Profiles of subgingival microbiomes and gingival crevicular metabolic signatures in patients with amnestic mild cognitive impairment and Alzheimer's disease.

Publisher: BioMed Central Ltd Country of Publication: England NLM ID: 101511643 Publication Model: Electronic Cited Medium: Internet ISSN: 1758-9193 (Electronic) NLM ISO Abbreviation: Alzheimers Res Ther Subsets: MEDLINE

Original Publication: [London] : BioMed Central Ltd.

Alzheimer Disease* ; Periodontitis* ; Microbiota* ; Cognitive Dysfunction*; Humans ; Cross-Sectional Studies ; RNA, Ribosomal, 16S/genetics ; Chromatography, Liquid ; Tandem Mass Spectrometry

Background: The relationship between periodontitis and Alzheimer's disease (AD) has attracted more attention recently, whereas profiles of subgingival microbiomes and gingival crevicular fluid (GCF) metabolic signatures in AD patients have rarely been characterized; thus, little evidence exists to support the oral-brain axis hypothesis. Therefore, our study aimed to characterize both the microbial community of subgingival plaque and the metabolomic profiles of GCF in patients with AD and amnestic mild cognitive impairment (aMCI) for the first time.
Methods: This was a cross-sectional study. Clinical examinations were performed on all participants. The microbial community of subgingival plaque and the metabolomic profiles of GCF were characterized using the 16S ribosomal RNA (rRNA) gene high-throughput sequencing and liquid chromatography linked to tandem mass spectrometry (LC-MS/MS) analysis, respectively.
Results: Thirty-two patients with AD, 32 patients with aMCI, and 32 cognitively normal people were enrolled. The severity of periodontitis was significantly increased in AD patients compared with aMCI patients and cognitively normal people. The 16S rRNA gene sequencing results showed that the relative abundances of 16 species in subgingival plaque were significantly correlated with cognitive function, and LC-MS/MS analysis identified a total of 165 differentially abundant metabolites in GCF. Moreover, multiomics Data Integration Analysis for Biomarker discovery using Latent cOmponents (DIABLO) analysis revealed that 19 differentially abundant metabolites were significantly correlated with Veillonella parvula, Dialister pneumosintes, Leptotrichia buccalis, Pseudoleptotrichia goodfellowii, and Actinomyces massiliensis, in which galactinol, sn-glycerol 3-phosphoethanolamine, D-mannitol, 1 h-indole-1-pentanoic acid, 3-(1-naphthalenylcarbonyl)- and L-iditol yielded satisfactory accuracy for the predictive diagnosis of AD progression.
Conclusions: This is the first combined subgingival microbiome and GCF metabolome study in patients with AD and aMCI, which revealed that periodontal microbial dysbiosis and metabolic disorders may be involved in the etiology and progression of AD, and the differential abundance of the microbiota and metabolites may be useful as potential markers for AD in the future.
(© 2024. The Author(s).)

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81971299 National Natural Science Foundation of China; 82071112 National Natural Science Foundation of China; 22ZR1437500 Science and Technology Commission of Shanghai Municipality; 2021ZD0201804 Ministry of Science and Technology of the People's Republic of China; SHDC2020CR5015 Clinical Research Plan of Shanghai Hospital Development Center; JYJC202005 Cross-disciplinary Research Fund of Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine

Keywords: Alzheimer’s disease; Metabolome; Microbiome; Mild cognitive impairment; Multiomics; Periodontitis

0 (RNA, Ribosomal, 16S)

Date Created: 20240219 Date Completed: 20240221 Latest Revision: 20240712

20240712

PMC10875772

10.1186/s13195-024-01402-1

38373985