Effect of Gut Microbiota on Blood Cholesterol: A Review on Mechanisms.

The gut microbiota serves as a pivotal mediator between diet and human health. Emerging evidence has shown that the gut microbiota may play an important role in cholesterol metabolism. In this review, we delve into five possible mechanisms by which the gut microbiota may influence cholesterol metabolism: (1) the gut microbiota changes the ratio of free bile acids to conjugated bile acids, with the former being eliminated into feces and the latter being reabsorbed back into the liver; (2) the gut microbiota can ferment dietary fiber to produce short-chain fatty acids (SCFAs) which are absorbed and reach the liver where SCFAs inhibit cholesterol synthesis; (3) the gut microbiota can regulate the expression of some genes related to cholesterol metabolism through their metabolites; (4) the gut microbiota can convert cholesterol to coprostanol, with the latter having a very low absorption rate; and (5) the gut microbiota could reduce blood cholesterol by inhibiting the production of lipopolysaccharides (LPS), which increases cholesterol synthesis and raises blood cholesterol. In addition, this review will explore the natural constituents in foods with potential roles in cholesterol regulation, mainly through their interactions with the gut microbiota. These include polysaccharides, polyphenolic entities, polyunsaturated fatty acids, phytosterols, and dicaffeoylquinic acid. These findings will provide a scientific foundation for targeting hypercholesterolemia and cardiovascular diseases through the modulation of the gut microbiota.

Metabolites of Gut Microbiota and Possible Implication in Development of Diabetes Mellitus.

Diabetes mellitus is characterized by having a disorder of glucose metabolism. The types of diabetes mellitus include type 1 diabetes mellitus, type 2 diabetes mellitus, gestational diabetes mellitus, and other specific types of diabetes mellitus. Many risk factors contribute to diabetes mellitus mainly including genetics, environment, obesity, and diet. In the recent years, gut microbiota has been shown to be linked to the development of diabetes. It has been reported that the gut microbiota composition of diabetic patients is different from that of healthy people. Although the mechanism behind the abnormality remains to be explored, most hypotheses focus on the inflammation response and leaky gut in relation to the changes in production of endotoxins and metabolites derived from the intestinal flora. Consequently, the above-mentioned abnormalities trigger a series of metabolic changes, gradually leading to development of hyperglycemia, insulin resistance, and diabetes. This review is (i) to summarize the differences in gut microbiota between diabetic patients and healthy people, (ii) to discuss the underlying mechanism(s) by which how lipopolysaccharide, diet, and metabolites of the gut microbiota affect diabetes, and (iii) to provide a new insight in the prevention and treatment of diabetes.

Life cycle assessment of car sharing models and the effect on GWP of urban transportation: A case study of Beijing.

Alongside a trend in lower private-vehicle ownership and the growing popularity of the shared use economy, car sharing is emerging as an alternative travel mode. The LCA model of car sharing is proposed, the global warming potential (GWP) of four car sharing models is determined, and the effect on GWP of urban transportation is explored. This study expanded the LCA of products to the LCA of services, by expanding the functional unit to service. In this study, the time dimension was considered during the functional unit setting. It was found that there are large GWP differences among different car sharing models. Electric vehicle car sharing models have less GWP than gasoline vehicle car sharing models. The dispatch distance and the numbers of passengers in one car are two key factors for GWP of car sharing models. When car sharing replaces ~10% and ~50% of private cars, the GWP reduction potentials of urban transportation are ~4% and ~20%, respectively. The overall distribution of car sharing should be set by considering the features of different models in different areas, to achieve the largest environment benefit by using car sharing in cities. Therefore, car sharing can be used as a measure for significant GWP reduction for city transportation.

Changes of urban nitrogen metabolism in the Beijing megacity of China, 2000-2016.

Rapid growth in metropolitan areas is associated with high nitrogen (N) flows and subsequent environmental and human health consequences. Many studies on the contemporary aspects of urban N metabolism have conducted in recent years, but comprehensive analysis from life cycle perspective is limited. In this study, a detailed quantitative framework for a coupled human-natural N flow model, comprising a full cycle analysis based on the substance flow analysis approach to cover and integrate all specific N flows and stocks associated with N production, consumption and emission, was developed to study the temporal changing patterns of N metabolism in Beijing megacity during 2000-2016. The results show that total N inputs continuously increased from 413.3 to 529.5 Gg N during the study period, primarily attributing to fossil fuel combustion (53%), fertilizer/feed import (19%), and food import (15%). Agriculture subsystem contains the largest N internal flows, and a decreasing trend is exhibited by a widening gap between local production and household consumption, reflecting Beijing's increasing dependence on the external environment. Moreover, N outputs (394.9 Gg in 2016) contribute to upstream air emissions, landfills accumulation and downstream wastewater discharges. Furthermore, driving force analysis demonstrates that population growth has the largest positive effect on N inputs, and a decoupling of N input with GDP growth is identified. Overall, N flows exhibit an inefficient and unsustainable trend, and possible options for optimizing more sustainable situations while simultaneously minimizing negative consequences are discussed. This study provides decision-makers with an integrated view of N management at the city scale.

An optimization method for energy structures based on life cycle assessment and its application to the power grid in China.

The optimization of energy structures, aimed at saving energy and reducing emissions, is an important precautionary measure against climate change. This study considers different environmental impacts of power systems, and investigates ways to optimize power structures and decrease their potential environmental impact. A multi-objectives optimization model of energy structures was created based on life cycle assessment (LCA). This model covers several environment impacts, rather than only focusing on carbon emissions. LCA was used to calculate the different environmental impacts and provided a new method for normalization. The model was applied to the power industry in China. Three kinds of environmental impacts were considered: material input (MI), global warming potential (GWP), and water deprivation (WD). The five major existing methods of electricity generation in China were considered: thermal power, nuclear power, hydro power, wind power, and solar photovoltaic power. The system boundary included all life cycle stages; specifically, extraction of raw materials and resources, production, energy generation processes, and power transport. The optimization results showed that the total environmental impacts were reduced; MI, GWP, and WD were decreased by 29.53%, 29.67%, and 19.06%, respectively. This method provides new insights into optimization of energy structures by considering multi-environment impacts.

Trace elements distribution and ecological risk assessment of seawater and sediments from Dingzi Bay, Shandong Peninsula, North China.

Selected trace elements (Hg, As, Pb, Cu, Cd, Cr and Zn) in seawater and sediments from Dingzi Bay, a semi-enclosed bay suffering from severe degradation located in the Shandong Peninsula, were investigated to evaluate the spatial distribution and potential ecological risk. Results indicated that higher concentrations occurred in the inner bay. Calculation of pollution load index (PLI) showed overall low values while the concentration factor (CF) indicated that Hg, As and Cd were at moderate risk levels in the region. Based on the effects-range classification, As was likely to pose environment risk. Principal component analysis (PCA) revealed that in addition to background contributions, the trace elements contamination could also be affected by anthropogenic pollution sources. The results of present study provide useful background information for further marine investigation and management in the region.