Table 1_A lipid-related transcriptomic signature for distinguishing Kawasaki disease from febrile infections: a Mendelian randomization and machine-learning study.xlsx

The immune microenvironment was estimated using CIBERSORT, acute-to-convalescent expression changes were tracked longitudinally, and selected markers were assessed in TNF-α-stimulated HUVECs.</p>Results<p>No exposure survived correction for the 422 tests performed (minimum Benjamini–Hochberg q = 0.979). All nine discovery-stage nominal hits were evaluated in a second KD GWAS; eight had directionally concordant point estimates, but none reached an unadjusted P < 0.05 in the second cohort.

The frozen penalized model achieved an external AUC of 0.851 (95% CI 0.768–0.934) in GSE68004 without cross-cohort batch correction. CIBERSORT indicated a higher estimated neutrophil fraction in KD, concordant with a mature-neutrophil marker score from the same matrix. CETP, KREMEN1, and CCL23 were lower in convalescent samples after IVIG (all FDR < 0.0001) and were induced in TNF-α-treated HUVECs, together with VCAM-1, ICAM-1, and IL-8.</p>Conclusions<p>The frozen penalized model showed moderate discrimination between KD and febrile controls, but the selected 12-gene set represents one candidate realization of a partition-sensitive modeling pipeline.

KREMEN1 warrants separate biological study on the basis of marginal, longitudinal, and immune-association findings rather than its selection stability. Recalibration and prospective validation are required before clinical use. The HUVEC findings are transcriptional and hypothesis-generating.</p>