Effect of Smoking on Blood Pressure and Resting Heart Rate: A Mendelian Randomization Meta-Analysis in the CARTA Consortium.

BACKGROUND: Smoking is an important cardiovascular disease risk factor, but the mechanisms linking smoking to blood pressure are poorly understood. METHODS AND RESULTS: Data on 141 317 participants (62 666 never, 40 669 former, 37 982 current smokers) from 23 population-based studies were included in observational and Mendelian randomization meta-analyses of the associations of smoking status and smoking heaviness with systolic and diastolic blood pressure, hypertension, and resting heart rate. For the Mendelian randomization analyses, a genetic variant rs16969968/rs1051730 was used as a proxy for smoking heaviness in current smokers. In observational analyses, current as compared with never smoking was associated with lower systolic blood pressure and diastolic blood pressure and lower hypertension risk, but with higher resting heart rate. In observational analyses among current smokers, 1 cigarette/day higher level of smoking heaviness was associated with higher (0.21 bpm; 95% confidence interval 0.19; 0.24) resting heart rate and slightly higher diastolic blood pressure (0.05 mm Hg; 95% confidence interval 0.02; 0.08) and systolic blood pressure (0.08 mm Hg; 95% confidence interval 0.03; 0.13). However, in Mendelian randomization analyses among current smokers, although each smoking increasing allele of rs16969968/rs1051730 was associated with higher resting heart rate (0.36 bpm/allele; 95% confidence interval 0.18; 0.54), there was no strong association with diastolic blood pressure, systolic blood pressure, or hypertension. This would suggest a 7 bpm higher heart rate in those who smoke 20 cigarettes/day. CONCLUSIONS: This Mendelian randomization meta-analysis supports a causal association of smoking heaviness with higher level of resting heart rate, but not with blood pressure. These findings suggest that part of the cardiovascular risk of smoking may operate through increasing resting heart rate.

Randomization tests for ABAB designs : Comparing-data-divisionspecific and common distributions. 'Pruebas de aleatorizaci??n para dise??os ABAB : comparaci??n entre distribuciones espec??ficas y comunes'

Resumen tomado de la publicaci??n. Resumen tambi??n en ingl??s

Causal relationship between obesity and vitamin D status: bi-directional mendelian randomization analysis of multiple cohorts

BACKGROUND: Obesity is associated with vitamin D deficiency, and both are areas of active public health concern. We explored the causality and direction of the relationship between body mass index (BMI) and 25-hydroxyvitamin D [25(OH)D] using genetic markers as instrumental variables (IVs) in bi-directional Mendelian randomization (MR) analysis. METHODS AND FINDINGS: We used information from 21 adult cohorts (up to 42,024 participants) with 12 BMI-related SNPs (combined in an allelic score) to produce an instrument for BMI and four SNPs associated with 25(OH)D (combined in two allelic scores, separately for genes encoding its synthesis or metabolism) as an instrument for vitamin D. Regression estimates for the IVs (allele scores) were generated within-study and pooled by meta-analysis to generate summary effects. Associations between vitamin D scores and BMI were confirmed in the Genetic Investigation of Anthropometric Traits (GIANT) consortium (n = 123,864). Each 1 kg/m(2) higher BMI was associated with 1.15% lower 25(OH)D (p = 6.52×10⁻²⁷). The BMI allele score was associated both with BMI (p = 6.30×10⁻⁶²) and 25(OH)D (-0.06% [95% CI -0.10 to -0.02], p = 0.004) in the cohorts that underwent meta-analysis. The two vitamin D allele scores were strongly associated with 25(OH)D (p≤8.07×10⁻⁵⁷ for both scores) but not with BMI (synthesis score, p = 0.88; metabolism score, p = 0.08) in the meta-analysis. A 10% higher genetically instrumented BMI was associated with 4.2% lower 25(OH)D concentrations (IV ratio: -4.2 [95% CI -7.1 to -1.3], p = 0.005). No association was seen for genetically instrumented 25(OH)D with BMI, a finding that was confirmed using data from the GIANT consortium (p≥0.57 for both vitamin D scores). CONCLUSIONS: On the basis of a bi-directional genetic approach that limits confounding, our study suggests that a higher BMI leads to lower 25(OH)D, while any effects of lower 25(OH)D increasing BMI are likely to be small. Population level interventions to reduce BMI are expected to decrease the prevalence of vitamin D deficiency.

Evaluation of genetic markers as instruments for Mendelian randomization studies on vitamin D

Combining SNPs into allele scores provides a more powerful instrument for MR analysis than a single SNP in isolation. Population stratification and the potential for pleiotropic effects need to be considered in MR studies on vitamin D.

Randomization in clinical trials in orthodontics: its significance in research design and methods to achieve it

Randomization is a key step in reducing selection bias during the treatment allocation phase in randomized clinical trials. The process of randomization follows specific steps, which include generation of the randomization list, allocation concealment, and implementation of randomization. The phenomenon in the dental and orthodontic literature of characterizing treatment allocation as random is frequent; however, often the randomization procedures followed are not appropriate. Randomization methods assign, at random, treatment to the trial arms without foreknowledge of allocation by either the participants or the investigators thus reducing selection bias. Randomization entails generation of random allocation, allocation concealment, and the actual methodology of implementing treatment allocation randomly and unpredictably. Most popular randomization methods include some form of restricted and/or stratified randomization. This article introduces the reasons, which make randomization an integral part of solid clinical trial methodology, and presents the main randomization schemes applicable to clinical trials in orthodontics.

Ragu: a free tool for the analysis of EEG and MEG event-related scalp field data using global randomization statistics

We present a program (Ragu; Randomization Graphical User interface) for statistical analyses of multichannel event-related EEG and MEG experiments. Based on measures of scalp field differences including all sensors, and using powerful, assumption-free randomization statistics, the program yields robust, physiologically meaningful conclusions based on the entire, untransformed, and unbiased set of measurements. Ragu accommodates up to two within-subject factors and one between-subject factor with multiple levels each. Significance is computed as function of time and can be controlled for type II errors with overall analyses. Results are displayed in an intuitive visual interface that allows further exploration of the findings. A sample analysis of an ERP experiment illustrates the different possibilities offered by Ragu. The aim of Ragu is to maximize statistical power while minimizing the need for a-priori choices of models and parameters (like inverse models or sensors of interest) that interact with and bias statistics.

Uric Acid and Cardiovascular Events: A Mendelian Randomization Study

Obesity and diets rich in uric acid-raising components appear to account for the increased prevalence of hyperuricemia in Westernized populations. Prevalence rates of hypertension, diabetes mellitus, CKD, and cardiovascular disease are also increasing. We used Mendelian randomization to examine whether uric acid is an independent and causal cardiovascular risk factor. Serum uric acid was measured in 3315 patients of the Ludwigshafen Risk and Cardiovascular Health Study. We calculated a weighted genetic risk score (GRS) for uric acid concentration based on eight uric acid-regulating single nucleotide polymorphisms. Causal odds ratios and causal hazard ratios (HRs) were calculated using a two-stage regression estimate with the GRS as the instrumental variable to examine associations with cardiometabolic phenotypes (cross-sectional) and mortality (prospectively) by logistic regression and Cox regression, respectively. Our GRS was not consistently associated with any biochemical marker except for uric acid, arguing against pleiotropy. Uric acid was associated with a range of prevalent diseases, including coronary artery disease. Uric acid and the GRS were both associated with cardiovascular death and sudden cardiac death. In a multivariate model adjusted for factors including medication, causal HRs corresponding to each 1-mg/dl increase in genetically predicted uric acid concentration were significant for cardiovascular death (HR, 1.77; 95% confidence interval, 1.12 to 2.81) and sudden cardiac death (HR, 2.41; 95% confidence interval, 1.16 to 5.00). These results suggest that high uric acid is causally related to adverse cardiovascular outcomes, especially sudden cardiac death.

Outcome-based adaptive randomization for binary data in clinical trials---Compare and contrast

Monte Carlo simulation has been conducted to investigate parameter estimation and hypothesis testing in some well known adaptive randomization procedures. The four urn models studied are Randomized Play-the-Winner (RPW), Randomized Pôlya Urn (RPU), Birth and Death Urn with Immigration (BDUI), and Drop-the-Loses Urn (DL). Two sequential estimation methods, the sequential maximum likelihood estimation (SMLE) and the doubly adaptive biased coin design (DABC), are simulated at three optimal allocation targets that minimize the expected number of failures under the assumption of constant variance of simple difference (RSIHR), relative risk (ORR), and odds ratio (OOR) respectively. Log likelihood ratio test and three Wald-type tests (simple difference, log of relative risk, log of odds ratio) are compared in different adaptive procedures. ^ Simulation results indicates that although RPW is slightly better in assigning more patients to the superior treatment, the DL method is considerably less variable and the test statistics have better normality. When compared with SMLE, DABC has slightly higher overall response rate with lower variance, but has larger bias and variance in parameter estimation. Additionally, the test statistics in SMLE have better normality and lower type I error rate, and the power of hypothesis testing is more comparable with the equal randomization. Usually, RSIHR has the highest power among the 3 optimal allocation ratios. However, the ORR allocation has better power and lower type I error rate when the log of relative risk is the test statistics. The number of expected failures in ORR is smaller than RSIHR. It is also shown that the simple difference of response rates has the worst normality among all 4 test statistics. The power of hypothesis test is always inflated when simple difference is used. On the other hand, the normality of the log likelihood ratio test statistics is robust against the change of adaptive randomization procedures. ^