Journal of Alzheimer's Disease最新文献

Background: The correlation between Alzheimer's disease (AD) and the glucose-triglyceride (TyG) index remains undetermined.

Objective: This study aimed to investigate the relationship between the TyG index and AD, as well as the relationship between the TyG index and cerebrospinal fluid (CSF) AD biomarkers and cognition.

Methods: Six hundred twenty-eight non-dementia participants were included. The TyG index, pathological markers, and cognitive measures were studied using multiple linear regression. Also calculated using a multivariate Cox regression model were the hazard ratio (HR) and its 95% confidence interval (CI). Ten thousand bootstrap iterative causal mediation analyses were performed to investigate the potential mediating effect of AD pathology on cognition.

Results: The TyG index was linked to CSF AD biomarkers (β_Aβ42= 0.880; β_Tau= -0.674; β_pTau= -0.884; β_Aβ42/pTau= 1.764; β_Aβ42/Tau= 1.554; β_pTau/Tau= -0.210) and cognitive measurements (β_MEM= 0.570; β_EF= 0.535; β_ADAS11= -0.789). Mediation analysis revealed that the TyG index may influence cognition via CSF AD biomarkers, including Aβ₄₂, tau, and pTau. Furthermore, each 1-unit increase in TyG index was associated with a 29.5% reduction in the risk of incident AD.

Conclusions: A delayed rate of cognitive decline and a reduced risk of AD were found to be correlated with higher levels of the TyG index, but this does not mean increasing TyG index levels is beneficial for health. Through AD pathology, the TyG index may influence AD and cognitive changes.

Background: Individuals with Alzheimer's disease (AD) have a heightened risk of epilepsy. However, the underlying mechanisms are not well-understood.

Objective: We aimed to elucidate the role of the glutamate-glutamine cycle in this mechanism and test the effect of ceftriaxone, a glutamate transporter-1 (GLT-1) enhancer, on seizure susceptibility in the Tg2576 mouse model of AD.

Methods: First, we assessed expression levels of key proteins in the glutamate-glutamine cycle in Tg2576 (n = 7) and wild-type littermates (n = 7), and subsequently in the kindling model of epilepsy (n = 6) and sham (n = 6). Then, kindling susceptibility was assessed in three groups: 200 mg/kg ceftriaxone-treated Tg2576 (Tg-Ceft, n = 9); saline-treated Tg2576 (Tg-Sal, n = 9); and saline-treated wild-type (WT-Sal, n = 15). Mice were treated for seven days before kindling, and seizure susceptibility compared between groups.

Results: Protein levels of GLT-1 (p = 0.0093) and glutamine synthetase (p = 0.0016) were reduced in cortex of Tg2576 mice, compared to WT. Kindling increased GLT-1 (cortex: p < 0.0001, hippocampus: p = 0.0075), and glutaminase (cortex: p = 0.0044) protein levels, compared to sham. Both Tg-Ceft and WT-Sal displayed Class IV seizures in response to the first stimulation (p > 0.99), while Tg-Sal displayed Class V seizure (p = 0.0212 versus WT-Sal). Seizure susceptibility of Tg-Ceft was not different from Tg-Sal (p > 0.05), and kindling rates did not differ between groups.

Conclusions: Disruptions to key components of the glutamate-glutamine cycle are observed in models of AD and epilepsy. However, increasing GLT-1 through ceftriaxone treatment did not influence seizure susceptibility in Tg2576 mice, suggesting this is not an effective strategy to lower seizure susceptibility in AD, or a higher dosage is needed.

Recent studies indicate that gut microbiota may play a crucial role in cognitive function. Individuals with cognitive impairment tend to have fewer beneficial gut bacteria and lower microbial diversity. Therefore, gut microbiota could be a potential biomarker for cognitive vulnerability. Further research is needed to understand the mechanisms and lifestyle factors affecting both microbiota composition and cognitive health. While the direct impact of microbiota and diet on cognitive impairment remains unconfirmed, this area holds promise for developing new preventive and treatment strategies.

Background: Distinguishing between mild cognitive impairment (MCI) and early dementia requires both neuropsychological and functional assessment that often relies on caregivers' insights. Contacting a patient's caregiver can be time-consuming in a physician's already-filled workday.

Objective: To assess the utility of a brief, machine learning (ML)-enabled digital cognitive assessment, the Digital Clock and Recall (DCR), for detecting functional dependence.

Methods: We evaluated whether the DCR can help identify individuals at risk of functional deficits as measured by the informant-rated Functional Activities Questionnaire (FAQ) in older individuals including cognitively unimpaired, MCI, and dementia likely due to Alzheimer's disease.

Results: The DCR scaled well with FAQ scores, and ML classifiers trained on multimodal DCR features demonstrated strong performance in predicting functional impairment on a held-out test set. Differences in FAQ scores between DCR-predicted classes were comparable across key demographic groups.

Conclusions: The DCR can streamline the clinical decision-making, triage, and intervention planning associated with functional impairment in primary care.

The cerebellum has been largely overlooked in Alzheimer's disease, despite increasing evidence implicating its cognitive capacities and functional networks, which interacts with cerebral cortex to subserve cognition. A study by Lin et al. has indicated that the cerebellum is part of the integrated network in amnestic mild cognitive impairment (aMCI), a prodromal state of Alzheimer's disease. The aMCI patients exhibited weaker cerebello-parietal functional connectivity but stronger cerebellar coupling with precuneus cortex, posterior cingulate gyrus, and caudate nucleus. These alterations in cerebello-cortical connectivity correlated with cognitive performance, suggesting a dynamic change of cerebello-cortical network related to cognitive change in aMCI.

Background: Bilateral oophorectomy (BO) confers immediate estradiol loss. We examined prevalence and predictors of Alzheimer's disease (AD) in women with early BO comparing their odds ratios of AD to those of women with spontaneous menopause (SM).

Methods: A cohort from UK Biobank (n = 34,603) included women aged 60 + at baseline with and without AD who had early BO or SM. AD was determined based on AD related ICD-10 or ICD-9 code. We used logistic regression to model the association of menopause type with AD. Model predictors included age, education, age at menopause, hormone therapy (HT), APOE4, body mass index (BMI), cancer history, and smoking history.

Results: Those with early BO had four times the odds of developing AD (OR = 4.12, 95% CI [2.02, 8.44]) compared to those with SM. APOE4 (OR = 4.29, 95% CI [2.43, 7.56]), and older age (OR = 1.16, 95% CI [1.05, 1.28]) were associated with increased odds of AD in the BO group. Greater years of education were associated with reduced odds of AD for both BO (OR = 0.91, 95% CI [0.85, 0.98]), and SM (OR = 0.95, 95% CI [0.90, 0.99]), while ever use of HT was associated with decreased odds of AD only for the BO group (OR = 0.43, 95% CI [0.23, 0.82]).

Conclusions: Women with early BO, particularly with an APOE4 allele, are at high risk of AD. Women with early BO who use HT and those with increased education have lower odds of developing AD.

Background: Reserves' mechanisms explain inconsistencies between accumulation of neuropathological damage and clinical manifestations. Leisure activities are believed to promote reserves.

Objective: This study evaluates whether cognitive, social, and physical leisure activities performed over life-span predict current cognitive functioning in normal aging and Alzheimer's disease (AD) continuum.

Methods: 35 AD, 24 amnestic-Mild Cognitive Impairment (a-MCI) patients, 21 individuals with subjective cognitive complaint (SCD), and 25 controls underwent a questionnaire developed to quantify leisure activities in different life periods, the Addenbrooke's Cognitive Examination-Revised (ACE-R), and T1-weighted 3T-MRI scans for brain volumetrics and cortical thickness quantification. Partial/total leisure activities' scores and demographic and brain variables were entered as predictors, while ACE-R scores as dependent variables in linear regression analyses.

Results: Current level of cognition was predicted by (i) social and physical activities performed in middle age and current cognitive activity in AD; (ii) cognitive and social activities performed in middle age, current age and cortical thickness in a-MCI; (iii) recreational activities the set of lifetime, current age, and brain features in SCD; (iv) education and the set of lifetime leisure activities over lifespan in controls.

Conclusions: This study shows a funnel effect due to gradual reduction of stimulatory activities in the transition from healthy aging to AD. Reserve indices taking into account different types of stimulatory activities allow to capture even smallest residual effects of reserves accumulated over lifespan, until their complete depletion at advanced AD stages. These results may help target tailored interventions during normal and pathological aging.

Background: Mild cognitive impairment (MCI) is a heterogeneous diagnostic entity, without a clear prognosis, often accompanied by psychiatric symptomatology and physical frailty.

Objective: Understanding the heterogeneity within MCI is a critical step in improving the early detection of cognitive decline and developing effective interventions.

Methods: Cross-sectional multivariate latent mixture analyses of data from patients evaluated between 2015 and 2019, who were routinely entered into a multidisciplinary database for research purposes. A sample of 538 community-dwelling older adults drawn from a large academic medical center, referred from within the Department of Neurology (63.7% Female, M_age= 67.8, SD_age= 10.6). Participants completed comprehensive neuropsychological assessments, psychiatric symptom measures, and frailty evaluations.

Results: Latent profile analyses supported five profiles of cognitive impairment: At-Risk, Pre-MCI, Amnestic MCI, Multiple Domain MCI, and Major Cognitive Impairment. The inclusion of concomitant psychiatric symptoms and frailty criteria revealed two additional profiles: Psychiatric/Frail, without cognitive impairments, and Multiple Cognitive Domains/Psychiatric/Frail. Critically, 55% of those classified as Healthy based on cognitive data alone were reclassified. Significant profile-wise differences emerged across auxiliary variables of brief cognitive screening, sociodemographics, and medical and psychosocial risk.

Conclusions: Results highlight heterogeneity represented by neurologic patients referred for neuropsychological evaluation that include key physical and emotional symptoms known to increase the risk of cognitive decline. Findings are in alignment with more recent research suggesting that the traditional paradigm cognitive impairment may need to be expanded to improve diagnostic accuracy and to develop more tailored, precision-driven interventions.

Background: Alzheimer's disease (AD) is characterized by the accumulation of amyloid-β and tau proteins, leading to neurofibrillary tangles. A biomarker-based diagnostic method called the ATN system categorizes AD pathology into amyloid-β (A), tau (T), and neurodegeneration (N). The relationship between regional tau deposition and reduced glucose metabolism in the preclinical AD stage is not well understood.

Objective: We presented voxel-by-voxel metabolic/tau deposition ratio (MTR) images to investigate the effects of tau deposition on metabolism in AD brains on a stage-by-stage basis.

Methods: We selected 174 subjects who underwent 3D-MRI, FDG-PET, amyloid PET, and tau PET scans. MTR images were created by normalizing FDG-PET toMK6240 PET images. Voxel-wise comparisons among 63 cognitively normal amyloid-negative (CNA) subjects, 49 subjects with AD dementia (ADD), 23 subjects with mild cognitive impairment due to AD (MCA), and 39 preclinical AD (PRC) subjects were conducted.

Results: There was reduced glucose metabolism in ADD and MCA groups compared to CNA, predominantly in parietotemporal areas. Tau deposition was observed in wider areas in ADD and restricted to the medial temporal lobes in MCA. MTR exhibited significant reductions in broader regions in ADD and MCA, indicating simultaneous glucose metabolism decrease and tau deposition. At the MCA and PRC stages, glucose metabolism impairment and tau deposition were shown in separate regions by FDG PET and tau PET, respectively, while MTR images showed impairment in both regions.

Conclusions: Our findings suggest that MTR imaging provides insights into AD pathophysiology by simultaneously assessing glucose metabolism and tau deposition. In the early stage of the AD continuum (MCA and PRC), metabolic decline and tau deposition occur independently in different brain regions.

Background: Plasma biomarkers have recently emerged for the diagnosis, assessment, and disease monitoring of Alzheimer's disease (AD), but have yet to be fully validated in preclinical AD. In addition to AD pathologic plasma biomarkers (amyloid-β (Aβ) and phosphorylated tau (p-tau) species), a proteomic panel can discriminate between symptomatic AD and cognitively unimpaired older adults in a dementia clinic population.

Objective: Examine the added value of a plasma proteomic panel, validated in symptomatic AD, over standard AD pathologic plasma biomarkers and demographic and genetic (apolipoprotein (APOE) ɛ4 status) risk factors in detecting preclinical AD.

Methods: 125 cognitively unimpaired older adults (mean age = 66 years) who completed Aβ PET and plasma draw were analyzed using multiple regression with Aβ PET status (positive versus negative) as the outcome to determine the best fit for predicting preclinical AD. Model 1 included age, education, and gender. Model 2 and 3 added predictors APOE ɛ4 status (carrier versus non-carrier) and AD pathologic blood biomarkers (Aβ_42/40 ratio, p-tau181), respectively. Random forest modeling established the 5 proteomic markers from the proteomic panel that best predicted Aβ PET status, and these markers were added in Model 4.

Results: The best model for predicting Aβ PET status included age, years of education, APOE ɛ4 status, Aβ_42/40 ratio, and p-tau181. Adding the top 5 proteomic markers did not significantly improve the model.

Conclusions: Proteomic markers in plasma did not add predictive value to standard AD pathologic plasma biomarkers in predicting preclinical AD in this sample.