The association between dietary patterns and severity of COVID-19: A cross-sectional study

Abstract

Background & aims: COVID-19 is an infectious disease caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). Studies show that nutrition plays a role in disease prevention and control. So, in this study, we investigate the association between dietary patterns and the severity of COVID-19 in adults. Methods: This cross-sectional study was performed on 3018 moderate/severe and 717 mild coronavirus patients with an average age of 47.29 +/- 13.73 years. The COVID-19 infection severity was defined following the World Health Organization criteria. The dietary patterns were extracted using the food frequency questionnaire (FFQ) and factor analysis with the PCA (Principal Components Analysis) approach. Multivariable logistic regression was used to assess the association between dietary patterns and COVID-19 severity. Results: Four patterns of Western, Semi-Mediterranean, Semi-Western, and high meat were extracted There wasn't a positive association between the Western pattern and the severity of COVID-19 in the crude model, but after adjusting for all covariates, the positive association was significant (OR: 1.50; 95 CI: 1.14-1.97). The highest tertile of the Semi-Mediterranean dietary pattern was associated with lower odds of severe disease (OR: 0.31; 95 CI: 0.24-0.41). A direct association between the Semi-Western dietary pattern and COVID-19 severity was seen in all models and remained strongly after adjustment for all covariates in final models (OR: 2.14; 95 CI: 1.66-2.77). Finally, after adjusting for confounders, participants in the highest quartile of a high-meat dietary pattern adherence had a lower chance of severe COVID-19(OR: 0.16; 95 CI: 0.11-0.21). Conclusion: The findings showed that adherence the Western and Semi-Western dietary patterns increases the odds of severity of COVID-19, while semi-Mediterranean and high-meat dietary patterns adherence may reduce the severity of COVID-19. (c) 2025 European Society for Clinical Nutrition and Metabolism. Published by Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.