Dietary carbohydrates interact with AMY1 polymorphisms to influence the incidence of type 2 diabetes in Korean adults

AbstractThe relationship between AMY1 single nucleotide polymorphisms (SNPs), dietary carbohydrates, and the risk of type 2 diabetes is unclear. We aimed to evaluate this association using an ongoing large-scale prospective study, namely the Korean Genome and Epidemiology Study. We selected six genetic variants of the AMY1 gene: rs10881197, rs4244372, rs6696797, rs1566154, rs1930212, and rs1999478. Baseline dietary data were obtained using a semi-quantitative food frequency questionnaire. Type 2 diabetes was defined according to the criteria of the World Health Organization and American Diabetes Association. During an average follow-up period of 12 years (651,780 person-years), 1082 out of 4552 (23.8%) patients had type 2 diabetes. Three AMY1 SNPs were significantly associated with diabetes incidence among patients with carbohydrate intake > 65% of total energy: rs6696797, rs4244372, and rs10881197. In multivariable Cox models, Korean women with the rs6696797 AG or AA genotype had 28% higher incidence of type 2 diabetes (hazard ratio 1.28, 95% confidence interval 1.06–1.55) than Korean women with the rs6696797 GG genotype. We did not observe significant associations between AMY1 SNPs, dietary carbohydrates, and diabetes incidence in Korean men. We conclude that AMY1 genetic variants and dietary carbohydrate intake influence the incidence of type 2 diabetes in Korean women only. Korean women who are minor carriers of the AMY1 rs6696797, rs4244372, and rs10881197 genotypes may benefit from a low-carbohydrate diet to prevent the future risk of type 2 diabetes.

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Genetic variants in promoter regions associated with type 2 diabetes mellitus: A large‐scale meta‐analysis and subgroup analysis

Journal of Cellular Biochemistry ◽

10.1002/jcb.28572 ◽

2019 ◽

Vol 120 (8) ◽

pp. 13012-13025 ◽

Cited By ~ 2

Author(s):

Ling Wu ◽

Chi Chiu Wang

Keyword(s):

Diabetes Mellitus ◽

Type 2 Diabetes ◽

Type 2 Diabetes Mellitus ◽

Subgroup Analysis ◽

Genetic Variants ◽

Large Scale ◽

Meta Analysis ◽

Promoter Regions

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Intake of Carbohydrate to Fiber Ratio Is a Useful Marker for Metabolic Syndrome in Patients with Type 2 Diabetes: A Cross-Sectional Study

Annals of Nutrition and Metabolism ◽

10.1159/000486550 ◽

2018 ◽

Vol 72 (4) ◽

pp. 329-335 ◽

Cited By ~ 6

Author(s):

Yoshitaka Hashimoto ◽

Muhei Tanaka ◽

Akane Miki ◽

Yukiko Kobayashi ◽

Sayori Wada ◽

...

Keyword(s):

Type 2 Diabetes ◽

Metabolic Syndrome ◽

Human Health ◽

Cross Sectional Study ◽

Carbohydrate Intake ◽

Protective Effects ◽

Sectional Study ◽

Cross Sectional ◽

Low Carbohydrate Diet

Background/Aims: The effect of low carbohydrate diet on human health is still controversial. Whole grain, which is carbohydrate rich in fiber, has protective effects on human health. Thus, we assumed that intake of carbohydrate to fiber ratio has an important role in human health. Methods: This is a post-hoc analysis of a cross-sectional study of 164 patients with type 2 diabetes. Habitual food and nutrient intake were assessed and estimated by a self-administered diet history questionnaire. Intake of carbohydrate to fiber ratio was defined as carbohydrate (g)/fiber intake (g). Logistic regression analyses were performed to reveal the association between intake of carbohydrate to fiber ratio and metabolic syndrome (MetS). Results: Intake of carbohydrate to fiber ratio has closely associated with metabolic parameters, including triglycerides (r = 0.21, p = 0.007) and high-density lipoprotein cholesterol (r = –0.23, p = 0.003). Intake of carbohydrate to fiber ratio was associated with MetS (OR 1.06 [95% CI 1.00–1.13], p = 0.047) after adjusting for covariates, whereas carbohydrate intake (1.00 [0.99–1.01], p = 0.752) or carbohydrate energy/total energy (1.00 [0.94–1.07], p = 0.962) was not associated with MetS. Conclusions: Intake of carbohydrate to fiber ratio was associated with MetS, whereas carbohydrate intake was not.

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Mendelian randomization studies of biomarkers and type 2 diabetes

Endocrine Connections ◽

10.1530/ec-15-0087 ◽

2015 ◽

Vol 4 (4) ◽

pp. 249-260 ◽

Cited By ~ 12

Author(s):

Ali Abbasi

Keyword(s):

Type 2 Diabetes ◽

Genetic Variants ◽

Large Scale ◽

Mendelian Randomization ◽

Association Studies ◽

Causal Effects ◽

Current Evidence ◽

Genome Wide Association Studies ◽

Glycaemic Traits

Many biomarkers are associated with type 2 diabetes (T2D) risk in epidemiological observations. The aim of this study was to identify and summarize current evidence for causal effects of biomarkers on T2D. A systematic literature search in PubMed and EMBASE (until April 2015) was done to identify Mendelian randomization studies that examined potential causal effects of biomarkers on T2D. To replicate the findings of identified studies, data from two large-scale, genome-wide association studies (GWAS) were used: DIAbetes Genetics Replication And Meta-analysis (DIAGRAMv3) for T2D and the Meta-Analyses of Glucose and Insulin-related traits Consortium (MAGIC) for glycaemic traits. GWAS summary statistics were extracted for the same genetic variants (or proxy variants), which were used in the original Mendelian randomization studies. Of the 21 biomarkers (from 28 studies), ten have been reported to be causally associated with T2D in Mendelian randomization. Most biomarkers were investigated in a single cohort study or population. Of the ten biomarkers that were identified, nominally significant associations with T2D or glycaemic traits were reached for those genetic variants related to bilirubin, pro-B-type natriuretic peptide, delta-6 desaturase and dimethylglycine based on the summary data from DIAGRAMv3 or MAGIC. Several Mendelian randomization studies investigated the nature of associations of biomarkers with T2D. However, there were only a few biomarkers that may have causal effects on T2D. Further research is needed to broadly evaluate the causal effects of multiple biomarkers on T2D and glycaemic traits using data from large-scale cohorts or GWAS including many different genetic variants.

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Type 2 Diabetes Risk Prediction Incorporating Family History Revealing a Substantial Fraction of Missing Heritability

10.1101/041335 ◽

2016 ◽

Author(s):

Jungsoo Gim ◽

Wonji Kim ◽

Soo Heon Kwak ◽

Kyong Soo Park ◽

Sungho Won

Keyword(s):

Type 2 Diabetes ◽

Family History ◽

Risk Prediction ◽

Genetic Variants ◽

Large Scale ◽

Threshold Model ◽

Penalized Regression ◽

Missing Heritability ◽

History Of

ABSTRACTDespite many successes of genome-wide association (GWA) studies, known susceptibility variants identified by GWAS have the modest effect sizes and we met noticeable skepticism about the risk prediction model building with large-scale genetic data. However, in contrast with genetic variants, family history of diseases has been largely accepted as an important risk factor in clinical diagnosis and risk prediction though; complicated structures of family history of diseases have limited their application to clinical use. Here, we develop a new method which enables the incorporation of general family history of diseases with the liability threshold model and a new analysis strategy for risk prediction with penalized regression incorporating large-scale genetic variants and clinical risk factors. An application of our model to type 2 diabetes (T2D) patients in Korean population (1846 cases out of 3692 subjects) demonstrates that SNPs accounts for 28.6% of T2D’s variability and incorporation of family history leads to additional improvement of 5.9%. Our result illustrates that family history of diseases can have an invaluable information for disease prediction and may bridge the gap originated from missing heritability.

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Association of decrease in carbohydrate intake with reduction in abdominal fat during 3-month moderate low-carbohydrate diet among non-obese Japanese patients with type 2 diabetes

Metabolism ◽

10.1016/j.metabol.2015.01.012 ◽

2015 ◽

Vol 64 (5) ◽

pp. 618-625 ◽

Cited By ~ 4

Author(s):

Tae Sasakabe ◽

Hajime Haimoto ◽

Hiroyuki Umegaki ◽

Kenji Wakai

Keyword(s):

Type 2 Diabetes ◽

Japanese Patients ◽

Abdominal Fat ◽

Carbohydrate Intake ◽

Carbohydrate Diet ◽

Low Carbohydrate Diet ◽

Low Carbohydrate

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Influence of genetic variants on gene expression in human pancreatic islets – implications for type 2 diabetes

10.1101/655670 ◽

2019 ◽

Cited By ~ 9

Author(s):

Ana Viñuela ◽

Arushi Varshney ◽

Martijn van de Bunt ◽

Rashmi B. Prasad ◽

Olof Asplund ◽

...

Keyword(s):

Type 2 Diabetes ◽

Genetic Variants ◽

Complex Traits ◽

Large Scale ◽

Association Studies ◽

Cell Types ◽

Genome Wide Association Studies ◽

Selective Enrichment ◽

Gwas Signal

AbstractMost signals detected by genome-wide association studies map to non-coding sequence and their tissue-specific effects influence transcriptional regulation. However, many key tissues and cell-types required for appropriate functional inference are absent from large-scale resources such as ENCODE and GTEx. We explored the relationship between genetic variants influencing predisposition to type 2 diabetes (T2D) and related glycemic traits, and human pancreatic islet transcription using RNA-Seq and genotyping data from 420 islet donors. We find: (a) eQTLs have a variable replication rate across the 44 GTEx tissues (<73%), indicating that our study captured islet-specific cis-eQTL signals; (b) islet eQTL signals show marked overlap with islet epigenome annotation, though eQTL effect size is reduced in the stretch enhancers most strongly implicated in GWAS signal location; (c) selective enrichment of islet eQTL overlap with the subset of T2D variants implicated in islet dysfunction; and (d) colocalization between islet eQTLs and variants influencing T2D or related glycemic traits, delivering candidate effector transcripts at 23 loci, including DGKB and TCF7L2. Our findings illustrate the advantages of performing functional and regulatory studies in tissues of greatest disease-relevance while expanding our mechanistic insights into complex traits association loci activity with an expanded list of putative transcripts implicated in T2D development.

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