Proteomic profiling of baseline CSF and serum from HDClarity identifies signatures for Huntington disease staging and stratification

BackgroundSensitive 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 spanning the disease spectrum. Here, we leveraged baseline proteomic data from HDClarity to characterize protein signatures associated with HD stage and clinical severity, compare measurements across analytical platforms and biofluid compartments, and identify candidate multi-protein panels for disease staging. MethodsBaseline proteomic data generated using Olink Explore ([~]3,000 proteins) and SomaScan v4.1 ([~]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. ResultsCross-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 and serum whose baseline abundance differed across HD-Integrated Staging System (HD-ISS) and clinical stages. Partial correlation analyses revealed additional candidate biomarkers associated with clinical severity and estimated time to disease onset. Machine-learning models derived compact CSF and serum protein panels that accurately classified participants across HD-ISS stages 0 and 1, as well as the transition from premanifest to early manifest disease. ConclusionsThis study provides the first large-scale orthogonal comparison of matched CSF and serum proteomes in HDClarity, establishing 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. Trial RegistrationNot applicable. One Sentence SummaryCaron et al. analyzed matched baseline CSF and serum proteomic data from the HDClarity study generated using two orthogonal proteomic platforms, identifying reproducible multi-protein panels capable of staging and stratifying Huntington disease.