ABCG5 and ABCG8 Gene Polymorphisms in Type 2 Diabetes Mellitus in the Turkish Population

Gök, Özlem; Karaali, Zeynep; Acar, Leyla Nesrin; Kılıç, Ülkan; Ergen, Arzu

doi:10.1016/j.jcjd.2015.04.004

Cited by 7 publications

(2 citation statements)

References 42 publications

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“…Zeng et al [40] had indicated that RNase L activation was responsible for type I diabetes, and it was also suggested that the increased expression of RNase L or down-regulated of its inhibitor (RLI) might enhance the insulin response in muscle cells of obese people [41]. Additionally, further studies demonstrated that the mutation of ABCB and gene polymorphisms of ABCG8 and ABCG5 have been linked to T2DM [42,43]. Moreover, ABC transporters are energydependent when transporting various molecules across the biological membranes, while HF is the consequence of insufficient energy supplement of the cardiac pump.…”

Section: Discussionmentioning

confidence: 99%

Identification of biomarkers and mechanisms of diabetic cardiomyopathy using microarray data

Guo

et al. 2020

Cardiol J.

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Background: The study aimed to uncover the regulation mechanisms of diabetic cardiomyopathy (DCM) and provide novel prognostic biomarkers. Methods: The dataset GSE62203 downloaded from the Gene Expression Omnibus database was utilized in the present study. After pretreatment using the Affy package, differentially expressed genes (DEGs) were identified by the limma package, followed by functional enrichment analysis and protein-protein interaction (PPI) network analysis. Furthermore, module analysis was conducted using MCODE plug-in of Cytoscape, and functional enrichment analysis was also performed for genes in the modules. Results: A set of 560 DEGs were screened, mainly enriched in the metabolic process and cell cycle related process. Hub nodes in the PPI network were LDHA (lactate dehydrogenase A), ALDOC (aldolase C, fructose-bisphosphate) and ABCE1 (ATP Binding Cassette Subfamily E Member 1), which were also highlighted in Module 1 or Module 2 and predominantly enriched in the processes of glycolysis and ribosome biogenesis. Additionally, LDHA were linked with ALDOC in the PPI network. Besides, activating transcription factor 4 (ATF4) was prominent in Module 3; while myosin heavy chain 6 (MYH6) was highlighted in Module 4 and was mainly involved in muscle cells related biological processes. Conclusions: Five potential biomarkers including LDHA, ALDOC, ABCE1, ATF4 and MYH6 were identified for DCM prognosis.

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Section: Discussionmentioning

confidence: 99%

Identification of biomarkers and mechanisms of diabetic cardiomyopathy using microarray data

Guo

et al. 2020

Cardiol J.

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“…Notably, the c.5897A > G p.(Glu1966Gly), c.5690T > C p.(Phe1897Ser), and c.6068A > T p.(Tyr2023Phe) variants in LPA and the c.169G > T p.(Asp57Tyr) variant in GCKR were classified as pathogen or likely pathogen according to the ACMG guidelines, suggesting that these novel variants are likely to be significantly related to hyperglycemia in individuals with MetS. In a cohort of patients with T2D, two variants (rs6720173 and rs4148211) in ABC transporter genes, ABCG5 and ABCG8 , have been found to increase the risk of T2D in humans [ 31 ]. The most significant SNP (rs4299376) in ABCG8 was also found to be associated with increased fasting plasma glucose levels [ 32 ].…”

Section: Discussionmentioning

confidence: 99%

Identification of genetic variants related to metabolic syndrome by next-generation sequencing

Lee¹,

Kim²,

Hong³

et al. 2022

Diabetol Metab Syndr

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Background Metabolic syndrome (MetS) is a cluster of conditions associated with glucose intolerance, hypertension, abdominal obesity, dyslipidemia, and insulin resistance that increase the risk of cardiovascular diseases (CVD) and type 2 diabetes (T2D). Since MetS is known as a complex symptom with a high incidence of genetic factors, it is important to identify genetic variants for each clinical characteristic of MetS. Methods We performed targeted next-generation sequencing (NGS) to identify genetic variants related to obesity, blood glucose, triacylglycerol (TG), and high-density lipoprotein (HDL)-cholesterol level, and hypertension in 48 subjects with MetS and in 48 healthy subjects. Results NGS analysis revealed that 26 of 48 subjects (54.2%) with MetS had putative non-synonymous variants related to the clinical features of MetS. Of the subjects with MetS, 8 (16.7%) had variants in 4 genes (COL6A2, FTO, SPARC, and MTHFR) related to central obesity, 17 (35.4%) had variants in 6 genes (APOB, SLC2A2, LPA, ABCG5, ABCG8, and GCKR) related to hyperglycemia, 3 (6.3%) had variants in 4 genes (APOA1, APOC2, APOA4, and LMF1) related to hypertriglyceridemia, 8 (16.7%) had variants in 4 genes (ABCA1, CETP, SCARB1, and LDLR) related to low HDL-cholesterolemia, and 5 (10.4%) had variants in ADD1 related to hypertension. Conclusions Our findings may contribute to broadening the genetic spectrum of risk variants related to the development of MetS.

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Effects of SNVs in ABCA1, ABCG1, ABCG5, ABCG8, and SCARB1 Genes on Plasma Lipids, Lipoproteins, and Adiposity Markers in a Brazilian Population

et al. 2021

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ABCG5 and ABCG8 Gene Polymorphisms in Type 2 Diabetes Mellitus in the Turkish Population

Cited by 7 publications

References 42 publications

Identification of biomarkers and mechanisms of diabetic cardiomyopathy using microarray data

Identification of biomarkers and mechanisms of diabetic cardiomyopathy using microarray data

Identification of genetic variants related to metabolic syndrome by next-generation sequencing

Effects of SNVs in ABCA1, ABCG1, ABCG5, ABCG8, and SCARB1 Genes on Plasma Lipids, Lipoproteins, and Adiposity Markers in a Brazilian Population

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