Table 1 Diagnostic performance of the LDS model across training and validation cohorts

From: A machine learning–enabled blood transcriptomic signature for digital diagnosis and subtyping of Alzheimer’s disease

Cohort

AUC (95% CI)

Sensitivity (%, 95% CI)

Specificity (%, 95% CI)

Overall accuracy (%, 95% CI)

Train

0.897 (0.849–0.934)

83.5 (75.8–89.0)

80.4 (71.6–86.9)

82.1 (76.5–86.5)

GSE122063

0.866 (0.792–0.925)

71.4 (58.5–81.6)

88.6 (76.0–95.0)

79.0 (70.0–85.8)

GSE132903

0.826 (0.762–0.879)

79.4 (70.3–86.2)

67.3 (57.6–75.8)

73.3 (66.7–79.0)

GSE28146

0.812 (0.602–0.989)

72.7 (51.8–86.8)

75.0 (40.9–92.9)

73.3 (55.6–85.8)

GSE29378

0.780 (0.659–0.882)

80.6 (63.7–90.8)

56.2 (39.3–71.8)

68.3 (56.0–78.4)

GSE36980

0.763 (0.648–0.867)

75.8 (59.0–87.2)

68.1 (53.8–79.6)

71.3 (60.5–80.0)

GSE37263

0.906 (0.719–1.000)

100.0 (67.6–100.0)

75.0 (40.9–92.9)

87.5 (64.0–96.5)

GSE48350

0.729 (0.665–0.791)

78.7 (68.6–86.3)

52.6 (45.2–59.9)

60.9 (54.7–66.7)

Independent clinical plasma cohort

0.772 (0.729–0.815)

48.1 (42.3–54.1)

96.7 (92.9–98.5)

67.6 (63.1–71.7)

  1. LDS Lactylation-Derived Score, AUC area under curve, CI confidence interval.