There is no discussion that a healthy diet is essential for obtaining and maintaining good human health. The WHO provides a definition of ‘a healthy diet’1, and from a scientific point of view, there is ample evidence that dietary factors are recognized contributors to common diseases, like obesity, type 2 diabetes and heart disease2.
Despite scientific substantiation of many diets and attributed benefits to general health or prevention of disease, there is no evidence based ‘diet that fits all’. Many (clinical) studies focus on occurrence of only certain health benefits, or (risk factors for) diseases in relation to consumption of a subset of nutrients, or a specific food or diet. Furthermore, many of these studies have been performed in very controlled settings in which subjects have been exposed to standardized diets and life style interventions, and findings cannot directly be translated to free-living individuals.
In addition, it is not just the food but also genetic make-up (genotype), biological constitution (phenotype) and environmental or life style factors that defines a person’s individual response to a certain food or diet3, including differential responses like blood glucose fluctuations, cholesterol alterations, allergies or intolerances, or metabolic errors.
Recently, the development of new technologies that enable wearable and non-invasive monitoring of health-related parameters, such as sleep, physical activity, but also biomarkers like blood sugar levels, has opened a whole new range of possibilities. In combination with a better understanding of the effect of diets on health, and the development of new tools to translate big data sets into customer friendly algorithms, programs and applications, it may provide a strong base for personalized food recommendations.
This is a new scientific area and although relatively little has been published on validation of algorithms for personalized nutrition thus far, it is expected that approaches like machine learning and artificial intelligence will bring additional insights5 and enhance this field further. Indeed, in 2015, Zeevi et al6, demonstrated that they could predict post-meal elevation of blood glucose by a machine-learning algorithm based on blood measurements, gut microbiota, dietary habits, and physical parameters, and that personalized dietary intervention may successfully alter post-meal blood glucose levels.
- World Health Organization https://www.who.int/behealthy/healthy-diet
- GBD 2015 Risk Factors Collaborators Global, regional, and national comparative risk assessment of 79 behavioral, environmental and occupational, and metabolic risks or clusters of risks, 1990-2015: a systematic analysis for the Global Burden of Disease Study 2015. Lancet (2016) 388:1659-724
- Frazier-Wood AC. Dietary patterns, genes, and health: challenges and obstacles to be overcome. Curr Nutr Rep (2015) 4:82-7
- Gorst C., et al. Long-term glycemic variability and risk of adverse outcomes: a systematic review and meta-analysis. Diabetes Care (2015) 38(12):2354-69.
- Chen CH., et al. PERSON-personalized expert recommendation system for optimized nutrition. IEEE Trans Biomed Circuits Syst (2018) 12:151-60
- Zeevi D., et al. Personalized nutrition by prediction of glycemic esponses. Cell (2015) 163:1079-94
The Clear Health Program’s proposition is based on the above described principles that:
- Food, nutrition or dietary habits have an effect on sugar (glucose) levels in the blood
- Stable blood glucose levels (little fluctuations) correspond with better health-outcomes, such as energy balance and vitality; and lower risk on e.g. insulin resistance, diabetes type 2 or obesity
- Not every person responds similarly to the same food: genetic make-up, biological characteristics (e.g. microbiome) and environmental or life style factors all contribute to an individual response
- Continuous blood glucose monitoring serves as a window through which we can observe what happens in the body in response to food and life style factors.
- Combining continuous blood glucose measurements with dietary and vitality parameters in an AI algorithm, provides an approach to develop a tool for personalized nutrition recommendations.
Understand your body’s unique responses to food
Choose foods that increase your energy, manage your weight, and promote your long term health. Measure your blood glucose levels, balance your diet and control your blood sugar levels.
Madelon Bracke, Head of Bioscience at Clear.
January 21st 2020