Personalized Nutrition: Tomorrow’s Approach to Nutrition

Personalized Nutrition: Tomorrow’s Approach to Nutrition
Personalized Nutrition: Tomorrow’s Approach to Nutrition

Personalized nutrition has made remarkable progress, moving from a concept to a tangible reality with significant potential to improve individual health and well-being. This innovative approach focuses on tailoring nutritional strategies to the unique needs and preferences of each individual, moving away from the traditional one-size-fits-all approach.

This article explores the significance of the innovative personalized nutrition approach and its ability to successfully implement personalized nutrition principles on a widespread scale.

Understanding personalized nutrition

Personalized nutrition incorporates factors such as physical activity, habits and behaviors, environmental exposures, continuous metabolite monitoring (continuous glucose monitoring), and continuous health-parameter monitoring (like heart rate and blood pressure). The overarching goal is to mitigate the risk of diet-related diseases by providing tailored dietary recommendations based on comprehensive data analysis, as well as managing existing chronic conditions.

The additional elements involved in the personalized nutrition approach can be analyzed using machine learning algorithms and artificial intelligence to propose precise diets most suitable for individuals at specific stages and circumstances of their lives. However, it is worth noting that manual analysis is still prevalent, posing challenges in terms of scalability and application to the wider population.

By leveraging algorithms to process information about the relationships between various factors and metabolic responses, medical doctors (MDs) and registered dietitian nutritionists (RDNs) can uncover predictions and correlations that traditional methods of analysis may not. This exciting new wave of personalized nutrition represents the convergence of nutrition science and data science.

Personalized nutrition algorithms act as roadmaps in the form of decision trees. They guide nutritionists through a logical sequence of steps, starting from specific physical and genetic traits, and help them make clear suggestions on adjusting our nutrient intake. By taking into account individual traits and genetic differences, these algorithms can offer personalized recommendations that are specifically designed for our unique requirements and how our bodies respond.

The idea of personalized nutrition has been on the surface for some time and has been recognized to be effective.

For instance, in 1908, Sir Archibald Garrod classified alkaptonuria as a “hereditary error of metabolism,” paving the way for a groundbreaking concept in personalized medicine. This important milestone demonstrated the connection between genetic factors and metabolic disorders, opening new avenues for targeted dietary interventions.

In addition, the discovery of phenylketonuria (PKU) in 1934, a condition that affects how the body processes phenylalanine, has provided valuable insights into the complex connection between nutrients, diet, and human well-being.

Precision nutrition vs. personalized nutrition

Although personalized nutrition and precision nutrition are two closely related concepts, they do differ in the assessed factors.

Precision nutrition focuses on using scientific data, biomarkers, and genetic information to provide targeted and precise nutritional recommendations. It emphasizes tailoring nutrition based on individual genetic variations and biological markers. Personalized nutrition, on the other hand, focuses on data collected from clients.

Customized plans for specific dietary needs can be created by considering specific genetic traits related to how nutrients are processed in the body. For example, someone can have a predisposition to lactose intolerance and may benefit from avoiding or reducing lactose intake.

Moreover, nutrigenomics and epigenetics are important areas of precision nutrition that explore how our genes and their interactions with the environment, including diet, can influence our health and well-being.

Meanwhile, personalized nutrition encompasses a broader range of factors beyond genetics. It integrates multiple sources of information, such as age, gender, body composition, medical history, dietary habits, and cultural background, to develop tailored nutrition plans.

Personalized nutrition survey

If we truly want to get the most out of our nutrition, we need to consider nutritionists’ work and if they do turn to personalized nutrition recommendations.

To review how personalized nutrition is used by nutritionists today, I conducted a survey involving 500+ nutritionists and healthcare professionals.

The data showed that 46.5% of dietitians most often use personalized nutrition principles in their daily practice. In addition, 42.72% of them have created at least one nutrition program based on advanced phenotypic and epigenetic data, and 43.09% are very confident in their knowledge of personalized or precision nutrition.

The survey also revealed that when creating a personalized nutrition program, the most used patient data includes:

  • Food allergy test (56.34%)
  • Other health conditions (such as hypercholesterolemia, hypertension, gout, etc.) (53.73%)
  • Current nutrition habits, including hydration (71.08%)
  • Lifestyle factors (58.20%)
  • Fitness levels (62.50%)

The least used patient data includes:

  • Social behavior (27.05%)
  • Data from personal lifestyle devices (27.61%)
  • Genetics and genomic data (30.22%)
  • Questionnaires (30.59%)
  • Phenotypic information on anthropometry (31.15%)
  • Screening for genetic disease (32.27%)

Personalized digital health platforms and tools

The primary goal of personalized digital health platforms is to empower individuals to take an active role in managing their nutrition. These platforms provide personalized guidance, educational resources, and monitoring tools to support this process.

By leveraging technology, data analytics, and user-friendly interfaces, including AI-powered solutions, these platforms deliver personalized health information, dietary recommendations, and tracking capabilities. This integration of AI enables individuals to make well-informed choices, accurately monitor their progress, and actively participate in managing their nutrition.

According to the personalized nutrition survey, nutritionists, on the other hand, utilize advanced/specialized data analysis tools (such as Nutritics, NutriAdmin, and similar) for analyzing the data. The survey revealed that 60.82% of nutritionists mostly rely on these tools for data analysis.


While personalized nutrition shows promising results and is an advanced approach to diet and nutrition, it still faces certain challenges.

One challenge is accessibility, as it often requires specialized healthcare professionals, which may limit its availability. Additionally, the cost of personalized nutrition services can be competitive, and potential customers need to be open to understanding and accepting new approaches to nutrition.

The biggest challenge in nutrition, however, is how to effectively apply healthy eating and lifestyle habits to improve the health of each individual on a larger scale. It becomes time-consuming.

Moreover, using machine learning to analyze patient data can raise privacy concerns. Machine learning strategies involve the analysis of large amounts of personal data and can raise questions about how personal information is stored and used.


In the future, research in personalized nutrition needs to focus on understanding the role of nutrition throughout the entire lifespan and address diet-related conditions through comprehensive interventions that go beyond simply choosing healthy foods.

This means considering various aspects of an individual’s life and implementing multifaceted approaches to promote overall well-being.

The next step would be to incorporate the precision nutrition approach, which would enhance our understanding of patient data and enable us to create a personalized approach with even greater efficiency.

Personalized and precision nutrition is the future of nutrition science. The integration of AI and machine learning techniques will provide opportunities we have never had before.

Given the immense potential, it’s crucial to direct attention and investments toward the field of personalized and precision nutrition. This will foster innovation and drive progress in healthcare.

Photo: fcafotodigital, Getty Images

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