Using combined ROC curves to improve the diagnostic usefulness of glutaminase, prostaglandins, and 8-isoprostane as biomarkers of autism spectrum disorders;Role in the Glu-GABA-Gln cycle
Abstract
Due to delayed symptoms and dependence of behavioral assessment, early diagnosis of autism spectrum disorder remains challenging. Identification of multivariate biomarker for the etiological mechanisms of ASD may enhance diagnostic accuracy. Multivariable logistic regression combines many predictors into a single risk score (linear predictor), resulting in an optimised ROC curve that enhances diagnostic accuracy over individual markers. The method comprises modelling a binary result, determining the likelihood, and visualising ROC based on the projected probabilities, which often improves individual marker AUCs. In the present study a diagnostic performance for a biomarker panel reflecting glutamatergic dysfunction, oxidative stress, and neuroinflammation was evaluated. Plasma levels of glutaminase, 8-isoprostane, and prostaglandin E₂ (PGE₂) obtained from 44 children with ASD and 40 age-matched controls were evaluated using receiver operating characteristic (ROC) analysis, both individually and in combined ROC models. Glutaminase showed significant negative correlations with both 8-isoprostane and PGE₂, whereas a positive correlation was observed between 8-isoprostane and PGE₂. All the three-biomarker showed good diagnostic performance for ASD on its own with statistically significant (p = 0.001) values of AUC of 0.830 for glutaminase, AUC of 0.815 for 8-Isoprostane and AUC of 0.818 for PGE₂. However combined ROC modeling substantially improved diagnostic accuracy by…
How strong is the evidence?
Evidence Hub reads this as a case-control, done in people. Its evidence level is 4 of 5 (Low · observational). Observational designs can show an association but not prove cause. Reported size: 44 participants or records.
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