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

Matviichuk et al., Frontiers in Endocrinology, doi:10.3389/fendo.2024.1459171
Dec 2024  
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PASC -5% Improvement Relative Risk Metformin for COVID-19  Matviichuk et al.  Prophylaxis Is prophylaxis with metformin beneficial for COVID-19? Retrospective 468 patients in Ukraine No significant difference in PASC c19early.org Matviichuk et al., Frontiers in Endocr.., Dec 2024 Favorsmetformin Favorscontrol 0 0.5 1 1.5 2+
Metformin for COVID-19
3rd treatment shown to reduce risk in July 2020, now with p < 0.00000000001 from 102 studies.
No treatment is 100% effective. Protocols combine treatments.
5,200+ studies for 112 treatments. c19early.org
Retrospective 469 patients with type 2 diabetes in Ukraine showing no significant difference in post-COVID-19 syndrome (PCS) with metformin. There was higher risk with Insulin analogs, but lower risk with human insulin.
risk of PASC, 5.0% higher, RR 1.05, p = 0.64, treatment 155 of 316 (49.1%), control 71 of 152 (46.7%), odds ratio converted to relative risk.
Effect extraction follows pre-specified rules prioritizing more serious outcomes. Submit updates
Matviichuk et al., 11 Dec 2024, retrospective, Ukraine, peer-reviewed, 9 authors. Contact: nazariikobyliak@gmail.com.
This PaperMetforminAll
Unveiling risk factors for post-COVID-19 syndrome development in people with type 2 diabetes
Anton Matviichuk, Viktoriia Yerokhovych, Sergii Zemskov, Yeva Ilkiv, Vitalii Gurianov, Zlatoslava Shaienko, Tetyana Falalyeyeva, Oksana Sulaieva, Nazarii Kobyliak
Frontiers in Endocrinology, doi:10.3389/fendo.2024.1459171
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 p=0.003) vs human insulin p=0.146). Although obesity aggravated COVID-19 severity, it did not impact PCS development. In ROC analysis, the 8factor 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%.
Ethics statement The studies involving humans were approved by Bogomolets National Medical University. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study. Author contributions Conflict of interest 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. Publisher's note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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People with ' 'type 2 diabetes (T2D) are at high risk for severe COVID-19 and ' 'PCS.</jats:p></jats:sec><jats:sec><jats:title>Aim</jats:title><jats:p>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.</jats:p></jats:sec><jats:sec><jats:title>Materials and ' 'methods</jats:title><jats:p>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.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>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&amp;lt;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%.</jats:p></jats:sec><jats:sec><jats:title>Conclusions</jats:title><jats:p>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.</jats:p></jats:sec>', 'DOI': '10.3389/fendo.2024.1459171', 'type': 'journal-article', 'created': { 'date-parts': [[2024, 12, 11]], 'date-time': '2024-12-11T05:18:00Z', 'timestamp': 1733894280000}, 'update-policy': 'https://doi.org/10.3389/crossmark-policy', 'source': 'Crossref', 'is-referenced-by-count': 0, 'title': 'Unveiling risk factors for post-COVID-19 syndrome development in people with type 2 diabetes', 'prefix': 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Endocrinol.', 'published': {'date-parts': [[2024, 12, 11]]}}
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