Evidence map›Paper›PMID 38604248›Full record

ArticleAging2024

Evidence of a pan-tissue decline in stemness during human aging.

Gabriel Arantes Dos Santos, Gustavo Daniel Vega Magdaleno, João Pedro de Magalhães

Open access · hybridAbstract read
In one paragraph

Article in Aging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
0.7field-weighted citation impact, top 32% of its field
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

5 citing papers in PubMed, 3 citations in OpenAlex.

  1. Article
  2. Review
  3. Article
  4. Review
  5. Immunological biomarkers of aging.Journal of immunology (Baltimore, Md. : 1950) · 2025
    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

3 authors at 3 institutions in 2 countries.

Gabriel Arantes Dos SantosLaboratory of Medical Investigation (LIM55), Urology Department, Faculdade de Medicina FMUSP, Universidade de Sao Paulo, Sao Paulo 01246 903, Brazil.
Gustavo Daniel Vega MagdalenoInstitute of Life Course and Medical Sciences, University of Liverpool, Liverpool L7 8TX, United Kingdom.
João Pedro de MagalhãesGenomics of Ageing and Rejuvenation Lab, Institute of Inflammation and Ageing, University of Birmingham, Birmingham B15 2WB, United Kingdom.
Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo · BRUniversity of Birmingham · GBUniversity of Liverpool · GB

Funding

Wellcome Trust
6 · The paper itself

Abstract

Despite their biological importance, the role of stem cells in human aging remains to be elucidated. In this work, we applied a machine learning methodology to GTEx transcriptome data and assigned stemness scores to 17,382 healthy samples from 30 human tissues aged between 20 and 79 years. We found that ~60% of the studied tissues exhibit a significant negative correlation between the subject's age and stemness score. The only significant exception was the uterus, where we observed an increased stemness with age. Moreover, we observed that stemness is positively correlated with cell proliferation and negatively correlated with cellular senescence. Finally, we also observed a trend that hematopoietic stem cells derived from older individuals might have higher stemness scores. In conclusion, we assigned stemness scores to human samples and show evidence of a pan-tissue loss of stemness during human aging, which adds weight to the idea that stem cell deterioration may contribute to human aging.

Indexed as

AgingCellular SenescenceAdultAgedCell ProliferationFemaleHematopoietic Stem CellsHumansMachine LearningMaleMiddle AgedStem CellsTranscriptomeYoung Adultlongevitysenescencestem cellstranscriptomics

Identifiers

PMID38604248
PMCPMC11042951
OpenAlexW4393952104

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.