Bias-reduced Regression Analysis in Nutrition (BRAiN) index: self-reported diet and predicted cognitive function.
Overview
- Human Performance Center, Parker University, Dallas, TX, United States
- Institute for Human and Machine Cognition, Pensacola, FL, United States
- Department of Psychology, University of Washington, Seattle, WA, United States
- Institute for Learning and Brain Sciences, University of Washington, Seattle, WA, United States
- Department of Pediatrics, University of Washington, Seattle, WA, United States
Abstract
Introduction: Evidence suggests the development of age-related dementia is strongly associated with numerous modifiable risk factors, particularly lifestyle behaviors such as diet, sleep, and physical activity. Yet, when exploring the dietary predictors of cognitive function, many scoring systems apply linear scales to eating patterns in a way that prioritizes simplicity over predictive accuracy.
Methods: The present study employed a cross-validated elastic net regression algorithm on a large dataset of self-reported dietary data (n = 28,968 individuals, mean age 56.3, age range 16–100) to identify the greatest nutritional predictors of participants’ performance on a validated online cognitive function test.
Results: Bias-reduced Regression Analysis in Nutrition (BRAiN) scores were computed, and their predictive performance in held-out data exceeded that of a conventional linear dietary scoring approach based on available MIND-related variables (Mediterranean-DASH Intervention for Neurodegenerative Delay). Non-linear relationships between intake level and cognition were noted for most foods, with greater consumption of animal and vegetable proteins, vegetables, nuts and seeds, and whole grains, being positively associated with cognitive function, while the regular consumption of both refined fats and refined carbohydrates was negatively associated with cognitive function.
Discussion: Shifts in dietary patterns by age suggested that some relationships between diet and cognitive function in nutritional epidemiology may be confounded by age-related dietary trends. Our data-driven model can be easily adjusted over time to reflect potential shifts in dietary practices at the population level and inform relevant health authorities in the timely design of effective nutritional recommendations and guidelines to prevent or slow down the onset of age-related cognitive decline.
Reproduced under the paper's license (CC BY), from the paper cited above.
Code
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Data
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Data availability statement
The datasets analysed during the current study are not publicly available due to privacy and data governance considerations associated with participant-derived health and lifestyle information collected through the Food for the Brain Cognitive Function Test platform. De-identified data may be made available upon reasonable request, subject to approval by the Food for the Brain Foundation and any applicable ethical or data-sharing requirements. Requests for access to the data should be directed to the Food for the Brain Foundation at . The R scripts used for data preprocessing, elastic net regression modelling, and BRAiN score calculation can be made available from the corresponding author upon request.
Reproduced under the paper's license (CC BY), from the paper cited above.
Versions
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Version 1, 27 September 2026: the first record
Recorded: type, language, journal, volume, pages, dates, 4 authors, 5 keywords, 47 references.
Cite
This paper
Conti, F., Gluck, K. A., Stocco, A., & Wood, T. R. (2026). Bias-reduced Regression Analysis in Nutrition (BRAiN) index: self-reported diet and predicted cognitive function. Frontiers in nutrition, 13, 1830204. https://
BibTeX
@article{conti2026bias,
author = {Conti, Federica and Gluck, Kevin A. and Stocco, Andrea and Wood, Thomas R.},
title = {{Bias-reduced Regression Analysis in Nutrition (BRAiN) index: self-reported diet and predicted cognitive function}},
journal = {Frontiers in nutrition},
year = {2026},
month = jul,
volume = {13},
pages = {1830204},
publisher = {Frontiers Media SA},
issn = {2296-861X},
doi = {10.3389/
url = {https://
pmid = {42490813},
pmcid = {PMC13375470}
}
RIS
TY - JOUR
AU - Conti, Federica
AU - Gluck, Kevin A.
AU - Stocco, Andrea
AU - Wood, Thomas R.
TI - Bias-reduced Regression Analysis in Nutrition (BRAiN) index: self-reported diet and predicted cognitive function
T2 - Frontiers in nutrition
J2 - Front Nutr
PY - 2026
DA - 2026/
VL - 13
SP - 1830204
SN - 2296-861X
PB - Frontiers Media SA
DO - 10.3389/
UR - https://
LA - en
ER -
CSL-JSON
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