Alzheimer's Research & Therapy最新文献

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.

Background: The accumulation of amyloid-β (Aβ) peptide in the brain is a hallmark of Alzheimer's disease (AD), occurring years before symptom onset. Current methods for quantifying in vivo amyloid load involve invasive or costly procedures, limiting accessibility. Early detection of amyloid positivity in non-demented individuals is crucial for aiding early AD diagnosis and for initiating anti-amyloid immunotherapies at early stages. This study aimed to develop and validate predictive models to identify brain amyloid positivity in non-demented patients, using routinely collected clinical data.

Methods: Predictive models for amyloid positivity were developed using data from 853 non-demented participants in the MEMENTO cohort. Amyloid levels were measured potentially repeatedly during study course through Positron Emision Tomography or CerebroSpinal Fluid analysis. The probability of amyloid positivity was modelled using mixed-effects logistic regression. Predictors included demographic information, cognitive assessments, visual brain MRI evaluations of hippocampal atrophy and lobar microbleeds, AD-related blood biomarkers (Aβ42/40 and P-tau181), and ApoE4 status. Models were subjected to internal cross-validation and external validation using data from the Amsterdam Dementia Cohort. Performance also was evaluated in a subsample that met the main criteria of the Appropriate Use Recommendations (AUR) for lecanemab.

Results: The most effective model incorporated demographic data, cognitive assessments, ApoE status, and AD-related blood biomarkers, achieving AUCs of 0.82 [95%CI 0.81-0.82] in MEMENTO sample and 0.90 [95%CI 0.86-0.94] in the external validation sample. This model significantly outperformed a reference model based solely on demographic and cognitive data, with an AUC difference in MEMENTO of 0.10 [95%CI 0.10-0.11]. A similar model without ApoE genotype achieved comparable discriminatory performance. MRI markers did not improve model performance. Performances in AUR of lecanemab subsample were comparable.

Conclusion: A predictive model integrating demographic, cognitive, and blood biomarker data offers a promising method to help identify amyloid status in non-demented patients. ApoE genotype and brain MRI data were not necessary for strong discriminatory ability, suggesting that ApoE genotyping may be deferred when assessing the risk-benefit ratio of immunotherapies in amyloid-positive patients who desire treatment. The integration of this model into clinical practice could reduce the need for lumbar puncture or PET examinations to confirm amyloid status.

Background: The associations of different obesity and metabolic phenotypes during midlife with the risk of incident dementia remain unclear. This study aimed to investigate the associations between metabolic heterogeneity of obesity and long-term risk of dementia.

Methods: We conducted prospective analyses from three cohorts, including the UK Biobank (UKB), Atherosclerosis Risk in Communities (ARIC) study, and Framingham Offspring Study (FOS). Eligible participants were those aged 45-65 years with valid assessments of body mass index (BMI) and metabolic status at the study baseline. Obesity was defined as a BMI of ≥ 30.0 kg/m², while metabolic abnormality was defined as meeting ≥ 2 of the National Cholesterol Education Program-Adult Treatment Panel III (NCEP-ATP III) criteria. Metabolic heterogeneity of obesity was evaluated based on obesity and metabolic phenotypes and grouped as metabolically normal non-obesity (MNNO), metabolically abnormal non-obesity (MANO), metabolically normal obesity (MNO), and metabolically abnormal obesity (MAO).

Results: Included in this study were 295,823 participants aged 56.3 ± 5.9 years from the UKB, 12,547 participants aged 54.0 ± 5.7 years from the ARIC, and 2,004 participants aged 53.9 ± 5.9 years from the FOS. Over 4,348,208 person-years, a total of 6,190 participants (3,601 in the UKB, 2,405 in the ARIC, and 184 in the FOS) developed incident dementia. In the pooled analysis of three cohorts, metabolic abnormality was associated with a hazard ratio (HR) of 1.41 (95% confidence interval [CI]: 1.10-1.80) for dementia, while obesity was associated with an HR of 1.20 (1.03-1.41). Compared with MNNO, individuals with MANO and MAO had increased risks of dementia (pooled HR: 1.33, 95% CI: 1.04-1.71 for MANO and 1.48, 1.16-1.89 for MAO). However, there was no significant difference in the risk of dementia among MNO (pooled HR: 1.10, 95% CI: 0.98-1.24). In addition, participants who recovered from MANO to MNNO had a lower risk of dementia (pooled HR: 0.79, 95% CI: 0.64-0.97), as compared with stable MANO.

Conclusions: Metabolic abnormality has a stronger association with dementia than obesity. Metabolically abnormal non-obesity and obesity, but not metabolically normal obesity, are associated with higher risks of incident dementia as compared with metabolically normal non-obesity. Recovering from an abnormal metabolic status to normal reduces the risk of dementia in populations without obesity. Our findings highlight the important role of metabolic status in the development of dementia and recommend the stratified management of obesity based on metabolic status.

Background: SHR-1707 is a novel humanized anti-Aβ IgG1 monoclonal antibody that binds to Aβ fibrils and monomers to block the formation of Aβ plaques or to promote the microglial phagocytosis of Aβ. Preclinical studies showed that SHR-1707 reduced brain Aβ deposition in 5xFAD transgenic mice. Herein, we conducted two phase 1 studies to evaluate the safety, tolerability, pharmacokinetics (PK) and pharmacodynamics (PD) of a single intravenous dose of SHR-1707 in healthy adult subjects.

Methods: Two randomized, double-blind, single-ascending-dose, phase 1 studies were conducted in China (Study CHN) and Australia (Study AUS). Study CHN consisted of 2 parts. In Part 1, eligible healthy young adults (18-45 years) were sequentially randomized 8:2 to receive SHR-1707 (five cohorts: 2, 6, 20, 40, and 60 mg/kg) or placebo in each cohort; in Part 2, elderly subjects (55-80 years) were randomized 8:4 to receive SHR-1707 (20 mg/kg) or placebo. A similar design was used in Study AUS, but with only healthy young adults enrolled across three dosing cohorts (2, 20, and 60 mg/kg).

Results: Sixty-two (part 1/2, n = 50/12; age range, 18-42/55-63 years) and 30 subjects (age range, 18-42 years) received SHR-1707 or placebo in Study CHN and Study AUS, respectively. In Study CHN, all treatment-related adverse events (TRAEs) were mild, with the most common being transient laboratory abnormalities. In Study AUS, TRAEs were mostly mild (1 moderate event each with SHR-1707/placebo); the most common TRAEs with SHR-1707 were dysgeusia and fatigue (8.3% each). In both studies, the exposure of SHR-1707 increased in a slightly greater than dose-proportional manner over the dose range of 2-60 mg/kg in young adults; there was a dose-dependent increase in plasma Aβ42 concentration following SHR-1707 administration compared with the placebo group. The safety and PK and PD profiles of SHR-1707 in the elderly subjects were consistent with the younger counterpart at the same dose level. No ethnic difference in safety, PK and PD of SHR-1707 was observed.

Conclusions: A single intravenous dose of SHR-1707 at 2-60 mg/kg was safe and well tolerated in healthy young adult and elderly subjects. The PK and PD profiles are supportive for further clinical development.

Trial registration: NCT04973189 (retrospectively registered on Jul.21, 2021) and NCT04745104 (registered on Feb.6, 2021) on clinicaltrials.gov.

Background: Alzheimer's Disease (AD) is the most common form of dementia. Its early stage, amnestic Mild Cognitive Impairment (aMCI), is characterized by disrupted information flow in the brain. Previous studies have yielded inconsistent results when using electrophysiological techniques to investigate functional connectivity changes in AD, and a contributing factor may be the study of brain activity divided into frequencies.

Methods: Our study aimed to address this issue by employing a cross-frequency approach to compare the functional networks of 172 healthy subjects and 105 aMCI patients. Using magnetoencephalography, we constructed source-based multilayer graphs considering both intra- and inter-frequency functional connectivity. We then assessed changes in network organization through three centrality measures, and combined them into a unified centrality score to provide a comprehensive assessment of centrality disruption in aMCI.

Results: The results revealed a noteworthy shift in centrality distribution in aMCI patients, both in terms of spatial distribution and frequency. Posterior brain regions decrease synchrony between their high-frequency oscillations and other regions' activity across all frequencies, while anterior regions increase synchrony between their low-frequency oscillations and other regions' activity across all frequencies. Thus, posterior regions reduce their relative importance in favor of anterior regions.

Conclusions: Our findings provide valuable insights into the intricate changes that occur in functional brain networks during the early stages of AD, demonstrating that considering the interplays between different frequency bands enhances our understanding of AD network dynamics and setting a precedent for the study of functional networks using a multilayer approach.

Background: There is a substantial body of observational research indicating an association between hearing impairment and dementia, yet the causal relationship and underlying mechanisms remain uncertain. This study aims to investigate the causal relationship between hearing impairment and its subtypes with dementia and cognitive function using two-sample Mendelian randomization (MR) analysis.

Methods: We performed two-sample MR analysis to examine the causal effects of hearing impairment and its subtypes, including conductive and sensorineural hearing loss (CSHL), conductive hearing loss (CHL), sensorineural hearing loss (SHL), and sudden sensorineural hearing loss (SIHL), on six dementia phenotypes (overall dementia, Alzheimer's disease [AD], Lewy body dementia [DLB], frontotemporal dementia [FTD], Parkinson's disease dementia, and vascular dementia) and four cognitive functions. Additionally, multivariable MR (MVMR) analysis was employed to investigate potential mediating mechanisms.

Results: Genetically determined CSHL was associated with an elevated risk of DLB (odds ratio [OR] 1.69; 95% CI 1.08 to 2.63; P = 0.021) and FTD (OR 1.66; 1.04 to 2.67; P = 0.035), but not AD (P = 0.958). Genetic predisposition to CHL was found to link with increased risks of AD (OR 1.07; 1.01 to 1.14; P = 0.031). Genetically determined SHL was causally associated with an elevated risk of semantic dementia (OR 3.81; 1.09 to 13.37; P = 0.037). Genetically predicted CHL and SIHL were both causally associated with lower general cognitive performance (β -0.015 and - 0.043; P = 0.007 and 0.013) and fluid intelligence score (β -0.045 and - 0.095; P = 0.037 and 0.040). In MVMR analysis, the causal relationship between hearing impairment and dementia was mediated by loneliness, depressed mood, and brain cortical volume, particularly the medial temporal lobe, but not by aging or ischemic stroke.

Conclusion: Overall, the study provides evidence supporting a causal relationship between hearing impairment and increased risks of different types of dementia (including AD, FTD, and DLB), as well as poorer general cognitive function. These findings underscore the importance of addressing hearing impairment as a modifiable risk factor for dementia.