Current Alzheimer Research - Online First

Type 2 diabetes mellitus (T2D) is a known risk factor for developing Alzheimer’s disease (AD). Recent research shows that both diseases share complex and related pathophysiological processes. Network medicine approaches can help to elucidate common dysregulated processes among different diseases, such as AD and T2D. Thus, the aim of this work was to determine differentially expressed genes (DEGs) in AD and T2D and to apply a network medicine approach to identify the microRNAs (miRNAs) involved in the AD-T2D association.

Gene expression microarray data sets consisting of 384 control samples and 399 samples belonging to AD and T2D disease were analyzed to obtain DEGs shared by both diseases; the miRNAs associated with these DEGs were predicted using a network medicine approach. Finally, potential small molecules targeting these potentially deregulated miRNAs were identified.

AD and T2D shared a subset of 82 downregulated DEGs. These genes were significantly associated (p < 0.01) with the ontology terms of chemical synaptic deregulation. DEGs were associated with 12 miRNAs expressed in specific tissues for AD and T2D. Such miRNAs were also primarily associated with the ontology terms related to synaptic deregulation and cancer, and AKT signaling pathways. Steroid anti-inflammatory drugs, antineoplastics, and glucose metabolites were predicted to be potential regulators of the 12 shared miRNAs.

The network medicine approach integrating DEGs and miRNAs enabled the identification of shared, potentially deregulated biological processes and pathways underlying the pathophysiology of AD and T2D. These common molecular mechanisms were also linked to drugs currently used in clinical practice, suggesting that this strategy may inform future drug repurposing efforts. Nonetheless, further in-depth biological validation is required to confirm these findings.

Network medicine allowed identifying 12 miRNAs involved in the AD-T2D association, and these could be drug targets for the design of new treatments; however, the identified miRNAs need further experimental confirmation.

Alzheimer's disease (AD) is a leading cause of death, making early detection critical to improve survival rates. Conventional manual techniques struggle with early diagnosis due to the brain's complex structure, necessitating the use of dependable deep learning (DL) methods. This research proposes a novel RESIGN model is a combination of Res-InceptionSeg for detecting AD utilizing MRI images.

The input MRI images were pre-processed using a Non-Local Means (NLM) filter to reduce noise artifacts. A ResNet-LSTM model was used for feature extraction, targeting White Matter (WM), Grey Matter (GM), and Cerebrospinal Fluid (CSF). The extracted features were concatenated and classified into Normal, MCI, and AD categories using an Inception V3-based classifier. Additionally, SegNet was employed for abnormal brain region segmentation.

The RESIGN model achieved an accuracy of 99.46%, specificity of 98.68%, precision of 95.63%, recall of 97.10%, and an F1 score of 95.42%. It outperformed ResNet, AlexNet, DenseNet, and LSTM by 7.87%, 5.65%, 3.92%, and 1.53%, respectively, and further improved accuracy by 25.69%, 5.29%, 2.03%, and 1.71% over ResNet18, CLSTM, VGG19, and CNN, respectively.

The integration of spatial-temporal feature extraction, hybrid classification, and deep segmentation makes RESIGN highly reliable in detecting AD. A 5-fold cross-validation proved its robustness, and its performance exceeded that of existing models on the ADNI dataset. However, there are potential limitations related to dataset bias and limited generalizability due to uniform imaging conditions.

The proposed RESIGN model demonstrates significant improvement in early AD detection through robust feature extraction and classification by offering a reliable tool for clinical diagnosis.

At present, there is limited research on the association between dietary intake of anthocyanidins and Alzheimer's disease (AD). More epidemiological studies are needed to better understand this relationship.

We explored the relationship between dietary Anthocyanidins intake and AD among 3806 American adults in the National Health and Nutrition Examination Survey (NHANES) and the United States Department of Agriculture’s Food and Nutrient Database for Dietary Studies (FNDDS) from 2007 to 2010, and 2017 to 2018. We use weighted logistic regression model, restricted cubic spline (RCS) and weighted quantile sum (WQS) regression analysis to analyze the relationship between anthocyanidins monomer and AD.

The weighted logistic regression model showed that the total intake of anthocyanidins was the fourth (OR:0.979; 95% CI: 0.966-0.992) quantile (relative to the lowest quantile) is related to the reduction of AD risk. RCS analysis showed that the total intake of anthocyanidins was negatively linearly correlated with AD (nonlinear P value was 0.002). The WQS regression analysis shows that cyanidin and malvidin are the main contributors to the comprehensive effects of six anthocyanidins.

Our findings indicate that higher dietary anthocyanin intake may reduce the risk of AD and alleviate neurodegenerative processes. However, the mechanisms underlying this relationship remain unclear. Future studies should confirm these associations and investigate the relevant biological pathways.

Our results show that a higher dietary intake of anthocyanidins is associated with a lower risk of AD.