Semi-quantitative inductively coupled plasma mass spectrometry (ICP-MS) enables simultaneous measurement of over 30 elements in biological samples, providing comprehensive profiles in a few minutes. While traditional clinical practice relies on protein and cellular biomarkers, analyzing complete mineral profiles through machine learning algorithms reveals complex patterns that conventional approaches might miss. The integration of artificial intelligence with these multi-elemental datasets enables the identification of subtle biological signatures indicative of disease states. This combination of rapid mineral profiling and advanced data analysis is particularly attractive for clinical implementation due to its efficiency, cost-effectiveness, and minimal sample preparation requirements.

In this study, we analyzed cerebrospinal fluid samples from 815 patients of the Homburg Hospital, to evaluate mineral profiles across different neurodegenerative conditions. Our heatmap visualization revealed distinct mineral signatures for specific pathologies: Alzheimer's Disease, Non-Alzheimer's Dementia, Parkinsonian Syndromes, and other neurodegenerative diseases. Each showed unique patterns of mineral alterations, with significant variations in elements such as copper, manganese, and zinc. Using these signatures, machine learning algorithms successfully distinguished between healthy and neurodegenerative cases with high accuracy.

These findings establish high-throughput mineral profiling as a promising tool for detecting and differentiating neurodegenerative conditions. The ability to identify disease-specific mineral patterns suggests potential applications in early diagnosis and monitoring. While current results are encouraging, integration with standard clinical parameters could further enhance diagnostic accuracy, establishing mineral profiling as a valuable addition to routine assessment.