Montserrat Alegret

Background: The group intervention aimed at caregivers of persons with dementia is regarded as an effective tool for support and education. However, these groups do not specifically cater to caregivers of individuals with spouses affected by young-onset dementia. Objective: To assess the effectiveness of a support and training group specifically targeted towards spouses of individuals with young onset dementia and its impact on reducing caregiver burden. Participants and Methods: Participants were recruited from a single memory clinic in Catalonia, Spain. The Zarit Burden Interview (ZBI) was utilized to evaluate caregiver burden both before and after participation in the support groups and a combined quantitative and qualitative analysis approach was employed. Results: A total of 77 caregivers were included and assessed, comprising 45.5% females with a mean age of 55 years and 54.5% males with a mean age of 63 years. While the overall caregiver burden, as measured by the ZBI, did not exhibit a significant reduction following participation in the group sessions, a notable decrease in ZBI scores was observed among caregivers with the highest burden at baseline. Three key stages were identified throughout the sessions: (1) discussions pertaining to the type of dementia and its associated changes; (2) the provision of care and attention to spouses along with the exchange of information among participants; and (3) identification of caregivers’ needs. Conclusions: The subgroup of caregivers of persons with young onset dementia who exhibited the highest burden at baseline derived the greatest benefit from the support groups. Various qualitative indicators.

Purpose: Amyloid PET imaging is capable of measuring brain amyloid load in vivo. The aim of this study is to assess the relationship of the baseline amyloid with its accumulation over time and with cognition in individuals with subjective cognitive decline (SCD), giving a focus on those below Aβ positivity thresholds. Methods: 118 of 197 individuals with SCD from the Fundació ACE Healthy Brain Initiative underwent three [¹⁸F]florbetaben scans and the remaining 79 underwent two scans in a 5-year span. Individuals were categorised based on baseline Centiloid values (CL) into amyloid positive (Aβ+; CL > 35.7), Grey Zone (GZ; 20 < CL ≤ 35.7), and amyloid negative (Aβ-; CL ≤ 20). Relationship between conversion to mild cognitive decline (MCI) and baseline amyloid levels was assessed. Then, to focus on sub-threshold individuals with amyloid accumulation, the Aβ- group was split into two groups (N1 (CL ≤ 13.5) and N2 (13.5 < CL ≤ 20)), Aβ accumulation was determined, and a parametric image analysis of the Aβ accumulators in the N1 group was performed. Results: At baseline, 20 individuals were Aβ+, 8 GZ, 160 N1, and 9 N2. Higher Aβ load, older and less educated individuals presented increased risk of MCI-conversion. Longitudinally, 19% of N1 individuals were accumulators despite very low Aβ burden at baseline. Meanwhile, 89% of the N2 group accumulated Aβ as well as all GZ individuals (which had the highest rate of amyloid accumulation, 5.1 CL/year). In the parametric image analysis of N1 accumulators, a region within the precuneus was linked to increased Aβ over time. Conclusion: Baseline amyloid levels differentiate individuals who accumulate amyloid over time and that are at risk for cognitive decline, including those at sub-threshold levels of Aβ. This can be valuable to identify pre-clinical AD in a SCD population.

High-throughput proteomic platforms are crucial to identify novel Alzheimer’s disease (AD) biomarkers and pathways. In this study, we evaluated the reproducibility and reliability of aptamer-based (SomaScan^® 7k) and antibody-based (Olink^® Explore 3k) proteomic platforms in cerebrospinal fluid (CSF) samples from the Ace Alzheimer Center Barcelona real-world cohort. Intra- and inter-platform reproducibility were evaluated through correlations between two independent SomaScan^® assays analyzing the same samples, and between SomaScan^® and Olink^® results. Association analyses were performed between proteomic measures, CSF biological traits, sample demographics, and AD endophenotypes. Our 12-category metric of reproducibility combining correlation analyses identified 2428 highly reproducible SomaScan CSF measures, with over 600 proteins well reproduced on another proteomic platform. The association analyses among AD clinical phenotypes revealed that the significant associations mainly involved reproducible proteins. The validation of reproducibility in these novel proteomics platforms, measured using this scarce biomaterial, is essential for accurate analysis and proper interpretation of innovative results. This classification metric could enhance confidence in multiplexed proteomic platforms and improve the design of future panels.

Background: Alzheimer’s disease (AD) dementia and mild cognitive impairment (MCI) are currently underdiagnosed in the community, and early detection of cognitive deficits is crucial for timely intervention. FACEmemory^®, the first completely self-administered online memory test with voice recognition, has been launched as an accessible tool to detect such deficits. This study aims to investigate the neuropsychological associations between FACEmemory subscores and cognitive composites derived from traditional paper-and-pencil neuropsychological tests and to develop an optimal algorithm using FACEmemory data and demographics to discriminate cognitively healthy (CH) individuals from those with MCI. Methods: A total of 669 participants (266 CH, 206 non-amnestic MCI [naMCI], and 197 amnestic MCI [aMCI]) were included. Multiple linear regression analyses were conducted using a cognitive composite as the dependent variable and FACEmemory subscores and demographic data (age, sex, and schooling) as independent variables. Machine learning models were compared to identify an optimal algorithm for distinguishing between CH and MCI (whole MCI, aMCI, and naMCI). Results: Multiple regression analyses showed associations between FACEmemory scores and the domains of memory (ρ = 0.67), executive functions (ρ = 0.63), visuospatial/visuoperceptual abilities (ρ = 0.55), language (ρ = 0.43), praxis (ρ = 0.52), and attention (ρ = 0.31). An optimal algorithm distinguished between CH and aMCI, achieving a FACEmemory cutoff score of 44.5, with sensitivity and specificity values of 0.81 and 0.72, respectively. Conclusions: FACEmemory is a promising online tool for identifying early cognitive impairment, particularly aMCI. It may contribute to addressing the underdiagnosis of MCI and dementia in the community and in promoting preventive strategies.

Background: Alzheimer's disease (AD) dementia and Mild Cognitive Impairment (MCI) are currently underdiagnosed in the community. Early detection of cognitive deficits is crucial for timely intervention. FACEmemory®, the first completely self-administered online memory test with voice recognition, has been launched as an accessible tool to detect such deficits. This study aims to correlate FACEmemory subscores with traditional neuropsychological tests and to develop an algorithm to distinguish between cognitively healthy (CH) individuals and those with MCI. Methods: A total of 669 participants (266 CH, 206 with non-amnestic MCI -naMCI-, and 197 with amnestic MCI -aMCI-) were included. Pearson’s correlations were conducted to examine the association between FACEmemory scores, demographics, and cognitive performance on classical neuropsychological tests (the Neuropsychological battery of Ace -NBACE-). Machine learning models were compared to find the best algorithm for distinguishing between CH and MCI (whole MCI, aMCI, and naMCI). Results: FACEmemory scores showed significant correlations (p< 0.001) with memory (r= 0.53), executive functions (r= 0.43), visuospatial/visuoperceptual (r= 0.32), language (r= 0.30), praxis (r= 0.30) and attention (r= 0.26) domains of the NBACE. The best algorithm distinguished between CH and MCI (especially aMCI), with a FACEmemory cutoff score of 44.5, yielding sensitivity and specificity values of 0.81 and 0.72, respectively. Conclusions: FACEmemory is a promising online tool for detecting MCI, particularly aMCI. It could play a key role in reducing underdiagnoses of MCI and AD and supporting early intervention efforts in the community.

Background: The FACEmemory® online platform comprises a complex memory test and sociodemographic, medical, and family questions. This is the first study of a completely self-administered memory test with voice recognition, pre-tested in a memory clinic, sensitive to Alzheimer’s disease, using information and communication technologies, and offered freely worldwide. Objective: To investigate the demographic and clinical variables associated with the total FACEmemory score, and to identify distinct patterns of memory performance on FACEmemory. Methods: Data from the first 3,000 subjects who completed the FACEmemory test were analyzed. Descriptive analyses were applied to demographic, FACEmemory, and medical and family variables; t-test and chi-square analyses were used to compare participants with preserved versus impaired performance on FACEmemory (cut-off = 32); multiple linear regression was used to identify variables that modulate FACEmemory performance; and machine learning techniques were applied to identify different memory patterns. Results: Participants had a mean age of 50.57 years and 13.65 years of schooling; 64.07% were women, and 82.10% reported memory complaints with worries. The group with impaired FACEmemory performance (20.40%) was older, had less schooling, and had a higher prevalence of hypertension, diabetes, dyslipidemia, and family history of neurodegenerative disease than the group with preserved performance. Age, schooling, sex, country, and completion of the medical and family history questionnaire were associated with the FACEmemory score. Finally, machine learning techniques identified four patterns of FACEmemory performance: normal, dysexecutive, storage, and completely impaired. Conclusions: FACEmemory is a promising tool for assessing memory in people with subjective memory complaints and for raising awareness about cognitive decline in the community.

Background: Alzheimer’s disease (AD) is a complex disease conditioned by environmental and genetic factors. Over the last few years, Genome‐Wide Association Studies (GWAS) have gained increasing interest representing the basis of a personalized medicine that aims to facilitate interventions at the preclinical stages. In most studies, the integration of the high‐dimensional GWAS data is accomplished via polygenic risk scores (PRSs) representing a measure of the genetic risk associated with a given phenotype. For the first time, this study aimed to explore the predictive value of a PRS developed to predict conversion to AD in patients with mild cognitive impairment (MCI) (AD‐PRS) (Bellenguez et al., 2022) on cognitive decline once the AD dementia stage has been establishedMethod: The modulatory effect of genetics on cognitive decline was studied using a sample of 3921 subjects with AD dementia (clinical dementia rating (CDR) ≥1) evaluated at Ace Alzheimer Center Barcelona. Cognitive decline, operationalized as changes in Mini Mental State Examination (MMSE) scores over time, was modeled using multivariate regression models considering clinical and sociodemographic data, and the AD‐PRS. Interaction effects between the AD‐PRS and the other covariates were explored, and subsequently, mediation analyses were applied. Afterward, Machine Learning techniques were used to select the single nucleotide polymorphisms (SNPs) forming the AD‐PRS with the highest predictive value on cognitive decline.Result: Multivariate regression models showed a significant interaction of the AD‐PRS with years of education (coef = .0006; p‐value = .005). Increased scores of AD‐PRS combined with higher formal education were found to be associated with greater cognitive decline. Furthermore, Machine Learning models identified 18 SNPs related to cognitive decline in AD, demonstrating that genetic information improves the predictive performance of the models.Conclusion: This is the first study that demonstrates the predictive capacity of the AD‐PRS on cognitive decline in AD dementia. Years of education were shown to modulate, but not mediate, the effect of the AD‐PRS on cognitive progression. Additionally, a subset of SNPs comprising the AD‐PRS with the highest predictive value of cognitive decline in AD was identified. Altogether, these results provide new insights into the genetics of the disease.

Background: The FACEmemory® online platform comprises a novel, self-administered memory test with embedded voice recognition technology and a questionnaire with relevant sociodemographic and medical/family history data. This is the first study about a completely self-administered memory test with voice recognition, pre-tested in a memory clinic, offered freely worldwide on a website platform. The aims of this study are to investigate the demographic and clinical variables associated with FACEmemory total score, and to identify differentiable patterns of memory performance among the first 3,000 individuals who completed the FACEmemory. Methods: A marketing campaign was carried out to make FACEmemory accessible worldwide to individuals whose native language was Spanish or Catalan. Data from the first 3,000 subjects over 18 years old who completed the FACEmemory were analysed. Descriptive analyses were applied to demographic, FACEmemory scores, and medical/family history variables reported in a questionnaire; t-test and chi-square analyses were used to compare participants with preserved (>31 points) versus impaired performance (Results: The study sample had a mean age of 50.57 years and 13.65 years of schooling. 64.1% were women and most (82.1%) participants reported memory complaints that worried them. The group with impaired FACEmemory performance (20.4%) was older, had fewer years of formal education and a higher prevalence of hypertension, diabetes mellitus, dyslipidemia, and family history of a neurodegenerative disease compared with the group with preserved FACEmemory performance. Multiple regression analysis showed that age, schooling, sex, country and completion of the questionnaire were statistically associated with FACEmemory total score. Finally, Machine Learning techniques identified 4 patterns of FACEmemory performance: normal, dysexecutive, storage and completely impaired. Conclusions: FACEmemory is a promising tool for the pre-screening of people with subjective memory complaints in the community in order to identify those with objective memory deficits and raise awareness about cognitive decline. The FACEmemory website platform is an opportunity to facilitate a free, online and self-administered episodic memory assessment to Spanish or Catalan speaking individuals worldwide, and potentially extensible to other languages.