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Exome Chip Analysis of 14,026 Koreans Reveals Known and Newly Discovered Genetic Loci Associated with Type 2 Diabetes Mellitus
Seong Beom Cho, Jin Hwa Jang, Myung Guen Chung, Sang Cheol Kim
Diabetes Metab J. 2021;45(2):231-240.   Published online July 28, 2020
DOI: https://doi.org/10.4093/dmj.2019.0163
  • 6,400 View
  • 195 Download
  • 6 Web of Science
  • 6 Crossref
Graphical AbstractGraphical Abstract AbstractAbstract PDFSupplementary MaterialPubReader   ePub   
Background

Most loci associated with type 2 diabetes mellitus (T2DM) discovered to date are within noncoding regions of unknown functional significance. By contrast, exonic regions have advantages for biological interpretation.

Methods

We analyzed the association of exome array data from 14,026 Koreans to identify susceptible exonic loci for T2DM. We used genotype information of 50,543 variants using the Illumina exome array platform.

Results

In total, 7 loci were significant with a Bonferroni adjusted P=1.03×10−6. rs2233580 in paired box gene 4 (PAX4) showed the highest odds ratio of 1.48 (P=1.60×10−10). rs11960799 in membrane associated ring-CH-type finger 3 (MARCH3) and rs75680863 in transcobalamin 2 (TCN2) were newly identified loci. When we built a model to predict the incidence of diabetes with the 7 loci and clinical variables, area under the curve (AUC) of the model improved significantly (AUC=0.72, P<0.05), but marginally in its magnitude, compared with the model using clinical variables (AUC=0.71, P<0.05). When we divided the entire population into three groups—normal body mass index (BMI; <25 kg/m2), overweight (25≤ BMI <30 kg/m2), and obese (BMI ≥30 kg/m2) individuals—the predictive performance of the 7 loci was greatest in the group of obese individuals, where the net reclassification improvement was highly significant (0.51; P=8.00×10−5).

Conclusion

We found exonic loci having a susceptibility for T2DM. We found that such genetic information is advantageous for predicting T2DM in a subgroup of obese individuals.

Citations

Citations to this article as recorded by  
  • Polygenic Risk Score, Lifestyles, and Type 2 Diabetes Risk: A Prospective Chinese Cohort Study
    Jia Liu, Lu Wang, Xuan Cui, Qian Shen, Dun Wu, Man Yang, Yunqiu Dong, Yongchao Liu, Hai Chen, Zhijie Yang, Yaqi Liu, Meng Zhu, Hongxia Ma, Guangfu Jin, Yun Qian
    Nutrients.2023; 15(9): 2144.     CrossRef
  • Celebrities in the heart, strangers in the pancreatic beta cell: Voltage‐gated potassium channels Kv7.1 and Kv11.1 bridge long QT syndrome with hyperinsulinaemia as well as type 2 diabetes
    Anniek F. Lubberding, Christian R. Juhl, Emil Z. Skovhøj, Jørgen K. Kanters, Thomas Mandrup‐Poulsen, Signe S. Torekov
    Acta Physiologica.2022;[Epub]     CrossRef
  • Substitution of Carbohydrates for Fats and Risk of Type 2 Diabetes among Korean Middle-Aged Adults: Findings from the Korean Genome and Epidemiology Study
    Hye-Ah Lee, Hyesook Park
    Nutrients.2022; 14(3): 654.     CrossRef
  • Ethnic-Specific Type 2 Diabetes Risk Factor PAX4 R192H Is Associated with Attention-Specific Cognitive Impairment in Chinese with Type 2 Diabetes
    Su Fen Ang, Serena Low, Tze Pin Ng, Clara S.H. Tan, Keven Ang, Ziliang Lim, Wern Ee Tang, Tavintharan Subramaniam, Chee Fang Sum, Su Chi Lim, Nagaendran Kandiah
    Journal of Alzheimer's Disease.2022; 88(1): 241.     CrossRef
  • TrustGWAS: A full-process workflow for encrypted GWAS using multi-key homomorphic encryption and pseudorandom number perturbation
    Meng Yang, Chuwen Zhang, Xiaoji Wang, Xingmin Liu, Shisen Li, Jianye Huang, Zhimin Feng, Xiaohui Sun, Fang Chen, Shuang Yang, Ming Ni, Lin Li, Yanan Cao, Feng Mu
    Cell Systems.2022; 13(9): 752.     CrossRef
  • Sex Differences in the Effects of CDKAL1 Variants on Glycemic Control in Diabetic Patients: Findings from the Korean Genome and Epidemiology Study
    Hye Ah Lee, Hyesook Park, Young Sun Hong
    Diabetes & Metabolism Journal.2022; 46(6): 879.     CrossRef
Adipose Gene Expression Profiles Related to Metabolic Syndrome Using Microarray Analyses in Two Different Models
Hye Jin Yoo, Hwan-Jin Hwang, Tae Woo Jung, Ja Young Ryu, Ho Cheol Hong, Hae Yoon Choi, Sei Hyun Baik, Kyung Mook Choi
Diabetes Metab J. 2014;38(5):356-365.   Published online October 17, 2014
DOI: https://doi.org/10.4093/dmj.2014.38.5.356
  • 4,356 View
  • 45 Download
  • 7 Web of Science
  • 7 Crossref
AbstractAbstract PDFPubReader   
Background

Peroxisome proliferator-activated receptor-γ (PPAR-γ) agonist has a wide-ranging influence on multiple components of metabolic syndrome. The Otsuka Long-Evans Tokushima Fatty (OLETF) rat is a useful animal model of metabolic syndrome. To determine genes related to metabolic syndrome, we examined overlapping genes that are simultaneously decreased by PPAR-γ agonists and increased in OLETF rats using microarrays in two different models.

Methods

In the first microarray analysis, PPAR-γ agonist-treated db/db mice were compared to standard diet-fed db/db mice. In the second microarray analysis, OLETF rats were compared to Long-Evans Tokushima Otsuka (LETO) rats (control of OLETF rats).

Results

Among the overlapping genes, in the present study, we validated that lipocalin-2 expression was significantly decreased in the visceral adipose tissue of PPAR-γ agonist-treated db/db mice compared to standard diet-fed db/db mice and increased in OLETF rats compared to LETO rats using real time reverse transcription polymerase chain reaction. Furthermore, we showed for the first time that lipocalin-2 expression was significantly increased in the visceral adipose tissues of obese humans compared with nonobese humans. In addition, the expression level of lipocalin-2 in human visceral adipose tissue had a significant positive correlation with body mass index, serum interleukin-6, adipocyte fatty acid binding protein levels, and white blood cell count.

Conclusion

Lipocalin-2 was confirmed to be a significant adipokine affected by PPAR-γ agonist and obesity in the present study. Also, for the first time in human visceral adipose tissue, it was determined that the expression of lipocalin-2 from obese humans was significantly increased and correlated with circulating inflammatory markers.

Citations

Citations to this article as recorded by  
  • Lipocalin‐2—The myth of its expression and function
    Dahui Li, Wai Yan Sun, Bowen Fu, Aimin Xu, Yu Wang
    Basic & Clinical Pharmacology & Toxicology.2020; 127(2): 142.     CrossRef
  • Lipocalin-2 counteracts metabolic dysregulation in obesity and diabetes
    Ioanna Mosialou, Steven Shikhel, Na Luo, Peristera Ioanna Petropoulou, Konstantinos Panitsas, Brygida Bisikirska, Nyanza J. Rothman, Roxane Tenta, Bertrand Cariou, Matthieu Wargny, Elisabeth Sornay-Rendu, Thomas Nickolas, Mishaela Rubin, Cyrille B. Confav
    Journal of Experimental Medicine.2020;[Epub]     CrossRef
  • Metabolism and adult neurogenesis: Towards an understanding of the role of lipocalin-2 and iron-related oxidative stress
    Ana Catarina Ferreira, Nuno Sousa, João M. Bessa, João Carlos Sousa, Fernanda Marques
    Neuroscience & Biobehavioral Reviews.2018; 95: 73.     CrossRef
  • LH-21, A Peripheral Cannabinoid Receptor 1 Antagonist, Exerts Favorable Metabolic Modulation Including Antihypertensive Effect in KKAy Mice by Regulating Inflammatory Cytokines and Adipokines on Adipose Tissue
    Ziqi Dong, Hui Gong, Yadan Chen, Hong Wu, Jun Wu, Yinghong Deng, Xinmao Song
    Frontiers in Endocrinology.2018;[Epub]     CrossRef
  • Lipocalin 2 produces insulin resistance and can be upregulated by glucocorticoids in human adipose tissue
    Prasad G. Kamble, Maria J. Pereira, Cherno O. Sidibeh, Sam Amini, Magnus Sundbom, Joey Lau Börjesson, Jan W. Eriksson
    Molecular and Cellular Endocrinology.2016; 427: 124.     CrossRef
  • Serum lipocalin-2 levels are positively associated with not only total body fat but also visceral fat area in Chinese men
    Yuqi Luo, Xiaojing Ma, Xiaoping Pan, Yiting Xu, Qin Xiong, Yunfeng Xiao, Yuqian Bao, Weiping Jia
    Medicine.2016; 95(30): e4039.     CrossRef
  • From the periphery to the brain: Lipocalin-2, a friend or foe?
    Ana C. Ferreira, Sandro Dá Mesquita, João C. Sousa, Margarida Correia-Neves, Nuno Sousa, Joana A. Palha, Fernanda Marques
    Progress in Neurobiology.2015; 131: 120.     CrossRef

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