{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T06:43:09Z","timestamp":1781073789767,"version":"3.54.1"},"reference-count":49,"publisher":"SAGE Publications","issue":"3","license":[{"start":{"date-parts":[[2021,2,15]],"date-time":"2021-02-15T00:00:00Z","timestamp":1613347200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["J Psychopharmacol"],"published-print":{"date-parts":[[2021,3]]},"abstract":"<jats:sec>\n                    <jats:title>Background:<\/jats:title>\n                    <jats:p>d-glutamate, which is involved in N-methyl-d-aspartate receptor modulation, may be associated with cognitive ageing.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Aims:<\/jats:title>\n                    <jats:p>This study aimed to use peripheral plasma d-glutamate levels to differentiate patients with mild cognitive impairment (MCI) and Alzheimer\u2019s disease (AD) from healthy individuals and to evaluate its prediction ability using machine learning.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods:<\/jats:title>\n                    <jats:p>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 na\u00efve Bayes) to build an optimal predictive model to distinguish patients with MCI or AD from healthy controls.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results:<\/jats:title>\n                    <jats:p>The MCI and AD groups had lower plasma d-glutamate levels (1097.79\u2009\u00b1\u2009283.99 and 785.10\u2009\u00b1\u2009720.06\u2009ng\/mL, respectively) compared to healthy controls (1620.08\u2009\u00b1\u2009548.80\u2009ng\/mL). The na\u00efve 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\u2009=\u20090.368, p\u2009&lt;\u20090.001). Multivariate regression analysis indicated that d-glutamate levels were significantly associated with the total MMSE score ( B\u2009=\u20090.003, 95% confidence interval 0.002\u20130.005, p\u2009&lt;\u20090.001).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions:<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/0269881120972331","type":"journal-article","created":{"date-parts":[[2021,2,15]],"date-time":"2021-02-15T06:26:28Z","timestamp":1613370388000},"page":"265-272","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":44,"title":["Plasma\n                    <scp>d<\/scp>\n                    -glutamate levels for detecting mild cognitive impairment and Alzheimer\u2019s disease: Machine learning approaches"],"prefix":"10.1177","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2949-5855","authenticated-orcid":false,"given":"Chun-Hung","family":"Chang","sequence":"first","affiliation":[{"name":"Institute of Clinical Medical Science, China Medical University, Taichung, Taiwan"},{"name":"Department of Psychiatry 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Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chih-Sheng","family":"Huang","sequence":"additional","affiliation":[{"name":"Artificial Intelligence Research and Development Department, ELAN Microelectronics Corporation, Hsinchu, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shaw-Ji","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Psychiatry, Mackay Memorial Hospital Taitung Branch, Taitung, Taiwan"},{"name":"Department of Medicine, Mackay Medical College, New Taipei, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wen-Cheng","family":"Lin","sequence":"additional","affiliation":[{"name":"Department of Medical Informatics, Tzu Chi University, Hualien, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hui-Ting","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Food Safety, Taipei Medical University, Taipei, 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