Serum creatinine levels, traditional cardiovascular risk factors and 10-year cardiovascular risk in Chinese patients with hypertension

A nonlinear correlation between the serum uric acid to creatinine ratio and the prevalence of hypertension: a large cross-sectional population-based study

OBJECTIVE

To explore the relationship between the serum uric acid to creatinine (UA/Cr) ratio and the prevalence of hypertension.

METHODS

In this cross-sectional study, we included 8571 individuals from the China Health and Nutrition Survey. Logistic regression analysis and restricted cubic spline (RCS) were used to analyze the relationship between the UA/Cr ratio and hypertension.

RESULTS

Compared with individuals without hypertension, individuals with hypertension had higher UA/Cr ratios. Multivariate logistic regression analysis showed that a higher UA/Cr ratio was closely related to a higher risk of hypertension (as a continuous variable, OR: 1.054, 95% CI: 1.014-1.095, p = 0.007; as a categorical variable, Q3 vs. Q1, OR: 1.183, 95% CI: 1.011-1.384, p = 0.035; Q4 vs. Q1, OR: 1.347, 95% CI: 1.146-1.582, p < 0.001). Subgroup analysis revealed that the correlation between the UA/Cr ratio and hypertension risk was stable in all subgroups except for the subgroup with diabetes and the subgroup with a BMI ≥ 28 kg/m2 (p < 0.05). Sensitivity analysis confirmed the robustness of the relationship between a higher UA/Cr ratio and a higher risk of hypertension (p < 0.05). The RCS showed that the UA/Cr ratio was nonlinearly related to hypertension risk. Further threshold effect showed that only a UA/Cr ratio less than 5.0 was related to hypertension risk (OR: 1.178, 95% CI: 1.086-1.278, p < 0.001), and the 2-piecewise linear regression model was superior to the 1-line linear regression model (p < 0.05).

CONCLUSION

The UA/Cr ratio was associated with the prevalence of hypertension.

Association between serum uric acid to serum creatinine ratio with cardiovascular and all-cause mortality in adults with hypertension

The serum uric acid to serum creatinine ratio (SUA/sCr) is a standardized index of renal function. More importance was attached to the significance of this ratio in the progression of hypertension. While the association between the prognosis of hypertension and SUA/sCr is unknown. Therefore, we aimed to prospectively examine the associations of serum uric acid to serum creatinine ratio and all-cause and CVD mortality in adults with hypertension. Participants with hypertension from NHANES 1999-2018 (n = 15,269) were included. They were stratified by 1 increment of SUA/sCr ratio and categorized into 6 groups as ≤ 4, > 4 to 5, > 5 to 6, > 6 to 7, > 7 to 8, and > 8. The reason for categorization in 6 groups was to analyze the influence of different ratios on outcomes accurately and provide more precise guidance. The sample size is large enough that even if divided into 6 groups, it does not affect the statistical power. The primary outcomes were all-cause and CVD mortality. Weighted multivariable Cox proportional hazards regression models were used to estimate hazard ratio (HRs) of mortality. Restricted cubic spline regression models were utilized to examine dose-response associations between the serum uric acid to serum creatinine ratio and all-cause and CVD mortality. Relatively comprehensive stratified analyses were conducted to confirm the accuracy and stability of the results. There were 15,269 total participants, 49.4% of whom were men, with an average age of 56.6 years. Weighted multivariable Cox proportional hazards regression models demonstrated participants in the lowest group (≤ 4) had the HRs (95% CIs) of 1.43 (1.18, 1.73) for all-cause mortality and 2.8 (1.92, 4.10) for CVD mortality when compared to the reference group. Participants in the highest group (> 8) had the HRs (95% CIs) of 0.47 (0.25, 0.89) for CVD mortality when compared to the reference group. There were progressively lower risks for all-cause and CVD mortality with the SUA/sCr ratio increased (both P trend < 0.01). The SUA/sCr ratio was (P for nonlinearity < 0.01) nonlinearly correlated with all-cause mortality, with inflection points of 6.25. In addition, the restricted cubic splines results indicated that the SUA/sCr ratio (P for nonlinearity = 0.32) showed linear and negative associations with cardiovascular mortality with inflection points of 6.54. The inverse associations between SUA/sCr ratio and all-cause mortality were consistent across all subgroups except for the subgroup of eGFR < 45 ml/min/1.73 m2 and never smokers (P trend = 0.20 and 0.13, respectively), and the inverse associations between low SUA/sCr ratio and CVD mortality were consistent across all subgroups (P trend < 0.01). Contrary to previous studies, outcomes suggest that lower SUA/sCr ratio was associated with higher risks of all-cause and CVD mortality in adults with hypertension.

Combining the Strengths of the Explainable Boosting Machine and Metabolomics Approaches for Biomarker Discovery in Acute Myocardial Infarction

Acute Myocardial Infarction (AMI), a common disease that can have serious consequences, occurs when myocardial blood flow stops due to occlusion of the coronary artery. Early and accurate prediction of AMI is critical for rapid prognosis and improved patient outcomes. Metabolomics, the study of small molecules within biological systems, is an effective tool used to discover biomarkers associated with many diseases. This study intended to construct a predictive model for AMI utilizing metabolomics data and an explainable machine learning approach called Explainable Boosting Machines (EBM). The EBM model was trained on a dataset of 102 prognostic metabolites gathered from 99 individuals, including 34 healthy controls and 65 AMI patients. After a comprehensive data preprocessing, 21 metabolites were determined as the candidate predictors to predict AMI. The EBM model displayed satisfactory performance in predicting AMI, with various classification performance metrics. The model's predictions were based on the combined effects of individual metabolites and their interactions. In this context, the results obtained in two different EBM modeling, including both only individual metabolite features and their interaction effects, were discussed. The most important predictors included creatinine, nicotinamide, and isocitrate. These metabolites are involved in different biological activities, such as energy metabolism, DNA repair, and cellular signaling. The results demonstrate the potential of the combination of metabolomics and the EBM model in constructing reliable and interpretable prediction outputs for AMI. The discussed metabolite biomarkers may assist in early diagnosis, risk assessment, and personalized treatment methods for AMI patients. This study successfully developed a pipeline incorporating extensive data preprocessing and the EBM model to identify potential metabolite biomarkers for predicting AMI. The EBM model, with its ability to incorporate interaction terms, demonstrated satisfactory classification performance and revealed significant metabolite interactions that could be valuable in assessing AMI risk. However, the results obtained from this study should be validated with studies to be carried out in larger and well-defined samples.

Machine Learning Approach for Cardiovascular Death Prediction among Nonalcoholic Steatohepatitis (NASH) Liver Transplant Recipients

Cardiovascular disease is the leading cause of mortality among nonalcoholic steatohepatitis (NASH) patients who undergo liver transplants. In the present study, machine learning algorithms were used to identify important risk factors for cardiovascular death and to develop a prediction model. The Standard Transplant Analysis and Research data were gathered from the Organ Procurement and Transplantation Network. After cleaning and preprocessing, the dataset comprised 10,871 patients and 92 features. Recursive feature elimination (RFE) and select from model (SFM) were applied to select relevant features from the dataset and avoid overfitting. Multiple machine learning algorithms, including logistic regression, random forest, decision tree, and XGBoost, were used with RFE and SFM. Additionally, prediction models were developed using a support vector machine, Gaussian naïve Bayes, K-nearest neighbors, random forest, and XGBoost algorithms. Finally, SHapley Additive exPlanations (SHAP) were used to increase interpretability. The findings showed that the best feature selection method was RFE with a random forest estimator, and the most critical features were recipient and donor blood type, body mass index, recipient and donor state of residence, serum creatinine, and year of transplantation. Furthermore, among all the outcomes, the XGBoost model had the highest performance, with an accuracy value of 0.6909 and an area under the curve value of 0.86. The findings also revealed a predictive relationship between features and cardiovascular death after liver transplant among NASH patients. These insights may assist clinical decision-makers in devising strategies to prevent cardiovascular complications in post-liver transplant NASH patients.

Correlation Analysis of NAMPT rs61330082 Polymorphism in Type 2 Diabetes Mellitus Combined with Hypertension

Introduction

This study aimed to investigate the association of Nicotinamide phosphoribosyl transferase (NAMPT) rs61330082 polymorphism with co-morbid hypertension (HTN) and the progression of hypertension in Chinese patients with type 2 diabetes mellitus (T2DM).

Methods

A total of 453 T2DM patients were genotyped for the polymorphism of rs61330082 using SNP-scan high-throughput technology. These patients were divided into T2DM group (261 patients) and T2DM combined with hypertension group (T2MH, 192 patients). The T2MH group was further categorized into Grade I, Grade II, and Grade III based on the results of the Hypertension Grade Score. Peripheral blood plasma urea, plasma creatinine, renin-angiotensin system (RAS) indexes, and lipid biochemistry indexes were measured in patients and analyzed in relation to NAMTP polymorphisms.

Results

We found that the presence of the NAMPT rs61330082-AA genotype was associated with a significantly increased risk of developing higher-grade hypertension in patients with T2MH. In addition, the A allele of the NAMPT rs61330082 gene displayed more associated in developing a higher grade of hypertension compared to the G allele. Also, the level of total cholesterol (TC) and low-density lipoprotein cholesterol (LDL-c) increased with hypertension grade in the NAMPT rs61330082-GG genotype.

Conclusion

NAMPT rs61330082 polymorphism was significantly associated with the progression of hypertension grade in T2MH patients and also affected plasma creatinine and LDL-c levels.

The Relationship Between C-reactive Protein Albumin Ratio and Long-Term Mortality in Patients With Acute Coronary Syndrome

Background Coronary artery disease (CAD) is one of the leading causes of death worldwide. CRP/albumin ratio is a sensitive indicator of inflammatory status. It has been shown that this parameter may be associated with poor short-term outcomes in CAD. In this study, we investigated the relationship between long-term mortality and the CRP/albumin ratio in patients with acute coronary syndromes (ACS). Material and methods This study was conducted on patients who applied to our hospital between January 2015 and December 2019 and were diagnosed with ACS. A total of 1689 patients were included in the study. Patients were divided into two groups according to mortality status, and long-term mortality predictors were investigated. Results The average follow-up period was 38.9±10.3 months. The mean age of the entire study group was 56.6±12.2 years, and 1440 (80.5%) of the patients were male. Comorbid diseases and blood parameters were significant between the two groups. In the regression analysis, creatine, hemoglobin, white blood cell count (WBC), neutrophil-lymphocyte ratio (NLR), and CRP albumin ratio (CAR) were found to be independent predictors. In the ROC analysis, it was observed that CAR had the best predictive value. Discussion An increased CAR level is an independent predictor of long-term mortality in ACS patients. It can be used in both short-term and long-term risk stratification for ACS patients.