Evidence map›Paper›PMID 42423517›Full record

ArticleDiabetes2026

Utility of Pancreatic Perivascular Adipose Tissue as a CT Imaging Biomarker for Diagnosing Type 2 Diabetes.

Anisa V Prasad, Tejas Sudharshan Mathai, Praveen T S Balamuralikrishna, Vivek Batheja, Abhinav Suri, Pritam Mukherjee, Jianfei Liu, Ronald M Summers

Abstract read
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In one paragraph

Article in Diabetes, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
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0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors.

Anisa V PrasadImaging Biomarkers and Computer-Aided Diagnosis Laboratory, Department of Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD.ORCID 0000-0002-1505-2079
Tejas Sudharshan MathaiImaging Biomarkers and Computer-Aided Diagnosis Laboratory, Department of Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD.
Praveen T S BalamuralikrishnaImaging Biomarkers and Computer-Aided Diagnosis Laboratory, Department of Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD.
Vivek BathejaImaging Biomarkers and Computer-Aided Diagnosis Laboratory, Department of Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD.
Abhinav SuriImaging Biomarkers and Computer-Aided Diagnosis Laboratory, Department of Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD.
Pritam MukherjeeImaging Biomarkers and Computer-Aided Diagnosis Laboratory, Department of Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD.
Jianfei LiuImaging Biomarkers and Computer-Aided Diagnosis Laboratory, Department of Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD.
Ronald M SummersImaging Biomarkers and Computer-Aided Diagnosis Laboratory, Department of Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD.

Funding

Intramural Research Program, National Institutes of Health Clinical Center 1Z01 CL040004
6 · The paper itself

Abstract

Intrapancreatic fat deposition is a well-established biomarker for type 2 diabetes mellitus (T2DM), but little is known about the role of pancreatic perivascular adipose tissue (PVAT), fat surrounding blood vessels supplying the pancreas. The objectives of this study were to develop a fully automated pipeline to derive imaging biomarkers of pancreatic PVAT from abdominal computed tomography (CT) scans and evaluate their utility for diagnosing T2DM. Contrast-enhanced CT (CECT) scans (n = 1,350) from the public PANORAMA data set were used to train a three-dimensional deep neural network to segment pancreatic anatomy (parenchyma, vasculature, ducts) and extrapancreatic structures. The model was applied to an internal data set containing 615 CECT scans. Intrapancreatic, extrapancreatic, and PVAT biomarkers were derived from these segmentations. Logistic regression with feature selection identified optimal biomarker subsets for classifying T2DM. All PVAT biomarkers differed significantly between individuals with diabetes and those without. The best multivariate model for T2DM achieved an area under the curve (AUC) of 0.93, sensitivity of 0.73, and specificity of 0.91. Exploratory analyses for incident diabetes (developed within the following 4 years) showed similarly high discrimination (AUC 0.93). Automated CT-derived PVAT biomarkers are associated with existing and future T2DM and may enable opportunistic identification of undiagnosed or high-risk individuals undergoing routine abdominal CT. ARTICLE HIGHLIGHTS: Pancreatic perivascular adipose tissue (PVAT) is a potentially important but understudied fat depot surrounding vessels supplying the pancreas, and its role in type 2 diabetes mellitus (T2DM) remains unclear. We aimed to quantify pancreatic PVAT automatically on routine abdominal computed tomography (CT) scans and assess whether PVAT-derived imaging biomarkers are associated with T2DM and future T2DM risk. PVAT biomarkers were associated with T2DM and were informative predictors in multivariable models. Exploratory analyses showed similar findings for incident T2DM (disease onset within 4 years of follow-up). PVAT may represent a novel CT-based biomarker for early metabolic dysfunction and T2DM risk stratification.

Indexed as

Adipose TissueDiabetes Mellitus, Type 2PancreasTomography, X-Ray ComputedAgedBiomarkersFemaleHumansMaleMiddle AgedBiomarkers

Identifiers

PMID42423517

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.