Association of hyperuricemia and hypertension phenotypes in hypertensive patients without uric acid lowering treatment

Quantified Metabolomics and Lipidomics Profiles Reveal Serum Metabolic Alterations and Distinguished Metabolites of Seven Chronic Metabolic Diseases

The co-occurrence of multiple chronic metabolic diseases is highly prevalent, posing a huge health threat. Clarifying the metabolic associations between them, as well as identifying metabolites which allow discrimination between diseases, will provide new biological insights into their co-occurrence. Herein, we utilized targeted serum metabolomics and lipidomics covering over 700 metabolites to characterize metabolic alterations and associations related to seven chronic metabolic diseases (obesity, hypertension, hyperuricemia, hyperglycemia, hypercholesterolemia, hypertriglyceridemia, fatty liver) from 1626 participants. We identified 454 metabolites were shared among at least two chronic metabolic diseases, accounting for 73.3% of all 619 significant metabolite-disease associations. We found amino acids, lactic acid, 2-hydroxybutyric acid, triacylglycerols (TGs), and diacylglycerols (DGs) showed connectivity across multiple chronic metabolic diseases. Many carnitines were specifically associated with hyperuricemia. The hypercholesterolemia group showed obvious lipid metabolism disorder. Using logistic regression models, we further identified distinguished metabolites of seven chronic metabolic diseases, which exhibited satisfactory area under curve (AUC) values ranging from 0.848 to 1 in discovery and validation sets. Overall, quantitative metabolome and lipidome data sets revealed widespread and interconnected metabolic disorders among seven chronic metabolic diseases. The distinguished metabolites are useful for diagnosing chronic metabolic diseases and provide a reference value for further clinical intervention and management based on metabolomics strategy.

A Study of Lipid Profile and the Correlation of Serum Uric Acid Levels in Patients With Hypertension

Aim We examine the lipid profile and correlation of serum uric acid (SUA) levels in cases of hypertension and normotensives. Methods The current observational study spanned between April 2022 and April 2024. Throughout the research, 200 patients were examined; 100 of these patients were classified as Stage 1 or Stage 2 hypertensive (as per the seventh report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure), while the other 100 served as controls, meaning they did not have hypertension or any other medical condition that could lead to elevated SUA levels. Results It was revealed that the proportion of hypertension was higher in males compared to females. Of the total male patients, most (41.1%) patients had grade 1 hypertension and grade 2 hypertension, while among females, 20% had grade 1 hypertension. It was seen that as age increases, systolic blood pressure (SBP) and diastolic blood pressure (DBP) also rise among the two study groups, although the correlation was not statistically significant between blood pressure level and age of study subjects. The hypertensive patients have increased SBP and DBP levels when compared to the control group, which is significant. The lipid profile shows that the hypertensive subjects had significantly higher mean low-density lipoprotein (LDL), very low-density lipoprotein (VLDL), and triglyceride levels than controls. SUA levels were observed to be elevated in the hypertensive subjects implying a positive correlation between the level of uric acid and blood pressures. Conclusion We found evidence that hyperuricemia and hypertension go hand in hand. A statistically noteworthy positive connection was found between the systolic blood pressures and lipid profiles of the patients. Hypertensive patients were found to have hyperlipidemia, whereas normotensive controls had normal lipid profiles. Moreover, it was seen that there was a positive correlation between SBP and chronological age in hypertensive cases, although this was statistically not significant.

Relationship between hyperuricemia and the risk of cardiovascular events and chronic kidney disease in both the general population and hypertensive patients: A systematic review and meta-analysis

BACKGROUND

To explore the relationships between hyperuricemia and the risk of cardiovascular diseases (CVD) and chronic kidney disease (CKD) in both the general population and hypertensive patients through meta-analysis.

METHODS AND RESULTS

We systematically searched PubMed, Embase, and Cochrane Library databases from January 2012. The eligibility criteria were predefined, and quality was assessed using the Newcastle-Ottawa Scale (NOS). Stata 15.1 was used for meta-analysis, heterogeneity and sensitivity analysis. Subgroup analysis was used to explore heterogeneity, funnel plots and Egger tests were used to assesse publication bias and applicability. A total of 10,662 studies were retrieved, 45 of which were included in this meta-analysis utilizing a random effects model. Hyperuricemia was significantly associated with an increased risk of new-onset hypertension (RR = 1.36, 95% CI 1.16-1.59; I2 = 98.8%), total CVD (RR = 1.53, 95% CI 1.23-1.89; I2 = 93.7%), stroke (RR = 1.97, 95% CI 1.71-2.26, I2 = 0.0%), coronary heart disease (CHD) (RR = 1.56, 95% CI 1.06-2.30, I2 = 93.3%), and CKD (RR = 1.71, 95% CI 1.56-1.87; I2 = 87.3%). However, subgroup analysis showed no significant associations between hyperuricemia and hypertension in non-Asian populations (RR = 0.88, 95% CI 0.59-1.33), or between hyperuricemia and CVD with a follow-up duration <5 years (RR = 1.26, 95% CI 0.97-1.63). Among hypertensive patients, hyperuricemia was significantly associated with total CVD (RR = 2.32, 95% CI 1.31-4.12, I2 = 90.2%), but not with stroke (RR = 1.48, 95% CI 0.86-2.55; I2 = 90.7%) or CHD (RR = 1.51, 95% CI 0.98-2.33; I2 = 71.7%).

CONCLUSION

Hyperuricemia was significantly associated with an increased risk of new-onset hypertension, total CVD, stroke, CHD, and CKD in the general population. Among hypertensive patients, hyperuricemia was associated with an increased risk of CVD but not stroke or CHD alone.

REGISTRATION NUMBER

CRD42022370692.

DHDIP: An interpretable model for hypertension and hyperlipidemia prediction based on EMR data

BACKGROUND AND OBJECTIVE

Traditional hypertension and hyperlipidemia prediction models suffer from uneven modeling data sources, small sample sizes, and a lack of uniform standards for the index system, resulting in the model failing to fulfill clinical applications. To address this issue, this work will offer DHDIP, an interpretable hypertension and hyperlipidemia prediction model based on EMR data.

METHODS

First, we will select massive high-dimensional, unstructured EMR data as a unified modeling data source, and propose a pre-processing algorithm for EMR data to solve the problem that EMR data cannot be directly processed by machine learning algorithms. Second, a variety of mainstream models such as XGBoost, CatBoost, and RandomForest are selected for modeling, and the best adaptation algorithms are identified by performance comparison. Finally, the SHAP framework was introduced into the DHDIP model, thus identifying the main factors contributing to hypertension and hyperlipidemia, effectively enhancing the interpretability of the model.

RESULTS

The DHDIP model's MSE value is 0.0285, and its LOSS value is 0.0054, both of which are better than previous studies.

CONCLUSION

The model balances performance and interpretability. Multi-objective learning allows for a more thorough analysis and prediction of the condition, which not only lowers the cost of disease prediction but also aids physicians in clinical diagnosis. In addition, the datasets and source code are available from this link: https://github.com/Xiaoyao-Jia/DHDIP.