{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T16:51:16Z","timestamp":1781715076003,"version":"3.54.5"},"reference-count":22,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2024,2,9]],"date-time":"2024-02-09T00:00:00Z","timestamp":1707436800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Cell. Infect. Microbiol."],"abstract":"<jats:sec><jats:title>Purpose<\/jats:title><jats:p>The aim of the work was to analyze the metabolites of the intestinal microbiota from the patients with mild cognitive impairment (MCI) and progressive MCI due to Alzheimer\u2019s disease (AD).<\/jats:p><\/jats:sec><jats:sec><jats:title>Method<\/jats:title><jats:p>Two cohorts were established. The first one included 87 subjects with 30 healthy controls (NC), 22 patients with MCI due to AD, and 35 patients with AD. The second cohort included 87 patients with MCI due to AD, who were followed up for 2 years and finally were divided into progressive MCI due to AD group (P-G) and unprogressive MCI due to AD group (U-G) according their cognitive levels. Fecal samples were collected to all patients at the baseline time point. Differential metabolites were subjected to pathway analysis by MetaboAnalyst.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>In the first cohort, we found 21 different metabolites among the three groups (AD, MCI, and NC). In the second cohort, we identified 19 differential metabolites between the P-G and U-G groups. By machine learning analysis, we found that seven characteristic metabolites [Erythrodiol, alpha-Curcumene, Synephrine, o-Hydroxylaminobenzoate, 3-Amino-4-hydroxybenzoic acid, 2-Deoxystreptamine, and 9(S] were of characteristic significance for the diagnosis of MCI due to AD, and six metabolites (Indolelactate, Indole-3-acetaldehyde, L-Proline, Perillyl, Mesaconate, and Sphingosine) were the characteristic metabolites of early warning for the progression of MCI due to AD. D-Glucuronic acid was negatively correlated with Apolipoprotein E4 (APOE4). Perillyl alcohol was negatively correlated with all of the five biomarkers [P-tau181, Neurofilament light chain (NF-light), A\u03b21-42, A\u03b21-40, and glial fibrillary acidic protein (GFAP)], but Indoleacetaldehyde was positively correlated with three biomarkers (P-tau181, A\u03b21-42, and GFAP). Three characteristic metabolites (3-Amino-4-hydroxybenzoate, 2-Deoxystreptamine, and p-Synephrine) were positively correlated with A\u03b21-42. 2-Deoxystreptamine, 9(S)-HPOT, and Indoleacetaldehyde were positively correlated with GFAP. L-Proline and Indoleacetaldehyde were positively correlated with NF-light.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>Specific metabolites of intestinal fora can be used as diagnostic and progressive markers for MCI.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fcimb.2024.1351523","type":"journal-article","created":{"date-parts":[[2024,2,9]],"date-time":"2024-02-09T14:38:28Z","timestamp":1707489508000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":7,"title":["Metabolites of intestinal fora can be used as diagnostic and progressive markers for mild cognitive impairment"],"prefix":"10.3389","volume":"14","author":[{"given":"Liquan","family":"Lu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei","family":"Qin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaohui","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zanhua","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoting","family":"Qiu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuo","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haihan","family":"Song","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Juan","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2024,2,9]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"1315","DOI":"10.1136\/gutjnl-2020-323476","article-title":"Integrated analysis of the faecal metagenome and serum metabolome reveals the role of gut microbiome-associated metabolites in the detection of colorectal cancer and adenoma","volume":"71","author":"Chen","year":"2022","journal-title":"Gut"},{"key":"B2","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1186\/s40529-022-00351-y","article-title":"Identification of the high-yield Monacolin K strain from Monascus spp. and its submerged fermentation using different medicinal plants","volume":"63","author":"Chen","year":"2022","journal-title":"Bot. 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