Current Alzheimer Research - Volume 19, Issue 10, 2022

Background: The clinical characteristics of symptomatic and asymptomatic carriers of early- onset autosomal dominant Alzheimer’s (EOADAD) due to a yet-undescribed chromosomal rearrangement may add to the available body of knowledge about Alzheimer’s disease and may enlighten novel and modifier genes. We report the clinical and genetic characteristics of asymptomatic and symptomatic individuals carrying a novel APP duplication rearrangement. Methods: Individuals belonging to a seven-generation pedigree with familial cognitive decline or intracerebral hemorrhages were recruited. Participants underwent medical, neurological, and neuropsychological evaluations. The genetic analysis included chromosomal microarray, Karyotype, fluorescence in situ hybridization, and whole genome sequencing. Results: Of 68 individuals, six females presented with dementia, and four males presented with intracerebral hemorrhage. Of these, nine were found to carry Chromosome 21 copy number gain (chr21:27,224,097-27,871,284, GRCh37/hg19) including the APP locus (APP-dup). In seven, Chromosome 5 copy number gain (Chr5: 24,786,234-29,446,070, GRCh37/hg19) (Chr5-CNG) cosegregated with the APP-dup. Both duplications co-localized to chromosome 18q21.1 and segregated in 25 pre-symptomatic carriers. Compared to non-carriers, asymptomatic carriers manifested cognitive decline in their mid-thirties. A third of the affected individuals carried a diagnosis of a dis-immune condition. Conclusion: APP extra dosage, even in isolation and when located outside chromosome 21, is pathogenic. The clinical presentation of APP duplication varies and may be gender specific, i.e., ICH in males and cognitive-behavioral deterioration in females. The association with immune disorders is presently unclear but may prove relevant. The implication of Chr5-CNG co-segregation and the surrounding chromosome 18 genetic sequence needs further clarification.

Objective: The aim of the present work was to investigate the features of the elderly population aged ≥65 yrs and with deteriorative mild cognitive impairment (MCI) due to Alzheimer’s disease (AD) to establish a prediction model. Methods: A total of 105 patients aged ≥65 yrs and with MCI were followed up, with a collection of 357 features, which were derived from the demographic characteristics, hematological indicators (serum Aβ1-40, Aβ1-42, P-tau and MCP-1 levels, APOE gene), and multimodal brain Magnetic Resonance Imaging (MRI) imaging indicators of 116 brain regions (ADC, FA and CBF values). Cognitive function was followed up for 2 yrs. Based on the Python platform Anaconda, 105 patients were randomly divided into a training set (70%) and a test set (30%) by analyzing all features through a random forest algorithm, and a prediction model was established for the form of rapidly deteriorating MCI. Results: Of the 105 patients enrolled, 41 deteriorated, and 64 did not come within 2 yrs. Model 1 was established based on demographic characteristics, hematological indicators and multi-modal MRI image features, the accuracy of the training set being 100%, the accuracy of the test set 64%, sensitivity 50%, specificity 67%, and AUC 0.72. Model 2 was based on the first five features (APOE4 gene, FA value of left fusiform gyrus, FA value of left inferior temporal gyrus, FA value of left parahippocampal gyrus, ADC value of right calcarine fissure as surrounding cortex), the accuracy of the training set being 100%, the accuracy of the test set 85%, sensitivity 91%, specificity 80% and AUC 0.96. Model 3 was based on the first four features of Model 1, the accuracy of the training set is 100%, the accuracy of the test set 97%, sensitivity100%, specificity 95% and AUC 0.99. Model 4 was based on the first three characteristics of Model 1, the accuracy of the training set being 100%, the accuracy of the test set 94%, sensitivity 92%, specificity 94% and AUC 0.96. Model 5 was based on the hematological characteristics, the accuracy of the training set is 100%, the accuracy of the test set 91%, sensitivity 100%, specificity 88% and AUC 0.97. The models based on the demographic characteristics, imaging characteristics FA, CBF and ADC values had lower sensitivity and specificity. Conclusion: Model 3, which has four important predictive characteristics, can predict the rapidly deteriorating MCI due to AD in the community.

Background: Poststroke cognitive impairment (PSCI) is a prevalent cause of disability in people with stroke. PSCI results from either lesion-dependent loss of cognitive function or augmentation of Alzheimer's pathology due to vascular insufficiency. The lack of prestroke cognitive assessments limits the clear understanding of the impact of PSCI on cognition. Objective: The present study aims to make a direct comparison of longitudinal cognitive assessment results to clarify the impact of ischemic stroke on PSCI and assess the cognitive decline in PSCI compared to people with Alzheimer's disease (AD). Methods: All study participants had their Mini-Mental State Examination (MMSE) at the chronic poststroke stage (≥6 months after stroke), which was compared with prestroke or acute poststroke (<6 months after stroke) MMSE to investigate the two aspects of PSCI. A group of patients with AD was used to reference the speed of neurodegenerative cognitive deterioration. Repeated measures analysis of variance was used to compare the longitudinal change of MMSE. Results: MMSE score between acute and chronic poststroke revealed a 1.8 ± 6.49 decline per year (n=76), which was not significantly different from the AD patients who underwent cholinesterase inhibitors treatment (-1.11 ± 2.61, p=0.35, n=232). MMSE score between prestroke and chronic poststroke (n=33) revealed a significant decline (−6.52 ± 6.86, p < 0.001). In addition, their cognitive deterioration was significantly associated with sex, age, and stroke over the white matter or basal ganglia. Conclusion: Ischemic stroke substantially affects cognition with an average six-point drop in MMSE. The rate of cognitive decline in PSCI was similar to AD, and those with white matter or basal ganglia infarct were at greater risk of PSCI.

Background: Early-onset Alzheimer’s disease (EOAD) is highly influenced by genetic factors. Numerous mutations in amyloid precursor protein (APP) and presenilin 1 and 2 (PSEN1 and PSEN2) have been identified for EOAD, but they can only account for a small proportion of EOAD cases. Objective: This study aimed to screen genetic mutations and variants associated with EOAD among Han Chinese adults. Methods: This study included 34 patients with EOAD and 26 controls from a population-based study and neurological ward. We first sequenced mutations in APP/PSENs and then performed whole-exome sequencing in the remaining patients with negative mutations in APP/PSENs to screen for additional potential genetic variants. Among patients who were negative in genetic screening tests, we further evaluated the risk burden of genes related to the Aβ metabolism-centered network to search for other probable causes of EOAD. Results: We identified 7 functional variants in APP/PSENs in 8 patients, including 1 APP mutation (p. Val715Met), 3 PSEN1 mutations (p. Phe177Ser; p. Arg377Met; p. Ile416Thr), and 3 PSEN2 mutations (p. Glu24Lys; p. Gly34Ser; p. Met239Thr). Of the remaining 26 EOAD cases without mutations in APP/PSENs, the proportion of carrying rare variants of genes involved in Aβ and APP metabolism was significantly higher than that of controls (84.6% vs. 73.1%, P=0.042). Thirty-one risk genes with 47 variants were identified in 22 patients. However, in 26 normal subjects, only 20 risk genes with 29 variants were identified in 19 subjects. Conclusions: Our findings demonstrate the role of APP/PSENs mutations in EOAD, identifying a new PSEN2 missense mutation, and further offer valuable insights into the potential genetic mechanisms of EOAD without APP/PSENs mutations among Han Chinese.

Background: Over a dozen studies have investigated the effect of the R219K variant in the ATP-binding cassette transporter A1 (ABCA1) gene on the risk of Alzheimer's disease (AD), but the results are conflicting. Objective: We performed a systematic review and meta-analysis to comprehensively assess the association between the ABCA1 R219K variant and the risk of AD. Methods: Studies included in the meta-analysis were obtained by searching PubMed, Web of Science and AlzGene. Review Manager 5.4 was used for meta-analysis. Both the odds ratio (OR) and its 95% confidence interval (CI) were used to evaluate the effect of ABCA1 R219K polymorphism on AD susceptibility. Heterogeneity between the included studies was assessed using I2 statistics and Cochran Qtest. Funnel plots were used to assess publication bias. Results: A total of 14 eligible studies involving 10084 subjects were retrieved from PubMed, Web of Science and AlzGene. Meta-analysis results showed that R219K polymorphism was significantly associated with a decreased risk of AD in Chinese under a recessive model (OR = 0.67; 95% CI = 0.51- 0.88; P = 0.004). Conclusion: The present meta-analysis indicated that the KK genotype of R219K polymorphism may act as a protective factor for AD in the Chinese population. Additional studies with larger sample sizes are needed to further confirm this association.