Alzheimer's Research & Therapy最新文献

Background: Tau-PET is a diagnostic tool with high sensitivity and specificity for discriminating Alzheimer's disease (AD) dementia from other neurodegenerative disorders in well-controlled research environments. The role of tau-PET in real-world clinical practice, however, remains to be established. The aim of the TAP-TAU study is therefore to investigate the impact of tau-PET in clinical practice.

Methods: TAP-TAU is a prospective, longitudinal multi-center study in 300 patients (≥ 50 years old) with mild cognitive impairment or mild dementia across five Dutch memory clinics. Patients are eligible if diagnostic certainty is < 85% after routine dementia screening and if the differential diagnosis includes AD. More specifically, we will include patients who (i) are suspected of having mixed pathology (e.g., AD and vascular pathology), (ii) have an atypical clinical presentation, and/or (iii) show conflicting or inconclusive outcomes on other tests (e.g., magnetic resonance imaging or cerebrospinal fluid). Participants will undergo a [¹⁸F]flortaucipir tau-PET scan, blood-based biomarker sampling, and fill out questionnaires on patient reported outcomes and experiences. The primary outcomes are change (pre- versus post- tau-PET) in diagnosis, diagnostic certainty, patient management and patient anxiety and uncertainty. Secondary outcome measures are head-to-head comparisons between tau-PET and less invasive and lower cost diagnostic tools such as novel blood-based biomarkers and artificial intelligence-based classifiers.

Results: TAP-TAU has been approved by the Medical Ethics Committee of the Amsterdam UMC. The first participant is expected to be included in October 2024.

Conclusions: In TAP-TAU, we will investigate the added clinical value of tau-PET in a real-world clinical setting, including memory clinic patients with diagnostic uncertainty after routine work-up. Findings of our study may contribute to recommendations regarding which patients would benefit most from assessment with tau-PET. This study is timely in the dawning era of disease modifying treatments as an accurate etiological diagnosis becomes increasingly important.

Trial registration: This trial is registered and authorized on December 21st, 2023 in EU Clinical Trials with registration number 2023-505430-10-00.

Background: Analysis of selected research cohorts has highlighted an association between plasma neurofilament light (NfL) protein and cross-sectional cognitive impairment as well as longitudinal cognitive decline. However, the findings have yielded inconsistent results regarding its possible application in clinical practice. Despite its potential prognostic significance, the role of plasma NfL in daily clinical practice with unselected patients suffering from cognitive impairment remains largely unexplored.

Methods: This retrospective, cross-sectional and longitudinal monocentric study enrolled 320 patients with Alzheimer's disease ([AD], n = 158), dementia with Lewy body ([DLB], n = 30), frontotemporal dementia ([FTD], n = 32), non-neurodegenerative diseases ([NND], n = 59) or subjective cognitive decline ([SCD], n = 41). Plasma NfL levels were measured at baseline on the Simoa platform. AD, DLB, and FTD patients were also analyzed altogether as a 'degenerative conditions' subgroup, whereas SCD and NND were grouped as a 'non-degenerative conditions' subgroup. We assessed the relationship between plasma NfL levels and cross-sectional cognitive performance, including global cognition and six specific cognitive domains. A subset of 239 patients had follow-up mini-mental state examinations (MMSE) up to 60 months. Models were adjusted on age, education level, glomerular filtration rate and body mass index.

Results: In 320 patients, baseline plasma NfL levels were negatively associated with global cognition (β=-1.28 (-1.81 ; -0.75) P < 0.001), memory (β=-1.48 (-2.38 ; -0.59), P = 0.001), language (β=-1.72(-2.49 ; -0.95) P < 0.001), praxis (β=-2.02 (-2.91 ; -1.13) P < 0.001) and executive functions (β=-0.81, P < 0.001). Across diagnosis, plasma NfL levels were negatively associated with cross-sectional global cognition in all but the SCD subgroup, specifically with executive functions and memory in AD (respectively β=-0.71(-1.21 ; -0.211), P = 0.005 and β=-1.29 (-2.17 ; -0.42), P = 0.004), and with attention in LBD (β=-0.81(-1.16 ; -0.002), P = 0.03). Linear mixed-effects models showed that plasma NfL predicted MMSE decline in the global population (β_{PlasmaNfLxTime}=-0.15 (-0.26 ; -0.04), P = 0.006), as in the neurodegenerative condition subgroup (β_{PlasmaNfLxTime}=-0.21 (-0.37 ; - 0.06), P = 0.007), but not in non-neurodegenerative condition subgroup.

Conclusion: In our clinical cohort, plasma NfL was associated with faster cognitive decline in neurodegenerative dementia, which corroborates data obtained in research cohorts. Yet, plasma NfL was not predictive of accelerated cognitive decline in individuals without neurodegeneration, suggesting its use as a neurodegeneration-specific predictive biomarker.

Background: Elevated concentrations of low-density lipoprotein cholesterol (LDL-C) are linked to dementia risk, and conversely, increased plasma concentrations of high-density lipoprotein cholesterol (HDL-C) and apolipoprotein-A1 (Apo-A1) associate with decreased dementia risk. Inhibition of cholesteryl ester transfer protein (CETP) meaningfully affects the concentrations of these blood lipids and may therefore provide an opportunity to treat dementia.

Methods: Drug target Mendelian randomization (MR) was employed to anticipate the on-target effects of lower CETP concentration (μg/mL) on plasma lipids, cardiovascular disease outcomes, autopsy confirmed Lewy body dementia (LBD), as well as Parkinson's dementia.

Results: MR analysis of lower CETP concentration recapitulated the blood lipid effects observed in clinical trials of CETP-inhibitors, as well as protective effects on coronary heart disease (odds ratio (OR) 0.92, 95% confidence interval (CI) 0.89; 0.96), heart failure, abdominal aortic aneurysm, any stroke, ischemic stroke, and small vessel stroke (0.90, 95%CI 0.85; 0.96). Consideration of dementia related traits indicated that lower CETP concentrations were associated higher total brain volume (0.04 per standard deviation, 95%CI 0.02; 0.06), lower risk of LBD (OR 0.81, 95%CI 0.74; 0.89) and Parkinson's dementia risk (OR 0.26, 95%CI 0.14; 0.48). APOE4 stratified analyses suggested the LBD effect was most pronounced in APOE-ε4 + participants (OR 0.61 95%CI 0.51; 0.73), compared to APOE-ε4- (OR 0.89 95%CI 0.79; 1.01); interaction p-value 5.81 × 10^- 4.

Conclusions: These results suggest that inhibition of CETP may be a viable strategy to treat dementia, with a more pronounced effect expected in APOE-ε4 carriers.

Background: The potential of microglia as a target for Alzheimer's disease (AD) treatment is promising, yet the clinical and pathological diversity within microglia, driven by genetic factors, poses a significant challenge. Subtyping AD is imperative to enable precise and effective treatment strategies. However, existing subtyping methods fail to comprehensively address the intricate complexities of AD pathogenesis, particularly concerning genetic risk factors. To address this gap, we have employed systems biology approaches for AD subtyping and identified potential therapeutic targets.

Methods: We constructed patient-specific microglial molecular regulatory network models by utilizing existing literature and single-cell RNA sequencing data. The combination of large-scale computer simulations and dynamic network analysis enabled us to subtype AD patients according to their distinct molecular regulatory mechanisms. For each identified subtype, we suggested optimal targets for effective AD treatment.

Results: To investigate heterogeneity in AD and identify potential therapeutic targets, we constructed a microglia molecular regulatory network model. The network model incorporated 20 known risk factors and crucial signaling pathways associated with microglial functionality, such as inflammation, anti-inflammation, phagocytosis, and autophagy. Probabilistic simulations with patient-specific genomic data and subsequent dynamics analysis revealed nine distinct AD subtypes characterized by core feedback mechanisms involving SPI1, CASS4, and MEF2C. Moreover, we identified PICALM, MEF2C, and LAT2 as common therapeutic targets among several subtypes. Furthermore, we clarified the reasons for the previous contradictory experimental results that suggested both the activation and inhibition of AKT or INPP5D could activate AD through dynamic analysis. This highlights the multifaceted nature of microglial network regulation.

Conclusions: These results offer a means to classify AD patients by their genetic risk factors, clarify inconsistent experimental findings, and advance the development of treatments tailored to individual genotypes for AD.

Background: The research community has historically failed to enroll diverse groups of participants in dementia clinical trials. A unique aspect of dementia care research is the requirement of a study partner, who can attest to the care recipient's clinical and functional capacity. The aim of this study is to assess racial and ethnic differences and the importance of various trial considerations among dementia caregivers, in their decision to participate in clinical research as study partners.

Method: We embedded a vignette about a hypothetical dementia clinical trial in a nationally representative survey of U.S. dementia caregivers, oversampling non-Hispanic Black and Hispanic caregivers. Dementia caregivers were asked about their willingness to participate in the trial with their care recipient and rated the importance of nine considerations in hypothetical decisions to participate. Caregiver demographic characteristics were analyzed as predictors of trial participation in a base demographic model. In a second reasons model caregiver demographic characteristics and the rated importance of the nine considerations were separately analyzed as predictors; both models used survey-weighted logistic regression.

Result: The sample consisted of 610 dementia caregivers, including 156 non-Hispanic Black and 122 Hispanic caregiver participants. In the base demographic model, hypothetical trial participation was negatively associated with older caregiver age (OR (odds ratio) = 0.72, p = < 0.001). In the reasons model, the rated importance of a social responsibility to help others by participating in research was significantly associated with participation (OR = 1.56, p = 0.049), while the importance of the possibility of the care recipient experiencing serious side effects was negatively associated with participation (OR = 0.51, p = 0.003). In both models there was no significant difference in hypothetical participation between non-Hispanic Black and non-Hispanic White caregivers, or between Hispanic and non-Hispanic White caregivers.

Conclusion: Hispanic and non-Hispanic Black dementia caregivers were not less likely than non-Hispanic White dementia caregivers to participate in a hypothetical dementia clinical trial. Our study suggests that failures to recruit diverse populations in dementia clinical research are not attributable to less willingness among members of underrepresented groups but may instead reflect structural barriers and historic exclusion from trial participation.

Background: Diagnosis of dementia with Lewy bodies (DLB) is challenging, especially in the earlier stages of the disease, owing to the clinical overlap with other neurodegenerative diseases such as Alzheimer's (AD) and Parkinson's disease (PD). We aimed to identify the transcranial sonography (TCS) parameters that can help us to detect early DLB patients.

Methods: In this cross-sectional study, we prospectively recruited newly diagnosed DLB patients with less than 3 years from the onset of cognitive symptoms. For comparison purposes, we also included AD and PD patients, with a disease duration of less than 3 years, and a control group. TCS was performed to assess the substantia nigra (SN) echogenicity, the width of the third ventricle, and the frontal horns of the lateral ventricles. Subsequently, TCS images were analyzed with the medical image viewer Horos in order to quantify the intensity of the echogenicity of the SN. Univariate analysis and a logistic regression model were used to identify which variables can predict the diagnosis of DLB.

Results: One hundred and seven participants were included (23 DLB, 26 AD, 27 PD and 31 controls). The median age of DLB patients was 75(72-77) years, with a disease duration of 2 years. DLB and PD patients showed higher SN hyperechogenicity rates (72.73% and 81.82%, respectively) and a greater area of the SN compared to AD patients and controls (p < 0.001). DLB and AD patients had wider ventricular systems than the other study groups. The SN hyperechogenicity predicted a diagnosis of DLB with an odds ratio of 22.67 (95%CI 3.98; 129.12, p < 0.001) when compared to AD patients. Unilateral and bilateral widened frontal horns predicted diagnosis of DLB compared to PD with an odds ratio of 9.5 (95%CI 0.97; 92.83, p = 0.053) and 5.7 (95%CI 0.97; 33.6, p = 0.054), respectively.

Conclusions: Echogenicity of the SN and widening of the frontal horns of lateral ventricles can predict the diagnosis of early DLB in this cohort of newly diagnosed patients, when compared to AD and PD patients. Transcranial sonography, a non-invasive tool, could be helpful for the diagnosis of DLB at its earlier stages.

Background: Multiple psychosocial factors have been associated with dementia, while the individual or joint effects of various psychosocial states on dementia remain unrevealed due to the complex interplay between those factors. Here, the authors examined the associations of psychosocial factors and patterns with subsequent risk of dementia, and if the associations could be modified by genetic susceptibility to dementia.

Methods: UK Biobank dementia-free participants were followed from one year after recruitment (median date: 24 January, 2010) until 31 October, 2022. Psychosocial states were measured by 22 items related to five dimensions, including psychiatric history, recent stressful life events, current psychiatric symptoms, social contact, and individual socioeconomic state. We identified clusters of individuals with distinct psychosocial patterns using latent class analysis. Cox proportional hazards models were used to evaluate the association between psychosocial items, as well as psychosocial patterns, and risk of dementia. We further performed stratification analyses by apolipoprotein E (APOE) genotype, polygenic risk score (PRS) of dementia, and family history of dementia.

Results: Of 497,787 included participants, 54.54% were female. During a median follow-up of 12.70 years, we identified 9,858 (1.98%) patients with newly diagnosed dementia. We identified seven clusters with distinct psychosocial patterns. Compared to individuals with a pattern of 'good state', individuals with other unfavorable patterns, featured by varying degrees of poor psychological state ('fair state' and 'mildly, moderately, and extremely poor psychological state'), low social contact or socioeconomic state ('living alone' and 'short education years'), were all at an increased risk of dementia (hazard ratios [HR] between 1.29 and 2.63). The observed associations showed no significant differences across individuals with varying APOE genotypes, levels of PRS, and family histories of dementia.

Conclusion: Unfavorable psychosocial patterns are associated with an increased risk of dementia, independent of genetic susceptibility. The findings highlight the importance of surveillance and prevention of cognitive decline among individuals with suboptimal psychosocial state.

Background: Dementia with Lewy Bodies (DLB) is a complex neurodegenerative disorder that often overlaps clinically with Alzheimer's disease (AD), presenting challenges in accurate diagnosis and underscoring the need for novel biomarkers. Lipidomic emerges as a promising avenue for uncovering disease-specific metabolic alterations and potential biomarkers, particularly as the lipidomics landscape of DLB has not been previously explored. We aim to identify potential diagnostic biomarkers and elucidate the disease's pathophysiological mechanisms.

Methods: This study conducted a lipidomic analysis of plasma samples from patients with DLB, AD, and healthy controls (HCs) at Xuanwu Hospital. Untargeted plasma lipidomic profiling was conducted via liquid chromatography coupled with mass spectrometry. Machine learning methods were employed to discern lipidomic signatures specific to DLB and to differentiate it from AD.

Results: The study enrolled 159 participants, including 57 with AD, 48 with DLB, and 54 HCs. Significant differences in lipid profiles were observed between the DLB and HC groups, particularly in the classes of sphingolipids and phospholipids. A total of 55 differentially expressed lipid species were identified between DLB and HCs, and 17 between DLB and AD. Correlations were observed linking these lipidomic profiles to clinical parameters like Unified Parkinson's Disease Rating Scale III (UPDRS III) and cognitive scores. Machine learning models demonstrated to be highly effective in distinguishing DLB from both HCs and AD, achieving substantial accuracy through the utilization of specific lipidomic signatures. These include PC(15:0_18:2), PC(15:0_20:5), and SPH(d16:0) for differentiation between DLB and HCs; and a panel includes 13 lipid molecules: four PCs, two PEs, three SPHs, two Cers, and two Hex1Cers for distinguishing DLB from AD.

Conclusions: This study presents a novel and comprehensive lipidomic profile of DLB, distinguishing it from AD and HCs. Predominantly, sphingolipids (e.g., ceramides and SPHs) and phospholipids (e.g., PE and PC) were the most dysregulated lipids in relation to DLB patients. The lipidomics panels identified through machine learning may serve as effective plasma biomarkers for diagnosing DLB and differentiating it from AD dementia.

Background: Misfolding and aggregation of amyloid β (Aβ), along with neurofibrillary tangles consisting of aggregated Tau species, are pathological hallmarks of Alzheimer's disease (AD) onset and progression. In this study, we hypothesized the clearance of Aβ aggregates from the brain and body into the gut.

Methods: To investigate this, we used surface-based fluorescence intensity distribution analysis (sFIDA) to determine the Aβ aggregate concentrations in feces from 26 AD patients and 31 healthy controls (HC).

Results: Aβ aggregates were detectable in human feces and their concentrations were elevated in AD patients compared to HC (specificity 90.3%, sensitivity 53.8%).

Conclusion: Thus, fecal Aβ aggregates constitute a non-invasive biomarker candidate for diagnosing AD. Whether digestion-resistant Aβ aggregates in feces are secreted via the liver and bile or directly from the enteric neuronal system remains to be elucidated.

Background: Emerging evidence suggested the association between gut dysbiosis and Alzheimer's disease (AD) progression. However, it remained unclear how the gut microbiome and neuroinflammation in the brain mutually interact or how these interactions affect brain functioning and cognition. Here we hypothesized that "gut-brain" axis mediated by microbial derived metabolites was expected to novel breakthroughs in the fields of AD research and development.

Methods: Multiple technologies, such as immunofluorescence, 16s rDNA sequencing, mass spectrometry-based metabolomics (LC-QQQ-MS and GC-MS), were used to reveal potential link between gut microbiota and the metabolism and cognition of the host.

Results: Microbial depletion induced by the antibiotics mix (ABX) verified that "gut-brain" can transmit information bidirectionally. Short-chain fatty acid-producing (SCFAs-producing) bacteria and amino acid-producing bacteria fluctuated greatly in 5×FAD mice, especially the reduction sharply of the Bifidobacteriaceae and the increase of the Lachnospiraceae family. Concentrations of several Tryptophan-kynurenine intermediates, lactic acid, CD4⁺ cell, and CD8⁺ cells were higher in serum of 5×FAD mice, whilst TCA cycle intermediates and Th1/Th2 were lower. In addition, the levels of iso-butyric acid (IBA) in feces, serum, and brain of 5×FAD mice were increased compared with WT-M mice, especially in serum. And IBA in the brain was positively correlated with Aβ and proinflammatory factors.

Conclusion: Together, our finding highlighted that the alternation in gut microbiota affected the effective communication between the "gut-brain" axis in 5×FAD mice by regulating the immune system, carbohydrate, and energy metabolism.