Evidence map›Paper›PMID 42440223›Full record

ArticleInternational urology and nephrology2026

Cellular senescence in kidney diseases: a bibliometric analysis of global trends, knowledge bases, and emerging therapeutic frontiers.

Ning Liu, Tao Liu

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

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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

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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

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

2 authors.

Ning LiuXi'an Medical College, No. 1, Weiwu Road, Huyi District, Xi'an, 710309, Shaanxi Province, China.
Tao LiuTongchuan People's Hospital, No. 10, West Section of Hongji Road, Yaozhou District, Tongchuan New Area, Tongchuan, 727031, Shaanxi Province, China. nyee123@sina.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeCellular senescence has emerged as an important mechanism linking renal ageing, acute kidney injury, diabetic kidney disease, chronic kidney disease, renal fibrosis, and kidney transplantation-related injury. This study aimed to map the global research landscape, knowledge bases, and emerging frontiers of cellular senescence-related kidney disease research.

methodsPublications were retrieved from the Science Citation Index Expanded of the Web of Science Core Collection. After screening by language and document type, 775 English-language articles and reviews were included. CiteSpace and VOSviewer were used to analyze publication trends, citation impact, collaboration networks, productive countries, institutions, authors, journals, highly cited documents, co-cited references, keyword co-occurrence, and citation bursts.

resultsThe field expanded rapidly after 2016 and reached its highest annual output in 2025. China and the United States were the leading contributors by publication volume. Co-cited reference clustering identified major knowledge bases related to renal ageing, chronic kidney disease-associated premature ageing, maladaptive repair, renal fibrosis, diabetic kidney disease, tubular epithelial injury, SASP, uremic toxicity, transplantation-related injury, and senescence-targeted interventions. Recent hotspots centered on diabetic kidney disease, acute kidney injury, kidney fibrosis, tubular epithelial cells, DNA damage, mitochondrial dysfunction, inflammaging, immunosenescence, extracellular vesicles, and senolytic therapy.

conclusionResearch on cellular senescence-related kidney diseases has shifted from descriptive studies of renal ageing and chronic kidney disease complications toward disease-specific, cell-type-specific, and translational investigations. Future studies should integrate single-cell and spatial multi-omics, establish reliable renal senescence biomarkers, and develop safer kidney-targeted senotherapeutic strategies.

Indexed as

BibliometricsCellular senescenceChronic kidney diseaseDiabetic kidney diseaseKidney diseaseRenal fibrosis

Identifiers

PMID42440223

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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.