A new strategy that takes advantage of the synergism between NMR and UHPLC-HRMS yields accurate concentrations of a high number of compounds in biofluids to delineate a personalized metabolic profile (SYNHMET). Metabolite identification and quantification by this method result in a higher accuracy compared to the use of the two techniques separately, even in urine, one of the most challenging biofluids to characterize due to its complexity and variability. We quantified a total of 165 metabolites in the urine of healthy subjects, patients with chronic cystitis, and patients with bladder cancer, with a minimum number of missing values. This result was achieved without the use of analytical standards and calibration curves. A patient's personalized profile can be mapped out from the final dataset's concentrations by comparing them with known normal ranges. This detailed picture has potential applications in clinical practice to monitor a patient's health status and disease progression.
Petrella, G., Montesano, C., Lentini, S., Ciufolini, G., Vanni, D., Speziale, R., et al. (2021). Personalized metabolic profile by synergic use of NMR and HRMS. MOLECULES, 26(14) [10.3390/molecules26144167].
Personalized metabolic profile by synergic use of NMR and HRMS
Lentini S.;Cicero D. O.
2021-01-01
Abstract
A new strategy that takes advantage of the synergism between NMR and UHPLC-HRMS yields accurate concentrations of a high number of compounds in biofluids to delineate a personalized metabolic profile (SYNHMET). Metabolite identification and quantification by this method result in a higher accuracy compared to the use of the two techniques separately, even in urine, one of the most challenging biofluids to characterize due to its complexity and variability. We quantified a total of 165 metabolites in the urine of healthy subjects, patients with chronic cystitis, and patients with bladder cancer, with a minimum number of missing values. This result was achieved without the use of analytical standards and calibration curves. A patient's personalized profile can be mapped out from the final dataset's concentrations by comparing them with known normal ranges. This detailed picture has potential applications in clinical practice to monitor a patient's health status and disease progression.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
