Precision Nutrition

Precision Nutrition (PN) is an approach to developing comprehensive and dynamic nutritional recommendations based on individual variables, including genetics, microbiome, metabolic profile, health status, physical activity, dietary pattern, food environment as well as socioeconomic and psychosocial characteristics. (Berciano, S ,2022)
Modern nutrition, which is a core element in the prevention and treatment of chronic diseases, has gradually entered the digital age and raised broader needs and higher standards for achieving individualized precision nutrition.
Digital nutrition intervention, and intelligent nutrition education is required in order to provide a reference for realizing national nutrition and health by digital means. (Wang, H. , 2022)
To conclude, a few limitations and drawbacks of digital technology and precision nutrition are to be mentioned:
1. Data accuracy, accessibility, and further research are required.
2. Frequent development and upgrade is needed in digital nutrition.
3. Lacks personal understanding and conversations with the expert and the client. (Maulucci, G. ,2024)

References:
1. Berciano, S., Figueiredo, J., Brisbois, T. D., Alford, S., Koecher, K., Eckhouse, S., Ciati, R., Kussmann, M., Ordovas, J. M., Stebbins, K., & Blumberg, J. B. (2022). Precision nutrition: Maintaining scientific integrity while realizing market potential. Frontiers in Nutrition, 9. //doi.org/10.3389/fnut.2022.979665

2. Zheng, J. L., Jiang, S., Li, X. G., & Wang, H. (2022). [Application and prospect of digital technology on personalized precision nutrition]. Zhonghua Yu Fang Yi Xue Za Zhi [Chinese Journal of Preventive Medicine], 56(12), 1872–1879. //doi.org/10.3760/cma.j.cn112150-20220628-00669

3.Alessio Abeltino, Alessia Riente, Bianchetti, G., Serantoni, C., Marco De Spirito, Stefano Capezzone, Esposito, R., & Maulucci, G. (2024). Digital applications for diet monitoring, planning, and precision nutrition for citizens and professionals: a state of the art. Nutrition Reviews. //doi.org/10.1093/nutrit/nuae035

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“Precision nutrition is defined as nutrition or dietary guidance designed to optimize health, facilitate disease prevention, and enhance therapeutic benefit through molecular (metabolomic, genomic, proteomic, metagenomic) profiling at the level of the individual.” Precision nutrition aims to understand these complex interrelations to optimize metabolic responses to diet and ultimately make sustainable and targeted individual nutritional recommendations to prevent and treat diseases and improve overall health and well-being.

Precision nutrition integrates genetic, metagenomic, metabolomic, physiopathological, behavioral and sociocultural cues to understand metabolism and human wellbeing and implement health actions. Such wide-ranging measures require advances in 1) high-throughput multi-omics techniques, and 2) integrative big data systems. Over recent decades, research in the fields of nutritional genetic, epigenetics, genomics, metabolomics and metagenomics has accelerated exponentially. These approaches provide deep genotypic and phenotypic insights into human variability in response to diet, which has informed a new era of personalized and precision nutrition interventions. Moreover, advances in big data and machine learning have paved the way for integrated precision nutrition applications across research, industry and healthcare.

Advancements in precision nutrition will mean better tailoring of dietary interventions to different people's circumstances and situations. It is not yet clear how specific this tailoring may need to be, as precision nutrition does not necessarily mean personalized nutrition when each person should get dietary recommendations specific to himself or herself. Much consideration and research will be needed before more precise nutrition recommendations can be achieved. This includes better understanding and accounting for variables such as age, sex, ethnicity, medical history, genetics, and social and environmental factors. The advent of new methods and technologies and the availability of considerably more data bring tremendous opportunity. However, we must proceed with appropriate levels of caution and make sure that the variables listed above are all appropriately considered, and systems approaches, and methods, are incorporated. Last, it will be important to develop and train an expanded workforce with the goal toward reducing health disparities and improving the precision of nutritional advice for all Americans.

References

//doi.org/10.1016/j.tifs.2022.08.017

Lee, B. Y., Ordovás, J. M., Parks, E. J., Anderson, C. A. M., Barabási, A. L., Clinton, S. K., de la Haye, K., Duffy, V. B., Franks, P. W., Ginexi, E. M., Hammond, K. J., Hanlon, E. C., Hittle, M., Ho, E., Horn, A. L., Isaacson, R. S., Mabry, P. L., Malone, S., Martin, C. K., Mattei, J., … Martinez, M. F. (2022). Research gaps and opportunities in precision nutrition: an NIH workshop report. The American journal of clinical nutrition, 116(6), 1877–1900. //doi.org/10.1093/ajcn/nqac237

The healthcare industry has seen a dramatic change in focus, moving from a reactive to a proactive approach that emphasises precision medicine and P4 (Preventive, Predictive, personalized, and Participatory). I would like to take your attention to related fields such as nutrigenomics and  Ayu Nutrigenomics,which also provide nutrition tailored to the individual, considering the composition of the body of the patient (Banerjee et al., 2015). The application of nutrigenomic technologies offered various insights on biomarkers of food consumption, undernutrition, metabolic syndrome, and its consequences. Crucially, nutrigenomic research also revealed a link between a number of genetic variants and variations in vitamin absorption and metabolism. The Ayurveda-inspired concept of personalized nutrition is a novel concept of nutrigenomic research for developing personalized functional foods and nutraceuticals suitable for one's genetic makeup with the help of Ayurveda.According to this concept, even if a food habit is harmful considering a person's genetic constitution, due to climatic (Ritu Satmya), geographical(Desha Satmya), disease (Roga Satmya), regular habit (Oka Satmya),and sociocultural (Jati Satmya) factors, his or her nutriome may become adjusted to that food habit. 

Reference: //doi.org/10.1016/j.jtcme.2014.12.009 ‌" target="_blank" rel="noopener">Banerjee, S., Debnath, P., & Debnath, P. K. (2015). Ayurnutrigenomics: Ayurveda-inspired personalized nutrition from inception to evidence. Journal of Traditional and Complementary Medicine, 5(4), 228–233. //doi.org/10.1016/j.jtcme.2014.12.009 ‌

//sci-hub.se///www.sciencedirect.com/science/article/pii/S222541101500019X ‌" target="_blank" rel="noopener">Sci-Hub | Ayurnutrigenomics: Ayurveda-inspired personalized nutrition from inception to evidence. Journal of Traditional and Complementary Medicine, 5(4), 228–233 | 10.1016/j.jtcme.2014.12.009. (2014). Sci-Hub.se. //sci-hub.se///www.sciencedirect.com/science/article/pii/S222541101500019X ‌

Precision nutrition studies health effects of nutritional exposure, considering human biology, environment, and social determinants. It offers beneficial nutrients to those most likely to benefit, while limiting harmful ones. Precision nutrition is an addition to healthy eating for diabetes prevention and management, but it does not replace other strategies like weight loss or glucose-lowering medications. Precision nutrition requires precise tools, such as objective biomarkers of diet, and clinical trials to capture dynamic nutritional responses. Deep phenotyping could detect subtle changes in health markers at the earliest possible time, allowing anticipatory prevention strategies targeted towards disrupted processes. However, the cost-effectiveness of precision nutrition in diabetes is not yet rigorously evaluated. Equitable access to effective precision nutrition advice should be ensured, especially in low- and middle-income countries, where diabetes prevalence is higher among ethnic minorities or those living in areas with higher deprivation. Long-term clinical trials are needed to assess head-to-head comparisons between precision nutrition interventions and population-wide approaches. Precision nutrition recommendations should be delivered through advanced methods of communication and education, incorporating real-time data collection and feedback to reinforce positive behaviors (1).

Precision nutrition is a personalized strategy for obesity management, highlighting the significance of personal variations in genetics, gut microbiome, metabolism, and behavior. It strives to deliver custom dietary advice to promote effective weight control and enhance overall well-being. Advances in technology, particularly in machine learning, have improved our ability to analyze genetic data and predict metabolic responses, which are crucial for creating personalized nutrition plans. While precision nutrition shows potential in revolutionizing obesity management, its effectiveness compared to traditional methods needs further validation. The progress of Precision nutrition relies on the outcomes of thorough scientific studies and the accumulation of more extensive evidence (2).

The precision nutrition research gaps and opportunities report on the holistic approach of precision nutrition to develop comprehensive nutrition recommendations and to optimize metabolic responses through tailored dietary approaches, considering factors like dietary habits, genetic makeup, health status, microbiome, metabolism, and environmental influences. It highlighted the use of artificial intelligence to create individualized dietary recommendations and addressed the need for research and education effort in the domain (3).

References:

1. Merino, J. (2022). Precision nutrition in diabetes: when population-based dietary advice gets personal. Diabetologia, 65(11), 1839–1848. //doi.org/10.1007/s00125-022-05721-6

2. Hande Gül Ulusoy-Gezer, & Neslişah Rakıcıoğlu. (2024). The Future of Obesity Management through Precision Nutrition: Putting the Individual at the Center. Current Nutrition Reports. //doi.org/10.1007/s13668-024-00550-y

3. Lee, B. Y., Ordovás, J. M., Parks, E. J., Cheryl AM Anderson, Albert-László Barabási, Clinton, S. K., Kayla, Duffy, V. B., Franks, P. W., Ginexi, E. M., Hammond, K. J., Hanlon, E. C., Hittle, M., Ho, E., Horn, A. L., Isaacson, R. S., Mabry, P. L., Malone, S., Martin, C. K., & Mattei, J. (2022). Research gaps and opportunities in precision nutrition: an NIH workshop report. ˜the œAmerican Journal of Clinical Nutrition, 116(6), 1877–1900. //doi.org/10.1093/ajcn/nqac237

People respond differently to food. This field of study, called precision nutrition, aims to understand these differences and create personalized diets for individuals. Researchers have long known that genes can affect how the body processes nutrients. New technologies allow scientists to study these effects in more detail. However, there are challenges to implementing precision nutrition. First, it is difficult to accurately measure what people eat. Second, researchers need to find ways to combine information from different sources. Third, healthcare professionals need training to interpret this information and create personalized plans. Finally, patients need help sticking to these plans. Despite these challenges, studies have shown that precision nutrition can be effective. In one study, participants who received personalized dietary recommendations based on their genes and other factors saw improvement in their health. Another study identified specific genes and dietary factors that can predict obesity. The ultimate goal of precision nutrition is to create customized diets that help people achieve optimal health. With new technologies and approaches, this field has the potential to revolutionize healthcare.

Reference:

Connell, J., Toma, R., Ho, C. H. C., Shen, N., Moura, P., Le, T., ... & Vuyisich, M. (2021). Data-driven precision nutrition improves clinical outcomes and risk scores for IBS, depression, anxiety, and T2D. American Journal of Lifestyle Medicine, 15598276231216393. doi:10.1101/2021.04.24.441290.

Lee, Y. C., Christensen, J. J., Parnell, L. D., Smith, C. E., Shao, J., McKeown, N. M., ... & Lai, C. Q. (2022). Using machine learning to predict obesity based on genome-wide and epigenome-wide gene–gene and gene–diet interactions. Frontiers in genetics, 12, 783845. doi:10.3389/fgene.
2021.783845.

Voruganti V. S. (2023). Precision Nutrition: Recent Advances in Obesity. Physiology (Bethesda, Md.), 38(1), 0. //doi.org/10.1152/physiol.00014.2022

The concept of precision nutrition represents a transformative shift in nutritional science, emphasizing the necessity for individualized dietary recommendations. Historically, nutrition science has predominantly operated under a one-size-fits-all paradigm, which often fails to account for the substantial variability in individual responses to dietary intake. Precision nutrition addresses this gap by aiming to tailor nutritional strategies based on an individual's unique genetic, metabolic, and environmental profiles.

This review highlights the evolving nature of precision nutrition by dissecting its framework into four pivotal components. First, it underscores the importance of defining individualized nutritional requirement phenotypes. This theoretical foundation is crucial because it recognizes that dietary needs and responses can vary significantly among individuals due to genetic differences, lifestyle factors, and health status.

Secondly, the review emphasizes the necessity for precise methods to measure dietary intake and evaluate nutritional status. Traditional dietary assessment tools often lack accuracy and fail to capture the complexity of dietary behaviors and nutrient absorption. Advancements in technology, such as wearable devices and biomarkers, are pivotal in providing more accurate and individualized nutritional assessments.

The third component involves developing multidimensional nutritional intervention strategies. These strategies must address not only what individuals should eat but also how and when they should eat. This holistic approach considers meal timing, food combinations, and personalized dietary plans that align with an individual's metabolic rhythms and health goals.

The fourth component focuses on the translation of scientific research into healthcare practices, facilitated by artificial intelligence (AI) and information platforms. AI can play a crucial role in processing large datasets, identifying patterns, and providing personalized dietary recommendations in real-time. Integrating these technologies into healthcare systems can enhance the delivery of precision nutrition interventions and improve health outcomes.

Despite the progress in these areas, the field of precision nutrition still faces several challenges. There is a notable lack of comprehensive review studies that map the landscape of precision nutrition, and more research is needed to fully understand the complex interactions between diet, genetics, and health. Furthermore, translating precision nutrition research into practical applications requires collaboration across disciplines, including genomics, bioinformatics, and behavioral science.

Future research should explore innovative ways to overcome these challenges, such as developing standardized protocols for nutritional phenotype assessments, enhancing the accuracy of dietary intake measurements, and creating scalable AI-driven platforms for personalized nutrition counseling. By addressing these gaps, precision nutrition has the potential to revolutionize dietary recommendations and significantly improve public health outcomes.

Reference : Tianshu Han a b 1 et al. (2024) The future landscape and framework of Precision Nutrition, Engineering. Available at: //www.sciencedirect.com/science/article/pii/S2095809924000754 (Accessed: 12 June 2024).

Precision nutrition is the practice of customizing dietary interventions or recommendations to an individual's genetic background, metabolic profile, and environmental exposures in order to prevent and manage chronic diseases. Recent advancements in genetics, metabolomics, and gut microbiome technologies have presented both potential and obstacles for using precision nutrition to prevent and control type 2 diabetes. Nutrigenomics research have found genetic variations that regulate specific nutrient intake and metabolism, as well as predict individual variability in response to dietary interventions. Metabolomics has revealed metabolomic fingerprints of food and nutrient consumption, as well as new metabolic pathways that diet may influence. Dietary interventions have been successful in changing the abundance, composition, and activity of gut microbiota that are relevant for food metabolism and glycemia.

References: //www.thelancet.com/journals/landia/article/PIIS2213-8587(18)30037-8/abstract

Nutrigenetics is considered the foundation of precision nutrition. Genetic variation in the form of single nucleotide polymorphisms (SNPs) is considered to account for the heterogeneity in individual dietary response and risk for obesity and type 2 diabetes. Nutrigenetic research has investigated the interactions between SNPs influencing body composition, insulin signaling, and dietary factors in relation to adiposity and glucose homeostasis in obesity and type 2 diabetes.

Reference:

//link.springer.com/article/10.1007/s13668-023-00491-y

Precision Nutrition is an individualized dietary approach that takes into account a person's genetic, environmental, and lifestyle factors,offering more personalized recommendations compared to traditional nutrition science, whichfocuses on average responses. Nutrigenomics, comprising nutrigenomics and nutrigenetics,explores how genetics and diet interact to influence various diseases. While monogenicdiseases can sometimes be managed through dietary adjustments, common conditions likeheart disease, diabetes, obesity, and cancer result from complex interactions between genes,environment, and diet. Nutrigenomics provides insights into disease prevention andpersonalized nutrition. Nutrigenetics focuses on how an individual's genetic makeup affectstheir responses to diet, impacting health, well-being, and disease susceptibility. Thisknowledge helps prevent or manage diet-related diseases and offers personalized dietaryguidance. In complex diseases like type 2 diabetes (T2DM), genetics play a significant role,but identifying causative genes is challenging due to multiple genes and environmental factors.Obesity is influenced by genetic and environmental factors, with over 600 gene markersassociated with human obesity. Recent technologies can analyze genetic variations involved.Precision nutrition, encompassing various omics technologies, tailors’ dietary guidance toindividual factors, providing personalized advice for better health outcomes. Challengesinclude the need for well-designed clinical trials, cost-effective technology, healthcare providereducation, ethical considerations, and accessibility. Precision nutrition has the potential torevolutionize nutrition science, offering tailored dietary recommendations based on individualdifferences in gene expression and enhancing overall health through the collaboration ofmultiple fields.

Reference: //www.researchgate.net/publication/377895758_PRECISION_NUTRITION_USING_NUTRIGENETIC_AND_NUTRIGENOMIC_CONCEPTS_IN_PERSONALIZED_NUTRITION [accessed Jun 17 2024].

Precision Nutrition utilizes human variability to design tailored dietary interventions to improve human health. It integrates metabolic, genetic, metagenomic, metabolomic, physio-pathological, behavioral, and sociocultural cues to understand metabolism and human well-being and implement health actions (Livingstone KM et al., 2022).

Mortazavi et al. (2023) provided an overview of current technology related to precision nutrition. These were explored to address the issues faced while tracking the dietary history and providing tailored interventions to the patients using conventional methods. They reviewed several technologies and digital tools that can significantly reduce the burden of dietary monitoring against traditional methods that require users to look up the nutritional content of foods in a calorie book and then manually enter the information in the records. Examples of tools that were explored in the study were:

1. Dieting mobile applications with the nutritional content of millions of food items. However, the accuracy of the database might be an issue. These databases can greatly simplify work by providing precise nutritional information about meals and guides about choosing portion sizes and meals. Another advantage of these apps is their ability to scan barcodes for packaged foods, which reduces the need to look up the food in a database or enter food nutrients manually. Finally, dieting apps can also be integrated with external devices, such as smart scales, fitness trackers, and continuous glucose monitors (CGMs) to help users understand the effect of diet and exercise on their weight trends and glucose patterns.
2. Physical sensors containing inertial measurement units for logging food intake by detecting specific gestures like eating. While these sensing systems provide accurate results in laboratory settings, accounting for accurate results in real-life environments remains a challenge using only wearable motion sensors though some success has been found in moving these motion sensors from the wrist to the head and mouth area (eg, jawbone).
3. Chemical sensors: dietary biomarkers that can be measured with wearable or handheld sensors, and continuous glucose monitoring (CGM) for managing type 1 diabetes. The mechanism by which CGMs may be used to monitor diet is based on the change in blood glucose after a meal, also known as the post-prandial glucose response (PPGR). The major determinant of post-prandial glucose is the amount of carbohydrates, but adding protein, fat, or fiber to a meal generally yields smaller increases and lengthier responses. Hence, the shape of the PPGR can be used to recover the macronutrient composition of the meal through the use of machine-learning techniques.

• Handheld devices are also available to analyze breath biomarkers associated with metabolism. A primary target of these devices is ketones. These devices are aimed at people attempting to lose weight through ketogenic diets, but may also be beneficial for people with diabetes who may be at risk of ketoacidosis.
• Metabolic biomarker, derived from breath analysis, metabolic fuel, a parameter that reflects the body’s fuel preference for energy production (ie, carbohydrates vs. fat). Metabolic fuel is generally estimated as the respiratory exchange ratio (RER), the ratio of CO2 produced during metabolism and oxygen used.
• Dietary biomarkers can be extracted ambulatorily with wearable sensors from other bodily fluids. A primary target of these devices is sweat since it can be measured at convenient body locations and is ideal for continuous monitoring. A variety of analytes present in sweat may be of interest for metabolic disorders, including various electrolytes, glucose, lactate, ammonia, ethanol, cortisol, and hydration markers

References:

Livingstone, K. M., Ramos-Lopez, O., Pérusse, L., Kato, H., Ordovas, J. M., & Martínez, J. A. (2022). Reprint of: Precision nutrition: A review of current approaches and future endeavors. Trends in Food Science & Technology, 130, 51-62.  //doi.org/10.1016/j.tifs.2022.10.010

Mortazavi, B. J., & Gutierrez-Osuna, R. (2023). A review of digital innovations for diet monitoring and precision nutrition. Journal of diabetes science and technology, 17(1), 217-223.

Precision nutrition is an emerging concept that aims to develop nutrition recommendations tailored to different people’s circumstances and biological characteristics. Responses to dietary changes and the resulting health outcomes from consuming different diets may vary significantly between people based on interactions between their genetic backgrounds, physiology, microbiome, underlying health status, behaviors, social influences, and environmental exposures(1).

Digital applications designed for citizens allow diet self-monitoring and can be effective tools for weight and diabetes management. These applications enable individuals to track their dietary intake, monitor their progress, and receive personalized feedback, thus empowering them to make informed decisions about their nutrition. For professionals, digital precision nutrition solutions provide scalability, personalized recommendations for patients, and a means of offering ongoing diet support. These solutions help healthcare providers to efficiently manage a larger number of patients while delivering customized dietary advice based on individual health data(2).

A number of sensor-based approaches are being developed to automate the process of tracking eating behaviors, thus reducing user burden and increasing measurement accuracy. These sensing modalities can be organized into two broad categories: physical sensors and chemical sensors. Physical sensors, such as wearable devices and smart utensils, monitor physical activity and food intake, while chemical sensors analyze biomarkers from biological samples like saliva or blood to provide insights into nutritional status(3).

Technologies and digital tools significantly reduce the burden of dietary monitoring compared to traditional methods that require users to look up the nutritional content of foods in a calorie book and manually enter the information in a log book. There has been a market explosion of direct-to-consumer (DTC) wearables and medical devices, along with associated apps, aimed at encouraging individuals to actively participate in their own health and wellness behavior changes or disease management(4).

Overall, the integration of digital technologies with precision nutrition offers a promising pathway to more personalized, efficient, and effective dietary management, potentially leading to better health outcomes for individuals.

References: 

1. Lee, B. Y., Ordovás, J. M., Parks, E. J., Anderson, C. A., Barabási, A.-L., Clinton, S. K., de la Haye, K., Duffy, V. B., Franks, P. W., Ginexi, E. M., Hammond, K. J., Hanlon, E. C., Hittle, M., Ho, E., Horn, A. L., Isaacson, R. S., Mabry, P. L., Malone, S., Martin, C. K., & Mattei, J. (2022). Research gaps and opportunities in precision nutrition: an NIH workshop report. The American Journal of Clinical Nutrition, 116(6), 1877–1900. //doi.org/10.1093/ajcn/nqac237
2. Alessio Abeltino, Alessia Riente, Bianchetti, G., Serantoni, C., Marco De Spirito, Stefano Capezzone, Esposito, R., & Maulucci, G. (2024). Digital applications for diet monitoring, planning, and precision nutrition for citizens and professionals: a state of the art. Nutrition Reviews. //doi.org/10.1093/nutrit/nuae035
3. Mortazavi, B. J., & Gutierrez-Osuna, R. (2021). A Review of Digital Innovations for Diet Monitoring and Precision Nutrition. Journal of Diabetes Science and Technology, 193229682110413. //doi.org/10.1177/19322968211041356
4.  Moore, J. B. (2020). From personalised nutrition to precision medicine: the rise of consumer genomics and digital health. Proceedings of the Nutrition Society, 79(3), 300–310. doi:10.1017/S0029665120006977

Precision nutrition (PN) is an emerging concept that focuses on understanding factors associated with an individual’s variations in metabolism as well as within the communities. With evolving advances in nutrition and technologies, it is essential to understand the role of PN in maintaining health and well-being.  

Application of PN is being used to provide customized dietary recommendations, one of the emerging areas is obesity, a recent review aimed to identify the components that are to be considered while applying it to manage obesity and issues associated. Interestingly the results show three major components that are gene-nutrient interactions, intestinal microbiota, and lifestyle factors. Various PN contributes to improving obesity conditions, and diet quality, and enhancing adherence to exercise or physical activities amongst obese patients.

Precision nutrition (PN) and its application has been recognized in data collection which aims to provide nutritional recommendations and improve nutritional status. Recent research revealed PN focuses on the stratification of at-risk groups which may contribute to and resolve issues in low-middle-income countries, however, certain prerequisites such as funds, affordability, training, etc are essential to address nutrition challenges in limited-resource communities.

Literature directs towards integration of AI and technology interventions, for regulating nutrition intake amongst beneficiaries, focusing on weight loss and dietary treatments which have indicated promising results, thus, indicating the potential of PN in addressing sustainable development goals (SDGs) and achieving universal health coverage.

References: 

Mansour, S., Alkhaaldi, S. M. I., Sammanasunathan, A. F., Ibrahim, S., Farhat, J., & AlOmari, B. (2024). Precision nutrition unveiled: Gene–Nutrient interactions, microbiota dynamics, and lifestyle factors in obesity management. Nutrients, 16(5), 581. //doi.org/10.3390/nu16050581

Antonelli M, Donelli D. Precision Nutrition and Artificial Intelligence Mobile Apps: A Narrative Review. Biology and Life Sciences Forum. 2023; 29(1):25. //doi.org/10.3390/IECN2023-15532  

Bedsaul-Fryer, J. R., Van Zutphen-Küffer, K. G., Monroy-Gomez, J., Clayton, D. E., Gavin-Smith, B., Worth, C., Schwab, C. N., Freymond, M., Surowska, A., Martins, L. B., Senn-Jakobsen, C., & Kraemer, K. (2023). Precision Nutrition Opportunities to help Mitigate nutrition and health challenges in Low- and Middle-Income Countries: an expert Opinion survey. Nutrients, 15(14), 3247. //doi.org/10.3390/nu15143247