| 1. |
- Langenberg, Claudia, et al.
(author)
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Gene-Lifestyle Interaction and Type 2 Diabetes: The EPIC InterAct Case-Cohort Study.
- 2014
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In: PLoS Medicine. - : Public Library of Science (PLoS). - 1549-1676 .- 1549-1277. ; 11:5
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Journal article (peer-reviewed)abstract
- Understanding of the genetic basis of type 2 diabetes (T2D) has progressed rapidly, but the interactions between common genetic variants and lifestyle risk factors have not been systematically investigated in studies with adequate statistical power. Therefore, we aimed to quantify the combined effects of genetic and lifestyle factors on risk of T2D in order to inform strategies for prevention.
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| 2. |
- Li, Sherly X., et al.
(author)
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Interaction between genes and macronutrient intake on the risk of developing type 2 diabetes : systematic review and findings from European Prospective Investigation into Cancer (EPIC)-InterAct
- 2017
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In: American Journal of Clinical Nutrition. - : American society for nutrition. - 0002-9165 .- 1938-3207. ; 106:1, s. 263-275
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Research review (peer-reviewed)abstract
- Background: Gene-diet interactions have been reported to contribute to the development of type 2 diabetes (T2D). However, to our knowledge, few examples have been consistently replicated to date. Objective: We aimed to identify existing evidence for genemacronutrient interactions and T2D and to examine the reported interactions in a large-scale study. Design: We systematically reviewed studies reporting genemacronutrient interactions and T2D. We searched the MEDLINE, Human Genome Epidemiology Network, and WHO International Clinical Trials Registry Platform electronic databases to identify studies published up to October 2015. Eligibility criteria included assessment of macronutrient quantity (e.g., total carbohydrate) or indicators of quality (e. g., dietary fiber) by use of self-report or objective biomarkers of intake. Interactions identified in the review were subsequently examined in the EPIC (European Prospective Investigation into Cancer)-InterAct case-cohort study (n = 21,148, with 9403 T2D cases; 8 European countries). Prentice-weighted Cox regression was used to estimate countryspecific HRs, 95% CIs, and P-interaction values, which were then pooled by random-effects meta-analysis. A primary model was fitted by using the same covariates as reported in the published studies, and a second model adjusted for additional covariates and estimated the effects of isocaloric macronutrient substitution. Results: Thirteen observational studies met the eligibility criteria (n < 1700 cases). Eight unique interactions were reported to be significant between macronutrients [carbohydrate, fat, saturated fat, dietary fiber, and glycemic load derived from self-report of dietary intake and circulating n-3 (v-3) polyunsaturated fatty acids] and genetic variants in or near transcription factor 7-like 2 (TCF7L2), gastric inhibitory polypeptide receptor (GIPR), caveolin 2 (CAV2), and peptidase D (PEPD) (P-interaction, 0.05). We found no evidence of interaction when we tried to replicate previously reported interactions. In addition, no interactions were detected in models with additional covariates. Conclusions: Eight gene-macronutrient interactions were identified for the risk of T2D from the literature. These interactions were not replicated in the EPIC-InterAct study, which mirrored the analyses undertaken in the original reports. Our findings highlight the importance of independent replication of reported interactions.
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| 3. |
- Podmore, Clara, et al.
(author)
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Association of Multiple Biomarkers of Iron Metabolism and Type 2 Diabetes : The EPIC-InterAct Study
- 2016
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In: Diabetes Care. - : American Diabetes Association. - 0149-5992 .- 1935-5548. ; 39:4, s. 572-581
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Journal article (peer-reviewed)abstract
- OBJECTIVE Observational studies show an association between ferritin and type 2 diabetes (T2D), suggesting a role of high iron stores in T2D development. However, ferritin is influenced by factors other than iron stores, which is less the case for other biomarkers of iron metabolism. We investigated associations of ferritin, transferrin saturation (TSAT), serum iron, and transferrin with T2D incidence to clarify the role of iron in the pathogenesis of T2D.RESEARCH DESIGN AND METHODS The European Prospective Investigation into Cancer and Nutrition-InterAct study includes 12,403 incident T2D cases and a representative subcohort of 16,154 individuals from a European cohort with 3.99 million person-years of follow-up. We studied the prospective association of ferritin, TSAT, serum iron, and transferrin with incident T2D in 11,052 cases and a random subcohort of 15,182 individuals and assessed whether these associations differed by subgroups of the population.RESULTS Higher levels of ferritin and transferrin were associated with a higher risk of T2D (hazard ratio [HR] [95% CI] in men and women, respectively: 1.07 [1.01-1.12] and 1.12 [1.05-1.19] per 100mg/L higher ferritin level; 1.11 [1.00-1.24] and 1.22 [1.12-1.33] per 0.5 g/L higher transferrin level) after adjustment for age, center, BMI, physical activity, smoking status, education, hs-CRP, alanine aminotransferase, and g-glutamyl transferase. Elevated TSAT (>= 45% vs. <45%) was associated with a lower risk of T2D in women (0.68 [0.54-0.86]) but was not statistically significantly associated in men (0.90 [0.75-1.08]). Serum iron was not associated with T2D. The association of ferritin with T2D was stronger among leaner individuals (P-interaction < 0.01).CONCLUSIONS The pattern of association of TSAT and transferrin with T2D suggests that the underlying relationship between iron stores and T2D is more complex than the simple link suggested by the association of ferritin with T2D.
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| 4. |
- Scott, Robert A., et al.
(author)
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Common Genetic Variants Highlight the Role of Insulin Resistance and Body Fat Distribution in Type 2 Diabetes, Independent of Obesity
- 2014
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In: Diabetes. - : American Diabetes Association. - 0012-1797 .- 1939-327X. ; 63:12, s. 4378-4387
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Journal article (peer-reviewed)abstract
- We aimed to validate genetic variants as instruments for insulin resistance and secretion, to characterize their association with intermediate phenotypes, and to investigate their role in type 2 diabetes (T2D) risk among normal-weight, overweight, and obese individuals. We investigated the association of genetic scores with euglycemic-hyperinsulinemic clamp- and oral glucose tolerance test-based measures of insulin resistance and secretion and a range of metabolic measures in up to 18,565 individuals. We also studied their association with T2D risk among normal-weight, overweight, and obese individuals in up to 8,124 incident T2D cases. The insulin resistance score was associated with lower insulin sensitivity measured by M/I value (beta in SDs per allele [95% CI], -0.03 [-0.04, -0.01]; P = 0.004). This score was associated with lower BMI (-0.01 [-0.01, -0.0]; P = 0.02) and gluteofemoral fat mass (-0.03 [-0.05,-0.02; P = 1.4x10(-6) and with higher alanine transaminase (0.02 [0.01, 0.03]; P = 0.002) and gamma-glutamyl transferase (0.02 [0.01, 0.03]; P = 0.001). While the secretion score had a stronger association with T2D in leaner individuals (P-interaction = 0.001), we saw no difference in the association of the insulin resistance score with T2D among BMI or waist strata (P-interaction > 0.31). While insulin resistance is often considered secondary to obesity, the association of the insulin resistance score with lower BMI and adiposity and with incident T2D even among individuals of normal weight highlights the role of insulin resistance and ectopic fat distribution in T2D, independently of body size.
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| 5. |
- Sluijs, Ivonne, et al.
(author)
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A Mendelian randomization study of circulating uric acid and type 2 diabetes.
- 2015
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In: Diabetes. - : American Diabetes Association. - 1939-327X .- 0012-1797. ; 64:8, s. 3028-3036
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Journal article (peer-reviewed)abstract
- We aimed to investigate the causal effect of circulating uric acid concentrations on type 2 diabetes risk. A Mendelian randomization study was performed using a genetic score with 24 uric acid associated loci. We used data of the EPIC-InterAct case-cohort study, comprising 24,265 individuals of European ancestry from eight European countries. During a mean (SD) follow-up of 10 (4) years, 10,576 verified incident type 2 diabetes cases were ascertained. Higher uric acid associated with higher diabetes risk following adjustment for confounders, with a HR of 1.20 (95%CI: 1.11,1.30) per 59.48 µmol/L (1 mg/dL) uric acid. The genetic score raised uric acid by 17 µmol/L (95%CI: 15,18) per SD increase, and explained 4% of uric acid variation. Using the genetic score to estimate the unconfounded effect found that a 59.48 µmol/L higher uric acid concentration did not have a causal effect on diabetes (HR 1.01, 95%CI: 0.87,1.16). Including data from DIAGRAM consortium, increasing our dataset to 41,508 diabetes cases, the summary OR estimate was 0.99 (95%CI: 0.92, 1.06). In conclusion, our study does not support a causal effect of circulating uric acid on diabetes risk. Uric acid lowering therapies may therefore not be beneficial in reducing diabetes risk.
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