Evidence map›Paper›PMID 41786761›Full record

ArticleScientific data2026

Proteomic profiling of human omental and subcutaneous adipose tissue in individuals with a broad range of BMI.

Alex Zelter, Yue Winnie Wen, Michael Riffle, Lindsay C Czuba, Aprajita S Yadav, Jerry Zhu, Jessica M Snyder, Aaron Maurais, Jeffrey LaFrance, Saurabh Khandelwal and 7 more

Abstract readDataset
In one paragraph

Article in Scientific data, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

17 authors.

Alex Zelter *Department of Genome Sciences, School of Medicine, University of Washington, Seattle, WA, USA.
Yue Winnie Wen *Department of Pharmaceutics, School of Pharmacy, University of Washington, Seattle, WA, USA.
Michael RiffleDepartment of Genome Sciences, School of Medicine, University of Washington, Seattle, WA, USA.ORCID http://orcid.org/0000-0003-1633-8607
Lindsay C CzubaDepartment of Pharmaceutics, School of Pharmacy, University of Washington, Seattle, WA, USA.
Aprajita S YadavDepartment of Pharmaceutics, School of Pharmacy, University of Washington, Seattle, WA, USA.
Jerry ZhuDepartment of Pharmaceutics, School of Pharmacy, University of Washington, Seattle, WA, USA.
Jessica M SnyderDepartment of Comparative Medicine, School of Medicine, University of Washington, Seattle, WA, USA.
Aaron MauraisDepartment of Genome Sciences, School of Medicine, University of Washington, Seattle, WA, USA.
Jeffrey LaFranceDepartment of Pharmaceutics, School of Pharmacy, University of Washington, Seattle, WA, USA.
Saurabh KhandelwalDivision of General Surgery, Department of Surgery, University of Washington, Seattle, WA, USA.
Judy Y ChenDivision of General Surgery, Department of Surgery, University of Washington, Seattle, WA, USA.
Estell WilliamsDivision of General Surgery, Department of Surgery, University of Washington, Seattle, WA, USA.
Zoe ParrDivision of General Surgery, Department of Surgery, University of Washington, Seattle, WA, USA.
Daniel KimDivision of General Surgery, Department of Surgery, University of Washington, Seattle, WA, USA.
Katya B RubinowDivision of Metabolism, Endocrinology and Nutrition, Department of Medicine, University of Washington, Seattle, WA, USA.
Michael J MacCossDepartment of Genome Sciences, School of Medicine, University of Washington, Seattle, WA, USA.ORCID http://orcid.org/0000-0003-1853-0256
Nina IsoherranenDepartment of Pharmaceutics, School of Pharmacy, University of Washington, Seattle, WA, USA. ni2@uw.edu.ORCID http://orcid.org/0000-0002-9548-3126

Funding

Pharmacological Sciences SupplementT32GM007750 · NIGMS · UNIVERSITY OF WASHINGTON · PI ATKINS, WILLIAM M · 1985 to 2023
$12.6M
Diabetes, Obesity and Metabolism Training ProgramT32DK007247 · NIDDK · UNIVERSITY OF WASHINGTON · PI GREGORY J MORTON, KRISTINA Marie UTZSCHNEIDER · 1986 to 2026
$10.0M
NRSA Training CoreTL1TR002318 · NCATS · UNIVERSITY OF WASHINGTON · PI Megan Moore · 2017 to 2026
$8.4M
Seattle Quant: A Resource for the Skyline Software EcosystemR24GM141156 · NIGMS · UNIVERSITY OF WASHINGTON · PI Michael MacCoss · 2021 to 2026
$6.7M
Quantifying proteins in plasma do democratize personalized medicine for patients with type 1 diabetesU01DK137097 · NIDDK · UNIVERSITY OF WASHINGTON · PI ANDREW N HOOFNAGLE, Michael MacCoss · 2023 to 2026
$3.4M
Mechanisms of Regulation of Retinoic Acid HomeostasisR01GM111772 · NIGMS · UNIVERSITY OF WASHINGTON · PI ISOHERRANEN, NINA · 2014 to 2021
$3.0M
Mechanisms of Regulation of Retinoic Acid HomeostasisR01DK143511 · NIDDK · UNIVERSITY OF WASHINGTON · PI Nina Isoherranen · 2025 to 2026
$1.2M
NIDDK NIH HHS R01 DK143511NIGMS NIH HHS R01 GM111772U.S. Department of Health & Human Services | National Institutes of Health (NIH) R01DK143511U.S. Department of Health & Human Services | National Institutes of Health (NIH) R01GM111772U.S. Department of Health & Human Services | National Institutes of Health (NIH) R24GM141156U.S. Department of Health & Human Services | National Institutes of Health (NIH) T32DK007247U.S. Department of Health & Human Services | National Institutes of Health (NIH) TL1TR002318U.S. Department of Health & Human Services | National Institutes of Health (NIH) U01DK137097U.S. Department of Health & Human Services | NIH | Office of Extramural Research, National Institutes of Health (OER) T32GM007750
6 · The paper itself

Abstract

Obesity is a major public health challenge affecting an ever-increasing proportion of the global population. It is associated with numerous comorbidities. Progressive expansion and remodeling of adipose tissue may lead to depot specific changes in adipose tissue biology and energy partitioning. Such changes likely precede the development of obesity-related complications. To facilitate a deeper understanding of adipose tissue biology, a comprehensive quantitative proteomic dataset is presented at the peptide and protein level. Data-independent acquisition LC-MS/MS data were acquired from matched subcutaneous and omental adipose tissues from metabolically healthy individuals with no comorbidities and covering a wide range of body mass indexes. Adipose tissue samples were collected during elective surgeries and immediately processed for histology or frozen until proteomic analysis. Internal and external quality control systems ensured high quality data. All data presented are available via ProteomeXchange. This dataset will allow new insights into biological changes that evolve with increasing adiposity captured before the onset of comorbidities. Matched sampling across fat depots provides an opportunity to uncover depot-specific physiological signatures.

Indexed as

Body Mass IndexOmentumProteomeSubcutaneous FatFemaleHumansObesityProteomicsTandem Mass SpectrometryProteome

Identifiers

PMID41786761
PMCPMC13079756

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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 OpenQuestion graph.