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Author(s):
Cynthia Al Hageh, Siobhán O'Sullivan, Andreas Henschel, Stephanie Chacar, Mireille Hantouche, Moni Nader, Pierre A. Zalloua
Publish date:
20 May 2024
Journal:
Acta Diabetologica
PubMed ID:
38767674

Abstract

AimsHypertension (HTN) and Type 2 Diabetes (T2D) often coexist, therefore understanding the relationship between both diseases is imperative to guide targeted prevention/therapy. This study aims to explore the relationship between HTN and T2D using genome-wide association study (GWAS) analysis and biochemical data to understand the implication of both clinical and genetic factors in these pathologies.MethodsA total of 2,876 patients were enrolled. Using GWAS and biochemical data, patients with both T2D and HTN were compared to patients with only HTN. Specificity was confirmed by testing the detected genetic variants for associations with HTN development in T2D patients, or with HTN in healthy subjects. Regression models were applied to examine the association of T2D in patients with HTN with cardiovascular risk factors. Replication was performed using UK Biobank dataset with 31,170 subjects.ResultsData showed that females with HTN are at higher risk of developing T2D due to dyslipidemia, while males faced higher risk due to high BMI (body mass index) and family history of T2D. GWAS identified Single Nucleotide Polymorphisms (SNPs) linked to T2D in patients with HTN. Notably, rs7865889, rs7756992, and rs10896290 were positively associated with T2D, whereas rs12737517 yielded negative association. Three SNPs were replicated in the UK Biobank (rs10896290, rs7865889, and rs7756992).ConclusionIncorporating clinical and genetic screening into risk assessment is important for the detection and prevention of T2D in patients with HTN. The detected SNPs (rs7865889, rs12737517, and rs10896290), especially the protective SNP (rs12737517), provide an opportunity for better diagnosis, prevention, and therapy of patients with T2D and HTN.

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Our motivation for studying the UK Biobank data set is based on the observation that traditional detection methods for finding indicators of a particular disease…

Institution:
Khalifa University of Science and Technology, United Arab Emirates

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