Sex-Specific Plasma Proteomic Signatures in Venous Thromboembolism: Differential Abundance and Machine-Learning-Based Feature Prioritization

Background: Sex differences influence venous thromboembolism (VTE) risk and clinical course, but corresponding differences in circulating proteomic profiles among individuals with established VTE remain incompletely characterized. We investigated female–male differences in the MARTHA plasma proteome and evaluated whether the identified signals formed a reproducible multivariable pattern. Methods: We performed a secondary analysis of the MARTHA plasma proteomics dataset, comprising 1388 participants with VTE (906 females and 482 males) and 376 antibody-derived assays targeting 234 candidate proteins. Assay identifiers were mapped to annotated genes and protein targets using the original MARTHA antibody-panel annotation. Sex-associated differential abundance was assessed using limma linear models with empirical Bayes moderation and Benjamini–Hochberg FDR correction, with robustness evaluated using SVA-adjusted limma, Welch’s t-test, Wilcoxon rank-sum testing, and 1000 sex-label permutations. To determine whether the proteomic differences collectively provided reproducible multivariable discrimination between female and male participants, we used repeated nested cross-validation with five outer folds repeated five times and five-fold inner cross-validation for model tuning. Starting from all 376 assays, limma-based FDR feature selection was performed independently within each outer training set. Elastic Net, LASSO, Ridge, Random Forest, and radial SVM models were then trained using the selected proteomic features and evaluated on the corresponding held-out outer test observations. Results: Fifty-five assays met the FDR <0.05 threshold, with 33 showing higher standardized signal in females and 22 in males. The strongest male-associated signal was RGS18 (HPA028727). Female-minus-Male difference =−0.513, FDR =3.28×10−17; Cohen’s d=−0.528), whereas the two strongest female-associated assays, Bsi3134 and Bsi0352, both targeted LRG1. Forty-six assays remained significant across all five inferential approaches, indicating substantial cross-method robustness. In the nested machine-learning analysis, subject-level out-of-fold AUCs were similar across model families, ranging from 0.804 for Ridge to 0.814 for radial SVM. Feature-selection stability was also high for a core subset of assays: 29 were selected in at least 80% of the 25 outer training evaluations, including stable signals targeting RGS18, LRG1, CFHR5, and VWF. Conclusions: The MARTHA VTE cohort exhibited a robust, bidirectional sex-associated proteomic pattern distributed across multiple assay signals. Concordance across differential-abundance analyses, complementary robustness testing, nested feature-selection stability, and held-out machine-learning performance supports the presence of a reproducible multivariable sex-associated proteomic signature, although female and male participants were not completely separable at the individual level. These findings require independent replication and should be interpreted in light of the lack of adjustment for important clinical and hormonal covariates.