The DOI matches its Crossref record; 17 of the 17 references it cites were found in a registry or an index.
Diabetes mellitus, the fastest growing global public health concern: Early detection should be focused
Open accessAbstract
Background and Aims: Diabetes is recognized as a significant factor in both mortality and morbidity worldwide, affecting various demographics regardless of geographic location, age group, or gender. This correspondence aims to express concern and draw the attention of leaders and policymakers worldwide to this critical public health issue. Methods: A thorough literature search was conducted utilizing various databases, including Google Scholar, PubMed, Science Direct, and the International Diabetes Federation (IDF) website, to collect the required data. Keywords were strategically applied to enhance search results, with preference given to English‐language articles containing pertinent information. Results: According to the 2021 report by the IDF, approximately 537 million individuals globally were affected with diabetes, constituting roughly 10.5% of the world's populace. This condition incurred healthcare expenditures totaling $966 billion. Projections indicate a surge in diabetes cases to 783 million by 2045, with associated healthcare costs estimated to surpass $1054 billion. However, almost half of all people with diabetes are unaware of their medical condition, with the highest prevalence of undiagnosed diabetes Mellitus (DM) found in low and middle‐income countries (LMICs) of the regions of Africa, the Western Pacific, and Southeast Asia. Conclusion: Collaborating with the World Health Organization (WHO), LMIC governments should improve healthcare accessibility, including more frequent diabetes screenings for individuals aged ≥ 45 years and younger individuals at elevated risk of having a family history.
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Sun, H., Saeedi, P., Karuranga, S., Pinkepank, M., Ogurtsova, K., Duncan, B. B., Stein, C., Basit, A., Chan, J. C. N., Mbanya, J. C., et al. (2022) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2021.109119 · Cited by 10,123
Ong, K. L., Stafford, L. K., McLaughlin, S. A., Boyko, E. J., Vollset, S. E., Smith, A. E., Dalton, B. E., Duprey, J., Cruz, J. A., Hagins, H., et al. (2023) · The Lancet · doi:10.1016/s0140-6736(23)01301-6 · Cited by 4,681
Self-citationPan, X.-R., Li, G.-W., Hu, Y.-H., Wang, J.-X., Yang, W.-Y., An, Z.-X., Hu, Z.-X., Juan-Lin, Xiao, J.-Z., Cao, H.-B., et al. (1997) · Diabetes Care · doi:10.2337/diacare.20.4.537 · Cited by 4,320
Ahmad, E., Lim, S., Lamptey, R., Webb, D. R., & Davies, M. J. (2022) · The Lancet · doi:10.1016/s0140-6736(22)01655-5 · Cited by 1,120
IDF diabetes Atlas: Global estimates of undiagnosed diabetes in adults for 2021
Ogurtsova, K., Guariguata, L., Barengo, N. C., Ruiz, P. L.-D., Sacre, J. W., Karuranga, S., Sun, H., Boyko, E. J., & Magliano, D. J. (2022) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2021.109118 · Cited by 952
The Growing Epidemic of Diabetes Mellitus
Lovic, D., Piperidou, A., Zografou, I., Grassos, H., Pittaras, A., & Manolis, A. (2020) · Current Vascular Pharmacology · doi:10.2174/1570161117666190405165911 · Cited by 504
Early detection of type 2 diabetes mellitus using machine learning-based prediction models
Kopitar, L., Kocbek, P., Cilar, L., Sheikh, A., & Stiglic, G. (2020) · Scientific Reports · doi:10.1038/s41598-020-68771-z · Cited by 369
Prevalence, Prevention, and Lifestyle Intervention of Gestational Diabetes Mellitus in China
Juan, J., & Yang, H. (2020) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph17249517 · Cited by 354
Early detection of undiagnosed diabetes mellitus: a US perspective
Harris, M. I., & Eastman, R. C. (2000) · Diabetes/Metabolism Research and Reviews · doi:10.1002/1520-7560(2000)9999:9999<::aid-dmrr122>3.0.co;2-w · Cited by 287
COVID-19 and metabolic disease: mechanisms and clinical management
Steenblock, C., Schwarz, P. E. H., Ludwig, B., Linkermann, A., Zimmet, P., Kulebyakin, K., Tkachuk, V. A., Markov, A. G., Lehnert, H., de Angelis, M. H., et al. (2021) · The Lancet Diabetes & Endocrinology · doi:10.1016/s2213-8587(21)00244-8 · Cited by 246
Diabetes: a defining disease of the 21st century
Lancet, T. (2023) · The Lancet · doi:10.1016/s0140-6736(23)01296-5 · Cited by 201
The double burden of diabetes and global infection in low and middle-income countries
Dunachie, S., & Chamnan, P. (2019) · Transactions of The Royal Society of Tropical Medicine and Hygiene · doi:10.1093/trstmh/try124 · Cited by 194
Diabetes and the COVID-19 pandemic
Khunti, K., Valabhji, J., & Misra, S. (2022) · Diabetologia · doi:10.1007/s00125-022-05833-z · Cited by 128
Utility of HbA1c for diagnosis of gestational diabetes mellitus
Rajput, R., YogeshYadav, Rajput, M., & Nanda, S. (2012) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2012.02.018 · Cited by 111
Diabetes Mellitus and COVID-19: Review Article
Nassar, M., Daoud, A., Nso, N., Medina, L., Ghernautan, V., Bhangoo, H. S., Nyein, A., Mohamed, M., Alqassieh, A., Soliman, K., et al. (2021) · Diabetes & Metabolic Syndrome Clinical Research & Reviews · doi:10.1016/j.dsx.2021.102268 · Cited by 99
Glezeva, N., Chisale, M., McDonald, K., Ledwidge, M. T., Gallagher, J., & Watson, C. J. (2017) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2017.12.019 · Cited by 48
Sarker, A., Das, R., Ether, S. T., & Saif‐Ur‐Rahman, K. (2020) · Systematic Reviews · doi:10.1186/s13643-020-01550-z · Cited by 19
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Kciuk, M., Kruczkowska, W., Gałęziewska, J., Wanke, K., Kałuzińska-Kołat, Ż., Aleksandrowicz, M., & Kontek, R. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms252211955 · Cited by 98
Bushi, G., Gaidhane, S., Ballal, S., Kumar, S., Bhat, M., Sharma, S., Kumar, M. R., Sinha, A., Khatib, M. N., Rai, N., et al. (2025) · Health Science Reports · doi:10.1002/hsr2.70490 · Cited by 13
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Adebisi, Y. A., Ngoma, C., Campagna, D., Ceriello, A., Alshahrani, N. Z., Misra, A., Basit, A., Russo, C., Battelino, T., Somasundaram, N., et al. (2025) · Diabetes & Metabolic Syndrome: Clinical Research & Reviews · doi:10.1016/j.dsx.2025.103331 · Cited by 8
Elfayoumi, M., AbouElazm, M., Mohamed, O., Abuhmed, T., & El-Sappagh, S. (2025) · 2025 19th International Conference on Ubiquitous Information Management and Communication (IMCOM) · doi:10.1109/imcom64595.2025.10857525 · Cited by 7
Metadata
- Authors
- Hossain, M. J., Al‐Mamun, M., & Islam, M. R.
- Venue
- Health Science Reports
- Publisher
- Wiley
- Published
- 2024-03
- Volume
- 7
- Issue
- 3
- Pages
- e2004-e2004
- ISSN
- 2398-8835 (ISSN-L)
- OpenAlex
- W4393072771
- Cited by
- 928
- References
- 17
- FWCI
- 252.19
- Type
- journal article
- Language
- en
- Open access
- gold
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
