Analysis of association between common variants of uncoupling proteins genes and diabetic retinopathy in a Chinese population

Abstract

Background: The aim of this study was to explore the association between diabetic retinopathy (DR) and the variants of uncoupling proteins (UCPs) genes in a Chinese population of type 2 diabetes, in total and in patients of different glycemic status separately. Methods: This case-control study included a total of 3107 participants from two datasets, among which 662 were DR patients (21.31%). Eighteen tag single nucleotide polymorphisms (SNPs) of UCP1, UCP2, and UCP3 were selected as genetic markers. TaqMan probes, Sequenom MassARRAY MALDI-TOF mass spectrometry platform and Affymetrix Genome-Wide Human SNP Array were used for genotyping. Online SHEsis software was used for association analysis. Bonferroni correction was used for multiple comparisons correction. Results: Three SNPs of UCP1: rs7688743 (A allele, OR = 1.192, p = 0.013), rs3811787 (T allele, OR = 0.863, p = 0.023), and rs10011540 (G allele, OR = 1.368, p = 0.004) showed association with DR after the adjustment of glucose, but only rs10011540 was marginally significantly associated with DR when Bonferroni correction was strictly applied (p adj = 0.048). In patients with uncontrolled glucose, rs7688743 (A allele, p = 0.012, OR = 1.309), rs10011540 (G allele, p = 0.033, OR = 1.432), and rs3811787 (T allele, p = 0.022, OR = 0.811) were associated with DR, while in participants with well controlled glucose, the rs2734827 of UCP3 was associated with DR (A allele, p = 0.017, OR = 0.532). Rs3811787 of UCP1 showed a protective effect to sight threatening DR (T allele, p = 0.007, OR = 0.490), and the association existed after the adjustment for environmental factors and the correction. In patients with uncontrolled glucose, the rs3811787 of UCP1 (T allele, p = 0.017, OR = 0.467) and the rs591758 of UCP3 (C allele, p = 0.026, OR = 0.103) were associated with STDR. While in those with well controlled glucose, only the rs7688743 of UCP1 showed a protective effect (A allele, p = 0.024, OR = 0.049). None of the associations remain significant when Bonferroni correction was strictly applied (all p < 0.05).

Association of uncoupling protein (Ucp) gene polymorphisms with cardiometabolic diseases

“…The A-112C polymorphism was not associated with T2D in the study, but acted as part of the haplotype [−3826A/−112C/229Met] to increase the risk of T2D in Indians (Vimaleswaran et al 2009). In a Chinese population, the -112C allelic variant was identified as a risk factor for diabetic retinopathy (OR = 1.368 P = 0.004) (Jin et al 2020).…”

Association of UCP1 and UCP2 variants with diabetic retinopathy susceptibility in type-2 diabetes mellitus patients: a meta-analysis

“…After screening on the abstracts and full-text reports, 11 studies met with the inclusion criteria (Brondani et al 2012; Jin et al 2017; Jin et al 2020; Montesanto et al 2018; Rudofsky et al 2007; Shen et al 2014; Zhang et al 2014; Zhou et al 2018; Zietz et al 2006) [ 20 , 26 , 27 , 32 – 37 ]. Jin et al 2020 is comprised of 2 datasets, of which dataset 1 has been reported in Jin et al 2017 [ 34 , 35 ]. For Jin et al 2020 and Montesanto et al 2018, only the allelic data was able to be extracted [ 35 , 36 ].…”

“…Jin et al 2020 is comprised of 2 datasets, of which dataset 1 has been reported in Jin et al 2017 [ 34 , 35 ]. For Jin et al 2020 and Montesanto et al 2018, only the allelic data was able to be extracted [ 35 , 36 ]. Therefore, dataset 2 of Jin et al 2020 as the data for Jin 2020 and Montesanto et al 2018 were used to calculate the pooled effect in the allelic model.…”

Association of UCP1 and UCP2 Variants With Diabetic Retinopathy Susceptibility in Type-2 Diabetes Mellitus Patients: A Meta-analysis

“…After screening on the abstracts and full-text reports, 11 studies met with the inclusion criteria [17,24,25,[30][31][32][33][34][35]. Jin et al 2020 is comprised of 2 datasets, of which dataset 1 has been reported in Jin et al 2017 [32,33]. For Jin et al 2020 and Montesanto et al 2018, only the allelic data was able to be extracted [33,34].…”

“…Jin et al 2020 is comprised of 2 datasets, of which dataset 1 has been reported in Jin et al 2017 [32,33]. For Jin et al 2020 and Montesanto et al 2018, only the allelic data was able to be extracted [33,34]. Therefore, dataset 2 of Jin et al 2020 as the data for Jin 2020 and Montesanto et al 2018 were used to calculate the pooled effect in the allelic model.…”