Analysis of neuropathological comorbid conditions in elderly patients with mild cognitive impairment in a tertiary care center in South India

Analysis of Convergent Validity of Performance-Based Activities of Daily Living Assessed by PA-IADL Test in Relation to Traditional (Standard) Cognitive Assessment to Identify Older Adults with Mild Cognitive Impairment

Difficulty in performing instrumental activities of daily living (IADLs) is currently considered an important indicator of cognitive impairment in the elderly. A non-experimental case-control investigation was conducted to assess the convergent validity of the PA-IADL with traditional (standard) cognitive assessment tests in its ability to identify adults with mild cognitive impairment. The analysis of the data was carried out by means of various multivariate statistical tests, and the sequence in its execution led to the conclusion that 8 of the 12 Tasks that make up the PA-IADL allow for the identification of people with mild cognitive impairment (MCI) to the same extent as traditional cognitive assessment tests and regardless of age. Age was found to be a moderating variable in the performance of the eight tasks; however, the results allow us to hypothesize that people with MCI experience a significant decline when it happens but thereafter, the deterioration that occurs does so at the same rate as the deterioration experienced by healthy people. They also allow us to hypothesize that the difference in the cognitive skills required by the eight functional tasks, and therefore also in the cognitive skills required by the traditional (standard) tests of a person with MCI compared to a person of the same age without MCI (Healthy), is approximately 10 years. These hypotheses have remarkable relevance and should be tested via longitudinal research. In the meantime, the results highlight the importance of the IADL assessment for the diagnosis of MCI as a complement to the standard cognitive assessment.

The relationship between protein modified folding molecular network and Alzheimer's disease pathogenesis based on BAG2-HSC70-STUB1-MAPT expression patterns analysis

Background

Alzheimer's disease (AD) is the most common cause of dementia and cognitive decline, while its pathological mechanism remains unclear. Tauopathies is one of the most widely accepted hypotheses. In this study, the molecular network was established and the expression pattern of the core gene was analyzed, confirming that the dysfunction of protein folding and degradation is one of the critical factors for AD.

Methods

This study analyzed 9 normal people and 22 AD patients' microarray data obtained from GSE1297 in Gene Expression Omnibus (GEO) database. The matrix decomposition analysis was used to identify the correlation between the molecular network and AD. The mathematics of the relationship between the Mini-Mental State Examination (MMSE) and the expression level of the genes involved in the molecular network was found by Neural Network (NN). Furthermore, the Support Vector Machine (SVM) model was for classification according to the expression value of genes.

Results

The difference of eigenvalues is small in first three stages and increases dramatically in the severe stage. For example, the maximum eigenvalue changed to 0.79 in the severe group from 0.56 in the normal group. The sign of the elements in the eigenvectors of biggest eigenvalue reversed. The linear function of the relationship between clinical MMSE and gene expression values was observed. Then, the model of Neural Network (NN) is designed to predict the value of MMSE based on the linear function, and the predicted accuracy is up to 0.93. For the SVM classification, the accuracy of the model is 0.72.

Conclusion

This study shows that the molecular network of protein folding and degradation represented by "BAG2-HSC70-STUB1-MAPT" has a strong relationship with the occurrence and progression of AD, and this degree of correlation of the four genes gradually weakens with the progression of AD. The mathematical mapping of the relationship between gene expression and clinical MMSE was found, and it can be used in predicting MMSE or classification with high accuracy. These genes are expected to be potential biomarkers for early diagnosis and treatment of AD.