# MULTI-OMIC DISSECTION OF ALZHEIMER’S DISEASE: DISENTANGLING AGING TRAJECTORIES, NEURODEGENERATION AND PATHOPHYSIOLOGICAL CONFOUNDERS

## TL;DR

This study uses multi-omic data to uncover hidden biological factors in Alzheimer’s disease, improving understanding of aging and disease progression.

## Contribution

The novel integration of multiple omic layers reveals two key biological components affecting Alzheimer’s biomarkers and disease modeling.

## Key findings

- Two components — CSF turnover and blood–brain barrier integrity — explain 75% of CSF omic variance.
- Adjusting for these factors improves biomarker classification and separates aging from disease signals.
- Multi-omic integration is essential to deconvolve causal layers in Alzheimer’s disease phenotypes.

## Abstract

Alzheimer’s disease (AD) is a heterogeneous, age-related neurodegenerative condition in which genetic risk, exposome, and disease biology interact in complex and non-linear ways. Multi-omic technologies now allow us to dissect this complexity at scale. We integrate genomics, CSF proteomics (SomaScan 7k), CSF lipidomics (LC–MS/MS), ancillary CSF measurements, and clinical phenotyping across deeply characterized individuals to identify latent molecular axes that shape CSF composition. Two orthogonal components — CSF turnover and blood–brain barrier integrity — explain ∼75% of CSF omic variance and drive strong artifactual inflation in biomarker signatures if not accounted for. Adjustment for these cryptic biological states improves classification performance, reveals more faithful disease-linked molecular signals, and allows a clearer separation of natural aging and disease trajectories. Our data show that multi-omic integration is not only descriptive — it is necessary to deconvolve the causal layers that structure AD phenotypes. This supports a precision therapeutics agenda in which molecular endophenotypes become the operative unit for disease modeling, biomarker development, and targeted intervention.