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Precision Nutrition and Lifespan Health Outcomes

A special issue of Nutrients (ISSN 2072-6643). This special issue belongs to the section "Nutrition and Public Health".

Deadline for manuscript submissions: 31 May 2025 | Viewed by 906

Special Issue Editor


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Guest Editor
National Heart Lung and Blood Institute (NHLBI), National Institutes of Health, Bethesda, MD, USA
Interests: precision nutrition; cardiovascular nutrition; nutrition in lung diseases; nutrition in blood diseases; chrononutrition

Special Issue Information

Dear Colleagues,

Precision nutrition is an approach that uses information on individual and population characteristics to develop targeted nutritional advice, products, or services to enable the achievement of lasting dietary behavior change that is beneficial for health and wellbeing. It is analogous to precision prevention and embraces the integration of individual- and community-level data, including biologic, behavioral, socioeconomic, and epidemiologic data, to create and implement strategies tailored to reducing diet-related chronic diseases and health conditions in specific individuals or populations. It acknowledges the heterogeneity in responses to dietary interventions and the need to tailor and/or adapt dietary recommendations to individuals’ or families’ of differing physiologic, socioeconomic, or cultural circumstances. It integrates public health with health professionals’ care by considering socioeconomic, psychological/behavioral, and environmental/cultural factors in creating programs to improve the quality of life of individuals and populations. The inclusion of social determinants of health in an analytic framework recognizes the powerful effects of structural factors that influence diet-related chronic diseases, such as geography, income, education, and structural racism. Advances in Artificial Intelligence (AI) and Machine Learning (ML) are enabling integration and harmonization of individual- (e.g., dietary data) and community-level data, and combined with implementation, have the potential to provide the right dietary intervention for the right person or community at the right time. The use of data analytic tools that identify phenotypes in whom dietary recommendations could have beneficial effects underscores the opportunity for precision nutrition to mitigate health disparities in individuals and communities.

In this Special Issue on precision nutrition and lifespan health outcomes, we welcome submissions focusing on epidemiologic studies and clinical trials in precision nutrition across the lifespan (i.e., developmental transitions—pregnancy through childhood and adulthood, menopause, and older adulthood) with the goal of reducing disparities in diet-related chronic diseases and improving health and wellbeing.

Dr. Charlotte A Pratt
Guest Editor

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Nutrients is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • precision nutrition
  • personalized nutrition
  • tailored nutrition
  • health equity
  • health disparities
  • health promotion
  • implementation science
  • chronic diseases
  • cardiovascular diseases
  • low-resource settings
  • data analytics
  • artificial intelligence
  • machine learning

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Published Papers (1 paper)

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Research

22 pages, 5879 KiB  
Article
Tlalpan 2020 Case Study: Enhancing Uric Acid Level Prediction with Machine Learning Regression and Cross-Feature Selection
by Guadalupe Gutiérrez-Esparza, Mireya Martínez-García, Manlio F. Márquez-Murillo, Malinalli Brianza-Padilla, Enrique Hernández-Lemus and Luis M. Amezcua-Guerra
Nutrients 2025, 17(6), 1052; https://doi.org/10.3390/nu17061052 - 17 Mar 2025
Viewed by 532
Abstract
Background/Objectives: Uric acid is a key metabolic byproduct of purine degradation and plays a dual role in human health. At physiological levels, it acts as an antioxidant, protecting against oxidative stress. However, excessive uric acid can lead to hyperuricemia, contributing to conditions like [...] Read more.
Background/Objectives: Uric acid is a key metabolic byproduct of purine degradation and plays a dual role in human health. At physiological levels, it acts as an antioxidant, protecting against oxidative stress. However, excessive uric acid can lead to hyperuricemia, contributing to conditions like gout, kidney stones, and cardiovascular diseases. Emerging evidence also links elevated uric acid levels with metabolic disorders, including hypertension and insulin resistance. Understanding its regulation is crucial for preventing associated health complications. Methods: This study, part of the Tlalpan 2020 project, aimed to predict uric acid levels using advanced machine learning algorithms. The dataset included clinical, anthropometric, lifestyle, and nutritional characteristics from a cohort in Mexico City. We applied Boosted Decision Trees (Boosted DTR), eXtreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), and Shapley Additive Explanations (SHAP) to identify the most relevant variables associated with hyperuricemia. Feature engineering techniques improved model performance, evaluated using Mean Squared Error (MSE), Root-Mean-Square Error (RMSE), and the coefficient of determination (R2). Results: Our study showed that XGBoost had the highest accuracy for anthropometric and clinical predictors, while CatBoost was the most effective at identifying nutritional risk factors. Distinct predictive profiles were observed between men and women. In men, uric acid levels were primarily influenced by renal function markers, lipid profiles, and hereditary predisposition to hyperuricemia, particularly paternal gout and diabetes. Diets rich in processed meats, high-fructose foods, and sugary drinks showed stronger associations with elevated uric acid levels. In women, metabolic and cardiovascular markers, family history of metabolic disorders, and lifestyle factors such as passive smoking and sleep quality were the main contributors. Additionally, while carbohydrate intake was more strongly associated with uric acid levels in women, fructose and sugary beverages had a greater impact in men. To enhance model robustness, a cross-feature selection approach was applied, integrating top features from multiple models, which further improved predictive accuracy, particularly in gender-specific analyses. Conclusions: These findings provide insights into the metabolic, nutritional characteristics, and lifestyle determinants of uric acid levels, supporting targeted public health strategies for hyperuricemia prevention. Full article
(This article belongs to the Special Issue Precision Nutrition and Lifespan Health Outcomes)
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