Precision nutrition: A systematic literature review
Open accessAbstract
Precision Nutrition research aims to use personal information about individuals or groups of individuals to deliver nutritional advice that, theoretically, would be more suitable than generic advice. Machine learning, a subbranch of Artificial Intelligence, has promise to aid in the development of predictive models that are suitable for Precision Nutrition. As such, recent research has applied machine learning algorithms, tools, and techniques in precision nutrition for different purposes. However, a systematic overview of the state-of-the-art on the use of machine learning in Precision Nutrition is lacking. Therefore, we carried out a Systematic Literature Review (SLR) to provide an overview of where and how machine learning has been used in Precision Nutrition from various aspects, what such machine learning models use as input features, what the availability status of the data used in the literature is, and how the models are evaluated. Nine research questions were defined in this study. We retrieved 4930 papers from electronic databases and 60 primary studies were selected to respond to the research questions. All of the selected primary studies were also briefly discussed in this article. Our results show that fifteen problems spread across seven domains of nutrition and health are present. Four machine learning tasks are seen in the form of regression, classification, recommendation and clustering, with most of these utilizing a supervised approach. In total, 30 algorithms were used, with 19 appearing more than once. Models were through the use of four groups of approaches and 23 evaluation metrics. Personalized approaches are promising to reduce the burden of these current problems in nutrition research, and the current review shows Machine Learning can be incorporated into Precision Nutrition research with high performance. Precision Nutrition researchers should consider incorporating Machine Learning into their methods to facilitate the integration of many complex features, allowing for the development of high-performance Precision Nutrition approaches.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
AI systems can integrate and analyze vast complex datasets to predict individual metabolic and behavioral responses to dietary and lifestyle interventions.
“Leveraging the power of M, these systems can integrate and analyze vast, complex datasets—including genomic, transcriptomic, metabolomic, hormonal, and environmental factors—to predict individual metabolic and behavioral responses to various dietary and lifestyle interventions.”
From Beyond Calories: Individual Metabolic and Hormonal Adaptations Driving Variability in Weight Management—A State-of-the-Art Narrative Review (2024) · cites this paper as [96]
AI is revolutionizing weight management by addressing inter-individual variability through real-time, data-driven insights.
“AI is revolutionizing weight management by addressing inter-individual variability through real-time, data-driven insights.”
From Beyond Calories: Individual Metabolic and Hormonal Adaptations Driving Variability in Weight Management—A State-of-the-Art Narrative Review (2024) · cites this paper as [96]
AI-aided platforms optimize caloric intake and macronutrient distribution based on the user's metabolic and hormonal profiles, addressing adaptive responses such as metabolic slowdown during caloric restriction.
“AI-aided platforms optimize caloric intake and macronutrient distribution based on the user’s metabolic and hormonal profiles, addressing individual adaptive responses such as metabolic slowdown during caloric restriction.”
From Beyond Calories: Individual Metabolic and Hormonal Adaptations Driving Variability in Weight Management—A State-of-the-Art Narrative Review (2024) · cites this paper as [96]
AI platforms integrate behavioral psychology principles, tracking adherence and identifying factors contributing to deviations.
“AI platforms also integrate behavioral psychology principles, tracking adherence trends and identifying factors contributing to deviations.”
From Beyond Calories: Individual Metabolic and Hormonal Adaptations Driving Variability in Weight Management—A State-of-the-Art Narrative Review (2024) · cites this paper as [96]
AI can integrate data streams to refine predictive models, enabling a dynamic, iterative approach to weight management.
“AI can integrate these data streams to refine predictive models, enabling a dynamic, iterative approach to weight management that evolves alongside the user’s changing physiology.”
From Beyond Calories: Individual Metabolic and Hormonal Adaptations Driving Variability in Weight Management—A State-of-the-Art Narrative Review (2024) · cites this paper as [96]
Wearable devices provide real-time physiological data, while advanced biomarkers offer deeper insights into metabolic states.
“Wearable devices, such as continuous glucose monitors (CGMs) and smartwatches, provide real-time physiological data, while advanced biomarkers, including metabolomic and proteomic profiles, offer deeper insights into metabolic states.”
From Beyond Calories: Individual Metabolic and Hormonal Adaptations Driving Variability in Weight Management—A State-of-the-Art Narrative Review (2024) · cites this paper as [96]
AI can adjust meal timing and composition based on detected variations in metabolic rate or hormonal fluctuations.
“For example, AI can adjust meal timing and composition based on detected variations in metabolic rate or hormonal fluctuations [96,97,98,99,100,101,102].”
From Beyond Calories: Individual Metabolic and Hormonal Adaptations Driving Variability in Weight Management—A State-of-the-Art Narrative Review (2024) · cites this paper as [96]
AI algorithms can incorporate circadian rhythms, physical activity levels, and psychological stressors to refine recommendations.
“These algorithms can also incorporate circadian rhythms, physical activity levels, and psychological stressors to refine the recommendations further [96,97,98,99,100,101,102].”
From Beyond Calories: Individual Metabolic and Hormonal Adaptations Driving Variability in Weight Management—A State-of-the-Art Narrative Review (2024) · cites this paper as [96]
Predictive analytics can anticipate challenges such as low motivation or high stress, offering proactive interventions.
“Through predictive analytics, these systems can anticipate challenges such as periods of low motivation or high stress, offering proactive interventions to maintain consistency.”
From Beyond Calories: Individual Metabolic and Hormonal Adaptations Driving Variability in Weight Management—A State-of-the-Art Narrative Review (2024) · cites this paper as [96]
Machine learning algorithms can identify specific macronutrient ratios that minimize hunger while maximizing fat oxidation and muscle preservation.
“For example, ML algorithms can identify the specific macronutrient ratios that minimize hunger while maximizing fat oxidation and muscle preservation, thereby enhancing the efficacy of precision nutrition strategies.”
From Beyond Calories: Individual Metabolic and Hormonal Adaptations Driving Variability in Weight Management—A State-of-the-Art Narrative Review (2024) · cites this paper as [96]
AI provides continuous real-time feedback through wearable devices, mobile applications, and connected health platforms.
“One of AI’s most transformative features is its ability to provide continuous, real-time feedback through wearable devices, mobile applications, and connected health platforms.”
From Beyond Calories: Individual Metabolic and Hormonal Adaptations Driving Variability in Weight Management—A State-of-the-Art Narrative Review (2024) · cites this paper as [96]
AI-powered platforms, wearable devices, and continuous glucose monitors enhance adherence by tracking biometrics and offering actionable insights to users, making weight management dynamic and adaptive to individual needs.
“These tools enhance adherence by tracking biometrics and offering actionable insights to users, making weight management dynamic and adaptive to individual needs [96,97,98,99,100,101,102].”
From Beyond Calories: Individual Metabolic and Hormonal Adaptations Driving Variability in Weight Management—A State-of-the-Art Narrative Review (2024) · cites this paper as [96]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
The citation lists haven’t been fetched yet
References 127 · Cited by 238
Research works printed but not linked: 1
- K.P. Murphy, Machine Learning: a Probabilistic Perspective (Adaptive Computation and Machine Learning Series), Mit Press, 2012.
- Authors
- Kirk, D., Catal, C., & Tekinerdogan, B.
- Affiliations
- Wageningen University & Research (NL)
- Qatar University (QA)
- Corresponding author
- Catal, Cagatay
- Venue
- Computers in Biology and Medicine
- Publisher
- Elsevier BV
- Published
- 2021-06
- Volume
- 133
- Pages
- 104365
- ISSN
- 0010-4825 (ISSN-L)
- OpenAlex
- W3145026862
- Cited by
- 238
- References
- 127
- Citations by year
- 2026: 49 · 2025: 73 · 2024: 51 · 2023: 34 · 2022: 25 · 2021: 6
- FWCI
- 4.27
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Computers in Biology and Medicine (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Socio-Environmental Systems Modeling (submittedVersion, cc-by)
- Figshare (submittedVersion, cc-by)
- Qatar University QSpace (Qatar University) (submittedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
- Funders
- Qatar National Library
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Nutrition, Genetics, and Disease
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- personalized nutrition, machine learning, nutritional advice, prediction model, supervised learning, regression analysis, classification, recommender systems, clustering, model evaluation metrics, input features, artificial intelligence in nutrition
- MeSH terms
- Machine Learning, Algorithms, Artificial Intelligence, Humans, Nutritional Status, Databases, Factual
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
