Biomarkers that accurately estimate intake of whole foods or dietary patterns are lacking, even though biomarkers exist to estimate the intake of some nutrients. The lack of effective biomarkers of whole food intake makes it difficult to conduct studies requiring this information, as it means it is necessary to rely on participant recall, which is subject to bias.
Existing work documents the impact of certain whole foods on intestinal microbiota, suggesting the possibility of using changes in the microbiome as a surrogate biomarker for whole food intakes. Shinn and colleagues conducted a study using data from 5 intervention studies to determine if there was a predictable pattern to microbial populations that were predictive of food intake. Results from that work are published in the February 2021 issue of The Journal of Nutrition.
Data for this work was derived from 5 controlled feeding studies that determined the impact of specific foods (almonds, avocados, broccoli, walnuts, and whole grain barley and oats) on human microbiota. Analyses were conducted to explore the differences in microbiota between control periods and the experimental food periods using aggregated data. Results used to predict study food consumption included the top 20 species from the initial analyses, and the authors decreased the number of taxa to determine the smallest and most accurate feature set.
Upcoming ASN Webinar: Research Designs to Account for Diversity and Inclusiveness and Impact Food Guidance Policy
March 11, 2022, 12:30 – 2:00 PM EST
This webinar focuses on the consideration of diversity and inclusion when conducting research to inform and implement food and nutrition guidance policy. This webinar is part of the CASP Nutrition Policy Webinar series, designed to help ASN members better understand the interface between research and nutrition policy.
Overall model accuracy was 70% when all 6 foods were included and differences in the abundance of 22 taxa were used. The accuracy of the overall model improved to 85% when the number of taxa was reduced to 15. The 15 unique taxa resulted in accuracy of predictions of 76% for almonds, 88% for avocados, 72% for walnuts, and 96% for whole grains. These data led the authors to conclude that in healthy people, it is possible to predict food consumption using fecal bacteria as biomarkers, and it is therefore possible to use those observations to determine fidelity in nutrition studies.
In a commentary on this article, Frankenfeld noted that the highest accuracy for predicting intake was achieved when broccoli was removed and both grains were collapsed into a single category. Dr. Frankenfeld points out that this type of result may make it hard to use fecal bacteria as a biomarker for the consumption of specific foods, but this should not be problematic when estimating dietary patterns. Frankenfeld concludes that studies should be expanded to include more diverse and larger populations, and that comparisons of more complex diets from less controlled intake studies are needed.
References
Shinn LM, Li Y, Mansharamani A, Auvil LS, Welge ME, Bushell C, et al. Fecal bacteria as biomarkers for predicting food intake in healthy adults. The Journal of Nutrition, Volume 151, Issue 2, February 2021, Pages 423–433, https://doi.org/10.1093/jn/nxaa285.
Frankenfeld CL. Fecal bacteria as an addition to the lineup of objective dietary biomarkers. The Journal of Nutrition, Volume 151, Issue 2, February 2021, Pages 273–274, https://doi.org/10.1093/jn/nxaa359.
Images via canva.com.
