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

2024 · doi:10.1101/2024.11.23.624811
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Abstract

Structured Abstract INTRODUCTION Changes to the relative abundance of amyloid-beta (Aβ) peptides are hallmarks of Alzheimer’s disease (AD). iPSC-derived neurons offer a physiological model of Aβ production. We employed unbiased, data-driven analyses to investigate combinations of Aβ peptides as AD biomarkers and the relative contribution of peptides to AD pathogenesis. METHODS We measured Aβ37, Aβ38, Aβ40, Aβ42 and Aβ43 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. RESULTS Data-driven approaches distinguished Aβ42 and Aβ43 from shorter peptides, providing unbiased evidence for their contribution to disease. Weighted linear combinations of Aβ peptides outperform Aβ42/40 and provide insights into relative peptide contribution as biomarkers; the optimal ratio for all data is represented as (21 · Aβ37 + 10 · Aβ38 + 69 · Aβ40)/(94 · Aβ42 + 6 · Aβ43). DISCUSSION The algorithm discovered herein can be further refined to improve biomarkers for AD.
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Introduction

Changes to the relative abundance of amyloid-beta (Aβ) peptides are hallmarks of Alzheimer’s disease (AD). iPSC-derived neurons offer a physiological model of Aβ production. We employed unbiased, data-driven analyses to investigate combinations of Aβ peptides as AD biomarkers and the relative contribution of peptides to AD pathogenesis.

Methods

We measured Aβ37, Aβ38, Aβ40, Aβ42 and Aβ43 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.

Results

Data-driven approaches distinguished Aβ42 and Aβ43 from shorter peptides, providing unbiased evidence for their contribution to disease. Weighted linear combinations of Aβ peptides outperform Aβ42/40 and provide insights into relative peptide contribution as biomarkers; the optimal ratio for all data is represented as (21 · Aβ37 + 10 · Aβ38 + 69 · Aβ40)/(94 · Aβ42 + 6 · Aβ43).

Discussion

The algorithm discovered herein can be further refined to improve biomarkers for AD. Competing Interest Statement The authors have declared no competing interest.

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