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Tatara Y, Kasai S, Kokubu D, Tsujita T, Mimura J, Itoh K. Emerging Role of GCN1 in Disease and Homeostasis. Int J Mol Sci 2024; 25:2998. [PMID: 38474243 PMCID: PMC10931611 DOI: 10.3390/ijms25052998] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Revised: 02/29/2024] [Accepted: 03/02/2024] [Indexed: 03/14/2024] Open
Abstract
GCN1 is recognized as a factor that is essential for the activation of GCN2, which is a sensor of amino acid starvation. This function is evolutionarily conserved from yeast to higher eukaryotes. However, recent studies have revealed non-canonical functions of GCN1 that are independent of GCN2, such as its participation in cell proliferation, apoptosis, and the immune response, beyond the borders of species. Although it is known that GCN1 and GCN2 interact with ribosomes to accomplish amino acid starvation sensing, recent studies have reported that GCN1 binds to disomes (i.e., ribosomes that collide each other), thereby regulating both the co-translational quality control and stress response. We propose that GCN1 regulates ribosome-mediated signaling by dynamically changing its partners among RWD domain-possessing proteins via unknown mechanisms. We recently demonstrated that GCN1 is essential for cell proliferation and whole-body energy regulation in mice. However, the manner in which ribosome-initiated signaling via GCN1 is related to various physiological functions warrants clarification. GCN1-mediated mechanisms and its interaction with other quality control and stress response signals should be important for proteostasis during aging and neurodegenerative diseases, and may be targeted for drug development.
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Affiliation(s)
- Yota Tatara
- Department of Stress Response Science, Biomedical Research Center, Hirosaki University Graduate School of Medicine, 5 Zaifu-cho, Hirosaki 036-8562, Aomori, Japan
| | - Shuya Kasai
- Department of Stress Response Science, Biomedical Research Center, Hirosaki University Graduate School of Medicine, 5 Zaifu-cho, Hirosaki 036-8562, Aomori, Japan
| | - Daichi Kokubu
- Diet and Well-Being Research Institute, KAGOME, Co., Ltd., 17 Nishitomiyama, Nasushiobara 329-2762, Tochigi, Japan
- Department of Vegetable Life Science, Hirosaki University Graduate School of Medicine, 5 Zaifu-cho, Hirosaki 036-8562, Aomori, Japan
| | - Tadayuki Tsujita
- Laboratory of Biochemistry, Department of Applied Biochemistry and Food Science, Faculty of Agriculture, Saga University, 1 Honjo-machi, Saga City 840-8502, Saga, Japan;
| | - Junsei Mimura
- Department of Stress Response Science, Biomedical Research Center, Hirosaki University Graduate School of Medicine, 5 Zaifu-cho, Hirosaki 036-8562, Aomori, Japan
| | - Ken Itoh
- Department of Stress Response Science, Biomedical Research Center, Hirosaki University Graduate School of Medicine, 5 Zaifu-cho, Hirosaki 036-8562, Aomori, Japan
- Department of Vegetable Life Science, Hirosaki University Graduate School of Medicine, 5 Zaifu-cho, Hirosaki 036-8562, Aomori, Japan
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2
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Pravednikova AE, Nikitich A, Witkowicz A, Karabon L, Flouris AD, Vliora M, Nintou E, Dinas PC, Szulińska M, Bogdański P, Metsios GS, Kerchev VV, Yepiskoposyan L, Bylino OV, Larina SN, Shulgin B, Shidlovskii YV. Genotypes of the UCP1 gene polymorphisms and cardiometabolic diseases: A multifactorial study of association with disease probability. Biochimie 2024; 218:162-173. [PMID: 37863280 DOI: 10.1016/j.biochi.2023.10.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Revised: 10/16/2023] [Accepted: 10/18/2023] [Indexed: 10/22/2023]
Abstract
Cardiometabolic diseases (CMDs) are complex disorders with a heterogenous phenotype, which are caused by multiple factors including genetic factors. Single nucleotide polymorphisms (SNPs) rs45539933 (p.Ala64Thr), rs10011540 (c.-112A>C), rs3811791 (c.-1766A>G), and rs1800592 (c.-3826A>G) in the UCP1 gene have been analyzed for association with CMDs in many studies providing controversial results. However, previous studies only considered individual UCP1 SNPs and did not evaluate them in an integrated manner, which is a more powerful approach to uncover genetic component of complex diseases. This study aimed to investigate associations between UCP1 genotype combinations and CMDs or CMD risk factors in the context of non-genetic factors. We performed multiple logistic regression analysis and proposed new methodology of testing different combinations of SNP genotypes. We found that probability of CMDs increased in presence of the three-SNP combination of genotypes with minor alleles of c.-3826A>G and p.Ala64Thr and wild allele of c.-112A>C, with increasing age, body mass index (BMI), body fat percentage (BF%) and may differ between sexes and between countries. The combination of genotypes with c.-3826A>G minor allele and wild homozygotes of c.-112A>C and p.Ala64Thr was associated with increased probability of diabetes. While combination of genotypes with minor alleles of all three SNPs reduced the CMD probability. The present results suggest that age, BMI, sex, and UCP1 three-SNP combinations of genotypes significantly contribute to CMD probability. Varying of c.-112A>C alleles in the genotype combination with minor alleles of c.-3826A>G and p.Ala64Thr markedly changes CMD probability.
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Affiliation(s)
- Anna E Pravednikova
- Laboratory of Gene Expression Regulation in Development, Institute of Gene Biology, Russian Academy of Sciences, Moscow, Russia.
| | - Antonina Nikitich
- Center for Mathematical Modeling in Drug Development, Institute of Biodesign and Complex Systems Modeling, I.M. Sechenov First Moscow State Medical University, Ministry of Health of the Russian Federation, Moscow, Russia
| | - Agata Witkowicz
- Institute of Immunology and Experimental Therapy, Polish Academy of Sciences, Wroclaw, Poland
| | - Lidia Karabon
- Institute of Immunology and Experimental Therapy, Polish Academy of Sciences, Wroclaw, Poland
| | - Andreas D Flouris
- FAME Laboratory, Department of Physical Education and Sport Science, University of Thessaly, Trikala, Greece
| | - Maria Vliora
- FAME Laboratory, Department of Physical Education and Sport Science, University of Thessaly, Trikala, Greece
| | - Eleni Nintou
- FAME Laboratory, Department of Physical Education and Sport Science, University of Thessaly, Trikala, Greece
| | - Petros C Dinas
- FAME Laboratory, Department of Physical Education and Sport Science, University of Thessaly, Trikala, Greece
| | - Monika Szulińska
- Department of Treatment of Obesity, Metabolic Disorders and Clinical Dietetics, Poznan University of Medical Sciences, Poznan, Poland
| | - Paweł Bogdański
- Department of Treatment of Obesity, Metabolic Disorders and Clinical Dietetics, Poznan University of Medical Sciences, Poznan, Poland
| | - George S Metsios
- School of Physical Education, Sport Science and Dietetics, University of Thessaly, Trikala, Greece
| | - Victor V Kerchev
- Laboratory of Gene Expression Regulation in Development, Institute of Gene Biology, Russian Academy of Sciences, Moscow, Russia; Department of Biology and General Genetics, I.M. Sechenov First Moscow State Medical University, Ministry of Health of the Russian Federation, Moscow, Russia
| | - Levon Yepiskoposyan
- Laboratory of Evolutionary Genomics, Institute of Molecular Biology, National Academy of Sciences of the Republic of Armenia, Yerevan, Armenia
| | - Oleg V Bylino
- Laboratory of Gene Expression Regulation in Development, Institute of Gene Biology, Russian Academy of Sciences, Moscow, Russia
| | - Svetlana N Larina
- Laboratory of Gene Expression Regulation in Development, Institute of Gene Biology, Russian Academy of Sciences, Moscow, Russia; Department of Biology and General Genetics, I.M. Sechenov First Moscow State Medical University, Ministry of Health of the Russian Federation, Moscow, Russia
| | - Boris Shulgin
- Center for Mathematical Modeling in Drug Development, Institute of Biodesign and Complex Systems Modeling, I.M. Sechenov First Moscow State Medical University, Ministry of Health of the Russian Federation, Moscow, Russia; Department of Mathematics, Mechanics and Mathematical Modeling, Institute of Computer Science and Mathematical Modeling, I.M. Sechenov First Moscow State Medical University, Ministry of Health of the Russian Federation, Moscow, Russia
| | - Yulii V Shidlovskii
- Laboratory of Gene Expression Regulation in Development, Institute of Gene Biology, Russian Academy of Sciences, Moscow, Russia; Department of Biology and General Genetics, I.M. Sechenov First Moscow State Medical University, Ministry of Health of the Russian Federation, Moscow, Russia
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Seike M, Asahara SI, Inoue H, Kudo M, Kanno A, Yokoi A, Suzuki H, Kimura-Koyanagi M, Kido Y, Ogawa W. l-Asparaginase regulates mTORC1 activity via a TSC2-dependent pathway in pancreatic beta cells. Biochem Biophys Res Commun 2023; 652:121-130. [PMID: 36842323 DOI: 10.1016/j.bbrc.2023.02.035] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Revised: 02/09/2023] [Accepted: 02/14/2023] [Indexed: 02/18/2023]
Abstract
Eif2ak4, a susceptibility gene for type 2 diabetes, encodes GCN2, a molecule activated by amino acid deficiency. Mutations or deletions in GCN2 in pancreatic β-cells increase mTORC1 activity by decreasing Sestrin2 expression in a TSC2-independent manner. In this study, we searched for molecules downstream of GCN2 that suppress mTORC1 activity in a TSC2-dependent manner. To do so, we used a pull-down assay to identify molecules that competitively inhibit the binding of the T1462 phosphorylation site of TSC2 to 14-3-3. l-asparaginase was identified. Although l-asparaginase is frequently used as an anticancer drug for acute lymphoblastic leukemia, little is known about endogenous l-asparaginase. l-Asparaginase, which is expressed downstream of GCN2, was found to bind 14-3-3 and thereby to inhibit its binding to the T1462 phosphorylation site of TSC2 and contribute to TSC2 activation and mTORC1 inactivation upon TSC2 dephosphorylation. Further investigation of the regulation of mTORC1 activity in pancreatic β-cells by l-asparaginase should help to elucidate the mechanism of diabetes and insulin secretion failure during anticancer drug use.
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Affiliation(s)
- Masako Seike
- Division of Diabetes and Endocrinology, Department of Internal Medicine, Kobe University Graduate School of Medicine, 7-5-1 Kusunoki-cho, Chuo-ku, Kobe, Hyogo, 650-0017, Japan.
| | - Shun-Ichiro Asahara
- Division of Diabetes and Endocrinology, Department of Internal Medicine, Kobe University Graduate School of Medicine, 7-5-1 Kusunoki-cho, Chuo-ku, Kobe, Hyogo, 650-0017, Japan.
| | - Hiroyuki Inoue
- Division of Medical Chemistry, Department of Metabolism and Diseases, Kobe University Graduate School of Health Sciences, 7-10-2 Tomogaoka, Suma-ku, Kobe, Hyogo, 654-0142, Japan.
| | - Michiyo Kudo
- Division of Medical Chemistry, Department of Metabolism and Diseases, Kobe University Graduate School of Health Sciences, 7-10-2 Tomogaoka, Suma-ku, Kobe, Hyogo, 654-0142, Japan.
| | - Ayumi Kanno
- Division of Diabetes and Endocrinology, Department of Internal Medicine, Kobe University Graduate School of Medicine, 7-5-1 Kusunoki-cho, Chuo-ku, Kobe, Hyogo, 650-0017, Japan.
| | - Aisha Yokoi
- Division of Diabetes and Endocrinology, Department of Internal Medicine, Kobe University Graduate School of Medicine, 7-5-1 Kusunoki-cho, Chuo-ku, Kobe, Hyogo, 650-0017, Japan.
| | - Hirotaka Suzuki
- Division of Diabetes and Endocrinology, Department of Internal Medicine, Kobe University Graduate School of Medicine, 7-5-1 Kusunoki-cho, Chuo-ku, Kobe, Hyogo, 650-0017, Japan.
| | - Maki Kimura-Koyanagi
- Division of Diabetes and Endocrinology, Department of Internal Medicine, Kobe University Graduate School of Medicine, 7-5-1 Kusunoki-cho, Chuo-ku, Kobe, Hyogo, 650-0017, Japan.
| | - Yoshiaki Kido
- Division of Medical Chemistry, Department of Metabolism and Diseases, Kobe University Graduate School of Health Sciences, 7-10-2 Tomogaoka, Suma-ku, Kobe, Hyogo, 654-0142, Japan.
| | - Wataru Ogawa
- Division of Diabetes and Endocrinology, Department of Internal Medicine, Kobe University Graduate School of Medicine, 7-5-1 Kusunoki-cho, Chuo-ku, Kobe, Hyogo, 650-0017, Japan.
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Watanabe H, Okada H, Hirose J, Omata Y, Matsumoto T, Matsumoto M, Nakamura M, Saito T, Miyamoto T, Tanaka S. Transcription factor Hhex negatively regulates osteoclast differentiation by controlling cyclin‐dependent kinase inhibitors. JBMR Plus 2022; 6:e10608. [PMID: 35434453 PMCID: PMC9009129 DOI: 10.1002/jbm4.10608] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/04/2021] [Revised: 01/05/2022] [Accepted: 01/19/2022] [Indexed: 11/11/2022] Open
Abstract
We investigated the role of hematopoietically expressed homeobox protein (Hhex) in osteoclast development. Trimethylation of lysine 27 of histone H3 at the cis‐regulatory element of Hhex was maintained and that of lysine 4 was reduced during receptor activator of nuclear factor κB ligand (RANKL)‐induced osteoclastogenesis, which was associated with a reduction of Hhex expression. Overexpression of Hhex in bone marrow–derived macrophages inhibited, whereas Hhex suppression promoted, RANKL‐induced osteoclastogenesis in vitro. Conditional deletion of Hhex in osteoclast‐lineage cells promoted osteoclastogenesis and reduced cancellous bone volume in mice, confirming the negative regulatory role of Hhex in osteoclast differentiation. Expression of cyclin‐dependent kinase inhibitors such as Cdkn2a and Cdkn1b in osteoclast precursors was negatively regulated by Hhex, and Hhex deletion increased the ratio of cells at the G1 phase of the cell cycle. In conclusion, Hhex is an inhibitor of osteoclast differentiation that is regulated in an epigenetic manner and regulates the cell cycle of osteoclast precursors and the skeletal homeostasis. © 2022 The Authors. JBMR Plus published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research.
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Affiliation(s)
- Hisato Watanabe
- Department of Orthopaedic Surgery, Faculty of Medicine The University of Tokyo, 7‐3‐1 Hongo, Bunkyo‐ku Tokyo Japan
| | - Hiroyuki Okada
- Center for Disease Biology and Integrative Medicine, Graduate School of Medicine The University of Tokyo Tokyo Japan
| | - Jun Hirose
- Department of Orthopaedic Surgery, Faculty of Medicine The University of Tokyo, 7‐3‐1 Hongo, Bunkyo‐ku Tokyo Japan
| | - Yasunori Omata
- Department of Orthopaedic Surgery, Faculty of Medicine The University of Tokyo, 7‐3‐1 Hongo, Bunkyo‐ku Tokyo Japan
| | - Takumi Matsumoto
- Department of Orthopaedic Surgery, Faculty of Medicine The University of Tokyo, 7‐3‐1 Hongo, Bunkyo‐ku Tokyo Japan
| | - Morio Matsumoto
- Department of Orthopaedic Surgery Keio University School of Medicine Tokyo Japan
| | - Masaya Nakamura
- Department of Orthopaedic Surgery Keio University School of Medicine Tokyo Japan
| | - Taku Saito
- Department of Orthopaedic Surgery, Faculty of Medicine The University of Tokyo, 7‐3‐1 Hongo, Bunkyo‐ku Tokyo Japan
| | - Takeshi Miyamoto
- Department of Orthopedic Surgery Kumamoto University Kumamoto Japan
| | - Sakae Tanaka
- Department of Orthopaedic Surgery, Faculty of Medicine The University of Tokyo, 7‐3‐1 Hongo, Bunkyo‐ku Tokyo Japan
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Asahara SI, Inoue H, Kido Y. Regulation of Pancreatic β-Cell Mass by Gene-Environment Interaction. Diabetes Metab J 2022; 46:38-48. [PMID: 35135077 PMCID: PMC8831821 DOI: 10.4093/dmj.2021.0045] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Accepted: 05/28/2021] [Indexed: 11/15/2022] Open
Abstract
The main pathogenic mechanism of diabetes consists of an increase in insulin resistance and a decrease in insulin secretion from pancreatic β-cells. The number of diabetic patients has been increasing dramatically worldwide, especially in Asian people whose capacity for insulin secretion is inherently lower than that of other ethnic populations. Causally, changes of environmental factors in addition to intrinsic genetic factors have been considered to have an influence on the increased prevalence of diabetes. Particular focus has been placed on "gene-environment interactions" in the development of a reduced pancreatic β-cell mass, as well as type 1 and type 2 diabetes mellitus. Changes in the intrauterine environment, such as intrauterine growth restriction, contribute to alterations of gene expression in pancreatic β-cells, ultimately resulting in the development of pancreatic β-cell failure and diabetes. As a molecular mechanism underlying the effect of the intrauterine environment, epigenetic modifications have been widely investigated. The association of diabetes susceptibility genes or dietary habits with gene-environment interactions has been reported. In this review, we provide an overview of the role of gene-environment interactions in pancreatic β-cell failure as revealed by previous reports and data from experiments.
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Affiliation(s)
- Shun-ichiro Asahara
- Division of Diabetes and Endocrinology, Department of Internal Medicine, Kobe University Graduate School of Medicine, Kobe, Japan
| | - Hiroyuki Inoue
- Division of Medical Chemistry, Department of Metabolism and Diseases, Kobe University Graduate School of Health Sciences, Kobe, Japan
| | - Yoshiaki Kido
- Division of Diabetes and Endocrinology, Department of Internal Medicine, Kobe University Graduate School of Medicine, Kobe, Japan
- Division of Medical Chemistry, Department of Metabolism and Diseases, Kobe University Graduate School of Health Sciences, Kobe, Japan
- Corresponding author: Yoshiaki Kido https://orcid.org/0000-0003-2433-5799 Department of Metabolism and Diseases, Kobe University Graduate School of Health Sciences, 7-10-2 Tomogaoka, Suma-ku, Kobe 654-0142, Japan E-mail:
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Hsieh AR, Fann CSJ, Lin HC, Tai J, Hsieh SY, Tai DI. Hepatitis B virus persistent infection-related single nucleotide polymorphisms in HLA regions are associated with viral load in hepatoma families. World J Gastroenterol 2021; 27:6262-6276. [PMID: 34712031 PMCID: PMC8515798 DOI: 10.3748/wjg.v27.i37.6262] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/08/2021] [Revised: 07/06/2021] [Accepted: 09/01/2021] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Genome-wide association studies from Asia indicate that HLA-DP and HLA-DQ loci are important in persistent hepatitis B virus (HBV) infections. One of the key elements for HBV-related carcinogenesis is persistent viral replication and inflammation. AIM To examine genetic and nongenetic factors with persistent HBV infection and viral load in families with hepatocellular carcinoma (HCC). METHODS The HCC families included 301 hepatitis B surface antigen (HBsAg) carriers and 424 noncarriers born before the nationwide vaccination program was initiated in 1984. Five HBV-related single nucleotide polymorphisms (SNPs) - rs477515, rs9272105, rs9276370, rs7756516, and rs9277535 - were genotyped. Factors associated with persistent HBV infection and viral load were analyzed by a generalized estimating equation. RESULTS In the first-stage persistent HBV study, all SNPs except rs9272105 were associated with persistent infection. A significantly higher area under the reciprocal operating characteristic curve for nongenetic factors vs genetic factors (P < 0.001) suggests that the former play a major role in persistent HBV infection. In the second-stage viral load study, we added 8 HBsAg carriers born after 1984. The 309 HBsAg carriers were divided into low (n = 162) and high viral load (n = 147) groups with an HBV DNA cutoff of 105 cps/mL. Sex, relationship to the index case, rs477515, rs9272105, and rs7756516 were associated with viral load. Based on the receiver operating characteristic curve analysis, genetic and nongenetic factors affected viral load equally in the HCC family cohort (P = 0.3117). CONCLUSION In these east Asian adults, the mechanism of persistent HBV infection-related SNPs was a prolonged viral replication phase.
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Affiliation(s)
- Ai-Ru Hsieh
- Department of Statistics, Tamkang University, New Taipei City 25137, Taiwan
| | - Cathy S J Fann
- Institute of Biomedical Sciences, Academia Sinica, Nankang, Taipei 11529, Taiwan
| | - Hung-Chun Lin
- Division of Hepatology, Department of Gastroenterology and Hepatology, Chang Gung Memorial Hospital, Linkou Medical Center, Taoyuan 33305, Taiwan
| | - Jennifer Tai
- Division of Hepatology, Department of Gastroenterology and Hepatology, Chang Gung Memorial Hospital, Linkou Medical Center, Taoyuan 33305, Taiwan
| | - Sen-Yung Hsieh
- Division of Hepatology, Department of Gastroenterology and Hepatology, Chang Gung Memorial Hospital, Linkou Medical Center, Taoyuan 33305, Taiwan
| | - Dar-In Tai
- Division of Hepatology, Department of Gastroenterology and Hepatology, Chang Gung Memorial Hospital, Linkou Medical Center, Taoyuan 33305, Taiwan
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van der Burgh AC, Moes A, Kieboom BCT, van Gelder T, Zietse R, van Schaik RHN, Hesselink DA, Hoorn EJ. Serum magnesium, hepatocyte nuclear factor 1β genotype and post-transplant diabetes mellitus: a prospective study. Nephrol Dial Transplant 2020; 35:176-183. [PMID: 31361318 DOI: 10.1093/ndt/gfz145] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2019] [Accepted: 06/12/2019] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Retrospective studies suggest that tacrolimus-induced hypomagnesaemia is a risk factor for post-transplant diabetes mellitus (PTDM), but prospective studies are lacking. METHODS This was a prospective study with measurements of serum magnesium and tacrolimus at pre-specified time points in the first year after living donor kidney transplantation (KT). The role of single nucleotide polymorphisms (SNPs) in hepatocyte nuclear factor 1β (HNF1β) was also explored because HNF1β regulates insulin secretion and renal magnesium handling. Repeated measurement and regression analyses were used to analyse associations with PTDM. RESULTS In our cohort, 29 out of 167 kidney transplant recipients developed PTDM after 1 year (17%). Higher tacrolimus concentrations were significantly associated with lower serum magnesium and increased risk of hypomagnesaemia. Patients who developed PTDM had a significantly lower serum magnesium trajectory than patients who did not develop PTDM. In multivariate analysis, lower serum magnesium, age and body mass index were independent risk factors for PTDM. In recipients, the HNF1β SNP rs752010 G > A significantly increased the risk of PTDM [odds ratio (OR) = 2.56, 95% confidence interval (CI) 1.05-6.23] but not of hypomagnesaemia. This association lost significance after correction for age and sex (OR = 2.24, 95% CI 0.90-5.57). No association between HNF1β SNPs and PTDM was found in corresponding donors. CONCLUSIONS A lower serum magnesium in the first year after KT is an independent risk factor for PTDM. The HNF1β SNP rs752010 G > A may add to this risk through an effect on insulin secretion rather than hypomagnesaemia, but its role requires further confirmation.
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Affiliation(s)
- Anna C van der Burgh
- Department of Internal Medicine, Division of Nephrology & Transplantation, Erasmus Medical Center, University Medical Center Rotterdam, Rotterdam, The Netherlands.,Department of Epidemiology, Erasmus Medical Center, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Arthur Moes
- Department of Internal Medicine, Division of Nephrology & Transplantation, Erasmus Medical Center, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Brenda C T Kieboom
- Department of Epidemiology, Erasmus Medical Center, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Teun van Gelder
- Department of Internal Medicine, Division of Nephrology & Transplantation, Erasmus Medical Center, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Robert Zietse
- Department of Internal Medicine, Division of Nephrology & Transplantation, Erasmus Medical Center, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Ron H N van Schaik
- Department of Clinical Chemistry, Erasmus Medical Center, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Dennis A Hesselink
- Department of Internal Medicine, Division of Nephrology & Transplantation, Erasmus Medical Center, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Ewout J Hoorn
- Department of Internal Medicine, Division of Nephrology & Transplantation, Erasmus Medical Center, University Medical Center Rotterdam, Rotterdam, The Netherlands
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Kanno A, Asahara SI, Furubayashi A, Masuda K, Yoshitomi R, Suzuki E, Takai T, Kimura-Koyanagi M, Matsuda T, Bartolome A, Hirota Y, Yokoi N, Inaba Y, Inoue H, Matsumoto M, Inoue K, Abe T, Wei FY, Tomizawa K, Ogawa W, Seino S, Kasuga M, Kido Y. GCN2 regulates pancreatic β cell mass by sensing intracellular amino acid levels. JCI Insight 2020; 5:128820. [PMID: 32376799 DOI: 10.1172/jci.insight.128820] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2019] [Accepted: 04/01/2020] [Indexed: 01/09/2023] Open
Abstract
EIF2AK4, which encodes the amino acid deficiency-sensing protein GCN2, has been implicated as a susceptibility gene for type 2 diabetes in the Japanese population. However, the mechanism by which GCN2 affects glucose homeostasis is unclear. Here, we show that insulin secretion is reduced in individuals harboring the risk allele of EIF2AK4 and that maintenance of GCN2-deficient mice on a high-fat diet results in a loss of pancreatic β cell mass. Our data suggest that GCN2 senses amino acid deficiency in β cells and limits signaling by mechanistic target of rapamycin complex 1 to prevent β cell failure during the consumption of a high-fat diet.
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Affiliation(s)
- Ayumi Kanno
- Division of Diabetes and Endocrinology, Department of Internal Medicine, and
| | - Shun-Ichiro Asahara
- Division of Diabetes and Endocrinology, Department of Internal Medicine, and
| | - Ayuko Furubayashi
- Division of Metabolism and Disease, Department of Biophysics, Kobe University Graduate School of Health Science, Kobe, Japan
| | - Katsuhisa Masuda
- Division of Metabolism and Disease, Department of Biophysics, Kobe University Graduate School of Health Science, Kobe, Japan
| | - Risa Yoshitomi
- Division of Metabolism and Disease, Department of Biophysics, Kobe University Graduate School of Health Science, Kobe, Japan
| | - Emi Suzuki
- Division of Diabetes and Endocrinology, Department of Internal Medicine, and
| | - Tomoko Takai
- Division of Diabetes and Endocrinology, Department of Internal Medicine, and
| | | | - Tomokazu Matsuda
- Division of Diabetes and Endocrinology, Department of Internal Medicine, and
| | - Alberto Bartolome
- Naomi Berrie Diabetes Center and Department of Medicine, Columbia University, New York, New York, USA
| | - Yushi Hirota
- Division of Diabetes and Endocrinology, Department of Internal Medicine, and
| | - Norihide Yokoi
- Division of Molecular and Metabolic Medicine, Department of Physiology and Cell Biology, Kobe University Graduate School of Medicine, Kobe, Japan
| | - Yuka Inaba
- Metabolism and Nutrition Research Unit, Institute for Frontier Science Initiative, Kanazawa University, Kanazawa, Ishikawa, Japan
| | - Hiroshi Inoue
- Metabolism and Nutrition Research Unit, Institute for Frontier Science Initiative, Kanazawa University, Kanazawa, Ishikawa, Japan
| | - Michihiro Matsumoto
- Department of Molecular Metabolic Regulation, Diabetes Research Center, Research Institute, National Center for Global Health and Medicine, Tokyo, Japan
| | | | - Takaya Abe
- Laboratory for Animal Resource Development and.,Laboratory for Genetic Engineering, RIKEN Center for Biosystems Dynamics Research, Kobe, Japan
| | - Fan-Yan Wei
- Department of Molecular Physiology, Faculty of Life Sciences, Kumamoto University, Kumamoto, Japan
| | - Kazuhito Tomizawa
- Department of Molecular Physiology, Faculty of Life Sciences, Kumamoto University, Kumamoto, Japan
| | - Wataru Ogawa
- Division of Diabetes and Endocrinology, Department of Internal Medicine, and
| | - Susumu Seino
- Division of Molecular and Metabolic Medicine, Department of Physiology and Cell Biology, Kobe University Graduate School of Medicine, Kobe, Japan
| | - Masato Kasuga
- National Center for Global Health and Medicine, Tokyo, Japan
| | - Yoshiaki Kido
- Division of Diabetes and Endocrinology, Department of Internal Medicine, and.,Division of Metabolism and Disease, Department of Biophysics, Kobe University Graduate School of Health Science, Kobe, Japan
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Patron J, Serra-Cayuela A, Han B, Li C, Wishart DS. Assessing the performance of genome-wide association studies for predicting disease risk. PLoS One 2019; 14:e0220215. [PMID: 31805043 PMCID: PMC6894795 DOI: 10.1371/journal.pone.0220215] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2019] [Accepted: 11/01/2019] [Indexed: 12/24/2022] Open
Abstract
To date more than 3700 genome-wide association studies (GWAS) have been published that look at the genetic contributions of single nucleotide polymorphisms (SNPs) to human conditions or human phenotypes. Through these studies many highly significant SNPs have been identified for hundreds of diseases or medical conditions. However, the extent to which GWAS-identified SNPs or combinations of SNP biomarkers can predict disease risk is not well known. One of the most commonly used approaches to assess the performance of predictive biomarkers is to determine the area under the receiver-operator characteristic curve (AUROC). We have developed an R package called G-WIZ to generate ROC curves and calculate the AUROC using summary-level GWAS data. We first tested the performance of G-WIZ by using AUROC values derived from patient-level SNP data, as well as literature-reported AUROC values. We found that G-WIZ predicts the AUROC with <3% error. Next, we used the summary level GWAS data from GWAS Central to determine the ROC curves and AUROC values for 569 different GWA studies spanning 219 different conditions. Using these data we found a small number of GWA studies with SNP-derived risk predictors that have very high AUROCs (>0.75). On the other hand, the average GWA study produces a multi-SNP risk predictor with an AUROC of 0.55. Detailed AUROC comparisons indicate that most SNP-derived risk predictions are not as good as clinically based disease risk predictors. All our calculations (ROC curves, AUROCs, explained heritability) are in a publicly accessible database called GWAS-ROCS (http://gwasrocs.ca). The G-WIZ code is freely available for download at https://github.com/jonaspatronjp/GWIZ-Rscript/.
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Affiliation(s)
- Jonas Patron
- Department of Biological Sciences, University of Alberta, Edmonton, Canada
| | | | - Beomsoo Han
- Department of Biological Sciences, University of Alberta, Edmonton, Canada
| | - Carin Li
- Department of Biological Sciences, University of Alberta, Edmonton, Canada
| | - David Scott Wishart
- Department of Biological Sciences, University of Alberta, Edmonton, Canada
- Department of Computing Science, University of Alberta, Edmonton, Canada
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Capcarova M, Kalafova A, Schwarzova M, Schneidgenova M, Svik K, Prnova MS, Slovak L, Kovacik A, Lory V, Zorad S, Brindza J. Cornelian cherry fruit improves glycaemia and manifestations of diabetes in obese Zucker diabetic fatty rats. Res Vet Sci 2019; 126:118-123. [PMID: 31446268 DOI: 10.1016/j.rvsc.2019.08.024] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2018] [Revised: 08/06/2019] [Accepted: 08/12/2019] [Indexed: 01/05/2023]
Abstract
Cornelian cherry (Cornus mas L.) was in the past frequently used in Slovak Republic; meanwhile fell into oblivion despite the fact that it is known as antidiabetic supplement. However, there is no research investigated its effect on animal model of Diabetes mellitus (DM) 2 type as it is Zucker diabetic fatty (ZDF) rats. Thus, the aim of this study was to determine the effect of C. mas fruit given orally on the development of DM symptoms in ZDF rats. In the experiment male ZDF rats (fa/fa) and their age-matched non-diabetic lean controls (fa/+) were used aged 12 weeks. Male ZDF rats were administered C. mas in two doses (500 and 1000 mg/kg body weight) using a gastric gavage for 10 weeks. One group of diabetic animals served as positive control and received only distilled water. We found significant decrease of glucose level after oral administration of C. mas in dose of 1000 mg/kg bw in pre-diabetic state of animals (until 7th week of the experiment) and significant restriction of water intake in both C. mas groups against the diabetic control. We presume that the higher dose of Cornelian cherry could be beneficial and helpful in prevention of diabetic symptoms when consumed regularly in young animals.
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Affiliation(s)
- Marcela Capcarova
- Department of Animal Physiology, Faculty of Biotechnology and Food Sciences, Slovak University of Agriculture in Nitra, 949 76 Nitra, Slovak Republic.
| | - Anna Kalafova
- Department of Animal Physiology, Faculty of Biotechnology and Food Sciences, Slovak University of Agriculture in Nitra, 949 76 Nitra, Slovak Republic
| | - Marianna Schwarzova
- Department of Human Nutrition, Faculty of Agrobiology and Food Resources, Slovak University of Agriculture in Nitra, 949 76 Nitra, Slovak Republic
| | - Monika Schneidgenova
- Department of Animal Physiology, Faculty of Biotechnology and Food Sciences, Slovak University of Agriculture in Nitra, 949 76 Nitra, Slovak Republic
| | - Karol Svik
- Centre of Experimental Medicine, Institute of Experimental Pharmacology and Toxicology, Slovak Academy of Science, Bratislava, Slovak Republic
| | - Marta Soltesova Prnova
- Centre of Experimental Medicine, Institute of Experimental Pharmacology and Toxicology, Slovak Academy of Science, Bratislava, Slovak Republic
| | - Lukas Slovak
- Centre of Experimental Medicine, Institute of Experimental Pharmacology and Toxicology, Slovak Academy of Science, Bratislava, Slovak Republic
| | - Anton Kovacik
- Department of Animal Physiology, Faculty of Biotechnology and Food Sciences, Slovak University of Agriculture in Nitra, 949 76 Nitra, Slovak Republic
| | - Viktoria Lory
- Biomedical Research Center, Institute of Experimental Endocrinology, Slovak Academy of Science, Bratislava, Slovak Republic
| | - Stefan Zorad
- Biomedical Research Center, Institute of Experimental Endocrinology, Slovak Academy of Science, Bratislava, Slovak Republic
| | - Jan Brindza
- Department of Genetics and Plant Breeding, Faculty of Agrobiology and Food Resources, Slovak University of Agriculture in Nitra, 949 76 Nitra, Slovak Republic; Institute of Biodiversity Conservation and Biosafety, Slovak University of Agriculture in Nitra, 949 76 Nitra, Slovak Republic
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11
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Inaishi J, Hirakawa Y, Horikoshi M, Akiyama M, Higashioka M, Yoshinari M, Hata J, Mukai N, Kamatani Y, Momozawa Y, Kubo M, Ninomiya T. Association Between Genetic Risk and Development of Type 2 Diabetes in a General Japanese Population: The Hisayama Study. J Clin Endocrinol Metab 2019; 104:3213-3222. [PMID: 30830152 DOI: 10.1210/jc.2018-01782] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/17/2018] [Accepted: 02/26/2019] [Indexed: 12/11/2022]
Abstract
CONTEXT Although recent genetic studies have identified many susceptibility loci associated with type 2 diabetes (T2D), the usefulness of such loci for precision medicine remains uncertain. OBJECTIVE This study investigated the impact of genetic risk score (GRS) on the development of T2D in a general Japanese population. PARTICIPANTS The current study consists of 1465 subjects aged 40 to 79 years without diabetes who underwent a health examination in 2002. DESIGN The GRS was generated using the literature-based effect size for T2D of 84 susceptibility loci for the Japanese population, and the risk estimates of GRS on the incidence of T2D were computed by using a Cox proportional hazard model in a 10-year follow-up study. The influence of GRS on the predictive ability was estimated with Harrell C statistics, integrated discrimination improvement (IDI), and continuous net reclassification improvement (cNRI). RESULTS During the 10-year follow-up, 199 subjects experienced T2D. The risk of developing T2D increased significantly with elevating quintiles of GRS (multivariable-adjusted hazard ratio for the fifth vs first quintile, 2.85; 95% CI, 1.83 to 4.44). When incorporating GRS into the multivariable model comprising environmental risk factors, the Harrell C statistics (95% CI) increased from 0.681 (0.645 to 0.717) to 0.707 (0.672 to 0.742) and the predictive ability of T2D was significantly improved (IDI, 0.0376; 95% CI, 0.0284 to 0.0494; cNRI, 0.3565; 95% CI, 0.1278 to 0.5829). GRS was also associated with the risk of T2D independently of environmental risk factors. CONCLUSIONS These findings suggest the usefulness of GRS for identifying a high-risk population together with environmental risk factors in the Japanese population.
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Affiliation(s)
- Jun Inaishi
- Department of Epidemiology and Public Health, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Yoichiro Hirakawa
- Department of Epidemiology and Public Health, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
- Department of Medicine and Clinical Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Momoko Horikoshi
- Laboratory for Endocrinology, Metabolism and Kidney Diseases, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Masato Akiyama
- Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Department of Ophthalmology, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Mayu Higashioka
- Department of Epidemiology and Public Health, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
- Department of Diabetes and Molecular Genetics, Graduate School of Medicine, Ehime University, Ehime, Japan
| | - Masahito Yoshinari
- Department of Epidemiology and Public Health, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
- Department of Medicine and Clinical Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Jun Hata
- Department of Epidemiology and Public Health, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
- Department of Medicine and Clinical Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Naoko Mukai
- Department of Epidemiology and Public Health, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
- Department of Medicine and Clinical Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Yoichiro Kamatani
- Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Yukihide Momozawa
- Laboratory for Genotyping Development, RIKEN Center for Integrative Medical, Sciences, Yokohama, Japan
| | - Michiaki Kubo
- RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Toshiharu Ninomiya
- Department of Epidemiology and Public Health, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
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12
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Xie F, Chan JCN, Ma RCW. Precision medicine in diabetes prevention, classification and management. J Diabetes Investig 2018; 9:998-1015. [PMID: 29499103 PMCID: PMC6123056 DOI: 10.1111/jdi.12830] [Citation(s) in RCA: 49] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/05/2018] [Accepted: 02/12/2018] [Indexed: 12/18/2022] Open
Abstract
Diabetes has become a major burden of healthcare expenditure. Diabetes management following a uniform treatment algorithm is often associated with progressive treatment failure and development of diabetic complications. Recent advances in our understanding of the genomic architecture of diabetes and its complications have provided the framework for development of precision medicine to personalize diabetes prevention and management. In the present review, we summarized recent advances in the understanding of the genetic basis of diabetes and its complications. From a clinician's perspective, we attempted to provide a balanced perspective on the utility of genomic medicine in the field of diabetes. Using genetic information to guide management of monogenic forms of diabetes represents the best-known examples of genomic medicine for diabetes. Although major strides have been made in genetic research for diabetes, its complications and pharmacogenetics, ongoing efforts are required to translate these findings into practice by incorporating genetic information into a risk prediction model for prioritization of treatment strategies, as well as using multi-omic analyses to discover novel drug targets with companion diagnostics. Further research is also required to ensure the appropriate use of this information to empower individuals and healthcare professionals to make personalized decisions for achieving the optimal outcome.
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Affiliation(s)
- Fangying Xie
- Department of Medicine and TherapeuticsPrince of Wales HospitalThe Chinese University of Hong KongShatinHong Kong
| | - Juliana CN Chan
- Department of Medicine and TherapeuticsPrince of Wales HospitalThe Chinese University of Hong KongShatinHong Kong
- Hong Kong Institute of Diabetes and ObesityPrince of Wales HospitalThe Chinese University of Hong KongShatinHong Kong
- Li Ka Shing Institute of Health SciencesPrince of Wales HospitalThe Chinese University of Hong KongShatinHong Kong
- CUHK‐SJTU Joint Research Centre in Diabetes Genomics and Precision MedicinePrince of Wales HospitalThe Chinese University of Hong KongShatinHong Kong
| | - Ronald CW Ma
- Department of Medicine and TherapeuticsPrince of Wales HospitalThe Chinese University of Hong KongShatinHong Kong
- Hong Kong Institute of Diabetes and ObesityPrince of Wales HospitalThe Chinese University of Hong KongShatinHong Kong
- Li Ka Shing Institute of Health SciencesPrince of Wales HospitalThe Chinese University of Hong KongShatinHong Kong
- CUHK‐SJTU Joint Research Centre in Diabetes Genomics and Precision MedicinePrince of Wales HospitalThe Chinese University of Hong KongShatinHong Kong
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13
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The high-energy diet affecting development of diabetes symptoms in Zucker diabetic fatty rats. Biologia (Bratisl) 2018. [DOI: 10.2478/s11756-018-0076-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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14
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Goto A, Noda M, Goto M, Yasuda K, Mizoue T, Yamaji T, Sawada N, Iwasaki M, Inoue M, Tsugane S. Predictive performance of a genetic risk score using 11 susceptibility alleles for the incidence of Type 2 diabetes in a general Japanese population: a nested case-control study. Diabet Med 2018; 35:602-611. [PMID: 29444352 DOI: 10.1111/dme.13602] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 02/09/2018] [Indexed: 01/05/2023]
Abstract
AIMS To assess the predictive ability of a genetic risk score for the incidence of Type 2 diabetes in a general Japanese population. METHODS This prospective case-control study, nested within a Japan Public Health Centre-based prospective study, included 466 participants with incident Type 2 diabetes over a 5-year period (cases) and 1361 control participants, as well as 1463 participants with existing diabetes and 1463 control participants. Eleven susceptibility single nucleotide polymorphisms, identified through genome-wide association studies and replicated in Japanese populations, were analysed. RESULTS Most single nucleotide polymorphism loci showed directionally consistent associations with diabetes. From the combined samples, one single nucleotide polymorphism (rs2206734 at CDKAL1) reached a genome-wide significance level (odds ratio 1.28, 95% CI 1.18-1.40; P = 1.8 × 10-8 ). Three single nucleotide polymorphisms (rs2206734 in CDKAL1, rs2383208 in CDKN2A/B, and rs2237892 in KCNQ1) were nominally significantly associated with incident diabetes. Compared with the lowest quintile of the total number of risk alleles, the highest quintile had a higher odds of incident diabetes (odds ratio 2.34, 95% CI 1.59-3.46) after adjusting for conventional risk factors such as age, sex and BMI. The addition to the conventional risk factor-based model of a genetic risk score using the 11 single nucleotide polymorphisms significantly improved predictive performance; the c-statistic increased by 0.021, net reclassification improved by 6.2%, and integrated discrimination improved by 0.003. CONCLUSIONS Our prospective findings suggest that the addition of a genetic risk score may provide modest but significant incremental predictive performance beyond that of the conventional risk factor-based model without biochemical markers.
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Affiliation(s)
- A Goto
- Epidemiology and Prevention Group, Centre for Public Health Sciences, National Cancer Centre, Tokyo
| | - M Noda
- Department of Endocrinology and Diabetes, Saitama Medical University, Saitama
| | - M Goto
- Department of Diabetes and Endocrinology, JCHO Tokyo Yamate Medical Centre, Tokyo
| | - K Yasuda
- Department of Metabolic Disorder, Diabetes Research Centre, National Centre for Global Health and Medicine, Tokyo, Japan
| | - T Mizoue
- Department of Epidemiology and Prevention, National Centre for Global Health and Medicine, Tokyo, Japan
| | - T Yamaji
- Epidemiology and Prevention Group, Centre for Public Health Sciences, National Cancer Centre, Tokyo
| | - N Sawada
- Epidemiology and Prevention Group, Centre for Public Health Sciences, National Cancer Centre, Tokyo
| | - M Iwasaki
- Epidemiology and Prevention Group, Centre for Public Health Sciences, National Cancer Centre, Tokyo
| | - M Inoue
- Epidemiology and Prevention Group, Centre for Public Health Sciences, National Cancer Centre, Tokyo
| | - S Tsugane
- Epidemiology and Prevention Group, Centre for Public Health Sciences, National Cancer Centre, Tokyo
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15
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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. Quantitative Relationship Between Cumulative Risk Alleles Based on Genome-Wide Association Studies and Type 2 Diabetes Mellitus: A Systematic Review and Meta-analysis. J Epidemiol 2017; 28:3-18. [PMID: 29093303 PMCID: PMC5742374 DOI: 10.2188/jea.je20160151] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
Many epidemiological studies have assessed the genetic risk of having undiagnosed or of developing type 2 diabetes mellitus (T2DM) using several single nucleotide polymorphisms (SNPs) based on findings of genome-wide association studies (GWAS). However, the quantitative association of cumulative risk alleles (RAs) of such SNPs with T2DM risk has been unclear. The aim of this meta-analysis is to review the strength of the association between cumulative RAs and T2DM risk. Systematic literature searches were conducted for cross-sectional or longitudinal studies that examined odds ratios (ORs) for T2DM in relation to genetic profiles. Logarithm of the estimated OR (log OR) of T2DM for 1 increment in RAs carried (1-ΔRA) in each study was pooled using a random-effects model. There were 46 eligible studies that included 74,880 cases among 249,365 participants. In 32 studies with a cross-sectional design, the pooled OR for T2DM morbidity for 1-ΔRA was 1.16 (95% confidence interval [CI], 1.13–1.19). In 15 studies that had a longitudinal design, the OR for incident T2DM was 1.10 (95% CI, 1.08–1.13). There was large heterogeneity in the magnitude of log OR (P < 0.001 for both cross-sectional studies and longitudinal studies). The top 10 commonly used genes significantly explained the variance in the log OR (P = 0.04 for cross-sectional studies; P = 0.006 for longitudinal studies). The current meta-analysis indicated that carrying 1-ΔRA in T2DM-associated SNPs was associated with a modest risk of prevalent or incident T2DM, although the heterogeneity in the used genes among studies requires us to interpret the results with caution.
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Affiliation(s)
- Satoru Kodama
- Department of Laboratory Medicine and Clinical Epidemiology for Prevention of Noncommunicable Niigata University Graduate School of Medical and Dental Sciences
| | - Kazuya Fujihara
- Department of Hematology, Endocrinology and Metabolism, Niigata University Faculty of Medicine
| | - Hajime Ishiguro
- Department of Hematology, Endocrinology and Metabolism, Niigata University Faculty of Medicine
| | - Chika Horikawa
- Department of Health and Nutrition, Faculty of Human Life Studies, University of Niigata Prefecture
| | - Nobumasa Ohara
- Department of Hematology, Endocrinology and Metabolism, Niigata University Faculty of Medicine
| | - Yoko Yachi
- Department of Administrative Dietetics, Faculty of Health and Nutrition, Yamanashi Gakuin University
| | - Shiro Tanaka
- Department of Clinical Trial, Design & Management, Translational Research Center, Kyoto University Hospital
| | - Hitoshi Shimano
- Department of Internal Medicine, University of Tsukuba Institute of Clinical Medicine
| | - Kiminori Kato
- Department of Laboratory Medicine and Clinical Epidemiology for Prevention of Noncommunicable Niigata University Graduate School of Medical and Dental Sciences
| | - Osamu Hanyu
- Department of Hematology, Endocrinology and Metabolism, Niigata University Faculty of Medicine
| | - Hirohito Sone
- Department of Hematology, Endocrinology and Metabolism, Niigata University Faculty of Medicine
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16
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Improving Disease Prediction by Incorporating Family Disease History in Risk Prediction Models with Large-Scale Genetic Data. Genetics 2017; 207:1147-1155. [PMID: 28899997 DOI: 10.1534/genetics.117.300283] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2017] [Accepted: 08/31/2017] [Indexed: 12/30/2022] Open
Abstract
Despite the many successes of genome-wide association studies (GWAS), the known susceptibility variants identified by GWAS have modest effect sizes, leading to notable skepticism about the effectiveness of building a risk prediction model from large-scale genetic data. However, in contrast to genetic variants, the family history of diseases has been largely accepted as an important risk factor in clinical diagnosis and risk prediction. Nevertheless, the complicated structures of the family history of diseases have limited their application in clinical practice. Here, we developed a new method that enables incorporation of the general family history of diseases with a liability threshold model, and propose a new analysis strategy for risk prediction with penalized regression analysis that incorporates both large numbers of genetic variants and clinical risk factors. Application of our model to type 2 diabetes in the Korean population (1846 cases and 1846 controls) demonstrated that single-nucleotide polymorphisms accounted for 32.5% of the variation explained by the predicted risk scores in the test data set, and incorporation of family history led to an additional 6.3% improvement in prediction. Our results illustrate that family medical history provides valuable information on the variation of complex diseases and improves prediction performance.
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17
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Tachibana K, Sakurai K, Watanabe M, Miyaso H, Mori C. Associations between changes in the maternal gut microbiome and differentially methylated regions of diabetes-associated genes in fetuses: A pilot study from a birth cohort study. J Diabetes Investig 2017; 8:550-553. [PMID: 27863092 PMCID: PMC5497035 DOI: 10.1111/jdi.12598] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/29/2016] [Revised: 11/13/2016] [Accepted: 11/15/2016] [Indexed: 01/04/2023] Open
Abstract
Several intrauterine environmental factors can increase the future risk of type 2 diabetes. The microbiome can influence the balance between health and disease. However, the influence of the maternal gut microbiome on the future risk of diabetes in the fetus is unknown. The present study investigated the associations between maternal gut microbiome and differentially methylated regions of diabetes‐associated genes in umbilical cord samples. The present study included 10 pregnant participants from a birth cohort study. 16S ribosomal ribonucleic acid metagenome analysis of maternal stool samples and deoxyribonucleic acid methylation assays of umbilical cord samples were carried out. The present study found that changes in the UBE2E2 and KCNQ1 methylation rates in umbilical cord samples were associated with the proportion of Firmicutes in the maternal gut, albeit with marginal correlations after adjustment for age and body mass index. These findings suggest a link between the methylation of diabetes‐associated genes in fetuses and maternal microbiota components during pregnancy.
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Affiliation(s)
- Kaori Tachibana
- Center for Preventive Medical Sciences, Chiba University, Chiba, Japan
| | - Kenichi Sakurai
- Center for Preventive Medical Sciences, Chiba University, Chiba, Japan
| | - Masahiro Watanabe
- Center for Preventive Medical Sciences, Chiba University, Chiba, Japan
| | - Hidenobu Miyaso
- Center for Preventive Medical Sciences, Chiba University, Chiba, Japan
| | - Chisato Mori
- Center for Preventive Medical Sciences, Chiba University, Chiba, Japan
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18
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Kamura Y, Iwata M, Maeda S, Shinmura S, Koshimizu Y, Honoki H, Fukuda K, Ishiki M, Usui I, Fukushima Y, Takano A, Kato H, Murakami S, Higuchi K, Kobashi C, Tobe K. FTO Gene Polymorphism Is Associated with Type 2 Diabetes through Its Effect on Increasing the Maximum BMI in Japanese Men. PLoS One 2016; 11:e0165523. [PMID: 27820839 PMCID: PMC5098825 DOI: 10.1371/journal.pone.0165523] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2015] [Accepted: 10/13/2016] [Indexed: 12/29/2022] Open
Abstract
Aim Several studies have demonstrated that polymorphisms within the fat-mass and obesity-associated gene (FTO) are associated with type 2 diabetes (T2D). However, whether the effects of the FTO locus on T2D susceptibility are independent of fat-mass increases remains controversial. To investigate this issue, we examined the association of FTO variants with T2D and various aspects of BMI history during adult life in a Japanese population. Methods We genotyped SNPs within FTO (rs1121980 and rs1558902) in 760 Japanese patients with T2D who had reached a lifetime maximum BMI (BMImax) before or at the time of diagnosis and 693 control individuals with information regarding their BMImax. Results The BMImax showed the strongest association with T2D risk among the BMIs evaluated in this study. In the sex-combined analysis, FTO SNPs were not associated with any of the BMI variables or with T2D, but in sex-stratified analyses, both SNPs were significantly associated with the BMImax and rs1558902 was associated with T2D in men. The association of the SNPs with T2D remained significant after adjustments for the current BMI and age, whereas the T2D association of the SNP was no longer significant after adjustments for BMImax and age. Conclusions These results suggest that the effects of FTO polymorphisms on T2D susceptibility in Japanese men are mediated through their effect on increasing the BMImax before or at the time of diagnosis.
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Affiliation(s)
- Yutaka Kamura
- First Department of Internal Medicine, Faculty of Medicine, University of Toyama, Toyama, Japan
| | - Minoru Iwata
- First Department of Internal Medicine, Faculty of Medicine, University of Toyama, Toyama, Japan
- Health Administration Center, University of Toyama, Toyama, Japan
- * E-mail:
| | - Shiro Maeda
- Laboratory for Endocrinology and Metabolism, RIKEN Center for Genomic Medicine, Yokohama, Kanagawa, Japan
- Department of Advanced Genomic and Laboratory Medicine, Graduate School of Medicine, University of the Ryukyus, Okinawa, Japan
- Division of Clinical Laboratory and Blood Transfusion, University of the Ryukyus Hospital, Okinawa, Japan
| | - Satomi Shinmura
- First Department of Internal Medicine, Faculty of Medicine, University of Toyama, Toyama, Japan
| | - Yukiko Koshimizu
- First Department of Internal Medicine, Faculty of Medicine, University of Toyama, Toyama, Japan
| | - Hisae Honoki
- First Department of Internal Medicine, Faculty of Medicine, University of Toyama, Toyama, Japan
| | - Kazuhito Fukuda
- First Department of Internal Medicine, Faculty of Medicine, University of Toyama, Toyama, Japan
| | - Manabu Ishiki
- First Department of Internal Medicine, Faculty of Medicine, University of Toyama, Toyama, Japan
| | - Isao Usui
- First Department of Internal Medicine, Faculty of Medicine, University of Toyama, Toyama, Japan
| | - Yasuo Fukushima
- Department of Internal Medicine, Asahi General Hospital, Asahi-machi, Toyama, Japan
| | - Atsuko Takano
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Saiseikai Takaoka Hospital, Takaoka, Toyama, Japan
| | - Hiromi Kato
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Japan Community Health care Organization Takaoka Fushiki Hospital, Takaoka, Toyama, Japan
| | - Shihou Murakami
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Toyama Rosai Hospital, Uozu, Toyama, Japan
| | - Kiyohiro Higuchi
- Department of Internal Medicine, Kouseiren Itoigawa General Hospital, Itoigawa, Niigata, Japan
| | - Chikaaki Kobashi
- Department of Internal Medicine, Kamiichi General Hospital, Kamiichi-machi, Toyama, Japan
| | - Kazuyuki Tobe
- First Department of Internal Medicine, Faculty of Medicine, University of Toyama, Toyama, Japan
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19
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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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Estimation of the risk of a qualitative phenotype: dependence on population risk. J Hum Genet 2016; 62:191-198. [PMID: 27557667 DOI: 10.1038/jhg.2016.106] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2016] [Revised: 07/10/2016] [Accepted: 07/13/2016] [Indexed: 11/09/2022]
Abstract
Individual disease risk estimated based on the data from single or multiple genetic loci is generally calculated using the genotypes of a subject, frequencies of alleles of interest, odds ratios and the average population risk. However, it is often difficult to estimate accurately the average population risk, and therefore it is often expressed as an interval. To better estimate the risk of a subject with given genotypes, we built R scripts using the R environment and constructed graphs to examine the change in the estimated risk as well as the relative risk according to the change of the average population risk. In most cases, the graph of the relative risk did not cross the line of y=1, thereby indicating that the order of the relative risk for given genotypes and the population average risk does not change when the average risk increases or decreases. In rare cases, however, the graph of the relative risk crossed the line of y=1, thereby indicating that the order of the relative risk for given genotypes and the population average risk does change owing to the change in the population risk. We propose that the relative risk should be estimated for not only the point average population risk but also for an interval of the average population risk. Moreover, when the graph crosses the line of y=1 within the interval, this information should be reported to the consumer.
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21
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Zhao F, Mamatyusupu D, Wang Y, Fang H, Wang H, Gao Q, Dong H, Ge S, Yu X, Zhang J, Wu L, Song M, Wang W. The Uyghur population and genetic susceptibility to type 2 diabetes: potential role for variants in CAPN10, APM1 and FUT6 genes. J Cell Mol Med 2016; 20:2138-2147. [PMID: 27374856 PMCID: PMC5082412 DOI: 10.1111/jcmm.12911] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2015] [Accepted: 05/20/2016] [Indexed: 02/06/2023] Open
Abstract
Genome‐wide association studies have successfully identified over 70 loci associated with the risk of type 2 diabetes mellitus (T2DM) in multiple populations of European ancestry. However, the risk attributable to an individual variant is modest and does not yet provide convincing evidence for clinical utility. Association between these established genetic variants and T2DM in general populations is hitherto understudied in the isolated populations, such as the Uyghurs, resident in Hetian, far southern Xinjiang Uyghur Autonomous Region, China. In this case–control study, we genotyped 13 single‐nucleotide polymorphisms (SNPs) at 10 genes associated with diabetes in 130 cases with T2DM and 135 healthy controls of Uyghur, a Chinese minority ethnic group. Three of the 13 SNPs demonstrated significant association with T2DM in the Uyghur population. There were significant differences between the T2DM patients and controls in the risk allele distributions of rs3792267 (CAPN10) (P = 0.002), rs1501299 (APM1) (P = 0.017), and rs3760776 (FUT6) (P = 0.031). Allelic carriers of rs3792267‐A, rs1501299‐T, and rs3760776‐T had a 2.24‐fold [OR (95% CI): 1.35–3.71], 0.59‐fold [OR (95% CI): 0.39–0.91], 0.57‐fold [OR (95% CI): 0.34–0.95] increased risk for T2DM respectively. We further confirmed that the cumulative risk allelic scores calculated from the 13 susceptibility loci for T2DM differed significantly between the T2DM patients and controls (P = 0.001), and the effect of obesity/overweight on T2DM was only observed in the subjects with a combined risk allelic score under a value of 17. This study observed that the SNPs rs3792267 in CAPN10, rs1501299 in APM1, and rs3760776 in FUT6 might serve as potential susceptible biomarkers for T2DM in Uyghurs. The cumulative risk allelic scores of multiple loci with modest individual effects are also significant risk factors in Uyghurs for T2DM, particularly among non‐obese individuals. This is the first investigation having observed/found genetic variations on genetic loci functionally linked with glycosylation associated with the risk of T2DM in a Uyghur population.
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Affiliation(s)
- Feifei Zhao
- School of Public Health, Capital Medical University, Beijing, China.,Municipal Key Laboratory of Clinical Epidemiology, Beijing, China
| | - Dolikun Mamatyusupu
- College of the Life Sciences and Technology, Xinjiang University, Urumqi, China
| | - Youxin Wang
- School of Public Health, Capital Medical University, Beijing, China.,Municipal Key Laboratory of Clinical Epidemiology, Beijing, China
| | - Honghong Fang
- School of Public Health, Capital Medical University, Beijing, China.,Municipal Key Laboratory of Clinical Epidemiology, Beijing, China
| | - Hao Wang
- School of Public Health, Capital Medical University, Beijing, China.,Municipal Key Laboratory of Clinical Epidemiology, Beijing, China
| | - Qing Gao
- School of Public Health, Capital Medical University, Beijing, China.,Municipal Key Laboratory of Clinical Epidemiology, Beijing, China
| | - Hao Dong
- School of Public Health, Capital Medical University, Beijing, China.,Municipal Key Laboratory of Clinical Epidemiology, Beijing, China
| | - Siqi Ge
- School of Public Health, Capital Medical University, Beijing, China.,Municipal Key Laboratory of Clinical Epidemiology, Beijing, China
| | - Xinwei Yu
- School of Public Health, Capital Medical University, Beijing, China.,Municipal Key Laboratory of Clinical Epidemiology, Beijing, China
| | - Jie Zhang
- School of Public Health, Capital Medical University, Beijing, China.,Municipal Key Laboratory of Clinical Epidemiology, Beijing, China
| | - Lijuan Wu
- School of Public Health, Capital Medical University, Beijing, China.,Municipal Key Laboratory of Clinical Epidemiology, Beijing, China
| | - Manshu Song
- School of Public Health, Capital Medical University, Beijing, China. .,Municipal Key Laboratory of Clinical Epidemiology, Beijing, China.
| | - Wei Wang
- School of Public Health, Capital Medical University, Beijing, China. .,Municipal Key Laboratory of Clinical Epidemiology, Beijing, China. .,School of Medical Sciences, Edith Cowan University, Joondalup, WA, Australia.
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22
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Association between IGF2BP2 Polymorphisms and Type 2 Diabetes Mellitus: A Case-Control Study and Meta-Analysis. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2016; 13:ijerph13060574. [PMID: 27294943 PMCID: PMC4924031 DOI: 10.3390/ijerph13060574] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/20/2016] [Revised: 05/26/2016] [Accepted: 06/01/2016] [Indexed: 12/26/2022]
Abstract
Background: Genome-wide association studies (GWAS) found that IGF2BP2 rs4402960 and rs1470579 polymorphisms were associated with type 2 diabetes mellitus (T2DM) risk. Many studies have replicated this association, but yielded inconsistent results. Materials and Methods: A case-control study consisting of 461 T2DM patients and 434 health controls was conducted to detect the genetic susceptibility of IGF2BP2 in a northern Han Chinese population. A meta-analysis was to evaluate the association more precisely in Asians. Results: In the case-control study, the carriers of TT genotype at rs4402960 had a higher T2DM risk than the G carriers (TG + GG) (adjusted odd ratio (AOR) = 1.962, 95% confidence interval (95% CI) = 1.065–3.612, p = 0.031]; CC carriers at rs1470579 were more susceptible to T2DM than A carriers (CA + AA) (AOR = 2.014, 95% CI = 1.114–3.642, p = 0.021). The meta-analysis containing 36 studies demonstrated that the two polymorphisms were associated with T2DM under the allele comparison, genetic models of dominant and recessive in Asians (p < 0.05). The rs4402960 polymorphisms were significantly associated with the T2DM risk after stratification by diagnostic criterion, size of sample and average age and BMI of cases, while there’re no consistent results for rs1470579. Conclusions: Our data suggests that IGF2BP2 polymorphisms are associated with T2DM in Asian populations.
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Ueyama M, Nishida N, Korenaga M, Korenaga K, Kumagai E, Yanai H, Adachi H, Katsuyama H, Moriyama S, Hamasaki H, Sako A, Sugiyama M, Aoki Y, Imamura M, Murata K, Masaki N, Kawaguchi T, Torimura T, Hyogo H, Aikata H, Ito K, Sumida Y, Kanazawa A, Watada H, Okamoto K, Honda K, Kon K, Kanto T, Mizokami M, Watanabe S. The impact of PNPLA3 and JAZF1 on hepatocellular carcinoma in non-viral hepatitis patients with type 2 diabetes mellitus. J Gastroenterol 2016; 51:370-379. [PMID: 26337813 DOI: 10.1007/s00535-015-1116-6] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/10/2015] [Accepted: 08/14/2015] [Indexed: 02/04/2023]
Abstract
BACKGROUND Type 2 diabetes mellitus (T2DM) is an established independent risk factor for hepatocellular carcinoma (HCC). T2DM is associated with non-alcoholic steatohepatitis (NASH), which is a major cause of non-HBV and non-HCV-related HCC; nevertheless, it has been difficult to identify those patients with T2DM who have a high risk of developing HCC. The aim of this study was to identify genetic determinants that predispose T2DM patients to HCC by genotyping T2DM susceptibility loci and PNPLA3. METHODS We recruited 389 patients with T2DM who satisfied the following three criteria: negative for HBs-Ag and anti-HCV Ab, alcohol intake <60 g/day, and history of T2DM >10 years. These patients were divided into two groups: T2DM patients with HCC (DM-HCC, n = 59) or those without HCC (DM-non-HCC, n = 330). We genotyped 51 single-nucleotide polymorphisms (SNPs) previously reported as T2DM or NASH susceptibility loci (PNPLA3) compared between the DM-HCC and DM-non-HCC groups with regard to allele frequencies at each SNP. RESULTS The SNP rs738409 located in PNPLA3 was the greatest risk factor associated with HCC. The frequency of the PNPLA3 G allele was significantly higher among DM-HCC individuals than DM-non-HCC individuals (OR 2.53, p = 1.05 × 10(-5)). Among individuals homozygous for the PNPLA3 G allele (n = 115), the frequency of the JAZF1 rs864745 G allele was significantly higher among DM-HCC individuals than DM-non-HCC individuals (OR 3.44, p = 0.0002). CONCLUSIONS PNPLA3 and JAZF1 were associated with non-HBV and non-HCV-related HCC development among Japanese patients with T2DM.
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Affiliation(s)
- Misuzu Ueyama
- The Research Center for Hepatitis and Immunology, National Center for Global Health and Medicine, 1-7-1 Kohnodai, Ichikawa, Chiba, 272-8516, Japan
- Department of Gastroenterology, Juntendo University School of Medicine, Bunkyo-Ku, Tokyo, Japan
| | - Nao Nishida
- The Research Center for Hepatitis and Immunology, National Center for Global Health and Medicine, 1-7-1 Kohnodai, Ichikawa, Chiba, 272-8516, Japan
| | - Masaaki Korenaga
- The Research Center for Hepatitis and Immunology, National Center for Global Health and Medicine, 1-7-1 Kohnodai, Ichikawa, Chiba, 272-8516, Japan.
| | - Keiko Korenaga
- The Research Center for Hepatitis and Immunology, National Center for Global Health and Medicine, 1-7-1 Kohnodai, Ichikawa, Chiba, 272-8516, Japan
| | - Erina Kumagai
- The Research Center for Hepatitis and Immunology, National Center for Global Health and Medicine, 1-7-1 Kohnodai, Ichikawa, Chiba, 272-8516, Japan
- Department of Gastroenterology, Juntendo University School of Medicine, Bunkyo-Ku, Tokyo, Japan
| | - Hidekatsu Yanai
- Department of Internal Medicine, National Center for Global Health and Medicine Kohnodai Hospital, Ichikawa, Chiba, Japan
| | - Hiroki Adachi
- Department of Internal Medicine, National Center for Global Health and Medicine Kohnodai Hospital, Ichikawa, Chiba, Japan
| | - Hisayuki Katsuyama
- Department of Internal Medicine, National Center for Global Health and Medicine Kohnodai Hospital, Ichikawa, Chiba, Japan
| | - Sumie Moriyama
- Department of Internal Medicine, National Center for Global Health and Medicine Kohnodai Hospital, Ichikawa, Chiba, Japan
| | - Hidetaka Hamasaki
- Department of Internal Medicine, National Center for Global Health and Medicine Kohnodai Hospital, Ichikawa, Chiba, Japan
| | - Akahito Sako
- Department of Internal Medicine, National Center for Global Health and Medicine Kohnodai Hospital, Ichikawa, Chiba, Japan
| | - Masaya Sugiyama
- The Research Center for Hepatitis and Immunology, National Center for Global Health and Medicine, 1-7-1 Kohnodai, Ichikawa, Chiba, 272-8516, Japan
| | - Yoshihiko Aoki
- The Research Center for Hepatitis and Immunology, National Center for Global Health and Medicine, 1-7-1 Kohnodai, Ichikawa, Chiba, 272-8516, Japan
| | - Masatoshi Imamura
- The Research Center for Hepatitis and Immunology, National Center for Global Health and Medicine, 1-7-1 Kohnodai, Ichikawa, Chiba, 272-8516, Japan
| | - Kazumoto Murata
- The Research Center for Hepatitis and Immunology, National Center for Global Health and Medicine, 1-7-1 Kohnodai, Ichikawa, Chiba, 272-8516, Japan
| | - Naohiko Masaki
- The Research Center for Hepatitis and Immunology, National Center for Global Health and Medicine, 1-7-1 Kohnodai, Ichikawa, Chiba, 272-8516, Japan
| | - Takumi Kawaguchi
- Division of Gastroenterology, Department of Medicine, Kurume University School of Medicine, Kurume, Japan
| | - Takuji Torimura
- Division of Gastroenterology, Department of Medicine, Kurume University School of Medicine, Kurume, Japan
| | - Hideyuki Hyogo
- Department of Gastroenterology and Metabolism, Hiroshima University, Hiroshima, Japan
| | - Hiroshi Aikata
- Department of Gastroenterology and Metabolism, Hiroshima University, Hiroshima, Japan
| | - Kiyoaki Ito
- Division of Gastroenterology, Department of Internal Medicine, Aichi Medical University School of Medicine, Nagakute, Japan
| | - Yoshio Sumida
- Center for Digestive and Liver Diseases, Nara City Hospital, Nara, Japan
- Department of Gastroenterology and Hepatology, Kyoto Prefectural University of Medicine, Kamigyo-ku, Kyoto, Japan
| | - Akio Kanazawa
- Department of Metabolism and Endocrinology, Juntendo University Graduate School of Medicine, Bunkyo-Ku, Tokyo, Japan
| | - Hirotaka Watada
- Department of Metabolism and Endocrinology, Juntendo University Graduate School of Medicine, Bunkyo-Ku, Tokyo, Japan
| | - Koji Okamoto
- Department of Nephrology and Endocrinology, Department of Hemodialysis and Apheresis, University Hospital, The University of Tokyo, Tokyo, Japan
| | - Kenjiro Honda
- Department of Nephrology and Endocrinology, Department of Hemodialysis and Apheresis, University Hospital, The University of Tokyo, Tokyo, Japan
| | - Kazuyoshi Kon
- Department of Gastroenterology, Juntendo University School of Medicine, Bunkyo-Ku, Tokyo, Japan
| | - Tatsuya Kanto
- The Research Center for Hepatitis and Immunology, National Center for Global Health and Medicine, 1-7-1 Kohnodai, Ichikawa, Chiba, 272-8516, Japan
| | - Masashi Mizokami
- The Research Center for Hepatitis and Immunology, National Center for Global Health and Medicine, 1-7-1 Kohnodai, Ichikawa, Chiba, 272-8516, Japan
| | - Sumio Watanabe
- Department of Gastroenterology, Juntendo University School of Medicine, Bunkyo-Ku, Tokyo, Japan
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24
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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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26
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Tanisawa K, Tanaka M, Higuchi M. Gene-exercise interactions in the development of cardiometabolic diseases. THE JOURNAL OF PHYSICAL FITNESS AND SPORTS MEDICINE 2016. [DOI: 10.7600/jpfsm.5.25] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Affiliation(s)
- Kumpei Tanisawa
- Faculty of Sport Sciences, Waseda University
- Department of Genomics for Longevity and Health, Tokyo Metropolitan Institute of Gerontology
- Japan Society for the Promotion of Science
| | - Masashi Tanaka
- Department of Genomics for Longevity and Health, Tokyo Metropolitan Institute of Gerontology
| | - Mitsuru Higuchi
- Faculty of Sport Sciences, Waseda University
- Institute of Advanced Active Aging Research, Waseda University
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27
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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: 22] [Impact Index Per Article: 2.2] [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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28
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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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29
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Tanisawa K, Ito T, Sun X, Ise R, Oshima S, Cao ZB, Sakamoto S, Tanaka M, Higuchi M. High cardiorespiratory fitness can reduce glycated hemoglobin levels regardless of polygenic risk for Type 2 diabetes mellitus in nondiabetic Japanese men. Physiol Genomics 2014; 46:497-504. [PMID: 24824210 DOI: 10.1152/physiolgenomics.00027.2014] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
Abstract
High cardiorespiratory fitness (CRF) is associated with a reduced risk of Type 2 diabetes mellitus (T2DM) and improved β-cell function; genetic factors also determine these risks. This cross-sectional study investigated whether CRF modifies the association of polygenic risk of T2DM with glucose metabolism in nondiabetic Japanese men. Fasting plasma glucose, insulin, and glycated hemoglobin (HbA1c) levels were measured in 174 Japanese men (age: 20-79 yr). β-Cell function and insulin resistance were evaluated by calculating HOMA-β and HOMA-IR, respectively. CRF was assessed by measuring maximal oxygen uptake (V̇o2max). Subjects were divided into the low and high CRF groups within each age group according to the median V̇o2max. Eleven single nucleotide polymorphisms (SNPs) associated with T2DM were analyzed and used to calculate genetic risk score (GRS); subjects were divided into the low, middle, and high GRS groups. The high GRS group had higher HbA1c levels than the low GRS group in both the low and high CRF groups (P < 0.05). Furthermore, the individuals with a high GRS had a lower HOMA-β than those with a low GRS regardless of CRF (P < 0.05). In multiple linear regression analysis, although GRS was a significant predictor of HbA1c (β = 0.153, P = 0.025), V̇o2max was also associated with HbA1c (β = -0.240, P = 0.041) independent of GRS. These results suggest that CRF is associated with HbA1c levels independent of GRS derived from T2DM-related SNPs; however, it does not modify the association of GRS with increased HbA1c or impaired β-cell function.
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Affiliation(s)
- Kumpei Tanisawa
- Graduate School of Sport Sciences, Waseda University, Tokorozawa, Saitama, Japan; Department of Genomics for Longevity and Health, Tokyo Metropolitan Institute of Gerontology, Itabashi, Tokyo, Japan
| | - Tomoko Ito
- Graduate School of Sport Sciences, Waseda University, Tokorozawa, Saitama, Japan
| | - Xiaomin Sun
- Graduate School of Sport Sciences, Waseda University, Tokorozawa, Saitama, Japan
| | - Ryuken Ise
- Graduate School of Sport Sciences, Waseda University, Tokorozawa, Saitama, Japan
| | - Satomi Oshima
- Faculty of Sport Sciences, Waseda University, Tokorozawa, Saitama, Japan; and
| | - Zhen-Bo Cao
- Faculty of Sport Sciences, Waseda University, Tokorozawa, Saitama, Japan; and
| | - Shizuo Sakamoto
- Faculty of Sport Sciences, Waseda University, Tokorozawa, Saitama, Japan; and Institute of Advanced Active Aging Research, Waseda University, Tokorozawa, Saitama, Japan
| | - Masashi Tanaka
- Department of Genomics for Longevity and Health, Tokyo Metropolitan Institute of Gerontology, Itabashi, Tokyo, Japan
| | - Mitsuru Higuchi
- Faculty of Sport Sciences, Waseda University, Tokorozawa, Saitama, Japan; and Institute of Advanced Active Aging Research, Waseda University, Tokorozawa, Saitama, Japan
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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: 53] [Impact Index Per Article: 4.8] [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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Li L, Gao K, Zhao J, Feng T, Yin L, Wang J, Wang C, Li C, Wang Y, Wang Q, Zhai Y, You H, Ren Y, Wang B, Hu D. Glucagon gene polymorphism modifies the effects of smoking and physical activity on risk of type 2 diabetes mellitus in Han Chinese. Gene 2013; 534:352-5. [PMID: 24185078 DOI: 10.1016/j.gene.2013.09.121] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2013] [Revised: 08/30/2013] [Accepted: 09/30/2013] [Indexed: 12/17/2022]
Abstract
Few genome-wide association studies have considered interactions between multiple genetic variants and environmental factors associated with disease. The interaction was examined between a glucagon gene (GCG) polymorphism and smoking, alcohol consumption and physical activity and the association with risk of type 2 diabetes mellitus (T2DM) in a case-control study of Chinese Han subjects. The rs12104705 polymorphism of GCG and interactions with environmental variables were analyzed for 9619 participants by binary multiple logistic regression. Smoking with the C-C haplotype of rs12104705 was associated with increased risk of T2DM (OR=1.174, 95% CI=1.013-1.361). Moderate and high physical activity with the C-C genotype was associated with decreased risk of T2DM as compared with low physical activity with the genotype (OR=0.251, 95% CI=0.206-0.306 and OR=0.190, 95% CI=0.164-0.220). However, the interaction of drinking and genotype was not associated with risk of T2DM. Genetic polymorphism in rs12104705 of GCG may interact with smoking and physical activity to modify the risk of T2DM.
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Affiliation(s)
- Linlin Li
- Department of Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, 450001, People's Republic of China
| | - Kaiping Gao
- Shenzhen University School of Medicine, Shenzhen, 518060, People's Republic of China
| | - Jingzhi Zhao
- Military Hospital of Henan Province, Zhengzhou, 450003, People's Republic of China
| | - Tianping Feng
- Military Hospital of Henan Province, Zhengzhou, 450003, People's Republic of China
| | - Lei Yin
- Military Hospital of Henan Province, Zhengzhou, 450003, People's Republic of China
| | - Jinjin Wang
- Discipline of Public Health and Preventive Medicine, Center of Preventive Medicine Research and Assessment, Henan University of Traditional Chinese Medicine, Zhengzhou, 450008, People's Republic of China
| | - Chongjian Wang
- Department of Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, 450001, People's Republic of China
| | - Chunyang Li
- Department of Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, 450001, People's Republic of China
| | - Yan Wang
- Department of Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, 450001, People's Republic of China
| | - Qian Wang
- Department of Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, 450001, People's Republic of China
| | - Yujia Zhai
- Department of Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, 450001, People's Republic of China
| | - Haifei You
- Department of Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, 450001, People's Republic of China
| | - Yongcheng Ren
- Department of Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, 450001, People's Republic of China
| | - Bingyuan Wang
- Department of Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, 450001, People's Republic of China
| | - Dongsheng Hu
- Shenzhen University School of Medicine, Shenzhen, 518060, People's Republic of China.
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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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Kido T, Kawashima M, Nishino S, Swan M, Kamatani N, Butte AJ. Systematic evaluation of personal genome services for Japanese individuals. J Hum Genet 2013; 58:734-41. [PMID: 24067293 DOI: 10.1038/jhg.2013.96] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2013] [Revised: 08/02/2013] [Accepted: 08/13/2013] [Indexed: 11/09/2022]
Affiliation(s)
- Takashi Kido
- 1] Rikengenesis CO., LTD., Tokyo, Japan [2] JST, PRESTO, Tokyo, Japan
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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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Affiliation(s)
- Valeriya Lyssenko
- Department of Clinical Sciences, Diabetes and Endocrinology, Lund University, Malmö, Sweden.
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Urano W, Taniguchi A, Inoue E, Sekita C, Ichikawa N, Koseki Y, Kamatani N, Yamanaka H. Effect of Genetic Polymorphisms on Development of Gout. J Rheumatol 2013; 40:1374-8. [DOI: 10.3899/jrheum.121244] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Objective.To validate the association between genetic polymorphisms and gout in Japanese patients, and to investigate the cumulative effects of multiple genetic factors on the development of gout.Methods.Subjects were 153 Japanese male patients with gout and 532 male controls. The genotypes of 11 polymorphisms in the 10 genes that have been indicated to be associated with serum uric acid levels or gout were determined. The cumulative effects of the genetic polymorphisms were investigated using a weighted genotype risk score (wGRS) based on the number of risk alleles and the OR for gout. A model to discriminate between patients with gout and controls was constructed by incorporating the wGRS and clinical factors. C statistics method was applied to evaluate the capability of the model to discriminate gout patients from controls.Results.Seven polymorphisms were shown to be associated with gout. The mean wGRS was significantly higher in patients with gout (15.2 ± 2.01) compared to controls (13.4 ± 2.10; p < 0.0001). The C statistic for the model using genetic information alone was 0.72, while the C statistic was 0.81 for the full model that incorporated all genetic and clinical factors.Conclusion.Accumulation of multiple genetic factors is associated with the development of gout. A prediction model for gout that incorporates genetic and clinical factors may be useful for identifying individuals who are at risk of gout.
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Tanaka D, Nagashima K, Sasaki M, Funakoshi S, Kondo Y, Yasuda K, Koizumi A, Inagaki N. Exome sequencing identifies a new candidate mutation for susceptibility to diabetes in a family with highly aggregated type 2 diabetes. Mol Genet Metab 2013; 109:112-7. [PMID: 23499280 DOI: 10.1016/j.ymgme.2013.02.010] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/15/2013] [Revised: 02/13/2013] [Accepted: 02/13/2013] [Indexed: 11/20/2022]
Abstract
The aim of this study was to investigate the genetic background of familial clustering of diabetes using genome-wide linkage analysis combined with exome sequencing. We recruited a Japanese family with a 3-generation history of diabetes. The family comprised 16 members, 13 having been diagnosed with diabetes. Nine members had been diagnosed before the age of 40. Linkage analysis was performed assuming an autosomal dominant model. Linkage regions were observed on chromosomes 4q34, 5q11-q13, and 12p11-q22 and the logarithm of odds (LOD) scores were 1.80. To identify the susceptibility variants, we performed exome sequencing of an affected family member. We predicted that the familial clustering of diabetes is caused by a rare non-synonymous variant, and focused our analysis on non-synonymous variants absent in dbSNP131. Exome sequencing identified 10 such variants in the linkage regions, 7 of which were concordant with the affection status in the family. One hundred five normal subjects and 67 lean diabetes subjects were genotyped for the 7 variants; the only variant found to be significantly more frequent in the diabetes subjects than in the normal subjects was the N1072K variant of the early endosome antigen 1 (EEA1) gene (0 in normal subjects and 4 in diabetes subjects, p=0.022). We therefore propose that the N1072K variant of the EEA1 gene is a candidate mutation for susceptibility to diabetes in the Japanese population.
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Affiliation(s)
- Daisuke Tanaka
- Department of Diabetes and Clinical Nutrition, Graduate School of Medicine, Kyoto University, 54 Shogoin-Kawahara-cho, Sakyo-ku, Kyoto, 606-8507, Japan
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Li H, Xu R, Peng X, Wang Y, Wang T. Association of glucokinase regulatory protein polymorphism with type 2 diabetes and fasting plasma glucose: a meta-analysis. Mol Biol Rep 2013; 40:3935-42. [PMID: 23307301 DOI: 10.1007/s11033-012-2470-6] [Citation(s) in RCA: 23] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2012] [Accepted: 12/18/2012] [Indexed: 01/15/2023]
Abstract
Glucokinase regulatory protein (GCKR) which binds to glucokinase (GCK) in the nucleus and inhibits its activity in the presence of fructose-6-phosphate is critical for glucose metabolism. In the past few years, a number of case-control studies have been carried out to investigate the relationship between the GCKR polymorphism and type 2 diabetes (T2D) since it was first identified to be associated with fasting plasma glucose levels, insulin resistance through genome-wide association approach. After that, a number of studies reported that the rs780094 polymorphism in GCKR has been implicated in T2D risk. However, these studies have yielded contradictory results. To investigate this inconsistency, we performed a meta-analysis of 19 studies involving a total of 298,977 subjects for GCKR rs780094 to evaluate its effect on genetic susceptibility for T2D. In a combined analysis, the summary per-allele odds ratio for T2D of the rs780094 polymorphism was 1.11 (95 % CI: 1.07-1.14, P < 10(-5)). Significant results were also observed using dominant (OR = 1.18, 95 % CI: 1.05-1.34, P < 10(-5)) or recessive genetic model (OR = 1.20, 95 % CI: 1.12-1.28, P < 10(-5)). Significant results were found in Asians and Caucasians when stratified by ethnicity. Besides, the polymorphism was found to be significantly associated with increased fasting plasma glucose level. There was strong evidence of heterogeneity, which largely disappeared after stratification by ethnicity. This meta-analysis suggests that the rs780094 polymorphism in GCKR is associated with elevated T2D risk, but these associations vary in different ethnic populations.
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Affiliation(s)
- Hong Li
- Department of Endocrinology, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, 725 South Wanpin Road, Shanghai, 200032, People's Republic of China.
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Iwata M, Maeda S, Kamura Y, Takano A, Kato H, Murakami S, Higuchi K, Takahashi A, Fujita H, Hara K, Kadowaki T, Tobe K. Genetic risk score constructed using 14 susceptibility alleles for type 2 diabetes is associated with the early onset of diabetes and may predict the future requirement of insulin injections among Japanese individuals. Diabetes Care 2012; 35:1763-70. [PMID: 22688542 PMCID: PMC3402252 DOI: 10.2337/dc11-2006] [Citation(s) in RCA: 74] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
OBJECTIVE We evaluated the clinical usefulness of a genetic risk score (GRS) based on 14 well-established variants for type 2 diabetes. RESEARCH DESIGN AND METHODS We analyzed 14 SNPs at HHEX, CDKAL1, CDKN2B, SLC30A8, KCNJ11, IGF2BP2, PPARG, TCF7L2, FTO, KCNQ1, IRS-1, GCKR, UBE2E2, and C2CD4A/B in 1,487 Japanese individuals (724 patients with type 2 diabetes and 763 control subjects). A GRS was calculated according to the number of risk alleles by counting all 14 SNPs (T-GRS) as well as 11 SNPs related to β-cell function (β-GRS) and then assessing the association between each GRS and the clinical features. RESULTS Among the 14 SNPs, 4 SNPs were significantly associated with type 2 diabetes in the present Japanese sample (P < 0.0036). The T-GRS was significantly associated with type 2 diabetes (P = 5.9 × 10(-21)). Among the subjects with type 2 diabetes, the β-GRS was associated with individuals receiving insulin therapy (β = 0.0131, SE = 0.006, P = 0.0431), age at diagnosis (β = -0.608, SE = 0.204, P = 0.0029), fasting serum C-peptide level (β = -0.032, SE = 0.0140, P = 0.022), and C-peptide index (β = -0.031, SE = 0.012, P = 0.0125). CONCLUSIONS Our data suggest that the β-GRS is associated with reduced β-cell functions and may be useful for selecting patients who should receive more aggressive β-cell-preserving therapy.
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Affiliation(s)
- Minoru Iwata
- First Department of Internal Medicine, Faculty of Medicine, Toyama University, Toyama, Japan.
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Yamakawa-Kobayashi K, Natsume M, Aoki S, Nakano S, Inamori T, Kasezawa N, Goda T. The combined effect of the T2DM susceptibility genes is an important risk factor for T2DM in non-obese Japanese: a population based case-control study. BMC MEDICAL GENETICS 2012; 13:11. [PMID: 22364391 PMCID: PMC3313886 DOI: 10.1186/1471-2350-13-11] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/02/2011] [Accepted: 02/24/2012] [Indexed: 11/10/2022]
Abstract
Background Type 2 diabetes mellitus (T2DM) is a complex endocrine and metabolic disorder. Recently, several genome-wide association studies (GWAS) have identified many novel susceptibility loci for T2DM, and indicated that there are common genetic causes contributing to the susceptibility to T2DM in multiple populations worldwide. In addition, clinical and epidemiological studies have indicated that obesity is a major risk factor for T2DM. However, the prevalence of obesity varies among the various ethnic groups. We aimed to determine the combined effects of these susceptibility loci and obesity/overweight for development of T2DM in the Japanese. Methods Single nucleotide polymorphisms (SNPs) in or near 17 susceptibility loci for T2DM, identified through GWAS in Caucasian and Asian populations, were genotyped in 333 cases with T2DM and 417 control subjects. Results We confirmed that the cumulative number of risk alleles based on 17 susceptibility loci for T2DM was an important risk factor in the development of T2DM in Japanese population (P < 0.0001), although the effect of each risk allele was relatively small. In addition, the significant association between an increased number of risk alleles and an increased risk of T2DM was observed in the non-obese group (P < 0.0001 for trend), but not in the obese/overweight group (P = 0.88 for trend). Conclusions Our findings indicate that there is an etiological heterogeneity of T2DM between obese/overweight and non-obese subjects.
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Affiliation(s)
- Kimiko Yamakawa-Kobayashi
- Laboratory of Human Genetics, School of Food and Nutritional Sciences, Graduate School of Nutritional and Environmental Sciences, Global COE Program, University of Shizuoka, Shizuoka 422-8526, Japan.
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Willems SM, Mihaescu R, Sijbrands EJG, van Duijn CM, Janssens ACJW. A methodological perspective on genetic risk prediction studies in type 2 diabetes: recommendations for future research. Curr Diab Rep 2011; 11:511-8. [PMID: 21947855 PMCID: PMC3207129 DOI: 10.1007/s11892-011-0235-6] [Citation(s) in RCA: 24] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Fueled by the successes of genome-wide association studies, numerous studies have investigated the predictive ability of genetic risk models in type 2 diabetes. In this paper, we review these studies from a methodological perspective, focusing on the variables included in the risk models as well as the study designs and populations investigated. We argue and show that differences in study design and characteristics of the study population have an impact on the observed predictive ability of risk models. This observation emphasizes that genetic risk prediction studies should be conducted in those populations in which the prediction models will ultimately be applied, if proven useful. Of all genetic risk prediction studies to date, only a few were conducted in populations that might be relevant for targeting preventive interventions.
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Affiliation(s)
- Sara M. Willems
- Department of Epidemiology, Erasmus University Medical Center, Dr. Molewaterplein 50, 3015 GE Rotterdam, the Netherlands
| | - Raluca Mihaescu
- Department of Epidemiology, Erasmus University Medical Center, Dr. Molewaterplein 50, 3015 GE Rotterdam, the Netherlands
| | - Eric J. G. Sijbrands
- Department of Internal Medicine, Erasmus University Medical Center, Dr. Molewaterplein 50, 3015 GE Rotterdam, the Netherlands
| | - Cornelia M. van Duijn
- Department of Epidemiology, Erasmus University Medical Center, Dr. Molewaterplein 50, 3015 GE Rotterdam, the Netherlands
| | - A. Cecile J. W. Janssens
- Department of Epidemiology, Erasmus University Medical Center, Dr. Molewaterplein 50, 3015 GE Rotterdam, the Netherlands
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Torkamani A, Scott-Van Zeeland AA, Topol EJ, Schork NJ. Annotating individual human genomes. Genomics 2011; 98:233-41. [PMID: 21839162 DOI: 10.1016/j.ygeno.2011.07.006] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2011] [Accepted: 07/26/2011] [Indexed: 02/03/2023]
Abstract
Advances in DNA sequencing technologies have made it possible to rapidly, accurately and affordably sequence entire individual human genomes. As impressive as this ability seems, however, it will not likely amount to much if one cannot extract meaningful information from individual sequence data. Annotating variations within individual genomes and providing information about their biological or phenotypic impact will thus be crucially important in moving individual sequencing projects forward, especially in the context of the clinical use of sequence information. In this paper we consider the various ways in which one might annotate individual sequence variations and point out limitations in the available methods for doing so. It is arguable that, in the foreseeable future, DNA sequencing of individual genomes will become routine for clinical, research, forensic, and personal purposes. We therefore also consider directions and areas for further research in annotating genomic variants.
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Travers ME, McCarthy MI. Type 2 diabetes and obesity: genomics and the clinic. Hum Genet 2011; 130:41-58. [PMID: 21647602 DOI: 10.1007/s00439-011-1023-8] [Citation(s) in RCA: 44] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2011] [Accepted: 05/26/2011] [Indexed: 12/11/2022]
Abstract
Type 2 diabetes (T2D) and obesity represent major challenges for global public health. They are at the forefront of international efforts to identify the genetic variation contributing to complex disease susceptibility, and recent years have seen considerable success in identifying common risk-variants. Given the clinical impact of molecular diagnostics in rarer monogenic forms of these diseases, expectations have been high that genetic discoveries will transform the prospects for risk stratification, development of novel therapeutics and personalised medicine. However, so far, clinical translation has been limited. Difficulties in defining the alleles and transcripts mediating association effects have frustrated efforts to gain early biological insights, whilst the fact that variants identified account for only a modest proportion of observed familiarity has limited their value in guiding treatment of individual patients. Ongoing efforts to track causal variants through fine-mapping and to illuminate the biological mechanisms through which they act, as well as sequence-based discovery of lower-frequency alleles (of potentially larger effect), should provide welcome acceleration in the capacity for clinical translation. This review will summarise recent advances in identifying risk alleles for T2D and obesity, and existing contributions to understanding disease pathology. It will consider the progress made in translating genetic knowledge into clinical utility, the challenges remaining, and the realistic potential for further progress.
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Affiliation(s)
- Mary E Travers
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Churchill Hospital, University of Oxford, Old Road, Headington, Oxford OX3 7LJ, UK
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Yang L, Zhou X, Luo Y, Sun X, Tang Y, Guo W, Han X, Ji L. Association between KCNJ11 gene polymorphisms and risk of type 2 diabetes mellitus in East Asian populations: a meta-analysis in 42,573 individuals. Mol Biol Rep 2011; 39:645-59. [PMID: 21573802 DOI: 10.1007/s11033-011-0782-6] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2010] [Accepted: 04/27/2011] [Indexed: 01/12/2023]
Abstract
A number of studies have been performed to identify the association between potassium inwardly-rectifying channel, subfamily J, member 11 (KCNJ11) gene and type 2 diabetes mellitus (T2DM) in East Asian populations, with inconsistent results. The main aim of this work was to evaluate more precisely the genetic influence of KCNJ11 on T2DM in East Asian populations by means of a meta-analysis. We identified 20 articles for qualitative analysis and 16 were eligible for quantitative analysis (meta-analysis) by database searching up to May 2010. The association was assessed under different genetic models, and the pooled odds ratios (ORs) with 95% confidence intervals (95% CIs) were calculated. The allelic and genotypic contrast demonstrated that the association between KCNJ11 and T2DM was significant for rs5210. However, not all results for rs5215 and rs5218 showed significant associations. For rs5219, the combined ORs (95% CIs) for allelic contrast, dominant and recessive models contrast (with allelic frequency and genotypic distribution data) were 1.139 (1.093-1.188), 1.177 (1.099-1.259) and 1.207 (1.094-1.332), respectively (random effect model). The analysis on the most completely adjusted ORs (95% CIs) by the covariates of rs5219 all presented significant associations under different genetic models. Population-stratified analysis (Korean, Japanese and Chinese) and sensitivity analysis verified the significant results. Cumulative meta-analysis including publication time and sample size illustrated the exaggerated genetic effect in the earliest studies. Heterogeneity and publication bias were assessed. Our study verified that single nucleotide polymorphisms (SNPs) of KCNJ11 gene were significantly associated with the risk of T2DM in East Asian populations.
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Affiliation(s)
- Lijuan Yang
- Department of Endocrinology and Metabolism, Peking University People's Hospital, No. 11 Xi Zhimen Nan Da Jie Main Street, Xi Cheng District, Beijing, 100044, China
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Koga M, Kawasaki A, Ito I, Furuya T, Ohashi J, Kyogoku C, Ito S, Hayashi T, Matsumoto I, Kusaoi M, Takasaki Y, Hashimoto H, Sumida T, Tsuchiya N. Cumulative association of eight susceptibility genes with systemic lupus erythematosus in a Japanese female population. J Hum Genet 2011; 56:503-7. [DOI: 10.1038/jhg.2011.49] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
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Tanaka D, Nagashima K, Sasaki M, Yamada C, Funakoshi S, Akitomo K, Takenaka K, Harada K, Koizumi A, Inagaki N. GCKR mutations in Japanese families with clustered type 2 diabetes. Mol Genet Metab 2011; 102:453-60. [PMID: 21236713 DOI: 10.1016/j.ymgme.2010.12.009] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/15/2010] [Accepted: 12/15/2010] [Indexed: 12/01/2022]
Abstract
OBJECTIVE The aim was to investigate the genetic background of familial clustering of type 2 diabetes. SUBJECTS AND METHODS We recruited Japanese families with a 3-generation history of diabetes. Genome-wide linkage analysis was performed assuming an autosomal dominant model. Genes in the linkage region were computationally prioritized using Endeavour. We sequenced the candidate genes, and the frequencies of detected nucleotide changes were then examined in normoglycemic controls. RESULTS To exclude known genetic factors, we sequenced 6 maturity onset diabetes of the young (MODY) genes in 10 familial cases. Because we detected a MODY3 mutation HNF1A R583G in one case, we excluded this case from further investigation. Linkage analysis revealed a significant linkage region on 2p25-22 (LOD score=3.47) for 4 families. The 23.6-Mb linkage region contained 106 genes. Those genes were scored by computational prioritization. Eleven genes, i.e., top 10% of 106 genes, were selected and considered primary candidates. Considering their functions, we eliminated 3 well characterized genes and finally sequenced 8 genes. GCKR ranked highly in the computational prioritization. Mutations (minor allele frequency less than 1%) in exons and the promoter of GCKR were found in index cases of the families (3 of 18 alleles) more frequently than in controls (0 of 36 alleles, P=0.033). In one pedigree with 9 affected members, the mutation GCKR g.6859C>G was concordant with affection status. No mutation in other 7 genes that ranked highly in the prioritization was concordant with affection status in families. CONCLUSIONS We propose that GCKR is a susceptibility gene in Japanese families with clustered diabetes. The family based approach seems to be complementary with a large population study.
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Affiliation(s)
- Daisuke Tanaka
- Department of Diabetes and Clinical Nutrition, Graduate School of Medicine, Kyoto University, Kyoto, Japan
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Sousa AGP, Lopes NH, Hueb WA, Krieger JE, Pereira AC. Genetic variants of diabetes risk and incident cardiovascular events in chronic coronary artery disease. PLoS One 2011; 6:e16341. [PMID: 21283728 PMCID: PMC3024434 DOI: 10.1371/journal.pone.0016341] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2010] [Accepted: 12/11/2010] [Indexed: 01/08/2023] Open
Abstract
Objective To determine whether information from genetic risk variants for diabetes is associated with cardiovascular events incidence. Methods From the about 30 known genes associated with diabetes, we genotyped single-nucleotide polymorphisms at the 10 loci most associated with type-2 diabetes in 425 subjects from the MASS-II Study, a randomized study in patients with multi-vessel coronary artery disease. The combined genetic information was evaluated by number of risk alleles for diabetes. Performance of genetic models relative to major cardiovascular events incidence was analyzed through Kaplan-Meier curve comparison and Cox Hazard Models and the discriminatory ability of models was assessed for cardiovascular events by calculating the area under the ROC curve. Results Genetic information was able to predict 5-year incidence of major cardiovascular events and overall-mortality in non-diabetic individuals, even after adjustment for potential confounders including fasting glycemia. Non-diabetic individuals with high genetic risk had a similar incidence of events then diabetic individuals (cumulative hazard of 33.0 versus 35.1% of diabetic subjects). The addition of combined genetic information to clinical predictors significantly improved the AUC for cardiovascular events incidence (AUC = 0.641 versus 0.610). Conclusions Combined information of genetic variants for diabetes risk is associated to major cardiovascular events incidence, including overall mortality, in non-diabetic individuals with coronary artery disease. Clinical Trial Registration Information Medicine, Angioplasty, or Surgery Study (MASS II). Unique identifier: ISRCTN66068876 URL.
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Affiliation(s)
- André Gustavo P Sousa
- Laboratory of Genetics and Molecular Cardiology, Medical School, Heart Institute, University of São Paulo, São Paulo, Brazil.
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Mihaescu R, Meigs J, Sijbrands E, Janssens AC. Genetic risk profiling for prediction of type 2 diabetes. PLOS CURRENTS 2011; 3:RRN1208. [PMID: 21278902 PMCID: PMC3024707 DOI: 10.1371/currents.rrn1208] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 01/11/2011] [Indexed: 11/29/2022]
Abstract
Type 2 diabetes (T2D) is a common disease caused by a complex interplay between many genetic and environmental factors. Candidate gene studies and recent collaborative genome-wide association efforts revealed at least 38 common single nucleotide polymorphisms (SNPs) associated with increased risk of T2D. Genetic testing of multiple SNPs is considered a potentially useful tool for early detection of individuals at high diabetes risk leading to improved targeting of preventive interventions.
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Affiliation(s)
- Raluca Mihaescu
- Erasmus University Medical Center Rotterdam; Massachusetts General Hospital and Dept. of Internal Medicine, Erasmus MC, Rotterdam, The Netherlands
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Abstract
Genome-wide association studies (GWAS) have facilitated a substantial and rapid rise in the number of confirmed genetic susceptibility variants for type 2 diabetes (T2D). Approximately 40 variants have been identified so far, many of which were discovered through GWAS. This success has led to widespread hope that the findings will translate into improved clinical care for the increasing numbers of patients with diabetes. Potential areas or clinical translation include risk prediction and subsequent disease prevention, pharmacogenetics, and the development of novel therapeutics. However, the genetic loci so far identified account for only a small fraction (approximately 10%) of the overall heritable risk for T2D. Uncovering the missing heritability is essential to the progress of T2D genetic studies and to the translation of genetic information into clinical practice.
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Affiliation(s)
- Minako Imamura
- Laboratory for Endocrinology and Metabolism, RIKEN Center for Genomic Medicine, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa, Japan
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