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.
Background Previous studies reported the value of blood-based biomarkers in predicting Alzheimer disease (AD) progression among individuals with different disease stages. However, evidence regarding the value of these markers in those with amnestic mild cognitive impairment (aMCI) is insufficient. Methods A cohort with 251 aMCI individuals were followed for up to 8 years. Baseline blood biomarkers were measured on a single-molecule array platform. Multipoint clinical diagnosis and domain-specific cognitive functions were assessed to investigate the longitudinal relationship between blood biomarkers and clinical AD progression. Results Individuals with low Aβ42/Aβ40 and high p-tau181 at baseline demonstrated the highest AD risk (hazard ratio = 4.83, 95% CI 2.37–9.86), and the most dramatic decline across cognitive domains. Aβ42/Aβ40 and p-tau181, combined with basic characteristics performed the best in predicting AD conversion (AUC = 0.825, 95% CI 0.771–0.878). Conclusions Combining Aβ42/Aβ40 and p-tau181 may be a feasible indicator for AD progression in clinical practice, and a potential composite marker in clinical trials.
Background: Previous studies indicated that blood-based biomarkers could predict cognitive decline in Alzheimer's disease (AD) continuum.Method: Two hundred and fifty-one participants with amnestic mild cognitive impairment (aMCI) from the Shanghai Memory Study were followed up for a maximum of 8 years. Baseline blood biomarkers were measured with the single-molecule array (Simoa) platform. Multipoint clinical diagnosis and domain-specific cognitive functions were assessed to investigate the longitudinal relationship between blood biomarkers and clinical AD progression.Result: Participants with high-risk plasma Aβ42/Aβ40 (A) and p-tau181 (T) level demonstrated the highest probability of incident AD (HR 5.54, 95% CI 2.99-10.27), and the most dramatic decline in global cognition, attention, executive function, visuospatial function, and language. Comparing to young-old participants, the old-old ones with low-and moderate-risk AT showed higher AD risks (HR 3.70, HR 3.15,, and faster cognitive deterioration. Conclusion:The results supported the use of plasma Aβ42/Aβ40 and p-tau181 as accessible and feasible indicators of AD progression and the long-term cognitive deterioration, especially in patients with older age.
Background The blood-based biomarkers are approaching the clinical practice of Alzheimer’s disease (AD). Chronic kidney disease (CKD) has a potential confounding effect on peripheral protein levels. It is essential to characterize the impact of renal function on AD markers. Methods Plasma phospho-tau181 (P-tau181), and neurofilament light (NfL) were assayed via the Simoa HD-X platform in 1189 dementia-free participants from the Shanghai Aging Study (SAS). The estimated glomerular filter rate (eGFR) was calculated. The association between renal function and blood NfL, P-tau181 was analyzed. An analysis of interactions between various demographic and comorbid factors and eGFR was conducted. Results The eGFR levels were negatively associated with plasma concentrations of NfL and P-tau181 (B = -0.19, 95%CI -0.224 to -0.156, P < 0.001; B = -0.009, 95%CI -0.013 to -0.005, P < 0.001, respectively). After adjusting for demographic characteristics and comorbid diseases, eGFR remained significantly correlated with plasma NfL (B = -0.010, 95%CI -0.133 to -0.068, P < 0.001), but not with P-tau181 (B = -0.003, 95%CI -0.007 to 0.001, P = 0.194). A significant interaction between age and eGFR was found for plasma NfL (Pinteraction < 0.001). In participants ≥ 70 years and with eGFR < 60 ml/min/1.73 m2, the correlation between eGFR and plasma NfL was significantly remarkable (B = -0.790, 95%CI -1.026 to -0,554, P < 0.001). Conclusions Considering renal function and age is crucial when interpreting AD biomarkers in the general aging population.
Background Mild cognitive impairment (MCI) is a critical prodromal stage of Alzheimer’s disease (AD) with high risk of conversion. It is of great challenge to construct reliable biomarkers for predicting conversion from MCI to AD, while the underlying mechanism is still not fully explored. Inter‐dataset generalizability is a prerequisite for clinical use of biomarkers and always a shortage of neuroimaging‐based studies. Method In this study, we propose a novel framework by integrating structural MRI (sMRI) and both static and dynamic resting‐state functional MRI (fMRI) measurements to investigate the differences between MCI converters (MCI_C) and non‐converters (MCI_NC), and then utilized support vector machine (SVM) to construct the prediction models based on selected features. A total of 186 MCI patients with both MRI and three‐year outcome data were selected from two independent cohorts: Shanghai Memory Study (SMS) cohort for selection of MRI predictors and internal cross‐validation, and ADNI cohort for external validation on the generalizability of these MRI predictors. Result In comparison with MCI_NC, the MRI converters were mainly characterized by alterations of medial temporal lobe (MTL) with atrophy extending to lateral temporal and regional hyperactivity and instability, posterior parietal cortex (PPC) with atrophy and inter‐regional hypo‐connectivity and connectional instability, and occipital cortex with functional instability. All of the imaging‐based prediction models achieved an AUC above 0.7 and ACC above 70% in both SMS and ADNI cohorts. The combination of static and dynamic fMRI features resulted in overall good performance as relative to static or dynamic fMRI solely, supporting the contribution of dynamic features into the fMRI model. In both cohorts, the best imaging model was the multi‐modality MRI model which provided excellent performance with AUC above 0.85 and average ACC/sensitivity/specificity around 80%. Conclusion This inter‐cohort validation study provides a new insight into the mechanisms of MCI conversion and paves a way for eventual clinical use of MRI biomarkers.
Alzheimer's disease (AD), the most common type of dementia, is a chronic, progressive degenerative disease, with the main clinical features being progressive impairment in cognition, behavior, and ability to do activities of daily living. Although the neuropathological changes in AD can be assessed by histopathologic examination, cerebrospinal fluid (CSF) or blood assays, and through positron emission tomography (PET) imaging, clinical application of these biomarkers is limited due to the cost, invasiveness, and availability. [1]
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