Gene Expression Biomarkers in Alzheimer's Disease Diagnosis

Alzheimer’s disease (AD) presents a pressing global health challenge with an urgent need for minimally invasive, early diagnostic tools. Gene expression biomarkers, derived primarily from peripheral blood transcriptomes, offer a window into the molecular processes underpinning neurodegeneration. By measuring patterns of RNA abundance, researchers can detect signatures of inflammation, mitochondrial dysfunction and synaptic decline that precede clinical symptoms. Advances in machine learning enable the construction of classifiers that combine multiple gene‐level features into predictive models with high sensitivity and specificity. Integration with imaging and proteomic data has further refined these signatures, revealing links between peripheral gene expression, brain pathology and cognitive performance. Despite promising results, challenges remain in distinguishing AD‐specific signals from those of other neurodegenerative disorders and in controlling for variability in cell‐type proportions. Ongoing efforts to validate biomarkers across diverse populations and assay platforms aim to translate transcriptomic profiles into routine screening tools, ushering in a new era of precision neurology and earlier therapeutic intervention.

Research from Nature Portfolio

Recent studies have demonstrated the utility of blood‐based transcriptomic profiling for AD classification. One foundational investigation applied multiple feature selection methods and classifiers across independent cohorts, achieving robust area under the curve (AUC) values by identifying genes enriched in inflammatory, mitochondrial and Wnt signalling pathways. This work validated that classifiers trained on one dataset could accurately predict AD status in external cohorts, supporting cross‐platform generalisability. Another systematic analysis compared differentially expressed genes in matched blood and brain samples, revealing hundreds of concordant transcripts. Machine learning models built on mitochondrial and ribosomal gene sets yielded classification accuracies approaching 80% and highlighted dysregulation of NF-κB and inducible nitric oxide synthase pathways. These seminal efforts established large‐scale transcriptome analysis and cross‐tissue comparison as pillars of AD biomarker discovery.

Gene Expression Biomarkers in Alzheimer's Disease Diagnosis publication trend

The graph below shows the total number of articles in gene expression biomarkers in alzheimer's disease diagnosis across all publications each year (not limited to Nature Index journals).

Technical terms

Biomarker: A measurable molecule indicating normal or pathological biological processes or responses to therapeutic intervention.

Transcriptomics: The large‐scale study of RNA transcripts produced by the genome under specific conditions.

Differential gene expression: Comparison of RNA levels between groups to identify genes whose expression varies significantly.

Machine learning classifier: A computational model that assigns samples to categories (e.g. disease versus control) based on input features.

Radiogenomics: The integration of imaging data with genomic or transcriptomic profiles to link structure and molecular function.

Mitophagy: The selective degradation of mitochondria by autophagy, a process implicated in cellular quality control and neurodegeneration.

References

1. A multi-cohort study of the hippocampal radiomics model and its associated biological changes in Alzheimer’s Disease. Translational Psychiatry (2024).
2. Blood-Based Transcriptomic Biomarkers Are Predictive of Neurodegeneration Rather Than Alzheimer’s Disease. International Journal of Molecular Sciences (2023).
3. Construction and evaluation of Alzheimer’s disease diagnostic prediction model based on genes involved in mitophagy. Frontiers in Aging Neuroscience (2023).
4. Prediction of Alzheimer’s disease using blood gene expression data. Scientific Reports (2020).
5. Systematic Analysis and Biomarker Study for Alzheimer’s Disease. Scientific Reports (2018).