Early Alzheimer's Disease Patients Research Articles

Novel Hominid-Specific IAPP Isoforms: Potential Biomarkers of Early Alzheimer's Disease and Inhibitors of Amyloid Formation.

(1) Background and aims: Amyloidosis due to aggregation of amyloid-β (Aβ42) is a key pathogenic event in Alzheimer's disease (AD), whereas aggregation of mature islet amyloid polypeptide (IAPP37) in human islets leads to β-cell dysfunction. The aim of this study is to uncover potential biomarkers that might additionally point to therapy for early AD patients. (2) Methods: We used bioinformatic approach to uncover novel IAPP isoforms and developed a quantitative selective reaction monitoring (SRM) proteomic assay to measure their peptide levels in human plasma and CSF from individuals with early AD and controls, as well as postmortem cerebrum of clinical confirmed AD and controls. We used Thioflavin T amyloid reporter assay to measure the IAPP isoform fibrillation propensity and anti-amyloid potential against aggregation of Aβ42 and IAPP37. (3) Results: We uncovered hominid-specific IAPP isoforms: hIAPPβ, which encodes an elongated propeptide, and hIAPPγ, which is processed to mature IAPP25 instead of IAPP37. We found that hIAPPβ was significantly reduced in the plasma of AD patients with the accuracy of 89%. We uncovered that IAPP25 and a GDNF derived DNSP11 were nonaggregating peptides that inhibited the aggregation of IAPP37 and Aβ42. (4) Conclusions: The novel peptides derived from hIAPP isoforms have potential to serve as blood-derived biomarkers for early AD and be developed as peptide based anti-amyloid medicine.

CD34+ progenitor cells as diagnostic and therapeutic targets in Alzheimer's disease.

CD34+ progenitor cells as diagnostic and therapeutic targets in Alzheimer's disease.

A Graph Convolutional Network Based on Univariate Neurodegeneration Biomarker for Alzheimer's Disease Diagnosis.

Alzheimer's disease (AD) is a progressive and irreversible neurodegenerative disease that is not easily detectable in the early stage. This study proposed an efficient method of applying a graph convolutional network (GCN) on the early prediction of AD. We proposed a univariate neurodegeneration biomarker (UNB) based GCN semi-supervised classification framework. We generated UNB by comparing the similarity of individual morphological atrophy pattern and the atrophy pattern of [Formula: see text] AD group according to the brain morphological abnormalities induced by AD. For the GCN semi-supervised classification model, we took the UNBs of individuals as the features of nodes and constructed the weight of edges according to the similarity of phenotypic information between individuals, which explored the essential features of individuals through spectral graph convolution. The attention module was constructed and embedded into the GCN framework, which may refine the input morphological features to highlight the main impact of AD on the cerebral cortex and weaken the instability caused by individual diversities, thereby identifying the significant ROIs affected by AD and improving the classification accuracy. We tested the UNB-GCN framework on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The estimated minimum sample sizes were 156, 349 and 423 for the longitudinal [Formula: see text] AD, [Formula: see text] mild cognitive impairment (MCI) and [Formula: see text] cognitively unimpaired (CU) groups, respectively. And the proposed UNB-GCN framework combined with the attention module can effectively improve the classification performance with 93.90% classification accuracy for AD vs. CU and 82.05% for AD vs. MCI on the validation set. The proposed UNB measures were superior to the conventional volume measures in describing the AD-induced cerebral cortex morphological changes. And the UNB-GCN framework combined with attention module may effectively improve the classification performance between MCI subjects and AD patients. Clinical and Translational Impact Statement: This study aims to predict the early AD patients, so as to help clinicians develop effective interventions to delay the deterioration of AD symptoms.

Sleep deprivation aggravated amyloid β oligomers-induced damage to the cerebellum of rats: Evidence from magnetic resonance imaging

For quite a long time, researches on Alzheimer's disease (AD) primarily focused on the cortex and hippocampus, while the cerebellum has been ignored because of its abnormalities considered to appear in the late stage of AD. In recent years, increasing evidence suggest that the cerebellar pathological changes possibly occur in the preclinical phase of AD, which is also associated with sleep disorder. Sleep disturbance is a high risk factor of AD. However, the changes and roles of cerebellum has rarely been reported under conditions of AD accompanied with sleep disorders. In this study, using an amyloid-β oligomers (AβO)-induced rat model of AD subjected to sleep deprivation, combining with a 7.0 T animals structural magnetic resonance imaging (MRI), we assessed structural changes of cerebellum in MRI. Our results showed that sleep deprivation combined with AβO led to an increased FA value in the anterior lobe of cerebellum, decreased ADC value in the cerebellar lobes and cerebellar nuclei, and increased cerebellum volume. Besides that, sleep deprivation exacerbated the damage of AβO to the cerebellar structural network. This study demonstrated that sleep deprivation could aggravate the damage to cerebellum induced by AβO. The present findings provide supporting evidence for the involvement of cerebellum in the early pathology of AD and sleep loss. Our data would contribute to advancing the understanding of the mysterious role of cerebellum in AD and sleep disorders, as well as would be helpful for developing non-invasive MRI biomarkers for screening early AD patients with self-reported sleep disturbances.

Synaptic proteins in neuron-derived extracellular vesicles as biomarkers for Alzheimer's disease: novel methodology and clinical proof of concept.

Blood biomarkers can improve drug development for Alzheimer's disease (AD) and its treatment. Neuron-derived extracellular vesicles (NDEVs) in plasma offer a minimally invasive platform for developing novel biomarkers that may be used to monitor the diverse pathogenic processes involved in AD. However, NDEVs comprise only a minor fraction of circulating extracellular vesicles (EVs). Most published studies have leveraged the L1 cell adhesion molecule (L1CAM) for NDEV immunocapture. We aimed to develop and optimize an alternative, highly specific immunoaffinity method to enrich blood NDEVs for biomarker development. After screening multiple neuronal antigens, we achieved NDEV capture with high affinity and specificity using antibodies against Growth-Associated Protein (GAP) 43 and Neuroligin 3 (NLGN3). The EV identity of the captured material was confirmed by electron microscopy, western blotting, and proteomics. The specificity for neuronal origin was demonstrated by showing enrichment for neuronal markers (proteins, mRNA) and recovery of spiked neuronal EVs. We performed NDEV isolation retrospectively from plasma samples from two cohorts of early AD patients (N = 19 and N = 40) and controls (N = 20 and N = 19) and measured p181-Tau, amyloid-beta (Aβ) 42, brain-derived neurotrophic factor (BDNF), precursor brain-derived neurotrophic factor (proBDNF), glutamate receptor 2 (GluR2), postsynaptic density protein (PSD) 95, GAP43, and syntaxin-1. p181-Tau, Aβ42, and NRGN were elevated in AD samples, whereas proBDNF, GluR2, PSD95, GAP43, and Syntaxin-1 were reduced. Differences for p181-Tau, proBDNF, and GluR2 survived multiple-comparison correction and were correlated with cognitive scores. A model incorporating biomarkers correctly classified 94.7% of AD participants and 61.5% of control participants. The observed differences in NDEVs-associated biomarkers are consistent with previous findings. NDEV isolation by GAP43 and NLGN3 immunocapture offers a robust novel platform for biomarker development in AD, suitable for large-scale validation.

Associations between cortical β-amyloid burden, fornix microstructure and cognitive processing of faces, places, bodies and other visual objects in early Alzheimer's disease.

Using two imaging modalities, that is, Pittsburgh compound B (PiB) positron emission tomography (PET) and diffusion tensor imaging (DTI) the present study tested associations between cortical amyloid-beta (Aβ) burden and fornix microstructural changes with cognitive deficits in early Alzheimer's disease (AD), namely deficits in working memory (1-back) processing of visual object categories (faces, places, objects, bodies and verbal material). Second, we examined cortical Aβ associations with fornix microstructure. Seventeen early AD patients and 17 healthy-matched controls were included. Constrained spherical deconvolution-based tractography was used to segment the fornix and a control tract the central branch of the superior longitudinal fasciculus (CB-SLF) previously implicated in working memory processes. Standard uptake value ratios (SUVR) of Aβ were extracted from 45 cortical/subcortical regions from the AAL atlas and subject to principal component analysis for data reduction. Patients exhibited (i) impairments in cognitive performance (ii) reductions in fornix fractional anisotropy (FA) and (iii) increases in a component that loaded highly on cortical Aβ. There were no group differences in CB-SLF FA and in a component loading highly on subcortical Aβ. Partial correlation analysis in the patient group showed (i) positive associations between fornix FA and performance for all the visual object categories and (ii) a negative association between the cortical Aβ component and performance for the object categories but not for the remaining classes of visual stimuli. A subsequent analysis showed a positive association between overall cognition (performance across distinct 1-back task conditions) with fornix FA but no association with cortical Aβ burden, in keeping with influential accounts on early onset AD. This indicates that the fornix degenerates early in AD and contributes to deficits in working memory processing of visual object categories; though it is also important to acknowledge the importance of prospective longitudinal studies with larger samples. Overall, the effect sizes of fornical degeneration on visual working memory appeared stronger than the ones related to amyloid burden.

Translational Inhibition of Multiple Neurotoxic Proteins Leads to Improved Cognition in Alzheimer’s Disease

AbstractBackgroundOverexpression of neurotoxic proteins drives downstream events that dysregulate axonal transport, lead to inflammation, nerve cell death, and loss of function. By inhibiting the translation of amyloid precursor protein, tau and alpha‐synuclein, our drug restored axonal transport, lowered inflammation, and protected nerve cells from dying in animal models. Buntanetap’s reversal of the toxic cascade leading to nerve cell death did preserve and/or restore full function in 7 different animal models of neurodegenerative diseases including Alzheimer’s disease (AD) and Parkinson’s disease (PD).MethodIn a double blind, placebo controlled Phase2a study, early AD patients were randomized into 80mg Buntanetap (n = 10) or placebo (n = 5) QD for 25±2 days. We evaluated the safety, pharmacokinetics of Buntanetap, biomarkers, and its efficacy.ResultIn clinical trials, Buntanetap was safe in over 200 subjects, both healthy volunteers and AD/PD patients. In our recent double‐blind, placebo controlled Phase2a study, patients randomized to either 80mg QD Buntanetap or placebo we measured biomarkers in AD patients and showed that Buntanetap can lower neurotoxic proteins, improve axonal integrity and synaptic function, and lower inflammation. Buntanetap has also improved AD patients’ cognitive functions as measured by ADASCog11 and WAIS coding test after 25±2 days treatment.ConclusionThese encouraging data support our further development of Buntanetap as a potential treatment for Alzheimer’s disease. We are planning three double‐blind, placebo‐controlled studies, a short 3‐month dose‐finding trial and two long‐term 12‐ and 18‐month trials in early and moderate AD patients.

The Impact Treatment Imbalance and Patient Enrichment has on Study Sample Size in Early/Mild Alzheimer’s Disease

AbstractBackgroundDisease modification trials in Alzheimer’s Disease (AD) are typically conducted in Early AD patients, combination of patients with mild cognitive impairment (MCI) and mild AD (mAD), despite vastly different progression rates between these two AD groups. A large sample size (SS) is required to show efficacy due to low MCI APRs. Furthermore, treatment group imbalances in patients likely to progress (PLP) may lead to underpowered studies. We quantified the impact on SS of: (1) potential treatment group imbalances in PLPs and (2) PLP enrichment strategies.MethodRoughly 10% of patients diagnosed with MCI progress to dementia within 1 year (Mitchell, 2009). Patients with mAD progress at an APR of 25% (Davis, 2018). To assess the impact on SS, we considered a 1:1 randomized study, stratified by equally sized AD strata (MCI, mAD) with 90% power and 1‐sided 0.025 level Cochran Mantel Haenszel (CMH) test to detect lower APRs in the experimental arm compared to placebo for an odds ratio (OR) = 0.5. To assess the impact of treatment group imbalances in PLPs, we computed SS for various departures from a balanced study. SS calculations were performed after enriching the APR in the MCI or both AD strata.ResultSS increases as more PLPs are assigned to the experimental arm than the control arm. For example, SS is 852 when PLPs are balanced 50%:50% between arms and 932 with a 55%:45% imbalance. When the MCI population is enriched to a 20% APR, SS is 672 for a balanced study and 732 with a 55%:45% imbalance. The following figure displays the impact of a PLP enrichment strategy on SS by increasing APRs by 5% in each AD strata, assuming no treatment group imbalances in PLPs.ConclusionPrediction (Davis, 2018) and machine learning (James, 2021) models have been developed to classify PLPs. Using such tools to stratify patients by PLP status at randomization minimizes the risk of having an underpowered study. The same tools can be used to enrich both the MCI and mild AD populations with PLPs to reduce SS requirements.

Selecting appropriate meaningful change thresholds for trials of early (prodromal‐to‐mild) AD: A caregiver‐rated, anchor‐based analysis based on the Tauriel Study

AbstractBackgroundClinically meaningful change thresholds for assessments used in Alzheimer’s disease (AD) trials, including the Clinical Dementia Rating Scale – Sum of Boxes (CDR‐SB), may differ by disease stage and are increasingly important to interpret efficacy. However, anchor‐based estimates for the CDR‐SB have largely used clinician‐ rather than caregiver‐rated anchors. We established meaningful change thresholds for the CDR‐SB using a caregiver‐rated anchor of global change in early (prodromal‐to‐mild) AD patients over an 18‐month clinical trial.MethodAnchor‐based analyses of the Tauriel data (NCT03289143) were conducted to establish CDR‐SB thresholds for biomarker confirmed early AD patients. The Caregiver Global Impression of Change – Alzheimer’s Disease (CaGI‐Alz), a 7‐point caregiver‐rated instrument, provided 2 independent anchors of meaningful decline in memory and activities of daily living (ADLs) (Figure 1). The anchor category of interest for establishing minimal meaningful change thresholds was the ‘Somewhat worse’ category. Average score changes were generated for the ‘No change’ category for comparison. Baseline CDR‐SB and Week 25, 49, and 73 CaGI‐Alz and CDR‐SB data were analyzed; correlations of >0.3 were deemed acceptable for anchor‐based analyses.ResultOur results focus on 49‐ and 73‐week time points based on strength of anchor‐measure correlation and group sample size (Table 1). At 49 weeks, prodromal participants in the ‘Somewhat worse’ category had average CDR‐SB changes of 1.54 (ADL anchor) and 1.08 (memory anchor), mild participants had changes of 2.22 (ADL) and 1.80 (memory), and the combined early AD population had changes of 1.97 (ADL) and 1.51 (memory). At 73 weeks, prodromal participants in the ‘Somewhat worse’ category had average CDR‐SB changes of 2.09 (ADL) and 1.52 (memory), mild participants had changes of 2.25 (ADL) and 2.32 (memory), and the combined early AD population had changes of 2.18 (ADL) and 2.01 (memory).ConclusionAlthough thresholds for meaningful change may differ across disease stages, AD is a continuum and the transition between prodromal to mild is often indistinct. In early (prodromal‐to‐mild) AD populations assessed over longer clinical trials, it may be appropriate to identify meaningful progression on the CDR‐SB using thresholds of 2–2.5 at the individual level.

Neurons derived from individual early Alzheimer's disease patients reflect their clinical vulnerability.

Establishing preclinical models of Alzheimer's disease that predict clinical outcomes remains a critically important, yet to date not fully realized, goal. Models derived from human cells offer considerable advantages over non-human models, including the potential to reflect some of the inter-individual differences that are apparent in patients. Here we report an approach using induced pluripotent stem cell-derived cortical neurons from people with early symptomatic Alzheimer's disease where we sought a match between individual disease characteristics in the cells with analogous characteristics in the people from whom they were derived. We show that the response to amyloid-β burden in life, as measured by cognitive decline and brain activity levels, varies between individuals and this vulnerability rating correlates with the individual cellular vulnerability to extrinsic amyloid-β in vitro as measured by synapse loss and function. Our findings indicate that patient-induced pluripotent stem cell-derived cortical neurons not only present key aspects of Alzheimer's disease pathology but also reflect key aspects of the clinical phenotypes of the same patients. Cellular models that reflect an individual's in-life clinical vulnerability thus represent a tractable method of Alzheimer's disease modelling using clinical data in combination with cellular phenotypes.

Buntanetap, a Novel Translational Inhibitor of Multiple Neurotoxic Proteins, Proves to Be Safe and Promising in Both Alzheimer’s and Parkinson’s Patients

BackgroundPreviously we reported the clinical safety and pharmacological activity of buntanetap (known as Posiphen or ANVS401) in healthy volunteers and mild cognitive impaired (MCI) patients (21). The data supported continued clinical evaluation of buntanetap for treating Alzheimer’s Disease (AD). Neurodegenerative diseases such as AD and Parkinson’s disease (PD) share several pathological manifestations, including increased levels of multiple neurotoxic protein aggregates. Therefore, a treatment strategy that targets toxic species common to both disorders can potentially provide better clinical outcomes than attacking one neurotoxic protein alone. To test this hypothesis, we recently completed a clinical study in early AD and early PD participants and report the data here.ObjectivesWe evaluated safety, pharmacokinetics, biomarkers, and efficacy of buntanetap in treating early AD and PD patients.DesignDouble-blind, placebo-controlled, multi-center study.Setting13 sites in the US participated in this clinical trial. The registration number is NCT04524351 at ClinicalTrials.gov.Participants14 early AD patients and 54 early PD patients.InterventionAD patients were given either 80mg buntanetap or placebo QD. PD patients were given 5mg, 10mg, 20mg, 40mg, 80mg buntanetap or placebo QD.MeasurementsPrimary endpoint is safety and tolerability; secondary endpoint is pharmacokinetics of buntanetap in plasma; exploratory endpoints are 1) biomarkers in cerebrospinal fluid (CSF) in both AD and PD patients 2) psychometric tests specific for AD (ADAS-Cogs & WAIS coding test) or PD (MDS-UPDRS & WAIS coding test).ResultsBuntanetap was safe and well tolerated. Biomarker data indicated a trend in lowering levels of neurotoxic proteins and inflammatory factors and improving axonal integrity and synaptic function in both AD and PD cohorts. Psychometric tests showed statistically significant improvements in ADAS-Cog11 and WAIS coding in AD patients and MDS-UPDRS and WAIS coding in PD patients.ConclusionsBuntanetap is well tolerated and safe at doses up to 80mg QD in both AD and PD patients. Cmax and AUC increase with dose without evidence for a plateau up to 80mg QD. The drug shows promising evidence in exploratory biomarker and efficacy measures. Further evaluation of buntanetap in larger, longer-term clinical trials for the treatment of AD and PD are warranted.

Increase in Mitochondrial D-Loop Region Methylation Levels in Mild Cognitive Impairment Individuals

Methylation levels of the mitochondrial displacement loop (D-loop) region have been reported to be altered in the brain and blood of Alzheimer’s disease (AD) patients. Moreover, a dynamic D-loop methylation pattern was observed in the brain of transgenic AD mice along with disease progression. However, investigations on the blood cells of AD patients in the prodromal phases of the disease have not been performed so far. The aim of this study was to analyze D-loop methylation levels by means of the MS-HRM technique in the peripheral blood cells of 14 mild cognitive impairment (MCI) patients, 18 early stage AD patients, 70 advanced stage AD patients, and 105 healthy control subjects. We found higher D-loop methylation levels in MCI patients than in control subjects and AD patients. Moreover, higher D-loop methylation levels were observed in control subjects than in AD patients in advanced stages of the disease, but not in those at early stages. The present pilot study shows that peripheral D-loop methylation levels differ in patients at different stages of AD pathology, suggesting that further studies deserve to be performed in order to validate the usefulness of D-loop methylation analysis as a peripheral biomarker for the early detection of AD.

First-in-human study of blood amyloid β removal from early Alzheimer's disease patients with normal kidney function.

Amyloid β (Aβ) is a brain protein that causes Alzheimer's disease (AD). This study aimed to verify whether hemadsorption using a hexadecyl-alkylated cellulose bead (HexDC) column removes blood Aβ and brain Aβ accumulation in mild cognitive impairment/mild AD cases with normal kidney function. Two patients with positive Aβ on brain imaging underwent HexDC hemadsorption weekly for 6 months. The Aβ removal efficiency of HexDC was 87-99%. Aβ1-40 /Aβ1-42 influx into the blood in one session was 596/56 and 489/48 ng for Case A and Case B, respectively. Although brain Aβ accumulation did not clearly change after 6 months of hemadsorption, cognitive functions measured by the two tests were maintained or slightly improved. Blood Aβ removal was performed in two early AD patients with normal kidney function without adverse events, and it slightly improved or maintained cognitive function.

Lack of association between cortical amyloid deposition and glucose metabolism in early stage Alzheimer´s disease patients.

BackgroundBeta amyloid (Aβ) causes synaptic dysfunction leading to neuronal death. It is still controversial if the magnitude of Aβ deposition correlates with the degree of cognitive impairment. Diagnostic imaging may lead to a better understanding the role of Aβ in development of cognitive deficits. The aim of the present study was to investigate if Aβ deposition in the corresponding brain region of early stage Alzheimer´s disease (AD) patients, directly correlates to neuronal dysfunction and cognitive impairment indicated by reduced glucose metabolism.Patients and methodsIn 30 patients with a clinical phenotype of AD and amyloid positive brain imaging, 2-[18F] fluoro-2-deoxy-d-glucose (FDG) PET/CT was performed. We extracted the average [18F] flutemetamol (Vizamyl) uptake for each of the 16 regions of interest in both hemispheres and computed the standardized uptake value ratio (SUVR) by dividing the Vimazyl intensities by the mean signal of positive and negative control regions. Data were analysed using the R environment for statistical computing and graphics.ResultsAny negative correlation between Aβ deposition and glucose metabolism in 32 dementia related and corresponding brain regions in AD patients was not found. None of the correlation coefficient values were statistically significant different from zero based on two-sided p- value.ConclusionsRegional Aβ deposition did not correlate negatively with local glucose metabolism in early stage AD patients. Our findings support the role of Aβ as a valid biomarker, but does not permit to conclude that Aβ is a direct cause for an aberrant brain glucose metabolism and neuronal dysfunction.

Identifying cognitive trajectories and predicting rapid decline of cognitive function in early Alzheimer’s disease

AbstractBackgroundThe clinical manifestations and progression of cognitive function vary between patients with Alzheimer disease (AD) and mild cognitive impairment (MCI). Predicting disease course is important for families and clinicians because most patients experience progressive and irreversible cognitive decline despite variations.MethodsThis study sought to identify groups of patients following a similar trajectory of cognitive decline over time, and then developed a prediction model to identify patients at higher risk for a speedy cognitive decline. This study was conducted on a clinic‐based cohort of 233 early AD and MCI patients who converted to AD within 2 years in Taiwan. A group‐based trajectory modeling technique characterized the trajectory groups of Mini‐Mental State Examination (MMSE) scores over time. Feature‐selection strategy was then used to investigate predictors of the trajectory of rapid cognitive decline. Finally, multivariable logistic regression (LR) was applied to predict the trajectory group membership from the predictors.ResultsThe best‐fitting model includes two meaningful trajectories: Group 1 shows a rapid decline in the MMSE scores over time (decrease of 5.4±3.9 points within 2 years); Group 2 shows a slow progression (decrease of 1.2±3.9 points within 2 years). The baseline MMSE score, instrumental activities daily living (IADL) total score, and APOE ε4 status are significant predictors in the prediction model for classifying the trajectory of cognitive decline. After bootstrapping validation, the performance of the LR model using these predictors to predict trajectory groups shows 85.3% of area under curve (AUC), 79.0% of sensitivity, 73.1% of specificity, and 75.4% of predictive accuracy.ConclusionsAmong various biomarkers and neuropsychological battles for patients with AD or MCI, baseline MMSE score, IADL total score and APOE ε4 status are the ones important and unchallenging to obtain in dementia clinics. The LR model for the prediction model is easy to understand and apply in the clinical practice. This model gives clinicians and patients a glimpse of future cognitive progression and aids further healthcare planning.

Building memories on prior knowledge: behavioral and fMRI evidence of impairment in early Alzheimer's disease

Impaired memory is a hallmark of prodromal Alzheimer's disease (AD). Prior knowledge associated with the memoranda improves memory in healthy individuals, but we ignore whether the same occurs in early AD. We used functional MRI to investigate whether prior knowledge enhances memory encoding in early AD, and whether the nature of this prior knowledge matters. Patients with early AD and Controls underwent a task-based fMRI experiment where they learned face-scene associations. Famous faces carried pre-experimental knowledge (PEK), while unknown faces with which participants were familiarized prior to learning carried experimental knowledge (EK). Surprisingly, PEK strongly enhanced subsequent memory in healthy controls, but importantly not in patients. Partly nonoverlapping brain networks supported PEK vs. EK associative encoding in healthy controls. No such networks were identified in patients. In addition, patients displayed impaired activation in a right sub hippocampal region where activity predicted successful associative memory formation for PEK stimuli. Despite the limited sample sizes of this study, these findings suggest that the role prior knowledge in new learning might have been so far overlooked and underestimated in AD patients. Prior knowledge may drive critical differences in the way healthy elderly and early AD patients learn novel associations.

Artificial Intelligence-based MRI Images for Brain in Prediction of Alzheimer's Disease.

The study aimed to explore the accuracy and stability of Deep metric learning (DML) algorithm in Magnetic Resonance Imaging (MRI) examination of Alzheimer's Disease (AD) patients. In this study, MRI data of patients obtained were from Alzheimer's Disease Neuroimaging Initiative (ADNI) database (A total of 180 AD cases, 88 women, 92 men; 188 samples in healthy conditions (HC), including 90 females and 98 males. 210 samples of mild cognitive impairment (MCI), 104 females and 106 males). On the basis of deep learning, an early AD diagnosis system was constructed using CNN (Convolutional Neural Network) and DML algorithms. Then, the system was used to classify AD, HC, and MCI, and the two algorithms were compared for the accuracy and stability of in classification of MRI images. It was found that in the classification of AD and HC, the classification accuracy and sensitivity of the deep measurement learning model are both 0.83, superior to the CNN model; in terms of specificity, the classification specificity of the DML model was 0.82, slightly lower than that of the CNN model; and that in the classification of MCI and HC, the classification accuracy and sensitivity of the DML model was 0.65, superior to the CNN model; and in terms of specificity, the classification specificity of the DML model was 0.66, slightly lower than that of the CNN model. It suggested that the DML model demonstrated better classification effects on early AD patients. The loss curve analysis results showed that, for classification of AD and HC or MCI and HC, the DML algorithm can improve the convergence speed of the AD early prediction model. Therefore, the DML algorithm can significantly improve the clarity and quality of MRI images, elevate the classification accuracy and stability of early AD patients, and accelerate the convergence of the model, providing a new way for early prediction of AD.

Aducanumab and the “post-amyloid” era of Alzheimer research?

Aducanumab and the “post-amyloid” era of Alzheimer research?

F-18 ML-104 tau PET imaging in mild cognitive impairment.

This study was undertaken to evaluate the tau distribution patterns in patients with amnestic mild cognitive impairment (aMCI) using PET radiotracer F-18 ML-104. Thirty patients, clinically diagnosed as aMCI [mini mental state evaluation ≥24] in the neurology or geriatric memory clinics, were included in the study. Each aMCI patient underwent F-18 fluorodeoxyglucose and F-18 ML-104 tau PET. Standardized uptake value ratios for cortical gray matter regions were evaluated for F-18 ML-104 tau PET and compared with normal controls and with early Alzheimer's disease (AD) patients (used from a previous study). aMCI revealed significantly higher standardized uptake value ratios in both medial temporal cortices, precuneus and posterior cingulate cortices in comparison to normal controls and a significantly lesser binding in bilateral medial and lateral temporal, precuneus and posterior cingulate cortices in comparison to early AD. A negative correlation was noted between F-18 fluorodeoxyglucose uptake and F-18 ML-104 retention in the precuneus and posterior cingulate cortices in aMCI, while F-18 ML-104 retention and mini mental state evaluation scores revealed a moderate negative correlation in the posterior cingulate cortices. We could demonstrate a significant increase in cortical tau deposition in aMCI patients in comparison to normal controls, thus providing in vivo evidence of the underlying pathological process in this subgroup of patients with high probability of conversion to AD.

A systems-biology clinical trial of a personalized multimodal lifestyle intervention for early Alzheimer's disease.

IntroductionThere is an urgent need to develop effective interventional treatments for people with Alzheimer's disease (AD). AD results from a complex multi‐decade interplay of multiple interacting dysfunctional biological systems that have not yet been fully elucidated. Epidemiological studies have linked several modifiable lifestyle factors with increased incidence for AD. Because monotherapies have failed to prevent or ameliorate AD, interventional studies should deploy multiple, targeted interventions that address the dysfunctional systems that give rise to AD.MethodsThis randomized controlled trial (RCT) will examine the efficacy of a 12‐month personalized, multimodal, lifestyle intervention in 60 mild cognitive impairment (MCI) and early stage AD patients (aged 50+, amyloid positivity). Both groups receive data‐driven, lifestyle recommendations designed to target multiple systemic pathways implicated in AD. One group receives these personalized recommendations without coaching. The other group receives personalized recommendations with health coaching, dietary counseling, exercise training, cognitive stimulation, and nutritional supplements. We collect clinical, proteomic, metabolomic, neuroimaging, and genetic data to fuel systems‐biology analyses. We will examine effects on cognition and hippocampal volume. The overarching goal of the study is to longitudinally track biological systems implicated in AD to reveal the dynamics between these systems during the intervention to understand differences in treatment response.ResultsWe have developed and implemented a protocol for a personalized, multimodal intervention program for early AD patients. We began enrollment in September 2019; we have enrolled a third of our target (20 of 60) with a 95% retention and 86% compliance rate.DiscussionThis study presents a paradigm shift in designing multimodal, lifestyle interventions to reduce cognitive decline, and how to elucidate the biological systems being targeted. Analytical efforts to explain mechanistic or causal underpinnings of individual trajectories and the interplay between multi‐omic variables will inform the design of future hypotheses and development of effective precision medicine trials.