Classifying Individuals Into a Dietary Pattern Based on Metabolomic Data

Orla Prendiville, Janette Walton, Albert Flynn, Anne P. Nugent, Breige A. McNulty, Lorraine Brennan

Research output: Contribution to journalArticlepeer-review

Abstract

Scope The objectives are to develop a metabolomic‐based model capable of classifying individuals into dietary patterns and to investigate the reproducibility of the model. Methods and Results K‐means cluster analysis is employed to derive dietary patterns using metabolomic data. Differences across the dietary patterns are examined using nutrient biomarkers. The model is used to assign individuals to a dietary pattern in an independent cohort, A‐DIET Confirm (n = 175) at four time points. The stability of participants to a dietary pattern is assessed. Four dietary patterns are derived: moderately unhealthy, convenience, moderately healthy, and prudent. The moderately unhealthy and convenience patterns has lower adherence to the alternative healthy eating index (AHEI) and the alternative mediterranean diet score (AMDS) compared to the moderately healthy and prudent patterns (AHEI = 24.5 and 22.9 vs 26.7 and 28.4, p < 0.001). The dietary patterns are replicated in A‐DIET Confirm, with good reproducibility across four time points. The stability of participants’ dietary pattern membership ranged from 25.0% to 61.5%. Conclusion The multivariate model classifies individuals into dietary patterns based on metabolomic data. In an independent cohort, the model classifies individuals into dietary patterns at multiple time points furthering the potential of such an approach for nutrition research. Open Research
Original languageEnglish
Pages (from-to)2001183
Number of pages1
JournalMolecular Nutrition & Food Research
Early online date05 May 2021
DOIs
Publication statusEarly online date - 05 May 2021

Keywords

  • Biotechnology
  • Food Science

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