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Updated: Nov 17, 2025
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Plasma d-glutamate levels for detecting mild cognitive impairment and Alzheimer's disease: Machine learning
Chun-Hung Chang1,2,3, Chieh-Hsin Lin1,4,5, Chieh-Yu Liu6
1Institute of Clinical Medical Science, China Medical University, Taichung, Taiwan.
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.
Insights
Lower plasma d-glutamate levels are linked to mild cognitive impairment (MCI) and Alzheimer's disease (AD). These findings suggest d-glutamate may serve as a biomarker for early detection of cognitive decline.
Area of Science:
- Neuroscience
- Biochemistry
- Medical Diagnostics
Background:
- d-glutamate plays a role in N-methyl-d-aspartate receptor modulation.
- d-glutamate may be implicated in the process of cognitive aging.
Purpose of the Study:
- To investigate peripheral plasma d-glutamate levels as a biomarker for differentiating mild cognitive impairment (MCI) and Alzheimer's disease (AD) from healthy individuals.
- To evaluate the predictive capability of d-glutamate levels for MCI and AD using machine learning algorithms.
Main Methods:
- Recruited 31 healthy controls, 21 MCI patients, and 133 AD patients.
- Measured serum d-glutamate levels using high-performance liquid chromatography (HPLC).
- Utilized machine learning algorithms (support vector machine, logistic regression, random forest, naïve Bayes) to build predictive models.
Main Results:
- MCI and AD groups exhibited significantly lower plasma d-glutamate levels compared to healthy controls.
- Naïve Bayes and random forest models showed strong performance in predicting MCI and AD susceptibility, respectively.
- Plasma d-glutamate levels positively correlated with cognitive function as assessed by the Mini-Mental Status Examination (MMSE).
Conclusions:
- Peripheral plasma d-glutamate levels are associated with cognitive impairment and show potential as a biomarker for detecting MCI and AD.
- HPLC and machine learning offer a rapid, cost-effective approach for assisting physicians in diagnosing MCI and AD in clinical settings.
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