Investigating the prevalence of hypertension and its associated risk factors in a population-based study: Fasa PERSIAN COHORT data

Medication adherence and blood pressure control in treated hypertensive patients: first follow-up findings from the PREDIcT-HTN study in Northern Bangladesh

INTRODUCTION

Adherence to antihypertensive medication is crucial to control blood pressure (BP) and hypertension management outcomes. In Bangladesh, as in many other countries, poor adherence to medication represents a challenge to effective hypertension management. This study aims to investigate the prevalence and relationship between medication adherence and BP management among hypertensive patients in Bangladesh.

METHODS

The PREDIcT-HTN study in Northern Bangladesh aims to evaluate major adverse clinical events in treated hypertensive patients. The study involves 2643 hypertensive patients from a medical center, with data collected through baseline information and yearly follow-ups until 2025. The first follow-up visit was conducted between January and March 2021. Following the 2020 ISH-global hypertension guideline, patients were classified as having controlled BP, grade-I, or grade-II uncontrolled BP. Patients were divided into three groups (good, moderate, and poor) based on their 9-item Hill-Bone medication adherence scale. A multinomial regression analysis was conducted to identify the association between medication adherence and BP control after adjusting potential confounders.

RESULTS

Analysis of 2276 hypertensive patients (mean age 51.31 ± 11.58 years) revealed that 36.1% had grade-I and 24.2% had grade-II uncontrolled hypertension. Most patients (78%) displayed moderate adherence, and 15% showed poor medication adherence. Certain patient subgroups had higher rates of poor adherence: females (17.1%) compared to males (12.2%), rural residents (22.4%) compared to city-dwellers (12.2%), and newly diagnosed patients (17.2%) compared to those diagnosed 2-5 years earlier (12.6%). Multivariable analysis found a strong association between medication adherence and BP control. Compared to poor adherence, moderate adherence (relative risk ratio (RRR):0.50, 95%CI:0.36-0.68) and good adherence (RRR:0.56, 95%CI:0.35-0.91) were associated with better control. Increasing age, rural living, and uncontrolled hypertension were also linked. Comorbidities worsened BP control, and managing multiple medications contributed to poor adherence and grade-II hypertension in patients.

CONCLUSION

The high prevalence of uncontrolled hypertension in Bangladesh underscores the need for improved treatment strategies. Addressing medication adherence is essential for better BP control, with particular attention needed for women, rural residents, and newly diagnosed individuals. A comprehensive approach is warranted, including strategies to enhance adherence, early diagnosis, personalized treatment, and simplified medication regimens. These efforts align with the UN's 2030 SDGs, emphasizing targeted interventions for equitable healthcare access and outcomes.

Application of machine learning algorithms in predicting new onset hypertension: a study based on the China Health and Nutrition Survey

BACKGROUND

Hypertension is a serious chronic disease that can significantly lead to various cardiovascular diseases, affecting vital organs such as the heart, brain, and kidneys. Our goal is to predict the risk of new onset hypertension using machine learning algorithms and identify the characteristics of patients with new onset hypertension.

METHODS

We analyzed data from the 2011 China Health and Nutrition Survey cohort of individuals who were not hypertensive at baseline and had follow-up results available for prediction by 2015. We tested and evaluated the performance of four traditional machine learning algorithms commonly used in epidemiological studies: Logistic Regression, Support Vector Machine, XGBoost, LightGBM, and two deep learning algorithms: TabNet and AMFormer model. We modeled using 16 and 29 features, respectively. SHAP values were applied to select key features associated with new onset hypertension.

RESULTS

A total of 4,982 participants were included in the analysis, of whom 1,017 developed hypertension during the 4-year follow-up. Among the 16-feature models, Logistic Regression had the highest AUC of 0.784(0.775∼0.806). In the 29-feature prediction models, AMFormer performed the best with an AUC of 0.802(0.795∼0.820), and also scored the highest in MCC (0.417, 95%CI: 0.400∼0.434) and F1 (0.503, 95%CI: 0.484∼0.505) metrics, demonstrating superior overall performance compared to the other models. Additionally, key features selected based on the AMFormer, such as age, province, waist circumference, urban or rural location, education level, employment status, weight, WHR, and BMI, played significant roles.

CONCLUSION

We used the AMFormer model for the first time in predicting new onset hypertension and achieved the best results among the six algorithms tested. Key features associated with new onset hypertension can be determined through this algorithm. The practice of machine learning algorithms can further enhance the predictive efficacy of diseases and identify risk factors for diseases.

A Study on Prevalence and Factors Affecting Hypertension in an Iranian Population: Results from the Fasa Cohort Study

Background

In recent years, hypertension has been one of the most important noncommunicable diseases worldwide. In this context, identifying the predictors of this disease can help health policymakers to reduce its burden. This study aimed to identify some of the most important influential factors of hypertension and present a model to predict this disease in the data from a large sample cohort study.

Methods

The data set included 10,138 people from the baseline phase of the Fasa cohort study during 2014 and 2016. The main outcome under study was having hypertension in the baseline phase of the study according to self-reports or medical examinations. To identify the related factors of hypertension, logistic regression, classification tree, and random forest models were utilized. Statistical analyses were performed in R.

Results

Among the 10,138 people examined, 2819 (27.8%) had hypertension. In the initial screening, 39 variables were regarded as potential indicators of hypertension. After preliminary analysis, 11 variables were recognized as important predictors based on the importance index: history of cardiovascular disease, cardiac disease, waist circumference to height ratio, body mass index, sex, hypertension in a first-degree relative, weight, fatty liver, cardiac disease in a first-degree relative, diabetes in a first-degree relative, and energy intake. The area under the receiving operating characteristic (ROC) curve for predicting hypertension using logistic regression, classification tree, and random forest models was about 72.8%, 73%, and 87.6%, respectively. Also, the accuracy of these models was 65.2%, 67.4% and 77.8%, respectively.

Conclusion

In general, our findings showed that machine learning-based approaches, such as random forest models, outperformed classical methods, such as logistic regression in predicting hypertension. Regarding the rather high prevalence of hypertension in the population under study, there is an urgent need to pay more attention to its indicators for early diagnosis of the patients and reducing the burden of this silent disease in our country.

Sacubitril/valsartan reverses cardiac structure and function in experimental model of hypertension-induced hypertrophic cardiomyopathy

This study evaluated the effect of sacubtril/valsartan on cardiac remodeling, molecular and cellular adaptations in experimental (rat) model of hypertension-induced hypertrophic cardiomyopathy. Thirty Wistar Kyoto rats, 10 healthy (control) and 20 rats with confirmed hypertension-induced hypertrophic cardiomyopathy (HpCM), were used for this study. The HpCM group was further subdivided into untreated and sacubitril/valsartan-treated groups. Myocardial structure and function were assessed using echocardiography, Langendorff's isolated heart experiment, blood sampling and qualitative polymerase chain reaction. Echocardiographic examinations revealed protective effects of sacubitril/valsartan by improving left ventricular internal diameter in systole and diastole and fractional shortening. Additionally, sacubitril/valsartan treatment decreased systolic and diastolic blood pressures in comparison with untreated hypertensive rats. Moreover, sacubitril/valsartan treatment reduced oxidative stress and apoptosis (reduced expression of Bax and Cas9 genes) compared to untreated rats. There was a regular histomorphology of cardiomyocytes, interstitium, and blood vessels in treated rats compared to untreated HpCM rats which expressed hypertrophic cardiomyocytes, with polymorphic nuclei, prominent nucleoli and moderately dilated interstitium. In experimental model of hypertension-induced hypertrophic cardiomyopathy, sacubitril/valsartan treatment led to improved cardiac structure, haemodynamic performance, and reduced oxidative stress and apoptosis. Sacubitril/valsartan thus presents as a potential therapeutic strategy resulted in hypertension-induced hypertrophic cardiomyopathy.

Associations of socioeconomic status and obesity with hypertension in tibetan adults in a Chinese plateau area

BACKGROUND

Previous studies have identified that socioeconomic status (SES) and obesity are associated with hypertension. However, their interaction on hypertension risk has not yet been assessed.

METHODS

The study used data from 6,069 Tibetan residents in Chengguan District in Lhasa, the Chinese Tibetan autonomous region's capital, based on a cohort study conducted from May 2018 to September 2019 in five provinces in southwest China. We used logistic regression models to assess the complex relations of SES and obesity with hypertension.

RESULTS

Compared with individuals of high SES, low and moderate SES were positively associated with high risk of hypertension. SES and obesity have significant additive interaction on hypertension (general obesity by BMI: RERI = 1.33, P < 0.001; abdominal obesity by WC: RERI = 0.76, P < 0.001; abdominal obesity by WHtR: RERI = 0.96, P < 0.001). In people from the low and moderate SES segments, obesity was linked to an increased risk of hypertension, but the correlations were stronger in people from the moderate SES category. Compared with people of high SES and non obese, those with moderate SES and obesity had a higher risk of hypertension, and ORs were 4.38 (2.80, 6.84) for general obesity by BMI, 3.38 (2.05, 5.57) for abdominal obesity by WC, and 3.18 (1.57, 6.42) for abdominal obesity by WHtR.

CONCLUSION

There is an independent and additive interaction effect of obesity and SES on the risk of hypertension. People with obesity, especially those of moderate and low SES, should reduce weight and waist circumference, and pay more attention to blood pressure. Moreover, the government, health administration departments, and society should prioritize improving the socioeconomic status of the Tibetan population and addressing risk factors like obesity.

Medication Beliefs, Cognitive Defusion, and Valued Living in Hypertensive Patients with Varying Medication Adherence

BACKGROUND

One of the most severe problems that patients with Hypertension (HPN) face are lack of medication adherence, which is influenced by psychological factors. Thus, the current survey sought to compare medication beliefs, cognitive defusion and valued living in hypertensive patients with varying medication adherence.

METHOD

A cross-sectional study with 162 HPN patients from three clinics at Isfahan University of Medical Sciences was conducted in 2019. Participants completed the BMQ (Beliefs about Medicines Questionnaire), MMAS (Morisky Medication Adherence Scale), CFQ (Cognitive Fusion Questionnaire), and VLQ (Valued Living Questionnaire). The data were analyzed using descriptive statistics, chi-square, and analysis of variance (ANOVA).

RESULTS

Only 22.2% of patients scored high in medication adherence (MA). MA levels increased with age in a significant correlation (P=0.03). ANOVA results revealed that the three MA levels (low, medium, and high) had substantial differences in both VLQ subscales (importance of person-valued living and allotted time for values; P=0.002 and P=0.023). However, no significant differences in MA levels were found in the CFQ (cognitive defusion and cognitive fusion) and BMQ subscales (specific necessity, specific concern, general overuse, and general harm).

CONCLUSIONS

This study discovered that a higher MA is associated with increasing age. In addition, patients with HPN who value living and devote more time to their values have higher MA.

Can anthropometric indices predict the chance of hypertension? A multicentre cross-sectional study in Iran

OBJECTIVES

This study aims to assess the prevalence of hypertension (HTN), and determine the relationship between HTN and anthropometric indices including fat distribution, body mass index (BMI), waist-to-hip ratio (WHR) and waist-to-height ratio (WHtR) in Shiraz Heart Study.

SETTING

In this cross-sectional study, subjects were enrolled in 25 clinics in Shiraz. I.R. Iran between 2019 and 2021.

PARTICIPANTS

A total number of 7225 individuals were selected, aged between 40 and 70 years of whom 52.3% were female. Among the people living in Shiraz, individuals living far from clinics, cases of mental or physical disabilitiy and documented cardiovascular diseases were excluded.

PRIMARY AND SECONDARY OUTCOME MEASURES

Primary outcome: The association of body composition, WHR, WHtR and BMI with HTN.

SECONDARY OUTCOME

The sensitivity and specificity of the WHtR for the prediction of HTN.

RESULTS

HTN prevalence was 19.3%. Obesity prevalence was estimated to be 28.5%. WHR and lean body mass showed a significant association with HTN (p<0.05). Receiver operating characteristic for WHtR yielded an area under the curve of 0.62 (95% CI 0.61 to 0.64) and 0.63 (95% CI 0.62 to 0.65) for males and females, respectively. The optimal threshold value yielded 0.54 in men and 0.61 in women. The sensitivity was 72.3% and 73.9% in women and men, with specificity of 48.4% and 44.3%, respectively.

CONCLUSION

HTN had a meaningful association with all the noted anthropometric indices. WHtR performed well as a predictor of HTN.

The AGT epistasis pattern proposed a novel role for ZBED9 in regulating blood pressure: Tehran Cardiometabolic genetic study (TCGS)

INTRODUCTION

High blood pressure is widely regarded as the most important risk factor for cardiovascular diseases. Epistasis analysis may provide additional insight into the genetic basis of hypertension.

METHODS

A nested case-control design was used on 4214 unrelated Tehran Cardiometabolic Genetic Study (TCGS) adults to evaluate 65 SNPs of previously associated genes, including ZBED9, AGT, and TNXB. The integrated effect of each gene was determined using the Sequence-based Kernel Association Test (SKAT). We used model-based multifactor dimension reduction (Mb-MDR) and entropy-based gene-gene interaction (IGENT) methods to determine interaction and epistasis patterns.

RESULTS

The integrated effect of each gene has a statistically significant association with blood pressure traits (P-value < 0.05). The single-locus analysis identified two missense variants in ZBED9 (rs450630) and AGT (rs4762) associated with hypertension. In the ZBED9 gene, significant local interactions were discovered. The G allele in rs450630 showed an antagonistic effect on hypertension, but interestingly, IGENT analysis revealed significant epistasis effects for different combinations of ZBED9, AGT, and TNXB loci.

CONCLUSION

We discovered a novel interaction effect between a significant variant in an essential gene for hypertension (AGT) and a missense variant in ZBED9, which has shifted our focus to ZBED9's role in blood pressure regulation.