Reno-protective Effect of Angiotensin Receptor Blocker in Spontaneously Hypertensive Rat Models: A Systematic Review

Background Chronic Kidney Disease (CKD) is highly prevalent among hypertensive population. Previous date from studies and experiments have confirmed the reno-protective effect and tolerability of angiotensin receptor blockers in diabetic nephropathy. However, clinical data are lacking in hypertensive population. Objectives This is the first meta-analysis to evaluate the surrogate outcomes of renal protection with ARBs in hypertensive rodents to serve as a baseline for further studies. Methods The systematic review was conducted following the PRISM checklist. Four different databases were searched including PubMed, EMBASE, ScienceDirect and SCOPUS. No restrictions were applied on dose, duration of follow up or ARBs used. Inclusion was restricted to experimental studies reporting means and SEM, published in English, addressing the PICO question and having a moderate to high quality. 37 total eligible articles were identified and Results: Of 25 reported primary outcomes in comparison to hypertensive untreated controls, 23 studies showed positive results supporting that ARBs induce reduction in proteinuria and/or albuminuria compared to hypertensive untreated controls. Similarly, in comparison to normotensive untreated controls, 9 of 12 studies showed positive results supporting that ARBs’ induced reduction in proteinuria and/or albuminuria can reach levels similar to normotensive controls. For the secondary outcomes, 12 of 18 reported outcomes showed significant improvement in CrCl or reduction in BUN compared to hypertensive untreated rats. Similarly, 10 of 17 outcomes reported showed that the improvement in secondary outcomes reached levels that are comparable to normotensive controls. Conclusion: Qualitative data from this systematic review support that ARBs have a Reno-protective effect in different hypertensive models and the effect is independent of BP lowering.

Predicting stroke risk in Chinese hypertensive population using machine learning

Background Stroke is the leading cause of death in China, and the stroke burden is especially high in rural areas. Risk prediction is essential for primary prevention of stroke. However, uncertainty remains about the optimal methodology for analyzing stroke risk. In this study, we aim to determine the most effective stroke prediction method in a targeted population and establish a general framework and pipeline for future analysis. Purpose 1) to determine the most effective stroke prediction method in a targeted population and 2) to establish a general framework and pipeline for future analysis. Methods Data were obtained from the China Stroke Primary Prevention Trial (CSPPT), a randomized, double-blind, multi-center clinical trial. 20,702 hypertensive patients without prior history of stroke were included in the study. The primary outcome was new nonfatal and fatal stroke (ischemic or hemorrhagic) occurring between baseline and follow-up (a median of 4.2 years). All suspected stroke cases were collected and further validated by the event adjudication committee. We compared two regression models (logistic regression and step wise logistic regression) and two machine learning methods (extreme gradient boosting and random forest). All models were trained using questionnaire data with and without laboratory data, then analyzed and compared. The primary outcome was defined as first stroke. Accuracy, sensitivity, specificity and AUCs (area under receiver operating characteristic curve) were used to assess each model. AUCs were used to evaluate the performance of each analysis method. Results In our data set with 20,702 samples and 127 variables, the highest AUCs (0.775 (0.725–0.826)) were observed with RUS (random under sampling) applied to RF (random forest). Before applying data balancing techniques, all analysis methods showed very low sensitivity (around 0.01), very high accuracy (around 0.97), and very high specificity (around 1.00). The mean AUCs were 0.741 (0.678–0.803). After data balancing techniques were applied, we observed an increase in sensitivity and decreases in accuracy and specificity. Different data balancing techniques had different effects on analysis methods. No significant effect on AUCs was observed; the range of increase and decrease was around 0.01. Similar overall patterns were observed when training with laboratory test data added. The mean AUCs were 0.739 (0.679–0.799) and 0.734 (0.674–9.795) for all models using data with and without laboratory test respectively. The 10 most important variables as determined by the model were selected as stroke risk predictors for all analysis models. Conclusion The most effective stroke prediction method in this Chinese rural hypertensive population is RUS applied to RF. The optimal analysis method and variable selection depends on data-specific features. FUNDunding Acknowledgement Type of funding sources: Public Institution(s). Main funding source(s): Key R&D Projects, Jiangxi [20203BBGL73173] National Key Research and Development Program [2016YFE0205400]

The association of lipid ratios with hyperuricemia in a rural Chinese hypertensive population

Background Current studies support lipid ratios [the total cholesterol (TC)/high-density lipoprotein cholesterol (HDL-C) ratio; the triglyceride (TG)/HDL-C ratio; the low-density lipoprotein cholesterol (LDL -C)/HDL-C ratio; and non-HDL-C] as reliable indicators of cardiovascular disease, stroke, and diabetes. However, whether lipid ratios could serve as markers for hyperuricemia (HUA) remains unclear due to limited research. This study aimed to explore the association between lipid ratios and HUA in hypertensive patients. Methods The data from 14,227 Chinese hypertensive individuals in the study were analyzed. Multiple logistic regression analysis and smooth curve fitting models examined the relationship between lipid ratios and HUA. Results The results showed positive associations between the lipid ratios and HUA (all P < 0.001). Furthermore, lipid ratios were converted from continuous variables to tertiles. Compared to the lowest tertile, the fully adjusted ORs (95 % CI) of the TC/HDL-C ratio, the TG/HDL-C ratio, the LDL-C/HDL-C ratio, and non-HDL-C in the highest tertile were 1.79 (1.62, 1.99), 2.09 (1.88, 2.32), 1.67 (1.51, 1.86), and 1.93 (1.74, 2.13), respectively (all P < 0.001). Conclusions The study suggested that high lipid ratios (TC/HDL-C ratio, TG/HDL-C ratio, LDL-C/HDL-C ratio, and non-HDL-C) are associated with HUA in a Chinese hypertensive population. This study’s findings further expand the scope of the application of lipid ratios. These novel and essential results suggest that lipid ratio profiles might be potential and valuable markers for HUA. Trial registration No. ChiCTR1800017274. Registered July 20, 2018.

The U-Shaped Association of Non-High-Density Lipoprotein Cholesterol Levels With All-Cause and Cardiovascular Mortality Among Patients With Hypertension

Background: Non-high-density lipoprotein cholesterol (non-HDL-C) is a valuable indicator in routine blood lipid tests, but the associations of non-HDL-C with mortality in hypertensive population still remain uncertain.Methods: In the National Health and Nutrition Examination Surveys from 1999 to 2014, participants having hypertension were included and grouped by non-HDL-C levels (&lt;130, 130–159, 160–189, 190–219, and ≥220 mg/dl). Multivariate Cox regression was conducted for calculation of hazard ratios (HR) and 95% confidence interval (CI). To reveal the relationship between non-HDL-C and mortality, Kaplan–Meier survival curves, restricted cubic spline, linear regression, and subgroup analysis were also applied.Results: A total of 12,169 participants (47.52% males, mean age 57.27 ± 15.79 years) were included. During average follow-up of 92.5 months, 1,946 (15.99%) all-cause deaths and 422 (3.47%) cardiovascular deaths occurred. After adjusting for confounders, the association of non-HDL-C with mortality was detected as U-shaped. Threshold values were observed at 158 mg/dl for all-cause mortality and 190 mg/dl as to cardiovascular mortality. Below the threshold, every 10 mg/dl increment in non-HDL-C attributed to relatively low all-cause mortality significantly (HR = 0.94, 95% CI: 0.92–0.96). Above the threshold, non-HDL-C has significant positive associations with both all-cause (HR = 1.03, 95% CI: 1.01–1.05) and cardiovascular mortality (HR = 1.09, 95% CI: 1.05–1.14). For subgroups analysis, similar results were found among participants age &lt;65 years old, non-white population, those were not taking lipid-lowering drugs, and subjects with body mass index (BMI) ≥25 kg/m2.Conclusion: The U-shaped association was detected between non-HDL-C and mortality among hypertensive population.

Association of TyG index and TG/HDL-C ratio with arterial stiffness progression in a non-normotensive population

Background Cross-sectional studies have reported that insulin resistance (IR) is associated with arterial stiffness. However, the relationship between IR and arterial stiffness progression remains unclear. This study aims to evaluate the association of triglyceride glucose (TyG) index and triglyceride to high-density lipoprotein cholesterol (TG/HDL-C) ratio with arterial stiffness progression in a non-normotensive population. Methods A total of 1895 prehypertensive (systolic pressure 120–139 mmHg or diastolic pressure 80–90 mmHg) or hypertensive (systolic pressure ≥ 140 mmHg or diastolic pressure ≥ 90 mmHg or using antihypertensive medication) participants were enrolled in 2013 and 2014, and followed until December 31, 2019. Arterial stiffness progression was measured by brachial-ankle pulse wave velocity (baPWV) change (absolute difference between baseline and last follow-up), baPWV change rate (change divided by following years), and baPWV slope (regression slope between examination year and baPWV). Results During a median follow-up of 4.71 years, we observed an increasing trend of baPWV in the population. There were linear and positive associations of the TyG index and TG/HDL-C ratio with the three baPWV parameters. The difference (95% CI) in baPWV change (cm/s) comparing participants in the highest quartile versus the lowest of TyG index and TG/HDL-C ratio were 129.5 (58.7–200.0) and 133.4 (52.0–214.9), respectively. Similarly, the evaluated baPWV change rates (cm/s/year) were 37.6 (15.3–60.0) and 43.5 (17.8–69.2), while the slopes of baPWV were 30.6 (9.3–51.8) and 33.5 (9.0–58.0). The observed association was stronger in the hypertensive population. Conclusion Our study indicates that the TyG index and TG/HDL-C ratio are significantly associated with arterial stiffness progression in hypertensive population, not in prehypertensive population.