Risk factors of the progression to hypertension and characteristics of natural history during progression: A national cohort study

Comparison of seven anthropometric indexes to predict hypertension plus hyperuricemia among U.S. adults

Purpose

This study aims to compare the association of hypertension plus hyperuricemia (HTN-HUA) with seven anthropometric indexes. These include the atherogenic index of plasma (AIP), lipid accumulation product (LAP), visceral adiposity index (VAI), triglyceride-glucose index (TyG), body roundness index (BRI), a body shape index (ABSI), and the cardiometabolic index (CMI).

Methods

Data was procured from the National Health and Nutrition Examination Survey (NHANES), which recruited a representative population aged 18 years and above to calculate these seven indexes. Logistic regression analysis was employed to delineate their correlation and to compute the odds ratios (OR). Concurrently, receiver operating characteristic (ROC) curves were utilized to evaluate the predictive power of the seven indexes.

Results

A total of 23,478 subjects were included in the study. Among these, 6,537 (27.84%) were patients with HUA alone, 2,015 (8.58%) had HTN alone, and 2,836 (12.08%) had HTN-HUA. The multivariate logistic regression analysis showed that the AIP, LAP, VAI, TyG, BRI, ABSI, and CMI were all significantly associated with concurrent HTN-HUA. The OR for the highest quartile of the seven indexes for HTN-HUA were as follows: AIP was 4.45 (95% CI 3.82-5.18), LAP was 9.52 (95% CI 7.82-11.59), VAI was 4.53 (95% CI 38.9-5.28), TyG was 4.91 (95% CI 4.15-5.80), BRI was 9.08 (95% CI 7.45-11.07), ABSI was 1.71 (95% CI 1.45 -2.02), and CMI was 6.57 (95% CI 5.56-7.76). Notably, LAP and BRI demonstrated significant discriminatory abilities for HTN-HUA, with area under the curve (AUC) values of 0.72 (95% CI 0.71 - 0.73) and 0.73 (95% CI 0.72 - 0.74) respectively.

Conclusion

The AIP, LAP, VAI, TyG, BRI, ABSI, and CMI all show significant correlation with HTN-HUA. Notably, both LAP and BRI demonstrate the capability to differentiate cases of HTN-HUA. Among these, BRI is underscored for its effective, non-invasive nature in predicting HTN-HUA, making it a superior choice for early detection and management strategies.

Trends and influence factors in the prevalence, awareness, treatment, and control of hypertension among US adults from 1999 to 2018

OBJECTIVE

We aimed to describe the trends and influence factors in the prevalence, awareness, treatment, and control of hypertension among US Adults from 1999 to 2018.

METHODS

We utilized data from the National Health and Nutrition Examination Survey (NHANES) spanning ten survey cycles (n = 53,496). Prevalence, awareness, treatment, and control of hypertension were calculated using survey weights. Joinpoint regression and survey-weighted generalized linear models were used to analyze trends and influence factors, respectively.

RESULTS

The estimated prevalence of hypertension increased significantly from 33.53% to 40.58% (AAPC = 0.896, P = 0.002) during 1999-2018 with dropping rate of newly diagnosed hypertension from 8.62% to 4.82% before 2014 (APC = -4.075, P = 0.001), and then rose to 7.51% in 2018 (APC = 12.302, P = 0.126). Despite modest improvements or stability in the awareness, treatment, and control since 1999, the latter two remained inadequate in 2018 at 59.52% and 51.71%. There was an uptrend in the use of angiotensin-converting enzyme inhibitors (from 24.02% to 45.71%) and angiotensin receptor blockers (from 20.22% to 38.38%), and downtrend in β-blocker (from 12.71% to 4.21%). Men were at higher risk of incidence, un-awareness, un-treatment, and un-control for hypertension. Lower income and education were associated with susceptibility to hypertension, while being married was favorable for treatment and control. Optimal health reduced the incidence of hypertension, and increased the awareness and treatment.

CONCLUSION

Although the rate of newly diagnosed hypertension has declined slightly since 2010 in the US, the prevalence of hypertension is increasing, and treatment and control rates remain inadequate. To manage hypertension effectively, we need to focus on screening and prevention for high-risk populations, while advocating for optimal health to improve the burden of hypertension.

Dose-Response Association Between Handgrip Strength and Hypertension: A Longitudinal Study of 76,503 European Older Adults

To examine the prospective dose-response association between handgrip strength and the incidence of hypertension in a representative sample of older European adults. We retrieved data from the Survey of Health, Ageing and Retirement in Europe (SHARE) waves 1, 2, 4, 5, 6, 7, and 8. Handgrip strength was measured and participants reported whether they had a medical diagnosis of hypertension. We assessed the longitudinal dose-response associations of handgrip strength with hypertension using restricted cubic splines. During the follow-up, 27,149 (35.5%) were diagnosed with incident hypertension. At the fully adjusted model, the minimum and optimal dose of handgrip strength for a significant reduction in the risk of hypertension was 28 Kg (HR: 0.92; 95% CI: 0.89-0.96) and 54 Kg (HR: 0.83; 95% CI: 0.78-0.89), respectively. There exists an association between increased handgrip strength and reduced risk of developing hypertension in older European adults.

Predicting the risk of hypertension using machine learning algorithms: A cross sectional study in Ethiopia

BACKGROUND AND OBJECTIVES

Hypertension (HTN), a major global health concern, is a leading cause of cardiovascular disease, premature death and disability, worldwide. It is important to develop an automated system to diagnose HTN at an early stage. Therefore, this study devised a machine learning (ML) system for predicting patients with the risk of developing HTN in Ethiopia.

MATERIALS AND METHODS

The HTN data was taken from Ethiopia, which included 612 respondents with 27 factors. We employed Boruta-based feature selection method to identify the important risk factors of HTN. The four well-known models [logistics regression, artificial neural network, random forest, and extreme gradient boosting (XGB)] were developed to predict HTN patients on the training set using the selected risk factors. The performances of the models were evaluated by accuracy, precision, recall, F1-score, and area under the curve (AUC) on the testing set. Additionally, the SHapley Additive exPlanations (SHAP) method is one of the explainable artificial intelligences (XAI) methods, was used to investigate the associated predictive risk factors of HTN.

RESULTS

The overall prevalence of HTN patients is 21.2%. This study showed that XGB-based model was the most appropriate model for predicting patients with the risk of HTN and achieved the accuracy of 88.81%, precision of 89.62%, recall of 97.04%, F1-score of 93.18%, and AUC of 0. 894. The XBG with SHAP analysis reveal that age, weight, fat, income, body mass index, diabetes mulitas, salt, history of HTN, drinking, and smoking were the associated risk factors of developing HTN.

CONCLUSIONS

The proposed framework provides an effective tool for accurately predicting individuals in Ethiopia who are at risk for developing HTN at an early stage and may help with early prevention and individualized treatment.

Appropriate screening interval to detect the development of chronic metabolic diseases

AIMS

We conducted a cohort study to determine the screening intervals of metabolic disorders.

METHOD

Participants without diabetes mellitus (DM), hypertension (HTN), dyslipidemia, and abdominal obesity who underwent health examinations (2005-2019) in Korea were included. Participants were grouped according to baseline fasting glucose, LDL-C level, blood pressure (BP), and waist circumference (WC). The time to develop metabolic disorders and the percentile of survival time was assessed in each group.

RESULT

The median follow-up duration was 4.94 years (n = 222,413;mean age 37.13 ± 7.49 years). After 8.32(95 %CI 8.22-8.41), 3.01(2.89-3.31), and 1.11(1.03-1.25) years, 10 % of participants developed DM in fasting glucose levels of 100-110, 110-120, and 120-125 mg/dL, respectively. After 8.40(8.33-8.45), 6.33(6.20-6.47), and 1.99(1.97-2.00) years, 10 % developed HTN in BP 120/70, 120/70-130/80, and 130/80-140/90 mmHg, respectively. After 5.99(5.94-6.04), 2.84(2.77-2.90), and 1.36(1.30-1.44) years, 10 % developed dyslipidemia in LDL-C 100-120, 120-140, and 140-160 mg/dL, respectively. After 4.62(4.41-4.80) and 1.67(1.64-1.69) years, 10 % developed abdominal obesity in baseline WC < 80(Women;W)/85(Men;M) and < 85(W)/90(M) cm, respectively.

CONCLUSION

In adults aged 30-40, the screening interval of metabolic disorders should be individualized based on the baseline metabolic derangement. An individual with borderline values may need an annual screening.

A longitudinal analysis of the progression from normal blood pressure to stage 2 hypertension: A 12-year Korean cohort

Background

The study aimed to estimate the incidence of and period of progression to stage 2 hypertension from normal blood pressure.

Methods

We selected a total of 21,172 normotensive individuals between 2003 and 2004 from the National Health Insurance Service-Health Screening and followed them up until 2015. The criteria for blood pressure were based on the American College of Cardiology/American Heart Association 2017 guideline (normal BP: SBP < 120 and DBP < 80 mmHg, elevated BP: SBP 120–129 and DBP < 80 mmHg, stage 1 hypertension: SBP 130–139 or DBP 80–89 mmHg, stage 2 hypertension: SBP ≥140 or DBP ≥ 90 mmHg). We classified the participants into four courses (Course A: normal BP → elevated BP → stage 1 hypertension→ stage 2 hypertension, Course B: normal BP → elevated BP → stage 2 hypertension, Course C: normal BP → stage 1 hypertension → stage 2 hypertension, Course D: normal BP → stage 2 hypertension) according to their progression from normal blood pressure to stage 2 hypertension.

Results

During the median 12.23 years of follow-up period, 52.8% (n= 11,168) and 23.6% (n=5004) of the participants had stage 1 and stage 2 hypertension, respectively. In particular, over 60 years old had a 2.8-fold higher incidence of stage 2 hypertension than 40–49 years old. After the follow-up period, 77.5% (n=3879) of participants with stage 2 hypertension were found to be course C (n= 2378) and D (n=1501). After the follow-up period, 77.5% (n=3879) of participants with stage 2 hypertension were found to be course C (n= 2378) and D (n=1501). The mean years of progression from normal blood pressure to stage 2 hypertension were 8.7±2.6 years (course A), 6.1±2.9 years (course B), 7.5±2.8 years (course C) and 3.2±2.0 years, respectively.

Conclusions

This study found that the incidence of hypertension is associated with the progression at each stage. We suggest that the strategies necessary to prevent progression to stage 2 hypertension need to be set differently for each target course.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-020-10115-7.