Evidence map›Paper›PMID 34384369›Full record

SynthesisBMC neurology2021

Factors associated with brain ageing - a systematic review.

Jo Wrigglesworth, Phillip Ward, Ian H Harding, Dinuli Nilaweera, Zimu Wu, Robyn L Woods, Joanne Ryan

Abstract readSystematic Review
In one paragraph

Synthesis in BMC neurology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 57 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
57citing papers in PubMed, 5 pooled it
–field-weighted citation impact
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

57 citing papers in PubMed, 5 syntheses or guidelines pooled it.

  1. Pooled it
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  3. BrainAGE, brain health, and mental disorders: A systematic review.Neuroscience and biobehavioral reviews · 2024
    Pooled it
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  6. Accelerated brain aging and anterior white matter hyperintensity burden in chronic post-stroke aphasia: a lesion-aware MRI analysis.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2026
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  9. Brain-age in ultra-low-field MRI: How well does it work?Imaging neuroscience (Cambridge, Mass.) · 2026
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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

7 authors.

Jo WrigglesworthSchool of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, 3004, Australia.
Phillip WardMonash Biomedical Imaging, Monash University, Clayton, Victoria, 3168, Australia.
Ian H HardingMonash Biomedical Imaging, Monash University, Clayton, Victoria, 3168, Australia.
Dinuli NilaweeraSchool of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, 3004, Australia.
Zimu WuSchool of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, 3004, Australia.
Robyn L WoodsSchool of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, 3004, Australia.
Joanne RyanSchool of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, 3004, Australia. joanne.ryan@monash.edu.ORCID http://orcid.org/0000-0002-7039-6325

Funding

National Health and Medical Research Council 1106533National Health and Medical Research Council 1135727
6 · The paper itself

Abstract

backgroundBrain age is a biomarker that predicts chronological age using neuroimaging features. Deviations of this predicted age from chronological age is considered a sign of age-related brain changes, or commonly referred to as brain ageing. The aim of this systematic review is to identify and synthesize the evidence for an association between lifestyle, health factors and diseases in adult populations, with brain ageing.

methodsThis systematic review was undertaken in accordance with the PRISMA guidelines. A systematic search of Embase and Medline was conducted to identify relevant articles using search terms relating to the prediction of age from neuroimaging data or brain ageing. The tables of two recent review papers on brain ageing were also examined to identify additional articles. Studies were limited to adult humans (aged 18 years and above), from clinical or general populations. Exposures and study design of all types were also considered eligible.

resultsA systematic search identified 52 studies, which examined brain ageing in clinical and community dwelling adults (mean age between 21 to 78 years, ~ 37% were female). Most research came from studies of individuals diagnosed with schizophrenia or Alzheimer's disease, or healthy populations that were assessed cognitively. From these studies, psychiatric and neurologic diseases were most commonly associated with accelerated brain ageing, though not all studies drew the same conclusions. Evidence for all other exposures is nascent, and relatively inconsistent. Heterogenous methodologies, or methods of outcome ascertainment, were partly accountable.

conclusionThis systematic review summarised the current evidence for an association between genetic, lifestyle, health, or diseases and brain ageing. Overall there is good evidence to suggest schizophrenia and Alzheimer's disease are associated with accelerated brain ageing. Evidence for all other exposures was mixed or limited. This was mostly due to a lack of independent replication, and inconsistency across studies that were primarily cross sectional in nature. Future research efforts should focus on replicating current findings, using prospective datasets.

trial registrationA copy of the review protocol can be accessed through PROSPERO, registration number CRD42020142817 .

Indexed as

BrainNeuroimagingAdultAgedAgingCross-Sectional StudiesFemaleHumansMiddle AgedProspective StudiesYoung AdultAge predictionAge-related brain changesBiomarkerBrainAGEBrain ageingMachine learningNeuroimagingPredicted age difference

Identifiers

PMID34384369
PMCPMC8359541

What OpenQuestion holds

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Registered trials

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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.