Development and testing of an algorithm to predict fruit and vegetable consumption

点击翻译按钮获取中文摘要

A high intake of fruit and vegetables (F&V) may reduce the risk of disease, particularly heart disease. However, it is important that F&V intake is measured accurately when trying to work out whether increasing consumption of these foods will reduce the risk of disease. It is currently very difficult to measure F&V intake accurately, as standard methods used are imprecise. The introduction of a blood test to measure F&V consumption would be a significant development in nutrition research as it would allow researchers to make better estimates of F&V intake in the general population, and also lead to a better understanding of whether and how F&V reduce disease risk. We propose that, because F&V are diverse and complex foods with many biologically active compounds, that a single blood test is never likely to accurately reflect F&V consumption. As an alternative, we propose that the results of measuring a panel of compounds from a single blood test could be combined using statistical methods, and that this approach would lead to a more accurate estimate of F&V consumption. We will carry out a study where we feed healthy volunteers a known number of portions of F&V per day and will carry out these blood tests, to allow the testing of the statistical methods. We will then test the results of these statistical analyses in a series of F&V studies that have already been carried out where we know how many portions of F&V the participants were consuming in order to compare reported intake with the more objective blood results.