Sustainable urban metabolism as a link between bio-physical sciences and urban planning: the BRIDGE project

Urban metabolism considers a city as a system with flows of energy and material between it and the environment. Recent advances in bio-physical sciences provide methods and models to estimate local scale energy, water, carbon and pollutant fluxes. However, good communication is required to provide this new knowledge and its implications to endusers (such as urban planners, architects and engineers). The FP7 project BRIDGE (sustainaBle uRban plannIng Decision support accountinG for urban mEtabolism) aimed to address this gap by illustrating the advantages of considering these issues in urban planning. The BRIDGE Decision Support System (DSS) aids the evaluation of the sustainability of urban planning interventions. The Multi Criteria Analysis approach adopted provides a method to cope with the complexity of urban metabolism. In consultation with targeted end-users, objectives were defined in relation to the interactions between the environmental elements (fluxes of energy, water, carbon and pollutants) and socioeconomic components (investment costs, housing, employment, etc.) of urban sustainability. The tool was tested in five case study cities: Helsinki, Athens, London, Florence and Gliwice; and sub-models were evaluated using flux data selected. This overview of the BRIDGE project covers the methods and tools used to measure and model the physical flows, the selected set of sustainability indicators, the methodological framework for evaluating urban planning alternatives and the resulting DSS prototype.

Aspects of in vitro and in vivo research approaches directed towards identifying probiotics and prebiotics for human use

The microbiota of the human gastrointestinal tract plays a key role in nutrition and health. Through the process of fermentation, gut bacteria metabolize various substrates (principally dietary components) to end products such as short-chain fatty acids and gases. This anaerobic metabolism is thought to contribute positively toward host daily energy requirements. However, under certain circumstances, the fermentative process may produce undesirable metabolites. This may cause the onset of gut disorders that can be manifest through both acute and chronic conditions. Moreover, the gut flora may become contaminated by transient pathogens that serve further to upset the normal community structure. There has been a recent increase in the use of dietary components that help to maintain, or even improve, the gut microflora "balance." Probiotics are live microbial feed supplements added to appropriate food vehicles (usually fermented milks), whereas prebiotics are dietary carbohydrates that have a selective metabolism in the colon and serve to increase numbers of bacteria seen as desirable. Because of their purported health-promoting properties, lactic acid-producing bacteria, including bifidobacteria, are the usual target organisms. The market value and biological potential of both approaches are enormous. This article will summarize how efficacious types can be identified.

Top single nucleotide polymorphisms affecting carbohydrate metabolism in Metabolic Syndrome: from the LIPGENE study

Rationale:Metabolic Syndrome (MetS) is a high prevalence condition characterized by altered energy metabolism, insulin resistance and elevated cardiovascular risk.Objectives:Although many individual single nucleotide polymorphisms (SNPs) have been linked to certain MetS features, there are few studies analyzing the influence of SNPs on carbohydrate metabolism in MetS.Methods:904 SNPs (tag SNPs and functional SNPs) were tested for influence in eight fasting and dynamic markers of carbohydrate metabolism, performing an intravenous glucose tolerance test in 450 participants of the LIPGENE study.Findings:From 382 initial gene-phenotype associations between SNPs and any phenotypic variables, 61 (a 16 % of the pre-selected) remained significant after Bootstrapping. Top SNPs affecting glucose metabolism variables were as follows: fasting glucose: rs26125 (PPARGC1B); fasting insulin: rs4759277 (LRP1); C peptide: rs4759277 (LRP1); HOMA-IR: rs4759277 (LRP1); QUICKI: rs184003 (AGER); SI: rs7301876 (ABCC9), AIRg: rs290481 (TCF7L2) and DI: rs12691 (CEBPA).Conclusions:We describe here the top SNPs linked to phenotypic features in carbohydrate metabolism among aproximately 1000 candidate gene variations in fasting and postprandial samples of 450 patients with MetS from the LIPGENE study.