Evidence map›Paper›PMID 39517107›Full record

ArticleObesity (Silver Spring, Md.)2025

Relationships between abdominal adipose tissue and neuroinflammation with diffusion basis spectrum imaging in midlife obesity.

Mahsa Dolatshahi, Paul K Commean, Farzaneh Rahmani, Yifei Xu, Jingxia Liu, Sara Hosseinzadeh Kassani, Mahshid Naghashzadeh, LaKisha Lloyd, Caitlyn Nguyen, Abby McBee Kemper and 12 more

Abstract read
In one paragraph

Article in Obesity (Silver Spring, Md.), 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
–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

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

  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

22 authors.

Mahsa DolatshahiMallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, Missouri, USA.ORCID https://orcid.org/0000-0002-4524-9462
Paul K CommeanMallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, Missouri, USA.
Farzaneh RahmaniMallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, Missouri, USA.
Yifei XuDivision of Public Health Sciences, Department of Surgery, Washington University School of Medicine, St. Louis, Missouri, USA.
Jingxia LiuDivision of Public Health Sciences, Department of Surgery, Washington University School of Medicine, St. Louis, Missouri, USA.
Sara Hosseinzadeh KassaniMallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, Missouri, USA.
Mahshid NaghashzadehMallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, Missouri, USA.
LaKisha LloydMallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, Missouri, USA.
Caitlyn NguyenMallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, Missouri, USA.
Abby McBee KemperMallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, Missouri, USA.
Nancy HantlerMallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, Missouri, USA.
Maria LyMallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, Missouri, USA.
Gary YuMallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, Missouri, USA.
Shaney FloresMallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, Missouri, USA.
Joseph E IppolitoMallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, Missouri, USA.
Sheng-Kwei SongMallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, Missouri, USA.
Claude B SirlinLiver Imaging Group, Department of Radiology, University of California, Los Angeles, California, USA.
Weiying DaiDepartment of Computer Science, State University of New York at Binghamton, Binghamton, New York, USA.
Bettina MittendorferDepartments of Medicine and Nutrition & Exercise Physiology, University of Missouri School of Medicine, Columbia, Missouri, USA.ORCID https://orcid.org/0000-0003-0049-2677
John C MorrisKnight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, Missouri, USA.
Tammie L S BenzingerMallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, Missouri, USA.
Cyrus A RajiMallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, Missouri, USA.ORCID https://orcid.org/0000-0002-9086-0105

Funding

Smartphone-Based "Burst" Cognitive AssessmentsP01AG003991 · NIA · WASHINGTON UNIVERSITY · PI Tammie Lee Smith Benzinger · 1985 to 2026
$69.5M
The natural history of AB accumulation in preclinical ADP01AG026276 · NIA · WASHINGTON UNIVERSITY · PI SCHINDLER, SUZANNE ELIZABETH · 2005 to 2025
$49.5M
Washington University Nutrition Obesity Research CenterP30DK056341 · NIDDK · WASHINGTON UNIVERSITY · PI Brian N Finck · 1999 to 2026
$30.2M
Neuroinflammation and Alzheimer's Disease Imaging Biomarkers in Midlife ObesityRF1AG072637 · NIA · WASHINGTON UNIVERSITY · PI RAJI, CYRUS A · 2021 to 2021
$2.3M
Neuroinflammation and Alzheimer's Disease Imaging Biomarkers in Midlife ObesityR01AG072637 · NIA · WASHINGTON UNIVERSITY · PI RAJI, CYRUS A · 2024 to 2025
$1.4M
NIA NIH HHS 1RF1AG072637-01NIA NIH HHS P01 AG003991NIA NIH HHS P01 AG026276NIA NIH HHS R01 AG072637NIA NIH HHS RF1 AG072637NIDDK NIH HHS P30 DK056341
6 · The paper itself

Abstract

objectiveThis study investigated how obesity, BMI ≥ 30 kg/m

methodsWe analyzed data from 98 cognitively normal midlife participants (mean age: 49.4 [SD 6.2] years; 34 males [34.7%]; 56 with obesity [57.1%]). Participants underwent brain and abdominal magnetic resonance imaging (MRI), blood tests, and amyloid positron emission tomography (PET) imaging. Abdominal visceral and subcutaneous adipose tissue (VAT and SAT, respectively) was segmented, and Centiloids were calculated. Diffusion basis spectrum imaging parameter maps were created using an in-house script, and tract-based spatial statistics assessed white matter differences in high versus low BMI values, VAT, SAT, insulin resistance, systemic inflammation, and Centiloids, with age and sex as covariates.

resultsObesity, high VAT, and high SAT were linked to lower axial diffusivity, reduced fiber fraction, and increased restricted fraction in white matter. Obesity was additionally associated with higher hindered fraction and lower fractional anisotropy. Also, individuals with high C-reactive protein showed lower axial diffusivity. Higher restricted fraction correlated with continuous BMI and SAT particularly in male individuals, whereas VAT effects were similar in male and female individuals.

conclusionsThe findings suggest that, at midlife, obesity and abdominal fat are associated with reduced brain axonal density and increased inflammation, with visceral fat playing a significant role in both sexes.

Indexed as

Body Mass IndexInflammationObesityAbdominal FatAdultBrainFemaleHumansInsulin ResistanceIntra-Abdominal FatMaleMiddle AgedNeuroinflammatory DiseasesPositron-Emission TomographyWhite Matter

Identifiers

PMID39517107
PMCPMC11664304

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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.