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Background: Subjective cognitive decline (SCD) is the earliest symptomatic manifestation of preclinical Alzheimer's disease (AD). Gut microbiota may serve as a susceptibility factor for AD. Altered gut microbiota has been reported in patients with mild cognitive impairment (MCI) and AD dementia. However, whether gut microbial compositions changed in SCD remains largely unknown.Objective: To characterize the gut microbiota in SCD.Methods: In this study, a total of 105 participants including 38 normal controls (NC), 53 individuals with SCD, and 14 patients with cognitive impairment (CI) were recruited. Gut microbiota of all participants isolated from fecal samples were investigated using 16S ribosomal RNA (rRNA) Illumina Miseq sequencing technique. The gut microbial compositions were compared among the three groups, and the association between altered gut microbiota and cognitive performance was analyzed. To validate the alteration of gut microbiota in SCD, we conducted amyloid positron emission tomography (PET) in selected participants and further compared the gut microbiota among subgroups.Results: The abundance of phylum Firmicutes, class Clostridia, order Clostridiales, family Ruminococcaceae, and genus Faecalibacterium showed a trend toward a progressive decline from NC to SCD and CI. Specifically, the abundance of the anti-inflammatory genus Faecalibacterium was significantly decreased in SCD compared with NC. In addition, altered bacterial taxa among the three groups were associated with cognitive performance. The findings were validated in SCD participants with positive amyloid evidence.Conclusion: The composition of gut microbiota is altered in individuals with SCD. This preliminary study will provide novel insights into the pathophysiological mechanism of AD. [ABSTRACT FROM AUTHOR]

Background: Education plays a potential important effect on the prevalence and incidence of dementia. However, most of the evidence based on convenience sampling.Objective: To explore effects of education on cognition in individuals with subjective cognitive decline (SCD) and cognitive impairment (CI) from a population-based study.Methods: We examined the effect of education on cognition among individuals with SCD (n = 451) and CI (n = 280) from a population-based study. A series of neuropsychological tests of memory, executive, language, and general cognitive function were used to assess the participants.Results: Multiple regression analyses revealed that education has a positive effect on cognition in both SCD and CI group in the population-based research. Further stratification study showed that the beneficial effect of education remains in the SCD group regardless of the education level, especially in the SCD participants with a low education level. However, that effect of education exists in the CI group with a low education level and disappears in the high education level.Conclusion: These results from a population-based sample suggest that high educational attainment may delay cognitive decline in the individuals with SCD regardless of high or low educational level, and high education only predicts cognition in those in the low educational level in CI group. [ABSTRACT FROM AUTHOR]

Background: Visual rating scales for medial temporal lobe atrophy (MTA) and posterior atrophy (PA) have been reported to be useful for Alzheimer's disease diagnosis in routine clinical practice.Objective: To investigate the efficacy of combined MTA and PA visual rating scales to discriminate amnestic mild cognitive impairment (aMCI) patients from healthy controls.Methods: This study included T1-weighted MRI images from two different cohorts. In the first cohort, we recruited 73 patients with aMCI and 48 group-matched cognitively normal controls for training and validation. Visual assessments of MTA and PA were carried out for each participant. Global gray matter volume and density were estimated using voxel-based morphometry analysis as the objective reference. We investigated the discriminative power of a single visual rating scale and the combination of the MTA and PA rating scales for identifying aMCI. The second cohort, consisting of 33 aMCI patients and 45 controls, was used to verify the reliability of the visual assessments.Results: Compared with the single visual rating scale, the combination of the MTA and PA exhibited the best discriminative power, with an AUC of 0.818±0.041, which was similar to the diagnostic accuracy of the gray matter volumetric measures. The discriminative power of the combined MTA and PA was verified in the second cohort (AUC 0.824±0.058).Conclusion: The combined MTA and PA rating scales demonstrated practical diagnostic value for distinguishing aMCI patients from controls, suggesting its potential to serve as a convenient and reproducible method to assess the degree of atrophy in clinical settings. [ABSTRACT FROM AUTHOR]

Background: Detecting subtle changes in visual attention from electroencephalography (EEG) and the perspective of eye movement in mild cognitive impairment (MCI) patients can be of great significance in screening early Alzheimer's disease (AD) in a large population at primary care.Objective: We proposed an automatic, non-invasive, and quick MCI detection approach based on multimodal physiological signals for clinical decision-marking.Methods: The proposed model recruited 152 patients with MCI and 184 healthy elderly controls (HC) who underwent EEG and eye movement signal recording under a visual stimuli task, as well as other neuropsychological assessments. Forty features were extracted from EEG and eye movement signals by linear and nonlinear analysis. The features related to MCI were selected by logistic regression analysis. To evaluate the efficacy of this MCI detection approach, we applied the same procedures to achieve the Clinical model, EEG model, Eye movement model, EEG+ Clinical model, Eye movement+ Clinical model, and Combined model, and compared the classification accuracy between the MCI and HC groups with the above six models.Results: After the penalization of logistic regression analysis, five features from EEG and eye movement features exhibited significant differences (p < 0.05). In the classification experiment, the combined model resulted in the best accuracy. The average accuracy for the Clinical/EEG/Eye movement/EEG+ Clinical/Eye movement+ Clinical/Combined model was 68.69%, 61.79%, 73.13%, 69.46%, 75.61%, and 81.51%, respectively.Conclusion: These results suggest that the proposed MCI detection tool has the potential to screen MCI patients from HCs and may be a powerful tool for personalized precision MCI screening in the large-scale population under primary care condition. [ABSTRACT FROM AUTHOR]

Subjective cognitive decline (SCD) is a risk factor for Alzheimer's disease (AD). Urinary Alzheimer-associated neuronal thread protein (AD7c-NTP) has been identified as a biomarker for AD. It was hypothesized that if urinary AD7c-NTP were also elevated in SCD, as it is in prodromal AD (mild cognitive impairment stage), it could be a convenient and efficient clinical biomarker for the early diagnosis of SCD. SCD is often accompanied by a depressive state (DS), and the impact of DS on urinary AD7c-NTP levels remains unknown. A total of 297 right-handed Chinese Han subjects were recruited, including 98 subjects with SCD, 92 patients with DS, and 107 well-matched cognitively normal controls (NC). The levels of AD7c-NTP in urine samples were measured using an enzyme-linked immunosorbent assay AD7c-NTP kit. Our results demonstrated that urinary AD7c-NTP levels in the SCD group (0.7561±0.5657 ng/mL) were not significantly higher than in either the DS (0.7527±0.5607 ng/mL) or NC (0.7214±0.5077 ng/mL) groups. Furthermore, urinary AD7c-NTP levels were not correlated with Hamilton Depression Rating Scale and Hamilton Anxiety Scale scores, but they were negatively associated with Mini-Mental State Examination scores (r = -0.222, p = 0.033) and Montreal Cognitive Assessment-Basic scores (r = -0.207, p = 0.048). Urinary AD7c-NTP level is not elevated in SCD and is unaffected by DS. Urinary AD7c-NTP may therefore not be a good potential biomarker for SCD and DS, although it may become elevated with more severe cognitive decline. [ABSTRACT FROM AUTHOR]

Identifying amnestic mild cognitive impairment (aMCI) is of great clinical importance because aMCI is a putative prodromal stage of Alzheimer's disease. The present study aimed to explore the feasibility of accurately identifying aMCI with a magnetic resonance imaging (MRI) biomarker. We integrated measures of both gray matter (GM) abnormalities derived from structural MRI and white matter (WM) alterations acquired from diffusion tensor imaging at the voxel level across the entire brain. In particular, multi-modal brain features, including GM volume, WM fractional anisotropy, and mean diffusivity, were extracted from a relatively large sample of 64 Han Chinese aMCI patients and 64 matched controls. Then, support vector machine classifiers for GM volume, FA, and MD were fused to distinguish the aMCI patients from the controls. The fused classifier was evaluated with the leave-one-out and the 10-fold cross-validations, and the classifier had an accuracy of 83.59% and an area under the curve of 0.862. The most discriminative regions of GM were mainly located in the medial temporal lobe, temporal lobe, precuneus, cingulate gyrus, parietal lobe, and frontal lobe, whereas the most discriminative regions of WM were mainly located in the corpus callosum, cingulum, corona radiata, frontal lobe, and parietal lobe. Our findings suggest that aMCI is characterized by a distributed pattern of GM abnormalities and WM alterations that represent discriminative power and reflect relevant pathological changes in the brain, and these changes further highlight the advantage of multi-modal feature integration for identifying aMCI. [ABSTRACT FROM AUTHOR]