ArticleNutrients2022
The Application of Clustering on Principal Components for Nutritional Epidemiology: A Workflow to Derive Dietary Patterns.
Article in Nutrients, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- The influence of the glucagon-like peptide-1 receptor agonist, liraglutide, on dietary patterns and nutrient intakes in patients with obesity and prediabetes: A secondary analysis of a randomized controlled trial.Diabetes, obesity & metabolism · 2025Trial
- Impact of dietary component clusters identified by K-means++ on renal function decline in a Taiwanese cohort.Renal failure · 2026Article
- Data-Driven Discovery of Plant-Based Dietary Subtypes and Their Association With Cancer, Cardiovascular Diseases, and Type 2 Diabetes in Postmenopausal Women: Insights From the Women's Health Initiative.Journal of the Academy of Nutrition and Dietetics · 2026Observational
- Associations of Overall Diet Quality and Individual Dietary Behaviours with Anthropometric Outcomes Among Chilean University Students.Healthcare (Basel, Switzerland) · 2026Article
- Associations between dietary nutrient intake and the risk of multiple age-related ocular diseases: evidence from longitudinal analysis.The journal of nutrition, health & aging · 2026Article
- Dietary Patterns and an Adverse Mental Health Indicator Among School-Attending Brazilian Adolescents: Analyses of the 2015 and 2019Nutrients · 2026Article
- Article
- Omnivorous and plant-based dietary patterns: a comparative analysis using data-driven and index-based approaches.European journal of nutrition · 2026Article
- Carbohydrate composition of infant formula and glycemic regulation in early infancy using continuous glucose monitoring: cross-sectional evidence of altered glucose patterns with corn syrup solid-based formulas.The American journal of clinical nutrition · 2026Observational
- The role of dietary patterns on epigenetic and inflammatory aging based on the INSPIRE-T study.Communications medicine · 2026Article
- Maternal Dietary Patterns, Socioeconomic Conditions, and Birth Outcomes in the MAMI-MED and Piccolipiù Italian Birth Cohorts.Nutrients · 2026Article
- Patterns and clusters of diet in relation to metabolic syndrome among adults in Ubungo (urban) and Kilindi (rural) districts, Tanzania: a cross-sectional study.BMC public health · 2026Article
- Multivariate characterization of the nutritional diversity of traditional Ecuadorian foods.Frontiers in nutrition · 2026Article
- Distinct Clusters of Testosterone Levels, Symptoms, and Serum Trace Elements in Young Men: A Cross-Sectional Analysis.Nutrients · 2025Article
- Diversity of dietary protein patterns across Europe - Impact on nutritional quality and environmental sustainability.Current research in food science · 2025Article
- The Association between Diet-Exercise Patterns and Cirrhosis: A Cross-Sectional Study from NHANES 2017-March 2020.Nutrients · 2024Article
- Profiling the socioeconomic characteristics, dietary intake, and health status of Korean older adults for nutrition plan customization: a comparison of principal component, factor, and cluster analyses.Epidemiology and health · 2024Article
- Article
- The Effect of Maternal Dietary Patterns on Birth Weight for Gestational Age: Findings from the MAMI-MED Cohort.Nutrients · 2023Article
- Dietary patterns and socioeconomic, demographic, and health-related behaviors during pregnancy. A cross-sectional study.Sao Paulo medical journal = Revista paulista de medicina · 2023Article
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Authors and funding
7 authors.
Funding
Abstract
In the last decades, different multivariate techniques have been applied to multidimensional dietary datasets to identify meaningful patterns reflecting the dietary habits of populations. Among them, principal component analysis (PCA) and cluster analysis represent the two most used techniques, either applied separately or in parallel. Here, we propose a workflow to combine PCA, hierarchical clustering, and a K-means algorithm in a novel approach for dietary pattern derivation. Since the workflow presents certain subjective decisions that might affect the final clustering solution, we also provide some alternatives in relation to different dietary data used. For example, we used the dietary data of 855 women from Catania, Italy. Our approach-defined as clustering on principal components-could be useful to leverage the strengths of each method and to obtain a better cluster solution. In fact, it seemed to disentangle dietary data better than simple clustering algorithms. However, before choosing between the alternatives proposed, it is suggested to consider the nature of dietary data and the main questions raised by the research.
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