AUTHOR=Li Yufeng , Ni Ruizi , An Yajing , Yang Ling , Ye Zhaoyang , Zhuang Li , Li Linsheng , Wang Liang , Gong Wenping 

TITLE=Bibliometric analysis and knowledge mapping of diabetes mellitus combined with tuberculosis research: trends from 1995 to 2023

JOURNAL=Frontiers in Immunology

VOLUME=Volume 16 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1571123

DOI=10.3389/fimmu.2025.1571123

ISSN=1664-3224

ABSTRACT=BackgroundThe synergistic epidemic of diabetes mellitus and tuberculosis (DM-TB) has created a dual disease burden, challenging global health systems with complex pathophysiological interactions and suboptimal treatment outcomes. To decode the evolving research landscape, this study presents the latest comprehensive bibliometric analysis mapping the intellectual architecture of DM-TB research over three decades.MethodsWe systematically analyzed 791 peer-reviewed articles from the Web of Science Core Collection (1995-2023) using CiteSpace, VOSviewer, and Bibliometrix. Advanced metrics including co-citation networks, keyword burst detection, and institutional collaboration patterns were employed to identify paradigm-shifting trends.ResultsThree distinct growth phases were observed: initial stagnation (1995-2007, <10 annual publications), exponential growth (2008-2019), and research diversification (2020-2023). The United States dominated scientific output (27.3% of total publications), while the London School of Hygiene & Tropical Medicine emerged as the central hub for international collaborations (TLS=176). Keyword evolution revealed three transformative phases: (1) Pathomechanistic exploration (1995-2016): Focused on hyperglycemia-immunity interplay and epidemiological surveillance; (2) Translational innovation (2017-2020): Shifted to preclinical models, pharmacokinetic optimization, and multidrug resistance; (3) Precision medicine era (2021-2023): Emerging hotspots in latent TB screening (burst strength=6.82), metformin-mediated immunomodulation, and AI-driven diagnostic biomarkers.ConclusionBeyond delineating historical trajectories, this study identifies critical knowledge gaps in inflammation-resolution mechanisms and insulin resistance pathways, proposing a roadmap for targeted biomarker discovery and global health policy formulation. The constructed knowledge framework empowers strategic resource allocation for combating the DM-TB syndemic.