Bibiliometrics Menggunakan Analisis R-Bibiloshiny Resistensi Insulin pada Obesitas Kelompok Dewasa Database Scopus (2019-2025)
DOI:
https://doi.org/10.57214/jka.v8i2.678Keywords:
insulin resistance, obesity, bibliometrics, metabolic syndrome, scopus analysisAbstract
contributing to the global burden of type 2 diabetes, cardiovascular diseases, and other metabolic complications. This bibliometric study aims to analyze trends, thematic focuses, and collaborations in scientific publications related to insulin resistance in adult obesity, utilizing data from the Scopus database covering the period from 2019 to 2025. Bibliometric tools such as RStudio, Biblioshiny, and VoS Viewer were employed to extract and visualize findings from 8,037 publications. The results reveal that research activity peaked in 2021, followed by a decline in subsequent years. Dominant keywords, such as "insulin resistance," "obesity," and "metabolic syndrome," highlight the clinical and metabolic focus of current research. Biomolecular markers, such as "glycated hemoglobin" and the "triglyceride-glucose index," reflect a growing interest in more precise diagnostic tools. The United States, China, and Italy emerged as major contributors, with journals like Nutrients and Frontiers in Endocrinology leading in publications. Despite significant progress, there remains a considerable gap, particularly in exploring the role of epigenetics and the microbiota in insulin resistance. Additionally, cross-disciplinary collaboration and participation from institutions in developing countries remain limited. This study emphasizes the importance of global, interdisciplinary efforts to address these gaps and drive innovation in interventions, providing a foundation for future research aimed at reducing the impact of insulin resistance in adult obesity.
References
Abarca-Gómez, L., Abdeen, Z. A., Hamid, Z. A., Abu-Rmeileh, N. M., Acosta-Cazares, B., Acuin, C., Adams, R. J., Aekplakorn, W., Afsana, K., Aguilar-Salinas, C. A., Agyemang, C., Ahmadvand, A., Ahrens, W., Ajlouni, K., Akhtaeva, N., Al-Hazzaa, H. M., Al-Othman, A. R., Al-Raddadi, R., Al Buhairan, F., … Ezzati, M. (2017). Worldwide trends in body-mass index, underweight, overweight, and obesity from 1975 to 2016: A pooled analysis of 2416 population-based measurement studies in 128.9 million children, adolescents, and adults. The Lancet, 390(10113), 2627–2642. https://doi.org/10.1016/S0140-6736(17)32129-3
Afrisham, R., Farrokhi, V., Ayyoubzadeh, S. M., Vatannejad, A., Fadaei, R., Moradi, N., Jadidi, Y., & Alizadeh, S. (2024a). CCN5/WISP2 serum levels in patients with coronary artery disease and type 2 diabetes and its correlation with inflammation and insulin resistance: A machine learning approach. Biochemistry and Biophysics Reports, 40, 101857. https://doi.org/10.1016/J.BBREP.2024.101857
Afrisham, R., Farrokhi, V., Ayyoubzadeh, S. M., Vatannejad, A., Fadaei, R., Moradi, N., Jadidi, Y., & Alizadeh, S. (2024b). CCN5/WISP2 serum levels in patients with coronary artery disease and type 2 diabetes and its correlation with inflammation and insulin resistance: A machine learning approach. Biochemistry and Biophysics Reports, 40, 101857. https://doi.org/10.1016/J.BBREP.2024.101857
Aria, M., & Cuccurullo, C. (2017a). bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975. https://doi.org/10.1016/J.JOI.2017.08.007
Aria, M., & Cuccurullo, C. (2017b). bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975. https://doi.org/10.1016/J.JOI.2017.08.007
Arslan, A. K., Yagin, F. H., Algarni, A., Karaaslan, E., Al-Hashem, F., & Ardigò, L. P. (2024). Enhancing type 2 diabetes mellitus prediction by integrating metabolomics and tree-based boosting approaches. Frontiers in Endocrinology, 15. https://doi.org/10.3389/fendo.2024.1444282
Belhayara, M. I., Mellouk, Z., Hamdaoui, M. S., Bachaoui, M., Kheroua, O., & Malaisse, W. J. (2020). The metabolic syndrome: Emerging novel insights regarding the relationship between the homeostasis model assessment of insulin resistance and other key predictive markers in young adults of western Algeria. Nutrients, 12(3). https://doi.org/10.3390/nu12030727
Boitard, C. (2020). Les diabètes: De la génétique à l’environnement. Bulletin de l’Académie Nationale de Médecine, 204(5), 493–499. https://doi.org/10.1016/J.BANM.2020.03.007
Depommier, C., Everard, A., Druart, C., Plovier, H., Van Hul, M., Vieira-Silva, S., Falony, G., Raes, J., Maiter, D., Delzenne, N. M., de Barsy, M., Loumaye, A., Hermans, M. P., Thissen, J. P., de Vos, W. M., & Cani, P. D. (2019). Supplementation with Akkermansia muciniphila in overweight and obese human volunteers: A proof-of-concept exploratory study. Nature Medicine, 25(7), 1096–1103. https://doi.org/10.1038/s41591-019-0495-2
Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133, 285–296. https://doi.org/10.1016/J.JBUSRES.2021.04.070
Elmitwalli, O., Darwish, R., Al-Jabery, L., Algahiny, A., Roy, S., Butler, A. E., & Hasan, A. S. (2024). The emerging role of p21 in diabetes and related metabolic disorders. International Journal of Molecular Sciences, 25(23). https://doi.org/10.3390/ijms252313209
Fasciolo, G., Napolitano, G., Aprile, M., Cataldi, S., Costa, V., Muscari Tomajoli, M. T., Lombardi, A., Di Meo, S., & Venditti, P. (2023a). Muscle oxidative stress plays a role in hyperthyroidism-linked insulin resistance. Antioxidants, 12(3), 592. https://doi.org/10.3390/antiox12030592
Fasciolo, G., Napolitano, G., Aprile, M., Cataldi, S., Costa, V., Muscari Tomajoli, M. T., Lombardi, A., Di Meo, S., & Venditti, P. (2023b). Muscle oxidative stress plays a role in hyperthyroidism-linked insulin resistance. Antioxidants, 12(3). https://doi.org/10.3390/antiox12030592
Fu, T., Liu, H., Shi, C., Zhao, H., Liu, F., & Xia, Y. (2024). Global hotspots and trends of nutritional supplements in sport and exercise from 2000 to 2024: A bibliometric analysis. Journal of Health, Population and Nutrition, 43(1), 146. https://doi.org/10.1186/s41043-024-00638-9
Gallardo-Garcia, J., Pagán-Castaño, E., Sánchez-Garcia, J., & Guijarro-García, M. (2023). Bibliometric analysis of the customer experience literature. Economic Research-Ekonomska Istrazivanja, 36(2). https://doi.org/10.1080/1331677X.2022.2137822
González-Martín, J. M., Torres-Mata, L. B., Cazorla-Rivero, S., Fernández-Santana, C., Gómez-Bentolila, E., Clavo, B., & Rodríguez-Esparragón, F. (2023). An Artificial Intelligence Prediction Model of Insulin Sensitivity, Insulin Resistance, and Diabetes Using Genes Obtained through Differential Expression. Genes, 14(12). https://doi.org/10.3390/genes14122119
Imierska, M., Zabielski, P., Roszczyc-Owsiejczuk, K., Pogodzińska, K., & Błachnio-Zabielska, A. (2025). Impact of reduced hepatic ceramide levels in high-fat diet mice on glucose metabolism. The Journal of Nutritional Biochemistry, 135, 109785. https://doi.org/10.1016/J.JNUTBIO.2024.109785
Khan, M. A. B., Hashim, M. J., King, J. K., Govender, R. D., Mustafa, H., & Al Kaabi, J. (2019). Epidemiology of Type 2 Diabetes – Global Burden of Disease and Forecasted Trends. Journal of Epidemiology and Global Health, 10(1), 107. https://doi.org/10.2991/jegh.k.191028.001
