Updated hypertension prevalence, awareness, and control rates based on the 2017ACC/AHA high blood pressure guideline.

In 2017, the American College of Cardiology/American Heart Association (ACC/AHA) updated the Guideline of Prevention, Detection, Evaluation and Management and Management of High Blood Pressure (HBP) in Adults. The purpose of the current study was to evaluate the potential impact of the 2017ACC/AHA HBP guideline on hypertension prevalence, awareness, and control rates. The data were collected from Physical Examination Center of the Second Hospital of Hebei Medical University from January 2012 to December 2017 (N = 66 977), including demographic information and risk factors of hypertension. The hypertension prevalence, awareness, and control rates of people were evaluated according to the new guideline. Additionally, the factors related to hypertension prevalence were also assessed. According to previous HBP guideline, hypertension prevalence, awareness, and control rate were 30.54%, 44.33%, and 13.04%, respectively. However, when the 2017 ACC/AHA HBP guideline was introduced, the population with hypertension increased from 20 453 to 34 460, the hypertension prevalence rate increased from 30.54% to 51.45%, the awareness rate decreased from 44.33% to 26.31%, and the control rate declined from 13.04% to 2.72%. The most newly diagnosed hypertension patients were from the low-risk population with young age and without the above histories. The 2017ACC/AHA HBP guideline indicated that high hypertension prevalence rate still existed with a substantial increase, while the awareness and control rates were relatively lowered.

Quantitative assessment of cell population diversity in single-cell landscapes.

Single-cell RNA sequencing (scRNA-seq) has become a powerful tool for the systematic investigation of cellular diversity. As a number of computational tools have been developed to identify and visualize cell populations within a single scRNA-seq dataset, there is a need for methods to quantitatively and statistically define proportional shifts in cell population structures across datasets, such as expansion or shrinkage or emergence or disappearance of cell populations. Here we present sc-UniFrac, a framework to statistically quantify compositional diversity in cell populations between single-cell transcriptome landscapes. sc-UniFrac enables sensitive and robust quantification in simulated and experimental datasets in terms of both population identity and quantity. We have demonstrated the utility of sc-UniFrac in multiple applications, including assessment of biological and technical replicates, classification of tissue phenotypes and regional specification, identification and definition of altered cell infiltrates in tumorigenesis, and benchmarking batch-correction tools. sc-UniFrac provides a framework for quantifying diversity or alterations in cell populations across conditions and has broad utility for gaining insight into tissue-level perturbations at the single-cell resolution.