ArticleRSC advances2026
LOESS-based normalization workflow for targeted HDL glycoproteomics in an Alzheimer's disease cohort.
Article in RSC advances, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Authors and funding
10 authors.
Funding
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%.
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Registered trials
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