The concept of “personalized medicine” has evolved based on the predicted pharmacogenetic responses of individuals. These response predictions were greatly enhanced by the Human Genome Project and the resultant detection of single nucleotide polymorphisms (SNPs) within human societies. Nutritional genomics provides genetic uprising, which incorporates (Subbiah 2007): 1. Nutrigenomics: To understand the changes occurring in the proteome and metabolome of an individual after the dietary compounds interact with the genome. 2. Nutrigenetics: Involves an individual’s gene-based reactions to various dietary compounds—thus, it helps in the development of a variety of nutraceuticals, well suited to the genetic makeup of the person.
Nutrigenomics explores the effects of nutrients on the genome, proteome and metabolome, and nutrigenetics. Figure 3.1 below depicts a scheme to estimate the risk of a particular disease and the health condition of an individual by utilizing several nutrigenomic techniques incorporating dietary, genetic, and metabolic knowledge.
On the basis of more than one interrelated dimension, a “systems” biomarker has been designed by the amalgamation of various aforesaid data.
Nutraceuticals are the products derived from food sources having extra health bene ts in addition to their basic nutritional value. They can be categorized as: dietary supplements, functional foods, medicinal foods, and pharmaceuticals. Nutrigenomics has a wide range of applications and can be used to understand the consequence of nutritional compounds on the genetic constitution, function, and expression pro le along with the transcriptome of an individual. Intake of nutraceutical substitutes in the form of tablets or pill capsules are also known to prevent multifactorial diseases like cancer, CVD, type II diabetes mellitus, and some monogenic disorders, for example, phenylketonuria, galactosemia, lactose intolerance, etc. Nutrigenetics and nutrigenomics are two different elds, which have two completely different approaches for understanding the genetic interaction with the dietary compounds. However, their common nal aspiration is to personalize diet, understand genetic polymorphisms, and to offer potent gateways and hence augment human health (Mutch et al. 2005). Nutritional interventions like antioxidants, vitamins (e.g., vitamin A, E, D and C, etc.), avonoids, omega-3 fatty acids, etc., aim to prevent the pathogenesis of diabetes mellitus, metabolic syndromes, and their complications. Plant-derived food products show also positive effects on the reduction of chronic diseases due to the presence of phytochemicals. Various food materials like green tea, vitamin E, soy, vitamin D, lycopene, and selenium are taken from natural sources and are being used to alleviate human health (Cencic and Chingwaru 2010). So, inborn error metabolism can be recti fied artifi cially by giving nutraceuticals, or personalized diet supports, which have future aspects of personalized nutrigenome medications. Data mining is the procedure of nding patterns in data to calculate a result or to predict future outputs. Using data mining tools and techniques, huge amounts of data can be handled in a short period of time. Cluster detection, memory-based reasoning, market basket analysis, genetic algorithms, link analysis, decision trees, and neural nets are some of the powerful techniques used for data mining and evaluation purposes. Data mining is playing a major role in nutrigenomics analysis, as it helps to study the present status of nutraceuticals in the market, as well as control the market value of those medicated products and the customers’ responses to them.