Abstract
High-density lipoprotein (HDL) particle diameter distribution is informative in the diagnosis of many conditions, including Alzheimer's disease (AD). However, obtaining an accurate HDL size measurement is challenging. We demonstrated the utility of measuring the diameter of more than 1,800,000 HDL particles with the deep learning model YOLOv7 (you only look once) from micrographs of 183 HDL samples, including patients with dementia or normal cognition (controls). This method was shown to be more efficient and accurate than conventional image analysis software. Using this method, we found a higher abundance of small HDLs in participants with dementia compared to controls in patients with the apolipoprotein E (APOE) ε3ε4 genotype, whereas patients with the APOE ε3ε3 genotype had higher variability in the abundance of different HDL subclasses. Our results show an example of accurate individual HDL particle diameter measurement for large-scale clinical samples, which can be expanded to characterize the relationship between disease risk and other nanoparticles in the sub-20-nm diameter size range.