Label free, machine learning informed plasma-based elemental biomarkers of Alzheimer's disease

Using inductively coupled plasma mass spectrometry (ICP-MS), we have measured the elemental concentrations of Na, Fe, Cu, P, Mg, Zn, K in plasma samples of 25 Alzheimer's disease (AD) patients and 34 healthy individuals. Given the multidimensional nature of the ICP-MS data, we used support vector machines and logistic regression to illustrate the elemental distribution of each donor and seek key features that may differentiate plasma samples of AD patients from those of healthy individuals. We found that ratios of the elemental concentrations of Na over K, Fe over Na, and P over Zn yield specificity, sensitivity, and accuracy of 79%, 84% and 81% respectively. This information was then used to seek from the mass spectrometric data a differentiation of the plasma samples from AD and healthy donors. Plotted as a function of the Na/K, Fe/Na, and P/Zn, the ICP-MS data reveals a linear delineation between the two groups of samples yielding to the correct classification 21 of 25 AD and 28 of 34 HC plasma samples. These findings highlight the importance of elemental ratios present in plasma and suggest that the ratios of the elemental concentrations of blood metals may be considered as biomarkers that can distinguish plasma samples of AD patients from healthy subjects. Machine learning analysis of ICP-MS data identifies elemental ratios that differentiates with great accuracy blood plasma of Alzheimer's patients and healthy donors.