Tryptophan metabolites as biomarkers to predict the severity and prognosis of acute ischemic stroke patients
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Background
A growing body of evidence indicates alterations in metabolite levels and enzyme activities associated with the conversion of tryptophan (TRP) throughout the course of cerebral ischemia. In this study we aim to explore the potential relationship between TRP metabolism and clinical prognosis in acute ischemic stroke (AIS) patients of mainland China.
Methods
Blood samples were obtained from a cohort of 304 patients diagnosed with AIS. The concentrations of ten TRP metabolites were quantified utilizing liquid chromatography-tandem mass spectrometry (LC-MS/MS). Stroke severity was evaluated upon admission using the National Institutes of Health Stroke Scale (NIHSS). A poor functional outcome was defined as modified Rankin scale (mRS) > 3, whereas a good functional outcome was defined by mRS ≤ 3 at 3 months post-stroke. LASSO regression and random forest algorithms were then employed to identify key TRP metabolism parameters associated with prognosis.
Results
Following the optimization of variable selection through Lasso regression, a prognostic risk model with 7-factors related to AIS was constructed, yielding an AUC of 0.917. Subsequently, a random forest analysis was conducted to establish an 11-factor prognostic risk model, which demonstrated an enhanced AUC of 1.000. Ultimately, three robust parameters related to TRP metabolism were identified. Multivariable logistic regression analysis, adjusted for covariates, revealed that TRP (odds ratio [OR] = 0.46, 95% confidence interval [CI]: 0.26 - 0.76, p = 0.004), the kynurenine (KYN)/TRP ratio (OR = 2.06, 95% CI: 1.23 - 3.60, p = 0.008), and the kynurenic acid (KYNA)/TRP ratio (OR = 2.15, 95% CI: 1.23 - 4.12, p = 0.014) were independently associated with poor functional prognosis.
Conclusions
The results of this study indicate that TRP metabolism is associated with the severity and prognosis of AIS. The TRP, KYN/TRP ratio and KYNA/TRP ratio may serve as potential biomarkers for 3-month prognostic evaluation.
