Using artificial intelligence analysis of blood tests may predict progression of neurodegeneration
Researchers at McGill University showed that analysis of blood samples using artificial intelligence (AI) could predict and provide a more comprehensive explanation for the progression of neurodegenerative diseases. The findings were published in the journal Brain.
The results were gathered from analyzing the blood-brain samples of over 1,900 patients with the presence of late-onset Alzheimer’s and Huntington’s disease. Researchers used a novel gene expression contrastive trajectory inference (GE-cTI) method able to unveil enriched temporal patterns, while also predicting neuropathological severity among affected participants.
Spanning decades, the machine learning algorithm identified how the patients’ genes expressed themselves uniquely, a first study of which revealed how molecular changes underlies neurodegeneration.
From the findings: “Evaluated on 1,969 subjects in the spectrum of late-onset Alzheimer’s and Huntington’s diseases , this unsupervised machine learning algorithm strongly predicts neuropathological severity (e.g. Braak, amyloid and Vonsattel stages).”
“Furthermore, when applied to in vivo blood samples at baseline (ADNI), it significantly predicts clinical deterioration and conversion to advanced disease stages, supporting the identification of a minimally invasive (blood-based) tool for early clinical screening,” the study reads.
“This technique also allows the discovery of genes and molecular pathways, in both peripheral and brain tissues, that are highly predictive of disease evolution. Eighty-five to ninety per cent of the most predictive molecular pathways identified in the brain are also top predictors in the blood.”
The findings conclude the GE-cTI method demonstrates clinical importance for identifying and detailing complex neuropathological mechanisms capable of aiding in the development of potential new treatments for such diseases.
“This test could one day be used by doctors to evaluate patients and prescribe therapies tailored to their needs,” concluded Yasser Iturria-Medina, co-author of the study. “It could also be used in clinical trials to categorize patients and better determine how experimental drugs impact their predicted disease progression.”