Elevated blood pressure accelerates white matter brain aging among late middle-aged women: a Mendelian Randomization study in the UK Biobank.

BACKGROUND: Elevated blood pressure (BP) is a modifiable risk factor associated with cognitive impairment and cerebrovascular diseases. However, the causal effect of BP on white matter brain aging remains unclear. METHODS: In this study, we focused on N  = 228 473 individuals of European ancestry who had genotype data and clinical BP measurements available (103 929 men and 124 544 women, mean age = 56.49, including 16 901 participants with neuroimaging data available) collected from UK Biobank (UKB). We first established a machine learning model to compute the outcome variable brain age gap (BAG) based on white matter microstructure integrity measured by fractional anisotropy derived from diffusion tensor imaging data. We then performed a two-sample Mendelian randomization analysis to estimate the causal effect of BP on white matter BAG in the whole population and subgroups stratified by sex and age brackets using two nonoverlapping data sets. RESULTS: The hypertension group is on average 0.31 years (95% CI = 0.13-0.49; P  < 0.0001) older in white matter brain age than the nonhypertension group. Women are on average 0.81 years (95% CI = 0.68-0.95; P  < 0.0001) younger in white matter brain age than men. The Mendelian randomization analyses showed an overall significant positive causal effect of DBP on white matter BAG (0.37 years/10 mmHg, 95% CI 0.034-0.71, P  = 0.0311). In stratified analysis, the causal effect was found most prominent among women aged 50-59 and aged 60-69. CONCLUSION: High BP can accelerate white matter brain aging among late middle-aged women, providing insights on planning effective control of BP for women in this age group.

Sample Size Determination for Food Sampling.

Industry and public health agencies sample and test food products for various purposes related to food safety and quality. Methods of sample selection and sample size determination are important in designing an optimal sampling plan. The appropriate sample size of a sampling plan depends on the objective. We examine the methods of sample size calculation for the following four objectives commonly associated with food sampling: (1) estimate prevalence (e.g., of contaminated products), (2) detect presence (e.g., of contaminated products), (3) estimate maximum prevalence, and (4) compare estimated prevalence with a specified value (e.g., a previous estimate or a threshold value). We illustrate these methods using examples and provide a web-based application (https://simple-sample.galaxytrakr.org/) written in R, using the shiny package, to help users with the application of each method.