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Genetics
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Genome-Wide Association Study on Longitudinal Change in Fasting Plasma Glucose in Korean Population
Heejin Jin, Soo Heon Kwak, Ji Won Yoon, Sanghun Lee, Kyong Soo Park, Sungho Won, Nam H. Cho
Diabetes Metab J. 2023;47(2):255-266.   Published online January 19, 2023
DOI: https://doi.org/10.4093/dmj.2021.0375
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  • 175 Download
  • 1 Web of Science
  • 1 Crossref
AbstractAbstract PDFSupplementary MaterialPubReader   ePub   
Background
Genome-wide association studies (GWAS) on type 2 diabetes mellitus (T2DM) have identified more than 400 distinct genetic loci associated with diabetes and nearly 120 loci for fasting plasma glucose (FPG) and fasting insulin level to date. However, genetic risk factors for the longitudinal deterioration of FPG have not been thoroughly evaluated. We aimed to identify genetic variants associated with longitudinal change of FPG over time.
Methods
We used two prospective cohorts in Korean population, which included a total of 10,528 individuals without T2DM. GWAS of repeated measure of FPG using linear mixed model was performed to investigate the interaction of genetic variants and time, and meta-analysis was conducted. Genome-wide complex trait analysis was used for heritability calculation. In addition, expression quantitative trait loci (eQTL) analysis was performed using the Genotype-Tissue Expression project.
Results
A small portion (4%) of the genome-wide single nucleotide polymorphism (SNP) interaction with time explained the total phenotypic variance of longitudinal change in FPG. A total of four known genetic variants of FPG were associated with repeated measure of FPG levels. One SNP (rs11187850) showed a genome-wide significant association for genetic interaction with time. The variant is an eQTL for NOC3 like DNA replication regulator (NOC3L) gene in pancreas and adipose tissue. Furthermore, NOC3L is also differentially expressed in pancreatic β-cells between subjects with or without T2DM. However, this variant was not associated with increased risk of T2DM nor elevated FPG level.
Conclusion
We identified rs11187850, which is an eQTL of NOC3L, to be associated with longitudinal change of FPG in Korean population.

Citations

Citations to this article as recorded by  
  • Unraveling the understudied influence of a lead variant in the 9p21 locus on the atherogenic index among type 2 diabetes patients with coronary artery disease
    Mahsa Naserian, Ahad Alizadeh, Mani Nosrati, Abdolkarim Mahrooz
    Journal of Diabetes & Metabolic Disorders.2024;[Epub]     CrossRef
Review
Islet Studies and Transplantation
Article image
Regulation of Pancreatic β-Cell Mass by Gene-Environment Interaction
Shun-ichiro Asahara, Hiroyuki Inoue, Yoshiaki Kido
Diabetes Metab J. 2022;46(1):38-48.   Published online January 27, 2022
DOI: https://doi.org/10.4093/dmj.2021.0045
  • 4,827 View
  • 200 Download
  • 5 Web of Science
  • 5 Crossref
Graphical AbstractGraphical Abstract AbstractAbstract PDFPubReader   ePub   
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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  • Increased risk of incident diabetes after therapy with immune checkpoint inhibitor compared with conventional chemotherapy: A longitudinal trajectory analysis using a tertiary care hospital database
    Minyoung Lee, Kyeongseob Jeong, Yu Rang Park, Yumie Rhee
    Metabolism.2023; 138: 155311.     CrossRef
  • The ameliorating effects of mesenchymal stem cells compared to α‐tocopherol on apoptosis and autophagy in streptozotocin‐induced diabetic rats: Implication of PI3K/Akt signaling pathway and entero‐insular axis
    Heba A. Mubarak, Manal M. Kamal, Yossra Mahmoud, Fatma S. Abd‐Elsamea, Eman Abdelbary, Marwa G. Gamea, Reham I. El‐Mahdy
    Journal of Cellular Biochemistry.2023; 124(11): 1705.     CrossRef
  • Leptin Rs7799039 polymorphism is associated with type 2 diabetes mellitus Egyptian patients
    Amal Ahmed Mohamed, Dina M. Abo-Elmatty, Alaa S. Wahba, Omnia Ezzat Esmail, Hadeer Saied Mahmoud Salim, Wafaa Salah Mohammed Hegab, Mona Mostafa Farid Ghanem, Nadia Youssef Riad, Doaa Ghaith, Lamiaa I Daker, Shorouk Issa, Noha Hassan Radwan, Eman Sultan,
    Archives of Physiology and Biochemistry.2023; : 1.     CrossRef
  • Association of Polygenic Variants with Type 2 Diabetes Risk and Their Interaction with Lifestyles in Asians
    Haeng Jeon Hur, Hye Jeong Yang, Min Jung Kim, Kyun-Hee Lee, Myung-Sunny Kim, Sunmin Park
    Nutrients.2022; 14(15): 3222.     CrossRef
  • Chemical Compounds and Ambient Factors Affecting Pancreatic Alpha-Cells Mass and Function: What Evidence?
    Gaia Chiara Mannino, Elettra Mancuso, Stefano Sbrignadello, Micaela Morettini, Francesco Andreozzi, Andrea Tura
    International Journal of Environmental Research and Public Health.2022; 19(24): 16489.     CrossRef
Original Article
Genome-Wide Association Study Identifies Two Novel Loci with Sex-Specific Effects for Type 2 Diabetes Mellitus and Glycemic Traits in a Korean Population
Min Jin Go, Joo-Yeon Hwang, Tae-Joon Park, Young Jin Kim, Ji Hee Oh, Yeon-Jung Kim, Bok-Ghee Han, Bong-Jo Kim
Diabetes Metab J. 2014;38(5):375-387.   Published online October 17, 2014
DOI: https://doi.org/10.4093/dmj.2014.38.5.375
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  • 43 Download
  • 28 Web of Science
  • 24 Crossref
AbstractAbstract PDFPubReader   
Background

Until recently, genome-wide association study (GWAS)-based findings have provided a substantial genetic contribution to type 2 diabetes mellitus (T2DM) or related glycemic traits. However, identification of allelic heterogeneity and population-specific genetic variants under consideration of potential confounding factors will be very valuable for clinical applicability. To identify novel susceptibility loci for T2DM and glycemic traits, we performed a two-stage genetic association study in a Korean population.

Methods

We performed a logistic analysis for T2DM, and the first discovery GWAS was analyzed for 1,042 cases and 2,943 controls recruited from a population-based cohort (KARE, n=8,842). The second stage, de novo replication analysis, was performed in 1,216 cases and 1,352 controls selected from an independent population-based cohort (Health 2, n=8,500). A multiple linear regression analysis for glycemic traits was further performed in a total of 14,232 nondiabetic individuals consisting of 7,696 GWAS and 6,536 replication study participants. A meta-analysis was performed on the combined results using effect size and standard errors estimated for stage 1 and 2, respectively.

Results

A combined meta-analysis for T2DM identified two new (rs11065756 and rs2074356) loci reaching genome-wide significance in CCDC63 and C12orf51 on the 12q24 region. In addition, these variants were significantly associated with fasting plasma glucose and homeostasis model assessment of β-cell function. Interestingly, two independent single nucleotide polymorphisms were associated with sex-specific stratification in this study.

Conclusion

Our study showed a strong association between T2DM and glycemic traits. We further observed that two novel loci with multiple diverse effects were highly specific to males. Taken together, these findings may provide additional insights into the clinical assessment or subclassification of disease risk in a Korean population.

Citations

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  • Interactions between Bitter Taste Receptor Gene Variants and Dietary Intake Are Associated with the Incidence of Type 2 Diabetes Mellitus in Middle-Aged and Older Korean Adults
    Kyung Won Lee, Dayeon Shin
    International Journal of Molecular Sciences.2023; 24(3): 2199.     CrossRef
  • Evaluating machine learning-powered classification algorithms which utilize variants in the GCKR gene to predict metabolic syndrome: Tehran Cardio-metabolic Genetics Study
    Mahdi Akbarzadeh, Nadia Alipour, Hamed Moheimani, Asieh Sadat Zahedi, Firoozeh Hosseini-Esfahani, Hossein Lanjanian, Fereidoun Azizi, Maryam S. Daneshpour
    Journal of Translational Medicine.2022;[Epub]     CrossRef
  • The potential effects of HECTD4 variants on fasting glucose and triglyceride levels in relation to prevalence of type 2 diabetes based on alcohol intake
    Yoo Jeong Lee, Hansongyi Lee, Han Byul Jang, Min-Gyu Yoo, Sumin Im, Soo Kyung Koo, Hye-Ja Lee
    Archives of Toxicology.2022; 96(9): 2487.     CrossRef
  • Impaired fasting glucose and development of chronic kidney disease in non-diabetic population: a Mendelian randomization study
    Hyoungnae Kim, Suyeon Park, Soon Hyo Kwon, Jin Seok Jeon, Dong Cheol Han, Hyunjin Noh
    BMJ Open Diabetes Research & Care.2020; 8(1): e001395.     CrossRef
  • Interactions of Habitual Coffee Consumption by Genetic Polymorphisms with the Risk of Prediabetes and Type 2 Diabetes Combined
    Taiyue Jin, Jiyoung Youn, An Na Kim, Moonil Kang, Kyunga Kim, Joohon Sung, Jung Eun Lee
    Nutrients.2020; 12(8): 2228.     CrossRef
  • Genetic predisposition in type 2 diabetes: A promising approach toward a personalized management of diabetes
    Mahmoud M. Sirdah, N. Scott Reading
    Clinical Genetics.2020; 98(6): 525.     CrossRef
  • Association of transcription factor 7-like 2 (TCF7L2) gene polymorphism with type 2 diabetes mellitus in Chinese Korean ethnicity population
    Kui-Chen Zhou, Hong-Wei Liu, Chen Wang, Yan-Jun Fu, Feng Jin
    Medicine.2019; 98(5): e14288.     CrossRef
  • Association of Fasting Glucose Level with Neutrophil-Lymphocyte Ratio Compared to Leukocyte Count and Serum C-Reactive Protein
    Jin-Kyu Kim, Ah-Young Lee, Jee-Hyun Kang, Byung-Yeon Yu, Seong-Ju Kim
    Korean Journal of Family Medicine.2018; 39(1): 42.     CrossRef
  • New Common and Rare Variants Influencing Metabolic Syndrome and Its Individual Components in a Korean Population
    Ho-Sun Lee, Yongkang Kim, Taesung Park
    Scientific Reports.2018;[Epub]     CrossRef
  • Association of genetic variants in RETN, NAMPT and ADIPOQ gene with glycemic, metabolic traits and diabetes risk in a Chinese population
    Qiang Zhou, Bo Chen, Tianxing Ji, Miaoshan Luo, Jiandong Luo
    Gene.2018; 642: 439.     CrossRef
  • Opposite Genetic Effects of CMIP Polymorphisms on the Risk of Type 2 Diabetes and Obesity: A Family-Based Study in China
    Yaying Cao, Tao Wang, Yiqun Wu, Juan Juan, Xueying Qin, Xun Tang, Tao Wu, Yonghua Hu
    International Journal of Molecular Sciences.2018; 19(4): 1011.     CrossRef
  • Type 2 Diabetes Genetic Variants and Risk of Diabetic Retinopathy
    Yong He Chong, Qiao Fan, Yih Chung Tham, Alfred Gan, Shu Pei Tan, Gavin Tan, Jie Jin Wang, Paul Mitchell, Tien Yin Wong, Ching-Yu Cheng
    Ophthalmology.2017; 124(3): 336.     CrossRef
  • Precision Nutrition: A Review of Personalized Nutritional Approaches for the Prevention and Management of Metabolic Syndrome
    Juan de Toro-Martín, Benoit Arsenault, Jean-Pierre Després, Marie-Claude Vohl
    Nutrients.2017; 9(8): 913.     CrossRef
  • 10-year trajectory of β-cell function and insulin sensitivity in the development of type 2 diabetes: a community-based prospective cohort study
    Jung Hun Ohn, Soo Heon Kwak, Young Min Cho, Soo Lim, Hak Chul Jang, Kyong Soo Park, Nam H Cho
    The Lancet Diabetes & Endocrinology.2016; 4(1): 27.     CrossRef
  • Sex and Gender Differences in Risk, Pathophysiology and Complications of Type 2 Diabetes Mellitus
    Alexandra Kautzky-Willer, Jürgen Harreiter, Giovanni Pacini
    Endocrine Reviews.2016; 37(3): 278.     CrossRef
  • No Interaction with Alcohol Consumption, but Independent Effect of C12orf51 (HECTD4) on Type 2 Diabetes Mellitus in Korean Adults Aged 40-69 Years: The KoGES_Ansan and Ansung Study
    Jihye Kim, Bermseok Oh, Ji Eun Lim, Mi Kyung Kim, C. Mary Schooling
    PLOS ONE.2016; 11(2): e0149321.     CrossRef
  • Risk Prediction Using Genome-Wide Association Studies on Type 2 Diabetes
    Sungkyoung Choi, Sunghwan Bae, Taesung Park
    Genomics & Informatics.2016; 14(4): 138.     CrossRef
  • Analysis of multiple related phenotypes in genome-wide association studies
    Sohee Oh, Iksoo Huh, Seung Yeoun Lee, Taesung Park
    Journal of Bioinformatics and Computational Biology.2016; 14(05): 1644005.     CrossRef
  • Recent progress in genetic and epigenetic research on type 2 diabetes
    Soo Heon Kwak, Kyong Soo Park
    Experimental & Molecular Medicine.2016; 48(3): e220.     CrossRef
  • Statistical power considerations in genotype-based recall randomized controlled trials
    Naeimeh Atabaki-Pasdar, Mattias Ohlsson, Dmitry Shungin, Azra Kurbasic, Erik Ingelsson, Ewan R. Pearson, Ashfaq Ali, Paul W. Franks
    Scientific Reports.2016;[Epub]     CrossRef
  • The role of vitamin D, obesity and physical exercise in regulation of glycemia in Type 2 Diabetes Mellitus patients
    Abdulbari Bener, Abdulla O.A.A. Al-Hamaq, Eda Merve Kurtulus, Waleed K. Abdullatef, Mahmoud Zirie
    Diabetes & Metabolic Syndrome: Clinical Research & Reviews.2016; 10(4): 198.     CrossRef
  • Recent advances in understanding the genetic architecture of type 2 diabetes
    Karen L. Mohlke, Michael Boehnke
    Human Molecular Genetics.2015; 24(R1): R85.     CrossRef
  • Letter: Genome-Wide Association Study Identifies Two Novel Loci with Sex-Specific Effects for Type 2 Diabetes Mellitus and Glycemic Traits in a Korean Population (Diabetes Metab J2014;38:375-87)
    Soo Heon Kwak
    Diabetes & Metabolism Journal.2014; 38(6): 484.     CrossRef
  • Response: Genome-Wide Association Study Identifies Two Novel Loci with Sex-Specific Effects for Type 2 Diabetes Mellitus and Glycemic Traits in a Korean Population (Diabetes Metab J2014;38:375-87)
    Min Jin Go, Bong-Jo Kim
    Diabetes & Metabolism Journal.2014; 38(6): 487.     CrossRef
Review
The Importance of Global Studies of the Genetics of Type 2 Diabetes
Mark I. McCarthy
Diabetes Metab J. 2011;35(2):91-100.   Published online April 30, 2011
DOI: https://doi.org/10.4093/dmj.2011.35.2.91
  • 4,387 View
  • 29 Download
  • 11 Crossref
AbstractAbstract PDFPubReader   

Genome wide association analyses have revealed large numbers of common variants influencing predisposition to type 2 diabetes and related phenotypes. These studies have predominantly featured European populations, but are now being extended to samples from a wider range of ethnic groups. The transethnic analysis of association data is already providing insights into the genetic, molecular and biological causes of diabetes, and the relevance of such studies will increase as human discovery genetics increasingly moves towards sequencing-based approaches and a focus on low frequency and rare variants.

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  • Validation and genetic heritability estimation of known type 2 diabetes related variants in the Korean population
    Hye-Mi Jang, Mi Yeong Hwang, Bong-Jo Kim, Young Jin Kim
    Genomics & Informatics.2021; 19(4): e37.     CrossRef
  • Distinct subtypes of polycystic ovary syndrome with novel genetic associations: An unsupervised, phenotypic clustering analysis
    Matthew Dapas, Frederick T. J. Lin, Girish N. Nadkarni, Ryan Sisk, Richard S. Legro, Margrit Urbanek, M. Geoffrey Hayes, Andrea Dunaif, Jenny E. Myers
    PLOS Medicine.2020; 17(6): e1003132.     CrossRef
  • Association of a genetic variant of the ZPR1 zinc finger gene with type 2 diabetes mellitus
    FUMITAKA TOKORO, REIKO MATSUOKA, SHINTARO ABE, MASAZUMI ARAI, TOSHIYUKI NODA, SACHIRO WATANABE, HIDEKI HORIBE, TETSUO FUJIMAKI, MITSUTOSHI OGURI, KIMIHIKO KATO, SHINYA MINATOGUCHI, YOSHIJI YAMADA
    Biomedical Reports.2015; 3(1): 88.     CrossRef
  • Insights into the Genetic Susceptibility to Type 2 Diabetes from Genome-Wide Association Studies of Glycaemic Traits
    Letizia Marullo, Julia S. El-Sayed Moustafa, Inga Prokopenko
    Current Diabetes Reports.2014;[Epub]     CrossRef
  • Towards Virtual Knowledge Broker services for semantic integration of life science literature and data sources
    Ian Harrow, Wendy Filsell, Peter Woollard, Ian Dix, Michael Braxenthaler, Richard Gedye, David Hoole, Richard Kidd, Jabe Wilson, Dietrich Rebholz-Schuhmann
    Drug Discovery Today.2013; 18(9-10): 428.     CrossRef
  • Genome-Wide Association Study for Type 2 Diabetes in Indians Identifies a New Susceptibility Locus at 2q21
    Rubina Tabassum, Ganesh Chauhan, Om Prakash Dwivedi, Anubha Mahajan, Alok Jaiswal, Ismeet Kaur, Khushdeep Bandesh, Tejbir Singh, Benan John Mathai, Yogesh Pandey, Manickam Chidambaram, Amitabh Sharma, Sreenivas Chavali, Shantanu Sengupta, Lakshmi Ramakris
    Diabetes.2013; 62(3): 977.     CrossRef
  • A replication study of 19 GWAS-validated type 2 diabetes at-risk variants in the Lebanese population
    Wassim Y. Almawi, Rita Nemr, Sose H. Keleshian, Akram Echtay, Fabiola Lisa Saldanha, Fatima A. AlDoseri, Eddie Racoubian
    Diabetes Research and Clinical Practice.2013; 102(2): 117.     CrossRef
  • Single nucleotide polymorphisms in JAZF1 and BCL11A gene are nominally associated with type 2 diabetes in African-American families from the GENNID study
    Kurt A Langberg, Lijun Ma, Neeraj K Sharma, Craig L Hanis, Steven C Elbein, Sandra J Hasstedt, Swapan K Das
    Journal of Human Genetics.2012; 57(1): 57.     CrossRef
  • Typ-2-Diabetes-assoziierte Gene
    J. Kriebel, H. Grallert, T. Illig
    Der Diabetologe.2012; 8(1): 26.     CrossRef
  • Genomweite Assoziationsstudien (GWAS) — Möglichkeiten und Grenzen
    Jennifer Kriebel, Thomas Illig, Harald Grallert
    BIOspektrum.2012; 18(5): 508.     CrossRef
  • T2DM: Why Epigenetics?
    Delphine Fradin, Pierre Bougnères
    Journal of Nutrition and Metabolism.2011; 2011: 1.     CrossRef
Sulwon Lecture 2009
The Search for Genetic Risk Factors of Type 2 Diabetes Mellitus
Kyong Soo Park
Diabetes Metab J. 2011;35(1):12-22.   Published online February 28, 2011
DOI: https://doi.org/10.4093/dmj.2011.35.1.12
  • 4,981 View
  • 58 Download
  • 23 Crossref
AbstractAbstract PDFPubReader   

Type 2 diabetes mellitus (T2DM) is caused by complex interplay between multiple genetic and environmental factors. The three major approaches used to identify the genetic susceptibility include candidate gene approach, familial linkage analysis and genome- wide association analysis. Recent advance in genome-wide association studies have greatly improved our understanding of the pathophysiology of T2DM. As of the end of 2010, there are more than 40 confirmed T2DM-associated genetic loci. Most of the T2DM susceptibility genes were implicated in decreased β-cell function. However, these genetic variations have a modest effect and their combination only explains less than 10% of the T2DM heritability. With the advent of the next-generation sequencing technology, we will soon identify rare variants of larger effect as well as causal variants. These advances in understanding the genetics of T2DM will lead to the development of new therapeutic and preventive strategies and individualized medicine.

Citations

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  • Diabetes: Risk factor and translational therapeutic implications for Alzheimer's disease
    Jeffrey Cummings, Andrew Ortiz, Janelle Castellino, Jefferson Kinney
    European Journal of Neuroscience.2022; 56(9): 5727.     CrossRef
  • Association of gene polymorphisms with body weight changes in prediabetic patients
    Farida V. Valeeva, Mariya S. Medvedeva, Kamilya B. Khasanova, Elena V. Valeeva, Tatyana A. Kiseleva, Emiliya S. Egorova, Craig Pickering, Ildus I. Ahmetov
    Molecular Biology Reports.2022; 49(6): 4217.     CrossRef
  • Analysis of the association of FTO, PPARG and PPARGC1A gene polymorphisms with carbohydrate metabolism disorders
    Farida V. Valeeva, Kamilya B. Khasanova, Elizaveta A. Sozinova, Tatyana A. Kiseleva, Elena V. Valeeva, Emiliya S. Egorova, Ildus I. Ahmetov
    Kazan medical journal.2022; 103(4): 592.     CrossRef
  • Associations between new and old anthropometric indices with type 2 diabetes mellitus and risk of metabolic complications: a cross-sectional analytical study
    Parichehr Amiri, Ahmad Zare Javid, Leila Moradi, Neda Haghighat, Rahim Moradi, Hossein Bavi Behbahani, Milad Zarrin, Hadi Bazyar
    Jornal Vascular Brasileiro.2021;[Epub]     CrossRef
  • When will individuals meet their personalized probabilities? A philosophical note on risk prediction
    Olaf M. Dekkers, Jesse M. Mulder
    European Journal of Epidemiology.2020; 35(12): 1115.     CrossRef
  • From Pre-Diabetes to Diabetes: Diagnosis, Treatments and Translational Research
    Radia Khan, Zoey Chua, Jia Tan, Yingying Yang, Zehuan Liao, Yan Zhao
    Medicina.2019; 55(9): 546.     CrossRef
  • Systematic analysis of genes and diseases using PheWAS-Associated networks
    Ali Khosravi, Morteza Kouhsar, Bahram Goliaei, B. Jayaram, Ali Masoudi-Nejad
    Computers in Biology and Medicine.2019; 109: 311.     CrossRef
  • Protective effects of asiatic acid in a spontaneous type 2 diabetic mouse model
    Wen Sun, Guangyuan Xu, Xuan Guo, Guangbin Luo, Lili Wu, Yi Hou, Xiangyu Guo, Jingxin Zhou, Tunhai Xu, Lingling Qin, Yixin Fan, Li Han, Motlalepula Matsabisa, Xuesheng Ma, Tonghua Liu
    Molecular Medicine Reports.2017; 16(2): 1333.     CrossRef
  • Are We in the Same Risk of Diabetes Mellitus? Gender- and Age-Specific Epidemiology of Diabetes in 2001 to 2014 in the Korean Population
    Bo Kyung Koo, Min Kyong Moon
    Diabetes & Metabolism Journal.2016; 40(3): 175.     CrossRef
  • Efficient Strategy to Identify Gene-Gene Interactions and Its Application to Type 2 Diabetes
    Donghe Li, Sungho Won
    Genomics & Informatics.2016; 14(4): 160.     CrossRef
  • Genetic polymorphisms associated with overweight and obesity in uncontrolled Type 2 diabetes mellitus
    Nor Bahirah Kasim, Hasniza Zaman Huri, Shireene Ratna Vethakkan, Luqman Ibrahim, Bashar Mudhaffar Abdullah
    Biomarkers in Medicine.2016; 10(4): 403.     CrossRef
  • A multicenter clinical study to determine the efficacy of a novel fenugreek seed (Trigonella foenum-graecum) extract (Fenfuro™) in patients with type 2 diabetes
    Narsingh Verma, Kauser Usman, Naresh Patel, Arvind Jain, Sudhir Dhakre, Anand Swaroop, Manashi Bagchi, Pawan Kumar, Harry G. Preuss, Debasis Bagchi
    Food & Nutrition Research.2016; 60(1): 32382.     CrossRef
  • Association between -308G/A TNFA Polymorphism and Susceptibility to Type 2 Diabetes Mellitus: A Systematic Review
    Geisa Izetti Luna, Izabel Cristina Rodrigues da Silva, Mauro Niskier Sanchez
    Journal of Diabetes Research.2016; 2016: 1.     CrossRef
  • Metabolomics – the complementary field in systems biology: a review on obesity and type 2 diabetes
    Mohamad Hafizi Abu Bakar, Mohamad Roji Sarmidi, Kian-Kai Cheng, Abid Ali Khan, Chua Lee Suan, Hasniza Zaman Huri, Harisun Yaakob
    Molecular BioSystems.2015; 11(7): 1742.     CrossRef
  • Predictive modeling for incident and prevalent diabetes risk evaluation
    Katya L Masconi, Justin Basile Echouffo-Tcheugui, Tandi E Matsha, Rajiv T Erasmus, Andre Pascal Kengne
    Expert Review of Endocrinology & Metabolism.2015; 10(3): 277.     CrossRef
  • Polymorphism of gene UBE2E2 and the risk of developing diabetes type 2
    Elena Vladimirovna Kazakova, Yanhui Wu, Meijun Chen, Tongtong Wang, Lulu Sun, Hong Qiao
    Diabetes mellitus.2015; 18(3): 46.     CrossRef
  • The Architecture of Risk for Type 2 Diabetes: Understanding Asia in the Context of Global Findings
    Noraidatulakma Abdullah, John Attia, Christopher Oldmeadow, Rodney J. Scott, Elizabeth G. Holliday
    International Journal of Endocrinology.2014; 2014: 1.     CrossRef
  • Translational medicine as a new clinical tool and application which improves metabolic diseases: perspectives from 2012 Sino‐American symposium on clinical and translational medicine
    Lin Shi, Elena López Villar, Chengshui Chen
    Clinical and Translational Medicine.2014;[Epub]     CrossRef
  • Frequency of Fat Mass and Obesity-Associated Gene rs9939609 and Peroxisome Proliferator-Activated Receptor Gamma 2 Gene rs1801282 Polymorphisms among Trinidadian Neonates of Different Ethnicities and Their Relationship to Anthropometry at Birth
    Candace E. Cuthbert, D. Dan Ramdath, Jerome E. Foster
    Lifestyle Genomics.2014; 7(1): 39.     CrossRef
  • Genetics of type 2 diabetes and potential clinical implications
    Soo Heon Kwak, Kyong Soo Park
    Archives of Pharmacal Research.2013; 36(2): 167.     CrossRef
  • Genetics in Diabetes Mellitus - Contribution to the Classification and Management
    Jeesuk Yu
    Annals of Pediatric Endocrinology & Metabolism.2012; 17(4): 211.     CrossRef
  • Genome-wide association studies with metabolomics
    Jerzy Adamski
    Genome Medicine.2012; 4(4): 34.     CrossRef
  • Typ-2-Diabetes-assoziierte Gene
    J. Kriebel, H. Grallert, T. Illig
    Der Diabetologe.2012; 8(1): 26.     CrossRef

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