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Abstract 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.

At present， most smart wearable devices are smart watches， wristbands， etc.， which have the problems of high rigidity， poor comfort and frequent charging， making it difficult to meet the requirements of ergonomics and clothing comfort. They cannot be worn for a long time to achieve all-weather monitoring. Textile triboelectric nanogenerator （T-TENG） can be integrated into shoes and clothing as flexible power sources and self-powered sensors， making it an ideal wearable device for active human health monitoring and enforcement. However， most of the reported T-TENG need to be packaged and then integrated into clothing before use， resulting in a reduced air permeability of clothing. In addition， most of the current studies are in the laboratory stage， and the properties of T-TENG such as durability， sensitivity and stability in actual use are not fully considered. In this paper， the basic working mode， material selection， manufacturing method， integrated footwear and clothing mode， and application scenarios of T-TENG were reviewed. The preparation methods of T-TENG for nanofiber membrane and textile composite materials， fiber/yarn-based T-TENG， and fabric-based T-TENG were mainly discussed. New strategies for the development and integration of comfortable T-TENG in the future were proposed， including the large-scale preparation of T-TENG， the integration of T-TENG and traditional clothing， the compatibility of T-TENG monitoring accuracy and comfort， and the durability and stability of T-TENG.