Plasma anandamide and other N-acylethanolamines are correlated with their corresponding free fatty acid levels under both fasting and non-fasting conditions in women

N-acylethanolamines (NAEs), such as anandamide (AEA), are a group of endogenous lipids derived from a fatty acid linked to ethanolamine and have a wide range of biological activities, including regulation of metabolism and food intake. We hypothesized that i) NAE plasma levels are associated with levels of total free fatty acids (FFAs) and their precursor fatty acid in fasting and non-fasting conditions and ii) moderate alcohol consumption alters non-fasting NAE levels. In a fasting and non-fasting study we sampled blood for measurements of specific NAEs and FFAs. In the fasting study blood was drawn after an overnight fast in 22 postmenopausal women. In the non-fasting study blood was sampled before and frequently after a standardized lunch with beer or alcohol-free beer in 19 premenopausal women. Fasting AEA levels correlated with total FFAs (r = 0.84; p <0.001) and arachidonic acid levels (r = 0.42; p <0.05). Similar results were observed for other NAEs with both total FFAs and their corresponding fatty acid precursors. In addition, AEA (r = 0.66; p <0.01) and OEA levels (r = 0.49;

Spartan Daily, November 1, 1999

Volume 113, Issue 44https://scholarworks.sjsu.edu/spartandaily/9473/thumbnail.jp

Spartan Daily, October 9, 2000

Volume 115, Issue 27https://scholarworks.sjsu.edu/spartandaily/9594/thumbnail.jp

Spartan Daily, October 9, 2000

Volume 115, Issue 27https://scholarworks.sjsu.edu/spartandaily/9594/thumbnail.jp

Spartan Daily, December 9, 1997

Volume 109, Issue 68https://scholarworks.sjsu.edu/spartandaily/9215/thumbnail.jp

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Analyzing the Language of Food on Social Media

We investigate the predictive power behind the language of food on social media. We collect a corpus of over three million food-related posts from Twitter and demonstrate that many latent population characteristics can be directly predicted from this data: overweight rate, diabetes rate, political leaning, and home geographical location of authors. For all tasks, our language-based models significantly outperform the majority-class baselines. Performance is further improved with more complex natural language processing, such as topic modeling. We analyze which textual features have most predictive power for these datasets, providing insight into the connections between the language of food, geographic locale, and community characteristics. Lastly, we design and implement an online system for real-time query and visualization of the dataset. Visualization tools, such as geo-referenced heatmaps, semantics-preserving wordclouds and temporal histograms, allow us to discover more complex, global patterns mirrored in the language of food.Comment: An extended abstract of this paper will appear in IEEE Big Data 201

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