Evidence map›Paper›PMID 39864732›Full record

ArticleOsteoarthritis and cartilage2025

Quantifying knee-adjacent subcutaneous fat in the entire OAI baseline dataset - Associations with cartilage MRI T

Gabby B Joseph, Felix Liu, Katharina Ziegeler, Zehra Akkaya, John A Lynch, Valentina Pedoia, Sharmila Majumdar, Nancy E Lane, Michael C Nevitt, Charles E McCulloch and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Osteoarthritis and cartilage, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Review
  2. Review
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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

11 authors.

Gabby B JosephDepartment of Radiology and Biomedical Imaging, University of California, San Francisco, USA. Electronic address: gabby.joseph@ucsf.edu.
Felix LiuDepartment of Radiology and Biomedical Imaging, University of California, San Francisco, USA.
Katharina ZiegelerDepartment of Radiology and Biomedical Imaging, University of California, San Francisco, USA.
Zehra AkkayaDepartment of Radiology and Biomedical Imaging, University of California, San Francisco, USA; Department of Radiology, Ankara University Faculty of Medicine, Ankara, Turkey.
John A LynchDepartment of Epidemiology and Biostatistics, University of California, San Francisco, USA.
Valentina PedoiaDepartment of Radiology and Biomedical Imaging, University of California, San Francisco, USA.
Sharmila MajumdarDepartment of Radiology and Biomedical Imaging, University of California, San Francisco, USA.
Nancy E LaneDepartment of Rheumatology, University of California, Davis, USA.
Michael C NevittDepartment of Epidemiology and Biostatistics, University of California, San Francisco, USA.
Charles E McCullochDepartment of Epidemiology and Biostatistics, University of California, San Francisco, USA.
Thomas M LinkDepartment of Radiology and Biomedical Imaging, University of California, San Francisco, USA.

Funding

Skeletal Biology and Biomechanics (SBB) CoreP30AR075055 · NIAMS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI Wenhan Chang · 2019 to 2026
$7.1M
Understanding the mechanisms by which weight change affects progression of knee osteoarthritis in obese and overweight individuals: An analysis of the Osteoarthritis Initiative DatasetR01AR078917 · NIAMS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI GABRIELLE JOSEPH, THOMAS M LINK · 2021 to 2026
$3.4M
The Study of Muscle, Mobility and Aging with Knee OAR01AG070647 · NIA · UNIVERSITY OF CALIFORNIA AT DAVIS · PI Nancy E Lane · 2021 to 2026
$2.5M
Impact of Weight loss on Knee Joint Biochemical and Structural DegenerationR01AR064771 · NIAMS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI LINK, THOMAS M · 2014 to 2019
$1.6M
NIAMS NIH HHS P30 AR075055NIAMS NIH HHS R01 AR064771NIAMS NIH HHS R01 AR078917NIA NIH HHS R01 AG070647
6 · The paper itself

Abstract

objectiveKnee-adjacent subcutaneous fat (kaSCF) has emerged as a potential biomarker and risk factor for osteoarthritis (OA) progression. This study aims to develop an artificial intelligence-based tool for the automatic segmentation of kaSCF thickness and evaluate the cross-sectional associations between kaSCF, cartilage thickness, magnetic resonance imaging-based cartilage T

designBaseline 3.0T MR images of the right knee from the entire Osteoarthritis Initiative cohort (n=4796) were used to quantify average values of kaSCF, cartilage thickness, and T

resultsModel prediction CVs for kaSCF thickness ranged from 3.57% to 9.87% across femoral and tibial regions. Greater average kaSCF was associated with thinner cartilage in men (std. β= -0.029, 95% CI: -0.050 to -0.007, p=0.010) and higher T

conclusionsGreater kaSCF was associated with thinner cartilage in men, higher T

Indexed as

ArthralgiaCartilage, ArticularKnee JointOsteoarthritis, KneeSubcutaneous FatAgedBody Mass IndexCross-Sectional StudiesFemaleHumansMagnetic Resonance ImagingMaleMiddle AgedMuscle StrengthCartilage T2Cartilage thicknessKnee-adjacent subcutaneous fatPain

Identifiers

PMID39864732
PMCPMC12412328

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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.