Background
Alzheimer’s disease (AD) is a neurodegenerative disorder that causes gradual memory loss. AD and its prodromal stage of mild cognitive impairment (MCI) are marked by significant gut microbiome perturbations, also known as gut dysbiosis. However, the direction and extent of gut dysbiosis have not been elucidated. Therefore, we performed a meta-analysis and systematic review of 16S gut microbiome studies to gain insights into gut dysbiosis in AD and MCI.
Methods
We searched MEDLINE, Scopus, EMBASE, EBSCO, and Cochrane for AD gut microbiome studies published between Jan 1, 2010 and Mar 31, 2022. This study has two outcomes: primary and secondary. The primary outcomes explored the changes in α-diversity and relative abundance of microbial taxa, which were analyzed using a variance-weighted random-effects model. The secondary outcomes focused on qualitatively summarized β-diversity ordination and linear discriminant analysis effect sizes. The risk of bias was assessed using a methodology appropriate for the included case-control studies. The geographic cohorts’ heterogeneity was examined using subgroup meta-analyses if sufficient studies reported the outcome. The study protocol has been registered with PROSPERO (CRD42022328141).
Findings
Seventeen studies with 679 AD and MCI patients and 632 controls were identified and analyzed. The cohort is 61.9% female with a mean age of 71.3±6.9 years. The meta-analysis shows an overall decrease in species richness in the AD gut microbiome. However, the phylum Bacteroides is consistently higher in US cohorts (standardised mean difference [SMD] 0.75, 95% confidence interval [CI] 0.37 to 1.13, p < 0.01) and lower in Chinese cohorts (SMD -0.79, 95% CI -1.32 to -0.25, p < 0.01). Moreover, the Phascolarctobacterium genus is shown to increase significantly, but only during the MCI stage.
Discussion
Notwithstanding possible confounding from polypharmacy, our findings show the relevance of diet and lifestyle in AD pathophysiology. Our study presents evidence for region-specific changes in abundance of Bacteroides, a major constituent of the microbiome. Moreover, the increase in Phascolarctobacterium and the decrease in Bacteroides in MCI subjects shows that gut microbiome dysbiosis is initiated in the prodromal stage. Therefore, studies of the gut microbiome can facilitate early diagnosis and intervention in Alzheimer’s disease and perhaps other neurodegenerative disorders.
Alzheimer's Disease (AD) is the most common form of dementia. It usually manifests through progressive loss of cognitive function and memory, subsequently impairing the person's ability to live without assistance and causing a tremendous impact on the affected individuals and society. Currently, AD diagnosis relies on cognitive tests, blood tests, behavior assessments, brain imaging, and medical history analysis. However, these procedures are subjective and inconsistent, making an accurate prediction for the early stages of AD difficult. This paper introduces a curvelet transform (CT) based-convolutional neural network (CNN) (DeepCurvMRI) model for improving the accuracy of early-stage AD disease detection using from Magnetic resonance imaging (MRI) images. The MRI images were first pre-processed using CT, and then a CNN model was trained using the new image representation. In this study, we used Alzheimer's MRI images dataset hosted on the Kaggle platform to train DeepCurvMRI for multi and binary classification tasks. DeepCurvMRI achieved an accuracy, sensitivity, specificity, and F1 score of 98.62% ± 0.10%, 99.05% ± 0.10%, 98.50% ± 0.03%, and 99.21 ± 0.08, respectively, using the leave-one-group-out (LOGO) cross-validation approach in multi-classification task. The highest accuracy obtained in binary classification is 98.71% ± 0.05%. In addition to LOGO, DeepCurvMRI was tested using randomly stratified 10-fold and 5-fold cross validation. These encouraging results are superior to the ones reported in related methods, showcasing the potentiality of DeepCurvMRI in capturing the key anatomical changes in MRI images that can be differentiated between various staged of Alzheimer's disease classes.INDEX TERMS Alzheimer's disease, curvelet transform, deep learning, CNN, MRI images.
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