Proteomic profiling of baseline CSF and serum from HDClarity identifies signatures for Huntington disease staging and stratification
Background: Sensitive biomarkers that objectively stage Huntington disease (HD) are needed to improve participant stratification and facilitate the enrichment of clinical trials with biologically and clinically homogeneous populations. The HDClarity study, an international longitudinal biofluid collection initiative for HD, provides a unique resource for large-scale proteomic profiling of matched CSF and serum samples from healthy controls and individuals across premanifest and manifest stages of HD. Here, we leveraged baseline proteomic data from HDClarity to characterize protein signatures associated with disease stage and clinical severity, compare measurements across analytical platforms and biofluid compartments, and identify candidate multi-protein panels for disease staging. Methods: Baseline proteomic data generated using Olink Explore (~3,000 proteins) and SomaScan (~7,000 proteins) were analyzed in matched CSF and serum samples from 315 HD gene-expansion carriers and 92 non-HD controls. A total of 2,119 proteins overlapped between Olink and SomaScan, enabling assessment of cross-platform concordance, while CSF-serum relationships were evaluated using all available protein measurements within each assay. Covariate-adjusted linear regression models were used to assess disease stage-associated differences in protein abundance, while partial correlation analyses evaluated relationships between protein abundance, clinical severity in HD gene-expansion carriers, and estimated years to disease onset in premanifest participants. A nested machine-learning pipeline incorporating univariate feature ranking, penalized regression-based feature selection, and repeated cross-validation was used to derive compact multi-protein classifiers for HD staging. Results: Cross-platform and CSF-serum correlations were highly protein-dependent, with some analytes showing strong concordance and others exhibiting weak or inverse relationships. These findings highlight substantial heterogeneity in biomarker behaviour across analytical platforms and biofluids. Adjusted models identified both known HD-associated markers (NEFL, GFAP, CHI3L1) and less well-characterized proteins in CSF (TNFRSF8, TPM3, NPPB) and serum (OMG, NPTXR, NCAN) whose baseline abundance differed across clinical and/or HD-Integrated Staging System (HD-ISS) stages. Partial correlation analyses revealed additional candidate biomarkers associated with clinical severity and years to predicted disease onset. Machine-learning models identified compact CSF and serum protein panels that achieved high classification accuracy (AUC > 0.9) across all clinical contrasts, with models distinguishing the transitions from HD-ISS stage 0 to 1 and from premanifest to early manifest disease demonstrating robust generalization to unseen data. Conclusions: This study provides the first large-scale comparison of matched CSF and serum proteomic profiles from the HDClarity cohort, identifying robust baseline proteomic signatures across the HD continuum. Our findings demonstrate the importance of considering both analytical platform and biofluid when interpreting protein biomarkers and identify compact protein panels with potential utility for objective disease staging, patient stratification, and clinical trial enrichment in HD.