The association between bilirubin and hypertension among a Chinese ageing cohort: a prospective follow-up study

Association of barium deficiency with Type 2 diabetes mellitus incident risk was mediated by mitochondrial DNA copy number (mtDNA-CN): a follow-up study

Accumulating evidence indicates that plasma metal levels may be associated with Type 2 diabetes mellitus (T2DM) incident risk. Mitochondrial function such as mitochondrial DNA copy number (mtDNA-CN) might be linked to metal exposure and physiological metabolism. Mediation analysis was conducted to determine the mediating roles of mtDNA-CN in the association between plasma metals and diabetes risk. In the present study, we investigated associations between plasma metals levels, mtDNA-CN, and T2DM incident in the elderly population with a 6-year follow-up (two times) study. Ten plasma metals [i.e. manganese, aluminum, calcium, iron, barium (Ba), arsenic, copper, selenium, titanium, and strontium] were measured using inductively coupled plasma mass spectrometry. mtDNA-CN was measured by real-time polymerase chain reaction. Multivariable linear regression and logistic regression analyses were carried out to estimate the relationship between plasma metal concentrations, mtDNA-CN, and T2DM incident risk in the current work. Plasma Ba deficiency and mtDNA-CN decline were associated with T2DM incident risk during the aging process. Meanwhile, plasma Ba was found to be positively associated with mtDNA-CN. Mitochondrial function mtDNA-CN demonstrated mediating effects in the association between plasma Ba deficiency and T2DM incident risk, and 49.8% of the association was mediated by mtDNA-CN. These findings extend the knowledge of T2DM incident risk factors and highlight the point that mtDNA-CN may be linked to plasma metal elements and T2DM incident risk.

Metabolic shifts during coffee consumption refresh the immune response: insight from comprehensive multiomics analysis

Coffee, a widely consumed beverage, has shown benefits for human health but lacks sufficient basic and clinical evidence to fully understand its impacts and mechanisms. Here, we conducted a cross-sectional observational study of coffee consumption and a 1-month clinical trial in humans. We found that coffee consumption significantly reshaped the immune system and metabolism, including reduced levels of inflammatory factors and a reduced frequency of senescent T cells. The frequency of senescent T cells and the levels of the senescence-associated secretory phenotype were lower in both long-term coffee consumers and new coffee consumers than in coffee nondrinking subjects, suggesting that coffee has anti-immunosenescence effects. Moreover, coffee consumption downregulated the activities of the The Janus kinase/signal transduction and activator of transcription (JAK/STAT) and mitogen-activated protein kinases (MAPK) signaling pathways and reduced systemic proinflammatory cytokine levels. Mechanistically, coffee-associated metabolites, such as 1-methylxanthine, 3-methylxanthine, paraxanthine, and ceramide, reduced the frequency of senescent CD4+CD57+ T cells in vitro. Finally, in vivo, coffee intake alleviated inflammation and immunosenescence in imiquimod-induced psoriasis-like mice. Our results provide novel evidence of the anti-inflammatory and anti-immunosenescence effects of coffee, suggesting that coffee consumption could be considered a healthy habit.

Machine learning-based models to predict the conversion of normal blood pressure to hypertension within 5-year follow-up

BACKGROUND

Factors contributing to the development of hypertension exhibit significant variations across countries and regions. Our objective was to predict individuals at risk of developing hypertension within a 5-year period in a rural Middle Eastern area.

METHODS

This longitudinal study utilized data from the Fasa Adults Cohort Study (FACS). The study initially included 10,118 participants aged 35-70 years in rural districts of Fasa, Iran, with a follow-up of 3,000 participants after 5 years using random sampling. A total of 160 variables were included in the machine learning (ML) models, and feature scaling and one-hot encoding were employed for data processing. Ten supervised ML algorithms were utilized, namely logistic regression (LR), support vector machine (SVM), random forest (RF), Gaussian naive Bayes (GNB), linear discriminant analysis (LDA), k-nearest neighbors (KNN), gradient boosting machine (GBM), extreme gradient boosting (XGB), cat boost (CAT), and light gradient boosting machine (LGBM). Hyperparameter tuning was performed using various combinations of hyperparameters to identify the optimal model. Synthetic Minority Over-sampling Technology (SMOTE) was used to balance the training data, and feature selection was conducted using SHapley Additive exPlanations (SHAP).

RESULTS

Out of 2,288 participants who met the criteria, 251 individuals (10.9%) were diagnosed with new hypertension. The LGBM model (determined to be the optimal model) with the top 30 features achieved an AUC of 0.67, an f1-score of 0.23, and an AUC-PR of 0.26. The top three predictors of hypertension were baseline systolic blood pressure (SBP), gender, and waist-to-hip ratio (WHR), with AUCs of 0.66, 0.58, and 0.63, respectively. Hematuria in urine tests and family history of hypertension ranked fourth and fifth.

CONCLUSION

ML models have the potential to be valuable decision-making tools in evaluating the need for early lifestyle modification or medical intervention in individuals at risk of developing hypertension.

Long-term ozone exposure is negatively associated with estimated glomerular filtration rate in Chinese middle-aged and elderly adults

Chronic kidney disease (CKD) is an inflammatory disease characterized by the deterioration of renal function, which imposes a significant burden on the healthcare system. In the recent decades, the ageing of the population and the increase of ozone pollution have accelerated. However, epidemiological associations between long-term ozone exposure and renal function in susceptible populations are understudied. In this study, we aimed to investigate the association of 1 y ozone exposure with renal function among the older adults in Xiamen City, China. We recruited 6024 eligible participants with a median age of 65.00 years, estimated their ozone exposure data, and collected questionnaires on demographic status and lifestyle factors as well as information on healthcare access. A generalized linear model was used to assess the association. An increase of 10 μg/m3 of 1 y ozone exposure was negatively associated with the estimated glomerular filtration rate (eGFR) [-3.12 (95% CI: -4.76, -1.48)]. The associations were stronger in men, non-smokers, and those with hypertension or T2DM. Clinical indicators of high-density lipoprotein, low-density lipoprotein, triglycerides, and total cholesterol were the main mediators to regulate the ozone-renal function association. Our results suggested that long-term ozone exposure is a potential risk factor for renal function in Chinese middle-aged and elderly adults.

Development of a convenient and effective hypertension risk prediction model and exploration of the relationship between Serum Ferritin and Hypertension Risk: a study based on NHANES 2017-March 2020

Background

Hypertension is a major public health problem, and its resulting other cardiovascular diseases are the leading cause of death worldwide. In this study, we constructed a convenient and high-performance hypertension risk prediction model to assist in clinical diagnosis and explore other important influencing factors.

Methods

We included 8,073 people from NHANES (2017-March 2020), using their 120 features to form the original dataset. After data pre-processing, we removed several redundant features through LASSO regression and correlation analysis. Thirteen commonly used machine learning methods were used to construct prediction models, and then, the methods with better performance were coupled with recursive feature elimination to determine the optimal feature subset. After data balancing through SMOTE, we integrated these better-performing learners to construct a fusion model based for predicting hypertension risk on stacking strategy. In addition, to explore the relationship between serum ferritin and the risk of hypertension, we performed a univariate analysis and divided it into four level groups (Q1 to Q4) by quartiles, with the lowest level group (Q1) as the reference, and performed multiple logistic regression analysis and trend analysis.

Results

The optimal feature subsets were: age, BMI, waist, SBP, DBP, Cre, UACR, serum ferritin, HbA1C, and doctors recommend reducing salt intake. Compared to other machine learning models, the constructed fusion model showed better predictive performance with precision, accuracy, recall, F1 value and AUC of 0.871, 0.873, 0.871, 0.869 and 0.966, respectively. For the analysis of the relationship between serum ferritin and hypertension, after controlling for all co-variates, OR and 95% CI from Q2 to Q4, compared to Q1, were 1.396 (1.176-1.658), 1.499 (1.254-1.791), and 1.645 (1.360-1.989), respectively, with P < 0.01 and P for trend <0.001.

Conclusion

The hypertension risk prediction model developed in this study is efficient in predicting hypertension with only 10 low-cost and easily accessible features, which is cost-effective in assisting clinical diagnosis. We also found a trend correlation between serum ferritin levels and the risk of hypertension.

Which Comes First, Nonalcoholic Fatty Liver Disease or Arterial Hypertension?

Non-alcoholic fatty liver disease (NAFLD) and arterial hypertension (AH) are widespread noncommunicable diseases in the global population. Since hypertension and NAFLD are diseases associated with metabolic syndrome, they are often comorbid. In fact, many contemporary published studies confirm the association of these diseases with each other, regardless of whether other metabolic factors, such as obesity, dyslipidemia, and type 2 diabetes mellites, are present. This narrative review considers the features of the association between NAFLD and AH, as well as possible pathophysiological mechanisms.

Retrospective Study of Aging and Sex-Specific Risk Factors of COVID-19 with Hypertension in China

Background

Coronavirus disease 2019 (COVID-19) has been a global threat that pushes healthcare to its limits. Hypertension is one of the most common risk factors for cardiovascular complications in COVID-19 and is strongly associated with disease severity and mortality. To date, clinical mechanisms by which hypertension leads to increased risk in COVID-19 are still unclear. Furthermore, additional factors might increase these risks, such as the consideration of age and sex, which are of interest when in search of personalized treatments for hypertensive COVID-19 patients.

Methods

We conducted a retrospective cohort study of 543 COVID-19 patients in seven provinces of China to examine the epidemiological and clinical characteristics of COVID-19 in this population and to determine risk factors of hypertensive COVID-19 patients. We also used univariable and multivariable logistic regression methods to explore the risk factors associated with hypertensive COVID-19 patients in different age and sex subgroups.

Results

Among the enrolled COVID-19 patients, the median age was 47 years (interquartile range (IQR) 34.0–57.0), and 99 patients (18.23%) were over 60 years old. With regard to comorbidities, 91 patients (16.75%) were diagnosed with hypertension, followed by diabetes, coronary disease, and cerebrovascular disease. Of the hypertensive COVID-19 patients, 51 (56.04%) were male. Multivariable analysis showed that old age, comorbid diabetes or coronary heart disease on admission, increased D-dimer, increased glucose, and decreased lymphocyte count were independent risk factors associated with hypertensive COVID-19 patients. Elevated total bilirubin (odds ratio [OR]: 1.014, 95% confidence interval [CI]: 0.23–1.05; p = 0.043) and triglycerides (OR: 1.173, 95% CI: 0.049–1.617; p = 0.007) were found to be associated with elderly hypertensive COVID-19 patients. In addition, we found that decreased lymphocytes, basophil, high-density lipoprotein, and increased fibrinogen and creatinine were related to a higher risk of disease severity in male patients. The most common abnormal clinical findings pertaining to female hypertensive COVID-19 patients were hemoglobin, total bile acid, total protein, and low-density lipoprotein.

Conclusions

Factors associated with increased risk of hypertensive COVID-19 patients were identified. Results to the different age and sex subgroups in our study will allow for better possible personalized care and also provide new insights into specific risk stratification, disease management, and treatment strategies for COVID-19 patients with hypertension in the future.