A Novel Approach to Improve the Estimation of a Diet Adherence Considering Seasonality and Short Term Variability - The NU-AGE Mediterranean Diet Experience

Enrico Giampieri, Rita Ostan, Giulia Guidarelli, Stefano Salvioli, Agnes A M Berendsen, Anna Brzozowska, Barbara Pietruszka, Amy Jennings, Nathalie Meunier, Elodie Caumon, Susan Fairweather-Tait, Ewa Sicinska, Edith J M Feskens, Lisette C P G M de Groot, Claudio Franceschi, Aurelia Santoro, Enrico Giampieri, Rita Ostan, Giulia Guidarelli, Stefano Salvioli, Agnes A M Berendsen, Anna Brzozowska, Barbara Pietruszka, Amy Jennings, Nathalie Meunier, Elodie Caumon, Susan Fairweather-Tait, Ewa Sicinska, Edith J M Feskens, Lisette C P G M de Groot, Claudio Franceschi, Aurelia Santoro

Abstract

In this work we present a novel statistical approach to improve the assessment of the adherence to a 1-year nutritional intervention within the framework of the NU-AGE project. This was measured with a single adherence score based on 7-days food records, under limitations on the number of observations per subject and time frame of intervention. The results of the NU-AGE dietary intervention were summarized by variations of the NU-AGE index as described in the NU-AGE protocol. Food and nutrient intake of all participants was assessed by means of 7-days food records at recruitment and after 10 to 14 months of intervention (depending on the subject availability). Sixteen food groups and supplementations covering the dietary goals of the NU-AGE diet have been used to estimate the NU-AGE index before and after the intervention. The 7-days food record is a reliable tool to register food intakes, however, as with other tools used to assess lifestyle dietary compliance, it is affected by uncertainty in this estimation due to the possibility that the observed week is not fully representative of the entire intervention period. Also, due to logistic limitations, the effects of seasonality can never be completely removed. These variabilities, if not accounted for in the index estimation, will reduce the statistical power of the analyses. In this work we discuss a method to assess these uncertainties and thus improve the resulting NU-AGE index. The proposed method is based on Hierarchical Bayesian Models. This model explicitly includes country-specific averages of the NU-AGE index, index variation induced by the dietary intervention, and country based seasonality. This information is used to evaluate the NU-AGE index uncertainty and thus to estimate the "real" NU-AGE index for each subject, both before and after the intervention. These corrections reduce the possibility of misinterpreting measurement variability as real information, improving the power of the statistical tests that are performed with the resulting index. The results suggest that this method is able to reduce the short term and seasonal variability of the measured index in the context of multicenter dietary intervention trials. Using this method to estimate seasonality and variability would allow one to obtain better measurements from the subjects of a study, and be able to simplify the scheduling of diet assessments. Clinical Trial Registration: www.ClinicalTrials.gov, identifier NCT01754012.

Keywords: Bayesian statistics; Mediterranean-like diet; diet assessment; hierarchical models; inflammaging; regression to the mean; seasonality.

Figures

FIGURE 1
FIGURE 1
Expected effect on the score correlation depending on the ratio between within-variability and between-variability of the subjects. This relationship can be inverted to obtain the expected ratio given the observed correlation value.
FIGURE 2
FIGURE 2
Posterior distribution for the variability components of each part of the model. The majority of the variability is composed of the individual variability in the baseline value and the measurement uncertainty. The measurement uncertainty is slightly higher than the between-individual variability, as expected from the model described in Figure 1.
FIGURE 3
FIGURE 3
Baseline NU-AGE index for the population by country. Each country is divided in 5 segments to assess the variability among simulations (each simulation corresponds to one line). The central point represents the mean of the posterior distribution, the thick line represents the 50% HDI, the thin line represents the 95% HDI.
FIGURE 4
FIGURE 4
NU-AGE index variation after 1 year intervention for the population divided by country. Four countries out of five behave similarly, while the French subjects had a noticeably higher NU-AGE index. Each country is divided into 5 segments to assess the variability among simulations (each simulation corresponds to one line). The central point represents the mean of the posterior distribution, the thick line the 50% HDI, the thin line the 95% HDI.
FIGURE 5
FIGURE 5
Representation of the effect of the correction on the NU-AGE index at T0 and T1 for both control and NU-AGE diet intervention group. Each arrow represents a subject, starting from the non-corrected values at (T0, T1) and arriving at the post-correction values. Longer arrows represent a stronger correction, while the barely visible arrows at the center of each cloud represent subjects whose NU-AGE index has not been corrected. The diagonal red line represents identical NU-AGE index before and after the intervention study.
FIGURE 6
FIGURE 6
Seasonal amplitude of the variation of NU-AGE index according to country. This represents the amplitude of maximum variation (above and below the yearly average) for the seasonality effect, independently from the estimated period of higher compliance. The difference among the countries is significant. In particular, the NU-AGE index in both England and the Netherlands seems to have a lower seasonal variability compared to the other countries.
FIGURE 7
FIGURE 7
Month of maximum variation of the seasonality of the NU-AGE index. The colored area represents the distribution of plausible values of the NU-AGE index for the peak period. Higher and thinner peaks represent a higher degree of certainty on the estimation of the peak period. The colored dots represent the average peak location (and degree of certainty). The NU-AGE index in the English and the Netherlands subjects undergoes a lower seasonality effect.

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Source: PubMed

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