Individual and joint effects of borderline ankle-brachial index and high plasma total homocysteine on all-cause death in hypertensive adults.

BACKGROUND: The cardiovascular hazards of total homocysteine (tHcy) are long known. In addition, despite the acknowledgment on the importance of low ankle-brachial index (ABI) (< 0.9), borderline ABI (0.91-0.99) was once commonly overlooked. This study aims to explore the independent and joint effect of tHcy level and borderline ABI on all-cause death in hypertensive population. METHODS: This study included 10,538 participants from China H-type Hypertension Registry Study. ABI was described into two groups: normal ABI (1.00-1.40) and borderline ABI. tHcy level was also divided into two groups: < 15.02 and ≥ 15.02 µmo/L. Four groups were analyzed, using COX proportional hazard regression model, separately and pairwise to observe the independent and joint effect on all-cause death. RESULTS: A total of 126 (1.2%) deaths were observed in the 1.7 years follow-up time. Borderline ABI has a higher predicted risk of death than normal ABI (HR = 1.87, 95%CI: 1.17-3.00) after adjusting for potential covariates. Compare with tHcy level < 15.02 µmo/L (low tHcy), those with tHcy ≥ 15.02 µmo/L (high tHcy) had higher risk to event outcome (HR = 1.99, 95% CI: 1.30-3.05). According to the cumulative hazard curve, group with borderline ABI and high tHcy level has significantly higher altitude and larger increasing rate over follow-up period compare to other groups. Among those with borderline ABI, participants with high tHcy had higher death risk than those with low tHcy, nevertheless, no significant different between borderline and normal ABI among those with low tHcy levels. CONCLUSIONS: Borderline ABI and tHcy level both have independent predictive value on all-cause death. The combined group of borderline ABI and high tHcy has highest risk factor of outcomes, which suggested the mutual additive value of borderline ABI and tHcy. More attention should be given to the importance of borderline ABI in hypertensive population, especially with elevated tHcy level.

Novel Insights on Establishing Machine Learning-Based Stroke Prediction Models Among Hypertensive Adults.

Background: Stroke is a major global health burden, and risk prediction is essential for the primary prevention of stroke. However, uncertainty remains about the optimal prediction model for analyzing stroke risk. In this study, we aim to determine the most effective stroke prediction method in a Chinese hypertensive population using machine learning and establish a general methodological pipeline for future analysis. Methods: The training set included 70% of data (n = 14,491) from the China Stroke Primary Prevention Trial (CSPPT). Internal validation was processed with the rest 30% of CSPPT data (n = 6,211), and external validation was conducted using a nested case-control (NCC) dataset (n = 2,568). The primary outcome was the first stroke. Four received analysis methods were processed and compared: logistic regression (LR), stepwise logistic regression (SLR), extreme gradient boosting (XGBoost), and random forest (RF). Population characteristic data with inclusion and exclusion of laboratory variables were separately analyzed. Accuracy, sensitivity, specificity, kappa, and area under receiver operating characteristic curves (AUCs) were used to make model assessments with AUCs the top concern. Data balancing techniques, including random under-sampling (RUS) and synthetic minority over-sampling technique (SMOTE), were applied to process this unbalanced training set. Results: The best model performance was observed in RUS-applied RF model with laboratory variables. Compared with null models (sensitivity = 0, specificity = 100, and mean AUCs = 0.643), data balancing techniques improved overall performance with RUS, demonstrating a more satisfactory effect in the current study (RUS: sensitivity = 63.9; specificity = 53.7; and mean AUCs = 0.624. Adding laboratory variables improved the performance of analysis methods. All results were reconfirmed in validation sets. The top 10 important variables were determined by the analysis method with the best performance. Conclusion: Among the tested methods, the most effective stroke prediction model in targeted population is RUS-applied RF. From the insights, the current study revealed, we provided general frameworks for building machine learning-based prediction models.

Visceral adiposity index is associated with arterial stiffness in hypertensive adults with normal-weight: the china H-type hypertension registry study.

BACKGROUND: Limited information is available on arterial stiffness risk among hypertensive patients with metabolically abnormal but normal weight. Visceral adiposity index (VAI) is a novel indicator for visceral fat mass and metabolism, however, whether can be used to assessed arterial stiffness in a normal-weight population remains unclear. The goal of this study was to examine the independent association of VAI with arterial stiffness in normal-weight hypertensive patients. METHODS: 3258 participants recruited from the China H-type Hypertension Registry Study. VAI value was calculated using sex-specific equations. High arterial stiffness was defined as baPWV ≥ 18 m/s. Multivariable regression analysis was used to identify the association of VAI with baPWV and high arterial stiffness. RESULTS: Of participants, 50.5% (1644) were males, the mean age was 65.5 (SD, 9.1) years. Mean VAI and baPWV were 2.0 (SD, 2.3) and 18.2 (SD, 3.9) m/s, respectively. For each unit increase of lg VAI in multivariable regression analysis, there was a 1.05 m/s increase in baPWV (95% CI 0.67, 1.43) and a 2.13-fold increase in the risk of high arterial stiffness (95% CI 1.59, 2.86). In all models, the VAI was consistently and significantly associated with baPWV after adjustment for different confounders. High VAI levels were stably associated with baPWV in all subgroups. CONCLUSIONS: We found positive association of VAI with baPWV and high arterial stiffness in normal-weight adults with hypertension. The establishment of this association could help the arterial stiffness risk stratification in normal-weight hypertensive populations, who are frequently overlooked in preventing cardiovascular disease.

A Review of Novel Cardiac Biomarkers in Acute or Chronic Cardiovascular Diseases: The Role of Soluble ST2 (sST2), Lipoprotein-Associated Phospholipase A2 (Lp-PLA2), Myeloperoxidase (MPO), and Procalcitonin (PCT).

While the received traditional predictors are still the mainstay in the diagnosis and prognosis of CVD events, increasing studies have focused on exploring the ancillary effect of biomarkers for the aspiring of precision. Under which circumstances, soluble ST2 (sST2), lipoprotein-associated phospholipase A2 (Lp-PLA2), myeloperoxidase (MPO), and procalcitonin (PCT) have recently emerged as promising markers in the field of both acute and chronic cardiovascular diseases. Existent clinical studies have demonstrated the significant associations between these markers with various CVD outcomes, which further verified the potentiality of markers in helping risk stratification and diagnostic and therapeutic work-up of patients. The current review article is aimed at illuminating the applicability of these four novels and often neglected cardiac biomarkers in common clinical scenarios, including acute myocardial infarction, acute heart failure, and chronic heart failure, especially in the emergency department. By thorough classification, combination, and discussion of biomarkers with clinical and instrumental evaluation, we hope the current study can provide insights into biomarkers and draw more attention to their importance.

High Estimated Glomerular Filtration Rate Is Associated With Worse Cognitive Performance in the Hypertensive Population: Results From the China H-Type Hypertension Registry Study.

BACKGROUND: Increasing studies have focused on the predictive value of high estimated glomerular filtration rate (eGFR) on cardiovascular diseases and mortality; however, the association between high eGFR with cognitive function is still not established. Thus, this study aimed to determine the co-relationship between high eGFR and cognitive performance in the hypertensive population. METHODS: We conducted a baseline cross-sectional study using data from the China H-type Hypertension Registry study. Mini-Mental State Examination (MMSE) assessment was performed to evaluate the cognitive function scale, and serum creatinine was collected to estimate eGFR level. Different MMSE cutoff values were applied in participants with the various educational background to define dementia: <24 in participants with secondary school and above education setting, <20 in those with primary school, and <17 in illiterate participants. RESULTS: A total of 9,527 hypertensive adults with mean age 63.7 ± 9.8 years and 67% female gender were analyzed. The eGFR cutoff value of 71.52 ml/min/1.73 m2 was found after adjusting for potential covariates in a threshold effect analysis. The MMSE increased significantly with the increment of eGFR (ß, 0.27; 95% CI: 0.12-0.41) in participants with eGFR < 71.52 ml/min/1.73 m2 and decreased (ß, -0.28; 95% CI: -0.39 to -0.17) in participants with eGFR ≥ 71.52 ml/min/1.73 m2. Individuals with eGFR ≥ 85 ml/min/1.73 m2 have an elevated risk of cognitive impairment than those with eGFR of 65-75 ml/min/1.73 m2. Subgroup analysis showed that a greater reduction degree of MMSE was observed in female individuals and those who had body mass index (BMI) ≥ 24 kg/m2 among participants with eGFR ≥ 71.52 ml/min/1.73 m2. CONCLUSION: Our findings observed an inverted U-shaped relationship between eGFR and cognitive function. Both the low and high levels of eGFR were independently associated with worse cognitive assessment in the hypertensive population.