High waist circumference is a risk factor of new‐onset hypertension: Evidence from the China Health and Retirement Longitudinal Study

Risk factors and prediction model for new-onset hypertensive disorders of pregnancy: a retrospective cohort study

Background and aims

Hypertensive disorders of pregnancy (HDP) is a significant cause of maternal and neonatal mortality. This study aims to identify risk factors for new-onset HDP and to develop a prediction model for assessing the risk of new-onset hypertension during pregnancy.

Methods

We included 446 pregnant women without baseline hypertension from Liyang People's Hospital at the first inspection, and they were followed up until delivery. We collected maternal clinical parameters and biomarkers between 16th and 20th weeks of gestation. Logistic regression was used to determine the effect of the risk factors on HDP. For model development, a backward selection algorithm was applied to choose pertinent biomarkers, and predictive models were created based on multiple machine learning methods (generalised linear model, multivariate adaptive regression splines, random forest, and k-nearest neighbours). Model performance was evaluated using the area under the curve.

Results

Out of the 446 participants, 153 developed new-onset HDP. The HDP group exhibited significantly higher baseline body mass index (BMI), weight change, baseline systolic/diastolic blood pressure, and platelet counts than the control group. The increase in baseline BMI, weight change, and baseline systolic and diastolic blood pressure significantly elevated the risk of HDP, with odds ratios and 95% confidence intervals of 1.10 (1.03-1.17), 1.10 (1.05-1.16), 1.04 (1.01-1.08), and 1.10 (1.05-1.14) respectively. Restricted cubic spline showed a linear dose-dependent association of baseline BMI and weight change with the risk of HDP. The random forest-based prediction model showed robust performance with the area under the curve of 0.85 in the training set.

Conclusion

This study establishes a prediction model to evaluate the risk of new-onset HDP, which might facilitate the early diagnosis and management of HDP.

The potential mechanisms underlying the modulating effect of perirenal adipose tissue on hypertension: Physical compression, paracrine, and neurogenic regulation

Hypertension, a prevalent global cardiovascular disease, affects approximately 45.4 % of adults worldwide. Despite advances in therapy, hypertension continues to pose a significant health risk due to inadequate management. It has been established that excessive adiposity contributes majorly to hypertension, accounting for 65 to 75 % of primary cases. Fat depots can be categorised into subcutaneous and visceral adipose tissue based on anatomical and physiological characteristics. The metabolic impact and the risk of hypertension are determined more significantly by visceral fat. Perirenal adipose tissue (PRAT), a viscera enveloping the kidney, is known for its superior vascularisation and abundant innervation. Although traditionally deemed as a mechanical support tissue, recent studies have indicated its contributing potential to hypertension. Hypertensive patients tend to have increased PRAT thickness compared to those without, and there is a positive correlation between PRAT thickness and elevated systolic blood pressure. This review encapsulates the anatomical characteristics and biogenesis of PRAT. We provide an overview of the potential mechanisms where PRAT may modulate blood pressure, including physical compression, paracrine effects, and neurogenic regulation. PRAT has become a promising target for hypertension management, and continuous effort is required to further explore the underlying mechanisms.

Association between waist circumference change after smoking cessation and incidence of hypertension in Korean adults

OBJECTIVES

This study investigates the association between smoking cessation and hypertension incidence, as well as the association between waist circumference change after smoking cessation and hypertension incidence.

STUDY DESIGN

This was a nationwide population-based cohort study.

METHODS

We used the Korean Health Screening Cohort data and included 158,505 participants who had undergone two or more health examinations between 2008 and 2011, with follow-ups throughout 2019. Smoking cessation and waist changes were captured based on difference between first and follow-up screening dates. Hazard ratio (HR) and 95% confidence interval (CI) for hypertension risk were estimated using multivariable Cox proportional hazard regression models.

RESULTS

There were 31,270 cases of hypertension during a median follow-up of 8.50 years. After adjusting for potential confounding factors, HR for hypertension were 1.01 (95% CI: 0.97-1.05), 0.91 (95% CI: 0.87-0.95), and 0.88 (95% CI: 0.85-0.91) for recent quitters, long-term quitters, and non-smokers, respectively, compared with current smokers. HR for hypertension, compared with current smokers, were 0.89 (95% CI: 0.84-0.94), 0.91 (95% CI: 0.85-0.97), and 0.99 (95% CI: 0.91-1.08) for long-term quitters with no waist gain, long-term quitters with waist gain of 0.1-5.0 cm, and long-term quitters with waist gain of ≥5.0 cm, respectively.

CONCLUSIONS

Long-term smoking cessation was significantly associated with decreased risk of hypertension, and long-term smoking cessation with no waist gain or less than 5.0 cm of waist gain was significantly associated with decreased risk of hypertension. However, more than 5.0 cm of waist gain can attenuate the effect of long-term smoking cessation on lowering the risk of hypertension.

Application of machine learning in measurement of ageing and geriatric diseases: a systematic review

BACKGROUND

As the ageing population continues to grow in many countries, the prevalence of geriatric diseases is on the rise. In response, healthcare providers are exploring novel methods to enhance the quality of life for the elderly. Over the last decade, there has been a remarkable surge in the use of machine learning in geriatric diseases and care. Machine learning has emerged as a promising tool for the diagnosis, treatment, and management of these conditions. Hence, our study aims to find out the present state of research in geriatrics and the application of machine learning methods in this area.

METHODS

This systematic review followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and focused on healthy ageing in individuals aged 45 and above, with a specific emphasis on the diseases that commonly occur during this process. The study mainly focused on three areas, that are machine learning, the geriatric population, and diseases. Peer-reviewed articles were searched in the PubMed and Scopus databases with inclusion criteria of population above 45 years, must have used machine learning methods, and availability of full text. To assess the quality of the studies, Joanna Briggs Institute's (JBI) critical appraisal tool was used.

RESULTS

A total of 70 papers were selected from the 120 identified papers after going through title screening, abstract screening, and reference search. Limited research is available on predicting biological or brain age using deep learning and different supervised machine learning methods. Neurodegenerative disorders were found to be the most researched disease, in which Alzheimer's disease was focused the most. Among non-communicable diseases, diabetes mellitus, hypertension, cancer, kidney diseases, and cardiovascular diseases were included, and other rare diseases like oral health-related diseases and bone diseases were also explored in some papers. In terms of the application of machine learning, risk prediction was the most common approach. Half of the studies have used supervised machine learning algorithms, among which logistic regression, random forest, XG Boost were frequently used methods. These machine learning methods were applied to a variety of datasets including population-based surveys, hospital records, and digitally traced data.

CONCLUSION

The review identified a wide range of studies that employed machine learning algorithms to analyse various diseases and datasets. While the application of machine learning in geriatrics and care has been well-explored, there is still room for future development, particularly in validating models across diverse populations and utilizing personalized digital datasets for customized patient-centric care in older populations. Further, we suggest a scope of Machine Learning in generating comparable ageing indices such as successful ageing index.

Hypertension in China: epidemiology and treatment initiatives

The past two to three decades have seen a steady increase in the prevalence of hypertension in China, largely owing to increased life expectancy and lifestyle changes (particularly among individuals aged 35-44 years). Data from the China hypertension survey conducted in 2012-2015 revealed a high prevalence of grade 3 hypertension (systolic blood pressure ≥180 mmHg and diastolic blood pressure ≥110 mmHg) in the general population, which increased with age to up to 5% among individuals aged ≥65 years. The risk profile of patients with hypertension in China has also been a subject of intense study in the past 30 years. Dietary sodium and potassium intake have remained largely the same in China in the past three decades, and salt substitution strategies seem to be effective in reducing blood pressure levels and the risk of cardiovascular events and death. However, the number of individuals with risk factors for hypertension and cardiovascular disease in general, such as physical inactivity and obesity, has increased dramatically in the same period. Moreover, even in patients diagnosed with hypertension, their disease is often poorly managed owing to a lack of patient education and poor treatment compliance. In this Review, we summarize the latest epidemiological data on hypertension in China, discuss the risk factors for hypertension that are specific to this population, and describe several ongoing nationwide hypertension control initiatives that target these risk factors, especially in the low-resource rural setting.

Decomposition analysis of health inequalities between the urban and rural oldest-old populations in China: Evidence from a national survey

The number of Chinese oldest-old (aged 80+) is growing rapidly and some studies have shown that the health status is unequal among older persons in different regions. However, to the best of our knowledge, no study to date has analyzed health inequalities among the oldest-old in urban and rural areas in China. This study therefore aimed to examine the correlation between health inequalities among the oldest-old in urban and rural areas of China. From the 8th wave of the Chinese Longitudinal Health Longevity Survey (CLHLS), we selected 8124 oldest-old participants who met the requirements of the study. Chi-square tests were used to analyze the distribution characteristics of indicators and a logistic model was performed to determine the factors associated with different self-rated health (SRH). The Fairlie model was adopted to decompose the causes and related contributions to health inequality. Our results found that of the Chinese oldest-old, 46.57% were in good health. Urban residents reported significantly better SRH than rural residents (50.17% vs. 45.13%). Variables associated with good and poor SRH had different distribution characteristics. The logistic model suggested that marital status, alcohol consumption, and annual income were important factors underlying the SRH differences. Our decomposition analysis indicated that 76.64% of the SRH differences were caused by observational factors, and validated that the difference in SRH between urban and rural areas was significantly (P<0.05) associated with exercise status (45.44%), annual income (37.64%), social activity status (3.75%), age (-5.27%), and alcohol consumption (-2.66%). Therefore, socioeconomic status and individual lifestyle status were the main factors underlying the health inequality between urban and rural Chinese oldest-old.

Waist Circumference Trajectories in Relation to Blood Pressure and the Risk of Hypertension in Chinese Adults

Central obesity is associated with a higher risk of hypertension. This study aimed to analyze waist circumference (WC) trajectories and discover their association with blood pressure and the risk of hypertension. The data were obtained from the China Health and Nutrition Survey (CHNS), with a sample of 11,885 adults aged 18 or older. Trajectory groups of WC were identified by group-based trajectory modeling. Three trajectory groups were identified in males: "normal-stable group" (group 1), "normal-increase to central obesity group" (group 2), and "central obesity-slight decrease group" (group 3). There were also three identified in females: "normal-increase to central obesity group" (group 1), "normal-stable group" (group 2), and "central obesity-increase group" (group 3). For males, compared with group 1, systolic blood pressure (SBP) and diastolic blood pressure (DBP) increased by 2.47 mmHg and 2.13 mmHg, respectively, in group 2, and by 3.07 mmHg and 2.54 mmHg, respectively, in group 3. The adjusted hazard ratios (HR) and 95% confidence interval (95% CI) of hypertension in groups 2 and 3 were 1.16 (1.06-1.28) and 1.29 (1.10-1.50), respectively. For females, compared with group 2, SBP and DBP increased by 1.69 mmHg and 1.68 mmHg, respectively, in group 1, and by 4.96 mmHg and 2.77 mmHg, respectively, in group 3. The HR and 95% CI of hypertension in groups 2 and 3 were 1.21 (1.07-1.36) and 1.52(1.17-1.99), respectively. We found that the WC trajectory was a risk factor for hypertension and elevated blood pressure independent of basal WC. Increased risk of hypertension was nonlinearly associated with annual WC increase.

Short sleep duration associated with increased risk for new-onset cardiovascular diseases in individuals with metabolic syndromes: Evidence from the China Health and Retirement Longitudinal Study

To explore the impact and risk of short sleep duration (sleep duration < 6 h/night) on new-onset cardiovascular and cerebrovascular diseases (CVDs) in people with metabolic syndromes (Mets), this study used the 2011 baseline and 2015 follow-up data from the China Longitudinal Study of Health and Retirement (CHARLS) to conduct a prospective study of people aged ≥ 45 years in China. A total of 5,530 individuals without pre-existing CVDs in baseline were included. Mets were defined according to the harmonized criteria. We applied the Logistic Regression (LR), the Deep Neural Networks (DNN), and the Adaptive Boosting (AdaBoost), to evaluate the association between Mets components, short sleep, and the risk of new-onset CVDs, and the importance of multiple variates for new-onset CVDs. During the 4-year follow-up period, 512 individuals developed CVDs, and short sleep increased the risk of CVD in individuals with Mets. The odds ratio for prevalent CVD in Mets with short sleep group was 3.73 (95%CI 2.95–4.71; P < 0.001) compared to the normal group, and 1.99 (95% CI 1.58–2.51; P < 0.001) compared to the Mets without short sleep group. The DNN method reached the highest precision of 92.24% and f1-score of 95.86%, and the Adaboost method reached the highest recall of 99.92%. Both DNN and Adaboost have better predictive performance than LR and revealed short sleep duration and components of Mets are all the strongest predictors of CVD onset.

Trends in general and abdominal obesity in US adults: Evidence from the National Health and Nutrition Examination Survey (2001-2018)

Aim

This study investigates the trend in general obesity and abdominal obesity in US adults from 2001 to 2018.

Methods

We included 44,184 adults from the nine cycles of the continuous NHANES (2001-2002, 2003-2004, 2005-2006, 2007-2008, 2009-2010, 2011-2012, 2013-2014, 2015-2016, and 2017-2018). The age-adjusted mean body mass index and waist circumference were calculated, and the sex-specific annual change was estimated by the survey cycle. We used the weighted sex-specific logistic regression models to analyze the prevalence of general obesity and abdominal obesity from 2001 to 2018. The weighted adjusted odds ratio (OR) with a 95% confidence interval (CI) was calculated.

Results

Our study showed that general obesity and abdominal obesity account for about 35.48 and 53.13% of the US population. From 2001-2002 to 2017-2018, the age-adjusted prevalence of general obesity increased from 33.09 to 41.36% in females and from 26.88 to 42.43% in males. During 2001-2018, the age-adjusted prevalence of abdominal obesity increased from 57.58 to 67.33% in females and from 39.07 to 49.73% in males. A significant time-dependent increase was observed in the prevalence of general obesity (adjusted OR, 1.007; 95% CI 1.005-1.009, P < 0.001) and abdominal obesity (adjusted OR, 1.006; 95% CI, 1.004-1.008; P < 0.001).

Conclusion

General obesity and abdominal obesity are a heavy health burden among US adults, and the increasing trend remains in both males and females from 2001 to 2018.

Integrating functional data analysis with case-based reasoning for hypertension prognosis and diagnosis based on real-world electronic health records

BACKGROUND

Hypertension is the fifth chronic disease causing death worldwide. The early prognosis and diagnosis are critical in the hypertension care process. Inspired by human philosophy, CBR is an empirical knowledge reasoning method for early detection and intervention of hypertension by only reusing electronic health records. However, the traditional similarity calculation method often ignores the internal characteristics and potential information of medical examination data.

METHODS

In this paper, we first calculate the weights of input attributes by a random forest algorithm. Then, the risk value of hypertension from each medical examination can be evaluated according to the input data and the attribute weights. By fitting the risk values into a risk curve of hypertension, we calculate the similarity between different community residents, and obtain the most similar case according to the similarity. Finally, the diagnosis and treatment protocol of the new case can be given.

RESULTS

The experiment data comes from the medical examination of Tianqiao Community (Tongling City, Anhui Province, China) from 2012 to 2021. It contains 4143 community residents and 43,676 medical examination records. We first discuss the effect of the influence factor and the decay factor on similarity calculation. Then we evaluate the performance of the proposed FDA-CBR algorithm against the GRA-CBR algorithm and the CS-CBR algorithm. The experimental results demonstrate that the proposed algorithm is highly efficient and accurate.

CONCLUSIONS

The experiment results show that the proposed FDA-CBR algorithm can effectively describe the variation tendency of the risk value and always find the most similar case. The accuracy of FDA-CBR algorithm is higher than GRA-CBR algorithm and CS-CBR algorithm, increasing by 9.94 and 16.41%, respectively.