Healthy diet

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 disease (2).

No evidence based ‘diet that fits all’

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 lifestyle 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 lifestyle factors that define a person’s individual response to a certain food or diet (3), including differential responses like blood glucose fluctuations, cholesterol alterations, allergies or intolerances, or metabolic errors (4).

New technologies open new range of possibilities for precision nutrition

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 insights (5) and enhance this field further. Indeed, in 2015, Zeevi et al (6), 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. 

Scientific base of Clear.bio: understand your body’s unique responses to food

Clear.bio’s new digital precision nutrition program is based on the principles described above:

• 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 lifestyle 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 lifestyle 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

With Clear.bio you understand your body’s unique responses to food. Clear.bio enables and coaches you to choose the foods that increase your energy, manage your weight, and promote your long term health.