ArticleMedicine2026
Bibliometrics and visualization analysis: Big data research on diabetic kidney disease from 2000 to 2025.
Article in Medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAs the prevalence of diabetes rises, diabetic kidney disease (DKD) has become a leading cause of end-stage renal disease. Big data analysis aids in DKD prediction, diagnosis, and personalized treatment. This bibliometric study summarizes the current research status and hotspots in big data-driven DKD research.
methodsOn March 26, 2025, DKD-related big data publications were retrieved from the Web of Science Core Collection. CiteSpace and VOSviewer were used for co-authorship, co-occurrence, and co-citation analyses to construct knowledge networks.
resultsThree hundred twenty documents were identified, involving 2176 authors, 695 institutions, and 51 countries/regions, published in 192 journals. Research grew gradually from 2002 to 2018 and then rapidly after 2019. Frontiers in Endocrinology (21 publications) and Journal of the American Society of Nephrology (421 citations) led in publications and citations, respectively. China (189 publications), Beijing University of Chinese Medicine (10 publications), and Donovan, Michael J (6 publications) were the most productive. Hotspots included DKD (192), machine learning (ML, 99), diabetes mellitus (82), prediction (61), risk (52), chronic kidney disease (51), biomarkers (41), progression (33), artificial intelligence (AI, 27), and expression (27). ML, AI, mechanisms, and cells may be future frontiers.
conclusionBig data-driven DKD research is growing, with multi-omics biomarkers underpinning AI/ML models that are hotspots for risk prediction and progression assessment; AI, ML, mechanisms, and cells are the frontiers, which together provide references for DKD precision medicine.
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Identifiers
What Socratic holds
Registered trials
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.