Background: Gut microbiota (GMB) alteration has been reported to influence the Alzheimer’s disease (AD) pathogenesis through immune, endocrine, and metabolic pathways. This study aims to investigate metabolic output of the dysbiosis of GMB in AD pathogenesis. In this study, the fecal microbiota and metabolome from 21 AD participants and 44 cognitively normal control participants were measured. Untargeted GMB taxa was analyzed through 16S ribosomal RNA gene profiling based on next-generation sequencing and fecal metabolites were quantified by using ultrahigh performance liquid chromatography-mass spectrometry (UPLC-MS). Results: Our analysis revealed that AD was characterized by 15 altered gut bacterial genera, of which 46.7% (7/15 general) was significantly associated with a series of metabolite markers. The predicted metabolic profile of altered gut microbial composition included steroid hormone biosynthesis, N-Acyl amino acid metabolism and piperidine metabolism. Moreover, a combination of 2 gut bacterial genera (Faecalibacterium and Pseudomonas) and 4 metabolites (N-Docosahexaenoyl GABA, 19-Oxoandrost-4-ene-3,17-dione, Trigofoenoside F and 22-Angeloylbarringtogenol C) was able to discriminate AD from NC with AUC of 0.955 in these 65 subjects. Conclusions: These findings demonstrate that gut microbial alterations and related metabolic output changes may be associated with pathogenesis of AD, and suggest that fecal markers might be used as a non-invasive examination to assist screening and diagnosis of AD.

Altered gut microbiota has been reported in individuals with mild cognitive impairment (MCI) and Alzheimer’s disease (AD). Previous research has suggested that specific bacterial species might be associated with the decline of cognitive function. However, the evidence was insufficient, and the results were inconsistent. To determine whether there is an alteration of gut microbiota in patients with MCI and AD and to investigate its correlation with clinical characteristics, the fecal samples from 94 cognitively normal controls (NC), 125 participants with MCI, and 83 patients with AD were collected and analyzed by 16S ribosomal RNA sequencing. The overall microbial compositions and specific taxa were compared. The clinical relevance was analyzed. There was no significant overall difference in the alpha and beta diversity among the three groups. Patients with AD or MCI had increased bacterial taxa including Erysipelatoclostridiaceae, Erysipelotrichales, Patescibacteria, Saccharimonadales, and Saccharimonadia, compared with NC group (p < 0.05), which were positively correlated with APOE 4 carrier status and Clinical Dementia Rating (correlation coefficient: 0.11~0.31, p < 0.05), and negatively associated with memory (correlation coefficient: −0.19~−0.16, p < 0.01). Our results supported the hypothesis that intestinal microorganisms change in MCI and AD. The alteration in specific taxa correlated closely with clinical manifestations, indicating the potential role in AD pathogenesis.

Mild cognitive impairment (MCI) is often considered a critical time window for predicting early conversion to Alzheimer’s disease (AD). Brain functional connectome data (i.e., functional connections, global and nodal graph metrics) based on resting-state functional magnetic resonance imaging (rs-fMRI) provides numerous information about brain networks and has been used to discriminate normal controls (NCs) from subjects with MCI. In this paper, Student’s t-tests and group-least absolute shrinkage and selection operator (group-LASSO) were used to extract functional connections with significant differences and the most discriminative network nodes, respectively. Based on group-LASSO, the middle temporal, inferior temporal, lingual, posterior cingulate, and middle frontal gyri were the most predominant brain regions for nodal observation in MCI patients. Nodal graph metrics (within-module degree, participation coefficient, and degree centrality) showed the maximum discriminative ability. To effectively combine the multipattern information, we employed the multiple kernel learning support vector machine (MKL-SVM). Combined with functional connectome information, the MKL-SVM achieved a good classification performance (area under the receiving operating characteristic curve = 0.9728). Additionally, the altered brain connectome pattern revealed that functional connectivity was generally decreased in the whole-brain network, whereas graph theory topological attributes of some special nodes in the brain network were increased in MCI patients. Our findings demonstrate that optimal feature selection and combination of all connectome features (i.e., functional connections, global and nodal graph metrics) can achieve good performance in discriminating NCs from MCI subjects. Thus, the combination of functional connections and global and nodal graph metrics of brain networks can predict the occurrence of MCI and contribute to the early clinical diagnosis of AD.

Caregivers of care-recipients with mild cognitive impairment (MCI) or dementia experience high caregiver burden; however, the psychiatric burden of caregivers of care-recipients with subjective cognitive decline (SCD) has not been investigated. We aimed to explore the prevalence of and risk factors for anxiety and depression symptoms among the caregivers of care-recipients with SCD and cognitive impairment. The Hospital Anxiety and Depression Scale (HADS) was used to examine the anxiety and depression symptoms among the caregivers of 343 care-recipients (84 with SCD, 120 with MCI and 139 with dementia) treated at the Memory Clinic of Huashan Hospital in Shanghai, China from May 2012 to October 2014. A logistic regression was used to explore the factors associated with caregiver’s anxiety and depression symptoms. In total, 26.5 % of caregivers had anxiety symptoms, and 22.4 % had depression symptoms. Totals of 17.9, 30.0 and 28.8 % of caregivers of care-recipients with SCD, MCI or dementia, respectively, had anxiety symptoms (P = 0.1140), whereas 22.6, 24.2 and 20.9 %, respectively, had depression symptoms (P = 0.8165). The risk factors for caregiver’s anxiety symptoms were increased caregiver age as well as having care-recipients who were male, had higher Cohen Mansfield Agitation Inventory (CMAI) scores, and higher Geriatric Depression Scale (GDS) scores. The risk factors for caregiver’s depression symptoms were increased caregiver age as well as caring for care-recipients with MCI or SCD, those with lower Toronto Empathy Questionnaire (TEQ) scores, and those with higher GDS scores. Caregivers of care-recipients with SCD showed the same level of depression symptoms as those of care-recipients with MCI. Caregiver’s depression and anxiety symptoms were associated with their care-recipients’ psychiatric and behavioral syndromes.

INTRODUCTION: Blood biomarkers showed values for predicting future cognitive impairment. Evidence from the community‐based cohort was limited only in high‐income countries.METHODS: This study included 1857 dementia‐free community residents recruited in 2009–2011 and followed up in waves 2014–2016 and 2019–2023 in the Shanghai Aging Study. We intended to explore the relationships of baseline plasma ALZpath phosphorylated tau 217 (p‐tau217), p‐tau181, neurofilament light chain (NfL) with follow‐up incident dementia, Alzheimer's disease (AD), and amyloidosis.RESULTS: Higher concentrations of plasma p‐tau217, p‐tau181, and NfL were correlated to higher decline speed of Mini‐Mental State Examination score, and higher risk of incident dementia and AD. The p‐tau217 demonstrated a significant correlation with longitudinal neocortical amyloid‐beta (Aβ) deposition (r = 0.57 [0.30, 0.76]) and a high accuracy differentiating Aβ+ from Aβ‐ at follow‐ups (area under the receiver operating characteristic curve = 0.821 [0.703, 0.940]).DISCUSSION: Plasma p‐tau217 may be an early predictive marker of AD and Aβ pathology in older community‐dwelling individuals.Highlights: Plasma p‐tau217, p‐tau181, and NfL were positively associated with long‐term cognitive decline and risk of incident dementia. Plasma p‐tau217 showed a better performance distinguishing Aβ+ individuals from Aβ‐ individuals at follow‐ups. Plasma NfL may be a suitable predictor of general cognitive decline in older community‐dwelling individuals.