Evidence map›Paper›PMID 36455869›Full record

ArticleAging2022

White matter hyperintensity load is associated with premature brain aging.

Natalie Busby, Sarah Newman-Norlund, Sara Sayers, Roger Newman-Norlund, Sarah Wilson, Samaneh Nemati, Chris Rorden, Janina Wilmskoetter, Nicholas Riccardi, Rebecca Roth and 2 more

Open access · hybridAbstract read
In one paragraph

Article in Aging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
1.7field-weighted citation impact, top 16% 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

13 citing papers in PubMed, 13 citations in OpenAlex.

  1. Brain Age in Adult Patients With Early-Treated Phenylketonuria.Journal of inherited metabolic disease · 2026
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  10. Longitudinal Progression of White Matter Hyperintensity Severity in Chronic Stroke Aphasia.Archives of rehabilitation research and clinical translation · 2023
    Article
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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

12 authors at 3 institutions in 1 country.

Natalie BusbyDepartment of Communication Sciences and Disorders, University of South Carolina, Columbia, SC 29201, USA.
Sarah Newman-NorlundDepartment of Communication Sciences and Disorders, University of South Carolina, Columbia, SC 29201, USA.
Sara SayersDepartment of Communication Sciences and Disorders, University of South Carolina, Columbia, SC 29201, USA.
Roger Newman-NorlundDepartment of Psychology, University of South Carolina, Columbia, SC 29201, USA.
Sarah WilsonDepartment of Communication Sciences and Disorders, University of South Carolina, Columbia, SC 29201, USA.
Samaneh NematiDepartment of Communication Sciences and Disorders, University of South Carolina, Columbia, SC 29201, USA.
Chris RordenDepartment of Psychology, University of South Carolina, Columbia, SC 29201, USA.
Janina WilmskoetterDepartment of Health and Rehabilitation Sciences, Medical University of South Carolina, Charleston, SC 29425, USA.
Nicholas RiccardiDepartment of Psychology, University of South Carolina, Columbia, SC 29201, USA.
Rebecca RothDepartment of Neurology, Emory University, Atlanta, GA 30322, USA.
Julius FridrikssonDepartment of Communication Sciences and Disorders, University of South Carolina, Columbia, SC 29201, USA.
Leonardo BonilhaDepartment of Neurology, Emory University, Atlanta, GA 30322, USA.
University of South Carolina · USEmory University · USMedical University of South Carolina · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundBrain age is an MRI-derived estimate of brain tissue loss that has a similar pattern to aging-related atrophy. White matter hyperintensities (WMHs) are neuroimaging markers of small vessel disease and may represent subtle signs of brain compromise. We tested the hypothesis that WMHs are independently associated with premature brain age in an original aging cohort.

methodsBrain age was calculated using machine-learning on whole-brain tissue estimates from T1-weighted images using the BrainAgeR analysis pipeline in 166 healthy adult participants. WMHs were manually delineated on FLAIR images. WMH load was defined as the cumulative volume of WMHs. A positive difference between estimated brain age and chronological age (BrainGAP) was used as a measure of premature brain aging. Then, partial Pearson correlations between BrainGAP and volume of WMHs were calculated (accounting for chronological age).

resultsBrain and chronological age were strongly correlated (

conclusionsWMHs are an independent factor associated with premature brain aging. This finding underscores the impact of white matter disease on global brain integrity and progressive age-like brain atrophy.

Indexed as

Aging, PrematureLeukoaraiosisWhite MatterAgingAtrophyBrainHumansMagnetic Resonance Imagingagingbrain agebrain healthhealthwhite matter hyperintensity

Identifiers

PMID36455869
PMCPMC9792198
OpenAlexW4310602687

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.