Objectives: The present study was conducted to explore the performance of micronutrients in the prediction and prevention of coronavirus disease 2019 (COVID-19).
Methods: This is an observational case-control study. 149 normal controls (NCs) and 214 COVID-19 patients were included in this study. Fat-soluble and water-soluble vitamins were determined by liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis, and inorganic elements were detected by inductively coupled plasma-mass spectrometry (ICP-MS) analysis. A logistic regression model based on six micronutrients were constructed using DxAI platform.
Results: Many micronutrients were dysregulated in COVID-19 compared to normal control (NC). 25-Hydroxyvitamin D3 [25(OH)D3], magnesium (Mg), copper (Cu), calcium (Ca) and vitamin B6 (pyridoxic acid, PA) were significantly independent risk factors for COVID-19. The logistic regression model consisted of 25(OH)D3, Mg, Cu, Ca, vitamin B5 (VB5) and PA was developed, and displayed a strong discriminative capability to differentiate COVID-19 patients from NC individuals [area under the receiver operating characteristic curve (AUROC) = 0.901]. In addition, the model had great predictive ability in discriminating mild/normal COVID-19 patients from NC individuals (AUROC = 0.883).
Conclusions: Our study showed that micronutrients were associated with COVID-19, and our logistic regression model based on six micronutrients has potential in clinical management of COVID-19, and will be useful for prediction of COVID-19 and screening of high-risk population.
Keywords: COVID-19; Logistic regression; Mass spectrometry; Micronutrient; SARS-CoV-2.
© 2024 The Authors.