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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 5938

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Dr. Francesca Iacoponi

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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

Dr. Silvia Di Giacomo

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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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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

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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 (6 papers)

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Research

12 pages, 660 KB

Open AccessArticle

Toward Precision Obesity Pharmacotherapy: Using the Eating Behavior Phenotype Scale (EFCA) in Real-World Clinical Practice

by Ronaldo José Pineda-Wieselberg, Andressa Heimbecher Soares, Thiago Fraga Napoli, Nilza Maria Scalissi and João Eduardo Nunes Salles

Nutrients 2026, 18(9), 1419; https://doi.org/10.3390/nu18091419 - 30 Apr 2026

Viewed by 125

Abstract

Background: Obesity is a heterogeneous chronic disease in which eating behavior phenotypes may influence treatment response. Yet, anti-obesity medication (AOM) selection is still largely guided by anthropometric and metabolic parameters, with limited use of behavioral phenotyping in routine practice. We evaluated whether multidimensional eating behavior changes, measured by the Brazilian Eating Behavior Phenotype Scale (Escala de Fenótipos do Comportamento Alimentar, EFCA), differ across commonly used AOMs in a real-world cohort. Methods: We conducted a retrospective, observational, real-world study in obesity outpatient care settings in São Paulo, Brazil. Adults with obesity (18–65 years) treated with a single principal AOM for 6 months and paired baseline/6-month follow-up EFCA and anthropometric data were included. Analyses focused on early responders (≥5% total body weight loss at 3 months). Five AOM groups available in Brazil were analyzed: semaglutide (oral or subcutaneous), naltrexone/bupropion, sibutramine, topiramate, and tirzepatide. Outcomes included percent weight loss, EFCA total score, and five EFCA subscales (hedonic, emotional, compulsive, hyperphagic, disorganized). Within-medication behavioral changes were assessed using paired tests and standardized effect sizes (Cohen’s dz, 95% CI), summarized in heatmap form. Results: The analytical cohort comprised 66 early responders with paired EFCA assessments at baseline and 6 months. EFCA profiling revealed distinct behavioral response fingerprints across AOMs. Effect size mapping showed predominantly large behavioral effects (many dz ≥ 0.8) in hedonic, emotional, hyperphagic, and compulsive domains. Strongest signals included emotional eating reductions with naltrexone/bupropion (dz 2.04), tirzepatide (dz 1.77), semaglutide (dz 1.52), and topiramate (dz 1.54); hedonic reductions with tirzepatide (dz 2.06), semaglutide (dz 1.55), and naltrexone/bupropion (dz 1.52); hyperphagic reductions with tirzepatide (dz 1.50) and semaglutide (dz 1.34); and compulsive reductions with topiramate (dz 1.41) and consistent effects across tirzepatide, semaglutide, and sibutramine (≈dz 0.95–0.96). Disorganized eating showed heterogeneous/attenuated responsiveness, from near-null with tirzepatide (dz 0.03) to large but imprecise effects in smaller groups (e.g., topiramate dz 1.24, wide CI). Conclusions: In this responder-enriched real-world cohort, AOMs showed distinct and reproducible EFCA behavioral signatures, supporting a clinically actionable phenotype-informed framework to prioritize, sequence, and monitor obesity pharmacotherapy beyond nonspecific weight reduction, while highlighting disorganization as a potential target for adjunctive behavioral strategies. Full article

(This article belongs to the Special Issue Dietary Patterns and Data Analysis Methods)

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15 pages, 1844 KB

Open AccessArticle

Evaluating the Accuracy of Declared Eating Schedules by Continuous Glucose Monitoring

by Pedro González-Romero, Juan Antonio Madrid, Pedro Francisco Almaida-Pagán and Maria Angeles Rol

Nutrients 2026, 18(5), 772; https://doi.org/10.3390/nu18050772 - 27 Feb 2026

Viewed by 693

Abstract

Background/Objectives: Chrononutrition is an emergent field concerning the effect of eating patterns on human health and their relationship with biological rhythms. Current evidence points towards the benefits of early eating in the prevention of non-communicable diseases and circadian health. Despite the importance of eating/fasting rhythm, current methods are neither specific nor validated against physiological variables. This work aimed to explore an objective metabolic outcome, postprandial glucose, as an accuracy indicator of self-declared meal schedules registered in a mobile app. Methods: A 1-week protocol of ambulatory monitoring of meal schedules, glucose, and circadian variables was performed in 20 young adults. Meal annotations were registered using KronoEat 1.0, a smartphone app, allowing for both prospective and recall entries. A circadian monitoring device provided data on movement intensity, distal skin temperature, and prospective food annotation. Results: Participants annotated an average of 3.5 food events/day/participant with KronoEat. Breakfast (92.7%) and lunch (86.4%) showed the highest proportion of food events related to a glycemic excursion, whereas this proportion was lower for dinner (79.7%) and snacks (67.7%). Postprandial glucose after main meals differed significantly from average glucose levels. Interesting couplings were found in circadian variables and glucose—for example, between post-breakfast glycemic excursions and the morning increase in activity. Conclusions: Meal schedules registered under free-living conditions in KronoEat show high levels of correlation with postprandial glucose and glycemic excursions derived from continuous glucose monitoring. Full article

(This article belongs to the Special Issue Dietary Patterns and Data Analysis Methods)

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25 pages, 1126 KB

Open AccessArticle

Traditional and Non-Traditional Clustering Techniques for Identifying Chrononutrition Patterns in University Students

by José Gerardo Mora-Almanza, Alejandra Betancourt-Núñez, Pablo Alejandro Nava-Amante, María Fernanda Bernal-Orozco, Andrés Díaz-López, José Alfredo Martínez and Barbara Vizmanos

Nutrients 2026, 18(2), 190; https://doi.org/10.3390/nu18020190 - 6 Jan 2026

Viewed by 892

Abstract

Background/Objectives: Chrononutrition—the temporal organization of food intake relative to circadian rhythms—has emerged as an important factor in cardiometabolic health. While meal timing is typically analyzed as an isolated variable, limited research has examined integrated meal timing patterns, and no study has systematically compared clustering approaches for their identification. This cross-sectional study compared four clustering techniques—traditional (K-means, Hierarchical) and non-traditional (Gaussian Mixture Models (GMM), Spectral)—to identify meal timing patterns from habitual breakfast, lunch, and dinner times. Methods: The sample included 388 Mexican university students (72.8% female). Patterns were characterized using sociodemographic, anthropometric, food intake quality, and chronotype data. Clustering method concordance was assessed via Adjusted Rand Index (ARI). Results: We identified five patterns (Early, Early–Intermediate, Late–Intermediate, Late, and Late with early breakfast). No differences were observed in BMI, waist circumference, or age among clusters. Chronotype aligned with patterns (morning types overrepresented in early clusters). Food intake quality differed significantly, with more early eaters showing healthy intake than late eaters. Concordance across clustering methods was moderate (mean ARI = 0.376), with the highest agreement between the traditional and non-traditional techniques (Hierarchical–Spectral = 0.485 and K-means-GMM = 0.408). Conclusions: These findings suggest that, while traditional and non-traditional clustering techniques did not identify identical patterns, they identified similar core structures, supporting complementary pattern detection across algorithmic families. These results highlight the importance of comparing multiple methods and transparently reporting clustering approaches in chrononutrition research. Future studies should generate meal timing patterns in university students from other contexts and investigate whether these patterns are associated with eating patterns and cardiometabolic outcomes. Full article

(This article belongs to the Special Issue Dietary Patterns and Data Analysis Methods)

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17 pages, 263 KB

Open AccessArticle

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 667

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: 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

Open AccessArticle

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 805

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 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

Open AccessArticle

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

Cited by 1 | Viewed by 1746

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 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 (K_w), 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 K_w: 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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