Evidence map›Paper›PMID 39037603›Full record

ArticleDiabetologia2024

Spatial proteomics of human diabetic kidney disease, from health to class III.

Ayano Kondo, Monee McGrady, Dhiraj Nallapothula, Hira Ali, Alexandro E Trevino, Amy Lam, Ryan Preska, H Blaize D'Angio, Zhenqin Wu, Lauren N Lopez and 9 more

Abstract read
PubMed Publisher
In one paragraph

Article in Diabetologia, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing 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

14 citing papers in PubMed.

  1. Article
  2. Article
  3. New Insights into the Role of Mitochondrial Dysfunction in Diabetic Kidney Disease in the Omics Era.Diabetes, metabolic syndrome and obesity : targets and therapy · 2026
    Review
  4. Review
  5. Article
  6. Article
  7. Review
  8. Review
  9. Article
  10. Integrated multiomic analyses: An approach to improve understanding of diabetic kidney disease.Diabetic medicine : a journal of the British Diabetic Association · 2025
    Review
  11. Article
  12. Article
  13. Article
  14. IL-6 and diabetic kidney disease.Frontiers in immunology · 2024
    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

19 authors.

Ayano KondoEnable Medicine, Menlo Park, CA, USA.
Monee McGradyEnable Medicine, Menlo Park, CA, USA.
Dhiraj Nallapothula *Division of Nephrology, University of California, Davis, CA, USA.
Hira Ali *Enable Medicine, Menlo Park, CA, USA.
Alexandro E TrevinoEnable Medicine, Menlo Park, CA, USA.
Amy LamEnable Medicine, Menlo Park, CA, USA.
Ryan PreskaEnable Medicine, Menlo Park, CA, USA.
H Blaize D'AngioEnable Medicine, Menlo Park, CA, USA.
Zhenqin WuEnable Medicine, Menlo Park, CA, USA.
Lauren N LopezDivision of Nephrology, University of California, Davis, CA, USA.
Harshanna K BadheshaDivision of Nephrology, University of California, Davis, CA, USA.
Chenoa R VargasDivision of Nephrology, University of California, Davis, CA, USA.
Achyuta RameshUniversity of California, San Diego, CA, USA.
Nasim WiegleyDivision of Nephrology, University of California, Davis, CA, USA.
Seung Seok HanDepartment of Internal Medicine, Seoul National University College of Medicine, Seoul, South Korea.
Marc Dall'EraDepartment of Urologic Surgery, University of California-Davis Medical Center, Sacramento, CA, USA.
Kuang-Yu JenDepartment of Pathology and Laboratory Medicine, University of California- Davis, Sacramento, CA, USA.
Aaron T MayerEnable Medicine, Menlo Park, CA, USA.
Maryam AfkarianDivision of Nephrology, University of California, Davis, CA, USA. mafkarian@ucdavis.edu.ORCID http://orcid.org/0000-0001-8428-8264

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

aims/hypothesisDiabetic kidney disease (DKD) is the leading cause of chronic and end-stage kidney disease in the USA and worldwide. Animal models have taught us much about DKD mechanisms, but translation of this knowledge into treatments for human disease has been slowed by the lag in our molecular understanding of human DKD.

methodsUsing our Spatial TissuE Proteomics (STEP) pipeline (comprising curated human kidney tissues, multiplexed immunofluorescence and powerful analysis tools), we imaged and analysed the expression of 21 proteins in 23 tissue sections from individuals with diabetes and healthy kidneys (n=5), compared to those with DKDIIA, IIA-B and IIB (n=2 each) and DKDIII (n=1).

resultsThese analyses revealed the existence of 11 cellular clusters (kidney compartments/cell types): podocytes, glomerular endothelial cells, proximal tubules, distal nephron, peritubular capillaries, blood vessels (endothelial cells and vascular smooth muscle cells), macrophages, myeloid cells, other CD45 CONCLUSIONS/

interpretationUsing the STEP pipeline, we found alterations in protein expression, cellular phenotypic composition and microenvironment structure with DKD progression, demonstrating the power of this pipeline to reveal the pathophysiology of human DKD.

Indexed as

Diabetic NephropathiesProteomicsFemaleHumansKidneyMaleMiddle AgedPodocytesDiabetic kidney diseaseSpatial biologyTissue proteomics

Identifiers

PMID39037603

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.