A novel predicted model for hypertension based on a large cross-sectional study

Abstract

Hypertension is a global public health issue and leading risk for death and disability. It is urgent to search novel methods predicting hypertension. Herein, we chose 73158 samples of physical examiners in central China from June 2008 to June 2018. After strict exclusion processes, 33570 participants with hypertension and 35410 healthy controls were included. We randomly chose 70% samples as the train set and the remaining 30% as the test set. Clinical parameters including age, gender, height, weight, body mass index, triglyceride, total cholesterol, low-density lipoprotein, blood urea nitrogen, uric acid, and creatinine were significantly increased, while high-density lipoprotein was decreased in the hypertension group versus controls. Nine optimal markers were identified by a logistic regression model, and achieved AUC value of 76.52% in the train set and 75.81% in the test set for hypertension. In conclusions, this study is the first to establish predicted models for hypertension using the logistic regression model in Central China, which provide risk factors and novel prediction method to predict and prevent hypertension. Hypertension which is a global public health issue is a leading risk for death and disability 1. It exacerbates the burden of kidney failure, stroke, and heart disease, as well as premature mortality and disability. The number of people with hypertension worldwide increased by 400 million in 2008, and about 40% of adults over the age of 25 are diagnosed with hypertension. The risk of death associated with systolic blood pressure increases significantly with increasing systolic blood pressure 2. Hypertension ranks first among all modifiable causes of cerebrovascular disease (CVD) and is the second preventable cause of death in the United States 3. By 2030, the number of global CVD-related deaths will exceed 23.3 million 4. It's reported by the World Health Organization (WHO) that global CAD cause approximately 17 million deaths each year 5 , of which approximately 9.4 million die from complications of hypertension 6. Hypertension accounts for at least 51% death of stroke and 45% death of heart diseases 5. In 2010, hypertension has been the leading cause of disability-adjusted life years and death. Then, the disability-adjusted life year caused by CAD was 366 million in 2017. Among them, the disability-adjusted life year caused by hypertensive heart disease was 16.5 million 7. The prevalence of hypertension in China was high (46.4%) in 2012-2015 according to the 2017 American College of Cardiology/American Heart Association guidelines 8. However, the awareness, control and treatment of hypertension are unsatisfactory 8. Thus, it is important and urgent to search a new method predicting hypertension. The increased prevalence of hypertension is due to behavioral risk factors, aging and population growth, such as persistent stress, being overweight, lack of physical exercise, harmful consumption of alcohol and unhealthy diet 9. The harmful effects of hypertension are complicated, including the in...

Development and validation of a nomogram to better predict hypertension based on a 10-year retrospective cohort study in China

“…Two studies mentioned above were carried out either in a single rural area or urban area. Another predicted model for hypertension based on a large cross-sectional study was established recently ( Ren et al, 2020 ). In spite of a large group of people (73,158 samples), the prediction performances of the model were simply assessed by probability of disease (POD) index and AUC values (76.52% in the train set and 75.81% in the test set).…”

Establishment and Evaluation of a Risk-prediction Model for HBP in Elderly Patients With NAFLD From a Health Management Perspective

“…An increase in UA implies an increased risk of future cardiovascular disease [21,45] . In a cross-sectional study of 33,570 patients with HBP by Ren et al, the established risk-prediction model included variables such as Gender, Age, Height, Weight, and UA [17] . Meanwhile, Rosa et al proposed that UA is an important NAFLD predictor variable [46] .…”

“…This study also found that SBP was one of the main predictors of concurrent HBP and NAFLD, and there is a correlation between BMI and HBP [34,35] . The results of an HBP prediction model study based on 135,715 individuals showed that a lower BMI reduced the risk of developing HBP [17] . Among patients with HBP in the United States, 49.5% are obese (BMI > 28) [36] .…”

“…However, most CPMs are established in developed countries, with only a few in developing and underdeveloped countries. Through a literature review, we found few reports on the establishment of the CPMs for HBP in China, and the CPMs in different regions are signi cantly different [7,16,17,18,19] . Therefore, it is of great practical value to establish CPMs for different regions and populations in China and propose health management strategies based on the research ndings.…”

Risk factors and Interaction analysis of Hypertension in People who were Overweight and obesity:Based on Machine Learning and Data Mining

“…interaction between risk factors, which were mainly carried out in the general population, and there were few relevant studies in people who were Overweight and obesity. Recently, Ren Z [29] and others have discussed the prediction model and independent risk factors of hypertension in Central China. Chen y [30] and others analyzed the population in northern China and constructed the corresponding hypertension prediction model.…”