A discovery protein panel for brain predicted age discordance using MRI in neurologically healthy individuals.
Paper
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The authors' code
Shell · 23 lines · 678 B · LGPL-3.0
- #!/bin/bash
- ## brainageR script for collating brain predicted age results within a single directory
- ## James Cole, King's College London [email hidden]
- ## software version 1.0 09 Aug 2018
- directory=$1
- output_name=$2
- if [ "$#" -ne 2 ]; then
- echo "You must specify two arguments, collate_brain_ages.sh <directory> <output.csv>"
- exit 1
- fi
- header=`echo File,brain.predicted_age`
- for i in `find $directory -type f -name \*csv | sort | xargs -I x grep -l brain.predicted_age x`; do
- j=`wc -l $i | awk '{print $1}'`
- if [ $j == 2 ]; then
- tail -n +2 $i >> tmp.data.file
- fi
- done
- echo $header > tmp.header.file
- cat tmp.header.file tmp.data.file > $output_name
- rm tmp.*.file
collate_brain_ages.sh at commit f944460, under LGPL-3.0 · at the source
Overview
- Johns Hopkins School of Nursing, Baltimore, MD, United States
- Johns Hopkins School of Medicine, Baltimore, MD, United States
- Department of Neurology, Uniformed Services University of the Health Sciences, Bethesda, MD, United States
- Department of Neurology, University of Utah School of Medicine, Salt Lake City, UT, United States
- Department of Neurology, George E. Wahlen Salt Lake City Veterans Affairs Healthcare System, Salt Lake City, UT, United States
- Department of Physical Medicine and Rehabilitation, Virginia Commonwealth University School of Medicine, Richmond, VA, United States
- Richmond Veterans Affairs Medical Center, Central Virginia VA Health Care System, Richmond, VA, United States
Abstract
Background and Objectives: Brain age is a global measure that compares structural brain MRI with large reference datasets. Predicted age deviation (PAD) is the deviation between predicted brain age and chronological age, with positive values indicating advanced aging. Identifying blood-based biomarkers that approximate brain PAD could provide an accessible and cost-effective measure of brain health as an alternative to MRI, but no blood-based biomarkers have yet been identified. This study aimed to investigate novel blood-based biomarkers associated with accelerated PAD using an unbiased proteomics approach to discover new biomarkers.
Methods: This study is a secondary analysis with a cross-sectional case-control design using the LIMBIC-CENC dataset as a discovery approach to understand novel biomarker patterns. Brain age was estimated using brainageR in 137 participants aged ≤40 years with no substantial cognitive deficits or neurological disorders. Cases (n = 76) included individuals with brain age ≥5 years older than chronological age, whereas controls (n = 61) had brain age equal to or younger than chronological age (PAD range: -1.3 to 0; mean = -0.9) and were otherwise matched on demographics and clinical features. Unbiased proteomic profiling of ∼5,400 proteins was performed using the Olink Explore platform. Differential protein expression between groups was assessed using Wilcoxon tests with Benjamini-Hochberg correction. Receiver operating characteristic (ROC) analysis was performed on probabilities derived from generalized linear models (GLMs) to identify optimal protein combinations, prioritizing maximizing both sensitivity and negative predictive value.
Results: Olink analyses identified 418 proteins that were significantly different between groups after multiple-comparison correction. Upregulated proteins in participants with PAD≥5 years included: component inhibitor-nuclear factor kappa-b kinase (CHUK), methenyltetrahydrofolate
Discussion: These discovery-based findings warrant validation in larger cohorts and suggest potential for blood-based protein panel detection of early, clinically silent, pre-pathological accelerated brain aging changes when interventions may be most effective.
Reproduced under the paper's license (CC BY), from the paper cited above.
Repository
Its files are read in the Code ↔ Paper reader above.
james-cole/brainageR
f9444605527337b07b9a8eb2f6c8c81261aa8b61, 14 September 2026Availability: 1 check, the latest on 26 September 2026: the link answers
- 26 September 2026: the link answers
9 files
- collate_brain_ages.sh, Shell, 23 lines
- generate_submit_scripts.
sh , Shell, 13 lines - predict_new_data_gm_wm_c
sf.R , R, 74 lines - sge_submit_template.sh, Shell, 18 lines
- slurm_submit_template.sh
, Shell, 14 lines - spm_preprocess_brainageR
.m , MATLAB, 120 lines - submit_template.sh, Shell, 14 lines
- LICENSE, License, 165 lines
- README.md, Text, 153 lines
Tracing map
Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.
What the map holds:
- 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
- 7 scripts, each with its path and the digest of its content;
- no match between paragraphs and code yet;
- neither the text of the paper nor the code itself.
Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.
Data
No dataset and no data link were found in the paper.
Data availability
Anonymized data not published within this article will be made available by request from any qualified investigator.
Reproduced under the paper's license (CC BY), from the paper cited above.
Data availability statement
The affinity proteomics data have been deposited to the PRIDE repository with the dataset identifier PAD000050.
Reproduced under the paper's license (CC BY), from the paper cited above.
Versions
The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.
Version 1, 27 September 2026: the first record
Recorded: type, language, journal, volume, pages, dates, 15 authors, 5 keywords, 1 funder, 44 references.
Cite
This paper
Gill, J. M., Lim, A., Esopenko, C., Yun, S., Yun, J., Dark, H. E., Alice, J., Kenney, K., Hentig, J., Pugh, M. J., Walker, W. C., Cifu, D., de Souza, N. L., Dennis, E. L., & Wilde, E. A. (2026). A discovery protein panel for brain predicted age discordance using MRI in neurologically healthy individuals. Frontiers in cell and developmental biology, 14, 1833866. https://
BibTeX
@article{gill2026discove
author = {Gill, Jessica M. and Lim, Arum and Esopenko, Carrie and Yun, Sijung and Yun, Joseph and Dark, Heather E. and Alice, John and Kenney, Kimbra and Hentig, James and Pugh, Mary Jo and Walker, William C. and Cifu, David and de Souza, Nicola L. and Dennis, Emily L. and Wilde, Elisabeth A.},
title = {{A discovery protein panel for brain predicted age discordance using MRI in neurologically healthy individuals}},
journal = {Frontiers in cell and developmental biology},
year = {2026},
month = jul,
volume = {14},
pages = {1833866},
publisher = {Frontiers Media SA},
issn = {2296-634X},
doi = {10.3389/
url = {https://
pmid = {42495719},
pmcid = {PMC13392082}
}
RIS
TY - JOUR
AU - Gill, Jessica M.
AU - Lim, Arum
AU - Esopenko, Carrie
AU - Yun, Sijung
AU - Yun, Joseph
AU - Dark, Heather E.
AU - Alice, John
AU - Kenney, Kimbra
AU - Hentig, James
AU - Pugh, Mary Jo
AU - Walker, William C.
AU - Cifu, David
AU - de Souza, Nicola L.
AU - Dennis, Emily L.
AU - Wilde, Elisabeth A.
TI - A discovery protein panel for brain predicted age discordance using MRI in neurologically healthy individuals
T2 - Frontiers in cell and developmental biology
J2 - Front Cell Dev Biol
PY - 2026
DA - 2026/
VL - 14
SP - 1833866
SN - 2296-634X
PB - Frontiers Media SA
DO - 10.3389/
UR - https://
LA - en
ER -
CSL-JSON
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