Circulating Fetuin-A and Risk of Type 2 Diabetes: A Mendelian Randomization Analysis.
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ABSTRACT:
SUBMITTER: Kroger J
PROVIDER: S-EPMC6278908 | biostudies-literature | 2018 Jun
REPOSITORIES: biostudies-literature
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Project description: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 European Prospective Investigation into Cancer and Nutrition (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 case subjects with type 2 diabetes were ascertained. Higher uric acid was associated with a higher diabetes risk after adjustment for confounders, with a hazard ratio (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. By using the genetic score to estimate the unconfounded effect, we 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 the Diabetes Genetics Replication And Meta-analysis (DIAGRAM) consortium, increasing our dataset to 41,508 case subjects with diabetes, the summary odds ratio 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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Project description:Background: The incidence of type 1 diabetes (T1D) is increasing globally. One hypothesis is that increasing childhood obesity rates may explain part of this increase, but, as T1D is rare, intervention studies are challenging to perform. The aim of this study was to assess this hypothesis with a Mendelian randomization approach that uses genetic variants as instrumental variables to test for causal associations.Methods and findings: We created a genetic instrument of 23 single nucleotide polymorphisms (SNPs) associated with childhood adiposity in children aged 2-10 years. Summary-level association results for these 23 SNPs with childhood-onset (<17 years) T1D were extracted from a meta-analysis of genome-wide association study with 5,913 T1D cases and 8,828 reference samples. Using inverse-variance weighted Mendelian randomization analysis, we found support for an effect of childhood adiposity on T1D risk (odds ratio 1.32, 95% CI 1.06-1.64 per standard deviation score in body mass index [SDS-BMI]). A sensitivity analysis provided evidence of horizontal pleiotropy bias (p = 0.04) diluting the estimates towards the null. We therefore applied Egger regression and multivariable Mendelian randomization methods to control for this type of bias and found evidence in support of a role of childhood adiposity in T1D (odds ratio in Egger regression, 2.76, 95% CI 1.40-5.44). Limitations of our study include that underlying genes and their mechanisms for most of the genetic variants included in the score are not known. Mendelian randomization requires large sample sizes, and power was limited to provide precise estimates. This research has been conducted using data from the Early Growth Genetics (EGG) Consortium, the Genetic Investigation of Anthropometric Traits (GIANT) Consortium, the Tobacco and Genetics (TAG) Consortium, and the Social Science Genetic Association Consortium (SSGAC), as well as meta-analysis results from a T1D genome-wide association study.Conclusions: This study provides genetic support for a link between childhood adiposity and T1D risk. Together with evidence from observational studies, our findings further emphasize the importance of measures to reduce the global epidemic of childhood obesity and encourage mechanistic studies.
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Project description:BackgroundConventional epidemiologic studies have evaluated associations between circulating lipid levels and breast cancer risk, but results have been inconsistent. As Mendelian randomization analyses may provide evidence for causal inference, we sought to evaluate potentially unbiased associations between breast cancer risk and four genetically predicted lipid traits.MethodsPrevious genome-wide association studies (GWAS) have identified 164 discrete variants associated with high density lipoprotein-cholesterol (HDL-C), low density lipoprotein-cholesterol (LDL-C), triglycerides and total cholesterol. We used 162 of these unique variants to construct weighted genetic scores (wGSs) for a total of 101 424 breast cancer cases and 80 253 controls of European ancestry from the Breast Cancer Association Consortium (BCAC). Unconditional logistic regression was used to estimate odds ratios (OR) and 95% confidence intervals (CI) for associations between per standard deviation increase in genetically predicted lipid traits and breast cancer risk. Additional Mendelian randomization analysis approaches and sensitivity analyses were conducted to assess pleiotropy and instrument validity.ResultsCorresponding to approximately 15?mg/dL, one standard deviation increase in genetically predicted HDL-C was associated with a 12% increased breast cancer risk (OR: 1.12, 95% CI: 1.08-1.16). Findings were consistent after adjustment for breast cancer risk factors and were robust in several sensitivity analyses. Associations with genetically predicted triglycerides and total cholesterol were inconsistent, and no association for genetically predicted LDL-C was observed.ConclusionsThis study provides strong evidence that circulating HDL-C may be associated with an increased risk of breast cancer, whereas LDL-C may not be related to breast cancer risk.
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Project description:BackgroundVitamin D deficiency has been associated with type 1 diabetes in observational studies, but evidence from randomized controlled trials (RCTs) is lacking. The aim of this study was to test whether genetically decreased vitamin D levels are causally associated with type 1 diabetes using Mendelian randomization (MR).Methods and findingsFor our two-sample MR study, we selected as instruments single nucleotide polymorphisms (SNPs) that are strongly associated with 25-hydroxyvitamin D (25OHD) levels in a large vitamin D genome-wide association study (GWAS) on 443,734 Europeans and obtained their corresponding effect estimates on type 1 diabetes risk from a large meta-analysis of 12 type 1 diabetes GWAS studies (Ntot = 24,063, 9,358 cases, and 15,705 controls). In addition to the main analysis using inverse variance weighted MR, we applied 3 additional methods to control for pleiotropy (MR-Egger, weighted median, and mode-based estimate) and compared the respective MR estimates. We also undertook sensitivity analyses excluding SNPs with potential pleiotropic effects. We identified 69 lead independent common SNPs to be genome-wide significant for 25OHD, explaining 3.1% of the variance in 25OHD levels. MR analyses suggested that a 1 standard deviation (SD) decrease in standardized natural log-transformed 25OHD (corresponding to a 29-nmol/l change in 25OHD levels in vitamin D-insufficient individuals) was not associated with an increase in type 1 diabetes risk (inverse-variance weighted (IVW) MR odds ratio (OR) = 1.09, 95% CI: 0.86 to 1.40, p = 0.48). We obtained similar results using the 3 pleiotropy robust MR methods and in sensitivity analyses excluding SNPs associated with serum lipid levels, body composition, blood traits, and type 2 diabetes. Our findings indicate that decreased vitamin D levels did not have a substantial impact on risk of type 1 diabetes in the populations studied. Study limitations include an inability to exclude the existence of smaller associations and a lack of evidence from non-European populations.ConclusionsOur findings suggest that 25OHD levels are unlikely to have a large effect on risk of type 1 diabetes, but larger MR studies or RCTs are needed to investigate small effects.
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