Current Alzheimer Research - Volume 21, Issue 5, 2024

In an ageing population, dementia has become an imminent healthcare emergency. Capgras syndrome, the most common delusion of misidentification (DMS), is frequently found alongside dementia. Previous research showed that Capgras syndrome has significant negative effects on people living with dementia and their carers due to its complex presentation and impact on their lives. This qualitative systematic review explores the evidence base of the effective management and treatment of Capgras syndrome in dementia.

As per our knowledge, this is the first systematic review exploring the symptomatology of Capgras syndrome across different types of dementia. Additionally, it aims to identify the treatments used and their efficacy.

Four databases (EMBASE, MEDLINE, PsycINFO, and CINHAL) were screened in March, 2023. Twenty-six studies met the inclusion criteria and were included in the review. Thematic analysis was performed to explore and synthesise the qualitative findings of the studies.

Three conceptual themes were identified: diagnostic tools, Capgras syndrome symptomatology, and Capgras syndrome treatment. Results showed that Capgras syndrome in dementia is not diagnosed and treated in a standardised manner. Following the pharmacological intervention, 28% of cases showed resolution of symptoms, and another 28% experienced improvement. However, 7% of cases reported worsening symptoms, and 10.7% experienced no change. While some patients had positive outcomes with specific medications, others either did not respond or experienced a deterioration of their condition.

The results highlight that there is no single treatment approach for Capgras syndrome in people living with dementia. This underscores the need for person-centred care, where treatment is tailored to individual needs. The review also reveals a heavy reliance on antipsychotic medications and a noticeable lack of psychosocial interventions. Given the limited benefits and significant risks associated with antipsychotics, future research should prioritise developing and testing psychosocial approaches. Additionally, establishing standardised diagnostic criteria and consistent outcome measures for Capgras syndrome in dementia is crucial for evaluating treatment effectiveness and improving care.

When Ant Colony Optimization algorithm (ACO) is adept at identifying the shortest path, the temporary solution is uncertain during the iterative process. All temporary solutions form a solution set.

Where each solution is random. That is, the solution set has entropy. When the solution tends to be stable, the entropy also converges to a fixed value. Therefore, it was proposed in this paper that apply entropy as a convergence criterion of ACO. The advantage of the proposed criterion is that it approximates the optimal convergence time of the algorithm.

In order to prove the superiority of the entropy convergence criterion, it was used to cluster gene chip data, which were sampled from patients of Alzheimer’s Disease (AD). The clustering algorithm is compared with six typical clustering algorithms. The comparison shows that the ACO using entropy as a convergence criterion is of good quality.

At the same time, applying the presented algorithm, we analyzed the clustering characteristics of genes related to energy metabolism and found that as AD occurs, the entropy of the energy metabolism system decreases; that is, the system disorder decreases significantly.

Amnestic Mild Cognitive Impairment (aMCI) is a prodromal phase of Alzheimer's disease. Although recent studies have focused on cortical thickness as a key indicator, cortical complexity has not been exhaustively investigated.

To investigate the altered patterns of cortical features in aMCI patients and their correlation with memory function for early identification.

25 aMCI patients and 54 normal controls underwent neuropsychological assessments and 3D-T1 MRI scans. Cortical thickness and complexity measures were calculated using CAT12 software. Differences between groups were analyzed using two-sample t-tests, and multiple linear regression was employed to identify features associated with memory function. A support vector machine (SVM) model was constructed using multidimensional structural indicators to evaluate diagnostic performance.

aMCI patients exhibited extensive reductions in cortical thickness (pFDR-corrected <0.05), with complexity reduction predominantly in the left parahippocampal, entorhinal, rostral anterior cingulate, fusiform, and orbitofrontal (pFWE-corrected <0.05). Cortical indicators exhibited robust correlations with auditory verbal learning test (AVLT) scores. Specifically, the fractal dimension of the left medial orbitofrontal region was independently and positively associated with AVLT-short delayed score (r=0.348, p=0.002), while the gyrification index of the left rostral anterior cingulate region showed independent positive correlations with AVLT-long delayed and recognition scores (r=0.408, p=0.000; r=0.332, p=0.003). Finally, the SVM model integrating these cortical features achieved an AUC of 0.91, with 82.28% accuracy, 76% sensitivity, and 85.19% specificity.

Cortical morphological indicators provide important neuroimaging evidence for the early diagnosis of aMCI. Integrating multiple structural indicators significantly improves diagnostic accuracy.

Disease progression in Alzheimer’s Dementia (AD) is typically characterized by accelerated cognitive and functional decline, where heterogeneous trajectories can impact the observed treatment response.

We hypothesized that unobserved heterogeneity could obscure treatment benefits in AD. The effect of unobserved heterogeneity was empirically quantified within the Alzheimer’s Management By Albumin Replacement (AMBAR) phase 2b trial data. The ADAS-Cog 12 cognition endpoint was reanalyzed in a 2-class latent growth mixture model initially fit to the treatment arm. The model with the best fit was then applied across both treatment arms to a larger (n=1000) simulated dataset that was representative of AMBAR trial cognitive data.

Two classes of patients were observed: a stable cognitive trajectory class and a highly variable class. Removal of the latter (n=48, 22%) from the analysis and refitting efficacy models comparing the stable class to full placebo yielded significant treatment efficacy on cognition (p=0.007, Cohen’s D=-0.4). Comparison of the stable class of each arm within the simulated dataset revealed a significant difference in treatment efficacy favoring the simulated stable treatment arm.

This post hoc exploratory analysis suggests that prespecified strategies for addressing unobserved heterogeneity may yield improved effect detection in AD trials. The generalizability of the analytic strategy is limited by latent stratification in only the treatment arm, a requirement given the small placebo arm in AMBAR. This limitation was partially addressed by the simulation modeling.

NCT01561053.