Display options
Share it on

Front Physiol. 2019 Mar 05;10:149. doi: 10.3389/fphys.2019.00149. eCollection 2019.

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

Frontiers in physiology

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

Affiliations

  1. Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna, Bologna, Italy.
  2. Interdepartmental Centre "L. Galvani", University of Bologna, Bologna, Italy.
  3. Division of Human Nutrition, Wageningen University & Research, Wageningen, Netherlands.
  4. Faculty of Human Nutrition and Consumer Sciences, Warsaw University of Life Sciences-SGGW, Warsaw, Poland.
  5. Norwich Medical School, University of East Anglia, Norwich, United Kingdom.
  6. Centre Hospitalier Universitaire de Clermont-Ferrand, Clermont-Ferrand, France.
  7. Institute of Neurological Sciences (IRCCS), Bologna, Italy.

PMID: 30890946 PMCID: PMC6413567 DOI: 10.3389/fphys.2019.00149

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.

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

References

  1. Ann N Y Acad Sci. 2000 Jun;908:244-54 - PubMed
  2. N Engl J Med. 2003 Jun 26;348(26):2599-608 - PubMed
  3. Int J Epidemiol. 2005 Feb;34(1):215-20 - PubMed
  4. Public Health Nutr. 2004 Oct;7(7):943-7 - PubMed
  5. BMJ. 2008 Sep 11;337:a1344 - PubMed
  6. J Epidemiol. 2013;23(3):178-86 - PubMed
  7. Mech Ageing Dev. 2013 Nov-Dec;134(11-12):523-30 - PubMed
  8. Mech Ageing Dev. 2014 Mar-Apr;136-137:3-13 - PubMed
  9. J Gerontol A Biol Sci Med Sci. 2014 Jun;69 Suppl 1:S4-9 - PubMed
  10. Curr Cardiovasc Risk Rep. 2014;8(12):416 - PubMed
  11. Cell. 2014 Nov 6;159(4):709-13 - PubMed
  12. Am J Epidemiol. 2015 Feb 15;181(4):225-33 - PubMed
  13. Nutr Rev. 2017 Jun 1;75(6):442-455 - PubMed
  14. Front Physiol. 2018 Oct 01;9:1359 - PubMed
  15. Nutrients. 2018 Dec 04;10(12):null - PubMed

Publication Types