Alcohol Intake Interacts with CDKAL1, HHEX, and OAS3 Genetic Variants, Associated with the Risk of Type 2 Diabetes by Lowering Insulin Secretion in Korean Adults

Park, Sunmin; Liu, Meiling; Kang, Suna

doi:10.1111/acer.13888

Cited by 26 publications

(23 citation statements)

References 36 publications

(55 reference statements)

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“…Few studies have focused on the interaction of alcohol with the genetic risk related to beta cell function. A Korean study reported that alcohol interacted with BC-related genetic variants but lowered insulin secretion [23]. Further well-powered studies investigating potential interactions are highly warranted.…”

Section: Discussionmentioning

confidence: 99%

Alcohol consumption and its interaction with genetic variants are strongly associated with the risk of type 2 diabetes: a prospective cohort study

Wang

Zhang

et al. 2019

Nutr Metab (Lond)

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Background Both genetic and lifestyle factors contribute to the incidence of type 2 diabetes. It yet remains controversial whether and how alcohol consumption, one of the most prevalent lifestyle habits, influences type 2 diabetes. Moreover, whether alcohol consumption interacts with genetic risk is inconclusive. Thus, we aimed to explore the effects of alcohol, genetic risk and their potential interactions on type 2 diabetes risk. Methods The Shanghai Diabetes study (SHDS) had a total of 2546 participants with 611 incident cases of combined type 2 diabetes and impaired glucose regulation (IGR). We constructed weighted genetic risk score (GRS) for type 2 diabetes and categorized the GRS into three strata. And the homeostatic model assessment of β-cell function (HOMA-B) and insulin resistance (HOMA-IR) were calculated. Then we used logistic regression models and multiple linear regression models to examine the influence of both baseline alcohol consumption and genetic risk on blood glucose deterioration, insulin resistance (IR) and beta cell function (BC), respectively. Moreover, we investigated the interactions of alcohol intake with: (1) GRSs for type 2 diabetes, IR, BC, body mass index (BMI) and waist-to-hip ratio (WHR); and (2) each of the single nucleotide polymorphisms (SNPs) used to establish the GRSs mentioned above. Results Alcohol consumption and higher T2D-GRS both contributed to a higher incidence rate of blood glucose deterioration [odds ratio (OR), 2.24, 95% confidence interval (CI), 1.76–2.87; OR, 1.25, 95% CI, 1.11–1.42; respectively]. Alcohol reduced insulin sensitivity and compensated by enhancing beta cell function (β = 1.98, P < .0001 and β = − 1.97, P < .0001 for HOMA-IR and inverse HOMA-β, respectively). T2D-GRS deteriorated insulin secretion (β = 0.10, P = 0.0069 for inverse HOMA-B) but not insulin sensitivity (P = 0.0856). Moreover, there was a significant interaction between alcohol and T2D-GRS (Pinteraction = 0.0318), suggesting the association between alcohol and type 2 diabetes was much stronger in the lower T2D-GRS group than in the higher T2D-GRS group. And this interaction was more pronounced in men (Pinteraction = 0.0176) than in women (Pinteraction = 0.3285). No single SNP interacted strongly with alcohol intake. Conclusions/interpretation Alcohol consumption strongly increased the risk of type 2 diabetes by increasing IR, especially in men with low T2D-GRS, highlighting the importance of refraining from drinking alcohol when making recommendations for healthy lifestyle habits to prevent diabetes.

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Section: Discussionmentioning

confidence: 99%

Alcohol consumption and its interaction with genetic variants are strongly associated with the risk of type 2 diabetes: a prospective cohort study

Wang

Zhang

et al. 2019

Nutr Metab (Lond)

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“…DEGs in GO terms and pathways might be linked with advancement of obesity associated type 2 diabetes mellitus. ERBB2 [36], DACT1 [37], ARAP1 [38], MYH9 [39], INPPL1 [40], SARM1 [41], NOTCH1 [42], ROBO1 [43], MAPK8IP1 [44], ANK1 [45], SARM1 [46], SREBF2 [47], SIK1 [48], PASK (PAS domain containing serine/threonine kinase) [49], NOS2 [50], OAS3 [51], KL (klotho) [52], PECAM1 [53], S100A12 [54], S100P [55], BATF3 [56], PLEK (pleckstrin) [57], ALOX5 [58], ARG1 [59], CXCL8 [60], CXCR1 [61], PTAFR (platelet activating factor receptor) [62], PYGL (glycogen phosphorylase L) [63], TCF4 [64], CAMP (cathelicidin antimicrobial peptide) [65], RUNX2 [66], PLA2G2A [67], GCG (glucagon) [68], RARRES2 [69] and HAP1 [70] were involved in the genesis of type 2 diabetes mellitus. Recent studies have reported that ACHE (acetylcholinesterase) [71], FGFR3 [72], VLDLR (very low density lipoprotein receptor) [73], SHC1 [74], HDAC6 [75], CHRNA2 [76], CASR (calcium sensing receptor) [77], ELK1 [78], TYK2 [79], CIITA (class II major histocompatibility complex transactivator) [80], ZAP70 [81], GPT (glutamic--pyruvic transaminase) [82], CHI3L1 [83], AIF1 [84], MMP9 [85], ITGB2 [86], CFD (complement factor D) [87], C3AR1 [88], LGALS1 [89], CD14 [90], TIMP1 [91], TLR2 [92], LTF (lactotransferrin) [93], BRCA2 [94] and IGFBP3 [95] promotes the development of obesity.…”

Section: Discussionmentioning

confidence: 99%

Bioinformatics Analysis of Key Genes and Pathways for Obesity Associated Type 2 Diabetes Mellitus as a Therapeutic Target

Vastrad¹,

Tengli

Vastrad³

et al. 2020

Preprint

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Obesity associated type 2 diabetes mellitus is one of the most common metabolic disorder worldwide. The prognosis of obesity associated type 2 diabetes mellitus patients has remained poor, though considerable efforts have been made to improve the treatment of this metabolic disorder. Therefore, identifying significant differentially expressed genes (DEGs) associated in metabolic disorder advancement and exploiting them as new biomarkers or potential therapeutic targets for metabolic disorder is highly valuable. Differentially expressed genes (DEGs) were screened out from gene expression omnibus (GEO) dataset (GSE132831) and subjected to GO and REACTOME pathway enrichment analyses. The protein - protein interactions network, module analysis, target gene - miRNA regulatory network and target gene - TF regulatory network were constructed, and the top ten hub genes were selected. The relative expression of hub genes was detected in RT-PCR. Furthermore, diagnostic value of hub genes in obesity associated type 2 diabetes mellitus patients was investigated using the receiver operating characteristic (ROC) analysis. Small molecules were predicted for obesity associated type 2 diabetes mellitus by using molecular docking studies. A total of 872 DEGs, including 439 up regulated genes and 432 down regulated genes were observed. Second, functional enrichment analysis showed that these DEGs are mainly involved in the axon guidance, neutrophil degranulation, plasma membrane bounded cell projection organization and cell activation. The top ten hub genes (MYH9, FLNA, DCTN1, CLTC, ERBB2, TCF4, VIM, LRRK2, IFI16 and CAV1) could be utilized as potential diagnostic indicators for obesity associated type 2 diabetes mellitus. The hub genes were validated in obesity associated type 2 diabetes mellitus. This investigation found effective and reliable molecular biomarkers for diagnosis and prognosis by integrated bioinformatics analysis, suggesting new and key therapeutic targets for obesity associated type 2 diabetes mellitus.

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“…Steinthorsdottir et al [ 16 ] reported that homozygous carriers of the risk allele G of rs7756992 had a 22% decreased insulin response to glucose load than A/A allele carriers. Previous findings suggest significant associations between CDKAL1 , alcohol intake, dietary fat, and energy intake in relation to diabetes [ 23 , 36 , 37 ]. In the Ansan/Ansung Korean cohort, diabetes risk increased by 1.549 (95% CI 1.207–1.720) when high alcohol intake was combined with the presence of CDKAL1 risk alleles [ 36 ].…”

Section: Discussionmentioning

confidence: 99%

“…Previous findings suggest significant associations between CDKAL1 , alcohol intake, dietary fat, and energy intake in relation to diabetes [ 23 , 36 , 37 ]. In the Ansan/Ansung Korean cohort, diabetes risk increased by 1.549 (95% CI 1.207–1.720) when high alcohol intake was combined with the presence of CDKAL1 risk alleles [ 36 ]. In Japanese men, the interaction between CDKAL1 gene variants and excessive energy intake increased glycated hemoglobin ( p = 0.037) [ 37 ].…”

Section: Discussionmentioning

confidence: 99%

Dietary Protein and Fat Intake Affects Diabetes Risk with CDKAL1 Genetic Variants in Korean Adults

Choi

Jin

Kim

et al. 2020

IJMS

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Cyclin-dependent kinase 5 regulatory subunit-associated protein 1-like 1 (CDKAL1) is one of the strongest diabetes loci identified to date; evidence suggests that it plays an important role in insulin secretion. Dietary factors that affect insulin demand might enhance the risk of diabetes associated with CDKAL1 variants. Our aim was to examine the interactions between dietary protein and fat intake and CDKAL1 genetic variants in relation to the risk of diabetes in Korean adults. Single nucleotide polymorphisms (SNPs) were selected with a genome-wide association study (GWAS) for diabetes after adjustment for age, gender, and examination site. Using data from the Health Examinees (HEXA) Study of the Korean Genome and Epidemiology Study (KoGES), 3988 middle-aged Korean adults between 40–76 years of age (2034 men and 1954 women) were included in the study. Finally, rs7756992 located within the CDKAL1 gene region was selected from GWAS (p-value < 5 × 10−8). Multivariable logistic regression models were used to evaluate the interactions between genotypes and dietary protein and fat intake in relation to diabetes risk after adjustment for age, gender, BMI, waist circumference, physical activity, smoking status, drinking habits, and examination site. Significant interactions between CDKAL1 rs7756992 and dietary protein and fat intake for the risk of diabetes were observed in men (p-value < 0.05). In women, significant interactions between dietary protein and fat intake and CDKAL1 variants (rs7756992) were associated with increased risk of diabetes (p-value < 0.05). Dietary protein and fat intake interacted differently with CDKAL1 variants in relation to the risk of diabetes in Korean adults of both genders. These findings indicate that CDKAL1 variants play a significant role in diabetes and that dietary protein and fat intake could affect these associations.

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Alcohol Intake Interacts with CDKAL1, HHEX, and OAS3 Genetic Variants, Associated with the Risk of Type 2 Diabetes by Lowering Insulin Secretion in Korean Adults

Abstract: Subjects with a High-GRS had an elevated risk of T2DM even with moderate alcohol intakes due to lower HOMA-B. High alcohol intake appears to be a risk factor for all Asians regardless of alcohol intake.

Cited by 26 publications

References 36 publications

Alcohol consumption and its interaction with genetic variants are strongly associated with the risk of type 2 diabetes: a prospective cohort study

Alcohol consumption and its interaction with genetic variants are strongly associated with the risk of type 2 diabetes: a prospective cohort study

Bioinformatics Analysis of Key Genes and Pathways for Obesity Associated Type 2 Diabetes Mellitus as a Therapeutic Target

Dietary Protein and Fat Intake Affects Diabetes Risk with CDKAL1 Genetic Variants in Korean Adults

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