Identification of diagnostic biomarkers in Alzheimer's disease by integrated bioinformatic analysis and machine learning strategies

Insulin-Like Growth Factor Signaling in Alzheimer's Disease: Pathophysiology and Therapeutic Strategies

Alzheimer's disease (AD) is the leading cause of dementia among the elderly population, posing a significant public health challenge due to limited therapeutic options that merely delay cognitive decline. AD is associated with impaired energy metabolism and reduced neurotrophic signaling. The insulin-like growth factor (IGF) signaling pathway, crucial for central nervous system (CNS) development, metabolism, repair, cognition, and emotion regulation, includes IGF-1, IGF-2, IGF-1R, IGF-2R, insulin receptor (IR), and six insulin-like growth factor binding proteins (IGFBPs). Research has identified abnormalities in IGF signaling in individuals with AD and AD models. Dysregulated expression of IGFs, receptors, IGFBPs, and disruptions in downstream phosphoinositide 3-kinase-protein kinase B (PI3K/AKT) and mitogen-activated protein kinase (MAPK) pathways collectively increase AD susceptibility. Studies suggest modulating the IGF pathway may ameliorate AD pathology and cognitive decline. This review explores the CNS pathophysiology of IGF signaling in AD progression and assesses the potential of targeting the IGF system as a novel therapeutic strategy. Further research is essential to elucidate how aberrant IGF signaling contributes to AD development, understand underlying molecular mechanisms, and evaluate the safety and efficacy of IGF-based treatments.

Identifying Hub Genes and miRNAs Associated with Alzheimer's Disease: A Bioinformatics Pathway to Novel Therapeutic Strategies

Alzheimer's disease (AD) is a neurodegenerative disorder that mainly affects the elderly population. It is characterized by cognitive impairment and dementia due to abnormal levels of amyloid beta peptide (Aβ) and axonal Tau protein in the brain. However, the complex underlying mechanisms affecting this disease are not yet known, and there is a lack of standardized biomarkers and therapeutic targets. Therefore, in this study, by means of bioinformatics analysis, AD-affected brain tissue was analyzed using the GSE138260 dataset, identifying 612 differentially expressed genes (DEGs). Functional analysis revealed 388 upregulated DEGs associated with sensory perception and 224 downregulated DEGs linked to the regulation and modulation of synaptic processes. Protein-protein interaction network analysis identified 20 hub genes. Furthermore, miRNA target gene networks revealed 1767 miRNAs linked to hub genes, among which hsa-mir-106a-5p, hsa-mir-17-5p, hsa-mir-26a-5p, hsa-mir-27a-3p and hsa-mir-34a-5p were the most relevant. This study presents novel biomarkers and therapeutic targets for AD by analyzing the information obtained with a comprehensive literature review, providing new potential targets to study their role in AD.

Improve the diagnosis of idiopathic normal pressure hydrocephalus by combining abnormal cortical thickness and ventricular morphometry

Background

The primary imaging markers for idiopathic Normal Pressure Hydrocephalus (iNPH) emphasize morphological measurements within the ventricular system, with no attention given to alterations in brain parenchyma. This study aimed to investigate the potential effectiveness of combining ventricular morphometry and cortical structural measurements as diagnostic biomarkers for iNPH.

Methods

A total of 57 iNPH patients and 55 age-matched healthy controls (HC) were recruited in this study. Firstly, manual measurements of ventricular morphology, including Evans Index (EI), z-Evans Index (z-EI), Cella Media Width (CMW), Callosal Angle (CA), and Callosal Height (CH), were conducted based on MRI scans. Cortical thickness measurements were obtained, and statistical analyses were performed using surface-based morphometric analysis. Secondly, three distinct models were developed using machine learning algorithms, each based on a different input feature: a ventricular morphology model (LVM), a cortical thickness model (CT), and a fusion model (All) incorporating both features. Model performances were assessed using 10-fold cross validation and tested on an independent dataset. Model interpretation utilized Shapley Additive Interpretation (SHAP), providing a visualization of the contribution of each variable in the predictive model. Finally, Spearman correlation coefficients were calculated to evaluate the relationship between imaging biomarkers and clinical symptoms.

Results

iNPH patients exhibited notable differences in cortical thickness compared to HC. This included reduced thickness in the frontal, temporal, and cingulate cortices, along with increased thickness in the supracentral gyrus. The diagnostic performance of the fusion model (All) for iNPH surpassed that of the single-feature models, achieving an average accuracy of 90.43%, sensitivity of 90.00%, specificity of 90.91%, and Matthews correlation coefficient (MCC) of 81.03%. This improvement in accuracy (6.09%), sensitivity (11.67%), and MCC (11.25%) compared to the LVM strategy was significant. Shap analysis revealed the crucial role of cortical thickness in the right isthmus cingulate cortex, emerging as the most influential factor in distinguishing iNPH from HC. Additionally, significant correlations were observed between the typical triad symptoms of iNPH patients and cortical structural alterations.

Conclusion

This study emphasizes the significant role of cortical structure changes in the diagnosis of iNPH, providing a novel insights for assisting clinicians in improving the identification and detection of iNPH.

Insulin resistance induces earlier initiation of cognitive dysfunction mediated by cholinergic deregulation in a mouse model of Alzheimer's disease

Although insulin resistance increases the risk of Alzheimer's disease (AD), the mechanisms remain unclear, partly because no animal model exhibits the insulin-resistant phenotype without persistent hyperglycemia. Here we established an AD model with whole-body insulin resistance without persistent hyperglycemia (APP/IR-dKI mice) by crossbreeding constitutive knock-in mice with P1195L-mutated insulin receptor (IR-KI mice) and those with mutated amyloid precursor protein (AppNL-G-F mice: APP-KI mice). APP/IR-dKI mice exhibited cognitive impairment at an earlier age than APP-KI mice. Since cholinergic dysfunction is a major characteristic of AD, pharmacological interventions on the cholinergic system were performed to investigate the mechanism. Antagonism to a nicotinic acetylcholine receptor α7 (nAChRα7) suppressed cognitive function and cortical blood flow (CBF) response to cholinergic-regulated peripheral stimulation in APP-KI mice but not APP/IR-dKI mice. Cortical expression of Chrna7, encoding nAChRα7, was downregulated in APP/IR-dKI mice compared with APP-KI. Amyloid β burden did not differ between APP-KI and APP/IR-dKI mice. Therefore, insulin resistance, not persistent hyperglycemia, induces the earlier onset of cognitive dysfunction and CBF deregulation mediated by nAChRα7 downregulation. Our mouse model will help clarify the association between type 2 diabetes mellitus and AD.