Evidence map›Paper›PMID 38424358›Full record

ArticleEuropean archives of psychiatry and clinical neuroscience2025

White matter brain age as a biomarker of cerebrovascular burden in the ageing brain.

Jing Du, Yuangang Pan, Jiyang Jiang, Ben C P Lam, Anbupalam Thalamuthu, Rory Chen, Ivor W Tsang, Perminder S Sachdev, Wei Wen

Open access · hybridAbstract read
In one paragraph

Article in European archives of psychiatry and clinical neuroscience, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 1 pooled it
2.0field-weighted citation impact, top 14% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed, 1 synthesis or guideline pooled it, 5 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Review
  4. Article
  5. Article
  6. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 2 institutions in 2 countries.

Jing DuCentre for Healthy Brain Aging (CHeBA), School of Psychiatry, UNSW Sydney, Kensington, New South Wales, 2052, Australia. jing.du@unsw.edu.au.ORCID http://orcid.org/0000-0002-0780-826X
Yuangang PanCentre for Frontier AI Research (CFAR), A*STAR, Singapore, 138623, Singapore.
Jiyang JiangCentre for Healthy Brain Aging (CHeBA), School of Psychiatry, UNSW Sydney, Kensington, New South Wales, 2052, Australia.
Ben C P LamCentre for Healthy Brain Aging (CHeBA), School of Psychiatry, UNSW Sydney, Kensington, New South Wales, 2052, Australia.
Anbupalam ThalamuthuCentre for Healthy Brain Aging (CHeBA), School of Psychiatry, UNSW Sydney, Kensington, New South Wales, 2052, Australia.
Rory ChenCentre for Healthy Brain Aging (CHeBA), School of Psychiatry, UNSW Sydney, Kensington, New South Wales, 2052, Australia.
Ivor W TsangCentre for Frontier AI Research (CFAR), A*STAR, Singapore, 138623, Singapore.
Perminder S SachdevCentre for Healthy Brain Aging (CHeBA), School of Psychiatry, UNSW Sydney, Kensington, New South Wales, 2052, Australia.
Wei WenCentre for Healthy Brain Aging (CHeBA), School of Psychiatry, UNSW Sydney, Kensington, New South Wales, 2052, Australia. w.wen@unsw.edu.au.
UNSW Sydney · AUAgency for Science, Technology and Research · SG

Funding

National Health and Medical Research Council 1093083National Health and Medical Research Council 350833National Health and Medical Research Council 568969National Health and Medical Research Council 630593
6 · The paper itself

Abstract

As the brain ages, it almost invariably accumulates vascular pathology, which differentially affects the cerebral white matter. A rich body of research has investigated the link between vascular risk factors and the brain. One of the less studied questions is that among various modifiable vascular risk factors, which is the most debilitating one for white matter health? A white matter specific brain age was developed to evaluate the overall white matter health from diffusion weighted imaging, using a three-dimensional convolutional neural network deep learning model in both cross-sectional UK biobank participants (n = 37,327) and a longitudinal subset (n = 1409). White matter brain age gap (WMBAG) was the difference between the white matter age and the chronological age. Participants with one, two, and three or more vascular risk factors, compared to those without any, showed an elevated WMBAG of 0.54, 1.23, and 1.94 years, respectively. Diabetes was most strongly associated with an increased WMBAG (1.39 years, p < 0.001) among all risk factors followed by hypertension (0.87 years, p < 0.001) and smoking (0.69 years, p < 0.001). Baseline WMBAG was associated significantly with processing speed, executive and global cognition. Significant associations of diabetes and hypertension with poor processing speed and executive function were found to be mediated through the WMBAG. White matter specific brain age can be successfully targeted for the examination of the most relevant risk factors and cognition, and for tracking an individual's cerebrovascular ageing process. It also provides clinical basis for the better management of specific risk factors.

Indexed as

AgingBrainCerebrovascular DisordersDiabetes MellitusHypertensionWhite MatterAgedCross-Sectional StudiesDeep LearningDiffusion Magnetic Resonance ImagingFemaleHumansLongitudinal StudiesMaleMiddle AgedRisk FactorsDeep learning networksDiffusion weighted imagingVascular risk factorsWhite matter brain age

Identifiers

PMID38424358
PMCPMC12638326
OpenAlexW4392283226

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.