Alzheimer's Research & Therapy最新文献

Background: Specific genetic variants in the ATP-binding cassette transporter A7 locus (ABCA7) are associated with an increased risk of Alzheimer's disease (AD). ABCA7 transports lipids from/across cell membranes, regulates Aβ peptide processing and clearance, and modulates microglial and T-cell functions to maintain immune homeostasis in the brain. During AD pathogenesis, neuroinflammation is one of the key mechanisms involved. Therefore, we wanted to investigate the specific role of ABCA7 in microglial activation via the NLRP3 inflammasome.

Methods: We developed the first humanized, Cre-inducible ABCA7^flx knock-in mouse model, crossbred it with the APPPS1-21 β-amyloidosis model, and generated constitutive ABCA7^ko and microglia Cx3cr1-specific conditional ABCA7^ko AD mice. The role of ABCA7 was analyzed using histological, biochemical, molecular and mass spectrometry methods.

Results: Constitutive knockout of the Abca7 gene in APPPS1 mice increased the levels of Aβ42 and the number of IBA1+ (microglia) and GFAP+ (astrocytes) cells. Changes in the levels of astrocytes and microglia are associated with the activation of the NLRP3 inflammasome and increased levels of proinflammatory cytokines, such as IL1β and TNFα. Interestingly, microglia-specific ABCA7^ko restored Aβ₄₂ peptide levels and IBA1⁺ and GFAP⁺ and NLRP3-related gene expression to the original APPPS1 mouse levels. In primary glial cell cultures of APPPS1-hA7^ko microglia and APPPS1 astrocytes from newborn pups, we observed that conditioned media from LPS-stimulated microglia was able to induce NLRP3 inflammasome expression and proinflammatory cytokine release in astrocytes.

Conclusions: Our data suggest that ABCA7 transporters regulate the communication between microglia and astrocytes through the NLRP3 inflammasome and the release of proinflammatory cytokines. This regulation implicates ABCA7 as a key driver ultimately involved in the persistence of the inflammatory response observed in AD.

Background: Degeneration of the basal forebrain cholinergic system is a hallmark feature shared by Alzheimer's disease (AD) and Lewy body disease (LBD) whereas hippocampus atrophy is more specifically related to AD. We aimed to investigate the relationship between basal forebrain and hippocampus atrophy, cognitive decline, and neuropathology in a large autopsy sample.

Methods: Data were obtained from the National Alzheimer's Coordinating Center (NACC). Basal forebrain and hippocampus volumes were extracted using an established automated MRI volumetry approach. Associations of regional volumes with pathological markers (Braak stage, CERAD score, and McKeith criteria for LB pathology) and cognitive performance were assessed using Bayesian statistical methods.

Results: We included people with autopsy-confirmed pure AD (N = 248), pure LBD (N = 22), and mixed AD/LBD (N = 185). Posterior basal forebrain atrophy was most severe in mixed AD/LB pathology compared to pure AD (Bayes factor against the null hypothesis BF₁₀ = 16.2) or pure LBD (BF₁₀ = 4.5). In contrast, hippocampal atrophy was primarily associated with AD pathology, independent of LB pathology (pure AD vs. pure LBD: BF₁₀ = 166, pure AD vs. mixed AD/LBD: BF₁₀ = 0.11, pure LBD vs. mixed AD/LBD: BF₁₀ = 350). Cognitive performance was more impaired in AD pathology groups, with Braak stage being the strongest predictor. Hippocampal volume partially mediated this relationship between tau pathology and cognitive impairment, while basal forebrain volume had a limited role in mediating the relationship between pathological burden and cognitive outcomes.

Conclusion: In a heterogeneous autopsy sample, AD and LB pathology both contribute to cholinergic basal forebrain degeneration whereas hippocampus atrophy is more specifically related to AD pathology. Cognitive deficits are primarily associated with tau pathology which is partly mediated by hippocampus, but not basal forebrain atrophy.

Background: Alzheimer's disease (AD) stands as the leading cause of dementia worldwide, and projections estimate over 150 million patients by 2050. AD prevalence is notably higher in women, nearly twice that of men, with discernible sex differences in certain risk factors. To enhance our understanding of how sex influences the characteristics of AD patients and its potential impact on the disease trajectory, we conducted a comprehensive analysis of demographic, clinical, cognitive, and genetic data from a sizable and well-characterized cohort of AD dementia patients at a memory clinic in Barcelona, Spain.

Methods: The study cohort comprised individuals with probable and possible AD dementia with a Clinical Dementia Rating (CDR) score between 1 and 3 diagnosed at the Memory Unit from Ace Alzheimer Center Barcelona, Spain, between 2008 and 2018. We obtained cognitive baseline data and follow up scores for the Mini-Mental State Examination (MMSE), the CDR scale, and the neuropsychological battery used in our center (NBACE). We employed various statistical techniques to assess the impact of sex on cognitive evolution in these dementia patients, accounting for other sex-related risk factors identified through Machine Learning methods.

Results: The study cohort comprised a total of 6108 individuals diagnosed with AD dementia during the study period (28.4% males and 71.6% females). MMSE scores exhibited an average decline of approximately two units per year, unaffected by sex. Similarly, the decline in most neuropsychological functions assessed by NBACE did not exhibit significant differences between males and females. However, we observed that women diagnosed with mild AD dementia progressed more rapidly based on their CDR score (HR = 2.57, 95%CI:2.33-2.84) than men (HR = 2.03, 95%CI: 1.71-2.41) (p-interaction = 0.01).

Conclusions: Our findings do not strongly support the notion that sex significantly modifies the clinical progression of AD dementia based on cognitive data. Further research is essential to validate whether women with mild AD dementia indeed progress more rapidly than men at a similar stage and to delve into the potential underlying reasons for this finding.

Background: For clinical implementation of Alzheimer's disease (AD) blood-based biomarkers (BBMs), knowledge of short-term variability, is crucial to ensure safe and correct biomarker interpretation, i.e., to capture changes or treatment effects that lie beyond that of expected short-term variability and considered clinically relevant. In this study we investigated short-term intra- and inter-individual variability of AD biomarkers in the intended use population, memory clinic patients.

Methods: In a consecutive sample of memory clinic patients (AD n = 27, non-AD n = 20), blood samples were collected on three separate days within a period of 36 days and analysed for plasma Aβ40, Aβ42, p-tau181, p-tau217, p-tau231, T-tau, neurofilament light (NfL), and glial fibrillary acidic protein (GFAP). We measured intra- and inter-individual variability and explored if the variability could be affected by confounding factors. Secondly, we established the minimum change required to detect a difference between two given blood samples that exceeds intra-individual variability and analytical variation (reference change value, RCV). Finally, we tested if classification accuracy varied across the three visits.

Results: Intra-individual variability ranged from ~ 3% (Aβ42/40) to ~ 12% (T-tau). Inter-individual variability ranged from ~ 7% (Aβ40) to ~ 39% (NfL). Adjusting the models for time, eGFR, Hba1c, and BMI did not affect the variation. RCV was lowest for Aβ42/Aβ40 (- ~ 15%/ + ~ 17%) and highest in p-tau181 (- ~ 30/ + ~ 42%). No variation in classification accuracies was found across visits.

Conclusion: We found low intra-individual variability, robust to various factors, appropriate to capture individual changes in AD BBMs, while moderate inter-individual variability may give rise to caution in diagnostic contexts. High RCVs may pose challenges for AD BBMs with low fold changes and consequently, short-term variability is important to take into consideration when, e.g., estimating intervention effect and longitudinal changes of AD BBM levels.

Trial registration: Clinicaltrials.gov (NCT05175664), date of registration 2021-12-01.

Background: Intracerebral amyloid β (Aβ) accumulation is considered the initial observable event in the pathological process of Alzheimer's disease (AD). Efficient screening for amyloid pathology is critical for identifying patients for early treatment. This study developed machine learning models to classify positron emission tomography (PET) Aβ-positivity in participants with preclinical and prodromal AD using data accessible to primary care physicians.

Methods: This retrospective observational study assessed the classification performance of combinations of demographic characteristics, routine blood test results, and cognitive test scores to classify PET Aβ-positivity using machine learning. Participants with mild cognitive impairment (MCI) or normal cognitive function who visited Oita University Hospital or had participated in the USUKI study and met the study eligibility criteria were included. The primary endpoint was assessment of the classification performance of the presence or absence of intracerebral Aβ accumulation using five machine learning models (i.e., five combinations of variables), each constructed with three classification algorithms, resulting in a total of 15 patterns. L2-regularized logistic regression, and kernel Support Vector Machine (SVM) and Elastic Net algorithms were used to construct the classification models using 34 pre-selected variables (12 demographic characteristics, 11 blood test results, 11 cognitive test results).

Results: Data from 262 records (260 unique participants) were analyzed. The mean (standard deviation [SD]) participant age was 73.8 (7.8) years. Using L2-regularized logistic regression, the mean receiver operating characteristic (ROC) area under the curve (AUC) (SD) in Model 0 (basic demographic characteristics) was 0.67 (0.01). Classification performance was similar in Model 1 (basic demographic characteristics and Mini Mental State Examination [MMSE] subscores) and Model 2 (demographic characteristics and blood test results) with a cross-validated mean ROC AUC (SD) of 0.70 (0.01) for both. Model 3 (demographic characteristics, blood test results, MMSE subscores) and Model 4 (Model 3 and ApoE4 phenotype) showed improved performance with a mean ROC AUC (SD) of 0.73 (0.01) and 0.76 (0.01), respectively. In models using blood test results, thyroid-stimulating hormone and mean corpuscular volume tended to be the largest contributors to classification. Classification performances were similar using the SVM and Elastic Net algorithms.

Conclusions: The machine learning models used in this study were useful for classifying PET Aβ-positivity using data from routine physician visits.

Trial registration: UMIN Clinical Trials Registry (UMIN000051776, registered on 31/08/2023).

Background: Alzheimer's disease (AD) remains incurable, yet its long prodromal phase offers a crucial window for early intervention. Pretangle tau, a precursor to neurofibrillary tangles, plays a key role in early AD pathogenesis. Intervening in pretangle tau pathology could significantly delay the progression of AD. The gut-brain axis, increasingly recognized as a contributor to AD, represents a promising therapeutic target due to its role in regulating neuroinflammation and neurodegeneration. While probiotics have shown cognitive benefits in amyloid-centered AD models, their effect on pretangle tau pathology remains elusive.

Methods: This study evaluates the effects of probiotics in a rat model of preclinical AD, specifically targeting hyperphosphorylated pretangle tau in the locus coeruleus. TH-CRE rats (N = 47; 24 females and 23 males) received either AAV carrying pseudophosphorylated human tau (htauE14) or a control virus at 3 months of age. Probiotic or control diets were administered at 9-12 months, with blood and fecal samples collected for ELISA and 16S rRNA gene sequencing. Behavioral assessments were conducted at 13-14 months, followed by analysis of brain inflammation, blood-brain barrier integrity, and GSK-3β activation.

Results: Rats expressing pseudophosphorylated tau displayed impairment in spatial Y-maze (F_1,39 = 4.228, p = 0.046), spontaneous object location (F_1,39 = 6.240, p = 0.017), and olfactory discrimination (F_1,39 = 7.521, p = 0.009) tests. Phosphorylation of tau at S262 (t₃ = -4.834) and S356 (t₃ = -3.258) in the locus coeruleus was parallelled by GSK-3β activation in the hippocampus (F_1,24 = 10.530, p = 0.003). Probiotic supplementation increased gut microbiome diversity (F_1,31 = 8.065, p = 0.007) and improved bacterial composition (F_1,31 = 3.4867, p = 0.001). The enhancement in gut microbiomes was associated with enhanced spatial learning (p < 0.05), reduced inflammation indexed by Iba-1 (F_1,25 = 5.284, p = 0.030) and CD-68 (F_1,26 = 8.441, p = 0.007) expression, and inhibited GSK-3β in female rats (p < 0.01 compared to control females).

Conclusions: This study underscores the potential of probiotics to modulate the gut-brain axis and mitigate pretangle tau-related pathology in preclinical AD. Probiotic supplementation could offer a novel early intervention strategy for AD, highlighting the pivotal role of gut health in neurodegeneration.

Background: Effective detection of cognitive impairment in the primary care setting is limited by lack of time and specialized expertise to conduct detailed objective cognitive testing and few well-validated cognitive screening instruments that can be administered and evaluated quickly without expert supervision. We therefore developed a model cognitive screening program to provide relatively brief, objective assessment of a geriatric patient's memory and other cognitive abilities in cases where the primary care physician suspects but is unsure of the presence of a deficit.

Methods: Referred patients were tested during a 40-min session by a psychometrist or trained nurse in the clinic on a brief battery of neuropsychological tests that assessed multiple cognitive domains. Short questionnaires covering subjective cognitive complaints, symptoms of depression, and medical history were also administered. Results were conveyed to a dementia specialist who reviewed them and returned their judgement of the validity of the cognitive complaint to the primary care provider. Retrospective medical records review was carried out for a random (stratified) half of the sample to determine how screening results were utilized. Screening tests were repeated after two years in a subset of 69 patients.

Results: The 638 patients screened (mean age = 75.9 years; mean education = 14.9 years; 58% women) were classified by screening as having normal cognition (n = 177), depression (with possible cognitive changes; n = 115), mild cognitive impairment (MCI; n = 107), or dementia (n = 239). Classification accuracy was shown by high agreement with the eventual clinical diagnosis in the medical record (69%; Cohen's Kappa = .38; p < .001; 77% if MCI and dementia were collapsed; Cohen's Kappa = .58; p < .001) and longitudinal decline in cognitive test scores only in those initially classified as having MCI or dementia. Medical records documented discussion of screening results with the patient in 69% of cases (80% if MCI or dementia was detected) and often referral to a specialist (62%), new brain imaging (54%), or change in medication (58%) when screening indicated potential cognitive impairment.

Conclusion: The cognitive screening program was well accepted by primary care providers as an efficient and effective way to evaluate concerns about cognitive decline in older adults.

Background: Many risk factors for dementia have been identified, but the timing of risk is less well understood. Here, we analyzed risk factors in a case-control study covering 10 years before an incident dementia diagnosis.

Methods: We designed a case-control study using insurance claims of outpatient consultations of patients with German statutory health insurance between January 1, 2012, and December 31, 2022. We included patients with an incident diagnosis of dementia and controls without a diagnosis of dementia matched 1:2 for age, sex, region, and earliest year of outpatient encounter. We selected exposures based on previous systematic reviews, case-control and cohort studies reporting on risk factors, comorbidities, and prodromal features of dementia. We calculated the prevalence of risk factors in cases and controls and odds ratios for each year before the index date, along with Bonferroni-corrected confidence intervals, using conditional logistic regression.

Results: We identified a total of 1,686,759 patients with incident dementia (mean (SD) age, 82.15 (6.90) years; 61.70% female) and 3,373,518 matched controls (mean (SD) age, 82.15 (6.90) years; 61.70% female). Study participants were followed up for a mean (SD) of 6.6 (2.3) years. Of the 63 risk factors and prodromal features examined, 56 were associated with an increased risk of dementia in all years during the 10th and the 1st year before the index date. These included established risk factors, such as depression, hypertension, hearing impairment, nicotine and alcohol abuse, obesity, hypercholesterolaemia, traumatic brain injury, and diabetes. The greatest risk, with odds ratios greater than 2.5, was conferred by delirium, memory impairment, mental retardation, personality and behavioral disorders, sensory disorders, schizophrenia, and psychosis. Cancer was associated with a reduced risk of dementia.

Conclusions: This large case-control study confirmed established risk factors of dementia. In addition, the study identified non-specific diagnoses that showed a steep increase in risk close to the index date, such as psychosis, conduct disorder, and other sensory disorders. Consideration of these diagnoses, which may represent prodromal features rather than risk factors for dementia, may help to identify people with dementia in routine care.

Background: Alzheimer's disease (AD) is a progressive neurodegenerative disorder with both genetic and environmental factors contributing to its pathogenesis. While early-onset AD has well-established genetic determinants, the genetic basis for late-onset AD remains less clear. This study investigates a large Italian family with late-onset autosomal dominant AD, identifying a novel rare missense variant in GRIN2C gene associated with the disease, and evaluates the functional impact of this variant.

Methods: Affected and unaffected members from a Northern Italian family were included. Genomic DNA from family members was extracted and initially screened for pathogenic mutations in APP, PSEN1, and PSEN2, and screened for 77 genes associated with neurodegenerative conditions using NeuroX array assay. Exome sequencing was performed on three affected individuals and two healthy relatives. Bioinformatics analyses were conducted. Functional analysis was performed using primary neuronal cultures, and the impact of the variant was assessed through immunocytochemistry and electrophysiology.

Results: Pathogenic variants were not identified in APP, PSEN1, or PSEN2, nor in the 77 genes in NeuroX array assay. Exome Sequencing revealed the c.3215C > T p.(A1072V) variant in GRIN2C gene (NM 000835.6), encoding for the glutamate ionotropic receptor N-methyl-D-aspartate receptor (NMDA) type subunit 2C (GluN2C). This variant segregated in 6 available AD patients in the family and was absent in 9 healthy relatives. Primary rat hippocampal neurons overexpressing GluN2C^A1072V showed an increase in NMDAR-induced currents, suggesting altered glutamatergic transmission. Surface expression assays demonstrated an elevated surface/total ratio of the mutant GluN2C, correlating with the increased NMDAR current. Additionally, immunocytochemistry revealed in neurons expressing the mutant variant a reduced colocalization between the GluN2C subunit and 14-3-3 proteins, which are known to facilitate membrane trafficking of NMDARs.

Discussion: We identified a rare missense variant in GRIN2C associated with late-onset autosomal dominant Alzheimer's disease. These findings highlight the role of GluN2C-containing NMDARs in glutamatergic signaling and their potential contribution to AD pathogenesis.

Background: The imbalance of glutamate (Glu) and gamma-aminobutyric acid (GABA) neurotransmitter system plays a crucial role in the pathogenesis of Alzheimer's disease (AD). Riluzole is a Glu modulator originally approved for amyotrophic lateral sclerosis that has shown potential neuroprotective effects in various neurodegenerative disorders. However, whether riluzole can improve Glu and GABA homeostasis in AD brain and its related mechanism of action remain unknown. This study utilized chemical exchange saturation transfer (CEST) imaging combined with proton magnetic resonance spectroscopy (¹H-MRS) to monitor the dynamic changes of Glu and GABA in riluzole-treated AD mice, aiming to evaluate the efficacy and mechanism of riluzole in AD treatment.

Methods: GluCEST, GABACEST and ¹H-MRS were used to longitudinally monitor Glu and GABA levels in 3xTg AD mice treated with riluzole (12.5 mg/kg/day) or vehicle for 20 weeks. Magnetic resonance measurements were performed at baseline, 6, 12, and 20 weeks post-treatment. Cognitive performance was assessed using the Morris Water Maze (MWM) at baseline, 10, and 20 weeks. At the study endpoint, immunohistochemistry, Nissl staining, and Western blot were used to evaluate the brain pathology, neuronal survival, and protein expression.

Results: GluCEST, GABACEST and ¹H-MRS consistently revealed higher levels of Glu and GABA in the brain of riluzole-treated AD mice compared to untreated controls, which were associated with improvements in spatial learning and memory. The cognitive improvements significantly correlated with the increased GluCEST signals and Glu levels. Immunohistochemistry and Nissl staining demonstrated that riluzole treatment reduced amyloid-beta (Aβ) deposition, tau hyperphosphorylation, GFAP-positive astrocyte activation, and prevented neuronal loss. Moreover, riluzole upregulated the expression of excitatory amino acid transporter 2 (EAAT2), glutamic acid decarboxylase 65/67 (GAD65/67), and glutamine synthetase (GS), suggesting enhanced neurotransmitter metabolism.

Conclusions: CEST imaging combined with ¹H-MRS demonstrated the effectiveness of riluzole in modulating Glu- and GABA-related changes and improving cognitive function in 3xTg AD mice, potentially through regulating key proteins involved in neurotransmitter metabolism. These findings suggest riluzole as a therapeutic agent for Alzheimer's disease and highlight the utility of multimodal MR imaging in monitoring treatment response and exploring disease mechanisms.