Combination of Serum and Plasma Biomarkers Could Improve Prediction Performance for Alzheimer’s Disease

Abstract

Alzheimer’s disease (AD) can be predicted either by serum or plasma biomarkers, and a combination may increase predictive power, but due to the high complexity of machine learning, it may also incur overfitting problems. In this paper, we investigated whether combining serum and plasma biomarkers with feature selection could improve prediction performance for AD. 150 D patients and 150 normal controls (NCs) were enrolled for a serum test, and 100 patients and 100 NCs were enrolled for the plasma test. Among these, 79 ADs and 65 NCs had serum and plasma samples in common. A 10 times repeated 5-fold cross-validation model and a feature selection method were used to overcome the overfitting problem when serum and plasma biomarkers were combined. First, we tested to see if simply adding serum and plasma biomarkers improved prediction performance but also caused overfitting. Then we employed a feature selection algorithm we developed to overcome the overfitting problem. Lastly, we tested the prediction performance in a 10 times repeated 5-fold cross validation model for training and testing sets. We found that the combined biomarkers improved AD prediction but also caused overfitting. A further feature selection based on the combination of serum and plasma biomarkers solved the problem and produced an even higher prediction performance than either serum or plasma biomarkers on their own. The combined feature-selected serum–plasma biomarkers may have critical implications for understanding the pathophysiology of AD and for developing preventative treatments.

Advances in blood biomarkers for Alzheimer disease (AD): A review

“…They predict treatment response based on baseline levels or changes during trials, aiding treatment stratification 77 . Additionally, blood tests forecast complications, enabling proactive intervention 78 . Blood tests streamline participant selection, treatment assessment, response prediction, and complication identification in AD clinical trials, enhancing trial efficacy and intervention development 17,79 …”

A Machine Learning-Based Multiple Imputation Method for the Health and Aging Brain Study–Health Disparities

“…Fasting blood samples were collected in accordance with the international guidelines for MCI and AD biomarker studies. The previously validated proteomic profile was then analyzed using electrochemiluminescence (ECL) as described in our published methods [21,22]. The measured biomarkers included fatty acid binding protein 3 (FABP3); beta 2 microglobulin (B2M); C-reactive protein (CRP); thrombopoietin (TPO); alpha 2 macroglobulin (A2M) eotaxin 3; tumor necrosis factor-alpha (TNFa); tenascin C (TNC); interleukin (IL)-5, IL-6, IL-7, IL-10, IL-18; I-309; factor VII (factor 7); soluble intercellular adhesion molecule 1 (sICAM1); circulating vascular cell adhesion molecule 1 (sVCAM1); and pancreatic polypeptide (PPY) as well as glucagon-like peptide 1 (GLP-1), insulin, homeostatic model assessment of insulin resistance (HOMA-IR), glucagon, and peptide YY (PYY).…”