Multi-omics: Discovering Biomarkers for Alzheimer’s Disease

The human brain is widely considered to be the most complex object in the universe, making the race to identify, diagnose, and treat neurodegenerative diseases crucial. In the 1990s, Alzheimer’s disease (AD) was diagnosed using cognitive tests and brain imaging, often requiring a post-mortem autopsy for a definitive diagnosis. Treatments focused on managing symptoms rather than targeting the cause, and the lack of understanding meant that it was often too late to intervene and stop it from worsening. However, by the early 2000s, a new technique called multi-omics integration was developed, becoming one of the most innovative and valuable methods used to discover neurodegenerative biomarkers for early diagnosis and treatment.

What is multi-omics integration?

Multi-omics integration combines data from various ‘-omics’ to analyse and map complex biological systems, providing a holistic view of molecular interactions and their role in disease. This technique helps accelerate the discovery of disease biomarkers and therapeutic targets for drug development. Currently, the most commonly used -omics within biomedical research are genomics, transcriptomics, proteomics, metabolomics, and epigenomics.

What are neurodegenerative biomarkers?

Biomarkers are objective indicators that can be used to measure and track the development of biological processes, diseases, and pharmacological responses. In neurodegenerative diseases, biomarkers are crucial for tracking disease progression and aiding in early diagnosis, leading to earlier treatments. For example, AD, one of the most common and debilitating neurodegenerative diseases, has specific and established biomarkers: amyloid-beta plaques and tau tangles. In AD, amyloid-beta plaques accumulate in the brain, causing a toxic clump of misfolded proteins between neuronal cells. This disrupts synaptic connections, triggers neuroinflammation, and leads to neuronal death. Tau proteins are crucial for maintaining the structure of axonal microtubules in neurons. However, when hyperphosphorylated, as in AD, they detach from microtubules and form neurofibrillary tangles, also causing neuronal death.

Multi-omics lead to the discovery of new AD biomarkers

Numerous studies using multi-omics have identified novel AD biomarkers. For one, a 2025 study found a strong positive correlation between high mRNA levels of the susceptibility locus (a chromosomal region containing genes that increase one’s risk of developing a disease) CD180, and amyloid-beta plaque accumulation in the hippocampus. A 2026 study found that low levels of blood-based circular mRNAs (circRNAs) act as early biomarkers for AD. These non-coding RNAs can cross the blood-brain barrier and are crucial for neuronal development. Their exceptional stability allows them to be detected before symptoms appear.

"Multi-omics integration combines data from various ‘-omics’ to analyse and map complex biological systems, providing a holistic view of molecular interactions and their role in disease."

Moreover, benefits are also seen through epigenomic profiling: the mapping of environmental factors which can affect gene expression by modifying chemical groups on DNA, without changing the sequence itself. This method revealed that AD is characterised by dysregulated epigenetic mechanisms, specifically DNA hypermethylation and histone deacetylation. This results in the increased downregulation of neuroprotective and synaptic plasticity genes, ultimately leading to the accumulation of amyloid-beta plaques and tau. Going deeper, research has discovered a novel multi-omics-integrated epigenetic biomarker for AD: the hypermethylation of the Neprilysin (NEP) gene promoter, a DNA region upstream of the NEP gene – coding for a neuroprotective enzyme that degrades toxic amyloid-beta plaques – that controls its transcription. When the NEP gene promoter is hypermethylated, NEP transcription and subsequent production are reduced. This leads to fewer amyloid-beta plaques being cleared, therefore accelerating the progression of AD.

How is systems biology involved?

So, how is multi-omics data is used to identify AD biomarkers? Scientists use systems biology to map thousands of disconnected molecular signals in multi-omics data to form disease pathways, demonstrating how biomarkers contribute to AD development. Within this field, network modelling is used to represent the interactions between biological entities like proteins or genes. These entities are represented as nodes, and their interactions are represented as straight lines connecting them. One example of this is a protein-protein interaction (PPI) network, mapping the physical and functional contact between proteins within a cell. PPI networks have revealed that the amyloid precursor protein is one of the central proteins in AD pathogenesis, as its breakdown produces amyloid-beta peptides that form plaques in patients’ brains. 

"Research has discovered a novel multi-omics-integrated epigenetic biomarker for AD: the hypermethylation of the Neprilysin (NEP) gene promoter, a DNA region upstream of the NEP gene"

To sum up, multi-omics and systems biology can enable scientists to better understand molecular interactions in AD, map the disease, and identify biomarkers, which in turn may benefit the development of novel therapeutics. Whilst there is still a lot to be discovered, this is definitely an exciting step towards helping improve the lives of those living with the disease.

References

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This article was written by Priya Chaudhuri and edited by Julia Dabrowska, with graphics produced by Ameesha Gehlot. If you enjoyed this article, be the first to be notified about new posts by signing up to become a WiNUK member (top right of this page)! Interested in writing for WiNUK yourself? Contact us through the blog page and the editors will be in touch.