Your browser doesn't support javascript.
loading
Urine Metabolites Enable Fast Detection of COVID-19 Using Mass Spectrometry.
Moura, Alexandre Varao; de Oliveira, Danilo Cardoso; Silva, Alex Ap R; da Rosa, Jonas Ribeiro; Garcia, Pedro Henrique Dias; Sanches, Pedro Henrique Godoy; Garza, Kyana Y; Mendes, Flavio Marcio Macedo; Lambert, Mayara; Gutierrez, Junier Marrero; Granado, Nicole Marino; Dos Santos, Alicia Camacho; de Lima, Iasmim Lopes; Negrini, Lisamara Dias de Oliveira; Antonio, Marcia Aparecida; Eberlin, Marcos N; Eberlin, Livia S; Porcari, Andreia M.
Affiliation
  • Moura AV; MS4Life Laboratory of Mass Spectrometry, Health Sciences Postgraduate Program, São Francisco University, Bragança Paulista 12916-900, SP, Brazil.
  • de Oliveira DC; MS4Life Laboratory of Mass Spectrometry, Health Sciences Postgraduate Program, São Francisco University, Bragança Paulista 12916-900, SP, Brazil.
  • Silva AAR; MS4Life Laboratory of Mass Spectrometry, Health Sciences Postgraduate Program, São Francisco University, Bragança Paulista 12916-900, SP, Brazil.
  • da Rosa JR; MS4Life Laboratory of Mass Spectrometry, Health Sciences Postgraduate Program, São Francisco University, Bragança Paulista 12916-900, SP, Brazil.
  • Garcia PHD; MS4Life Laboratory of Mass Spectrometry, Health Sciences Postgraduate Program, São Francisco University, Bragança Paulista 12916-900, SP, Brazil.
  • Sanches PHG; MS4Life Laboratory of Mass Spectrometry, Health Sciences Postgraduate Program, São Francisco University, Bragança Paulista 12916-900, SP, Brazil.
  • Garza KY; Department of Chemistry, The University of Texas at Austin, Austin, TX 78712, USA.
  • Mendes FMM; MS4Life Laboratory of Mass Spectrometry, Health Sciences Postgraduate Program, São Francisco University, Bragança Paulista 12916-900, SP, Brazil.
  • Lambert M; MS4Life Laboratory of Mass Spectrometry, Health Sciences Postgraduate Program, São Francisco University, Bragança Paulista 12916-900, SP, Brazil.
  • Gutierrez JM; MS4Life Laboratory of Mass Spectrometry, Health Sciences Postgraduate Program, São Francisco University, Bragança Paulista 12916-900, SP, Brazil.
  • Granado NM; MS4Life Laboratory of Mass Spectrometry, Health Sciences Postgraduate Program, São Francisco University, Bragança Paulista 12916-900, SP, Brazil.
  • Dos Santos AC; Department of Material Engineering and Nanotechnology, Mackenzie Presbyterian University, São Paulo 01302-907, SP, Brazil.
  • de Lima IL; Department of Material Engineering and Nanotechnology, Mackenzie Presbyterian University, São Paulo 01302-907, SP, Brazil.
  • Negrini LDO; Municipal Department of Health, Bragança Paulista 12916-900, SP, Brazil.
  • Antonio MA; Integrated Unit of Pharmacology and Gastroenterology, UNIFAG, Bragança Paulista 12916-900, SP, Brazil.
  • Eberlin MN; Department of Material Engineering and Nanotechnology, Mackenzie Presbyterian University, São Paulo 01302-907, SP, Brazil.
  • Eberlin LS; Department of Chemistry, The University of Texas at Austin, Austin, TX 78712, USA.
  • Porcari AM; Department of Surgery, Baylor College of Medicine, Houston, TX 77030, USA.
Metabolites ; 12(11)2022 Nov 02.
Article in En | MEDLINE | ID: mdl-36355139
The COVID-19 pandemic boosted the development of diagnostic tests to meet patient needs and provide accurate, sensitive, and fast disease detection. Despite rapid advancements, limitations related to turnaround time, varying performance metrics due to different sampling sites, illness duration, co-infections, and the need for particular reagents still exist. As an alternative diagnostic test, we present urine analysis through flow-injection-tandem mass spectrometry (FIA-MS/MS) as a powerful approach for COVID-19 diagnosis, targeting the detection of amino acids and acylcarnitines. We adapted a method that is widely used for newborn screening tests on dried blood for urine samples in order to detect metabolites related to COVID-19 infection. We analyzed samples from 246 volunteers with diagnostic confirmation via PCR. Urine samples were self-collected, diluted, and analyzed with a run time of 4 min. A Lasso statistical classifier was built using 75/25% data for training/validation sets and achieved high diagnostic performances: 97/90% sensitivity, 95/100% specificity, and 95/97.2% accuracy. Additionally, we predicted on two withheld sets composed of suspected hospitalized/symptomatic COVID-19-PCR negative patients and patients out of the optimal time-frame collection for PCR diagnosis, with promising results. Altogether, we show that the benchmarked FIA-MS/MS method is promising for COVID-19 screening and diagnosis, and is also potentially useful after the peak viral load has passed.
Key words

Full text: 1 Collection: 01-internacional Database: MEDLINE Type of study: Diagnostic_studies Language: En Journal: Metabolites Year: 2022 Document type: Article Affiliation country: Brasil Country of publication: Suiza

Full text: 1 Collection: 01-internacional Database: MEDLINE Type of study: Diagnostic_studies Language: En Journal: Metabolites Year: 2022 Document type: Article Affiliation country: Brasil Country of publication: Suiza