A little bit of Greek never hurt anybody so lets first look at the term ‘omics’. It is widely used in scientific literature to refer to the new sciences of genomics, which is derived from the term ‘genome’. According to Wikipedia, the term (‘Genom’), coined by a German scientist Professor Hans Winkler of the University of Hamburg in 1920, is from the Greek word ‘I become’. The term ‘ome’ is also of Greek origin and means ‘totality’. From the term genome, came the term genomics, the study of the genome. We then entered the freewheel of ‘omics’ where the study, not just of one protein, but of all proteins measurable within a biological sample, became proteomics. Not to be outdone, those interested in metabolites coined the term ‘metabolomics’ to refer to the science of studying not one but hundreds of metabolites at one time, using pattern recognition technology to seek patterns within the vast amount of data generated. There the matter rested although a proposal for the entry of a new term ‘astrobolomics’ was mooted at a significant scientific meeting on Copenhagen just a few years back. The thinking behind this was that all of these omics could no more predict the future that astrology and that it was all a load of b******. Hence the proposal for astrobolomics.
All of these omics were eradicating traditional whole body physiology in what can only be described as a technology driven reductionist biomedical Klondike. They had wonderful terms such as ‘knock out’ and ‘knock in’, ‘upstream’ and ‘downstream’, ‘introns’ and exons’ and others, which I always thought would be excellent as the lyrics of some, rap song. Not only were all the omics the only show in town, but when they were blended together they ascended into a new level of mind blowing stratospheric science called ‘systems biology’, in which their blend, through great number-crunching brontobyte computers (a 1 followed by 27 zeros....forget a google) would lead us to biological Nirvana. This was the future, the autobahn of the new biology.
But as the song goes, with minor adaptations: “And then they went and spoiled it all by saying something stupid like phenomics”. This very new term refers to the very, very old term ‘phenotype’ which comes from the Greek words phainen (‘to show’) and typos (“type”). It really means the study of what you are like: height, weight, eye colour, IQ, fitness, blood this and blood that and just about everything measurable in the human body. To study phenotype is really to study form and not function. The emergence of phenomics is not actually a surrender note from the reductionist hoards on the omics autobahn, just a realization that when you add up all your biology, you ultimately end up with a phenotype. Some of us, blessed with a traditional reverence of the totality of human biology, nutritionists in particular, saw the need for this a long time ago. One could ay that the concept of the nutritional phenotype was born about ten years ago and several people need to be mentioned here for promoting this concept: Ben van Ommen of TNO, head of the European Nutrigenomics Organisation
, Jim Kaput who headed up the FDA’s Division of Personalised Nutrition (now with the Nestle Health Institute) and if I do say so myself, mé féin (copy, paste and Google translate).
In Ireland, a consortium of four universities (Joint Irish Nutrigenomics Organisation: JINGO) received state funding to create a National Nutritional Phenotype Database
. It contains data on several cohorts which have had their phenotype characterised to a remarkable extent (food intake, physical activity, bone density, body fatness, energy expenditure at rest and at exercise, blood this and blood that, muscle function, post prandial function and so on. In addition we payed homage to the gods of omics and collected complementary data on genomics, proteomics and metabolomics. The difference between this approach and that of systems biology is that we begin with phenotype in either the healthy state or the diseased state and we work back from there. Generally speaking, systems biology builds upwards from genomics, proteomics and metabolomics data to try to understand the mechanisms that lead to disease. Of course it isn’t a competition between systems biology and the construction of major phenotyping databases but the subtle difference is that the latter is driven by phenotype, the former less so and maybe more so now that they have discovered ‘phenomics’.
Such large phenotypic databases need to have several cohorts, central to which should be a large, healthy, nationally representative cohort. This database will always act as the reference database. If you want to know anything about the nutritional phenotype of the Irish, then we have 1,500 such subjects deeply characterised for their phenotype and of course their ‘omics’. Then you need at least one database, which involves a challenge to metabolism. We have two such. One involves a small number (210) who received test meals on two separate occasions and had their metabolic phenotype characterised after either a carbohydrate or fat meal. Almost everybody knows that when you have to attend for a blood test, you usually have to fast from the night before. If everyone were to arrive in at different times, having eaten very different breakfasts, then the interpretation of the blood tests would be confounded by this variation in food intake. So, virtually all the scientific data we have relating diet to health has blood samples measured in the fasting state. This is convenient for the clinician and researcher but it avoids the truth, which is that we eat about 5-7 times a day, and thus we spend most of our day in the postprandial or post-fed state. So, a knowledge of how dietary patterns relate to blood values at fasting is simply a measure of convenience, a means of reducing what is truly complex to a simple and manageable form. Metabolism is asleep in the fasting state and only comes alive in the fed state. Two individuals with identical levels of say fasting blood glucose may behave very differently when given a carbohydrate rich test meal. These test meals really sort out the chaff from the straw in metabolic terms. The second stressed cohort that we have is a very large cohort of older persons: 2,000 with bone disease, 2,000 with impaired cognitive function and 2,000 with high blood pressure.
We have spent the last 5 years building this database, which I equate to the building of a telescope. Now that it is almost finished, we will be equipped to peer deeper into the interaction of diet, genes and metabolism than many others can.