Evidence map›Paper›PMID 41527259›Full record

ArticleThe British journal of radiology2026

Automated CT-based visceral fat density predicts mortality regardless of visceral fat area.

Adam J Kuchnia, Glen M Blake, Matthew H Lee, Jevin Lortie, John W Garrett, Perry J Pickhardt

Abstract read
In one paragraph

Article in The British journal of radiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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

6 authors.

Adam J KuchniaDepartment of Nutritional Sciences, University of Wisconsin, Madison, WI 53706, United States.ORCID 0000-0002-3418-0965
Glen M BlakeSchool of Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas' Hospital, London SE1 7EH, United Kingdom.
Matthew H LeeDepartment of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, WI 53792-3252, United States.
Jevin LortieDepartment of Nutritional Sciences, University of Wisconsin, Madison, WI 53706, United States.ORCID 0000-0001-9647-2579
John W GarrettDepartment of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, WI 53792-3252, United States.
Perry J PickhardtDepartment of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, WI 53792-3252, United States.

Funding

Institutional Career Development CoreKL2TR002374 · NCATS · UNIVERSITY OF WISCONSIN-MADISON · PI Bo Liu, Marin Leigh Schweizer · 2017 to 2026
$9.7M
Clinical and Translational Science AwardNCATS NIH HHS KL2TR002374
6 · The paper itself

Abstract

objectivesWe evaluated whether automated CT-based adiposity tools can predict all-cause mortality in a large retrospective adult population.

methodsThis study included 151 177 patients who underwent abdominal CT between 2000 and 2021. An AI-based algorithm measured abdominal visceral adipose tissue (VAT) cross-sectional area and density at the L3. Kaplan-Meier survival curves and hazard ratios assessed VAT and mortality.

resultsAmong 136 895 patients included, 9059 died within 1 year and 18 829 died within 2 to 20 years post-CT. Higher VAT density predicted 1-year mortality (hazard ratio [HR] up to 3.8) and over 2-20 years (HR up to 2.1). In contrast, VAT area did not significantly predict mortality. High VAT density was associated with the poorest survival, regardless of area. Low VAT density predicted better survival, regardless of area. VAT density consistently predicted mortality across age groups and sexes, whereas BMI did not differentiate risk.

conclusionsAI-enabled CT measures of VAT density are superior to VAT area for predicting all-cause mortality. Furthermore, we analysed VAT density vs. BMI in our largest age group (40-59) and found BMI was unable to adequately predict risk of mortality. Automated assessment of VAT density may enhance patient risk assessment and management. ADVANCES IN KNOWLEDGE: Assessing visceral fat density using fully automated AI-based CT tools offers a significant advancement in predicting health risk, leading to targeted interventions and improved management strategies. This study is novel due to its large patient population, offering evidence that prognostication with VAT density is broadly generalizable across varying patient populations.

Indexed as

Intra-Abdominal FatTomography, X-Ray ComputedAdiposityAdultAgedAlgorithmsBody Mass IndexFemaleHumansMaleMiddle AgedPredictive Value of TestsRetrospective StudiesRisk Assessmentautomated AI-based CTHounsfield units (HU)mortalityVAT areaVAT HUvisceral adipose tissue (VAT)

Identifiers

PMID41527259
PMCPMC13016999

What Socratic 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 Socratic graph.