Given the ever-increasing prevalence of food-related non-communicable diseases, we can surmise that mainstream nutrition research methodologies applied thus far are not providing us with an accurate view of the cause/effect relations, which is what we badly need to grasp to better understand what is happening. Indeed, current claims are often based on observations of correlations rather than causality.
If somebody who doesn’t exercise is obese, there may well be a relationship between the lack of exercise and increased weight, but it doesn’t necessarily mean that one directly causes the other. We have probably all heard the phrase “you cannot outrun a bad diet”, and that perfectly sums up the relationship between exercise and weight loss. Exercise undoubtedly has a relationship with weight gain and loss, but food intake is causal in this example. The problem is that every day, the world’s media publish “proof” of the latest “claim” regarding healthy eating, and under constant pressure to improve our well-being, they are hard for us to ignore. Yet, they are for the largest part highly inaccurate.
This leads us to believe that we need to conduct our research in a radically different way, taking into account three macro-factors: lifestyle and food choices (what people put on the table and consume), ingredients (what they are using in their food preparation) and finally the chemical and nutritional values of the foodstuffs selected.
To be able to take a step back and objectively and radically redefine in a rigorous scientific way how we look at food and how we assess its value is the direction in which we need to be moving. Understanding how different chemicals within the foods interact with each other and with the chemicals in other foods, gaining knowledge of the functions of various nutrients, are crucial to be able to better create the big picture. Based on scientific data and mathematical model-based analyses as it is, only the latter will give us a far better way forward into preparing guidelines and recommendations.
The scientific network was identified and established, which means selecting the partners and the tools to make this research relevant and credible. It was then possible to create the data warehouse and begin data collection. Once data was incoming, then artificial intelligence and a systems-based approach could be adopted. This in turn led to the publication of scientific papers on different aspects of the work undertaken to date.