Alzheimer's Research & Therapy最新文献

Alzheimer's disease (AD) is a debilitating neurodegenerative disorder with a global impact, yet its pathogenesis remains poorly understood. While age, metabolic abnormalities, and accumulation of neurotoxic substances are potential risk factors for AD, their effects are confounded by other factors. To address this challenge, we first utilized multi-omics data from 87 well phenotyped AD patients and generated plasma proteomics and metabolomics data, as well as gut and saliva metagenomics data to investigate the molecular-level alterations accounting the host-microbiome interactions. Second, we analyzed individual omics data and identified the key parameters involved in the severity of the dementia in AD patients. Next, we employed Artificial Intelligence (AI) based models to predict AD severity based on the significantly altered features identified in each omics analysis. Based on our integrative analysis, we found the clinical relevance of plasma proteins, including SKAP1 and NEFL, plasma metabolites including homovanillate and glutamate, and Paraprevotella clara in gut microbiome in predicting the AD severity. Finally, we validated the predictive power of our AI based models by generating additional multi-omics data from the same group of AD patients by following up for 3 months. Hence, we observed that these results may have important implications for the development of potential diagnostic and therapeutic approaches for AD patients.

Background: Recent magnetic resonance imaging (MRI) studies have established that brain iron accumulation might accelerate cognitive decline in Alzheimer's disease (AD) patients. Both normal aging and AD are associated with cerebral atrophy in specific regions. However, no studies have investigated aging- and AD-selective iron deposition-related cognitive changes during normal aging. Here, we applied quantitative susceptibility mapping (QSM) to detect iron levels in cortical signature regions and assessed the relationships among iron, atrophy, and cognitive changes in older adults.

Methods: In this Taizhou Imaging Study, 770 older adults (mean age 62.0 ± 4.93 years, 57.5% women) underwent brain MRI to measure brain iron and atrophy, of whom 219 underwent neuropsychological tests nearly every 12 months for up to a mean follow-up of 2.68 years. Global cognition was assessed using the Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA). Domain-specific cognitive scores were obtained from MoCA subscore components. Regional analyses were performed for cortical regions and 2 signature regions where atrophy affected by aging and AD only: Aging (AG) -specific and AD signature meta-ROIs. The QSM and cortical morphometry means of the above ROIs were also computed.

Results: Significant associations were found between QSM levels and cognitive scores. In particular, after adjusting for cortical thickness of regions of interest (ROIs), participants in the upper tertile of the cortical and AG-specific signature QSM exhibited worse ZMMSE than did those in the lower tertile [ $\beta$ = -0.104, p = 0.026; $\beta$ = -0.118, p = 0.021, respectively]. Longitudinal analysis suggested that QSM values in all ROIs might predict decline in ZMoCA and key domains such as attention and visuospatial function (all p < 0.05). Furthermore, iron levels were negatively correlated with classic MRI markers of cortical atrophy (cortical thickness, gray matter volume, and local gyrification index) in total, AG-specific signature and AD signature regions (all p < 0.05).

Conclusion: AG- and AD-selective iron deposition was associated with atrophy and cognitive decline in elderly people, highlighting its potential as a neuroimaging marker for cognitive aging.

Background: Cholinesterase inhibitors (ChEIs) are prescribed for Alzheimer's disease (AD) and sometimes for mild cognitive impairment (MCI) without knowing underlying pathologies and its effect on cognition. We investigated the frequency of ChEI prescriptions in amyloid-negative MCI and their association with cognitive changes in the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort.

Methods: We included participants with amyloid positron emission tomography (PET)-negative MCI from the ADNI. We analyzed the associations of ChEI use with cognitive changes, brain volume, and cerebrospinal fluid (CSF) total tau (t-tau), hyperphosphorylated tau₁₈₁ (p-tau₁₈₁), and p-tau₁₈₁/t-tau ratio.

Results: ChEIs were prescribed in 27.4% of amyloid PET-negative MCI and were associated with faster cognitive decline, reduced baseline hippocampal volume and entorhinal cortical thickness, and a longitudinal decrease in the frontal lobe cortical thickness.

Conclusions: The association between ChEI use and accelerated cognitive decline may stem from underlying pathologies involving reduced hippocampal volume, entorhinal cortical thickness and faster frontal lobe atrophy. We suggest that ChEI use in amyloid PET-negative MCI patients might need further consideration, and studies investigating the causality between ChEI use and cognitive decline are warranted in the future.

Background: Glucagon-like peptide-1 receptor agonists are a viable option for the prevention of Alzheimer's disease (AD) but the mechanisms of this potential disease modifying action are unclear. We investigated the effects of once-weekly exenatide (EQW) on AD associated proteomic clusters.

Methods: The Exenatide Study of Cardiovascular Event Lowering study compared the cardiovascular effects of EQW 2 mg with placebo in 13,752 people with type 2 diabetes mellitus. 4,979 proteins were measured (Somascan V0.4) on baseline and 1-year plasma samples of 3,973 participants. C-reactive protein (CRP), ficolin-2 (FCN2), plasminogen activator inhibitor 1 (PAI-1), soluble vascular cell adhesion protein 1 (sVCAM1) and 4 protein clusters were tested in multivariable mixed models.

Results: EQW affected FCN2 (Cohen's d -0.019), PAI-1 (Cohen's d -0.033), sVCAM-1 (Cohen's d 0.035) and a cytokine-cytokine cluster (Cohen's d 0.037) significantly compared with placebo. These effects were sustained in individuals over the age of 65 but not in those under 65.

Conclusions: EQW treatment was associated with significant change in inflammatory proteins associated with AD.

Trial registration: EXSCEL is registered on ClinicalTrials.gov: NCT01144338 on 10th of June 2010.

Background: Apolipoprotein E (APOE) genotypes have been suggested to influence cognitive impairment and clinical onset in presenilin-1 (PSEN1) E280A carriers for autosomal dominant Alzheimer's disease (ADAD). Less is known about their impact on the trajectory of biomarker changes. Neurofilament light chain (NfL), a marker of neurodegeneration, begins to accumulate in plasma about 20 years prior to the clinical onset of ADAD. In this study we investigated the impact of APOE ε4 and ε2 variants on age-related plasma NfL increases and cognition in PSEN1 E280A mutation carriers.

Methods: We analyzed cross-sectional data from PSEN1 E280A mutation carriers and non-carriers recruited from the Alzheimer's Prevention Initiative Registry of ADAD. All participants over 18 years with available APOE genotype, plasma NfL, and neuropsychological evaluation were included in this study. APOE genotypes and plasma NfL concentrations were characterized for each participant. Cubic spline models using a Hamiltonian Markov chain Monte Carlo method were used to characterize the respective impact of at least one APOE ε4 or ε2 allele on age-related log-transformed plasma NfL increases. Linear regression models were estimated to explore the impact of APOE ε4 and ε2 variants and plasma NfL on a composite cognitive test score in the ADAD mutation carrier and non-carrier groups.

Results: Analyses included 788 PSEN1 E280A mutation carriers (169 APOE ε4 + , 114 ε2 +) and 650 mutation non-carriers (165 APOE ε4 + , 80 ε2 +), aged 18-75 years. APOE ε4 allele carriers were distinguished from ε4 non-carriers by greater age-related NfL elevations in the ADAD mutation carrier group, beginning about three years after the mutation carriers' estimated median age at mild cognitive impairment onset. APOE ε2 allele carriers had lower plasma NfL concentrations than ε2 non-carriers in both the ADAD mutation carrier and non-carrier groups, unrelated to age, and an attenuated relationship between higher NfL levels on cognitive decline in the ADAD mutation carrier group.

Conclusions: APOE ε4 accelerates age-related plasma NfL increases and APOE ε2 attenuates the relationship between higher plasma NfL levels and cognitive decline in ADAD. NfL may be a useful biomarker to assess clinical efficacy of APOE-modifying drugs with the potential to help in the treatment and prevention of ADAD.

Background: Several blood-based biomarkers offer the opportunity of in vivo detection of brain pathology and neurodegeneration in Alzheimer disease with high specificity and sensitivity, but the performance of amyloid-β (Aβ) measurements remains under evaluation. Autosomal dominant Alzheimer disease (ADAD) with mutations in PSEN1, PSEN2 and APP can be studied as a model for sporadic Alzheimer disease. However, clarifying the genetic effects on the Aβ-levels in different matrices such as cerebrospinal fluid or plasma is crucial for generalizability and utility of data. We aimed to explore plasma Aβ concentrations over the Alzheimer disease continuum in a longitudinal cohort of genetic Alzheimer disease.

Methods: 92 plasma samples were collected from at-risk individuals (n = 47) in a Swedish cohort of ADAD, including 18 mutation carriers (13 APPswe (p.KM670/671NL) MC), 5 PSEN1 (p.H163Y) MC) and 29 non-carriers (NC) as the reference group. Concentrations of Aβ1-38, Aβ1-40 and Aβ1-42 were analyzed in plasma using immunoprecipitation coupled to tandem liquid chromatography mass spectrometry (IP-LC-MS/MS). Cross-sectional and repeated-measures data analyses were investigated family-wise, applying non-parametric tests as well as mixed-effects models.

Results: Cross-sectional analysis at baseline showed more than a 3-fold increase in all plasma Aβ peptides in APPswe MC, regardless of clinical status, compared to controls (p < 0.01). PSEN1 (p.H163Y) presymptomatic MC had a decrease of plasma Aβ1-38 compared to controls (p < 0.05). There was no difference in Aβ1-42/1-40 ratio between APPswe MC (PMC and SMC), PSEN1 MC (PMC) and controls at baseline. Notably, both cross-sectional data and repeated-measures analysis suggested that APPswe MC have a stable Aβ1-42/1-40 ratio with increasing age, in contrast to the decrease seen with aging in both controls and PSEN1 (p.H163Y) MC.

Conclusion: These data show very strong mutation-specific effects on Aβ profiles in blood, most likely due to a ubiquitous production outside of the CNS. Hence, analyses in an unselected clinical setting might unintentionally disclose genetic status. Furthermore, our findings suggest that the Aβ ratio might be a poor indicator of brain Aβ pathology in selected genetic cases. The very small sample size is a limitation that needs to be considered but reflects the scarcity of longitudinal in vivo data from genetic cohorts.