Affiliations: [a] College of Information Science and Engineering, Northeastern University, Shenyang, China
| [b] Hebei Key Laboratory of Micro-Nano Precision Optical Sensing and Measurement Technology, Qinhuangdao, China
| [c] Department of Neurology, Tianjin Huanhu Hospital, Tianjin, China
| [d] Tianjin Key Laboratory of Cerebral Vascular and Neurodegenerative Diseases, Tianjin Dementia Institute, Tianjin, China
| [e]
First Hospital of Qinhuangdao, Qinhuangdao, China
Correspondence:
[*]
Correspondence to: Yong Ji, Department of Neurology, Tianjin Huanhu Hospital, Tianjin, 300350, P. R. China; Tianjin Key Laboratory of Cerebral Vascular and Neurodegenerative Diseases, Tianjin Dementia Institute, Tianjin, 300350, P. R. China. Tel.: +86 022 59065906; E-mail: jiyongusa@126.com.
Note: [1] These authors contributed equally to this work.
Abstract: Background:Alzheimer’s disease (AD) is the most prevalent neurodegenerative disease. The detection of early-stage AD is particularly desirable because it would allow early intervention. However, a minimally invasive, low-cost, and accurate discrimination or diagnostic method for AD is especially difficult in the earliest stage of AD. Objective:The aim of this research is to discover blood plasma spectral digital biomarkers of AD, develop a novel intelligent method for the discrimination of AD and accelerate the translation of Fourier transform infrared (FTIR) spectral-based disease discrimination methods from the laboratory to clinical practice. Methods:Since vibration spectroscopy can provide the structure and chemical composition information of biological samples at the molecular level, we investigated the potential of FTIR spectral biomarkers of blood plasma to differentiate between AD patients and healthy controls. Combined with machine learning technology, we designed a hierarchical discrimination system that provides reagent-free and accurate AD discrimination based on blood plasma spectral digital biomarkers of AD. Results:Accurate segregation between AD patients and healthy controls was achieved with 89.3% sensitivity and 85.7% specificity for early-stage AD patients, 92.8% sensitivity and 87.5% specificity for middle-stage AD patients, and 100% sensitivity and 100% specificity for late-stage AD patients. Conclusions:Our results show that blood plasma spectral digital biomarkers hold great promise as discrimination markers of AD, indicating the potential for the development of an inexpensive, reagent-free, and less laborious clinical test. As a result, our research outcome will accelerate the clinical application of spectral digital biomarkers and machine learning.
