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Metabolomic-based identification of clusters that reflect dietary patterns
Helena Gibbons1, Eibhlin Carr1, Breige A McNulty1
1Institute of Food and Health, UCD School of Agriculture and Food Science, University College Dublin, Dublin, Republic of Ireland.
Molecular Nutrition & Food Research
|June 7, 2017
Summary
Researchers developed an objective method to classify dietary patterns using metabolomic data, moving beyond error-prone self-reporting. This new model accurately categorizes individuals into "healthy" or "unhealthy" dietary groups.
Area of Science:
- Metabolomics
- Nutritional Science
- Biomarker Discovery
Background:
- Dietary pattern classification traditionally relies on self-reported data, which is subject to significant inaccuracies.
- Objective methods are needed to accurately assess dietary habits and their health implications.
Purpose of the Study:
- To develop and validate a metabolomic data-driven model for objective classification of individuals into distinct dietary patterns.
- To overcome the limitations of subjective dietary assessment methods.
Main Methods:
- Utilized urinary and dietary metabolomic data from the National Adult Nutrition Survey (NANS) (n=567).
- Applied two-step cluster analysis to identify dietary patterns within the metabolomic data.
- Validated the developed classification model on an independent cohort.
Main Results:
- Identified two distinct dietary patterns: a "healthy" cluster (high intake of cereals, fruits, fish) and an "unhealthy" cluster (high intake of processed foods, high-energy beverages).
- Classification was supported by significant differences in nutrient status between clusters.
- Achieved 94% accuracy in classifying subjects into dietary patterns within an independent validation group.
Conclusions:
- A novel model effectively classifies individuals into dietary patterns using objective metabolomic data.
- This approach offers a promising tool for rapid and accurate dietary assessment in research and clinical settings.
