Dietary patterns and breast cancer risk: a systematic review and meta-analysis

Background: Dietary patterns, which represent whole-diet and possible food and nutrient interactions, have been linked to the risk of various cancers. However, the associations of these dietary patterns with breast cancer remain unclear. Objective: We critically appraised the literature and conducted meta-analyses to pool the results of studies to clarify the relation between dietary patterns and breast cancer risk.
Design: MEDLINE and EMBASE were searched for relevant articles that identified common dietary patterns published up to November 2009. Multivariable-adjusted odds ratios (ORs) comparing highest and lowest categories of dietary pattern scores and multi-variable-adjusted ORs for a 20th-percentile increase in dietary pattern scores were combined by using random-effects meta-analyses. Results: Case-control and cohort studies were retrieved that identified prudent/healthy (n = 18), Western/unhealthy (n = 17), and drinker (n = 4) dietary patterns. There was evidence of a decrease in the risk of breast cancer in the highest compared with the lowest categories of prudent/healthy dietary patterns (OR = 0.89; 95% CI: 0.82, 0.99; P = 0.02) in all studies and in pooled cohort studies alone. An increase in the risk of breast cancer was shown for the highest compared with the lowest categories of a drinker dietary pattern (OR = 1.21; 95% CI: 1.04, 1.41; P = 0.01). There was no evidence of a difference in the risk of breast cancer between the highest and the lowest categories of Western/unhealthy dietary patterns (OR = 1.09; 95% CI: 0.98, 1.22; P = 0.12). Conclusion: The results of this systematic review and meta-analysis indicate that some dietary patterns may be associated with breast cancer risk.

Discovery of novel Agonists and antagonists of the free fatty acid receptor 1 (FFAR1) using virtual screening

The G-protein-coupled receptor free fatty acid receptor 1 (FFAR1), previously named GPR40, is a possible novel target for the treatment of type 2 diabetes. In an attempt to identify new ligands for this receptor, we performed virtual screening (VS) based on two-dimensional (2D) similarity, three-dimensional (3D) pharmacophore searches, and docking studies by using the structure of known agonists and our model of the ligand binding site, which was validated by mutagenesis. VS of a database of 2.6 million compounds followed by extraction of structural neighbors of functionally confirmed hits resulted in identification of 15 compounds active at FFAR1 either as full agonists, partial agonists, or pure antagonists. Site-directed mutagenesis and docking studies revealed different patterns of ligand-receptor interactions and provided important information on the role of specific amino acids in binding and activation of FFAR1.