Several companies constructed personalized nutrition products based on modern scientific methods. For example, personalized information about specific biomarkers, genes or gut bacteria can be extracted from body fluids. Other personalized information can be obtained by using questionnaires focusing on dietary habits and physical activity. Personalized diet programs, consultations or dietary packages are developed based on this information. In addition, biometric data monitors can adhere to the specific, personalized nutrition plan. Although these models are providing personalized nutrition programs, dietary recommendations or dietary products, their products are not really personalized. Instead, they are more of a slightly improved “one size fitsall” model.
DNA-based dietary recommendation programs are constructed from the personal information of specific genes. These programs suggest that this information can impact a person’s fitness, body weight, aging, metabolism and other factors involved with a healthy lifestyle. Other DNA analysis business models provide personalized supplements based on the personal genotype and SNP analyses showing genetic association with certain medical conditions or diseases. DNA analysis is also helping prepare personalized diet plans with exercise training programs and personal health coaching for fitness improvement. Alternatively, DNA data provided by other companies are used to produce personalized dietary supplement products.
Another approach has been analyzing biomarkers from an individual’s blood test. These results are used to make dietary and lifestyle changes, which will be monitored and adjusted with repeated blood testing. However, this approach looks more like a regular doctor-office visit with the personal recommendation to change the diet for improving personal levels of specific biomarkers, such as cholesterol.
Questionnaires about lifestyles and goals are used for recommendations and service delivery of vitamins and dietary supplements. Another approach is the service delivery of vitamins and supplements. These are based on personal pharmaceutical drug use, importantly to avoid specific metabolic interactions decreasing the activity of used drugs and vitamins or supplements.
Although all these products for personalized nutrition are using scientific approaches and scientific information, these products are still far from true, personalized nutrition. The major problem is in the reductionist approach focused on the interaction of nutrients or macromolecules on specific gene or gene products. However, an effective personalized nutrition solution requires a holistic understanding of health evaluating the interactions among diet, genes, gene products, health and environmental exposures.
A novel approach for targeted personal nutrition recommendation was recently developed by researchers at Virginia Tech (Front Nutr. 2018 Nov 29;5:117). This method is integrating data from diet, gut microbiome, electronic health records, measures of physical activity and data from wearable sensors, and based on artificial intelligence (AI) algorithms modeling. This data is used to suggest personalized lifestyle adjustments. Although this method will identify a tailored personal nutritional recommendation or products with verified predictable effectiveness, this approach is unfeasible for the food industry given the amount of data that’s required.
Moreover, consumers generally prefer a simple, effective solution, and nutrition companies prefer the easiest, and therefore cheaper, development of their products. On the other hand, a certain group of educated consumers may be interested in truly effective and tailored personalized nutrition products with proven effectiveness.
Since the universal mechanisms of pharmacology and metabolism are the same for pharmaceutical drugs as for nutrition supplements, real personalized nutrition must be comparable to the science applied in personalized medicine, focusing on targeted disease prevention and therapies.
For example, obesity and diabetes are civilization diseases associated with increased blood glucose levels after a meal. People have highly varied responses to identical meals. A machine-learning algorithm integrating blood parameters, dietary habits, anthropometrics, physical activity and gut microbiota was devised to accurately predict personalized post-meal glycemic response to real-life meals (Cell. 2015 Nov 19;163(5):1079-1094). Based on the microbiome analysis of stool samples, a personalized nutrition planning kit called Day Two, which balances blood sugar level, was successfully created.
However, for other medical conditions the mechanism of preventative and therapeutic effects of food is not fully elucidated. An alternative to the expensive AI algorithms modeling is personalized dynamic adjustment of selected laboratory validated outcome markers (PDAM).
PDAM consists of: 1) identification of a specific health condition and its molecular basis on the cellular level (personal biomarkers [PB]), 2) development and validation of PB in the personalized cell-based assay, 3) identification and development of nutritional/dietary intervention modulating PB, and 4) dynamic adjustment of personalized nutritional/dietary intervention based on PB changes. PDAM will use several laboratory techniques to process personal samples. Therefore, a protection of personal data must be in place by applying the standard HIPAA Privacy Rule (outlined in the Health Insurance Portability and Accountability Act of 1996). Although PDAM is not as simple as one-time DNA testing analysis or completing a health questionnaire, it is not as complicated as the complex AI algorithms modeling. Moreover, PDAM will allow a personal adjustment of nutrition/dietary intervention based on personal biomarkers in real time. Although PDAM is not a cheaper alternative to the current personalized nutrition approaches, PDAM can have a real impact on human well-being and health. However, as practically everybody can drive Toyota, not everybody can afford to drive Mercedes. Therefore, only a certain number of customers may embrace this new approach to personalized nutrition.
While PDAM is the future of real personalized nutrition, the major question is, how far away is this future?
Daniel Sliva, Ph.D., CEO & founder, DSTest Labs (dstest-lab.com), is a senior investigator at Indiana University Health, and an adjunct associate professor at the Indiana University School of Medicine. He has master’s degrees in food and biochemical technology, as well as biochemistry, and a doctoral degree in molecular biology and genetics. Sliva also completed postdoctoral studies at the department of medical nutrition, Karolinska Institute, Sweden, and at the Indiana University School of Medicine, Indianapolis. He founded DSTest Laboratories at Purdue Research Park in 2014 for evaluating and standardizing efficacy of ingredients, nutraceuticals and dietary supplements. In addition to authoring more than 82 peer-reviewed papers and three book chapters, he is an international speaker.