Potential Shared Immunometabolic Signatures Between Familial Hypercholesterolemia and Major Depressive Disorder: Integrative Transcriptomic Analysis, Mendelian Randomization, and Clinical Validation - Chenxi Liu; Yuting Li; Xiang Cao; Zixuan Ye; Quanzhou Shi; Feng Chen; Zihao Li; Jiamei Guo; Tian Qiu (International Journal of Molecular Sciences, 2026)
Familial hypercholesterolemia (FH) has been associated with an increased risk of major depressive disorder (MDD), but their shared molecular signatures remain unclear. This study aimed to identify candidate genes associated with FH-MDD co-occurrence through integrative transcriptomic analysis, machine learning, summary-data-based Mendelian randomization (SMR), and clinical validation. Transcriptomic datasets from the Gene Expression Omnibus were analyzed, including GSE6054 and GSE13985 for FH and GSE98793 for MDD. Differential expression analysis and weighted gene co-expression network analysis were used to identify shared candidate genes. Candidate biomarkers were screened using least absolute shrinkage and selection operator regression and Random Forest algorithms, followed by the construction of an exploratory nomogram. SMR analysis was performed to assess genetically supported associations between candidate-gene expression and MDD risk. Selected genes were validated by PBMC RT-qPCR in an independent four-group clinical cohort comprising healthy controls and participants with FH, MDD, or co-occurring FH and MDD. Flow cytometry was subsequently used to characterize the peripheral CD4+ T-cell profile. A total of 54 shared candidate genes were identified. CD4, MRPS21, and CRTC2 were selected by machine learning and incorporated into the nomogram, which showed good discrimination in the training set and moderate performance in external validation. Immune infiltration and enrichment analyses highlighted monocyte alterations, reduced T-cell-related signals, and enrichment of T-cell receptor and TNF-mediated pathways. SMR analysis indicated that genetically predicted higher CD4 expression was inversely associated with MDD risk, whereas RT-qPCR showed higher CD4 expression in patients with FH-MDD. Flow-cytometric analysis showed that FH-MDD participants had a higher circulating CD3+CD4+ T-cell proportion, an increased effector-memory CD4+ T-cell proportion, and a higher proportion of HLA-DR+ CD4+ T cells than participants in the comparison groups. These findings suggest that CD4 may represent a promising immune-related candidate signature associated with the co-occurrence of FH and MDD, warranting further functional validation.