Additive effect of non-alcoholic fatty liver disease on metabolic syndrome-related endothelial dysfunction in hypertensive patients

Metabolic syndrome (MS) is characterized by an increased risk of incident diabetes and cardiovascular (CV) events, identifying insulin resistance (IR) and endothelial dysfunction as key elements. Moreover, non-alcoholic fatty liver disease (NAFLD) is bidirectionally linked with MS as a consequence of metabolic and inflammatory abnormalities. We addressed the question if the evolution in NAFLD might worsen endothelium-dependent vasodilating response in MS hypertensives. We recruited 272 Caucasian newly-diagnosed never-treated hypertensive outpatients divided into three groups according to the presence/absence of MS alone or in combination with NAFLD. MS and NAFLD were defined according to the National Cholesterol Education Program-Adult Treatment Panel III (NCEP-ATPIII) and non-invasive fatty liver index, respectively. We determined IR by using the homeostasis model assessment (HOMA) index. Vascular function, as forearm blood flow (FBF), was determined through strain-gauge plethysmography after intra-arterial infusion of acetylcholine (ACh) and sodium nitroprusside. MS+NAFLD+ group showed worse metabolic, inflammatory and vascular profiles compared with MS-NAFLD- and MS+NAFLD-. HOMA resulted in being the strongest predictor of FBF both in the MS+NAFLD- and in the MS+NAFLD+ groups, accounting for 20.5% and 33.2% of its variation, respectively. In conclusion, we demonstrated that MS+NAFLD+ hypertensives show a worse endothelium-dependent vasodilation compared with MS+NAFLD-, allowing for consideration of NAFLD as an early marker of endothelial dysfunction in hypertensives

Additive effect of non-alcoholic fatty liver disease on metabolic syndrome-related endothelial dysfunction in hypertensive patients

Metabolic syndrome (MS) is characterized by an increased risk of incident diabetes and cardiovascular (CV) events, identifying insulin resistance (IR) and endothelial dysfunction as key elements. Moreover, non-alcoholic fatty liver disease (NAFLD) is bidirectionally linked with MS as a consequence of metabolic and inflammatory abnormalities. We addressed the question if the evolution in NAFLD might worsen endothelium-dependent vasodilating response in MS hypertensives. We recruited 272 Caucasian newly-diagnosed never-treated hypertensive outpatients divided into three groups according to the presence/absence of MS alone or in combination with NAFLD. MS and NAFLD were defined according to the National Cholesterol Education Program-Adult Treatment Panel III (NCEP-ATPIII) and non-invasive fatty liver index, respectively. We determined IR by using the homeostasis model assessment (HOMA) index. Vascular function, as forearm blood flow (FBF), was determined through strain-gauge plethysmography after intra-arterial infusion of acetylcholine (ACh) and sodium nitroprusside. MS+NAFLD+ group showed worse metabolic, inflammatory and vascular profiles compared with MS-NAFLD- and MS+NAFLD-. HOMA resulted in being the strongest predictor of FBF both in the MS+NAFLD- and in the MS+NAFLD+ groups, accounting for 20.5% and 33.2% of its variation, respectively. In conclusion, we demonstrated that MS+NAFLD+ hypertensives show a worse endothelium-dependent vasodilation compared with MS+NAFLD-, allowing for consideration of NAFLD as an early marker of endothelial dysfunction in hypertensives

Renal function is impaired in normotensive chronic HCV patients: role of insulin resistance

Renal dysfunction is an independent predictor for cardiovascular morbidity and mortality. We investigated whether chronic hepatitis C virus (HCV) infection and the related insulin resistance/hyperinsulinemia influence renal function in comparison with a group of healthy subjects and with another group with metabolic syndrome. We enrolled 130 newly diagnosed HCV outpatients matched for age and gender with 130 patients with metabolic syndrome and 130 healthy subjects. Renal function was evaluated by calculation of glomerular filtration rate (e-GFR, mL/min/1.73 m2) using the CKD-EPI equation. The following laboratory parameters were measured: fasting plasma glucose and insulin, total, LDL- and HDL-cholesterol, triglyceride, creatinine, and HOMA to evaluate insulin sensitivity. HCV patients with respect to both healthy subjects and metabolic syndrome patients have a decreased e-GFR: 86.6 ± 16.1 vs 120.2 ± 23.1 mL/min/1.73 m2 (P < 0.0001) and 94.9 ± 22.6 mL/min/1.73 m2 (P = 0.003), respectively. Regarding biochemical variables, HCV patients, in comparison with healthy subjects, have a higher triglyceride level, creatinine, fasting insulin and HOMA (3.4 ± 1.4 vs 2.6 ± 1.3; P < 0.0001). At linear regression analysis, the correlation between e-GFR and HOMA is similar in the metabolic syndrome (r = -0.555, P < 0.0001) and HCV (r = -0.527, P < 0.0001) groups. At multiple regression analysis, HOMA is the major determinant of e-GFR in both groups, accounting for, respectively, 30.8 and 27.8 % of its variation in the metabolic syndrome and HCV. In conclusion, we demonstrate that HCV patients have a significant reduction of e-GFR and that insulin resistance is the major predictor of renal dysfunction

A new methodology to model interdependency of Critical Infrastructure Systems during Hurricane Sandy’s event

The paper proposes a methodology to evaluate the resilience of the critical infrastructures networks hit by Hurricane Sandy in October 2012. The region analyzed in the case study is New York metropolitan area which includes New York City and the nearby state of New Jersey. This region was the most affected by the storm and it is one of the most densely populated regions of the United States due to its high concentration of businesses and several critical infrastructures. The identified critical infrastructure systems are highly interconnected, forming a heterogeneous network that is very vulnerable to catastrophic events, such as hurricanes. Due to several existing interdependencies, the systems are subjected to disruptive cascading effects. The disruption of one or more of these systems directly affects people, businesses, the government and leads to additional indirect damages. After a critical comparison of the different methodologies to analyze infrastructure interdependency, the input-output method is selected in order to indentify and rank the different types of dependencies in the network as well as to prioritize the different actions during the restoration process. Previous analyses have shown that power, transportation, and fuel were the most damaged networks in the region generating severe cascading effects due to the interdependencies between them. A series of recommendations to improve the global resilience in the region are provided which will be able to prevent cascading effects and prioritize the recovery effort in the future

Modelling cascading failures in lifelines using temporal networks

Lifelines are critical infrastructure systems with high interdependency. During a disaster, the interdependency between the lifelines can lead to cascading failures. In the literature, the approaches used to analyze infrastructure interdependencies within the social, political, and economic domains do not properly describe the infrastructures’ emergency management. During an emergency, the response phase is very condensed in time, and the failures that occur are usually amplified through cascading effects in the long-term period. Because of these peculiarities, interdependencies need to be modeled considering the time dimension. The methodology proposed in this paper is based on a modified version of the Input-output Inoperability Model. The lifelines are modeled using graph theory, and perturbations are applied to the elements of the graph, simulating natural or man-made disasters. The cascading effect among the interdependent networks has been simulated using a spatial multilayer approach. The adjancency tensor has been used to for the temporal dimension and its effects. Finally, the numerical results of the simulations with the proposed model are represented by probabilities of failure for each node of the system. As a case study, the methodology has been applied to a nuclear power plant. The model can be adopted to run analysis at different scales, from the regional to the local scales

Disaster Resilience Assessment of Building and Transportation System

The paper presents a new methodology to assist decision-makers in the management of critical events such as earthquakes evaluating the recovery time, and the resilience index of a building system that is a component of the physical infrastructure dimension of the PEOPLES Resilience framework. The interdependencies between building system and transportation network in term of accessibility are modeled. Finally, the methodology has been implemented in a software and has been applied in two case studies: (a) the old medieval center of L’Aquila town and (b) the Treasure Island in the San Francisco Bay area

Managing debris clearance from road transportation networks after earthquakes

This research proposes a framework that allows to define a debris removal strategy from a road transportation network after a seismic event. The case study is a hypothetical large-scale city consisting of many interdependent infrastructure. Once the debris generated by the collapse of buildings have been estimated, blocked roads are identified. Cleanup operations are then prioritized based on road importance and travel time. The goal is to first verify that evacuation routes and important paths connecting strategic facilities such as hospitals, shelters, fire stations, etc., are available. In case some roads within these paths are blocked, alternative routes are considered. If the pre-event travel time does not significantly increase, clearing equipment and resources could be managed accordingly and directed towards other areas. The objective of this work is to help emergency managers to successfully improve disaster response avoiding delays during rescue and recovery operations

A new methodology to model interdependency of Critical Infrastructure Systems during Hurricane Sandy’s event

The paper proposes a methodology to evaluate the resilience of the critical infrastructures networks hit by Hurricane Sandy in October 2012. The region analyzed in the case study is New York metropolitan area which includes New York City and the nearby state of New Jersey. This region was the most affected by the storm and it is one of the most densely populated regions of the United States due to its high concentration of businesses and several critical infrastructures. The identified critical infrastructure systems are highly interconnected, forming a heterogeneous network that is very vulnerable to catastrophic events, such as hurricanes. Due to several existing interdependencies, the systems are subjected to disruptive cascading effects. The disruption of one or more of these systems directly affects people, businesses, the government and leads to additional indirect damages. After a critical comparison of the different methodologies to analyze infrastructure interdependency, the input-output method is selected in order to indentify and rank the different types of dependencies in the network as well as to prioritize the different actions during the restoration process. Previous analyses have shown that power, transportation, and fuel were the most damaged networks in the region generating severe cascading effects due to the interdependencies between them. A series of recommendations to improve the global resilience in the region are provided which will be able to prevent cascading effects and prioritize the recovery effort in the future

A first order evaluation of the capacity of a healthcare network under emergency

Immediately after an earthquake a healthcare system within a city, comprising several hospitals, endures an extraordinary demand. This paper proposes a new methodology to estimate whether the hospital network has enough capacity to withstand the emergency caused by an earthquake. The ability of healthcare facilities and to provide a broad spectrum of emergency services immediately after a seismic event is assessed through a metamodel that assumes waiting time as main response parameter to assess the hospital network performance. The First Aid network of San Francisco subjected to a 7.2 Mw magnitude earthquake has been used as case study. The total number of injuries and their distributions among the six major San Francisco’s Emergency Departments have been assessed and compared with their capacity that has been determined using a survey conducted by the medical staff of the hospitals. The numerical results have shown that three of the six considered San Francisco’s hospitals cannot provide emergency services to the estimated injured. Two alternatives have been proposed to improve the performance of the network. The first one redistributes existing resources while the second one considers additional resources by designing a new Emergency Department