No abstract

Food is widely acknowledged as a significant contributor to climate change. Yet, estimates of food-related greenhouse gas emissions frequently consider supply chain stages only up to farm gate or regional distribution centres. Here, we estimate greenhouse gas emissions associated with different cooking methods and appliances in the UK. Data on current cooking practices were collected through a survey with more than 700 respondents. Results reveal that home cooking accounts for as much as 61% of total emissions associated with specific foods, and that this can be substantially reduced through alternative, readily available cooking practices.

BackgroundProinflammatory biomarkers levels are increased among patients with cardiovascular disease, and it is known that both the presence of insulin resistance and diet may influence those levels. However, these associations are not well studied among patients with established cardiovascular disease. Our objective is to compare inflammatory biomarker levels among cardiovascular disease secondary prevention patients with and without insulin resistance, and to evaluate if there is any association between plasma fatty acid levels and inflammatory biomarker levels among them.MethodsIn this cross-sectional sub-study from the BALANCE Program Trial, we collected data from 359 patients with established cardiovascular disease. Plasma fatty acids and inflammatory biomarkers (interleukin (IL)-1β, IL-6, IL-8, IL-10, IL-12, high sensitive C-reactive protein (hs-CRP), adiponectin, and tumor necrosis factor (TNF)-alpha) were measured. Biomarkers and plasma fatty acid levels of subjects across insulin resistant and not insulin resistant groups were compared, and general linear models were used to examine the association between plasma fatty acids and inflammatory biomarkers.ResultsSubjects with insulin resistance had a higher concentration of hs-CRP (p = 0.002) and IL-6 (p = 0.002) than subjects without insulin resistance. Among subjects without insulin resistance there was a positive association between stearic fatty acid and IL-6 (p = 0.032), and a negative association between alpha-linolenic fatty acid and pro-inflammatory biomarkers (p < 0.05). Among those with insulin resistance there was a positive association between monounsaturated fatty acids and arachidonic fatty acid and adiponectin (p < 0.05), and a negative association between monounsaturated and polyunsaturated fatty acids and pro-inflammatory biomarkers (p < 0.05), as well as a negative association between polyunsaturated fatty acids and adiponectin (p < 0.05). Our study has not found any association between hs-CRP and plasma fatty acids.ConclusionsSubjects in secondary prevention for cardiovascular disease with insulin resistance have a higher concentration of hs-CRP and IL-6 than individuals without insulin resistance, and these inflammatory biomarkers are positively associated with saturated fatty acids and negatively associated with unsaturated fatty acids.Electronic supplementary materialThe online version of this article (10.1186/s12937-018-0342-1) contains supplementary material, which is available to authorized users.

In this paper, we discuss the use of natural language processing and artificial intelligence to analyze nutritional and sustainability aspects of recipes and food. We present the state-of-the-art and some use cases, followed by a discussion of challenges. Our perspective on addressing these is that while they typically have a technical nature, they nevertheless require an interdisciplinary approach combining natural language processing and artificial intelligence with expert domain knowledge to create practical tools and comprehensive analysis for the food domain.