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Dietary Patterns and Data Analysis Methods

A special issue of Nutrients (ISSN 2072-6643). This special issue belongs to the section "Nutrition Methodology & Assessment".

Deadline for manuscript submissions: 5 June 2026 | Viewed by 2598

Special Issue Editors


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Guest Editor
Unit of Human Nutrition and Health, Department of Food Safety, Nutrition and Veterinary Public Health, Italian National Institute of Health, 00161 Rome, Italy
Interests: statistical analysis; nutrition; nutritional epidemiology; food frequency questionnaires; questionnaire validation; dietary assessment; food safety; dietary patterns; foodborne diseases

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Guest Editor
Unit of Human Nutrition and Health, Department of Food Safety, Nutrition and Veterinary Public Health, Italian National Institute of Health, 00161 Rome, Italy
Interests: food supplements; nutrition; nutritional epidemiology; food safety; botanical safety; nutrient intake; obesity; lifestyle; sport nutrition; food consumption; nutritional status; vitamins; minerals; phytochemicals
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Special Issue Information

Dear Colleagues,

Dietary patterns play a crucial role in understanding the relationship between nutrition and health. Unlike single-nutrient approaches, which focus on individual dietary components, the study of dietary patterns considers the overall consumption of foods and their combinations. This holistic perspective allows us to capture the complexity of human diets and their impact on chronic diseases, metabolic health, and overall well-being.

To analyze dietary patterns, various data analysis methods are employed, ranging from traditional statistical techniques to advanced machine learning approaches. Common methods include factor analysis and principal component analysis (PCA) to identify dietary patterns, cluster analysis to classify individuals based on their eating habits, and dietary indices that assess adherence to predefined healthy eating guidelines. Additionally, newer computational methods, such as supervised and unsupervised machine learning models, are increasingly used to uncover complex relationships in dietary data.

Understanding dietary patterns through rigorous data analysis helps inform public health policies, guides nutritional recommendations, and supports personalized nutrition strategies. As data collection methods continue to evolve—through the improvement of food frequency questionnaires, dietary recalls, and digital tracking tools—so do the techniques used to extract meaningful insights from dietary data.

Authors are invited to submit original research articles, narrative or systematic reviews, meta-analyses, and clinical trials exploring the impact of dietary patterns on health using both conventional and alternative statistical methods. Submissions on food frequency questionnaire validation are also welcome.

Dr. Francesca Iacoponi
Dr. Silvia Di Giacomo
Guest Editors

Manuscript Submission Information

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Keywords

  • dietary patterns
  • nutrition
  • food consumption
  • food supplements
  • dietary assessment
  • data analysis
  • food safety
  • nutritional epidemiology
  • food frequency questionnaires

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Published Papers (3 papers)

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Research

17 pages, 263 KB  
Article
Evaluating Intake Estimation Methods for Young Children’s Diets
by Xiaoshu Zhu, Christine Borger, Jill DeMatteis and Brenda Sun
Nutrients 2025, 17(24), 3874; https://doi.org/10.3390/nu17243874 - 11 Dec 2025
Viewed by 223
Abstract
Objectives: This paper illustrates the use of the National Cancer Institute (NCI) Markov Chain Monte Carlo (MCMC) method for usual intake (UI) analyses of 5-year-old children’s diets by comparing results from the MCMC method with results from other estimation methods. Methods: [...] Read more.
Objectives: This paper illustrates the use of the National Cancer Institute (NCI) Markov Chain Monte Carlo (MCMC) method for usual intake (UI) analyses of 5-year-old children’s diets by comparing results from the MCMC method with results from other estimation methods. Methods: This study involves secondary analysis of data from the Infant and Toddler Feeding Practices Study-2 (ITFPS-2), a nationally representative prospective cohort study that followed children from around birth through age 9. Dietary data analyzed were collected between April 2018 and August 2019. All study participants in the longitudinal cohort (n = 1030) had 1 day of dietary recall data, and 122 participants had 2 days of recall. We compare differences in intake distributions for sodium, added sugars, whole grains, energy, and Healthy Eating Index (HEI) scores using the NCI UI methods, as well as single-day and two-day methods. We use regression analysis to assess associations by intake estimation method. Results: Across the methods examined, means for daily consumed nutrients differed by less than 2 percentage points and mean HEI component scores differed by less than half a point. However, for episodically consumed whole grains, the NCI UI methods yielded mean intake estimates that differed by 37%, with the univariate method indicating higher mean intake than the MCMC method. Conclusions: For the daily consumed nutrients examined, the NCI MCMC method is a useful alternative to the univariate method. However, for episodically consumed whole grains, the NCI UI methods yield notably different mean estimates. For episodically consumed dietary constituents, abandoning the NCI univariate method may exacerbate differences between recommended and estimated population mean intake levels for young children. Full article
(This article belongs to the Special Issue Dietary Patterns and Data Analysis Methods)
15 pages, 438 KB  
Article
Design and Validation of the Index of Adherence to the Dietary Guidelines for Chile 2022 (GABAS-Index 17)
by Catalina Ramírez-Contreras, Jaime Crisosto-Alarcón, Solange Parra-Soto, Jorge Burdiles-Aguirre, Gianella Liabeuf and Lautaro Briones-Suárez
Nutrients 2025, 17(22), 3621; https://doi.org/10.3390/nu17223621 - 20 Nov 2025
Viewed by 431
Abstract
Background/Objectives: Adherence to national dietary guidelines is essential for promoting healthy eating and preventing chronic diseases. In Chile, the 2022 update introduced new evidence-based recommendations, but no validated tool is currently available to assess adherence. The aim of this study was to [...] Read more.
Background/Objectives: Adherence to national dietary guidelines is essential for promoting healthy eating and preventing chronic diseases. In Chile, the 2022 update introduced new evidence-based recommendations, but no validated tool is currently available to assess adherence. The aim of this study was to develop and validate a tool to assess adherence to the updated Chilean dietary guidelines. Methods: For this purpose, five expert judges evaluated the content validity using Aiken’s V (V ≥ 0.80). Reliability was assessed through a 21-day test–retest in 30 participants (≥18 years, mean age 38.9 years; 63.3% women) using the Intraclass Correlation Coefficient (ICC(3,1)), a two-way mixed-effects model to assess the absolute agreement of individual measurements, Standard Error of Measurement (SEM), and Minimal Detectable Change (MDC95) at the 95% confidence level. Internal consistency was assessed in 152 participants (≥18 years) examined via McDonald’s ω, and construct validity through confirmatory factor analysis (CFA) using the WLSMV estimator. Results: The GABAS-Index 17 showed high content validity (Aiken’s V = 0.93–1.00), good internal consistency (ω = 0.64–0.71), and accurate reliability (ICC = 0.905; SEM < 1; MDC95 = 2.1). Confirmatory factor analysis supported the proposed four-dimensional structure (CFI = 1.00; TLI = 1.02; RMSEA = 0.00), confirming strong factorial validity and internal coherence. Conclusions: These findings support the GABAS-Index 17 as an adequate and reliable tool for assessing adherence to the updated Chilean dietary guidelines. Although some psychometric aspects, such as the factorial structure, could be improved, the instrument performs well for its intended purpose of providing an overall adherence score. Its use can facilitate monitoring dietary patterns, support nutrition research, and inform public health strategies to improve diet quality in the Chilean population. Full article
(This article belongs to the Special Issue Dietary Patterns and Data Analysis Methods)
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19 pages, 1724 KB  
Article
Relative Validity and Reproducibility of a Semi-Quantitative Web-Based Food Frequency Questionnaire for Swiss Adults
by Sarah T. Pannen, Elsa Chevillard, Angeline Chatelan, Pedro Marques-Vidal, Silvia Stringhini, Robert Vorburger, Sabine Rohrmann, Nina Steinemann and Janice Sych
Nutrients 2025, 17(9), 1555; https://doi.org/10.3390/nu17091555 - 30 Apr 2025
Viewed by 1337
Abstract
Background/Objectives: Food frequency questionnaires (FFQs) are widely used in large epidemiological studies to assess diet and elucidate its impacts on health. However, they must be validated in the target population before use. Methods: We assessed the relative validity, reproducibility, and usability [...] Read more.
Background/Objectives: Food frequency questionnaires (FFQs) are widely used in large epidemiological studies to assess diet and elucidate its impacts on health. However, they must be validated in the target population before use. Methods: We assessed the relative validity, reproducibility, and usability of the Swiss eFFQ, a web-based, 83-item food frequency questionnaire, using a convenience sample of 177 adults (53.1% females, aged 18–75 years) from German- and French-speaking regions of Switzerland. The participants completed the Swiss eFFQ twice and kept a 4-day estimated food record (4-d FR). The dietary data were compared for energy, nutrient, and food group intakes by calculating mean group-level bias, performing the Wilcoxon signed-rank test, quartile cross-classification, weighted Cohen’s kappa (Kw), and correlation coefficients. Results: The Swiss eFFQ was highly rated by the participants, with a completion time under 35 min, although it tended to underestimate nutrient and food intake compared to the 4-d FR. For 31 of 36 nutrients, fewer than 10% of the participants were classified in opposite quartiles. The median proportion of subjects classified in the same or adjacent quartile was 74.7% (median Kw: 0.25). The median crude and de-attenuated Spearman correlation coefficients were 0.37 and 0.42 for nutrients and 0.45 and 0.52 for food groups, respectively. The median Spearman and intraclass correlation coefficients for the reproducibility of the Swiss eFFQ were 0.70 and 0.69 for nutrients and 0.70 and 0.61 for food groups, respectively. Conclusions: The Swiss eFFQ was shown to be reproducible and user-friendly, with acceptable accuracy in categorizing study participants based on food intake, and offers several advantages for dietary assessment of Swiss adult populations. Full article
(This article belongs to the Special Issue Dietary Patterns and Data Analysis Methods)
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