N-acylphosphatidylethanolamine, a gut- derived circulating factor induced by fat ingestion, inhibits food intake.

N-acylphosphatidylethanolamines (NAPEs) are a relatively abundant group of plasma lipids of unknown physiological significance. Here, we show that NAPEs are secreted into circulation from the small intestine in response to ingested fat and that systemic administration of the most abundant circulating NAPE, at physiologic doses, decreases food intake in rats without causing conditioned taste aversion. Furthermore, (14)C-radiolabeled NAPE enters the brain and is particularly concentrated in the hypothalamus, and intracerebroventricular infusions of nanomolar amounts of NAPE reduce food intake, collectively suggesting that its effects may be mediated through direct interactions with the central nervous system. Finally, chronic NAPE infusion results in a reduction of both food intake and body weight, suggesting that NAPE and long-acting NAPE analogs may be novel therapeutic targets for the treatment of obesity.

Implicit bias of encoded variables: frameworks for addressing structured bias in EHR-GWAS data.

The 'discovery' stage of genome-wide association studies required amassing large, homogeneous cohorts. In order to attain clinically useful insights, we must now consider the presentation of disease within our clinics and, by extension, within our medical records. Large-scale use of electronic health record (EHR) data can help to understand phenotypes in a scalable manner, incorporating lifelong and whole-phenome context. However, extending analyses to incorporate EHR and biobank-based analyses will require careful consideration of phenotype definition. Judgements and clinical decisions that occur 'outside' the system inevitably contain some degree of bias and become encoded in EHR data. Any algorithmic approach to phenotypic characterization that assumes non-biased variables will generate compounded biased conclusions. Here, we discuss and illustrate potential biases inherent within EHR analyses, how these may be compounded across time and suggest frameworks for large-scale phenotypic analysis to minimize and uncover encoded bias.