In a recent study published within the journal Nature Mental Health, researchers investigated the brain health domains of people identified with 4 distinct dietary subtypes, namely starch-free, vegetarian, high-protein-low-fiber, and balanced. Using neuroimaging and behavioral, biochemical, and genetic analyses, they found that individuals within the balanced weight loss program subtype showed higher cognitive functions and mental health than the others.

Study: Associations of dietary patterns with brain health from behavioral, neuroimaging, biochemical and genetic analyses. Image Credit: Elena Eryomenko / Shutterstock

Background

Food liking, a key driver of dietary patterns, significantly influences health outcomes, including chronic diseases and mental health. Understanding its impact on brain health is important for developing effective dietary interventions to boost overall well-being. A growing body of evidence suggests that dietary patterns significantly impact cognitive function and mental health.

The connection between dietary patterns and brain health potentially involves alterations in molecular biomarkers, gut microbiota, and brain structure and performance. Links have been observed between high sugar and saturated fat intake to cognitive decline and psychiatric disorders. Moreover, unhealthy diets, just like the Western pattern, are shown to be related to higher risks of depression and other psychiatric conditions in comparison with balanced diets wealthy in plant-based foods. Contrarily, the Mediterranean weight loss program is shown to be related to higher brain health and a reduced risk of neurodegenerative diseases.

Various traditional dietary patterns, including Western, Mediterranean, and vegetarian/plant-based patterns, have emerged based on food quantities, variety, and frequency of consumption. Nonetheless, the findings on their association with brain health are inconsistent as a consequence of variations in study scopes, sample sizes, and criteria for outlining dietary patterns, highlighting the necessity for a standardized classification system and studies across diverse populations. To handle this gap, researchers in the current study utilized data-driven methods to discover dietary patterns and their associations with brain health outcomes.

Concerning the study

The current study obtained food-liking data from the UK (UK) Biobank. A complete of 181,990 participants who accomplished a food-liking questionnaire were included. The mean age of the participants was 70.7 years, and about 57% were female. The information were then analyzed using principal component evaluation (PCA) and hierarchical clustering to discover food-liking subtypes. Further, differences in various brain health indicators, including mental health, cognitive function, biomarkers, and brain magnetic resonance imaging (MRI) traits, were assessed amongst these subtypes using one-way evaluation of covariance (ANCOVA). The study included measures of tension, depressive symptoms, mental distress, psychotic experiences, self-harm, trauma, and well-being as indicators of brain health.

Longitudinal data on mental disorders were also analyzed using Cox proportional hazards models to look at the differences among the many subtypes. Structural equation models (SEMs) were used to look at the relationships between dietary patterns and brain health. Finally, genome-wide association evaluation (GWAS) and gene expression evaluation were conducted to check the genetic basis of food-liking subtypes and potential biological pathways.

Results and discussion

4 distinct food-liking subtypes were identified among the many studied participants: (1) starch-free or low-starch pattern (18.09%), (2) vegetarian pattern (5.54%), (3) high protein and low fiber pattern (19.39%), and (4) balanced pattern (56.98%). Quantitative scores confirmed the robustness of the connection between food liking and actual food consumption patterns among the many individuals.

The balanced pattern, subtype 4, showed the bottom measures for mental health issues and the best scores for overall well-being and cognitive functions, indicating improved brain health and cognition than the opposite subtypes. Alternatively, subtypes 2 and three showed lower scores in well-being and better scores in mental health issues. In comparison with subtype 4, subtype 3 exhibited reduced gray matter volumes in regions just like the postcentral gyrus, indicating potential neurological differences. In contrast, subtype 2 displayed increased volumes within the thalamus and precuneus. Sixteen genes were found to differ between subtype 3 and subtype 4, they usually were related to biological processes linked to mental health and cognition. Further, subtype 3 showed differences in 127 biomarkers and 1,266 single nucleotide polymorphisms as in comparison with subtype 4.

This huge-scale study provides pioneering insights into the intricate relationship between food preferences and brain health, cognition, and mental well-being, offering the potential for targeted interventions and academic practices to advertise overall health. Nonetheless, the study is proscribed by its reliance on food-liking data moderately than actual consumption, potential selection bias within the UK Biobank sample, potential oversimplification of mental health assessment measures, and an incomplete consideration of key dietary components like tryptophan and omega-3/6 fatty acids.

Conclusion

In conclusion, the current study reveals that dietary patterns among the many older population may significantly impact mental health, cognitive functions, genetics, and brain imaging. Emphasizing the importance of balanced diets, the findings call for early-age dietary education to advertise long-term brain health. Further research is urged to explore the long-term associations between dietary patterns and brain health across various age groups, especially during adolescence and middle age.