The genetic architecture of blood pressure variability: A genome‐wide association study of 9370 participants from UK Biobank

Abstract Long‐term blood pressure variability (BPV) is a risk factor for cardiovascular diseases, dementia, and stroke. However, its genetic architecture is not fully understood. This study aims to explore its genetic factors and provide more evidence on the mechanisms and further pathological study of BPV. The genome‐wide association study (GWAS) is based on the UK Biobank cohort. There were four data collection rounds from 2006 to 2020, and 9370 participants with more than three blood pressure measurements were included. They had a median age of 55 and a male percentage of 50.1%. The phenotypes (BPV) were calculated by four methods and the genetic data contains 6 884 260 single nucleotide polymorphisms (SNPs) after imputation and quality control. A linear regression model was performed with adjustments for sex, age, genotype array, and a significant principal component. Subgroup analysis was performed on hypertension‐free participants. The significant and suggestive significant P thresholds were set as 5 × 10−8 and 1 × 10−6. Six genetic loci (BAD, CCDC88B, GPR137, PLCB3, RPS6KA4 for systolic BPV, and WWC2 for diastolic BPV) were identified by coding region SNPs at the suggestive significant P threshold (1 × 10−6). Among them, gene CCDC88B and RPS6KA4 reached the significant P threshold (5 × 10−8), with the strongest signal of SNP rs1229536170 (P = 6.36 × 10−8, β = –.29). The annotation results indicate that genes CCDC88B, GPR137, RPS6KA4, and BAD are associated with long‐term SBPV. Their functions of inflammation, epithelial dysfunction, and apoptosis are related to artery stiffness, which was reported as potential mechanisms of BPV

How Often Does an Individual Trial Agree with Its Corresponding Meta-Analysis? A Meta-Epidemiologic Study

<div><p>Objective</p><p>A meta-analysis may provide more conclusive results than a single trial. The major cost of meta-analysis is the time of waiting before the meta-analysis becomes available and resources spent on consequent trials that may not be necessary. The objective of this study is to address how often the result of a single trial, in particular the first trial, differs from that of its corresponding meta-analysis so as to reduce unnecessary waiting time and subsequent trials.</p><p>Study Design and Settings</p><p>A meta-epidemiologic study was conducted by collecting meta-analyses from the Cochrane Database of Systematic Reviews and five major medical journals. Effect size of a single trial was compared with that of its corresponding meta-analysis. The single trial includes the first trial, last trial and any trial randomly selected from the meta-analysis.</p><p>Results</p><p>647 meta-analyses are included and the median number of trials in a meta-analysis is 5. 233 (36.0%) meta-analyses have the first trial with a statistically significant result. When the first trial is statistically significant, 84.1% (95% CI: 79.4%, 88.8%) of the corresponding meta-analyses is both in the same direction and statistically significant. When the first trial is statistically insignificant, 57.9% (95% CI: 53.2%, 62.8%) of the meta-analysis is also statistically insignificant regardless of direction. The median number of years is 6.5 years from the first to the 5^th trial.</p><p>Conclusion</p><p>The conclusion of the first trial that the treatment is effective or harmful is mostly likely correct. A statistically significant trial agrees more often with its corresponding meta-analysis than a large trial. These findings imply that particularly in some urgent, life-saving or other critical circumstances for which no other effective methods are available, cautious recommendation based on the significant result of the first trial seems justifiable and could start use of an effective intervention by 5–8 years earlier.</p></div

The way forward after COVID-19 vaccination : vaccine passports with blockchain to protect personal privacy

The COVID-19 pandemic has been circulating in the world for over a year since 2019, resulting in over 80 million cases with almost 1.8 million deaths in 2020. The first vaccine that hit the global market is BNT162b2, given by Pfizer/BioNTech, which was approved in December 2020. Stepping into 2021, more COVID-19 vaccines are becoming accessible in the global market. Until February 2021, four vaccines have been approved for full use, while six more have been authorised for early or limited use in different countries around the world

Characteristics of the 647 meta-analyses according to where they are found.

#<p>: p is the p-value for the Chi-square test for the difference of percentages between the 2 sources of meta-analyses.</p><p>Characteristics of the 647 meta-analyses according to where they are found.</p

Flow chart of literature search.

<p>Flow chart of literature search.</p

Number of years from the time of publication of the first trial to that of the fifth trial in the same meta-analysis.

<p>Only meta-analyses with 5 or more trials were eligible for this analysis.</p

Agreement rate and relative difference in the odds ratio between the first trial and its corresponding meta-analysis.

<p>P_Chi is the p-value from the Chi-square test, and* indicates the trend test is also significant; P_NP is the p-value of the non-parametric test for median;</p>#<p>^, median absolute z-score for the combined effect of the meta-analysis and the effect size of standardized mean difference or mean difference was converted to odds ratio by using standard formula.</p><p>Agreement rate and relative difference in the odds ratio between the first trial and its corresponding meta-analysis.</p

Applications of artificial intelligence in dementia research

More than 50 million older people worldwide are suffering from dementia, and this number is estimated to increase to 150 million by 2050. Greater caregiver burdens and financial impacts on the healthcare system are expected as we wait for an effective treatment for dementia. Researchers are constantly exploring new therapies and screening approaches for the early detection of dementia. Artificial intelligence (AI) is widely applied in dementia research, including machine learning and deep learning methods for dementia diagnosis and progression detection. Computerized apps are also convenient tools for patients and caregivers to monitor cognitive function changes. Furthermore, social robots can potentially provide daily life support or guidance for the elderly who live alone. This review aims to provide an overview of AI applications in dementia research. We divided the applications into three categories according to different stages of cognitive impairment: (1) cognitive screening and training, (2) diagnosis and prognosis for dementia, and (3) dementia care and interventions. There are numerous studies on AI applications for dementia research. However, one challenge that remains is comparing the effectiveness of different AI methods in real clinical settings