Genetic Variants in Peroxisome Proliferator-Activated Receptor-<i>γ</i> and Retinoid X Receptor-<i>α</i> Gene and Type 2 Diabetes Risk: A Case-Control Study of a Chinese Han Population

Lu, Ying; Ye, Xinhua; Cao, Yuanyuan; Li, Qian; Yu, Xiang; Cheng, Jinquan; Gao, Ying; Ma, Jianhua; Du, Wei‐Dong; Zhou, Ling

doi:10.1089/dia.2010.0122

Cited by 14 publications

(7 citation statements)

References 19 publications

(24 reference statements)

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“…By virtue of its role as a heterodimerization partner of other regulatory receptors involved in cell proliferation, cholesterol metabolism or insulin sensitivity as mentioned above, the effects of NR2B1 polymorphisms have mostly been investigated in relation to disease susceptibilities such as Alzheimer's disease, diabetes and various cancers to date. [31][32][33][34] For instance, IVS2¹46C>A (rs1805352) was previously identified as one of the markers within the 6-SNP haplotype significantly associated with colon cancer. 33) The remaining 5 SNPs were deep intronic SNPs which were not covered in the present study.…”

Section: A Utr and Flanking Region (Supplementarymentioning

confidence: 99%

Genetic Variations of NR1I3 and NR2B1 in Asian Populations

Chew

Lim

Singh

et al. 2013

Drug Metabolism and Pharmacokinetics

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Several nuclear receptors are being increasingly recognized for their role as master xenosensors. Among them, CAR-RXRα heterodimer, as encoded by NR1I3 and NR2B1, responds to the presence of drug compounds and regulates the transcription of a wide array of genes involved in their disposition. To investigate the frequency distribution and linkage disequilibrium patterns of NR1I3 and NR2B1 genetic variations, these genes were screened in 168 healthy local Asian subjects, namely Chinese, Malays, and Indians (n=56 subjects each). A total of 38 and 88 SNPs were identified in NR1I3 and NR2B1, respectively. Among them, there were 13 and 43 novel SNPs present at low allelic frequencies (<10%) in NR1I3 and NR2B1, respectively. Notably, the genetic variations in the NR1I3 and NR2B1 genes were mainly confined to the introns whilst the exons were highly conserved across the ethnic populations. Indians harboured distinct frequency distributions from Chinese and Malays in both genes. Based on the linkage disequilibrium patterns of both genes, a number of tag-SNPs were selected for each population (n=8-13 for NR1I3; n=12-18 for NR2B1). In-silico prediction analyses revealed a number of possible functional SNPs. Our data would be valuable for future pharmacogenetic studies on the drug substrates of CAR-RXRα target genes.

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Section: A Utr and Flanking Region (Supplementarymentioning

confidence: 99%

Genetic Variations of NR1I3 and NR2B1 in Asian Populations

Chew

Lim

Singh

et al. 2013

Drug Metabolism and Pharmacokinetics

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“…[26] and Liu et al . [27] found that this SNP was associated with T2DM in a Chinese population. While Cho et al .…”

Section: Discussionmentioning

confidence: 99%

PPARGC1A rs3736265 G>A polymorphism is associated with decreased risk of type 2 diabetes mellitus and fasting plasma glucose level

et al. 2017

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It has been reported that peroxisome proliferator-activated receptor gamma (PPARG) and peroxisome proliferator-activated receptor gamma co-activator 1 (PPARGC1) family (e.g. PPARGC1A and PPARGC1B) are key agents in the development and pathophysiology of type 2 diabetes mellitus (T2DM). In this study, we designed a case-control study and selected PPARG rs1801282 C>G, PPARG rs3856806 C>T, PPARGC1A rs8192678 C>T, PPARGC1A rs2970847 C>T, PPARGC1A rs3736265 G>A, PPARGC1B rs7732671 G>C and PPARGC1B rs17572019 G>A polymorphisms to assess the relationship between these polymorphisms and T2DM using the SNPscan method. A total of 502 T2DM patients and 784 non-diabetic controls were enrolled. We found that PPARGC1A rs3736265 G>A polymorphism was correlated with a borderline decreased susceptibility of T2DM. In a subgroup analysis by age, sex, alcohol use, smoking status and body mass index, a significantly decreased risk of T2DM in <65 years and female groups was found. Haplotype comparison analysis indicated that CTTCGGG and CTCTGGG haplotypes with the order of PPARG rs1801282 C>G, PPARG rs3856806 C>T, PPARGC1A rs8192678 C>T, PPARGC1A rs2970847 C>T, PPARGC1A rs3736265 G>A, PPARGC1B rs7732671 G>C and PPARGC1B rs17572019 G>A polymorphisms in gene position significantly increased the risk of T2DM. However, CCCCACA haplotype conferred a decreased risk to T2DM. We also found that PPARGC1A rs3736265 A allele decreased the level of fasting plasma glucose (FPG), while increased the level of Triglyceride. In conclusion, Our findings suggest that variants of PPARGC1A rs3736265 G>A polymorphism decrease the level of FPG, improving the expectation of study in individual's prevention strategies to T2DM.

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“…BPANN as a new approach to etiology research avoids the limitations of case control study and logistic regression without the distribution form and independence of variables. Our group has previously found the association of single nucleotide polymorphisms (SNPs) in the PPAR-γ and RXR-α genes with metabolic syndrome or type 2 diabetes risks in different populations[11],[12]. In the present study, we further explored the application characteristics of BPANN in studying the combined effects of genetic variants in the PPAR-γ and RXR-α gene and metabolic syndrome risks in a Chinese Han population.…”

Section: Introductionmentioning

confidence: 91%

Application of the back-error propagation artificial neural network (BPANN) on genetic variants in the PPAR-γ and RXR-α gene and risk of metabolic syndrome in a Chinese Han population

Zhao¹,

Xu²,

Shi³

et al. 2014

J Biomed Res

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This study was aimed to explore the associations between the combined effects of several polymorphisms in the PPAR-γ and RXR-α gene and environmental factors with the risk of metabolic syndrome by back-error propagation artificial neural network (BPANN). We established the model based on data gathered from metabolic syndrome patients (n = 1012) and normal controls (n = 1069) by BPANN. Mean impact value (MIV) for each input variable was calculated and the sequence of factors was sorted according to their absolute MIVs. Generalized multifactor dimensionality reduction (GMDR) confirmed a joint effect of PPAR-γ and RXR-α based on the results from BPANN. By BPANN analysis, the sequences according to the importance of metabolic syndrome risk factors were in the order of body mass index (BMI), serum adiponectin, rs4240711, gender, rs4842194, family history of type 2 diabetes, rs2920502, physical activity, alcohol drinking, rs3856806, family history of hypertension, rs1045570, rs6537944, age, rs17817276, family history of hyperlipidemia, smoking, rs1801282 and rs3132291. However, no polymorphism was statistically significant in multiple logistic regression analysis. After controlling for environmental factors, A1, A2, B1 and B2 (rs4240711, rs4842194, rs2920502 and rs3856806) models were the best models (cross-validation consistency 10/10, P = 0.0107) with the GMDR method. In conclusion, the interaction of the PPAR-γ and RXR-α gene could play a role in susceptibility to metabolic syndrome. A more realistic model is obtained by using BPANN to screen out determinants of diseases of multiple etiologies like metabolic syndrome.

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Genetic Variants in Peroxisome Proliferator-Activated Receptor-γ and Retinoid X Receptor-α Gene and Type 2 Diabetes Risk: A Case-Control Study of a Chinese Han Population

Abstract: The PPAR-γ and RXR-α gene variants associated with the development of T2DM in this study must be investigated in a larger population to reveal any potential effects on metabolism.

Cited by 14 publications

References 19 publications

Genetic Variations of NR1I3 and NR2B1 in Asian Populations

Genetic Variations of NR1I3 and NR2B1 in Asian Populations

PPARGC1A rs3736265 G>A polymorphism is associated with decreased risk of type 2 diabetes mellitus and fasting plasma glucose level

Application of the back-error propagation artificial neural network (BPANN) on genetic variants in the PPAR-γ and RXR-α gene and risk of metabolic syndrome in a Chinese Han population

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