Literature DB >> 27744072

Predictive utility of a genetic risk score of common variants associated with type 2 diabetes in a black South African population.

Tinashe Chikowore1, Tertia van Zyl2, Edith J M Feskens3, Karin R Conradie2.   

Abstract

AIMS: To determine the predictive utility of polygenic risk scores of common variants associated with type 2 diabetes derived from the European and Asian ethnicities among a black South African population.
METHOD: Our study was a case-control study nested within the Prospective Urban and Rural Epidemiological (PURE) study of 178 male and female cases, matched for age and gender with 178 controls. Four types of genetic risk scores (GRS) were developed from 66 selected SNPs. These comprised of beta cell related variants (GRSb), variants which had significant associations with T2D in our study (GRSn), variants from the trans-ethnic meta-analysis (GRStrans) and all the 66 selected SNPs (GRSt).
RESULTS: Of the GRS's, only GRSn was associated with increased risk of T2D as indicated by an OR (95CI) of 1.21 (1.02-1.43) p-value=0.015. Stratified analysis of age and BMI, indicated the GRSn to be significantly associated with T2D among the non-obese and participants less than 50years. The area under the ROC of the T2D risk factors only was 0.652 (p value<0.001) and with the addition of GRSn it was 0.665 (p value<0.001).
CONCLUSIONS: The GRS of European and Asian derived variants have limited clinical utility in the black South African population. The inclusion of population specific variants in the GRS is pivotal.
Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Entities:  

Keywords:  BMI; Clinical use; GRS; Polygenic risk score

Mesh:

Year:  2016        PMID: 27744072     DOI: 10.1016/j.diabres.2016.09.019

Source DB:  PubMed          Journal:  Diabetes Res Clin Pract        ISSN: 0168-8227            Impact factor:   5.602


  7 in total

1.  A Genetic Risk Score Improves the Prediction of Type 2 Diabetes Mellitus in Mexican Youths but Has Lower Predictive Utility Compared With Non-Genetic Factors.

Authors:  América Liliana Miranda-Lora; Jenny Vilchis-Gil; Daniel B Juárez-Comboni; Miguel Cruz; Miguel Klünder-Klünder
Journal:  Front Endocrinol (Lausanne)       Date:  2021-03-12       Impact factor: 5.555

Review 2.  Genetic Basis of Obesity and Type 2 Diabetes in Africans: Impact on Precision Medicine.

Authors:  Ayo P Doumatey; Kenneth Ekoru; Adebowale Adeyemo; Charles N Rotimi
Journal:  Curr Diab Rep       Date:  2019-09-14       Impact factor: 4.810

3.  Genetic Risk Score Increased Discriminant Efficiency of Predictive Models for Type 2 Diabetes Mellitus Using Machine Learning: Cohort Study.

Authors:  Yikang Wang; Liying Zhang; Miaomiao Niu; Ruiying Li; Runqi Tu; Xiaotian Liu; Jian Hou; Zhenxing Mao; Zhenfei Wang; Chongjian Wang
Journal:  Front Public Health       Date:  2021-02-17

Review 4.  Circulating Nucleic Acid-Based Biomarkers of Type 2 Diabetes.

Authors:  Felipe Padilla-Martinez; Gladys Wojciechowska; Lukasz Szczerbinski; Adam Kretowski
Journal:  Int J Mol Sci       Date:  2021-12-28       Impact factor: 5.923

5.  Polygenic Prediction of Type 2 Diabetes in Africa.

Authors:  Tinashe Chikowore; Kenneth Ekoru; Marijana Vujkovi; Dipender Gill; Fraser Pirie; Elizabeth Young; Manjinder S Sandhu; Mark McCarthy; Charles Rotimi; Adebowale Adeyemo; Ayesha Motala; Segun Fatumo
Journal:  Diabetes Care       Date:  2022-03-01       Impact factor: 19.112

Review 6.  Genetics of Type 2 Diabetes: Implications from Large-Scale Studies.

Authors:  Natalie DeForest; Amit R Majithia
Journal:  Curr Diab Rep       Date:  2022-03-19       Impact factor: 5.430

7.  Comparing distributions of polygenic risk scores of type 2 diabetes and coronary heart disease within different populations.

Authors:  Sulev Reisberg; Tatjana Iljasenko; Kristi Läll; Krista Fischer; Jaak Vilo
Journal:  PLoS One       Date:  2017-07-05       Impact factor: 3.240

  7 in total

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