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.

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.