Risks and burdens of incident diabetes in long COVID: a cohort study
Open accessAbstract
Background There is growing evidence suggesting that beyond the acute phase of SARS-CoV-2 infection, people with COVID-19 could experience a wide range of post-acute sequelae, including diabetes. However, the risks and burdens of diabetes in the post-acute phase of the disease have not yet been comprehensively characterised. To address this knowledge gap, we aimed to examine the post-acute risk and burden of incident diabetes in people who survived the first 30 days of SARS-CoV-2 infection.Methods In this cohort study, we used the national databases of the US Department of Veterans Affairs to build a cohort of 181 280 participants who had a positive COVID-19 test between March 1, 2020, and Sept 30, 2021, and survived the first 30 days of COVID-19; a contemporary control (n=4 118 441) that enrolled participants between March 1, 2020, and Sept 30, 2021; and a historical control (n=4 286 911) that enrolled participants between March 1, 2018, and Sept 30, 2019. Both control groups had no evidence of SARS-CoV-2 infection. Participants in all three comparison groups were free of diabetes before cohort entry and were followed up for a median of 352 days (IQR 245-406). We used inverse probability weighted survival analyses, including predefined and algorithmically selected high dimensional variables, to estimate post-acute COVID-19 risks of incident diabetes, antihyperglycaemic use, and a composite of the two outcomes. We reported two measures of risk: hazard ratio (HR) and burden per 1000 people at 12 months.Findings In the post-acute phase of the disease, compared with the contemporary control group, people with COVID-19 exhibited an increased risk (HR 1·40, 95% CI 1·36-1·44) and excess burden (13·46, 95% CI 12·11-14·84, per 1000 people at 12 months) of incident diabetes; and an increased risk (1·85, 1·78-1·92) and excess burden (12·35, 11·36-13·38) of incident antihyperglycaemic use. Additionally, analyses to estimate the risk of a composite endpoint of incident diabetes or antihyperglycaemic use yielded a HR of 1·46 (95% CI 1·43-1·50) and an excess burden of 18·03 (95% CI 16·59-19·51) per 1000 people at 12 months. Risks and burdens of post-acute outcomes increased in a graded fashion according to the severity of the acute phase of COVID-19 (whether patients were non-hospitalised, hospitalised, or admitted to intensive care). All the results were consistent in analyses using the historical control as the reference category.Interpretation In the post-acute phase, we report increased risks and 12-month burdens of incident diabetes and antihyperglycaemic use in people with COVID-19 compared with a contemporary control group of people who were enrolled during the same period and had not contracted SARS-CoV-2, and a historical control group from a pre-pandemic era. Post-acute COVID-19 care should involve identification and management of diabetes.Funding US Department of Veterans Affairs and the American Society of Nephrology.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Research has shown a 60% increase in risk for new-onset diabetes in people suffering from COVID-19 compared with people who were never infected.
“Research has shown a 60% increase in risk for new-onset diabetes in people suffering from COVID-19 compared with people who were never infected”
From Is Pollution the Primary Driver of Infectious Syndemics? (2024) · cites this paper as [68]
The Results report an increased risk of incident diabetes among 30-day COVID-19 survivors compared with contemporary controls, but the adjusted hazard ratio is 1.40 (95% CI 1.36–1.44), indicating a 40% rather than a 60% increase. The finding also concerns diabetes after the first 30 days of infection, not risk during acute COVID-19. (LLM, conf=0.88)
Experimental — accuracy not yet measuredCardiovascular/cerebrovascular conditions and diabetes are potential sequelae of SARS-CoV-2 infection.
“and potential sequelae of SARS-CoV-2 infection39,40.”
From Attributed causes of excess mortality during the COVID-19 pandemic in a south Indian city (2023) · cites this paper as [40]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Austin, P. C. (2011) · Multivariate Behavioral Research · doi:10.1080/00273171.2011.568786 · Cited by 12,242 · see citing works
Making Neighborhood-Disadvantage Metrics Accessible — The Neighborhood Atlas
Kind, A. J. H., & Buckingham, W. R. (2018) · New England Journal of Medicine · doi:10.1056/nejmp1802313 · Cited by 3,310 · see citing works
Multiple imputation of discrete and continuous data by fully conditional specification
van Buuren, S. (2007) · Statistical Methods in Medical Research · doi:10.1177/0962280206074463 · Cited by 2,827 · see citing works
Ziegler, C. G. K., Allon, S. J., Nyquist, S. K., Mbano, I. M., Miao, V. N., Tzouanas, C. N., Cao, Y., Yousif, A. S., Bals, J., Hauser, B. M., et al. (2020) · Cell · doi:10.1016/j.cell.2020.04.035 · Cited by 2,284 · see citing works
Long-term cardiovascular outcomes of COVID-19
Xie, Y., Xu, E., Bowe, B. C., & Al‐Aly, Z. (2022) · Nature Medicine · doi:10.1038/s41591-022-01689-3 · Cited by 2,065 · see citing works
Self-citationNegative Controls: A Tool for Detecting Confounding and Bias in Observational Studies
Lipsitch, M., Tchetgen Tchetgen, E., & Cohen, T. (2010) · Epidemiology · doi:10.1097/ede.0b013e3181d61eeb · Cited by 1,583 · see citing works
High-dimensional characterization of post-acute sequelae of COVID-19
Al-Aly, Z., Xie, Y., & Bowe, B. (2021) · Nature · doi:10.1038/s41586-021-03553-9 · Cited by 1,518 · see citing works
Schneeweiss, S., Rassen, J. A., Glynn, R. J., Avorn, J., Mogun, H., & Brookhart, M. A. (2009) · Epidemiology · doi:10.1097/ede.0b013e3181a663cc · Cited by 1,128 · see citing works
Post-covid syndrome in individuals admitted to hospital with covid-19: retrospective cohort study
Ayoubkhani, D., Khunti, K. K., Nafilyan, V., Maddox, T., Humberstone, B., Diamond, I. D., & Banerjee, A. (2021) · BMJ · doi:10.1136/bmj.n693 · Cited by 843 · see citing works
Thomas (2020) · doi:10.1001/jama.2020.7819 · Cited by 835 · see citing works
New-onset diabetes in Covid-19
Rubino, F., Amiel, S. A., Zimmet, P., Alberti, G., Bornstein, S., Eckel, R. H., Mingrone, G., Boehm, B., Cooper, M. E., Chai, Z., et al. (2020) · New England Journal of Medicine · doi:10.1056/nejmc2018688 · Cited by 801 · see citing works
Addressing extreme propensity scores via the overlap weights
Li, F., & Thomas, L. E. (2018) · American Journal of Epidemiology · doi:10.1093/aje/kwy201 · Cited by 694 · see citing works
Yang, L., Han, Y., Nilsson-Payant, B. E., Gupta, V., Wang, P., Duan, X., Tang, X., Zhu, J., Zhao, Z., Jaffré, F., et al. (2020) · Cell stem cell · doi:10.1016/j.stem.2020.06.015 · Cited by 632 · see citing works
SARS-CoV-2 infects and replicates in cells of the human endocrine and exocrine pancreas
Müller, J. A., Groß, R. M., Conzelmann, C., Krüger, J., Merle, U., Steinhart, J., Weil, T., Koepke, L., Bozzo, C. P., Read, C., et al. (2021) · Nature Metabolism · doi:10.1038/s42255-021-00347-1 · Cited by 555 · see citing works
Acute and long-term disruption of glycometabolic control after SARS-CoV-2 infection
Montefusco, L. B., Nasr, M. B., 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 426 · see citing works
SARS-CoV-2 infects human pancreatic β cells and elicits β cell impairment
Wu, C.-T., Lidsky, P. V., Xiao, Y., Lee, I. T., Cheng, R., Nakayama, T., Jiang, S., Demeter, J., Bevacqua, R. J., Chang, C., et al. (2021) · Cell Metabolism · doi:10.1016/j.cmet.2021.05.013 · Cited by 368 · see citing works
Bowe, B., Xie, Y., Yan, Y., & Al-Aly, Z. (2019) · JAMA Network Open · doi:10.1001/jamanetworkopen.2019.15834 · Cited by 367 · see citing works
Self-citationBowe, B. C., Xie, Y., Xu, E., & Al-Aly, Z. (2021) · Journal of the American Society of Nephrology · doi:10.1681/asn.2021060734 · Cited by 350 · see citing works
Self-citationRisks of mental health outcomes in people with covid-19: cohort study
Xie, Y., Xu, E., & Al-Aly, Z. (2022) · BMJ · doi:10.1136/bmj-2021-068993 · Cited by 333 · see citing works
Self-citationCOVID-19, Hyperglycemia, and New-Onset Diabetes
Khunti, K. K., Prato, S. D., Mathieu, C., Kahn, S. E., Gabbay, R. A., & Buse, J. B. (2021) · Diabetes Care · doi:10.2337/dc21-1318 · Cited by 331 · see citing works
Xie, Y., Bowe, B. C., & Al‐Aly, Z. (2021) · Nature Communications · doi:10.1038/s41467-021-26513-3 · Cited by 293 · see citing works
Self-citationBarrett, C. E., Koyama, A. K., Alvarez, P., Chow, W., Lundeen, E. A., Perrine, C. G., Pavkov, M. E., Rolka, D. B., Wiltz, J. L., Bull–Otterson, L., et al. (2022) · MMWR Morbidity and Mortality Weekly Report · doi:10.15585/mmwr.mm7102e2 · Cited by 293 · see citing works
Wei, Y., Wang, Y., Di, Q., Choirat, C., Wang, Y., Koutrakis, P., Zanobetti, A., Dominici, F., & Schwartz, J. D. (2019) · BMJ · doi:10.1136/bmj.l6258 · Cited by 282 · see citing works
Proportion of newly diagnosed diabetes in COVID ‐19 patients: A systematic review and meta‐analysis
Sathish, T., Kapoor, N., Cao, Y., Tapp, R. J., & Zimmet, P. (2020) · Diabetes Obesity and Metabolism · doi:10.1111/dom.14269 · Cited by 253 · see citing works
COVID-19 and metabolic disease: mechanisms and clinical management
Steenblock, C., Schwarz, P. E. H., Ludwig, B., Linkermann, A., Zimmet, P., Kulebyakin, K. Y., Va, T., 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 248 · see citing works
Xie, Y., Bowe, B. C., Yan, Y., Xian, H., Li, T., & Al‐Aly, Z. (2019) · BMJ · doi:10.1136/bmj.l1580 · Cited by 226 · see citing works
Self-citationAcute Kidney Injury in a National Cohort of Hospitalized US Veterans with COVID-19
Bowe, B. C., Cai, M., Xie, Y., Gibson, A. K., Maddukuri, G. S., & Al‐Aly, Z. (2020) · Clinical Journal of the American Society of Nephrology · doi:10.2215/cjn.09610620 · Cited by 201 · see citing works
Self-citationHigher blood urea nitrogen is associated with increased risk of incident diabetes mellitus
Xie, Y., Bowe, B. C., Li, T., Xian, H., Yan, Y., & Al‐Aly, Z. (2017) · Kidney International · doi:10.1016/j.kint.2017.08.033 · Cited by 198 · see citing works
Self-citationA Selective Review of Negative Control Methods in Epidemiology
Shi, X., Miao, W., & Tchetgen, E. T. (2020) · Current Epidemiology Reports · doi:10.1007/s40471-020-00243-4 · Cited by 194 · see citing works
SARS-CoV-2 infection induces beta cell transdifferentiation
Tang, X., Uhl, S. A., Zhang, T., Xue, D., Li, B., Vandana, J. J., Acklin, J. A., Bonnycastle, L. L., Narisu, N., Erdos, M. R., et al. (2021) · Cell Metabolism · doi:10.1016/j.cmet.2021.05.015 · Cited by 192 · see citing works
Xie, Y., Bowe, B. C., Maddukuri, G. S., & Al‐Aly, Z. (2020) · BMJ · doi:10.1136/bmj.m4677 · Cited by 183 · see citing works
Self-citationGreene, M. L., Steinman, M. A., McNicholl, I. R., & Valcour, V. (2014) · Journal of the American Geriatrics Society · doi:10.1111/jgs.12695 · Cited by 171 · see citing works
Xie, Y., Bowe, B. C., Gibson, A. K., McGill, J. B., Maddukuri, G. S., Yan, Y., & Al‐Aly, Z. (2020) · Diabetes Care · doi:10.2337/dc20-1890 · Cited by 155 · see citing works
Self-citationAccili, D. (2021) · Nature Metabolism · doi:10.1038/s42255-020-00339-7 · Cited by 141 · see citing works
Bowe, B. C., Xie, Y., Gibson, A. K., Cai, M., van Donkelaar, A., Martin, R. V., Burnett, R. T., & Al‐Aly, Z. (2021) · Environment International · doi:10.1016/j.envint.2021.106564 · Cited by 101 · see citing works
Self-citationAssociation between Monocyte Count and Risk of Incident CKD and Progression to ESRD
Bowe, B. C., Xie, Y., Xian, H., Li, T., & Al‐Aly, Z. (2017) · Clinical Journal of the American Society of Nephrology · doi:10.2215/cjn.09710916 · Cited by 78 · see citing works
Self-citationOlvey, E. L., Clauschee, S. F., & Malone, D. C. (2009) · Clinical Pharmacology & Therapeutics · doi:10.1038/clpt.2009.198 · Cited by 65 · see citing works
Geographic Variation and US County Characteristics Associated With Rapid Kidney Function Decline
Bowe, B. C., Xie, Y., Xian, H., Lian, M., & Al‐Aly, Z. (2016) · Kidney International Reports · doi:10.1016/j.ekir.2016.08.016 · Cited by 54 · see citing works
Self-citationXie, Y., Bowe, B. C., Gibson, A. K., McGill, J. B., Maddukuri, G. S., & Al‐Aly, Z. (2021) · JAMA Internal Medicine · doi:10.1001/jamainternmed.2021.2488 · Cited by 48 · see citing works
Self-citationLimited extent and consequences of pancreatic SARS-CoV-2 infection
van der Heide, V., Jangra, S., Cohen, P., Rathnasinghe, R., Aslam, S., Aydillo, T., Geanon, D., Handler, D., Kelley, G., Lee, B., et al. (2022) · Cell Reports · doi:10.1016/j.celrep.2022.110508 · Cited by 47 · see citing works
Xie, Y., Bowe, B. C., Gibson, A. K., McGill, J. B., Maddukuri, G. S., & Al‐Aly, Z. (2021) · Journal of the American Heart Association · doi:10.1161/jaha.120.020237 · Cited by 46 · see citing works
Self-citationCai, M., Bowe, B. C., Xie, Y., & Al‐Aly, Z. (2021) · BMJ Open · doi:10.1136/bmjopen-2020-047369 · Cited by 41 · see citing works
Self-citationXie, Y., Bowe, B. C., Gibson, A. K., McGill, J. B., Yan, Y., Maddukuri, G. S., & Al‐Aly, Z. (2020) · Diabetes Care · doi:10.2337/dc20-1231 · Cited by 40 · see citing works
Self-citationBest Definitions of Multimorbidity to Identify Patients With High Health Care Resource Utilization
Aubert, C. E., Schnipper, J. L., Roumet, M., Marques-Vidal, P. M., Stirnemann, J., Auerbach, A. D., Zimlichman, E., Kripalani, S. B., Vasilevskis, E. E., Robinson, E. J., et al. (2020) · Mayo Clinic Proceedings Innovations Quality & Outcomes · doi:10.1016/j.mayocpiqo.2019.09.002 · Cited by 34 · see citing works
County-Level Contextual Characteristics and Disparities in Life Expectancy
Xie, Y., Bowe, B. C., Yan, Y., Cai, M., & Al‐Aly, Z. (2021) · Mayo Clinic Proceedings · doi:10.1016/j.mayocp.2020.04.043 · Cited by 19 · see citing works
Self-citationCollaborative, T. O., Tazare, J., Walker, A. J., et al. (2021) · medRxiv · doi:10.1101/2021.01.22.21250304 · Cited by 19 · see citing works
Flagged first, then by citations
Research works printed but not linked: 3
- [40]McKeigue PM Relation of incident type 1 diabetes to recent COVID-19 infection: cohort study using e-health record linkage in Scotland medRxiv 2022 published online Feb 11. 10.1101/2022.02.11.22270785 (preprint). 35880797 · DOI
- [41]Sathish T Kapoor N Cao Y Tapp RJ Zimmet P Proportion of newly diagnosed diabetes in COVID-19 patients: a systematic review and meta-analysis Diabetes Obes Metab 23 2021 870 874 33245182 PMC7753574
- [42]Ayoubkhani D Khunti K Nafilyan V Post-COVID syndrome in individuals admitted to hospital with COVID-19: retrospective cohort study BMJ 372 2021 n693 33789877 PMC8010267
Cited by
Sorted by citations
Long COVID: major findings, mechanisms and recommendations
Davis, H. E., McCorkell, L., Vogel, J. M., & Topol, E. J. (2023) · Nature Reviews Microbiology · doi:10.1038/s41579-022-00846-2 · Cited by 4,153 · see citing works
Long-term neurologic outcomes of COVID-19
Xu, E., Xie, Y., & Al-Aly, Z. (2022) · Nature Medicine · doi:10.1038/s41591-022-02001-z · Cited by 530 · see citing works
Self-citationUse of Continuous Glucose Monitors by People Without Diabetes: An Idea Whose Time Has Come?
Klonoff, D. C., Nguyen, K. T., Xu, N. Y., Gutierrez, A., Espinoza, J. C., & Vidmar, A. P. (2023) · Journal of Diabetes Science and Technology · doi:10.1177/19322968221110830 · Cited by 93 · see citing works
Robinson, D. J., Hanson, K., Jain, A. B., Kichler, J. C., Mehta, G., Melamed, O. C., Vallis, M., Bajaj, H. S., Barnes, T., Gilbert, J., et al. (2023) · Canadian Journal of Diabetes · doi:10.1016/j.jcjd.2023.04.009 · Cited by 73 · see citing works
COVID-19 as a trigger for type 1 diabetes
Wang, Y., Guo, H., Wang, G., Zhai, J., & Du, B. (2023) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/clinem/dgad165 · Cited by 50 · see citing works
Attributed causes of excess mortality during the COVID-19 pandemic in a south Indian city
Lewnard, J. A., B, C. M., Kang, G., & Laxminarayan, R. (2023) · Nature Communications · doi:10.1038/s41467-023-39322-7 · Cited by 23 · see citing works
Is Pollution the Primary Driver of Infectious Syndemics?
Singer, M. (2024) · Pathogens · doi:10.3390/pathogens13050370 · Cited by 8 · see citing works
“Research has shown a 60% increase in risk for new-onset diabetes in people suffering from COVID-19 compared with people who were never infected [68].”
Background
- Authors
- Xie, Y., & Al-Aly, Z.
- Affiliations
- VA St. Louis Health Care System (US)
- Washington University in St. Louis (US)
- Corresponding author
- Al-Aly, Ziyad
- ORCID
- Al-Aly, Ziyad
- Venue
- The Lancet Diabetes & Endocrinology
- Publisher
- Elsevier BV
- Published
- 2022-05
- Volume
- 10
- Issue
- 5
- Pages
- 311-321
- ISSN
- 2213-8587 (ISSN-L)
- PMC
- PMC8937253
- OpenAlex
- W4220740062
- Cited by
- 564
- References
- 46
- Citations by year
- 2026: 53 · 2025: 76 · 2024: 151 · 2023: 179 · 2022: 105
- FWCI
- 67.76
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- The Lancet Diabetes & Endocrinology (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- U.S. Department of Veterans Affairs
- American Society of Nephrology
- Topics
- Health Sciences › Medicine › Neurology › Long-Term Effects of COVID-19
- Health Sciences › Medicine › Infectious Diseases › COVID-19 Clinical Research Studies
- Health Sciences › Medicine › Infectious Diseases › SARS-CoV-2 and COVID-19 Research
- Keywords
- long COVID, new-onset diabetes, COVID-19 pandemic, veterans, antidiabetic drugs, diabetes risk, diabetes burden, deadweight loss, hospitalization, veterans' health care, Department of Veterans Affairs, high-dimensional variable selection, historical control comparison
- MeSH terms
- COVID-19, SARS-CoV-2, Post-Acute COVID-19 Syndrome, Diabetes Mellitus, Humans, Hypoglycemic Agents, United States, Cohort Studies
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
