Importance of risk factor management in diabetic patients and reduction in Stage B heart failure

BACKGROUND: A number of studies have demonstrated the presence of a diabetic cardiomyopathy, increasing the risk of heart failure development in this population. Improvements in present-day risk factor control may have modified the risk of diabetes-associated cardiomyopathy.

AIM: We sought to determine the contemporary impact of diabetes mellitus (DM) on the prevalence of cardiomyopathy in at-risk patients with and without adjustment for risk factor control.

DESIGN: A cross-sectional study in a population at risk for heart failure.

METHODS: Those with diabetes were compared to those with other cardiovascular risk factors, unmatched, matched for age and gender and then matched for age, gender, body mass index, systolic blood pressure and low density lipoprotein cholesterol.

RESULTS: In total, 1399 patients enrolled in the St Vincent's Screening to Prevent Heart Failure (STOP-HF) cohort were included. About 543 participants had an established history of DM. In the whole sample, Stage B heart failure (asymptomatic cardiomyopathy) was not found more frequently among the diabetic cohort compared to those without diabetes [113 (20.8%) vs. 154 (18.0%), P = 0.22], even when matched for age and gender. When controlling for these risk factors and risk factor control Stage B was found to be more prevalent in those with diabetes [88 (22.2%)] compared to those without diabetes [65 (16.4%), P = 0.048].

CONCLUSION: In this cohort of patients with established risk factors for Stage B heart failure superior risk factor management among the diabetic population appears to dilute the independent diabetic insult to left ventricular structure and function, underlining the importance and benefit of effective risk factor control in this population on cardiovascular outcomes.

Management of public water supply to reduce power cost and improve wind power uptake

This paper presents a study on the implementation of Real-Time Pricing (RTP) based Demand Side Management (DSM) of water pumping at a clean water pumping station in Northern Ireland, with the intention of minimising electricity costs and maximising the usage of electricity from wind generation. A Genetic Algorithm (GA) was used to create pumping schedules based on system constraints and electricity tariff scenarios. Implementation of this method would allow the water network operator to make significant savings on electricity costs while also helping to mitigate the variability of wind generation.

Management of public water supply to reduce energy cost and improve wind power uptake

This paper presents a study on the implementation of Real-Time Pricing (RTP) based Demand Side Management (DSM) of water pumping at a clean water pumping station in Northern Ireland, with the intention of minimising electricity costs and maximising the usage of electricity from wind generation. A Genetic Algorithm (GA) was used to create pumping schedules based on system constraints and electricity tariff scenarios. Implementation of this method would allow the water network operator to make significant savings on electricity costs while also helping to mitigate the variability of wind generation.

Management of public water supply to reduce energy cost and improve wind power uptake

This paper presents a study on the implementation of Real-Time Pricing (RTP) based Demand Side Management (DSM) of water pumping at a clean water pumping station in Northern Ireland, with the intention of minimising electricity costs and maximising the usage of electricity from wind generation. A Genetic Algorithm (GA) was used to create pumping schedules based on system constraints and electricity tariff scenarios. Implementation of this method would allow the water network operator to make significant savings on electricity costs while also helping to mitigate the variability of wind generation.