Alzheimer's Research & Therapy最新文献

Background: Alzheimer's Disease (AD) is characterized by progressive declines in cognitive and motor functions, impairing daily activities. Traditionally, AD diagnosis relies on cognitive assessments, but emerging evidence highlights motor function deficits as early indicators of AD and Mild Cognitive Impairment (MCI). These motor declines, which often precede cognitive symptoms, include slower and less accurate reaching movements. This study explored reaching actions in a Virtual Reality (VR) environment in AD and MCI patients to identify motor deficits and their link to cognitive decline.

Methods: The study involved 61 right-handed participants (19 AD, 21 MCI, and 21 healthy age-matched controls), screened for cognitive health using a Mini-Mental State Examination (MMSE). Participants performed upper-limb motor tasks (sequentially reaching targets) in a Virtual Reality (VR). Kinematic data was recorded and analyzed focusing on task success rate, frequency of anticipatory responses, and direction of anticipatory responses. Statistical analysis was performed using Generalized Linear Mixed Models to differentiate the three groups of participants based on performance metrics, anticipation behavior, and the correlation between anticipation rate and MMSE score.

Results: Both AD and MCI patients showed more anticipatory responses than healthy controls (HC), inversely related to success rates and cognitive function. AD patients exhibited lower success rates and a higher frequency of anticipatory responses, often biased toward previous trial targets, suggesting impaired motor planning or difficulty adapting to new cues. MCI patients showed an intermediate pattern, with more anticipatory responses than HC but comparable success rates. These results highlight the crucial role of anticipatory behavior in motor task performance, with AD patients displaying the most pronounced deficits.

Conclusions: This study highlights significant impairments of reaching movements in AD patients, particularly in terms of anticipatory behavior and success rates. The observed deficits suggest that kinematic metrics could serve as early biomarkers for diagnosis and intervention. These findings emphasize the importance of combining cognitive and sensorimotor assessments for the early detection of AD-related motor dysfunctions. Additionally, they highlight the potential of VR-based motor rehabilitation as a promising approach to address sensorimotor deficits in the AD continuum, improving both motor and cognitive outcomes.

In this article, we have carefully read the author's comments on our published article regarding the post-marketing safety concerns of lecanemab based on the Food and Drug Administration Adverse Event Reporting System (FAERS) database. Pharmacovigilance studies based on the disproportionality analysis through the case/non-case design are common, and the details of this method deserve attention. We acknowledged the author's perspectives on the term "signal of disproportionate reporting (SDR)", and make some explanations on the SDR results for pancreatic carcinoma and the deduplication methods.

Cognitive decline is a condition affecting almost one sixth of the elder population and is widely regarded as one of the first manifestations of Alzheimer's disease. Despite the extensive body of knowledge on the condition, there is no clear consensus on the structural defects and neurodegeneration processes determining cognitive decline evolution. Here, we introduce a Brain Network Model (BNM) simulating the effects of neurodegeneration on neural activity during cognitive processing. The model incorporates two key parameters accounting for distinct pathological mechanisms: synaptic degeneration, primarily leading to hyperexcitation, and brain disconnection. Through parameter optimization, we successfully replicated individual electroencephalography (EEG) responses recorded during task execution from 145 participants spanning different stages of cognitive decline. The cohort included healthy controls, patients with subjective cognitive decline (SCD), and those with mild cognitive impairment (MCI) of the Alzheimer type. Through model inversion, we generated personalized BNMs for each participant based on individual EEG recordings. These models revealed distinct network configurations corresponding to the patient's cognitive condition, with virtual neurodegeneration levels directly proportional to the severity of cognitive decline. Strikingly, the model uncovered a neurodegeneration-driven phase transition leading to two distinct regimes of neural activity underlying task execution. On either side of this phase transition, increasing synaptic degeneration induced changes in neural activity that closely mirrored experimental observations across cognitive decline stages. This enabled the model to directly link synaptic degeneration and hyperexcitation to cognitive decline severity. Furthermore, the model pinpointed posterior cingulum fiber degeneration as the structural driver of this phase transition. Our findings highlight the potential of BNMs to account for the evolution of neural activity across stages of cognitive decline while elucidating the underlying neurodegenerative mechanisms. This approach provides a novel framework for understanding how structural and functional brain alterations contribute to cognitive deterioration along the Alzheimer's continuum.

Background: Sleep disturbances have been associated with an increased risk of dementia. The mechanisms remain unclear, although neurodegenerative and vascular pathways are potentially involved. Hence, our study aims to investigate the relationships between several clinical sleep and polysomnographic features and volumes of hippocampus (indicative of neurodegeneration) and white matter hyperintensities (WMH) (reflecting vascular processes).

Methods: In this cross-sectional study, 678 participants aged 65-80 from the French population-based ESPRIT cohort with MRI-measured hippocampus and/or WMH volumes were included. Self-reported sleep data were collected at baseline, and 176 participants underwent ambulatory polysomnography (PSG). We performed multivariable logistic regression to assess associations between sleep characteristics and hippocampal and WMH volumes.

Results: Participants' median age was 70.7 years (Q1-Q3 = 67.8-74.0), with 52.4% being women. Early (≤ 6 am; odds ratio (OR) = 2.03, 95% confidence interval (CI) = 1.17;3.53) and late (> 8 am; OR = 2.14, 95%CI = 1.33;3.43) rising times were associated with low hippocampal volume. Early rising time (OR = 2.06, 95%CI = 1.24;3.43) and insomnia symptoms (OR = 1.84, 95%CI = 1.18;2.86 for 1 symptom, OR = 1.91, 95%CI = 1.18;3.09 for 2-3 symptoms) were associated with large WMH volume, whereas late bedtime (≥ 11 pm; OR = 0.56, 95%CI = 0.39;0.80) was associated with low WMH volume. Based on PSG data, higher rapid-eye movement (REM) sleep percentage (OR = 0.70, 95%CI = 0.50;0.96) was associated with low WMH volume, with similar trends for long sleep bouts duration, N3 and REM sleep durations (p = 0.05 to 0.07). Conversely, higher N2 sleep percentage (OR = 1.69, 95%CI = 1.09;2.62), longer NREM sleep bouts (OR = 1.46, 95%CI = 1.02;2.09), and higher periodic leg movements index (OR = 1.55, 95%CI = 1.02;2.26) were associated with large WMH volume. However, no PSG parameter associations remained after false discovery rate correction.

Conclusions: Distinct associations between sleep characteristics and hippocampal and WMH volumes were observed, highlighting the important relationships between sleep, sleep timing and brain structure.

Background: Disruptions in circadian rhythms are commonly observed in patients with Alzheimer's disease (AD) and could potentially accelerate the progression of the condition. However, the relationship between circadian rhythm disruptions and AD development, as well as the mechanisms involved, remain poorly understood.

Methods: This study investigated the circadian behavior, rhythmic gene expression in multiple brain regions, and its correlation with sleep architecture of AD mice at two disease stages: plaque-free stage (2-month-old) and plaque-burdened stage (10-month-old) as compared to age-matched wild-type (WT) mice.

Results: Two-month-old AD mice already displayed alteration in the activity patterns compared to WT mice, showing increased activity during the light phase and decreased activity during the dark phase, and the change in the activity pattern of 10-month-old AD mice was more significant. Further, electroencephalogram (EEG) examination showed increased wakefulness and reduced non-rapid eye movement (NREM) sleep in 2- and 10-month-old AD mice. In addition, we documented a significant change in circadian core clock genes in the suprachiasmatic nucleus (SCN), hippocampus, and cortex of 2- and 10-month-old AD mice. Correlation analyses demonstrated the close relationship between circadian clock gene expression level and specific sleep-wake parameters, especially within the SCN and hippocampus.

Conclusions: These findings revealed that circadian rhythm disturbances in AD mice preceded Aβ deposition. The circadian rhythm disturbances observed in the early AD might be attributed to the abnormal expression of core clock genes in the brain regions involved in circadian rhythm regulation.

Background: Tau pathology in Alzheimer's disease (AD) propagates trans-synaptically along structurally connected brain networks and in synergy with amyloid pathology it induces synaptic damage. However, the in vivo relationship of amyloid, tau and synaptic density with white matter (WM) structural changes has been studied rather limitedly. Recent advances in diffusion MRI processing allow quantification of apparent fibre density and fibre cross-section on the fixel level, i.e., individual fibre populations within one voxel. The aim of this study was to investigate the hypothesis of axonal loss due to tau propagation and amyloid pathology and its association with synaptic density in early disease stages.

Methods: Twenty-four patients with amnestic mild cognitive impairment (aMCI) and 23 healthy controls (HC) underwent baseline amyloid (¹¹C-PiB/¹⁸F-NAV4694), tau (¹⁸F-MK-6240) and synaptic density (¹¹C-UCB-J binding to SV2A) PET/MR in combination with diffusion MRI and cognitive assessments. A subset of 14 aMCI patients underwent follow-up visits after 2 years. First, a whole-brain fixel-based analysis was performed to identify differences in fibre density and fibre cross-section between HC and aMCI and longitudinally in the aMCI group. Next, a tract-of-interest analysis was performed, focusing on the temporal-cingulum bundle where most alterations have been shown in early AD. Tau and SV2A PET were quantified in the connected regions, i.e., hippocampus and posterior cingulate/precuneus (PCC-P). Amyloid PET centiloids were measured in the commonly used cortical composite volume-of-interest.

Results: At baseline, multiple WM tracts showed lower fibre density and lower fibre cross-section in aMCI compared to HC, and these parameters further decreased longitudinally in the aMCI group. In the temporal cingulum bundle, reduced fibre density was significantly associated with reduced hippocampal synaptic density while increased hippocampal and PCC-P tau specifically correlated with reduced fibre cross-section. Increased global amyloid burden was associated with reduced fibre density and fibre cross-section in the temporal cingulum bundle.

Conclusions: Our results suggest that WM degeneration already occurs in the aMCI stage of AD and alterations in apparent fibre density and fibre cross-section of the temporal cingulum bundle are associated with AD hallmark pathology.

Background: Alzheimer's disease (AD) is a neurodegenerative disease. Amyloid β-protein (Aβ) is one of the key pathological features of AD, which is cytotoxic and can damage neurons, thereby causing cognitive dysfunction. Photobiomodulation (PBM) is a non-invasive physical therapy that induces changes in the intrinsic mechanisms of cells and tissues through low-power light exposure. Although PBM has been employed in the treatment of AD, the effect and precise mechanism of PBM on AD-induced neurological damage are still unclear.

Methods: In vivo experiments, PBM (808 nm, 20 mW/cm²) was used to continuously interfere with APP/PS1 mice for 6 weeks, and then their cognitive function and AD pathological changes were evaluated. In vitro experiments, lipopolysaccharide (LPS) was used to induce microglia to model inflammation, and the effect of PBM treatment on microglia polarization status and phagocytic Aβ ability was evaluated. Hexokinase 2 (HK2) inhibitor 3-bromopyruvate (3BP) was used to study the effect of PBM treatment on mitochondrial energy metabolism in microglia.

Results: PBM further ameliorates AD-induced cognitive impairment by alleviating neuroinflammation and neuronal apoptosis, thereby attenuating nerve damage. In addition, PBM can also reduce neuroinflammation by promoting microglial anti-inflammatory phenotypic polarization; Promotes Aβ clearance by enhancing the ability of microglia to engulf Aβ. Among them, PBM regulates microglial polarization and inhibits neuronal apoptosis, which may be related to its regulation of mitochondrial energy metabolism, promotion of oxidative phosphorylation, and inhibition of glycolysis.

Conclusion: PBM regulates neuroinflammatory response and inhibits neuronal apoptosis, thereby repairing Aβ-induced neuronal damage and cognitive dysfunction. Mitochondrial energy metabolism plays an important role in PBM in improving nerve injury in AD mice. This study provides theoretical support for the subsequent application of PBM in the treatment of AD.

Background: Ethnic variations and detection methods may lead to differences in diagnostic biomarkers of dementia, and few comparative studies have evaluated the six plasma biomarkers of Alzheimer's disease (AD) and other neurodegenerative dementias in the Chinese population.

Methods: A cross-sectional cohort of 668 participants were enrolled, including 245 amnesic mild cognitive impairment (aMCI) or AD patients with Aβ positive pathology, 67 with frontotemporal dementia (FTD), 100 with progressive supranuclear palsy (PSP), 72 with dementia with Lewy bodies (DLB) and 184 healthy controls. Additionally, a longitudinal subset of 19 aMCI and 30 AD patients was followed for an average period of 1 year. Plasma biomarkers, including p-tau181, p-tau217, p-tau231, NfL, GFAP, and α-synuclein, were simultaneously measured using a novel single molecular array method. Aβ42 and p-tau181 levels in CSF, amyloid PET and structural MRI were measured.

Results: Plasma p-tau217 and p-tau231 were most effective in diagnosing aMCI/AD (AUC = 0.95 and 0.93, respectively), while p-tau217, p-tau231 and p-tau181 presented the best differential diagnosis for AD from PSP, FTD and DLB respectively (AUC = 0.84, 0.81 and 0.83). α-synuclein was presented as the best biomarker for PSP variant and behavior variant FTD subtypes (AUC = 0.81 and 0.74, respectively). Among them, p-tau217, p-tau231, GFAP and a-synuclein were negatively correlated with CSF Aβ42/40, while p-tau217 and GFAP were positively correlated with CSF p-tau181. Besides, p-tau181, p-tau217, and GFAP were associated with temporal lobe volume, while p-tau231 and GFAP were associated with frontal lobe volume. Longitudinal analysis showed the higher p-tau181 could predict the cognitive decline progression.

Conclusions: This study validate the practicality of blood biomarkers in the Chinese Han population using a novel single molecule immune detection method. Through the clinical performance study for several biomarkers, we found the plasma p-tau217 was the most effective biomarker in AD diagnosis, and p-tau showed high accuracy for differential diagnosis of AD from other dementia, GFAP is associated with multiple aspects of AD pathology, and frontal and temporal lobe volume, and p-tau181 can reflect the dynamic cognitive decline of AD.

Background: Although interactions between amyloid-beta and tau proteins have been implicated in Alzheimer's disease (AD), the precise mechanisms by which these interactions contribute to disease progression are not yet fully understood. Moreover, despite the growing application of deep learning in various biomedical fields, its application in integrating networks to analyze disease mechanisms in AD research remains limited. In this study, we employed BIONIC, a deep learning-based network integration method, to integrate proteomics and protein-protein interaction data, with an aim to uncover factors that moderate the effects of the Aβ-tau interaction on mild cognitive impairment (MCI) and early-stage AD.

Methods: Proteomic data from the ROSMAP cohort were integrated with protein-protein interaction (PPI) data using a Deep Learning-based model. Linear regression analysis was applied to histopathological and gene expression data, and mutual information was used to detect moderating factors. Statistical significance was determined using the Benjamini-Hochberg correction (p < 0.05).

Results: Our results suggested that astrocytes and GPNMB + microglia moderate the Aβ-tau interaction. Based on linear regression with histopathological and gene expression data, GFAP and IBA1 levels and GPNMB gene expression positively contributed to the interaction of tau with Aβ in non-dementia cases, replicating the results of the network analysis.

Conclusions: These findings suggest that GPNMB + microglia moderate the Aβ-tau interaction in early AD and therefore are a novel therapeutic target. To facilitate further research, we have made the integrated network available as a visualization tool for the scientific community (URL: https://igcore.cloud/GerOmics/AlzPPMap ).

Background: Personalized repetitive transcranial magnetic stimulation (rTMS) of the precuneus (PC) is emerging as a new non-invasive therapeutic approach in treating Alzheimer's disease (AD). Here we sought to investigate the effects of 52 weeks of rTMS applied over the PC on cognitive functions in patients with mild-to-moderate dementia due to AD.

Methods: Forty-eight patients with mild-to-moderate dementia due to AD were enrolled for the study. Of those 31 patients were extended to 52 weeks after being included in a 24-week trial (NCT03778151) with the same experimental design. The trial included a 52-week treatment with a 2-week intensive course where rTMS (or sham) was applied over the PC daily (5 times per week, Monday to Friday), followed by a 50-week maintenance phase in which the same stimulation was applied once weekly. Personalization of rTMS treatment was established using neuronavigated TMS in combination with electroencephalography (TMS-EEG). The primary outcome measure was change from baseline to week 52 of the Clinical Dementia Rating Scale-Sum of Boxes (CDR-SB). Secondary outcomes included score changes in the Alzheimer's Disease Assessment Scale- Cognitive Subscale (ADAS-Cog)₁₁, Mini Mental State Examination (MMSE), Alzheimer's Disease Cooperative Study-Activities of Daily Living scale (ADCS-ADL) and Neuropsychiatric Inventory (NPI). Changes in cortical activity and connectivity were monitored by TMS-EEG.

Results: Among 48 patients randomized (mean age 72.8 years; 56% women), 32 (68%) completed the study. Repetitive TMS of the PC (PC-rTMS) had a significant effect on the primary outcome measure. The estimated mean change in CDR-SB after 52 week was 1.36 for PC-rTMS (95% confidence interval (CI) [0.68, 2.04]) and 2.45 for sham-rTMS group (95%CI [1.85, 3.05]). There were also significant effects for the secondary outcomes ADAS-Cog₁₁, ADCS-ADL and NPI scores. Stronger DMN connectivity at baseline was associated with favorable response to rTMS treatment.

Conclusions: Fifty-two weeks of PC-rTMS may slow down the impairment of cognitive functions, activities of daily living and behavioral disturbances in patients with mild-to-moderate AD. Further multicenter studies are needed to confirm the clinical potential of DMN personalized rTMS.

Trial registration: The study was registered on the clinicaltrial.gov website on 07-07-2022 (NCT05454540).