Comparison between dietary assessment methods and biomarkers in estimating dietary (poly)phenol intake

Abstract

Background: although widely used, there is limited understanding of the suitability of different dietary assessment tools to estimate (poly)phenol intake. This study aims to compare the agreement between a food frequency questionnaire (FFQ) and a 7-day food diary (7DD) in assessing (poly)phenol intake and explore their associations with the urinary and plasma (poly)phenol metabolites. Methods: healthy free-living participants aged 18–80 years (n = 413) completed a 7DD and an FFQ (EPIC-Norfolk) and provided a 24 h urine and a fasting plasma sample. A comprehensive in-house (poly)phenol database was used to estimate (poly)phenol intake. The phenolic metabolite levels were analysed using a validated LC-MS method. The agreement between dietary assessment methods and biomarkers were evaluated by intraclass correlation coefficients (ICC), weighted kappa, quartile classification, Bland–Altman plots and correlations. Results: the total (poly)phenol intake estimated from FFQ was higher than from 7DD (median 1463 and 1042 mg d⁻¹, respectively). The agreement between FFQ and 7DD were moderate (ICC 0.51–0.59) for total (poly)phenols, flavan-3-ols, total phenolic acids, hydroxycinnamic acids and alkylmethoxyphenols, and were poor for all the other classes and subclasses (ICC 0.00–0.48). Positive correlations with total urine phenolic metabolites were found in FFQ estimated anthocyanins, dihydroflavonols, total lignans, tyrosols, alkylmethoxyphenols, total phenolic acids, and total stilbenes and the 7DD estimated theaflavins and thearubigins (all FDR adjusted p values < 0.1). No significant correlations were found between total plasma phenolic metabolites and (poly)phenol intake. Conclusion: agreements between dietary assessment tools were moderate for the major classes of (poly)phenols, while agreements between (poly)phenol intake and biomarkers were poor. Future research using biomarker approaches to increase the accuracy of estimating (poly)phenol exposure in larger populations is needed.