Assessing glucose-6-phosphate dehydrogenase (G6PD) during COVID-19 requires caution: evidence on the impact of the infection upon enzyme activity (preprint)

Glucose-6 phosphate dehydrogenase deficiency (G6PDd) was suggested as a risk factor of severity in patients with COVID-19. In this article, we assessed the influence of G6PDd on the infection, severity, and clinical progression of patients with COVID-19. This prospective cohort study included adult participants ([≥]18 years old) who had clinical and/or radiological COVID-19 findings or positive RT-PCR results. Epidemiological and clinical data were extracted from electronic medical records. G6PD activity was measured in SD Biosensor STANDARD G6PD(R) equipment at admission and one year after discharge. Samples were genotyped for the three most common single nucleotide polymorphisms (SNPs) for G6PDd in the Brazilian Amazon s1050828, rs1050829 and rs5030868, corresponding to G6PD African A-(G202A, A376G), G6PD African A+(A376G) and G6PD Mediterranean(C563T), respectively. Seven hundred fifty-three patients were included, of which 123 (16.3%) were G6PDd. The G6PDd group had a higher mean hemoglobin, and lower values of C-reactive protein and leukocytes at admission. There was no association between G6PDd and COVID-19 severity, considering that the frequency of G6PDd who needed to be hospitalized (1.9%) or demanding invasive mechanical ventilation (16.0%) or died (21.1%) was lower than G6PD normal patients. Only 29 out of 116 (25%) participants carried the African genotype. Out of 30 participants tested as G6PDd during disease, only 11 (36.7%) results agreed one year after discharge. In conclusion, caution must be taken when G6PDd screening in patients with acute COVID-19.

Covid-19 automated diagnosis and risk assessment through Metabolomics and Machine-Learning (preprint)

COVID-19 is still placing a heavy health and financial burden worldwide. Impairments in patient screening and risk management play a fundamental role on how governments and authorities are directing resources, planning reopening, as well as sanitary countermeasures, especially in regions where poverty is a major component in the equation. An efficient diagnostic method must be highly accurate, while having a cost-effective profile. We combined a machine learning-based algorithm with instrumental analysis using mass spectrometry to create an expeditious platform that discriminate COVID-19 in plasma samples within minutes, while also providing tools for risk assessment, to assist healthcare professionals in patient management and decision-making. A cross-sectional study with 728 patients (369 confirmed COVID-19 and 359 controls) was enrolled from three Brazilian epicentres (Sao Paulo capital, Sao Paulo countryside and Manaus) in the months of April, May, June and July 2020. We were able to elect and identify 21 molecules that are related to the diseases pathophysiology and 26 features to patients health-related outcomes. With specificity >97% and sensitivity >83% from blinded data, this screening approach is understood as a tool with great potential for real-world application.