMC3636649 DOI: 10.2337/db12-1077] [Citation(s) in RCA: 136] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/13/2012] [Accepted: 11/22/2012] [Indexed: 12/16/2022]
Abstract
We performed a genome-wide association study (GWAS) and a multistage meta-analysis of type 2 diabetes (T2D) in Punjabi Sikhs from India. Our discovery GWAS in 1,616 individuals (842 case subjects) was followed by in silico replication of the top 513 independent single nucleotide polymorphisms (SNPs) (P < 10⁻³) in Punjabi Sikhs (n = 2,819; 801 case subjects). We further replicated 66 SNPs (P < 10⁻⁴) through genotyping in a Punjabi Sikh sample (n = 2,894; 1,711 case subjects). On combined meta-analysis in Sikh populations (n = 7,329; 3,354 case subjects), we identified a novel locus in association with T2D at 13q12 represented by a directly genotyped intronic SNP (rs9552911, P = 1.82 × 10⁻⁸) in the SGCG gene. Next, we undertook in silico replication (stage 2b) of the top 513 signals (P < 10⁻³) in 29,157 non-Sikh South Asians (10,971 case subjects) and de novo genotyping of up to 31 top signals (P < 10⁻⁴) in 10,817 South Asians (5,157 case subjects) (stage 3b). In combined South Asian meta-analysis, we observed six suggestive associations (P < 10⁻⁵ to < 10⁻⁷), including SNPs at HMG1L1/CTCFL, PLXNA4, SCAP, and chr5p11. Further evaluation of 31 top SNPs in 33,707 East Asians (16,746 case subjects) (stage 3c) and 47,117 Europeans (8,130 case subjects) (stage 3d), and joint meta-analysis of 128,127 individuals (44,358 case subjects) from 27 multiethnic studies, did not reveal any additional loci nor was there any evidence of replication for the new variant. Our findings provide new evidence on the presence of a population-specific signal in relation to T2D, which may provide additional insights into T2D pathogenesis.
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Affiliation(s)
- Richa Saxena
- Center for Human Genetic Research and Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - Danish Saleheen
- Center for Non-Communicable Diseases, Karachi, Pakistan
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, U.K
- Departments of Biostatistics and Epidemiology and Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Latonya F. Been
- Department of Pediatrics, College of Medicine, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma
| | - Martha L. Garavito
- Department of Pediatrics, College of Medicine, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma
| | - Timothy Braun
- Department of Pediatrics, College of Medicine, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma
| | - Andrew Bjonnes
- Center for Human Genetic Research and Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - Robin Young
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, U.K
| | - Weang Kee Ho
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, U.K
| | - Asif Rasheed
- Center for Non-Communicable Diseases, Karachi, Pakistan
| | | | - Xueling Sim
- Center for Statistical Genetics and Department of Statistics, University of Michigan, Ann Arbor, Michigan
- Centre for Molecular Epidemiology, National University of Singapore, Singapore
| | - Neelam Hassanali
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Churchill Hospital, Oxford, U.K
| | | | | | - Samuel Liju
- Madras Diabetes Research Foundation, Chennai, India
| | - Simon D. Rees
- College of Medical and Dental Sciences, University of Birmingham, Birmingham, U.K
- Diabetes Centre, Heart of England National Health Service Foundation Trust, Birmingham, U.K
| | - Daniel Peng-Keat Ng
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore
| | - Tien-Yin Wong
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
- Department of Ophthalmology, National University of Singapore, Singapore
- Centre for Eye Research Australia, University of Melbourne, Melbourne, Australia
| | - Toshimasa Yamauchi
- Department of Diabetes and Metabolic Diseases, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Kazuo Hara
- Department of Diabetes and Metabolic Diseases, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
- Department of Integrated Molecular Science on Metabolic Diseases, 22nd Century Medical and Research Center, The University of Tokyo, Tokyo, Japan
| | - Yasushi Tanaka
- Department of Internal Medicine, Division of Metabolism and Endocrinology, St. Marianna University School of Medicine, Kanagawa, Japan
| | - Hiroshi Hirose
- Health Center, Keio University School of Medicine, Tokyo, Japan
| | - Mark I. McCarthy
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Churchill Hospital, Oxford, U.K
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, U.K
- Oxford National Institute for Health Research Biomedical Research Centre, Churchill Hospital, Oxford, U.K
| | - Andrew P. Morris
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, U.K
| | | | | | | | - Abdul Basit
- Baqai Institute of Diabetology and Endocrinology, Karachi, Pakistan
| | - Anthony H. Barnett
- Diabetes Centre, Heart of England National Health Service Foundation Trust, Birmingham, U.K
| | - Prasad Katulanda
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Churchill Hospital, Oxford, U.K
- Diabetes Research Unit, Department of Clinical Medicine, University of Colombo, Colombo, Sri Lanka
| | - David Matthews
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Churchill Hospital, Oxford, U.K
| | - Viswanathan Mohan
- Madras Diabetes Research Foundation, Chennai, India
- Dr. Mohan's Diabetes Specialities Centre, Chennai, India
| | - Gurpreet S. Wander
- Hero Dayanand Medical College and Heart Institute, Ludhiana, Punjab, India
| | - Jai Rup Singh
- Central University of Punjab, Bathinda, Punjab, India
| | - Narinder K. Mehra
- All India Institute of Medical Sciences and Research, New Delhi, India
| | - Sarju Ralhan
- Hero Dayanand Medical College and Heart Institute, Ludhiana, Punjab, India
| | - M. Ilyas Kamboh
- Department of Human Genetics, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - John J. Mulvihill
- Department of Pediatrics, College of Medicine, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma
| | - Hiroshi Maegawa
- Department of Medicine, Shiga University of Medical Science, Shiga, Japan
| | - Kazuyuki Tobe
- First Department of Internal Medicine, University of Toyama, Toyama, Japan
| | - Shiro Maeda
- Laboratory for Endocrinology and Metabolism, RIKEN Center for Genomic Medicine, Kanagawa, Japan
| | - Yoon S. Cho
- Department of Biomedical Science, Hallym University, Chuncheon, Gangwon-do 200-702, Republic of Korea
| | - E. Shyong Tai
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Duke-NUS Graduate Medical School Singapore, Singapore
| | - M. Ann Kelly
- College of Medical and Dental Sciences, University of Birmingham, Birmingham, U.K
- Diabetes Centre, Heart of England National Health Service Foundation Trust, Birmingham, U.K
| | - John C. Chambers
- Ealing Hospital National Health Service Trust, Middlesex, U.K
- Imperial College Healthcare National Health Service Trust, London, U.K
- Epidemiology and Biostatistics, Imperial College London, London, U.K
| | - Jaspal S. Kooner
- Ealing Hospital National Health Service Trust, Middlesex, U.K
- Imperial College Healthcare National Health Service Trust, London, U.K
- National Heart and Lung Institute, Imperial College London, Hammersmith Hospital, London, U.K
| | - Takashi Kadowaki
- Department of Diabetes and Metabolic Diseases, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | | | - Daniel J. Rader
- Department of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - John Danesh
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, U.K
| | - Dharambir K. Sanghera
- Department of Pediatrics, College of Medicine, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma
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