Editor’s Note: Eva Everloo is senior investment analyst and Dr. Anđela Martinović is scientific analyst at agrifood tech investor PeakBridge.
The views expressed in this guest commentary are the authors’ own and do not necessarily reflect those of AgFunderNews.
From accelerated drug discovery and turbo-charged R&D to sparkling marketing copy and improved customer service, artificial intelligence (AI) and machine learning (ML) are being deployed in every field. But what do they bring to the food industry?
The rise of personalized nutrition
Chronic diseases such as cardiovascular diseases, cancer, diabetes, and respiratory conditions are the leading causes of illness, morbidity, and mortality in the United States and Europe, and constitute a significant portion of the global disease burden.
In the US alone, they are responsible for 90% of the country’s $4.1 trillion annual healthcare, and they’re are on the rise, with 6 out of 10 US adults now having chronic diseases. Meanwhile, the Global Cancer Observatory (GLOBOCAN) forecasts that the global cancer burden is expected to increase to 28.4 million new cancer cases per year by 2040, with most cases occurring in low- and middle-income countries. Mental wellbeing should also be prioritized given the 13% rise in mental health conditions in the last decade.
Factors exacerbating this situation include:
- Rise of unhealthy lifestyles: Diets low in fruits and vegetables and high in sodium and saturated fats are on the rise, alongside high rates of chronic stress, and sedentary lifestyles.
- Global dietary shifts: The globalization of food markets and the westernization of dietary habits have led to a surge in the availability and consumption of ultra-processed foods, pushing traditional diets to the sideline. This dietary shift has significant implications for healthcare as it affects overall well-being and health outcomes; this is exacerbated in regions where access to comprehensive healthcare and healthcare education is limited, and where there are stark economic disparities.
- Aging populations: The global life expectancy has shown an upward trajectory, rising from 67 years in 2000 to 73 years in 2019. With older adults more susceptible to chronic conditions such as diabetes, arthritis, and heart disease, the prevalence of these diseases is intensifying worldwide.
Addressing this chronic disease public health epidemic calls for a paradigm shift—one that recognizes the therapeutic potential of a well-balanced and nutritious diet.
The concept of food as medicine seeks to harness the healing power of proper nutrition with the aim to live a healthy life and prevent diseases down the road. Furthermore, globally, there exists a notable nine-year discrepancy between lifespan (the total years one lives) and health span (the years lived in robust health.
While increasing lifespan is desirable, improving health span ensures those years are spent in good health and wellness. Realizing this vision on a global scale requires a synergy of public health strategies, educational outreach, and policy shifts.
Furthermore, it is crucial to establish an incentive structure that rewards the promotion of holistic health and well-being by healthcare professionals, rather than focusing only on disease treatment.
“Only 20% of our longevity is genetically determined. The rest is what we do, how we live our lives and increasingly the molecules that we take.”
Dr. David Sinclair
Yet, amid this shift, it is also important to acknowledge the fundamental finding that in healthcare, one size does not fit all. Every individual, characterized by their unique genetic makeup and dietary reactions, brings a layer of complexity to healthcare.
This emphasizes the need for personalized solutions, that can begin by tailoring wellness approaches to specific demographic groups based on their nuanced specific dietary and lifestyle requirements.
These groups could then be provided by specialized and targeted health interventions. This wave of personalization in healthcare signifies a transformative change, steering our focus from a broad-spectrum approach to one that’s precisely tailored to an individual’s needs, and data driven. This transformation does not only result in large-scale healthcare cost savings, but it also holds the promise of improving health outcomes and quality of life for individuals around the world.
Coupling this personalization with a preventive approach in healthcare amplifies the potential of personalized nutrition for enhanced wellbeing.
The personalized nutrition inflection point. Why now?
Several elements propel healthcare towards personalization:
- Availability of data: With 90% of the world’s data created in the last two years, we now have access to an unprecedented wealth of health data that underpins personalized approaches.
- AI and deep learning insights: The vast volume of health data has become a catalyst for training sophisticated models. These models are now translating data into actionable insights. These insights are revolutionizing areas from dietary recommendations tailored to individual needs to understanding the intricate nature of the human microbiome. With AI, our insight into the nuanced interplay between the microbiome, nutrition, and various health conditions has reached unparalleled depths.
- Scientific progress: In recent years, scientific advancements and innovations have deepened our understanding of individual health variability. For example, the completion of the Human Genome Project in 2003 provided a comprehensive map of human genes. Furthermore, breakthroughs in understanding the human microbiome have highlighted its pivotal role in digestion, immunity, and mental health. These insights pave the way for truly personalized interventions based on both genes and microbiome compositions.
- Technological breakthroughs: Technological innovations such as electronic health records and advanced diagnostic tools have allowed precision medicine in healthcare. In parallel, wearable health trackers and mobile apps have equipped consumers with the tools to delve into the intricacies of personalized health.
- Health consciousness: In today’s information-driven world, individuals, particularly those from higher socioeconomic backgrounds, proactively seek longevity and prevention against chronic illnesses. This has become even more apparent during the COVID-19 pandemic as it became evident that individuals with pre-existing health conditions faced a higher risk of unfavorable outcomes when confronted with a severe illness. Practices like biohacking are also on the rise.
- Gaps in conventional healthcare: The gaps in the current healthcare system include limited emphasis on prevention; fragmented care, which leads to lack of coordination and miscommunication among different specialists of comprehensive disease management, insufficient patient engagement; and lack of a holistic health approach. These gaps are one of the drivers for patient-oriented, personalized, preventive, and integrated healthcare models.
Given its profound impact on various aspects of health, nutrition is often considered the first line of defense in preventive healthcare. By making informed, personalized, and healthy food choices, individuals can significantly reduce their risk of developing chronic diseases and enhance their overall quality of life.
In this article, that follows our previous exploration of AI’s role in new product development, and the food value chain (see below), we will delve deeper into the topic of personalization in healthcare, with a particular focus on personalized nutrition, enabled by AI.
Setting the scene: AI Across the healthcare value chain
AI’s influence extends across the entire healthcare value chain, from prevention and diagnosis to treatment and drug discovery. All of these stages encompass nutrition aspects that contribute to personalized dietary recommendations, preventive strategies, and insights into the interplay between the microbiome, nutrition, and various health conditions.
AI-driven systems collect and analyze data from diverse sources, including (anonymized) electronic health records, genomic sequencing, and lifestyle tracking to assess an individual’s health profile. AI helps identify risk factors and provides personalized recommendations for preventive measures. For instance, AI can analyze genetic data to determine a person’s susceptibility to certain diseases, enabling proactive steps toward disease prevention.
Nutrigenomix (CA) is an example of a startup that offers genetic tests for tailored nutrition based on genes affecting metabolism, body composition, and eating habits, while Integrative phenomics (FR) crafts nutrition guidance utilizing lifestyle and gut microbiome insights.
AI plays a key role in analyzing various forms of data, from MRI scans to drug trial protocols and diagnostic propositions. Its inherent capability to identify patterns and anomalies in vast datasets has enhanced diagnostic precision.
For instance, companies such as Sleepiz (CH) diagnose sleep disorders in realistic home settings, while Prenuovo (US) integrates scans and AI analysis for early disease detection, ranging from cancers to liver and kidney conditions. Freenome (US) uses AI to develop non-invasive early detection tests for cancer by analyzing the body’s natural response to tumor presence rather than looking for the tumor itself.
Leveraging the principles of precision medicine, AI integrates genetic data, medical history, and lifestyle specifics to formulate highly targeted treatment plans. This results in personalized medication dosages, diminished side effects, and enhanced success rates.
Digbi Health (US) is an example of a company using AI in combination with clinical, genetic, gut microbiome, and behavioral data to deliver personalized holistic care, targeting the root cause of disease. From a surgery perspective, Touch Surgery by Medtronic (UK) is a mobile surgical simulation platform that uses AI to provide training for surgical procedures, ensuring that surgeons are well-prepared and can make precise decisions during actual surgeries.
Drug and treatment discovery
AI-driven drug discovery accelerates the identification of potential compounds for new treatments. Through advanced algorithms and data analysis, AI can predict how specific compounds interact with biological targets. This expedites the drug development process, reducing costs and time-to-market. Additionally, AI helps identify patient subgroups for clinical trials, leading to more successful outcomes and broader treatment access.
Genesis Therapeutics (US) is an example of a company leveraging AI for the design and development of new pharmaceuticals, using neural networks, biophysical simulation, and a scalable computing platform.
In essence, personalization thrives on data-driven insights. Harnessing a variety of data sources, AI supports in making precise diagnoses, customizing treatments, and in predicting potential diseases.
Concepts like digital twins take personalization a step further by creating virtual replicas of individuals for healthcare purposes. These virtual models enable more efficient clinical trials by swiftly identifying subgroups, thereby reducing the cost and time required for preliminary trials, ultimately leading to greater commercial success and impact – paralleling AI’s influence in NPD as elaborated in part one of this series.
This data-driven approach results in more precise diagnoses, more effective treatments, and proactive disease prevention.
AI for scaling personalized nutrition
As part of the prevention part of the healthcare value chain, personalized nutrition takes into account an individual’s unique dietary requirements. By analyzing factors like genetics, metabolism, and dietary preferences, nutritionists and dietitians can create custom meal plans that optimize health and well-being. This targeted approach, whether the goal is longevity, performance, recovery, or weight management, ensures optimized health and well-being.
For instance, a diet favoring a diversity of antioxidant-rich fruits and vegetables may optimize for longevity, while one emphasizing protein may optimize for muscle recovery and growth. Another example is that someone genetically predisposed to high blood pressure might receive dietary recommendations aimed at reducing sodium intake while increasing consumption of potassium-rich foods like leafy greens and bananas.
AI’s ability to analyze vast and complex datasets has revolutionized how personalized nutrition is approached. It can thoroughly examine an individual’s genetic makeup, blood biomarkers, health history, dietary habits, and more. By doing so, AI can provide data-driven insights into an individual’s specific nutritional needs.
This approach sharpens the precision of personalized nutrition, guiding individuals in their food choices. Large Language Models (LLMs) add a new layer that allows us to navigate the overwhelming amount of nutritional information and generate personalized meal plans by sifting through vast nutritional databases[i].
When considering solutions in the domain of personalized nutrition, it’s vital to approach the challenge in 3D:
- Diagnose the problem
- Deliver the solution
- Demonstrate the solution’s positive impact
The third “D” is paramount. Demonstrating the efficacy of a solution ensures a user’s continued engagement. If people don’t perceive a tangible benefit, they’re unlikely to adhere to the proposed regimen or treatment. This cycle of diagnosis, delivery, and demonstration is pivotal for sustained consumer engagement and optimal health outcomes.
Data for personalized nutrition can be collected from a variety of sources, including:
- Genetic testing: Insights into genetic makeup. Delivered by healthcare entities or direct-to-consumer companies like 23andMe (US), which elucidate the genetic profiles of particular demographic segments based on specific traits.
- Blood testing: Provides insights on nutrient levels, deficiencies, and metabolic health markers. For instance, Inside Tracker (US) analyzes up to 48 blood biomarkers found to identify where your health is optimized, where there are potential concerns, and where there’s room for improvement.
- Urine testing: Offers non-invasive data on nutrient statuses, mental health, and metabolic health. Examples of companies leveraging urine-derived insights include Bisu (US) and Healthy-Longer (CH).
- Gut microbiome (stool) testing: Provides insights into metagenomics and whole genome sequencing, among others. A company using microbiome insights for personalized nutrition is Day Two (US).
- Saliva testing: Primarily captures data on hormonal status and both oral and gut microbiota. A notable example of a company working on this is Viome (US)
- Breath testing: Used to assess lung health (e.g., VO2max), digestive processes, and gut health, such as bacterial overgrowth. RespiQ (NL) is a known company in this domain.
- Medical imaging: Incorporates MRI, CT scans, BIA, and DXA scans for diverse health assessments. Ezra (US) is an example of a company offering annual scans to prevent cancer.
- Lifestyle tracking devices: Fitness trackers by e.g., Fitbit, Oura, and Whoop can provide lifestyle insights into heart rate, activity levels and sleep patterns, while continuous glucose monitors (CGMs) provide real-time glucose readings used for diabetes management. The latter technology is leveraged by companies Una Health (DE) and Nutrisense (US).
Important to note is that health is intrinsically holistic, with each data collection method offering a piece of the broader landscape. A combination of diverse data sources is needed to fully grasp well-being. As companies navigate this integration, the market may see collaborations or the emergence of all-encompassing hubs for personalized health, ultimately serving consumer’s best interests.
Personalized nutrition offerings
Building on the insights gleaned from data, the personalized nutrition sector has evolved to offer a wide array of tailored services and products to cater to individual health needs:
- Recipes: Personalized recipes designed to meet individual dietary health needs and preferences, often paired with convenient meal (kit) delivery services.
- Nutritional coaching: Access to digital expert guidance from nutritionists and dietitians providing personalized advice and support, often supported by AI-driven chatbots.
- Nutrient and food recommendations: Daily suggestions on nutrient intake and specific foods to include or avoid based on an individual’s health profile.
- Lifestyle recommendations: Personalized lifestyle recommendations encompassing sleep patterns, exercise routines, fasting schedules, and optimal eating windows to enhance overall well-being.
- Supplements & functional foods: Sale of dietary supplements designed to address specific health goals and deficiencies.
These offerings reach consumers through various distribution channels, including employee wellness programs, insurance reimbursement schemes, direct-to-consumer (DTC) platforms, and strategic partnerships with retailers.
The role of wearable technologies
The rise of digital health tools, including wearable devices, mobile apps, and health trackers, has democratized personalized healthcare. Wearable technologies complement the comprehensive vision of personalized nutrition, offering:
- Continuous health monitoring: They provide ongoing monitoring of vital statistics, activity levels, and sleep patterns.
- Personalized recommendations: These devices generate tailored feedback based on users’ data. And this feedback is dynamic; for instance, the system might ask users how they feel if an irregular heart rate is detected or send medication reminders if high blood pressure is observed.
Within the realm of wearables, there are important distinctions to consider between at-home and in-clinic testing. Wearables afford users the convenience of routine health monitoring in the comfort of their home, reducing the necessity for regular clinic visits. Furthermore, while non-invasive data collection methods like physical activity trackers and dietary logs are preferred for their ease of use in routine monitoring, there are instances where more invasive methods, like in-clinic blood tests, offer deeper insights. A blend of both approaches can offer a nuanced view of an individual’s health.
Wearable devices for health and nutrition have seamlessly integrated into various aspects of our lives. From personalized wellness solutions such as InsideTracker to corporate employee benefits programs and insurance schemes, these devices play a pivotal role in enhancing health monitoring and management. Whether individuals seek data-driven insights into their well-being or employers aim to promote a healthier workforce, wearables have become instrumental tools in achieving these objectives.
Startups to watch in personalized nutrition
- InsideTracker (US): Offers comprehensive data-based personalized nutrition services, helping individuals optimize their health through tailored dietary recommendations. Based on a decade of gathering data.
- ZOE (UK): A personalized nutrition program, integrating scientific research with AI, assesses your gut microbiome, blood sugar, and fats via at-home tests. This analysis then guides tailored nutritional advice, such as optimal food timing and ingredient combinations for the best glucose and fat responses.
- Prevess (DE): personalized nutrition program for sport clubs and athletes to improve their meals, snacking and supplements based on biometric data, and inputs from health trackers.
- Nutrigenomix (US): genetic testing for insight into how genes impact body composition, metabolism, heart health, performance, fertility, food intolerances.
- Care/of (US): Personalized daily vitamin supplement packs and powders.
- Heali (US): Platform that combines machine learning and a proprietary database to create an AI-powered nutritionist that allows users to select and customize from a comprehensive list of therapeutic diets to fit their nutrition preferences and lifestyles.
- Verdify (NL): Provides recipes tailored to users’ needs and lifestyle preferences, allowing them to establish a personalized food profile. This includes detailed dietary requirements, empowering users to adapt recipes to plant-based or gluten-free alternatives.
- Anydish (IL): Analyzes unstructured recipes and personalizes them to the user’s unique clinical requirements and distinct culinary tastes, enabling endless opportunities for people with chronic diseases and other medical conditions.
- GorMonjee (US): Fitness platform designed for making healthy food choices by adding specific minerals or a common vitamin to balance the diet, enabling users to stay healthy.
- Google’s acquisition of FitBit in 2019 for $2.1 billion marked a significant move into personalized health. FitBit’s expertise in wearable health technology and data analytics aligned with Google’s mission to provide personalized health recommendations.
- Unilever’s collaboration with Holobiome focuses on exploring the microbiome’s role in personalized nutrition. This strategic move positions Unilever to investigate innovative personalized nutrition solutions.
- In 2020, Kraft Heinz acquired Wellio, a company that employs machine learning and behavioral science to provide personalized meal suggestions and fresh ingredient delivery, promoting healthier lifestyles.
- In 2023, [Peakbridge portfolio company] InsideTracker announced its partnership and platform integration with Oura ring, unlocking a range of functionalities and insights with the convergence of advanced, real-time analytics and evidence-based health recommendations, particularly focused on sleep.
- Bayer acquired Care / of in 2020 which reinforces Bayer’s focus on self-care and advances its understanding of personalized nutrition and digital capabilities. Bayer aims to leverage Care/of’s expertise to expand its digital presence in the personalized supplements market, providing accessible health solutions.
These collaborations and acquisitions highlight the industry’s commitment to data-driven and tailored approaches to health and well-being, with substantial investments driving innovation in the field of personalized nutrition.
Challenges and investment risks
In the dynamic landscape of personalized nutrition, stakeholders need to design comprehensive solutions that seamlessly integrate with mainstream healthcare avenues, such as insurance, and democratize the reach of personalized nutrition. The goal is to provide cost-effective diagnostic tools that cater to individualized needs on a scalable level. As investors and innovators dive into this domain, several challenges emerge:
- Data accuracy, quality and reliability: While (generative) AI holds great potential in personalized nutrition, its reliance on precise health data is paramount. Medical data, especially in nutrition, can be influenced by factors like self-reporting errors, varying food portions, or unclear food descriptions. Mistakes here can drastically skew personalization efforts, potentially leading to incorrect or harmful dietary suggestions. Companies with proprietary validated data sets have an advantage.
- Data privacy: Obtaining and storing personal health data raises concerns about unauthorized access, data breaches, or misuse of sensitive information.
- Complexities around data integration: Combining data from genomics, proteomics, and metabolomics to generate a holistic nutrition profile requires nuanced expertise in both biology and data science. Any misalignment can lead to ineffective or even counterproductive dietary recommendations.
- Consumer behavior and sustained adherence: Even the most tailored nutritional advice is fruitless if not followed. The challenge lies in maintaining user engagement, especially with tracking apps, and ensuring that users adhere to personalized recommendations over time.
- Cost and accessibility: Sophisticated personalized nutrition interventions, particularly those involving genetic testing or intricate metabolomic analyses, can be costly. This limits their reach and could inadvertently exclude those who might benefit most. This is not a problem which AI can solve in isolation, but rather requires the commoditization of diagnostic tools to go mainstream.
- Limited evidence base: While personalized nutrition is a promising field, many of its strategies, like individualized nutrient supplementation, still lack extensive, peer-reviewed research to back their efficacy.
- Healthcare system integration challenges: Incorporating personalized nutrition into mainstream healthcare isn’t just about innovation; it’s also about integration. Current obstacles include digitalizing old health records, training professionals on personalized nutrition principles, ensuring consistent data formats across borders, and harmonizing the healthcare value chain’s various phases.
- Regulatory compliance: Startups may face challenges in navigating complex and changing regulations since the regulatory environment for personalized nutrition and health just started evolving.
- Reliability of wearables: Concerns persist regarding the precision and reliability of wearable technologies in health monitoring. The quality of insights can be improved by combining input from various data sources.
The startup landscape
The personalized nutrition sector is undergoing rapid transformation, with startups leveraging AI for bespoke health solutions. Our analysis categorizes these startups into two main dimensions: their focus, which ranges from preventive strategies for the general population; to intervention for those with health conditions, and their offerings, which span products to services.
By integrating artificial intelligence with deep nutritional insights, these pioneering ventures are setting the stage for a data-driven, tailored nutrition and health ecosystem.
A ‘profound’ transformation for healthcare
Building upon the themes explored in our previous articles about AI’s transformative impact on new product development and the food value chain, it becomes evident that the promise of AI extends beyond these realms.
Personalization, driven by the potent combination of AI and data-driven insights, signifies a profound transformation within the realm of healthcare. The escalating prevalence of chronic diseases underscores the urgency of shifting towards a “food as medicine” paradigm. This transition is fueled by the abundance of health data, the capabilities of AI, scientific advancements, technological innovations, heightened health awareness, and recognition of gaps in traditional healthcare.
Looking ahead, the future of healthcare relies on personalized, data-driven methodologies that empower individuals to enhance their well-being and actively prevent the development of chronic illnesses.
To realize this vision, it is imperative for companies to engage in robust collaboration, nurturing ecosystems that provide a comprehensive 3D-supported approach. This multifaceted approach ensures that personalized nutrition reaches all corners of society, offering not only extended lifespans but also an elevated quality of life.
As people assume a more proactive role in overseeing and managing their health data, a fresh era of preventive healthcare emerges, lessening the dependence on physician visits and paving the way toward a healthier future infused with data. We are only at the beginning of this exciting journey, where the intersection of AI, nutrition, and healthcare promises a revolutionary transformation that will shape our well-being for generations to come.
Read parts one and two below: