Table 3.

Classification/Prediction Potential of Biomarker Panels

References	Biomarker Panel	Discriminant Model	Discriminant Group; Precision
Mitchell et al.29	159 differential features	SVM	Training set; balanced accuracy rate = 96.1% test set; balanced accuracy rate = 75.6%, AUC = 0.83
Laíns et al.30	87 differential features	Logistic regression	Differential material modeling (AUC = 0.8) Only contains age, gender, BMI and smoking status (AUC = 0.71)
Laíns et al.25	*	Logistic regression	Baseline model (AUC = 0.725; 95% CI:0.671–0.779) All-Met + EN model (AUC = 0.745; 95% CI:0.692–0.797) Stage + 2Eye model (AUC = 0.815; 95% CI: 0.771–0.860) AMD/Control model (AUC = 0.789; 95% CI: 0.738–0.840)
Kersten et al.10	Glutamine (Glu:Gln ratio, glutaminolysis, and PC.aa.C28.1)	sPLS-DA	Glutamine, Glu:Gln ratio, glutaminolysis, and PC.aa.C28.1 (AUC of 0.71, 95% CI: 0.62–0.79) glutamine (AUC of 0.66, 95% CI: 0.57–0.75)
Li et al.4	41 differential features	AUC	LPA (18:2), LPC (20:4), PC (20:1p/19:1), SM (d16:0/22:2), PAF (35:4), PC (16:0/22:5) and PC (18:1/20:4) are evaluated separately, AUC ≥ 0.8

SVM, support vector machine; sPLS-DA, sparse partial least squares discriminant analysis; CI, confidence interval; BMI, body mass index; Glu:Gln ratio, the ratio between glutamine and glutamate.

*Baseline: baseline model including only demographic covariates; All-Met + EN: all metabolites plus elastic net model including baseline + metabolites selected using elastic net regression with all metabolites; AMD/Control: AMD/Control model including baseline + metabolites identified in the logistic regression; Stage + 2Eye: stage + 2eye model including baseline + metabolites identified in the permutation-based cumulative logistic regression.