The DOI matches its Crossref record; 16 of the 16 references it cites were found in a registry or an index.
COVID-19 in people with diabetes: understanding the reasons for worse outcomes
Open accessAbstract
Since the initial COVID-19 outbreak in China, much attention has focused on people with diabetes because of poor prognosis in those with the infection. Initial reports were mainly on people with type 2 diabetes, although recent surveys have shown that individuals with type 1 diabetes are also at risk of severe COVID-19. The reason for worse prognosis in people with diabetes is likely to be multifactorial, thus reflecting the syndromic nature of diabetes. Age, sex, ethnicity, comorbidities such as hypertension and cardiovascular disease, obesity, and a pro-inflammatory and pro-coagulative state all probably contribute to the risk of worse outcomes. Glucose-lowering agents and anti-viral treatments can modulate the risk, but limitations to their use and potential interactions with COVID-19 treatments should be carefully assessed. Finally, severe acute respiratory syndrome coronavirus 2 infection itself might represent a worsening factor for people with diabetes, as it can precipitate acute metabolic complications through direct negative effects on β-cell function. These effects on β-cell function might also cause diabetic ketoacidosis in individuals with diabetes, hyperglycaemia at hospital admission in individuals with unknown history of diabetes, and potentially new-onset diabetes.
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Zhou, F., Yu, T., Du, R., Fan, G., Liu, Y., Liu, Z., Xiang, J., Wang, Y., Song, B., Gu, X., et al. (2020) · The Lancet · doi:10.1016/s0140-6736(20)30566-3 · Cited by 27,263
Chen, N., Zhou, M., Dong, X., Qu, J., Gong, F., Han, Y., Qiu, Y., Wang, J., Liu, Y., Wei, Y., et al. (2020) · The Lancet · doi:10.1016/s0140-6736(20)30211-7 · Cited by 21,415
Pathological findings of COVID-19 associated with acute respiratory distress syndrome
Xu, Z., Shi, L., Wang, Y., Zhang, J., Huang, L., Zhang, C., Liu, S., Zhao, P., Liu, H., Zhu, L., et al. (2020) · The Lancet Respiratory Medicine · doi:10.1016/s2213-2600(20)30076-x · Cited by 8,545
Collaborative, T. O., Williamson, E., Walker, A. J., Bhaskaran, K., Bacon, S., Bates, C., Morton, C. E., Curtis, H. J., Mehrkar, A., Evans, D., et al. (2020) · doi:10.1101/2020.05.06.20092999 · Cited by 6,674
Factors associated with COVID-19-related death using OpenSAFELY
Williamson, E. J., Walker, A. J., Bhaskaran, K., Bacon, S., Bates, C., Morton, C. E., Curtis, H. J., Mehrkar, A., Evans, D., Inglesby, P., et al. (2020) · Nature · doi:10.1038/s41586-020-2521-4 · Cited by 6,674
Pulmonary Vascular Endothelialitis, Thrombosis, and Angiogenesis in Covid-19
Ackermann, M., Verleden, S. E., Kuehnel, M., Haverich, A., Welte, T., Laenger, F., Vanstapel, A., Werlein, C., Stark, H., Tzankov, A., et al. (2020) · New England Journal of Medicine · doi:10.1056/nejmoa2015432 · Cited by 5,613
Dipeptidyl peptidase 4 is a functional receptor for the emerging human coronavirus-EMC
Raj, V. S., Mou, H., Smits, S. L., Dekkers, D. H. W., Müller, M. A., Dijkman, R., Muth, D., Demmers, J. A. A., Zaki, A., Fouchier, R. A. M., et al. (2013) · Nature · doi:10.1038/nature12005 · Cited by 2,061
Henry, B. M., de Oliveira, M. H. S., Benoit, S., Plebani, M., & Lippi, G. (2020) · Clinical Chemistry and Laboratory Medicine (CCLM) · doi:10.1515/cclm-2020-0369 · Cited by 1,964
Improved Docking of Polypeptides with Glide
Tubert-Brohman, I., Sherman, W., Repasky, M., & Beuming, T. (2013) · Journal of Chemical Information and Modeling · doi:10.1021/ci400128m · Cited by 1,308
Bode, B., Garrett, V., Messler, J., McFarland, R., Crowe, J., Booth, R., & Klonoff, D. C. (2020) · Journal of Diabetes Science and Technology · doi:10.1177/1932296820924469 · Cited by 766
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The burden and risks of emerging complications of diabetes mellitus
Tomic, D., Shaw, J. E., & Magliano, D. J. (2022) · Nature Reviews Endocrinology · doi:10.1038/s41574-022-00690-7 · Cited by 1,163
Garg, D., Muthu, V., Sehgal, I. S., Ramachandran, R., Kaur, H., Bhalla, A., Puri, G. D., Chakrabarti, A., & Agarwal, R. (2021) · Mycopathologia · doi:10.1007/s11046-021-00528-2 · Cited by 579
Global pandemics interconnected — obesity, impaired metabolic health and COVID-19
Stefan, N., Birkenfeld, A. L., & Schulze, M. B. (2021) · Nature Reviews Endocrinology · doi:10.1038/s41574-020-00462-1 · Cited by 452
An overview on the role of bioactive α-glucosidase inhibitors in ameliorating diabetic complications
Hossain, U., Das, A. K., Ghosh, S., & Sil, P. C. (2020) · Food and Chemical Toxicology · doi:10.1016/j.fct.2020.111738 · Cited by 438
Acute and long-term disruption of glycometabolic control after SARS-CoV-2 infection
Montefusco, L., Ben Nasr, M., D’Addio, F., Loretelli, C., Rossi, A., Pastore, I., Daniele, G., Abdelsalam, A., Maestroni, A., Dell’Acqua, M., et al. (2021) · Nature Metabolism · doi:10.1038/s42255-021-00407-6 · Cited by 423
Metadata
- Authors
- Apicella, M., Campopiano, M. C., Mantuano, M., Mazoni, L., Coppelli, A., & Del Prato, S.
- Venue
- The Lancet Diabetes & Endocrinology
- Publisher
- Elsevier BV
- Published
- 2020-09
- Volume
- 8
- Issue
- 9
- Pages
- 782-792
- ISSN
- 2213-8587 (ISSN-L)
- OpenAlex
- W3042557581
- Cited by
- 903
- References
- 16
- FWCI
- 23.45
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Apicella, M., Campopiano, M. C., Mantuano, M., Mazoni, L., Coppelli, A., & Del Prato, S. (2020). COVID-19 in people with diabetes: understanding the reasons for worse outcomes. The Lancet Diabetes & Endocrinology. https://doi.org/10.1016/s2213-8587(20)30238-2
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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