Background & Aims: The pathogenesis of hypertension involves a diverse range of genetic, environmental, hemodynamic, and more causative factors. Recent evidence points to an association between the gut microbiome and hypertension. Given that the microbiota is in part determined by host genetics, we used the two-sample Mendelian randomization (MR) analysis to address the bidirectional causal link between gut microbiota and hypertension.Methods: We selected genetic variants (P < 1 × 10⁻⁵) for gut microbiota (n = 18,340) from the MiBioGen study. Genetic association estimates for hypertension were extracted from genome-wide association study (GWAS) summary statistics on 54,358 cases and 408,652 controls. Seven complementary MR methods were implemented, including the inverse-variance weighted (IVW) method, followed by sensitivity analyses to verify the robustness of the results. Reverse-direction MR analyses were further conducted to probe if there was a reverse causative relationship. Bidirectional MR analysis then examines a modulation of gut microbiota composition by hypertension.Results: At the genus level, our MR estimates from gut microbiome to hypertension showed that there were 5 protective factors Allisonella, Parabacteroide, Phascolarctobacterium, Senegalimassilia, and unknowngenus (id.1000000073), while 6 genera Clostridiuminnocuum, Eubacteriumcoprostanoligenes, Eubacteriumfissicatena, Anaerostipes, LachnospiraceaeFCS020, and unknowngenus (id.2041) are risk factors. The Alcaligenaceae and ClostridialesvadinBB60 were detrimental and beneficial at the family level, respectively. In contrast, the MR results of hypertension-gut flora showed hypertensive states can lead to an increased abundance of Eubacteriumxylanophilum, Eisenbergiella, and Lachnospiraceae and a lower abundance of Alistipes, Bilophila, Butyricimonas, and Phascolarctobacterium.Conclusion: Altered gut microbiota is a causal factor in the development of hypertension, and hypertension causes imbalances in the intestinal flora. Substantial research is still needed to find the key gut flora and explore the specific mechanisms of their effects so that new biomarkers can be found for blood pressure control.

Although several staging systems have been proposed for pancreatic neuroendocrine tumors (pNETs), the optimal staging system remains unclear. Here, we aimed to assess the application of the newly revised 8th edition American Joint Committee on Cancer (AJCC) staging system for exocrine pancreatic carcinoma (EPC) to pNETs, in comparison with that of other staging systems. We identified pNETs patients from the Surveillance, Epidemiology, and End Results (SEER) database (2004–2014). Overall survival was analyzed using Kaplan–Meier curves with the log‐rank test. The predictive accuracy of each staging system was assessed by the concordance index (c‐index). Cox proportional hazards regression was conducted to calculate the impact of different stages. In total, 2424 patients with pNETs, including 2350 who underwent resection, were identified using SEER data. Patients with different stages were evenly stratified based on the 8th edition AJCC staging system for EPC. Kaplan–Meier curves were well separated in all patients and patients with resection using the 8th edition AJCC staging system for EPC. Moreover, the hazard ratio increased with worsening disease stage. The c‐index of the 8th edition AJCC staging system for EPC was similar to that of the other systems. For pNETs patients, the 8th edition AJCC staging system for EPC exhibits good prognostic discrimination among different stages in both all patients and those with resection.

This study aimed to investigate the association of the vascular aging and left ventricular geometry in OSAHS-related hypertension population with good blood pressure control. A retrospective study was conducted at the Third Xiangya Hospital, collecting data from patients admitted between August 1, 2022, and January 1, 2024. Vascular aging parameters were collected, including ankle-brachial index (ABI), brachial-ankle pulse wave velocity (baPWV), and carotid intima-media thickness (cIMT). Left ventricular mass index (LVMI), relative wall thickness (RWT), left ventricular hypertrophy (LVH), and left ventricular concentric geometry (LVCG) were collected to assess left ventricular geometry patterns. Spearman correlation analysis, generalized linear regression, and logistic regression were used to assess the associations of the vascular aging and left ventricular geometry. A total of 238 patients were included. Significant positive correlations were found between baPWV and LVMI (Rs = 0.26, P < 0.001), baPWV and RWT (Rs = 0.37, P < 0.001), cIMT and LVMI (Rs = 0.24, P < 0.001), and cIMT and RWT (Rs = 0.34, P < 0.001). Generalized linear regression model indicated that both baPWV (β = 0.84, P = 0.01 for LVMI; β = 0.005, P < 0.001 for RWT) and cIMT (β = 27.54, P = 0.01 for LVMI; β = 0.13, P < 0.001 for RWT) were associated with LVMI and RWT, respectively. Logistic regression revealed that baPWV (OR = 1.08, 95%CI 1.02–1.15) and cIMT (OR = 1.30, 95%CI 1.06–1.60) were associated with LVH. baPWV (OR = 1.44, 95%CI 1.28–1.65) and cIMT (OR = 1.54, 95%CI 1.21–1.96) also showed associations with LVCG. Subgroup analyses based on gender and BMI suggested that the results were robust. This study showed that vascular aging parameters may be used to assess changes in left ventricular geometry of patients with OSAHS-related hypertension.