Recently, the potential role of gut microbiome in metabolic diseases has been revealed, especially in cardiovascular diseases. Hypertension is one of the most prevalent cardiovascular diseases worldwide, yet whether gut microbiota dysbiosis participates in the development of hypertension remains largely unknown. To investigate this issue, we carried out comprehensive metagenomic and metabolomic analyses in a cohort of 41 healthy controls, 56 subjects with pre-hypertension, 99 individuals with primary hypertension, and performed fecal microbiota transplantation from patients to germ-free mice. Compared to the healthy controls, we found dramatically decreased microbial richness and diversity, Prevotella-dominated gut enterotype, distinct metagenomic composition with reduced bacteria associated with healthy status and overgrowth of bacteria such as Prevotella and Klebsiella, and disease-linked microbial function in both pre-hypertensive and hypertensive populations. Unexpectedly, the microbiome characteristic in pre-hypertension group was quite similar to that in hypertension. The metabolism changes of host with pre-hypertension or hypertension were identified to be closely linked to gut microbiome dysbiosis. And a disease classifier based on microbiota and metabolites was constructed to discriminate pre-hypertensive and hypertensive individuals from controls accurately. Furthermore, by fecal transplantation from hypertensive human donors to germ-free mice, elevated blood pressure was observed to be transferrable through microbiota, and the direct influence of gut microbiota on blood pressure of the host was demonstrated. Overall, our results describe a novel causal role of aberrant gut microbiota in contributing to the pathogenesis of hypertension. And the significance of early intervention for pre-hypertension was emphasized.

Background: SII and SIRI are two novel systemic inflammation indexes that were suggested in predicting poor outcomes in cancers. However, no studies have examined their effect on cardiovascular diseases (CVDs) and all-cause mortality. Thus, this study aims to investigate associations between SII, SIRI, and the risks for CVDs and all-cause mortality. Methods: A total of 85,154 participants from the Kailuan cohort were included and followed up for incidents of CVDs (including MI, stroke) and all-cause death for 10 years. Multiple Cox regression was used to calculate the adjusted hazard ratios (HRs). Results: During the follow-up period, 4262 stroke events, 1233 MI events, and 7225 all-cause deaths were identified, respectively. Compared with the lowest quantile (Q1) of SII or SIRI, after adjusted for most cardiovascular risk factors, both indexes showed positive associations with the risk for stroke (adjusted HRs in Q4 were 1.264 (95% CI: 1.157,1.382) for SII, 1.194 (95% CI: 1.087,1.313) for SIRI), and all-cause death (adjusted HRs in Q4 were 1.246 (95% CI: 1.165,1.331) for SII, 1.393 (95% CI: 1.296,1.498) for SIRI). Additionally, higher SII and SIRI are also associated with increased risk of hemorrhagic stroke and ischemic stroke. Higher SIRI but not SII exhibited a higher MI risk, the adjusted HR in Q4 was 1.204 (1.013,1.431). The significant association remained after additional adjustment for CRP. Subgroup analysis and sensitivity analysis displayed consistent results except for SIRI with MI, where the association did not arrive at significance in subjects aged ≥ 60. Conclusion: Elevated SII and SIRI increased the risk of stroke, two stroke subtypes, and all-cause death. Higher SIRI, but not SII associated with increased MI incidence, and the association of SIRI was only significant in subjects aged < 60.

In 2010, the American Heart Association published its 2020 Strategic Impact Goals to improve the cardiovascular health of all Americans and changed the focus from primary prevention of cardiovascular disease (CVD) to primordial prevention.¹ The committee proposed a new concept of ideal cardiovascular health, defined as the presence of 7 favorable health metrics and the absence of clinical CVD (eg, coronary heart disease and stroke).¹ The 7 health metrics include 4 health behaviors (smoking, body weight, physical activity, and diet) and 3 health factors (blood pressure, plasma glucose level, and cholesterol level).¹ Built on this concept and intended to capture individual-level changes in cardiovascular health factors and behaviors, a cardiovascular health score (CHS) was created based on the individual-level composite of all 7 cardiovascular health metrics to evaluate overall cardiovascular health status.²

Rationale: Previous studies on the relationship between diabetes and arterial stiffness were mostly cross-sectional. A few longitudinal studies focused on one single direction. Whether the association between arterial stiffness and diabetes is bidirectional remains unclear to date. Objective: To explore the temporal relationship between arterial stiffness and fasting blood glucose (FBG) status. Methods and Results: Included were 14,159 participants of the Kailuan study with assessment of brachial-ankle pulse wave velocity (baPWV) from 2010 to 2015, and free of diabetes, cardiovascular and cerebrovascular diseases and chronic kidney disease at baseline. FBG and baPWV were repeatedly measured at baseline and follow-ups. Cox proportional hazard regression model was used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) of incident diabetes across baseline baPWV groups: <1,400 cm/s (ref), 1,400 {less than or equal to} baPWV < 1,800 cm/s, and {greater than or equal to}1,800cm/s. Path analysis was used to analyze the possible temporal causal relationship between baPWV and FBG, among 8,956 participants with repeated assessment of baPWV and FBG twice in 2010-2017. The mean baseline age of the observed population was 48.3{plus minus}12.0 years. During mean 3.72 years of follow-up, 979 incident diabetes cases were identified. After adjusting for potential confounders, the HR (95% CI) for risk of diabetes was 1.59 (1.34, 1.88) for the borderline arterial stiffness group and 2.11 (1.71, 2.61) for the elevated arterial stiffness group, compared with the normal ideal arterial stiffness group. In the path analysis, baseline baPWV was associated with follow-up FBG (the standard regression coefficient was 0.09; 95% CI: 0.05 to 0.10). In contrast, the standard regression coefficient of baseline FBG for follow-up baPWV (β = 0.00, 95% CI: -0.02 to 0.02) was not significant. Conclusions: Arterial stiffness, as measured by baPWV, was associated with risk of developing diabetes. Arterial stiffness appeared to precede the increase in FBG.

Background and Purpose—: Previous studies showed an inverse association between ideal cardiovascular health (CVH) metrics and the total risk of cardiovascular diseases and stroke. This study aimed to investigate the relationship between ideal CVH metrics and the risks of ischemic and hemorrhagic stroke, respectively. Methods—: We collected information on the 7 ideal CVH metrics (including smoking status, body mass index, dietary intake, physical activity, blood pressure, total cholesterol, and fasting blood glucose) among 91 698 participants from the Kailuan study, China (72 826 men and 18 872 women between the ages of 18 and 98 years), free of myocardial infarction and stroke at baseline (2006–2007). Cox proportional hazards models were used to estimate stroke risk. Results—: During the 4-year follow-up, we identified 1486 incident stroke events (1057 ischemic, 386 intracerebral hemorrhagic, and 43 subarachnoid hemorrhagic). The hazard ratios (95% confidence interval) for total stroke with adherence to 0 (reference), 1, 2, 3, 4, 5, and 6/7 ideal CVH metrics were: 1, 0.92 (0.69–1.23), 0.69 (0.52–0.92), 0.52 (0.39–0.68), 0.38 (0.28–0.51), 0.27 (0.18–0.40), and 0.24 (0.11–0.54), respectively ( P trend <0.01), after adjusting for age, sex, education, income, and hospital. Similar inverse associations were observed for both ischemic and intracerebral hemorrhagic stroke (both P trend <0.01). Conclusions—: We observed a clear inverse gradient relationship between the number of ideal CVH metrics and the risk of stroke in a Chinese population, supporting the importance of ideal health behaviors and factors in stroke prevention.