Propelling the paradigm shift from reductionism to systems nutrition

Abstract

The complex physiology of living organisms represents a challenge for mechanistic understanding of the action of dietary bioactives in the human body and of their possible role in health and disease. Animal, cell, and microbial models have been extensively used to address questions that could not be pursued experimentally in humans, posing an additional level of complexity in translation of the results to healthy and diseased metabolism. The past few decades have witnessed a surge in development of increasingly sensitive molecular techniques and bioinformatic tools for storing, managing, and analyzing increasingly large datasets. Application of such powerful means to molecular nutrition research led to a major leap in study designs and experimental approaches yielding experimental data connecting dietary components to human health. Scientific journals bear major responsibilities in the advancement of science. As primary actors of dissemination to the scientific community, journals can impose rigid criteria for publishing only sound, reliable, and reproducible data. Journal policies are meant to guide potential authors to adopt the most updated standardization guidelines and shared best practices. Such policies evolve in parallel with the evolution of novel approaches and emerging challenges and therefore require constant updating. We highlight in this manuscript the major scientific issues that led to formulating new, updated journal policies for Genes & Nutrition, a journal which targets the growing field of nutritional systems biology interfacing personalized nutrition and preventive medicine, with the ultimate goal of promoting health and preventing or treating disease. We focus here on relevant issues requiring standardization in nutrition research. We also introduce new sections on human genetic variation and nutritional bioinformatics which follow the evolution of nutritional science into the twenty-first century.

Will guidelines on alcohol consumption be personalized by a genetic approach?

“…The complete understanding of gene-diet interaction is still far and, even though gigantic leaps ahead are being made every day, we cannot provide yet any conclusive answer. The necessary next step will necessarily involve the characterization of specific genetic profiles and identify subjects with a high polygenic risk score for a high risk of harmful consequences of alcohol intake, even at very low quantities to specifically “tailor” and target our recommendations [ 39 ].…”

Targeting the delivery of dietary plant bioactives to those who would benefit most: from science to practical applications

“…Data fusion approaches exploiting studies that have been published, or are underway, may allow for the creation of larger data sets that would have sufficient statistical power to reveal relationships between endogenous factors and cardiometabolic health outcomes on the individual level. Such approaches would also allow the implementation of novel computational methods to extend additive genetic risk scores to include nonlinear gene–gene, gene–environment, and epigenetic interactions that influence responses to plant bioactives and other nutritional variables [29]. However, we found that data fusion approaches were inherently difficult because of ethical and logistical factors that limit access to data and that differences in study designs and outcomes make it almost impossible to merge data.…”

Contribution of genetic ancestry and polygenic risk score in meeting vitamin B12 needs in healthy Brazilian children and adolescents

“…The majority of reports found statistical correlations but effect sizes were uniformly very small (usually < 1% of total phenotype) and reproducibility between studies is low. These results can be explained by gene–gene interactions, especially between different ancestral populations, and variation in diet and environmental/lifestyle factors that underlay gene-nutrient interactions 11 – 13 . In addition, reductionistic approaches ignore the many metabolite–protein (and therefore gene) and protein–protein interactions (i.e., gene–gene interactions) that produce an observable and measurable phenotype 14 .…”