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The Application of Machine Learning in Nutritional Epidemiology

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Dear Colleagues,

Nutritional epidemiology explores the relationships between diet, health, and disease within human populations. The emergence of non-communicable diseases (NCDs)—such as heart disease, diabetes, cancer, and chronic respiratory diseases—as leading global causes of mortality underscores the critical need to better understand the long-term impact of diet on health. Traditional methods in nutritional epidemiology face challenges, including the complexity of dietary patterns, measurement errors, and the multifactorial nature of diet-disease relationships.

The application of machine learning (ML) offers innovative opportunities to address these challenges by uncovering complex patterns, improving dietary assessments, and identifying causal pathways. ML methods have the potential to enhance data-driven insights, integrate multidimensional datasets, and provide personalised dietary recommendations for the prevention and management of NCDs.

This Special Issue aims to showcase feature papers (including original research and review articles) on the use of machine learning in nutritional epidemiology. Topics of interest include, but are not limited to, the following:

Development and application of ML techniques for dietary assessment.
ML-driven identification of dietary patterns linked to NCDs.
Integration of machine learning with causal inference methods to study diet–health relationships.
ML approaches for addressing measurement errors and missing data in dietary studies.
Predictive modelling of dietary impacts on human and planetary health.
Ethical and methodological considerations in applying ML to nutritional research.

By advancing the intersection of machine learning and nutritional epidemiology, this Special Issue seeks to foster innovative solutions to reduce diet-related disease burdens, promote sustainable dietary changes, and contribute to the development of healthier and more equitable food systems.

We look forward to your contributions to this important topic.

Dr. Yohannes A. Melaku
Dr. Zumin Shi
Guest Editors

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17 pages, 838 KB

Open AccessArticle

Causal Analysis of Multidimensional Dietary Data to Assess Effects on All-Cause Mortality

by Yohannes Adama Melaku and Zumin Shi

Nutrients 2026, 18(10), 1629; https://doi.org/10.3390/nu18101629 - 21 May 2026

Viewed by 607

Abstract

Background: Methods applied under explicit causal assumptions can provide estimates that support potential causal interpretations of the effects of dietary factors on health outcomes. However, the high dimensionality inherent in dietary data presents a challenge. Objectives: Using multivariate analysis methods under causal assumptions, we identified dietary patterns and estimated their associations with all-cause mortality, as well as the effects of a 100 g/day increase in individual components. Methods: Data from 12,635 individuals aged 20 years and above from the National Health and Nutrition Examination Survey (NHANES), United States, were analyzed. K-means clustering was first used to identify dietary patterns, and then their associations with mortality risk were estimated both with and without inverse probability weighting (IPW). Second, the multivariate generalized propensity score (mvGPS) method was employed to estimate the average effects of dietary components on all-cause mortality under causal assumptions. Third, mutually adjusted models (non-mvGPS) were utilized to determine the effects of each dietary component. Relative risks (RR) and 95% confidence intervals (CI) were computed using fully adjusted Poisson generalized linear models. Results: In a 15-year follow-up period, 400 (3.2%) deaths were recorded. ‘Healthy’, ‘unhealthy,’ and ‘mixed’ dietary patterns were identified. Variations in estimates of ‘healthy’ and ‘unhealthy’ patterns with mortality were observed in non-IPW (RR = 0.96; 95% CI: 0.67–1.13 and RR = 0.79; 0.56–1.11) and IPW models (RR = 0.75; 0.55–1.04 and RR = 0.92; 0.63–1.36, respectively) compared to the ‘mixed’ pattern. In the mvGPS model, added sugar (RR = 1.21; 1.06–1.36), processed meat (RR = 1.20; 0.96–1.48), and legumes (RR = 0.82; 0.73–0.90) showed the strongest effects. Only whole grains (RR = 0.68; 0.46–0.98) had an effect in the non-mvGPS model. Conclusions: Applying mvGPS to multidimensional dietary data may help improve covariate balance across measured confounders and support more interpretable analysis of correlated dietary components. However, findings from this observational study should be interpreted as estimates under explicit causal assumptions, rather than definitive causal effects. Future studies should apply and further evaluate these approaches in larger and more diverse populations. Full article

(This article belongs to the Special Issue The Application of Machine Learning in Nutritional Epidemiology)

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