Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2024 Dec 11:15:1459171.
doi: 10.3389/fendo.2024.1459171. eCollection 2024.

Unveiling risk factors for post-COVID-19 syndrome development in people with type 2 diabetes

Anton Matviichuk  1 , Viktoriia Yerokhovych  1 , Sergii Zemskov  1 , Yeva Ilkiv  1 , Vitalii Gurianov  1 , Zlatoslava Shaienko  2 , Tetyana Falalyeyeva  3   4 , Oksana Sulaieva  4   5 , Nazarii Kobyliak  1   4
Affiliations

Unveiling risk factors for post-COVID-19 syndrome development in people with type 2 diabetes

Anton Matviichuk et al. Front Endocrinol (Lausanne). .

Abstract

Introduction: Post-COVID-19 syndrome (PCS) is a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection-associated chronic condition characterized by long-term violations of physical and mental health. People with type 2 diabetes (T2D) are at high risk for severe COVID-19 and PCS.

Aim: The current study aimed to define the predictors of PCS development in people with T2D for further planning of preventive measures and improving patient outcomes.

Materials and methods: The data were collected through the national survey targeting persons with T2D concerning the history of COVID-19 course and signs and symptoms that developed during or after COVID-19 and continued for more than 12 weeks and were not explained by an alternative diagnosis. In total, 469 patients from different regions of Ukraine were enrolled in the study. Among them, 227 patients reported PCS development (main group), while 242 patients did not claim PCS symptoms (comparison group). Stepwise multivariate logistic regression and probabilistic neural network (PNN) models were used to select independent risk factors.

Results: Based on the survey data, 8 independent factors associated with the risk of PCS development in T2D patients were selected: newly diagnosed T2D (OR 4.86; 95% CI 2.55-9.28; p<0.001), female sex (OR 1.29; 95% CI 0.86-1.94; p=0.220), COVID-19 severity (OR 1.35 95% CI 1.05-1.70; p=0.018), myocardial infarction (OR 2.42 95% CI 1.26-4.64; p=0.002) and stroke (OR 3.68 95% CI 1.70-7.96; p=0.001) in anamnesis, HbA1c above 9.2% (OR 2.17 95% CI 1.37-3.43; p=0.001), and the use of insulin analogs (OR 2.28 95% CI 1.31-3.94; p=0.003) vs human insulin (OR 0.67 95% CI 0.39-1.15; p=0.146). Although obesity aggravated COVID-19 severity, it did not impact PCS development. In ROC analysis, the 8-factor multilayer perceptron (MLP) model exhibited better performance (AUC 0.808; 95% CІ 0.770-0.843), allowing the prediction of the risk of PCS development with a sensitivity of 71.4%, specificity of 76%, PPV of 73.6% and NPV of 73.9%.

Conclusions: Patients who were newly diagnosed with T2D, had HbA1c above 9.2%, had previous cardiovascular or cerebrovascular events, and had severe COVID-19 associated with mechanical lung ventilation were at high risk for PCS.

Keywords: COVID-19 infection; SARS-CoV-2; long COVID-19; post-COVID-19 Syndrome; type 2 diabetes.

PubMed Disclaimer

Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as potential conflicts of interest. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision.

Figures

Figure 1
Figure 1
The distribution of PCS symptoms: (A) among persons with T2D (main group); (B) sub analysis depending on presence of obesity in patients among PCS group.
Figure 2
Figure 2
ROC analysis for predicting PCS in patients with T2D. (A) logistic regression model; (B) MLP model; (C) pairwise comparison between models.
See this image and copyright information in PMC

Similar articles

See all similar articles

References

    1. Ong KL, Stafford LK, McLaughlin SA, Boyko EJ, Vollset SE, Smith AE, et al. . Global, regional, and national burden of diabetes from 1990 to 2021, with projections of prevalence to 2050: a systematic analysis for the Global Burden of Disease Study 2021. Lancet (London England). (2023) 402:203–34. doi: 10.1016/S0140-6736(23)01301-6 - DOI - PMC - PubMed
    1. Yousri NA, Suhre K, Yassin E, Al-Shakaki A, Robay A, Elshafei M, et al. . Metabolic and metabo-clinical signatures of type 2 diabetes, obesity, retinopathy, and dyslipidemia. Diabetes. (2022) 71:184–205. doi: 10.2337/db21-0490 - DOI - PMC - PubMed
    1. Marushchak M, Kozak K, Krynytska I. Comorbid overweight/obesity and chronic pancreatitis exacerbate the dyslipidemia progression in type 2 diabetic patients. Endocrine Regulations. (2022) 56:168–77. doi: 10.2478/enr-2022-0018 - DOI - PubMed
    1. Sharma A, Tiwari S, Deb MK, Marty JL. Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2): a global pandemic and treatment strategies. Int J antimicrobial Agents. (2020) 56:106054. doi: 10.1016/J.IJANTIMICAG.2020.106054 - DOI - PMC - PubMed
    1. Sulaieva O, Dudin O, Botsun P, Pischanska S, Karpachova V, Omelianenko I, et al. . The detrimental consequences of two consecutive disasters impacting cytopathology in Ukraine: COVID followed by the war. Cytopathology: Off J Br Soc Clin Cytology. (2023) 34:442–9. doi: 10.1111/CYT.13236 - DOI - PubMed

LinkOut - more resources