Maternal Plasma Proteomic Signatures Distinguish Preeclampsia from Healthy Pregnancy but Not Early-Onset from Late-Onset Subtypes: A Leakage-Free Machine-Learning and Explainability Study

Abstract

Early-onset (EOPE) and late-onset (LOPE) preeclampsia are clinically distinct, but their molecular differences are incompletely defined. Using a strictly leakage-free pipeline, we re-examined maternal plasma proteomes of EOPE, LOPE, and healthy pregnancy (HP) and asked whether the subtypes are separable at the proteome level. Label-free LC–MS/MS proteomes comprising 370 proteins from 46 women (HP, n = 18; EOPE, n = 17; LOPE, n = 11) were analysed using limma and fully nested cross-validation, in which feature selection, class balancing, and tuning were confined to the outer training folds; model explanations used SHAP and feature-selection stability. Observed selection frequencies far exceeded a random-selection null (for example, complement factor D was selected in all 15 outer folds, Wilson 95% confidence interval 0.80 to 1.00, against an expected frequency of 0.07), indicating that the stability was not a chance artefact. Discrimination is summarised as the mean ± standard deviation across the 5-fold × 3-repeat (15) outer test folds; because the single best-performing classifier was chosen on outer-fold performance, the reported area under the curve is modestly optimistic (on average by 0.04, and by up to 0.10 relative to the median of the three classifiers), even though the leakage-free procedure was applied identically to each classifier. Disease-versus-healthy contrasts were strong and concordant, with 10 and eight differentially abundant proteins identified for EOPE and LOPE, respectively, and AUROCs ranging from 0.88 to 0.90. EOPE showed a complement/acute-phase/coagulation signature, whereas LOPE showed an apolipoprotein/lipid signature, with limma, stability analysis, and SHAP yielding convergent findings. By contrast, the EOPE-versus-LOPE comparison identified no differentially abundant proteins and showed no reliable multivariate separation. A post hoc sensitivity analysis (two-sample t-test, two-sided, α = 0.05) indicated that the discovery cohort (early-onset n = 17, late-onset n = 11) had 80% power only for very large per-protein standardised differences (Cohen’s d ≥ 1.13, or d ≥ 2.10 after Bonferroni correction across the 370 proteins), and only 24% and 51% power to detect medium (d = 0.5) and large (d = 0.8) effects; the absence of a detectable early-versus-late difference is therefore consistent with limited power for small-to-moderate effects rather than proven biological equivalence. In an independent ELISA cohort (n = 87), EOPE differed from HP for seven of nine analytes, with AUCs of up to 0.82, led by sFlt-1 (fold-changes paralleled these areas under the curve: the largest early-onset median disease-to-control ratios were fibronectin 2.9, soluble fms-like tyrosine kinase-1 2.8 and α1-antitrypsin 2.5, whereas placental growth factor decreased to 0.6 and apolipoprotein E was unchanged at 1.0); however, the subtypes again did not separate. On adjustment for maternal age, body mass index and smoking, the leading early-onset markers remained significant (soluble fms-like tyrosine kinase-1, serum amyloid A and fibronectin robust; complement factor D and alpha-1-antitrypsin borderline), whereas maternal chronic hypertension, diabetes and thyroid disease were not recorded and could not be modelled. Benchmarked against soluble fms-like tyrosine kinase-1 (DeLong test), no novel candidate exceeded its discrimination and several were significantly lower; only glycoprotein hormones alpha chain added cross-validated incremental value in early-onset disease (cross-validated area under the curve 0.71 to 0.76), and none improved on soluble fms-like tyrosine kinase-1 in late-onset disease; apolipoprotein E did not validate, and placental growth factor decreased as expected. Plasma proteomics distinguishes preeclampsia from healthy pregnancy and recovers coherent subtype-leaning signatures but provides little evidence of a sharp EOPE–LOPE boundary; the prioritised panel warrants validation in larger cohorts.