Abstract
Context: Endocrinology plays a critical role in understanding hormonal systems that regulate metabolism, growth, and reproductive health. Global research in this field has surged in response to the rising prevalence of endocrine disorders such as diabetes, thyroid diseases, and metabolic syndrome. Collaborative dynamics among researchers have driven advancements in diagnostic techniques, therapeutic approaches, and disease-prevention strategies.
Objective: This study aims to analyze the collaborative structure of co-authorship networks in endocrinology research from 2000 to 2023. It investigates patterns of international cooperation, identifies influential researchers and institutions, and examines how collaboration impacts research innovation and productivity.
Methodology: The research analyzed 19,127 endocrinology-related articles from the Web of Science (2000–2023). Using Python (version 3.10.5), the analysis applied macro-level metrics (network density, clustering coefficient, components, and average distance) and micro-level metrics (degree, closeness, and betweenness centrality) to evaluate network structures and identify key contributors.
Results: Endocrinology research networks evolved from fragmented structures with low density (2000-2009) to increasingly clustered and interconnected networks by 2020-2023. Key researchers, including Savage MO, Murad M. Hassan, and Ji Linong, demonstrated consistently high centrality measures and served as pivotal collaborators. Influential researchers and collaborative clusters shaping the field were identified.
Conclusion: This study highlights the significance of collaborative networks in shaping endocrinology research. By identifying influential contributors and collaboration clusters, the findings emphasize the value of international partnerships in addressing complex endocrine disorders. These insights provide a framework for enhancing scientific cooperation in medical research and offer strategies to optimize collaboration for future innovations in endocrinology.
References
Iwen KA, Oelkrug R, Kalscheuer H, Brabant G. Metabolic syndrome in thyroid disease. In: Popovic V, Korbonits M, eds. Metabolic Syndrome Consequent to Endocrine Disorders. Basel: S. Karger AG; 2018:48-66.
Apostol DM, Cretu LM, Craciun AM, Mercore Hutanu E. Considerations on research in the field of lifestyle medicine. J Life Med Res Rev. 2023;1(1):25-31. https://doi.org/10.37897/LMRR.2023.1.4
Rubin EH, Allen JD, Nowak JA, Bates SE. Developing precision medicine in a global world. Clin Cancer Res. 2014;20(6):1419-1427. https://doi.org/10.1158/1078-0432.CCR-13-2166
Papakonstantinou E, Lambadiari V, Dimitriadis G, Zampelas A. Metabolic syndrome and cardiometabolic risk factors. Curr Vasc Pharmacol. 2013;11(6):858-879. https://doi.org/10.2174/15701611113116660176
Mottillo S, Filion KB, Genest J, Joseph L, Pilote L, Poirier P, et al. The metabolic syndrome and cardiovascular risk a systematic review and meta-analysis. J Am Coll Cardiol. 2010;56(14):1113-1132. https://doi.org/10.1016/j.jacc.2010.05.034
Wasir JS, Misra A. The metabolic syndrome in Asian Indians: Impact of nutritional and socio-economic transition in India. Metab Syndr Relat Disord. 2004;2(1):14-23. https://doi.org/10.1089/met.2004.2.14
Prasad DS, Kabir Z, Dash AK, Das BC. Prevalence and risk factors for metabolic syndrome in Asian Indians: A community study from urban Eastern India. J Cardiovasc Dis Res. 2012;3(3):204-211. https://doi.org/10.4103/0975-3583.98895
Lin Y, Wu Y. Trends in incidence and overdiagnosis of thyroid cancer in China, Japan, and South Korea. Cancer Sci. 2023;114(10):4052-4062. https://doi.org/10.1111/cas.15812
Ward J, Friche AA, Caiaffa WT, Proietti FA, Xavier CC, Roux AV. Association of socioeconomic factors with body mass index, obesity, physical activity, and dietary factors in Belo Horizonte, Minas Gerais State, Brazil: The BH Health Study. Cad Saude Publica. 2015;31(Suppl 1):182-194. https://doi.org/10.1590/0102-311X00126914
Sartorelli DS, Franco LJ. Tendências do diabetes mellitus no Brasil: o papel da transição nutricional. Cad Saude Publica. 2003;19(Suppl 1):S29-36. https://doi.org/10.1590/S0102-311X2003000700004
Chatterjee S, Ghosh R, Biswas P, Dubey S, Guria RT, Sharma CB, et al. COVID-19: the endocrine opportunity in a pandemic. Minerva Endocrinol. 2020;45(3):204-227. https://doi.org/10.23736/S0391-1977.20.03216-2
Wasserman S, Faust K. Social Network Analysis: Methods and Applications. Cambridge: Cambridge University Press; 1994. https://doi.org/10.1017/CBO9780511815478
Newman M. Scientific collaboration networks. II. Shortest paths, weighted networks, and centrality. Phys Rev E. 2001;64(1):016132. https://doi.org/10.1103/PhysRevE.64.016132
Newman ME. The structure of scientific collaboration networks. Proc Natl Acad Sci USA. 2001:6;98(2):404-409. https://doi.org/10.1073/pnas.98.2.404
Barabasi AL, Albert R. Emergence of scaling in random networks. Science. 1999;286(5439):509-512. https://doi.org/10.1126/science.286.5439.509

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Copyright (c) 2025 Revista Colombiana de Endocrinología, Diabetes & Metabolismo

