Bibliometrics and visualization analysis: Big data research on diabetic kidney disease from 2000 to 2025.

BACKGROUND: As 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. METHODS: On 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. RESULTS: Three 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. CONCLUSION: Big 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.