Background： This study was to establish a disease differentiation model for ST-segment elevation myocardial infarction （STEMI） youth patients experiencing ischemia and reperfusion via ultra-performance liquid chrom...Background： This study was to establish a disease differentiation model for ST-segment elevation myocardial infarction （STEMI） youth patients experiencing ischemia and reperfusion via ultra-performance liquid chromatography and mass spectrometry （UPLC/MS） platform, which searches for closely related characteristic metabolites and metabolic pathways to evaluate their predictive value in the prognosis after discharge. Methods： Forty-seven consecutive STEMI patients （23 patients under 45 years of age, referred to here as ＂youth,＂ and 24 elderly patients） and 48 healthy control group members （24 youth, 24 elderly） were registered prospectively. The youth patients were required to provide a second blood draw during a follow-up visit one year after morbidity （n - 22, one lost）. Characteristic metabolites and relative metabolic pathways were screened via UPLC/MS platform base on the Kyoto encyclopedia of genes and genomes （KEGG） and Human Metabolome Database. Receiver operating characteristic （ROC） curves were drawn to evaluate the predictive value of characteristic metabolites in the prognosis after discharge. Results： We successfully established an orthogonal partial least squares discriminated analysis model （R2X = 71.2%, R2Y = 79.6%, and Q2 55.9%） and screened out 24 ions; the sphingolipid metabolism pathway showed the most drastic change. The ROC curve analysis showed that ceramide [Cer（dl 8：0/16：0）, Cer（t 18：0/12：0）] and sphinganine in the sphingolipid pathway have high sensitivity and specificity on the prognosis related to major adverse cardiovascular events after youth patients were discharged. The area under curve （AUC） was 0.67 1, 0.750, and 0.711, respectively. A follow-up validation one year after morbidity showed corresponding AUC of 0.778, 0.833, and 0.806. Conclusions： By analyzing the plasma metabolism of myocardial infarction patients, we successfully established a model that can distinguish two different factors simultaneously： patholog展开更多
文摘Background： This study was to establish a disease differentiation model for ST-segment elevation myocardial infarction （STEMI） youth patients experiencing ischemia and reperfusion via ultra-performance liquid chromatography and mass spectrometry （UPLC/MS） platform, which searches for closely related characteristic metabolites and metabolic pathways to evaluate their predictive value in the prognosis after discharge. Methods： Forty-seven consecutive STEMI patients （23 patients under 45 years of age, referred to here as ＂youth,＂ and 24 elderly patients） and 48 healthy control group members （24 youth, 24 elderly） were registered prospectively. The youth patients were required to provide a second blood draw during a follow-up visit one year after morbidity （n - 22, one lost）. Characteristic metabolites and relative metabolic pathways were screened via UPLC/MS platform base on the Kyoto encyclopedia of genes and genomes （KEGG） and Human Metabolome Database. Receiver operating characteristic （ROC） curves were drawn to evaluate the predictive value of characteristic metabolites in the prognosis after discharge. Results： We successfully established an orthogonal partial least squares discriminated analysis model （R2X = 71.2%, R2Y = 79.6%, and Q2 55.9%） and screened out 24 ions; the sphingolipid metabolism pathway showed the most drastic change. The ROC curve analysis showed that ceramide [Cer（dl 8：0/16：0）, Cer（t 18：0/12：0）] and sphinganine in the sphingolipid pathway have high sensitivity and specificity on the prognosis related to major adverse cardiovascular events after youth patients were discharged. The area under curve （AUC） was 0.67 1, 0.750, and 0.711, respectively. A follow-up validation one year after morbidity showed corresponding AUC of 0.778, 0.833, and 0.806. Conclusions： By analyzing the plasma metabolism of myocardial infarction patients, we successfully established a model that can distinguish two different factors simultaneously： patholog
