LOESS-based normalization workflow for targeted HDL glycoproteomics in an Alzheimer's disease cohort

Abstract

High-density lipoprotein (HDL) carries proteins and glycoproteins involved in lipid metabolism and inflammatory regulation, yet quantitative characterization of HDL-associated peptides in Alzheimer's disease (AD) cohorts remains challenging due to small biological effect sizes superimposed on substantial technical variability. We applied a Locally Estimated Scatterplot Smoothing (LOESS)-based drift correction and internal-standard-guided normalization workflow to targeted multiple reaction monitoring (MRM) glycoproteomic data generated from HDL isolates collected from 194 participants spanning the cognitive spectrum. Of the 164 transitions originally targeted, 59 features passed quality control (QC), and 21 HDL-associated peptide and glycopeptide features showed consistent signal across all 194 samples; these 21 analytes were used for analysis. Normalization improved analytical reproducibility, reducing median HDL pooled QC coefficients of variation from 69.1% to 55.2%. APOE genotype analyses identified six peptides with statistically significant differences between APOE3/E3 and APOE3/E4 carriers, five of which remained statistically significant after false-discovery rate correction, and all six of which remained significant in covariate-adjusted models, whereas disease-related differences within APOE3/E3 carriers were modest and did not remain statistically significant after covariate adjustment. These findings demonstrate that LOESS-based drift correction combined with feature-specific internal-standard selection stabilizes quantitative HDL glycoproteomic measurements and support downstream comparisons. This workflow provides a practical framework for QC-informed normalization in targeted glycoproteomics and highlights APOE-associated variation in HDL peptides within an aging clinical cohort.