Journal of Clinical Hypertension最新文献

Cardiovascular diseases (CVDs) remain the leading cause of morbidity and mortality globally. Emerging evidence suggests that inflammation plays a pivotal role in the pathogenesis of atherosclerosis and subsequent cardiovascular events. Traditional treatments primarily focus on lipid-lowering and antithrombotic strategies; however, these approaches do not fully address the inflammatory component of CVD. Recent advancements have highlighted the potential of targeted anti-inflammatory therapies in mitigating cardiovascular risk. This review explores the efficacy and safety of these novel therapeutic agents. Interleukin (IL)-1β inhibitors, such as canakinumab, have shown promising results in reducing recurrent cardiovascular events in post-myocardial infarction patients. By directly modulating inflammatory pathways, canakinumab significantly lowered the incidence of major adverse cardiovascular events (MACE) independent of lipid levels. Similarly, colchicine, an ancient anti-inflammatory drug, has gained renewed interest due to its efficacy in reducing cardiovascular events in patients with chronic coronary disease and recent myocardial infarction. Furthermore, emerging therapies targeting other inflammatory mediators like IL-6 and tumor necrosis factor-α are under investigation, offering additional avenues for intervention. Despite these advancements, challenges such as identifying appropriate patient populations, long-term safety, and cost-effectiveness remain. Ongoing research aims to refine these therapies, ensuring a balance between risk reduction and adverse effects. In conclusion, targeted anti-inflammatory therapy represents a promising adjunct to traditional CVD treatments, potentially revolutionizing the management of cardiovascular events. Future studies are essential to optimize these strategies and fully integrate them into clinical practice, enhancing outcomes for patients with CVD.

This study aims to investigate the impact of exacerbated systemic inflammatory status on the degree of myocardial fibrosis and strain impairment in hypertensive patients with preserved ejection fraction, as well as the role played by epicardial adipose tissue (EAT) in this process. A total of 236 hypertensive patients who underwent cardiovascular magnetic resonance (CMR) and blood routine examinations at two medical centers in China were included. Thirty healthy volunteers were included as the control group. Compared with the low systemic inflammatory response index (SIRI) group, patients in the high SIRI group exhibited greater EAT volume, higher Native T1 value, and increased extracellular volume (ECV) (all p < 0.01). Additionally, significant differences were observed between the two groups in cardiac MRI parameters (all p < 0.001). Hypertensive patients had a significantly higher SIRI than healthy controls (p < 0.001). Binary logistic regression analysis indicated that SIRI and indexed EAT volume were independently associated with high ECV value (SIRI: p < 0.001; indexed EAT volume: p < 0.001), with results remaining stable after adjusting for confounding factors. Furthermore, mediation analysis showed that even after adjusting for confounding factors, EAT continued to play a role in SIRI-mediated changes in ECV (indirect effect: 0.1773 [95% CI 0.0173–0.3147]). HTN may contribute to the increase in systemic inflammatory severity. The relationship between the degree of myocardial fibrosis and the severity of systemic inflammatory status in patients with early HTN is mediated by EAT. Early mitigation of systemic inflammatory status in patients with early-stage HTN can reduce the adverse effects of EAT, thereby alleviating myocardial fibrosis and strain impairment.

Patients with hypertension (HTN) face an increased risk of complications and mortality from influenza; a risk that may be modified by influenza vaccination. There is limited evidence on the effectiveness of high-dose (HD-IIV) compared with standard-dose (SD-IIV) inactivated influenza vaccines in hypertensive individuals. This study, a post hoc analysis of DANFLU-1, a pragmatic, and open-label, individually randomized trial of HD-IIV vs. SD-IIV conducted during the 2021–2022 influenza season among participants aged 65–79 years. Prespecified outcomes in DANFLU-1 included hospitalization for influenza or pneumonia, cardiovascular, cardiorespiratory, and respiratory hospitalizations, all-cause hospitalization, and all-cause mortality. Outcomes were analyzed as both time-to-first and recurrent events. DANFLU-1 randomized 12 477 participants randomized to HD-IIV or SD-IIV, of these 6469 (51.9%) had a history of HTN. HD-IIV was associated with lower hazards for hospitalizations for pneumonia or influenza, respiratory disease, and all-cause mortality compared with SD-IIV and these associations were consistent regardless of HTN status (p_interaction = 0.09, = 0.09, and = 0.59, respectively). HD-IIV was associated with lower incidence rates of recurrent hospitalizations for pneumonia or influenza and all-cause hospitalizations compared with SD-IIV, irrespective of HTN status (p_interaction = 0.09 and = 0.75, respectively). There was evidence of potential effect modification of HD-IIV vs. SD-IIV in relation to recurrent respiratory hospitalizations, where the relative effect may be greater among those without vs. with HTN (p_interaction = 0.04). In conclusion, this post hoc analysis of a large-scale pragmatic trial, HD-IIV was associated with lower risk of clinical outcomes, including hospitalizations for pneumonia or influenza, all-cause mortality, and all-cause hospitalizations irrespective of the presence of HTN.

Trial Registration: ClinicalTrials.gov identifier: NCT05048589

Hypertensive disorders in pregnancy (HDP) are a major cause of maternal and perinatal morbidity and mortality. The impact of HDP on labor stage duration and maternal and neonatal outcomes in nulliparous women remains unclear. To assess labor stage duration and maternal and neonatal outcomes in nulliparous women with HDP. A retrospective cohort of 31 472 singleton pregnancies from January 2018 to December 2023 at the Obstetrics and Gynecology Hospital of Fudan University was analyzed using 1:4 propensity score matching (PSM) and inverse probability of treatment weighting (IPTW). Labor stage durations and maternal and neonatal outcomes were analyzed between women with and without HDP. HDP pregnancies had shorter first and total labor stages but longer second and third stages. The HDP group also had higher oxytocin (OCT) use, reduced fetal distress, intrapartum fever, and increased postpartum hemorrhage. Subgroup analysis by labor onset showed that for spontaneous onset, the HDP group had a significantly shorter first stage and total labor duration, with a significantly longer second and third stage duration. In subgroup analysis by HDP type, among pregnancies with spontaneous onset, those with superimposed preeclampsia on chronic hypertension had the shortest labor duration, followed by gestational hypertension and preeclampsia groups, with chronic hypertension alone showing the longest. Similarly, in the induced labor subgroup, gestational hypertension had the shortest duration, followed by superimposed preeclampsia and preeclampsia, with chronic hypertension again exhibiting the longest. The study indicates HDP affects labor duration, with implications for obstetric policies and childbirth safety.

For over half a century, the scientific community has been trying to optimize the tools to classify the risk of future fatal and non-fatal cardiovascular (CV) disease in the general population as well as in different clinical settings (i.e., diabetes, hypertension, obesity). A milestone in this journey is represented by the Framingham Heart Study begun in 1948, in which factors such as age, gender, cigarette-smoking, blood cholesterol, high-density lipoprotein (HDL) cholesterol, systolic blood pressure (BP), left ventricular hypertrophy (LVH), and diabetes mellitus have been used for the prediction of coronary artery disease (CAD) in a population-based cohort of 5573 participants (53% men) aged 30 to 74 years at baseline [1]. Estimates generated from the Framingham data showed that the 10-year incidence of CAD in a hypothetical 42-year-old adult increased progressively from 5.2% and 2.8% in men and women, respectively, with a single risk factor, to approximately 40% in both sexes with six risk factors.

Starting from the experience of the Framingham study, numerous CV risk prediction models have been developed and validated in recent decades to stratify individuals into various risk categories. The rationale behind CV risk stratification is to identify high-risk patients deserving prompt and more aggressive intervention, thus personalizing the intensity of lifestyle and risk factor management [2, 3]. In this perspective, several risk assessment tools have reached clinical relevance and have been incorporated in the current guidelines for the prevention of CV diseases.

Addressing the issue of CV risk assessment, the International and European guidelines on arterial hypertension underline that hypertension-mediated organ damage (HMOD) is a condition that identifies a high CV risk regardless of BP levels and conventional risk factors [4-6]. Consequently, these guidelines provide, through ad hoc tables and/or figures, incisive information on high CV risk conditions that include cardiac and extracardiac HMOD, warranting BP-lowering treatment. This practical approach has the merit of making the risk stratification algorithm easier and more applicable in everyday clinical practice.

Extending the landscape on the clinical significance of CV risk assessment methods, the study by Palomo-Piñón et al. [7] provides new insights into this area of research, comparing the prevalence of CV risk categories using three validated equations (Globorisk, SCORE2, and PREVENT) and assessing their association with HMOD in adult patients with hypertension. For this purpose, cross-sectional data of 4512 hypertensive patients (mean age 64 years, 61% women, BMI 28.8 kg/m², 38% with type 2 diabetes) from primary care enrolled in the Registry of Arterial Hypertension in Mexico were analyzed. The prevalence of CV risk categories varied widely across three risk equations, and this was also the case

This study aimed to compare the blood pressure–lowering efficacy and safety of different renal denervation (RDN) techniques. We systematically searched PubMed, Ovid, and Embase up to September 4, 2025. The primary outcome was the change in 24 h ambulatory systolic blood pressure from baseline to the end of follow-up. Secondary outcomes included changes in 24 h ambulatory diastolic blood pressure and the incidence of major adverse events. Two reviewers independently conducted study screening, data extraction, and risk of bias assessment. A network meta-analysis, along with sensitivity and subgroup analyses, was performed. Our analysis indicated that both radiofrequency RDN of the main renal artery and branches (RFB-RDN) and ultrasound RDN (US-RDN) were associated with significant reductions in 24 h ambulatory blood pressure, with comparable efficacy between the two approaches, whereas radiofrequency RDN of the main renal artery (RFM-RDN) and alcohol-mediated RDN (ALC-RDN) showed limited efficacy. Compared with sham, US-RDN and RFM-RDN showed trends toward fewer adverse events, whereas RFB-RDN and ALC-RDN exhibited numerically higher risks; however, these differences did not reach statistical significance. Subgroup analyses suggested that hypertension subtype, ethnicity, and baseline blood pressure may influence treatment effects, particularly for RFB-RDN.

While both cardiovascular health (CVH) and urinary albumin-to-creatinine ratio (UACR) are individually associated with mortality, their combined prognostic significance and potential mechanistic interplay in adults with hypertension remain unclear. This cohort study analyzed data from 9154 hypertensive adults in the National Health and Nutrition Examination Survey 2007–2018. CVH was assessed using the American Heart Association's Life's Essential 8 score, and UACR was measured from spot urine samples. Multivariable Cox proportional hazards models, restricted cubic spline analyses, joint exposure modeling, and causal mediation analysis were used to evaluate the independent, combined, and mediating effects of UACR and CVH on all-cause mortality. Both lower CVH scores and higher UACR levels were independently associated with increased mortality. A nonlinear association was observed for each. Individuals with severely elevated UACR and poor CVH had the highest mortality risk (HR = 6.61; 95% CI, 3.72–11.74), while those with normal UACR (<10 mg/g) showed no significant mortality difference across CVH strata. Notably, even mildly elevated UACR (10–29.9 mg/g), considered within the conventional “normal” range, was associated with significantly increased mortality. Mediation analysis revealed that UACR explained 4.01% (95% CI, 2.83%–6.40%; p < 0.001) of the association between CVH and mortality. This study is the first to demonstrate that UACR not only modifies but also mediates the association between CVH and mortality in individuals with hypertension. These findings underscore the prognostic value of integrating renal and cardiovascular metrics and suggest that even low-grade albuminuria has clinical relevance.

To the Editor:

We thank Bashir et al. for their interest in our article and for the constructive comments. We respond point by point, citing pertinent literature and our own results.

First, we agree that retrospective cohort studies are susceptible to selection bias and unmeasured confounding. To mitigate these risks, we applied strict inclusion and exclusion criteria (e.g., exclusion of any baseline target organ damage [TOD]) and adjusted for established confounders (age, sex, blood pressure, and comorbidities). We explicitly acknowledged in the discussion that a single-center, retrospective design limits control of residual confounding. Even so, we consider our findings valuable preliminary evidence. As noted in our article, future multicenter, prospective, large-scale studies are warranted to validate these findings, and we plan such studies to minimize bias and better assess causality.

Second, regarding TyG-BMI as a surrogate for insulin resistance (IR): we agree that the hyperinsulinemic–euglycemic clamp is the gold standard, but it is impractical for large human cohorts. Consequently, simple non-insulin-based indices are commonly used. A systematic review of the TyG index reported moderate diagnostic accuracy versus the clamp (AUC, 0.59–0.88 across studies) [1]. Evidence also suggests that adding BMI enhances performance [2]. In addition to being a low-cost surrogate that correlates closely with established IR markers, TyG-BMI is associated with increased cardiovascular risk and confers measurable prognostic value for adverse cardiovascular outcomes [3, 4]. Thus, while standardized cut-offs are still evolving, using TyG-BMI as a continuous or stratified predictor is reasonable and has been validated in diverse cohorts. We did not include the clamp or HOMA-IR, but our TyG-BMI findings align with expected metabolic associations. Future work will incorporate direct IR measures where feasible to strengthen validation.

Third, we appreciate the concern that fasting glucose and triglycerides—the components of TyG-BMI—were measured only once at baseline, which may not capture long-term variability. However, many validated cardiovascular risk algorithms (e.g., Framingham [5] and SCORE [6]) are derived from single baseline measurements and maintain robust predictive performance. In our real-world cohort, we deliberately used the first fasting measurement to mirror initial clinical risk stratification, avoid time-dependent bias and reverse causation from post-baseline treatment or behavior change, and maximize comparability given heterogeneous testing intervals in routine care. Nonetheless, we acknowledge this limitation and plan prospective studies with serial measurements and time-updated and trajectory analyses of TyG-BMI to determine whether dynamic changes improve prediction of TOD.

Fourth, regarding anthropometric and lifestyle factors: BMI was incl

Hypertension management requires precise treatment decisions that balance medication efficacy with patient-specific factors. While clinical guidelines exist, physician decision-making often incorporates nuanced experience that remains challenging to quantify. This study aimed to develop and validate a deep learning model capable of simulating hypertension specialists' prescription patterns and predicting subsequent physiological responses using clinical trial data. We designed a dual-block deep neural network (DNN) framework, where one block predicts optimal medication prescriptions and the other forecasts next-day blood pressure (BP) and heart rate (HR). The model was trained simultaneously using a multi-objective approach that captures the relationship between drug selection and physiological outcomes. Training employed the Huber loss function for robustness, and performance was evaluated using mean absolute error (MAE), error variance, and mean relative error (MRE). The model demonstrated high predictive accuracy, with post-medication BP prediction errors consistently below 10 mmHg (MAE = 6.2 ± 1.8 mmHg). Drug dosage predictions showed strong alignment with actual prescriptions (MRE = 0.12%). These results indicate that the DNN framework effectively replicates physician decision-making within clinically acceptable margins. Our findings suggest that deep learning models trained on structured clinical data can accurately simulate hypertension specialists' treatment strategies. This approach may assist in standardizing care, reducing decision variability, and enhancing precision medicine in hypertension management. This study serves as a proof-of-concept investigation, demonstrating the feasibility of our dual-block DNN architecture. While performance on our single-center dataset is encouraging, future multicenter collaborations with larger datasets are essential to validate this approach for clinical decision support.