Association and Risk Factors for Hypertension and Dyslipidemia in Young Adults from Poland

Urinary neonicotinoid exposure and its association with hypertension and dyslipidemia among the elderly: A cross-sectional study in Shenzhen, China

In recent years, neonicotinoids (NEOs) as a new type of insecticide have been increasingly used worldwide, causing significant impacts on human health. This study collected urine samples from 1147 elderly individuals (including 714 in the control group and 433 in the hypertension group) in Shenzhen, China, and detected the concentrations of six types of NEOs and four metabolites of NEOs (mNEOs). The aim of this study is to investigate the association between NEOs exposure and hypertension and dyslipidemia. After measurement, we find that the lowest detection rate (DR) among NEOs is imidacloprid (IMI), at only 39.3%. The NEO with the highest urine median concentration is dinotefuran (DIN) (1.31 μg/L), while the mNEO with the highest median concentration is DM-ACE (2.74 μg/L). Through univariate analysis, we found that DM-THM may promote the development of hypertension, while logistic regression indicated that IMI-OF could be a risk factor for hypertension. As prototypes of these two metabolites, thiamethoxam (THM) and IMI may also be risk factors for hypertension. Linear regression analysis revealed a negative correlation between the concentration of thiamethoxam (THD) and low-density lipoprotein (LDL) level, while DIN was positively correlated with triglyceride (TG) level and negatively correlated with high-density lipoprotein (HDL) level. Mediation effect analysis showed that THD may influence the risk of hypertension in the elderly by affecting LDL level. Based on this study, we believe that exposure to NEOs may increase the risk of hypertension in the elderly population.

Unraveling the link between hypertriglyceridemia, dampness syndrome, and chronic diseases: A comprehensive observational study

To investigate the dampness syndrome score in hypertriglyceridemia and the correlations between hypertriglyceridemia and other chronic diseases and lifestyle factors. Data were retrospectively obtained from individuals who underwent physical examinations at Guangzhou Cadres Health Management Centre from May 2022 to May 2023. t Test, variance analysis, and chi-square test were used to compare the score of dampness syndrome and the prevalence of hypertriglyceridemia among different subgroups. Pearson, Spearman correlation analysis, and regression analysis were used to explore the correlations between hypertriglyceridemia and dampness syndrome, chronic diseases, and lifestyle factors. The prevalence of hypertriglyceridemia was 26.70%. Clinical test index and dampness syndrome score were significant differences between hypertriglyceridemia group and normal group (P < .05). Subgroup analyses as a function of the degree of triglyceridemia indicated that the dampness syndrome score increased with increasing degree of triglyceridemia (P < .05). Correlation analysis showed that hypertriglyceridemia was correlated with dampness syndrome, overweight/obesity, hypertension, diabetes, and other chronic diseases (P < .05). Multivariate logistic regression analysis showed that age, sex, marriage, education level, smoking, drinking, fruit consumption, vegetable consumption, milk and dairy product consumption, dessert or snack consumption, the degree of dampness syndrome, and engagement in exercise were associated with hypertriglyceridemia (P < .05). Hypertriglyceridemia is associated with a variety of chronic diseases and lifestyle factors, and is closely related to dampness syndrome. The score of dampness syndrome can reflect hypertriglyceridemia to a certain extent. It provides more clinical reference for the treatment of hypertriglyceridemia combined with the analysis of dampness syndrome of traditional Chinese medicine.

Predicting dyslipidemia incidence: unleashing machine learning algorithms on Lifestyle Promotion Project data

BACKGROUND

Dyslipidemia, characterized by variations in plasma lipid profiles, poses a global health threat linked to millions of deaths annually.

OBJECTIVES

This study focuses on predicting dyslipidemia incidence using machine learning methods, addressing the crucial need for early identification and intervention.

METHODS

The dataset, derived from the Lifestyle Promotion Project (LPP) in East Azerbaijan Province, Iran, undergoes a comprehensive preprocessing, merging, and null handling process. Target selection involves five distinct dyslipidemia-related variables. Normalization techniques and three feature selection algorithms are applied to enhance predictive modeling.

RESULT

The study results underscore the potential of different machine learning algorithms, specifically multi-layer perceptron neural network (MLP), in reaching higher performance metrics such as accuracy, F1 score, sensitivity and specificity, among other machine learning methods. Among other algorithms, Random Forest also showed remarkable accuracies and outperformed K-Nearest Neighbors (KNN) in metrics like precision, recall, and F1 score. The study's emphasis on feature selection detected meaningful patterns among five target variables related to dyslipidemia, indicating fundamental shared unities among dyslipidemia-related factors. Features such as waist circumference, serum vitamin D, blood pressure, sex, age, diabetes, and physical activity related to dyslipidemia.

CONCLUSION

These results cooperatively highlight the complex nature of dyslipidemia and its connections with numerous factors, strengthening the importance of applying machine learning methods to understand and predict its incidence precisely.

Trends in hypertension prevalence, awareness, treatment, and control in South Korea, 1998-2021: a nationally representative serial study

The impact of the pandemic on hypertension management is unknown, particularly regarding changes in demographic risk factors. We conducted a comprehensive study between 1998 and 2021 on the long-term trends in hypertension prevalence in South Korea, including a comparison of the pre-pandemic and pandemic eras. Data from 1998 to 2021 of 108,687 Korean adults were obtained through a nationwide, large-scale, and serial study. We conducted a weighted complex sampling analysis on the estimates of national prevalence and compared the slope of hypertension prevalence before and during the pandemic to determine the trend dynamics. We included 108,687 participants over 24 years, 1998-2021. While the prevalence of patients with hypertension consistently increased before the pandemic from 25.51% [95% CI: 24.27-26.75] in 1998-2005 to 27.81% [95% CI: 26.97-28.66] in 2016-2019, the increasing slope in hypertension prevalence slowed during the pandemic period (28.07% [95% CI: 26.16-29.98] for 2021; βdiff, -0.012 [-0.023 to 0.000]). Hypertension awareness, treatment, control, and control rates among patients receiving treatment followed similar trends. Compared to the pre-pandemic era, individuals aged 19-59 years or male had significantly increased control rates among the treated patients during the pandemic. This study investigated long-term trends in hypertension prevalence, awareness, treatment, and control among Korean adults. The absence of a reduction in the health indicators associated with hypertension during the pandemic implies that medical services for individuals with hypertension remain unaffected.

[Spatial analysis of hypertension in Peruvian adults, 2022]

Objectives

To perform a spatial analysis of arterial hypertension in the Peruvian adult population to identify geographic patterns with a higher concentration of cases.

Materials and methods

A spatial analysis was conducted using data from the Demographic and Family Health Survey (ENDES) 2022. A sample of 29,422 adults was included, and the global Moran's index and Getis-Ord Gi* analysis were used to evaluate spatial autocorrelation and cluster concentration.

Results

The age-standardized prevalence of arterial hypertension was 19.2%. Clusters with a high concentration of arterial hypertension were observed in departments along the Peruvian coast such as Tumbes, Piura, Lambayeque, La Libertad, Ancash, and Lima, as well as in the northern regions of the Highlands. Clusters were also found in the regions of Loreto and Madre de Dios in the Peruvian jungle.

Conclusions

This study revealed geographic patterns of arterial hypertension in Peru, with a higher concentration of cases along the Peruvian coast and in certain regions of the Highlands and Jungle. These findings highlight the need to develop strategies for the prevention and control of the disease, especially in the areas identified as high-prevalence clusters.