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EDITORIAL
Year : 2014  |  Volume : 4  |  Issue : 3  |  Page : 1  

Prevention and management of type 2 diabetes: Potential role of genomics


1 Department of Pharmacology, Adesh Institute of Medical Sciences and Research, Bathinda, Punjab, India
2 Department of Biochemistry, Adesh Institute of Medical Sciences and Research, Bathinda, Punjab, India

Date of Web Publication15-Sep-2014

Correspondence Address:
Rajiv Mahajan
Department of Pharmacology, Adesh Institute of Medical Sciences and Research, Bathinda - 151 101, Punjab
India
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/2229-516X.140704

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How to cite this article:
Mahajan R, Gupta K. Prevention and management of type 2 diabetes: Potential role of genomics. Int J App Basic Med Res 2014;4, Suppl S1:1

How to cite this URL:
Mahajan R, Gupta K. Prevention and management of type 2 diabetes: Potential role of genomics. Int J App Basic Med Res [serial online] 2014 [cited 2019 Oct 17];4, Suppl S1:1. Available from: http://www.ijabmr.org/text.asp?2014/4/3/1/140704

Type 2 diabetes is a disease with a growing public health burden. Nearly 26 million people already have it and 79 million more have prediabetes. [1] Prevention and intervention are important. Innovative solutions are required to tackle it. Precisely because of the same reason, the American Medical Association Council on Science and Public Health decided to review current genomic strategies to control type 2 diabetes and improve clinical care. [2]

More than 65 genetic variations have been identified, which can increase the risk of type 2 diabetes by 10-45%. The Ser/Thr protein kinase mechanistic target of rapamycin (mTOR) is one such variant, which plays a significant role in regulating insulin signaling. In combination with several other molecules, mTOR can form two complexes, mTORC1 and mTORC2. Each complex has a distinct role in regulating insulin sensitivity - mTORC1 inhibits insulin signaling via its substrate S6K1, whereas mTORC2 has been shown to have a positive effect on glucose uptake and tolerance. [3] A quantified algorithm of all these risk factors can add value to diabetic risk prediction.

Individualizing therapy on the basis of the patient's genotype is another field of interest. For example, patients with gene variants for cytochrome P450 2C9 (P4502C9*3 allele) have decreased tolbutamide metabolism and thus have larger decreases in blood glucose. Such variants require a lower dose of tolbutamide to regulate their serum blucose levels. [4] Similarly, patients with genetic variations in organic cation transporter 1 (OCT1) that reduce liver uptake of metformin (OCT1-420del allele) show less glucose-lowering response (more dose required), compared to those without this genetic variant. [5] Carriers of other variants of enzymes that regulate glucose metabolism also show variation in response with antidiabetic drugs; like a variant of peroxisome proliferator-activated receptor-γ (Pro12Ala) shows more blood sugar lowering with pioglitazone therapy. [6]

Further perspectives of genetic polymorphism in Type 2 diabetes lay in predicting Type 2 diabetes complications, such as retinopathy, peripheral neuropathy, and nephropathy. The promise is there, but for the time being the potential has not been tapped fully and the small studies so far have not yet changed clinical practice. However with the advent of next-generation sequencing and whole-genome sequencing, genomics has many potential future uses in diabetes risk assessment, prevention, and management.

 
   References Top

1.Fryhofer SA. The fight against Type 2 diabetes: The promise of Genomics. Medscape family medicine. Available from: http://www.medscape.com/viewarticle/828019?nlid=61503_430 and src=wnl_edit_medp_fmed and uac=134696SR and spon=34. [Last accessed on 2014 Jul 21].  Back to cited text no. 1
    
2.American medical association council on science and public health. Genomic-based approaches to the risk assessment, management and prevention of type 2 diabetes. Available from: https://www download.ama-assn.org/resources/doc/csaph/x-pub/a14csaph 2-summaryonly.pdf. [Last accessed on 2014 Jul 21].  Back to cited text no. 2
    
3.Laplante M, Sabatini DM. mTOR signaling in growth control and disease. Cell 2012;149:274-93.  Back to cited text no. 3
    
4.Becker ML, Visser LE, Trienekens PH, Hofman A, van Schaik RH, Stricker BH. Cytochrome P450 2C9 FNx012 and FNx013 polymorphisms and the dose and effect of sulfonylurea in type II diabetes mellitus. Clin Pharmacol Ther 2008;83:288-92.  Back to cited text no. 4
    
5.Shu Y, Sheardown SA, Brown C, Owen RP, Zhang S, Castro RA, et al. Effect of genetic variation in the organic cation transporter 1 (OCT1) on metformin action. J Clin Invest 2007;117:1422-31.  Back to cited text no. 5
    
6.Hsieh MC, Lin KD, Tien KJ, Tu ST, Hsiao JY, Chang SJ, et al. Common polymorphisms of the peroxisome proliferator-activated receptor-gamma (Pro12Ala) and peroxisome proliferator-activated receptor-gamma coactivator-1 (Gly482Ser) and the response to pioglitazone in Chinese patients with type 2 diabetes mellitus. Metabolism 2010;59:1139-44.  Back to cited text no. 6
    




 

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