Table 3 Performance assessment of the noninvasive indices and models for the prediction of MAFLD in the Western China cohort

METS-IR

0.896 (0.888–0.905)

0.876 (0.859–0.892)

0.760 (0.745–0.774)

0.630 (0.609–0.650)

0.929 (0.919–0.939)

35.223

TyG

0.776 (0.762–0.789)

0.685 (0.662–0.708)

0.736 (0.721–0.751)

0.547 (0.525–0.570)

0.834 (0.820–0.847)

8.680

TyG-BMI

0.903 (0.895–0.911)

0.836 (0.817–0.854)

0.811 (0.798–0.825)

0.674 (0.653–0.695)

0.914 (0.904–0.924)

211.515

TyG-WC

0.873 (0.864–0.883)

0.865 (0.848–0.882)

0.724 (0.709–0.739)

0.594 (0.574–0.614)

0.920 (0.909–0.930)

714.871

TyG-WtHR

0.866 (0.856–0.876)

0.904 (0.889–0.919)

0.667 (0.651–0.683)

0.559 (0.539–0.578)

0.937 (0.927–0.947)

4.198

HSI

0.873 (0.863–0.883)

0.842 (0.824–0.860)

0.736 (0.721–0.751)

0.598 (0.578–0.619)

0.909 (0.898–0.920)

33.032

VAI

0.773 (0.759–0.786)

0.755 (0.733–0.776)

0.657 (0.641–0.673)

0.506 (0.486–0.527)

0.852 (0.838–0.865)

1.426

FLI

0.879 (0.869–0.888)

0.848 (0.830–0.866)

0.751 (0.736–0.765)

0.613 (0.593–0.634)

0.914 (0.903–0.924)

25.876

LAP

0.854 (0.843–0.864)

0.810 (0.790–0.829)

0.740 (0.725–0.755)

0.592 (0.571–0.613)

0.893 (0.882–0.905)

28.720

ZJU

0.900 (0.891–0.908)

0.865 (0.848–0.882)

0.773 (0.759–0.787)

0.640 (0.620–0.661)

0.925 (0.915–0.935)

34.549

FSI

0.872 (0.863–0.882)

0.904 (0.889–0.919)

0.676 (0.660–0.692)

0.565 (0.546–0.585)

0.938 (0.928–0.948)

-2.408

K-NAFLD

0.836 (0.825–0.847)

0.810 (0.790–0.829)

0.715 (0.700-0.731)

0.570 (0.550–0.591)

0.890 (0.878–0.902)

-2.589