Alzheimer's Research & Therapy最新文献

Background: Alzheimer's disease (AD) exhibits sex differences in prevalence, symptoms and risk factors. Understanding the effect of sex in AD cerebrospinal fluid (CSF) biomarkers and their association with amyloid-beta (Aβ) pathology in preclinical stages have important implications for their use in prevention trials. The objective of this study was to examine sex differences in core AD CSF biomarkers used in early diagnosis and prevention trials, as well as in CSF biomarkers reflecting downstream pathophysiological mechanisms, and in their associations with Aβ pathology as measured by Positron Emission Tomography (PET).

Methods: Cognitively Unimpaired (CU) participants from the ALFA + (N = 400) and the WRAP/WADRC (N = 548) cohorts were included in the study. CSF biomarkers for core AD pathology (Aβ42, Aβ42/40, p-tau181/Aβ42, p-tau181, p-tau217 and p-tau231), neurodegeneration (NfL, t-tau), synaptic dysfunction (neurogranin, GAP-43, SNAP25, synaptotagmin-1, α-synuclein), glial reactivity (GFAP, S100B, sTREM2, YKL-40), neuroinflammation (IL-6, MCP-1), and vascular dysregulation (sICAM-1, sVCAM-1) were measured. Participants underwent Aβ PET at baseline and follow-up visit. We used Analyses of Covariance (ANCOVA) to evaluate sex differences in CSF biomarker levels and performed sex-stratified Receiver-Operating Characteristic (ROC) analyses to test their performance to identify Aβ PET-positive individuals. Additionally, we run linear regression models to study the modifying effect of sex on the association of baseline CSF biomarkers with cross-sectional and longitudinal Aβ PET uptake.

Results: Men had higher CSF NfL, glial reactivity and vascular dysregulation biomarkers (Cohen's d ranging from -0.22 to -0.44, P < 0.05), and lower synaptic biomarkers (Cohen's d ranging from 0.18 to 0.30, P < 0.05) compared to women at baseline. There were no sex differences in the core AD CSF biomarkers' performance to identify Aβ PET-positive individuals (DeLong's test P values > 0.05), with CSF p-tau181/Aβ42 and p-tau217 showing the highest performance in both sexes (Areas Under the Curve (AUCs) ranging from 87.1 to 96.3). However, sex modified the associations of baseline CSF biomarkers with Aβ PET uptake, which were more pronounced in women than in men.

Conclusions: Our results suggest that tailoring core AD CSF biomarkers by sex is not necessary for detecting Aβ PET positivity in CU individuals. However, sex differences in their association with Aβ deposition could influence their prognostic or monitoring applications.

Background: KLOTHO-VS (KL-VS) heterozygosity, a variant of the KLOTHO gene, and its encoded protein, α-Klotho, are associated with brain health and show neuroprotective potential against Alzheimer's disease (AD). We aimed to assess whether KL-VS heterozygosity, cerebrospinal fluid (CSF) and serum soluble α-Klotho (sαKl) levels, would be associated with a lower likelihood of AD and better performance on memory and other cognitive domains in individuals with AD dementia, amnestic mild cognitive impairment (aMCI) due to AD, and cognitively unimpaired controls.

Methods: In this cross-sectional study, we analyzed two partially overlapping subsamples derived from 296 participants from the Czech Brain Aging Study. The first subsample included 196 participants with KL-VS haplotype data: 71 with AD dementia, 84 with aMCI due to AD, and 41 cognitively unimpaired controls. The second subsample included 147 participants with CSF and/or serum sαKl measurements, including 58 with AD dementia, 59 with aMCI due to AD, and 30 cognitively unimpaired controls. Diagnoses of aMCI and AD dementia were confirmed by positive CSF biomarkers and/or amyloid PET imaging. Logistic regression assessed how KL-VS heterozygosity influenced the odds of aMCI or dementia due to AD. Linear regression investigated associations between cognitive performance and either KL-VS heterozygosity or CSF/serum sαKl levels. Analysis of variance and analysis of covariance with post-hoc tests were used to compare sαKl levels across study groups.

Results: KL-VS heterozygosity carriers showed a consistent trend towards lower odds of being classified with aMCI and dementia due to AD, with similar patterns in both Apolipoprotein E ε4 (APOE ε4) allele carriers and non-carriers, although none of the associations reached statistical significance despite moderate (rather than small) effect sizes. Among individuals with aMCI due to AD, KL-VS heterozygotes displayed better memory performance (β = 0.61, p = .008), particularly those who also carried the APOE ε4 allele (β = 0.64, p = .042). Results with other cognitive domains were non-significant. No significant differences in sαKl levels were found between study groups, and soluble α-Klotho levels did not associate with memory performance.

Conclusions: KL-VS heterozygosity may be linked to lower likelihood of classification as aMCI or dementia due to AD, and its association with memory might be specific to the aMCI stage of AD and modulated by APOE ε4 status.

Background: Early detection of amyloid-β (Aβ) pathology is critical for timely intervention in Alzheimer's disease (AD). While Aβ positron emission tomography (PET) and cerebrospinal fluid (CSF) biomarkers are accurate, their high cost and limited accessibility hinder routine use. We developed a computed tomography (CT)-based, two-stage workflow combining CT-derived atrophy patterns with plasma phosphorylated tau 217 (p-Tau217) to predict Aβ PET positivity.

Methods: In this cohort of 616 participants (521 with mild cognitive impairment (MCI], 95 with early dementia of Alzheimer's type (DAT]; age 60-93 years), CT, p-Tau217 assays, and Aβ PET were performed. A random forest model incorporating CT-derived regional W-scores and apolipoprotein E ε4 (APOE ε4) status stratified individuals into low-, intermediate-, and high-risk groups. p-Tau217 testing was reserved for the intermediate-risk group.

Results: At a 95% sensitivity/specificity threshold, CT-based stratification yielded a low-risk negative predictive value (NPV) of 95.8% (93.0-98.6%) and a high-risk positive predictive value (PPV) of 98.4% (96.8-100.0%), with 28.2% classified as intermediate-risk. Targeted plasma testing of intermediate-risk group improved the overall PPV to 92.8% (88.5-97.1%) and the overall NPV to 88.9% (78.6-99.2%), achieving an overall accuracy of 95.8% (94.2-97.4%). The CT-based workflow's accuracy was non-inferior to our MRI-based method (area under the curve 0.96 vs. 0.95; p = 0.14).

Conclusions: This CT-based, two-stage approach is a cost-effective, scalable alternative to MRI-based strategies, leveraging routine CT and selective p-Tau217 testing to enhance early AD detection and optimize resource utilization in clinical practice.

Background: Previous literature indicate retinal hyperspectral imaging as a non-invasive method with the potential for identifying amyloid-beta (Aβ) protein deposits. Current diagnostic methods, such as cerebrospinal fluid analysis or positron emission tomography, are costly, invasive, and non-scalable. Hyperspectral imaging offers a potentially accessible alternative for early detection of Alzheimer's disease. The aim of this study is to investigate the potential of retinal hyperspectral imaging in identifying Aβ-positive patients within a clinical cohort from a memory clinic.

Methods: A prospective cross-sectional cohort study was conducted between January 2023 and May 2024 at a single memory clinic in Sweden. The study recruited 57 patients (35 Aβ-positive and 22 Aβ-negative) who underwent lumbar puncture as part of their diagnostic workup for cognitive complaints. Retinal hyperspectral images were captured from all participants at the time of their lumbar puncture and again 2-4 weeks later. Data was collected from five anatomical regions of the retina (Superior 1, Superior 2, Inferior 1, Inferior 2, and the center of the Fovea).The main outcome was the Aβ status (Aβ-positive or Aβ-negative). Catboost machine learning models were trained on hyperspectral imaging data to predict Aβ status. A nested cross-validation approach was used to train and evaluate classification models. Performance metrics included area under the curve (AUC), accuracy, sensitivity, and specificity.

Results: The best-performing model used the combination of regions Superior 1, Superior 2, and center of the fovea, achieving a mean AUC of 0.77 (0.05), mean accuracy of 0.66 (0.03), and mean sensitivity of 0.73 (0.13) and mean specificity of 0.55 (0.12). Performance was consistent across outer folds. Models using all five regions or less-informative combinations yielded lower and more variable results.

Conclusions: Retinal hyperspectral imaging combined with the Catboost algorithm demonstrated significant potential as a non-invasive biomarker for detecting Alzheimer's disease in a consecutive clinical cohort. Further studies should validate these findings in larger, more diverse populations and explore the integration of hyperspectral imaging with other diagnostic modalities. Limited sample size and imaging constraints highlight the need for validation in diverse clinical settings.

Trial registration: ClinicalTrials.gov, ID: NCT05604183 (registration date: 2022-10-27).

Background: Alzheimer's disease (AD) involves cognitive decline, amyloid-beta (Aβ) accumulation, tau hyperphosphorylation, and neuroinflammation. CREB1, a key transcription factor for memory, is downregulated in AD, contributing to disease progression. Phosphodiesterases 4 and 10 (PDE4 and PDE10) are key enzymes that degrade cAMP, a second messenger involved in CREB signaling, synaptic plasticity, and neuroprotection. Dysregulation of PDE activity has been implicated in AD and other neurodegenerative disorders.

Methods: We used human iPSC-derived cortical neurons and microglia, along with the APP/PS1 mouse model, to investigate the role of CREB1 and assess the therapeutic potential of dual PDE4/10A inhibition in AD.

Results: CREB1 deficiency in neurons increased Aβ and p-tau231 accumulation. Dual inhibition of PDE4 and PDE10A activated the cAMP-PKA-CREB pathway, restoring CREB1 activity, reducing Aβ and p-tau231, and mitigating neuroinflammation. This intervention improved synaptic plasticity and cognitive performance in vivo.

Conclusions: Our findings demonstrate that dual PDE4/10A inhibition synergistically enhances the cAMP-PKA-CREB signaling, promoting neuroprotection and synaptic remodeling. This approach offers a promising therapeutic strategy for modifying AD pathology and restoring cognitive function.

Background: Although individuals with Mild Behavioral Impairment (MBI) show an increased rate of developing dementia, it remains uncertain whether MBI should be considered a risk factor or an actual early sign of neurocognitive disease.

Objectives: This systematic review and meta-analysis aimed to explore the association between MBI and neurobiological correlates of dementia.

Methods: The study protocol followed PRISMA guidelines and was registered in PROSPERO (CRD42024589059). Five databases and gray literature were systematically searched from inception to January 31, 2025 to identify studies that explored the relationship between MBI and brain imaging findings or neurodegenerative and neuroinflammatory fluid biomarker levels. When studies employed comparable methodologies, a random-effects meta-analysis was performed to summarize the results; conversely, a qualitative synthesis was conducted. The Newcastle-Ottawa Quality Assessment Scale was used to assess the study quality.

Results: Of the 834 records, 27 studies were included. Most studies were cross-sectional and examined the presence of structural or functional abnormalities through brain imaging in individuals with MBI. Six studies, 4 of which were longitudinal, focused on MBI and cerebrospinal fluid or plasma biomarkers of neurodegeneration and neuroinflammation. Due to the high methodological heterogeneity across studies, five random-effects meta-analyses were conducted, each including two studies. These analyses reported a positive, cross-sectional correlation between MBI burden and brain deposition of amyloid-beta (Aβ) or tau. Conversely, MBI was not significantly associated with either plasma phosphorylated-tau181 levels or Magnetic Resonance Imaging (MRI) brain atrophy markers. Nevertheless, based on the qualitative synthesis of the 27 included studies, MBI was frequently linked to Alzheimer's disease (AD) abnormalities - both in biomarkers and brain imaging studies.

Conclusions: Across studies, MBI appears to be linked to specific neurobiological markers of AD, including Aβ and tau brain deposition, as well as alterations in the mesolimbic pathway and neurodegenerative and neuroinflammatory fluid biomarker levels. Although emerging evidence supports MBI as a potential early clinical sign of AD, heterogeneity across studies precludes definitive conclusions regarding its precise role in the onset and progression of the disease.

There is a critical unmet need for scalable, accessible and objective diagnostic tests for stratification in dementia. Biofluid Raman spectroscopy (RS) due to its simplicity, holistic and label-free nature, is a powerful approach that has the potential to offer differential diagnosis across dementia types including Alzheimer's disease (AD). RS is a laser-based optical method that can rapidly provide chemically rich information ('spectral biomarkers') from biofluids but its utility for AD diagnosis has not been established in a 'real-world' context, specifically from a clinically heterogenous cohort of patients. We carried out RS measurements on cerebrospinal fluid (CSF) samples of patients from a mixed clinical cohort (N = 143). All patients reported cognitive complaints and were clinically diagnosed over 2 years with conditions including AD and other neurodegenerative diseases, as well as developmental and long-term chronic conditions. Machine-learning algorithms were trained, optimised and evaluated on Raman spectra to classify AD from non-AD. AD was classified with 93% accuracy for patients in the testing set. Time from sample to classification was < 1 h. Spectral biomarkers explaining AD classification were identified and primarily assigned to protein-derived aromatic amino acids, representing a difference in proteome signature between AD and non-AD groups. Signals from a subset of spectral biomarkers directly correlated with pathological CSF biomarker concentrations including amyloid-β 42, phosphorylated-tau 181, and total tau. This pre-clinical study is a first step towards realising the real-world application of RS for dementia diagnosis. Compared to current and emerging methods, RS does not require sophisticated instrumentation or specialised labs. It is reagentless and simple, offering unprecedented rapidity, scalability, accessibility for dementia diagnosis.

Background: Cumulative exposure to multiple drugs with anticholinergic properties, known as anticholinergic burden, has been linked to dementia risk. We aimed to thoroughly assess this relationship by examining not only anticholinergic potency, cumulative dose, and drug class, but also dementia subtype and severity.

Methods: This Swedish nationwide case-control study included 199,526 individuals aged ≥ 40 years with incident dementia between July 2008 and December 2017, with 1:1 matched controls based on age, sex, and region of residence. Each case-control group was allocated an identical drug exposure period. Using the Anticholinergic Cognitive Burden (ACB) scale, we separately calculated the total defined daily doses (DDDs) of drugs with weak (ACB score of 1) and strong anticholinergic properties (ACB score of 2-3) prescribed ≥ 1 year before dementia onset. Conditional logistic regression was used to estimate the adjusted odds ratios (AORs) for the association between anticholinergic burden and all-cause dementia, with additional stratified analyses by drug class, dementia subtypes, and severity.

Results: A nonlinear dose-response relationship with all-cause dementia was found for the cumulative use of strong anticholinergics (e.g., 1-89 DDDs: AOR, 1.10 [95% CI, 1.08-1.12]; ≥1095 DDDs: AOR, 1.66 [95% CI, 1.55-1.79]), notably within the class of urinary antispasmodics, antihistamines, and psychotropic drugs. In contrast, a subtle association lacking a dose-response pattern was observed for weak anticholinergics (e.g., 1-89 DDDs: AOR, 1.11 [95% CI, 1.08-1.13]; ≥1095 DDDs: AOR, 1.01 [95% CI, 0.98-1.03]). The associations with strong anticholinergics were more pronounced in men, younger people, those diagnosed with vascular dementia or Lewy body dementia (versus Alzheimer's disease), and those with milder-stage dementia.

Conclusions: Use of strong anticholinergics, but not weak ones, was associated with a higher risk of dementia, with the strength of association depending on cumulative dose, drug class, and dementia subtype. Caution should be exercised when prescribing drugs with strong anticholinergic properties to middle-aged and older individuals.

Background: Current methods for the early detection of Alzheimer's disease (AD) are constrained by high costs, invasiveness, and limited accessibility, underscoring the urgent need for alternative approaches that are accessible, affordable, and patient-friendly. Previous research has identified speech analysis as a promising tool for the early diagnosis of cognitive impairment (CI). However, the correlation between speech tests and underlying pathology remains undetermined or even obscure. Its clinical utility still lacks pathological validation. We need to further explore the relationship through large-sample analysis and further construct models that can diagnose CIf.

Methods: 1223 participants including probable AD or AD (n = 238), amnestic mild cognitive impairment (aMCI) (n = 461) and cognitively unimpaired (CU) (n = 524) were recruited. The participants underwent neuropsychological tests, speech recordings of the "cookie-theft" task, serum biomarker quantification, APOE genotyping, and part of them underwent Aβ PET imaging. Partial Correlation Analysis and LOWESS were used to explore the correlation between speech digital biomarkers and other core AD biomarkers. Finally, machine learning such as XGBoost and Logistic regression were used for constructing the most cost-effective models for CI and Aβ status, leveraging SHAP values for screening.

Results: Significant differences in AD biomarkers were observed among different groups. Notably, the speech digital biomarker percentage of silence duration (PSD) was correlated with cognitive level, serum glial fibrillary acidic protein (GFAP), neurofilament light chain (NFL), phosphorylated tau protein 217 (p-Tau217) and amyloid deposition in specific brain regions. Additionally, we discovered that as the different stages of Aβ deposition progress, PSD, p-Tau217, and GFAP exhibit a two-stage change pattern. Based on the findings, a machine learning CI diagnostic model (AUC = 0.928, 95% CI 0.897 to 0.960) incorporating PSD, APOE, GFAP, and demographic information was developed. Furthermore, an Aβ status classification model (AUC = 0.845, 95% CI 0.783 to 0.907) with PSD, APOE, p-Tau217, and demographic data was also constructed.

Conclusion: Combining speech digital markers with serum and other biomarkers helps identify CI, representing a promising advance in AD detection. This study serves as a preliminary yet encouraging step toward applying speech digital biomarkers in AD diagnostics.

Background: In this two-part investigation, we examined whether Alzheimer's disease (AD) phenotypes are distinct clinical entities or represent positions within a graded multidimensional space.

Methods: First, using a large retrospective dataset of past research participants (n = 413) from memory clinics, we examined the comparative distributions of cognitive performance in people diagnosed with typical amnestic AD (tAD), logopenic variant of primary progressive aphasia (lvPPA), and posterior cortical atrophy (PCA), across a broad range of disease severities. Secondly, a prospective deep phenotyping study of lvPPA (n = 18) compared to typical AD (n = 9) addressed the following questions: (1) Does the multidimensional cognitive pattern of impairment only emerge in advanced lvPPA, and how does it compare to tAD? (2) Do memory deficits in lvPPA appear in a simple clinic-level cognitive assessment or require in-depth neuropsychological investigation? (3) To what extent is performance on verbal episodic memory attributable to language impairment? (4) Do the patterns of decline in lvPPA and tAD stay categorical or multidimensional over time? We explored the associations between scores derived from a principal component analysis of cognitive measures, and grey matter volumes in key memory- and language-related brain regions, at baseline and longitudinally.

Results: The clinic-level assessment revealed similar results in both the prospective and retrospective data: (i) patients showed graded distinctions (e.g., predominant visual versus language impairment in people with PCA versus lvPPA) and overlap (e.g., shared weakness in domains such as memory); and (ii) people with lvPPA and tAD were equally impaired on both verbal and non-verbal memory tests. Longitudinal assessment showed phenotypic dispersion: (i) people with tAD showed varied patterns of phenotypic differentiation; and (ii) people with lvPPA and lvPPA + exhibited a multidimensional pattern of decline with decreasing principal component scores and worsening multi-domain cognitive performance. The results of Bayesian linear regressions showed evidence for the association of grey matter volumes in language and memory networks with principal component analysis derived scores.

Conclusions: The graded distinctions amongst typical amnestic and atypical (language and visual) phenotypes of AD support the proposal for a transdiagnostic, multidimensional phenotype geometry that spans all AD subtypes.