Evidence map›Paper›PMID 40435724›Full record

ArticleEBioMedicine2025

Radiomic and proteomic signatures of body mass index on brain ageing and Alzheimer's-like patterns of brain atrophy.

Filippos Anagnostakis, Michail Kokkorakis, Keenan A Walker, Ioanna Skampardoni, Junhao Wen, Guray Erus, Duygu Tosun, Vasiliki Tassopoulou, Yuhan Cui, Sindhuja T Govindarajan and 7 more

Abstract read
In one paragraph

Article in EBioMedicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–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

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Association of sleep duration with Alzheimer's disease and cognition.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026
    Article
  4. Article
  5. 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

17 authors.

Filippos AnagnostakisDepartment of Medical and Surgical Sciences, Alma Mater University of Bologna, Bologna, Italy; Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA. Electronic address: filippos.anagnostakis@pennmedicine.upenn.edu.
Michail KokkorakisDepartment of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA; Department of Clinical Pharmacy and Pharmacology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands.
Keenan A WalkerLaboratory of Behavioral Neuroscience, National Institute on Aging, Intramural Research Program, Baltimore, MD, USA.
Ioanna SkampardoniCentre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA.
Junhao WenLaboratory of AI and Biomedical Science (LABS), Columbia University, New York, NY, USA.
Guray ErusCentre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA.
Duygu TosunDepartment of Radiology and Biomedical Imaging, University of California, San Francisco, USA.
Vasiliki TassopoulouCentre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA.
Yuhan CuiCentre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA.
Sindhuja T GovindarajanCentre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA.
Dhivya SrinivasanCentre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA.
Randa MelhemCentre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA.
Elizabeth MamourianCentre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA.
Haochang ShouCentre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA.
Ilya M NasrallahCentre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA; Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA.
Christos S MantzorosDepartment of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA; Section of Endocrinology, Boston VA Healthcare System, Harvard Medical School, Boston, MA, USA.
Christos DavatzikosCentre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA. Electronic address: Christos.davatzikos@pennmedicine.upenn.edu.

Funding

Research Education ComponentP30AG066507 · NIA · JOHNS HOPKINS UNIVERSITY · PI Karen J. Bandeen-Roche · 2020 to 2026
$29.3M
Machine Learning and Large-scale Imaging analytics for dimensional representations of brain trajectories in aging and preclinical Alzheimer's Disease: The brain aging chart and the iSTAGING consortiumRF1AG054409 · NIA · UNIVERSITY OF PENNSYLVANIA · PI DAVATZIKOS, CHRISTOS · 2017 to 2023
$6.3M
The Neuroimaging Brain Chart Software SuiteU24NS130411 · NINDS · UNIVERSITY OF PENNSYLVANIA · PI Christos Davatzikos, Yong Fan · 2023 to 2026
$3.7M
Machine Learning and Large-scale Imaging analytics for dimensional representations of brain trajectories in aging and preclinical Alzheimer's Disease: The brain aging chart and the iSTAGING consortiumR01AG054409 · NIA · UNIVERSITY OF PENNSYLVANIA · PI Christos Davatzikos · 2025 to 2026
$1.4M
NIA NIH HHS P30 AG066507NIA NIH HHS R01 AG054409NIA NIH HHS RF1 AG054409NINDS NIH HHS U24 NS130411
6 · The paper itself

Abstract

backgroundThe impact of high body mass index (BMI) states and associated proteomic factors on brain ageing and Alzheimer's disease (AD) remains unclear.

methodsWe sought to evaluate machine learning (ML)-based neuroimaging markers of brain age and AD-like brain atrophy in participants with obesity or overweight without diagnosed cognitive impairment (WODCI), in a harmonised study of 46,288 participants in 15 studies (the Imaging-Based Coordinate System for Aging and Neurodegenerative Diseases (iSTAGING) consortium). We also assessed the association between cognition, serum proteins, and brain ageing indices. Data were acquired between 1999 and 2020 and analysed from November 2024 onwards.

findingsThe study comprised 46,288 participants, including 24,897 females and 21,391 males, with a mean age of 64.33 years (SD = 8.13) and a mean BMI of 26.81 kg/m

interpretationThe findings demonstrate that higher BMI states are associated with accelerated brain ageing and AD-like atrophy, particularly in males, while females with normal weight demonstrated higher brain ageing and AD-like atrophy than males with normal weight. Moreover, the impact of obesity on brain ageing and AD-like brain atrophy becomes weaker with increasing age. Further research is needed to investigate sex-specific mechanisms by which weight gain influences brain ageing.

fundingNational Institute on Aging's Intramural Research Program, National Institute on Aging, Intramural Research Program.

Indexed as

AgingAlzheimer DiseaseBody Mass IndexBrainProteomeProteomicsAgedAged, 80 and overAtrophyBiomarkersFemaleHumansMagnetic Resonance ImagingMaleMiddle AgedNeuroimagingBiomarkersProteomeAgeingAlzheimer'sBrain age gapProteomics

Identifiers

PMID40435724
PMCPMC12159507

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.