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Unbiased data-driven analysis of five amyloid-beta peptides for biomarker investigations in familial Alzheimer's disease

Llorente-Saguer, I.; Gabriele, R.; Bradshaw, T.; Leckey, C. A.; Belder, C. R. S.; Chavez-Gutierrez, L.; de Silva, R.; Fox, N. C.; Wray, S.; Oxtoby, N. P.; Arber, C.

2024-11-23 neuroscience
10.1101/2024.11.23.624811 bioRxiv
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Structured AbstractO_ST_ABSINTRODUCTIONC_ST_ABSChanges to the relative abundance of amyloid-beta (A{beta}) peptides are hallmarks of Alzheimers disease (AD). iPSC-derived neurons offer a physiological model of A{beta} production. We employed unbiased, data-driven analyses to investigate combinations of A{beta} peptides as AD biomarkers and the relative contribution of peptides to AD pathogenesis. METHODSWe measured A{beta}37, A{beta}38, A{beta}40, A{beta}42 and A{beta}43 in ten iPSC-neuronal cultures from PSEN1 mutation carriers. We combined these data with published cell model data and used linear weighted combinations to 1) distinguish AD from controls, and 2) predict age-at-onset for PSEN1 mutations. RESULTSData-driven approaches distinguished A{beta}42 and A{beta}43 from shorter peptides, providing unbiased evidence for their contribution to disease. Weighted linear combinations of A{beta} peptides outperform A{beta}42/40 and provide insights into relative peptide contribution as biomarkers; the optimal ratio for all data is represented as (21 {middle dot} A{beta}37 + 10 {middle dot} A{beta}38 + 69 {middle dot} A{beta}40)/(94 {middle dot} A{beta}42 + 6 {middle dot} A{beta}43). DISCUSSIONThe algorithm discovered herein can be further refined to improve biomarkers for AD.

Published in Brain Communications (predicted rank #15) · training set

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