Chun-Hung Chang1,2,3, Chieh-Hsin Lin1,4,5, Chieh-Yu Liu6, Chih-Sheng Huang7, Shaw-Ji Chen8,9, Wen-Cheng Lin10, Hui-Ting Yang11, Hsien-Yuan Lane1,2,4,12. 1. Institute of Clinical Medical Science, China Medical University, Taichung, Taiwan. 2. Department of Psychiatry and Brain Disease Research Centre, China Medical University Hospital, Taichung, Taiwan. 3. An Nan Hospital, China Medical University, Tainan, Taiwan. 4. Graduate Institute of Biomedical Sciences, China Medical University, Taichung, Taiwan. 5. Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung, Taiwan. 6. Biostatistical Consulting Lab, Department of Speech Language Pathology and Audiology, National Taipei University of Nursing and Health Sciences, Taipei, Taiwan. 7. Artificial Intelligence Research and Development Department, ELAN Microelectronics Corporation, Hsinchu, Taiwan. 8. Department of Psychiatry, Mackay Memorial Hospital Taitung Branch, Taitung, Taiwan. 9. Department of Medicine, Mackay Medical College, New Taipei, Taiwan. 10. Department of Medical Informatics, Tzu Chi University, Hualien, Taiwan. 11. School of Food Safety, Taipei Medical University, Taipei, Taiwan. 12. Department of Psychology, College of Medical and Health Sciences, Asia University, Taichung, Taiwan.
Abstract
BACKGROUND: d-glutamate, which is involved in N-methyl-d-aspartate receptor modulation, may be associated with cognitive ageing. AIMS: This study aimed to use peripheral plasma d-glutamate levels to differentiate patients with mild cognitive impairment (MCI) and Alzheimer's disease (AD) from healthy individuals and to evaluate its prediction ability using machine learning. METHODS: Overall, 31 healthy controls, 21 patients with MCI and 133 patients with AD were recruited. Serum d-glutamate levels were measured using high-performance liquid chromatography (HPLC). Cognitive deficit severity was assessed using the Clinical Dementia Rating scale and the Mini-Mental Status Examination (MMSE). We employed four machine learning algorithms (support vector machine, logistic regression, random forest and naïve Bayes) to build an optimal predictive model to distinguish patients with MCI or AD from healthy controls. RESULTS: The MCI and AD groups had lower plasma d-glutamate levels (1097.79 ± 283.99 and 785.10 ± 720.06 ng/mL, respectively) compared to healthy controls (1620.08 ± 548.80 ng/mL). The naïve Bayes model and random forest model appeared to be the best models for determining MCI and AD susceptibility, respectively (area under the receiver operating characteristic curve: 0.8207 and 0.7900; sensitivity: 0.8438 and 0.6997; and specificity: 0.8158 and 0.9188, respectively). The total MMSE score was positively correlated with d-glutamate levels (r = 0.368, p < 0.001). Multivariate regression analysis indicated that d-glutamate levels were significantly associated with the total MMSE score (B = 0.003, 95% confidence interval 0.002-0.005, p < 0.001). CONCLUSIONS: Peripheral plasma d-glutamate levels were associated with cognitive impairment and may therefore be a suitable peripheral biomarker for detecting MCI and AD. Rapid and cost-effective HPLC for biomarkers and machine learning algorithms may assist physicians in diagnosing MCI and AD in outpatient clinics.
BACKGROUND:d-glutamate, which is involved in N-methyl-d-aspartate receptor modulation, may be associated with cognitive ageing. AIMS: This study aimed to use peripheral plasma d-glutamate levels to differentiate patients with mild cognitive impairment (MCI) and Alzheimer's disease (AD) from healthy individuals and to evaluate its prediction ability using machine learning. METHODS: Overall, 31 healthy controls, 21 patients with MCI and 133 patients with AD were recruited. Serum d-glutamate levels were measured using high-performance liquid chromatography (HPLC). Cognitive deficit severity was assessed using the Clinical Dementia Rating scale and the Mini-Mental Status Examination (MMSE). We employed four machine learning algorithms (support vector machine, logistic regression, random forest and naïve Bayes) to build an optimal predictive model to distinguish patients with MCI or AD from healthy controls. RESULTS: The MCI and AD groups had lower plasma d-glutamate levels (1097.79 ± 283.99 and 785.10 ± 720.06 ng/mL, respectively) compared to healthy controls (1620.08 ± 548.80 ng/mL). The naïve Bayes model and random forest model appeared to be the best models for determining MCI and AD susceptibility, respectively (area under the receiver operating characteristic curve: 0.8207 and 0.7900; sensitivity: 0.8438 and 0.6997; and specificity: 0.8158 and 0.9188, respectively). The total MMSE score was positively correlated with d-glutamate levels (r = 0.368, p < 0.001). Multivariate regression analysis indicated that d-glutamate levels were significantly associated with the total MMSE score (B = 0.003, 95% confidence interval 0.002-0.005, p < 0.001). CONCLUSIONS: Peripheral plasma d-glutamate levels were associated with cognitive impairment and may therefore be a suitable peripheral biomarker for detecting MCI and AD. Rapid and cost-effective HPLC for biomarkers and machine learning algorithms may assist physicians in diagnosing MCI and AD in outpatient clinics.
Authors: Courtney A Marshall; Jennifer D McBride; Lakshmi Changolkar; Dawn M Riddle; John Q Trojanowski; Virginia M-Y Lee Journal: Acta Neuropathol Commun Date: 2022-03-04 Impact factor: 7.801