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Updated: May 16, 2026
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Abbiategrasso Brain Bank Protocol for Collecting, Processing and Characterizing Aging Brains
Published on: June 3, 2020
Artificial Intelligence Decodes Brain Elemental Signatures to Stratify Aging and Neurological Diseases.
Augustin Tillement1,2,3, Eszter Nemeth4, Laurent David1
1Ingénierie des Matériaux Polymères (IMP), UMR 5223, Universite Claude Bernard Lyon 1, INSA de Lyon, Université Jean Monnet, CNRS, F-69622 Villeurbanne, France.
Research (Washington, D.C.)
|May 15, 2026
Summary
Brain elemental composition changes with age and disease. Metallomics analysis of cerebrospinal fluid and serum in 1,608 individuals reveals distinct elemental signatures for aging and neurological disorders, improving diagnostic accuracy.
Area of Science:
- Neuroscience
- Biochemistry
- Computational Biology
Background:
- Elemental composition of the brain changes with age, but its role as a diagnostic biomarker in neurological diseases is underexplored.
- Metallomics, the study of metal elements in biological systems, offers potential for novel biomarker discovery.
Purpose of the Study:
- To comprehensively analyze inorganic element profiles in cerebrospinal fluid and serum across healthy aging and various neurological conditions.
- To investigate the relationship between elemental signatures, aging, blood-brain barrier permeability, and specific neurological pathologies.
- To evaluate the diagnostic potential of metallomics, integrated with machine learning, for neurological disorders.
Main Methods:
- Analysis of 24 inorganic elements in paired cerebrospinal fluid and serum samples from 1,608 individuals.
- Machine learning algorithms to identify age- and disease-associated elemental signatures.
- Correlation analysis with blood-brain barrier permeability markers (albumin quotient).
- Ensemble learning to integrate elemental data with clinical parameters for diagnostic accuracy assessment.
Main Results:
- Distinct cerebrospinal fluid elemental signatures associated with aging, independent of serum changes, indicating blood-brain barrier alterations.
- Two primary patterns of elemental dysregulation identified: barrier-mediated leakage in inflammatory conditions and intrinsic metal dyshomeostasis in neurodegenerative disorders.
- Age-stratified analysis showed evolving elemental signatures across the lifespan for different pathologies.
- Integration of metallomics with clinical data significantly enhanced diagnostic accuracy for neurological conditions.
Conclusions:
- Brain metallomics is a promising emerging field for understanding neurological aging and disease.
- Elemental signatures in cerebrospinal fluid serve as valuable biomarkers reflecting both blood-brain barrier integrity and disease-specific metal dysregulation.
- Artificial intelligence-driven metallomics analysis opens new avenues for precision medicine in age-related neurological disorders.
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