How will SNSB achieve that? By generating a science-based blueprint for the production and consumption of sustainable and healthy nutrition, for everyone, the world over. Essentially, in basic terms, we are talking about a “what to eat and how to grow it” manual, for both people and for governments.
It is vital that we build on past experience, and see how we can move forward in making our nutritional guidelines ever more accurate and pertinent. Non-communicable diseases, which are not transmissible between human beings, mostly resulting from lifestyle choices and genetic factors, are sky-rocketing. Just an example: obesity and diabetes; all of us can probably think of at least one person in our immediate social circle who is afflicted by one of them.
Over recent decades, so-called scientific studies have made all sorts of claims about food, both in terms of production and consumption, and as to how these will directly affect our well-being and life expectancy. Yet, we are witnessing the general human condition getting worse rather than improving.
The fact is that there is a further layer of complexity to take into consideration. We have probably all heard of “follow the individual” studies (often recounted on ratings-oriented television programmes too) where an individual or number of individuals are selected and their consumption of defined categories of food is monitored. This kind of study, however, besides being time-consuming, costly and with no golden standard as a shareable methodology, does not take into account what we can call “confounding factors”: geographical location, culture, genetics, individual responses, lack of objectivity, etc., all of which can throw the results into unimaginable disarray.
As strange as it might sound, apparently altogether unrelated fields, such as complex systems, network science, artificial intelligence and big data to name just a few, actually look set to approach some of the crucial issues facing the sustainable and healthy nutrition industry.
Only sophisticated tools from such frontier disciplines are able to grasp the mass of data generated when so many “confounding” issues come into play. For example, in cases where at one extreme of the scale, gut bacteria specific to individuals, geographic areas, race etc., vs. cultural norms, behavior, deviance and convergence at the other, exhibit a multitude of other confounding factors in between. Simple observation and recording of consumption will never help untangle this web.
We need to convert that data into something we can analyze, interpret and create models on. Without the assistance of 21st-century tools and skill sets, this would be an impossible task. Whilst data on such complex issues will never be complete or perfect, we can certainly go a long way in the process of standardizing data and recognizing, measuring and taking into account confounding factors.
Another minefield is the characterization of causation. That means we have to be careful about making assumptions about what we observe. We need to adopt mathematical models that will assist us in forecasting outcomes of different behaviors – in response to a variety of proxies – to a higher degree of accuracy rather than just using observational methods. Correlation does not imply causation, it simply implies a connection or a relationship, yet we have used just that as a basis for the representation of certain issues … and publication of so many fallacies.
The current guidelines and parameters which define healthy nutrition are not achieving the desired results given the ever-increasing prevalence of non-communicable diseases (remember, that means chronic diseases, such as the metabolic or cardio-vascular ones, that are not transmissible person-to-person). So, we have a big challenge in our hands. Our scientific methodology to date is not moving us in the right direction; we need to up our game and re-examine how we can change the way we collect data, analyze and interpret results, and apply guidelines.
While we are right now focused on consumption, the big picture will inevitably involve food production too; they are two facets of the same problem, and need to develop and progress hand in hand. We firmly believe that the foundations of the roadmap we are creating are built on big data, artificial intelligence, and complex systems sciences, thus creating a decisive change in nutrition recommendations and policy.
SNSB intends to roll out the initial phase of this innovative way of studying food requirements and agricultural production with a particular focus on vegetable oils, to be closely followed by other basic nutrients.