Epidemiological studies have documented a high incidence of diabetes in hypertensive patients.Insulin resistance is defined as a less than expected biologic response to a given concentration of the hormone and plays a pivotal role in the pathogenesis of diabetes. However, over the last decades, it became evident that insulin resistance is not merely a metabolic abnormality, but is a complex and multifaceted syndrome that can also affect blood pressure homeostasis. The dysregulation of neuro-humoral and neuro-immune systems is involved in the pathophysiology of both insulin resistance and hypertension. These mechanisms induce a chronic low grade of inflammation that interferes with insulin signalling transduction. Molecular abnormalities associated with insulin resistance include the defects of insulin receptor structure, number, binding affinity, and/or signalling capacity. For instance, hyperglycaemia impairs insulin signalling through the generation of reactive oxygen species, which abrogate insulin-induced tyrosine autophosphorylation of the insulin receptor. Additional mechanisms have been described as responsible for the inhibition of insulin signalling, including proteasome-mediated degradation of insulin receptor substrate 1/2, phosphatase-mediated dephosphorylation and kinase-mediated serine/threonine phosphorylation of both insulin receptor and insulin receptor substrates. Insulin resistance plays a key role also in the pathogenesis and progression of hypertension-induced target organ damage, like left ventricular hypertrophy, atherosclerosis and chronic kidney disease. Altogether these abnormalities significantly contribute to the increase the risk of developing type 2 diabetes.

There is consensus that Heart Rate Variability is associated with the risk of vascular events. However, Heart Rate Variability predictive value for vascular events is not completely clear. The aim of this study is to develop novel predictive models based on data-mining algorithms to provide an automatic risk stratification tool for hypertensive patients. A database of 139 Holter recordings with clinical data of hypertensive patients followed up for at least 12 months were collected ad hoc. Subjects who experienced a vascular event (i.e., myocardial infarction, stroke, syncopal event) were considered as high-risk subjects. Several data-mining algorithms (such as support vector machine, tree-based classifier, artificial neural network) were used to develop automatic classifiers and their accuracy was tested by assessing the receiver-operator characteristics curve. Moreover, we tested the echographic parameters, which have been showed as powerful predictors of future vascular events. The best predictive model was based on random forest and enabled to identify high-risk hypertensive patients with sensitivity and specificity rates of 71.4% and 87.8%, respectively. The Heart Rate Variability based classifier showed higher predictive values than the conventional echographic parameters, which are considered as significant cardiovascular risk factors. Combination of Heart Rate Variability measures, analyzed with data-mining algorithm, could be a reliable tool for identifying hypertensive patients at high risk to develop future vascular events.

Background: Emerging evidence suggests that elevated circulating levels of HDL-C (high-density lipoprotein cholesterol) could be linked to an increased mortality risk. However, to the best of our knowledge, the relationship between HDL-C and specific cardiovascular events has never been investigated in patients with hypertension. Methods: To fill this knowledge gap, we analyzed the relationship between HDL-C levels and cardiovascular events in hypertensive patients within the Campania Salute Network in Southern Italy. Results: We studied 11 987 patients with hypertension, who were followed for 25 534 person-years. Our population was divided in 3 groups according to the HDL-C plasma levels: HDL-C80 mg/dL (high HDL-C). At the follow-up analysis, adjusting for potential confounders, we observed a total of 245 cardiovascular events with a significantly increased risk of cardiovascular events in the low HDL-C group and in the high HDL-C arm compared with the medium HDL-C group. The spline analysis revealed a nonlinear U-shaped association between HDL-C levels and cardiovascular outcomes. Interestingly, the increased cardiovascular risk associated with high HDL-C was not confirmed in female patients. Conclusions: Our data demonstrate that there is a U-shaped association between HDL-C and the risk of cardiovascular events in male patients with hypertension.

Background: Regression of left ventricular ( LV ) hypertrophy ( LVH ) has been a goal in clinical trials. This study tests the external validity of results of clinical trials on LVH regression using a large registry from a tertiary care center, to identify phenotypes less likely to achieve regression of LVH . Methods and Results: Patients from the Campania Salute Network, free of prevalent cardiovascular disease, but with echocardiographic LVH (defined as LV mass index [LVMi] >47 g/m ^2.7 in women and >50 g/m ^2.7 in men) were included. During a median follow‐up of 67 months, clear‐cut regression of LVH was documented in 14% of patients (13±8% reduction of initial LVMi ) or 23% when also considering those with a reduction of LVMi ≥5 g/m ^2.7 . Patients with persistent LVH were older with longer duration of hypertension, suboptimal blood pressure (BP) control, larger body mass index, LV mass, and carotid intima‐media thickness and included more women and subjects with diabetes mellitus, isolated systolic hypertension, and metabolic syndrome (all P <0.05). Number and class of antihypertensive drugs during follow‐up did not differ between groups. In multiple logistic regression analysis, older age, female sex, obesity, higher baseline LVMi and carotid intima‐media thickness, and suboptimal BP control were significant covariates of persistent LVH (all P ≤0.01), independent of diabetes, duration of hypertension, isolated systolic hypertension, follow‐up time and number and class of antihypertensive drugs. Conclusions: Early initiation of antihypertensive treatment, aggressive BP control, and attention to metabolic aspects are critical to avoid irreversible LVH .

Background—: Pentraxin 3 (PTX3), the prototype of long pentraxins, has been described to be associated with endothelial dysfunction in different cardiovascular disorders. No study has yet evaluated the possible direct effect of PTX3 on vascular function. Methods and Results—: Through in vitro experiments of vascular reactivity and ultrastructural analyses, we demonstrate that PTX3 induces dysfunction and morphological changes in the endothelial layer through a P-selectin/matrix metalloproteinase-1 pathway. The latter hampered the detachment of endothelial nitric oxide synthase from caveolin-1, leading to an impairment of nitric oxide signaling. In vivo studies showed that administering PTX3 to wild-type mice induced endothelial dysfunction and increased blood pressure, an effect absent in P-selectin–deficient mice. In isolated human umbilical vein endothelial cells, PTX3 significantly blunted nitric oxide production through the matrix metalloproteinase-1 pathway. Finally, using ELISA, we found that hypertensive patients (n=31) have higher plasma levels of PTX3 and its mediators P-selectin and matrix metalloproteinase-1 than normotensive subjects (n=21). Conclusions—: Our data show for the first time a direct role of PTX3 on vascular function and blood pressure homeostasis, identifying the molecular mechanisms involved. The findings in humans suggest that PTX3, P-selectin, and matrix metalloproteinase-1 may be novel biomarkers that predict the onset of vascular dysfunction in hypertensive patients.