Abstract Background Glycoprotein acetylation (GlycA) is a nuclear magnetic resonance–derived biomarker of systemic inflammation. GlycA shows statistical interaction with small HDL particle number (s-HDL-P) in relation to coronary artery disease (CAD). This study examines the confounding effects of GlycA-related glycoproteins, hypertriglyceridemia, and s-HDL-P on CAD risk. Methods Plasma samples from 752 participants in the CATHGEN biorepository were size-fractionated by asymmetric flow field-flow fractionation and analyzed by targeted proteomics and lipidomics. Fraction concentrations were converted to HDL particle number using a volumetric approach based on apolipoprotein A-I (APOA1) concentration, HDL size, and lipoprotein lipid composition. Correlations between GlycA, glycoproteins, total triglyceride (TG), HDL composition and HDL particle number, and their associations with CAD were assessed using multivariate least-squares fit and nominal logistic regression analysis, respectively. Results Acute-phase inflammation-related glycoproteins (GlycA-proteins) correlated strongly with GlycA. However, the size-distribution profiles of GlycA-proteins were consistent with circulation as free dimers/trimers, and their lack of overlap with APOA1 profiles suggested no detectable HDL binding. In contrast, apolipoproteins and lipid transfer proteins showed size-profile shifts relative to APOA1 consistent with HDL binding. s-HDL-P showed an inverse association with GlycA, most significantly at low TG levels. In CAD models, significant GlycA × s-HDL-P interaction emerged only when TG was included as a covariate. CAD correlated with lipoprotein lipids, apolipoproteins, and lipid transfer proteins that influence lipoprotein remodeling. Conclusions GlycA-proteins circulate freely without direct HDL binding. The empirical GlycA × s-HDL-P interaction is consistent with indirect coupling between systemic inflammation and altered lipoprotein remodeling associated with CAD risk.
Abstract Background Mass spectrometry (MS) remains underutilized in the evaluation of formalin-fixed, paraffin-embedded (FFPE) tissue. Clinical deployment of MS in anatomic pathology is largely confined to data-dependent acquisition (DDA) work flows for amyloid typing. Alternative acquisition strategies such as data-independent acquisition (DIA) enable quantitative, peptide-centric tissue analysis and can expand MS use in biopsy evaluation. Methods We performed DIA-based LC-MS/MS analyses of amyloid-positive tissue specimens alongside an established DDA clinical work flow. The DIA pipeline incorporated automated data processing to support standardization and transferability. Peptide peak areas were extracted using Skyline, and machine-learning classifiers and peptide scoring rules were developed to predict amyloid type. Results DIA peptide measurements were evaluated across 304 amyloid-positive tissue specimens representing 16 discrete amyloid types. In a development group (n = 259), a random forest classifier enabled accurate classification of the five most common amyloid types from individual LC-DIA-MS/MS injections: λ and κ immunoglobulin light chain, transthyretin, leukocyte chemotactic factor 2, and serum amyloid A. In prospective evaluation (n = 45), the combined DIA classification strategy was fully concordant with the reference DDA clinical assignments. Amyloid types not represented in classifier training were resolved using peptide-level diagnostic signals evaluated against empirically derived intensity thresholds. Conclusions DIA-based mass spectrometry enables reproducible peptide-level classification of amyloid deposits in FFPE tissue. Combined with standardized data processing, machine-learning classification, and rule-based peptide scoring, this approach provides a scalable framework for proteomic amyloid typing. More broadly, it may support wider adoption of mass spectrometry for peptide biomarker quantification in anatomic pathology.
