The dynamics of biomarkers across the clinical spectrum of Alzheimer's disease

Analysis of Cerebrospinal Fluid, Plasma β-Amyloid Biomarkers, and Cognition from a 2-Year Phase 2 Trial Evaluating Oral ALZ-801/Valiltramiprosate in APOE4 Carriers with Early Alzheimer's Disease Using Quantitative Systems Pharmacology Model

INTRODUCTION

ALZ-801/valiltramiprosate is an oral, small-molecule inhibitor of beta-amyloid (Aβ) aggregation and oligomer formation in late-stage development as a disease-modifying therapy for early Alzheimer's disease (AD). The present investigation provides a quantitative systems pharmacology (QSP) analysis of amyloid fluid biomarkers and cognitive results from a 2-year ALZ-801 Phase 2 trial in APOE4 carriers with early AD.

METHODS

The single-arm, open-label phase 2 study evaluated effects of ALZ-801 265 mg two times daily (BID) on cerebrospinal fluid (CSF) and plasma amyloid fluid biomarkers over 104 weeks in APOE4 carriers with early AD [Mini-Mental State Examination (MMSE) ≥ 22]. Subjects with positive CSF biomarkers for amyloid (Aβ42/Aβ40) and tau pathology (p-tau181) were enrolled, with serial CSF and plasma levels of Aβ42 and Aβ40 measured over 104 weeks. Longitudinal changes of CSF Aβ42, plasma Aβ42/Aβ40 ratio, and cognitive Rey Auditory Verbal Learning Test (RAVLT) were compared with the established natural disease trajectories in AD using a QSP approach. The natural disease trajectory data for amyloid biomarkers and RAVLT were extracted from a QSP model and an Alzheimer's disease neuroimaging initiative population model, respectively. Analyses were stratified by disease severity and sex.

RESULTS

A total of 84 subjects were enrolled. Excluding one subject who withdrew at the early stage of the trial, data from 83 subjects were used for this analysis. The ALZ-801 treatment arrested the progressive decline in CSF Aβ42 level and plasma Aβ42/Aβ40 ratio, and stabilized RAVLT over 104 weeks. Both sexes showed comparable responses to ALZ-801, whereas mild cognitive impairment (MCI) subjects (MMSE ≥ 27) exhibited a larger biomarker response compared with more advanced mild AD subjects (MMSE 22-26).

CONCLUSIONS

In this genetically defined and biomarker-enriched early AD population, the QSP analysis demonstrated a positive therapeutic effect of oral ALZ-801 265 mg BID by arresting the natural decline of monomeric CSF and plasma amyloid biomarkers, consistent with the target engagement to prevent their aggregation into soluble neurotoxic oligomers and subsequently into insoluble fibrils and plaques over 104 weeks. Accompanying the amyloid biomarker changes, ALZ-801 also stabilized the natural trajectory decline of the RAVLT memory test, suggesting that the clinical benefits are consistent with its mechanism of action. This sequential effect arresting the disease progression on biomarkers and cognitive decline was more pronounced in the earlier symptomatic stages of AD. The QSP analysis provides fluid biomarker and clinical evidence for ALZ-801 as a first-in-class, oral small-molecule anti-Aβ oligomer agent with disease modification potential in AD.

TRIAL REGISTRY

https://clinicaltrials.gov/study/NCT04693520.

Iterative Design of Near-Infrared Fluorescent Probes for Early Diagnosis of Alzheimer's Disease by Targeting Aβ Oligomers

Amyloid-β oligomers (AβOs), crucial toxic proteins in early Alzheimer's disease (AD), precede the formation of Aβ plaques and cognitive impairment. In this context, we present our iterative process for developing novel near-infrared fluorescent (NIRF) probes specifically targeting AβOs, aimed at early AD diagnosis. An initial screening identified compound 18 as being highly selective for AβOs. Subsequent analysis revealed that compound 20 improved serum stability while retaining affinity for AβOs. The most promising iteration, compound 37, demonstrated exceptional qualities: a high affinity for AβOs, emission in the near-infrared region, and good biocompatibility. Significantly, ex vivo double staining indicated that compound 37 detected AβOs in AD mouse brain and in vivo imaging experiments showed that compound 37 could differentiate between 4-month-old AD mice and age-matched wild-type mice. Therefore, compound 37 has emerged as a valuable NIRF probe for early detection of AD and a useful tool in exploring AD's pathological mechanisms.

A pipeline for the development and analysis of extracellular vesicle-based transcriptomic biomarkers in molecular diagnostics

Extracellular vesicles are shed by every cell type and can be found in any biofluid. They contain different molecules that can be utilized as biomarkers, including several RNA species which they protect from degradation. Here, we present a pipeline for the development and analysis of extracellular vesicle-associated transcriptomic biomarkers that our group has successfully applied multiple times. We highlight the key steps of the pipeline and give particular emphasis to the necessary quality control checkpoints, which are linked to numerous available guidelines that should be considered along the workflow. Our pipeline starts with patient recruitment and continues with blood sampling and processing. The purification and characterization of extracellular vesicles is explained in detail, as well as the isolation and quality control of extracellular vesicle-associated RNA. We point out the possible pitfalls during library preparation and RNA sequencing and present multiple bioinformatic tools to pinpoint biomarker signature candidates from the sequencing data. Finally, considerations and pitfalls during the validation of the biomarker signature using RT-qPCR will be elaborated.

Novel Biomarkers for Alzheimer's Disease: Plasma Neurofilament Light and Cerebrospinal Fluid

Neurodegenerative disorders such as Alzheimer's disease (AD) represent an increasingly significant public health concern. As clinical diagnosis faces challenges, biomarkers are becoming increasingly important in research, trials, and patient assessments. While biomarkers like amyloid-β peptide, tau proteins, CSF levels (Aβ, tau, and p-tau), and neuroimaging techniques are commonly used in AD diagnosis, they are often limited and invasive in monitoring and diagnosis. For this reason, blood-based biomarkers are the optimal choice for detecting neurodegeneration in brain diseases due to their noninvasiveness, affordability, reliability, and consistency. This literature review focuses on plasma neurofilament light (NfL) and CSF NfL as blood-based biomarkers used in recent AD diagnosis. The findings revealed that the core CSF biomarkers of neurodegeneration (T-tau, P-tau, and Aβ42), CSF NFL, and plasma T-tau were strongly associated with Alzheimer's disease, and the core biomarkers were strongly associated with mild cognitive impairment due to Alzheimer's disease. Elevated levels of plasma and cerebrospinal fluid NfL were linked to decreased [18F]FDG uptake in corresponding brain areas. In participants with Aβ positivity (Aβ+), NfL correlated with reduced metabolism in regions susceptible to Alzheimer's disease. In addition, CSF NfL levels correlate with brain atrophy and predict cognitive changes, while plasma total tau does not. Plasma P-tau, especially in combination with Aβ42/Aβ40, is promising for symptomatic AD stages. Though not AD-exclusive, blood NfL holds promise for neurodegeneration detection and assessing treatment efficacy. Given the consistent levels of T-tau, P-tau, Aβ42, and NFL in CSF, their incorporation into both clinical practice and research is highly recommended.

Clinical utility of plasma Aβ42/40 ratio by LC-MS/MS in Alzheimer's disease assessment

Introduction

Plasma Aβ42/40 ratio can help predict amyloid PET status, but its clinical utility in Alzheimer's disease (AD) assessment is unclear.

Methods

Aβ42/40 ratio was measured by LC-MS/MS for 250 specimens with associated amyloid PET imaging, diagnosis, and demographic data, and for 6,192 consecutive clinical specimens submitted for Aβ42/40 testing.

Results

High diagnostic sensitivity and negative predictive value (NPV) for Aβ-PET positivity were observed, consistent with the clinical performance of other plasma LC-MS/MS assays, but with greater separation between Aβ42/40 values for individuals with positive vs. negative Aβ-PET results. Assuming a moderate prevalence of Aβ-PET positivity, a cutpoint was identified with 99% NPV, which could help predict that AD is likely not the cause of patients' cognitive impairment and help reduce PET evaluation by about 40%.

Conclusion

High-throughput plasma Aβ42/40 LC-MS/MS assays can help identify patients with low likelihood of AD pathology, which can reduce PET evaluations, allowing for cost savings.

Interaction between Aβ and tau on reversion and conversion in mild cognitive impairment patients: After 2-year follow-up

Background

The role of amyloid-β (Aβ) and tau in reversion and conversion in patients with mild cognitive impairment (MCI) remains unclear. This study aimed to investigate the influence of cerebrospinal fluid (CSF) Aβ and tau on reversion and conversion and the temporal sequence of their pathogenicity in MCI patients.

Methods

179 MCI patients were recruited from the Alzheimer's Disease Neuroimaging Initiative database and classified into two groups based on cognitive changes after follow-up: reversal group (MCI to cognitively normal) and conversion group (MCI to Alzheimer's disease). CSF biomarkers and cognitive function were measured at baseline and 2-year follow-up. Partial correlation was used to analyze the association between CSF biomarkers and cognitive function, and multivariable logistic regression to identify independent risk factors for cognitive changes at baseline and 2-year follow-up. Receiver operating characteristic (ROC) curves were utilized to evaluate the predictive ability of these risk factors for cognitive changes.

Results

The differences in cognitive function and CSF biomarkers between the two groups remained consistent with baseline after 2-year follow-up. After controlling for confounding variables, there was still a correlation between CSF biomarkers and cognitive function at baseline and 2-year follow-up. Multivariable regression analysis found that at baseline, only Aβ level was independently associated with cognitive changes, while Aβ and tau were both predictive factors after 2-year follow-up. ROC curve analysis revealed that the combination of Aβ and tau [area under the curve (AUC) 0.91, sensitivity 84%, specificity 86%] in predicting cognitive changes after 2-year follow-up had better efficacy than baseline Aβ alone (AUC 0.81).

Conclusion

Aβ may precede Tau in causing cognitive changes, and the interaction between the two mediates cognitive changes in patients. This study provides new clinical evidence to support the view that Aβ pathology precedes tau pathology, which together contribute to the changes in cognitive function.

Cognitively defined Alzheimer's dementia subgroups have distinct atrophy patterns

INTRODUCTION

We sought to determine structural magnetic resonance imaging (MRI) characteristics across subgroups defined based on relative cognitive domain impairments using data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and to compare cognitively defined to imaging-defined subgroups.

METHODS

We used data from 584 people with Alzheimer's disease (AD) (461 amyloid positive, 123 unknown amyloid status) and 118 amyloid-negative controls. We used voxel-based morphometry to compare gray matter volume (GMV) for each group compared to controls and to AD-Memory.

RESULTS

There was pronounced bilateral lower medial temporal lobe atrophy with relative cortical sparing for AD-Memory, lower left hemisphere GMV for AD-Language, anterior lower GMV for AD-Executive, and posterior lower GMV for AD-Visuospatial. Formal asymmetry comparisons showed substantially more asymmetry in the AD-Language group than any other group (p = 1.15 × 10-10 ). For overlap between imaging-defined and cognitively defined subgroups, AD-Memory matched up with an imaging-defined limbic predominant group.

DISCUSSION

MRI findings differ across cognitively defined AD subgroups.

A data-driven approach to complement the A/T/(N) classification system using CSF biomarkers

AIMS

The AT(N) classification system not only improved the biological characterization of Alzheimer's disease (AD) but also raised challenges for its clinical application. Unbiased, data-driven techniques such as clustering may help optimize it, rendering informative categories on biomarkers' values.

METHODS

We compared the diagnostic and prognostic abilities of CSF biomarkers clustering results against their AT(N) classification. We studied clinical (patients from our center) and research (Alzheimer's Disease Neuroimaging Initiative) cohorts. The studied CSF biomarkers included Aβ(1-42), Aβ(1-42)/Aβ(1-40) ratio, tTau, and pTau.

RESULTS

The optimal solution yielded three clusters in both cohorts, significantly different in diagnosis, AT(N) classification, values distribution, and survival. We defined these three CSF groups as (i) non-defined or unrelated to AD, (ii) early stages and/or more delayed risk of conversion to dementia, and (iii) more severe cognitive impairment subjects with faster progression to dementia.

CONCLUSION

We propose this data-driven three-group classification as a meaningful and straightforward approach to evaluating the risk of conversion to dementia, complementary to the AT(N) system classification.

The potential of blood neurofilament light as a marker of neurodegeneration for Alzheimer's disease

Over the past several years, there has been a surge in blood biomarker studies examining the value of plasma or serum neurofilament light (NfL) as a biomarker of neurodegeneration for Alzheimer's disease. However, there have been limited efforts to combine existing findings to assess the utility of blood NfL as a biomarker of neurodegeneration for Alzheimer's disease. In addition, we still need better insight into the specific aspects of neurodegeneration that are reflected by the elevated plasma or serum concentration of NfL. In this review, we survey the literature on the cross-sectional and longitudinal relationships between blood-based NfL levels and other, neuroimaging-based, indices of neurodegeneration in individuals on the Alzheimer's continuum. Then, based on the biomarker classification established by the FDA-NIH Biomarker Working group, we determine the utility of blood-based NfL as a marker for monitoring the disease status (i.e. monitoring biomarker) and predicting the severity of neurodegeneration in older adults with and without cognitive decline (i.e. a prognostic or a risk/susceptibility biomarker). The current findings suggest that blood NfL exhibits great promise as a monitoring biomarker because an increased NfL level in plasma or serum appears to reflect the current severity of atrophy, hypometabolism and the decline of white matter integrity, particularly in the brain regions typically affected by Alzheimer's disease. Longitudinal evidence indicates that blood NfL can be useful not only as a prognostic biomarker for predicting the progression of neurodegeneration in patients with Alzheimer's disease but also as a susceptibility/risk biomarker predicting the likelihood of abnormal alterations in brain structure and function in cognitively unimpaired individuals with a higher risk of developing Alzheimer's disease (e.g. those with a higher amyloid-β). There are still limitations to current research, as discussed in this review. Nevertheless, the extant literature strongly suggests that blood NfL can serve as a valuable prognostic and susceptibility biomarker for Alzheimer's disease-related neurodegeneration in clinical settings, as well as in research settings.

Disentangling Accelerated Cognitive Decline from the Normal Aging Process and Unraveling Its Genetic Components: A Neuroimaging-Based Deep Learning Approach

Background

The progressive cognitive decline, an integral component of Alzheimer's disease (AD), unfolds in tandem with the natural aging process. Neuroimaging features have demonstrated the capacity to distinguish cognitive decline changes stemming from typical brain aging and AD between different chronological points.

Objective

To disentangle the normal aging effect from the AD-related accelerated cognitive decline and unravel its genetic components using a neuroimaging-based deep learning approach.

Methods

We developed a deep-learning framework based on a dual-loss Siamese ResNet network to extract fine-grained information from the longitudinal structural magnetic resonance imaging (MRI) data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study. We then conducted genome-wide association studies (GWAS) and post-GWAS analyses to reveal the genetic basis of AD-related accelerated cognitive decline.

Results

We used our model to process data from 1,313 individuals, training it on 414 cognitively normal people and predicting cognitive assessment for all participants. In our analysis of accelerated cognitive decline GWAS, we identified two genome-wide significant loci: APOE locus (chromosome 19 p13.32) and rs144614292 (chromosome 11 p15.1). Variant rs144614292 (G > T) has not been reported in previous AD GWA studies. It is within the intronic region of NELL1, which is expressed in neurons and plays a role in controlling cell growth and differentiation. The cell-type-specific enrichment analysis and functional enrichment of GWAS signals highlighted the microglia and immune-response pathways.

Conclusions

Our deep learning model effectively extracted relevant neuroimaging features and predicted individual cognitive decline. We reported a novel variant (rs144614292) within the NELL1 gene.

The supersaturation perspective on the amyloid hypothesis

Development of therapeutic interventions for Alzheimer's over the past three decades has been guided by the amyloid hypothesis, which puts Aβ deposition as the initiating event of a pathogenic cascade leading to dementia. In the current form, the amyloid hypothesis lacks a comprehensive framework that considers the complex nature of Aβ aggregation. The explanation of how Aβ deposition leads to downstream pathology, and how reducing Aβ plaque load via anti-amyloid therapy can lead to improvement in cognition remains insufficient. In this perspective we integrate the concept of Aβ supersaturation into the amyloid hypothesis, laying out a framework for the mechanistic understanding and therapeutic intervention of Alzheimer's disease. We discuss the important distinction between in vitro and in vivo patterns of Aβ aggregation, the impact of different aggregation stages on therapeutic strategies, and how future investigations could integrate this concept in order to produce a more thorough understanding and better treatment for Alzheimer's and other amyloid-related disorders.

Clinical utility of plasma Aβ42/40 ratio by LC-MS/MS in Alzheimer's disease assessment

INTRODUCTION

Plasma Aβ42/40 ratio can be used to help predict amyloid PET status, but its clinical utility in Alzheimer's disease (AD) assessment is unclear.

METHODS

Aβ42/40 ratio was measured by LC-MS/MS in 250 specimens with associated amyloid PET imaging, diagnosis, and demographic data, and 6,192 consecutive clinical specimens submitted for Aβ42/40 testing.

RESULTS

High diagnostic sensitivity and negative predictive value (NPV) for Aβ-PET positivity were observed, consistent with the clinical performance of other plasma LC-MS/MS assays, but with greater separation between Aβ42/40 values for individuals with positive vs negative Aβ-PET results. Assuming a moderate prevalence of Aβ-PET positivity, a cutpoint was identified with 99% NPV, which could help predict that AD is likely not the cause of patients' cognitive impairment and help reduce PET evaluation by about 40%.

DISCUSSION

Using high-throughput plasma Aβ42/40 LC-MS/MS assays can help reduce PET evaluations in patients with low likelihood of AD pathology, allowing for cost savings.

Disentangling accelerated cognitive decline from the normal aging process and unraveling its genetic components: A neuroimaging-based deep learning approach

Background

The progressive cognitive decline that is an integral component of AD unfolds in tandem with the natural aging process. Neuroimaging features have demonstrated the capacity to distinguish cognitive decline changes stemming from typical brain aging and Alzheimer's disease between different chronological points.

Methods

We developed a deep-learning framework based on dual-loss Siamese ResNet network to extract fine-grained information from the longitudinal structural magnetic resonance imaging (MRI) data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study. We then conducted genome-wide association studies (GWAS) and post-GWAS analyses to reveal the genetic basis of AD-related accelerated cognitive decline.

Results

We used our model to process data from 1,313 individuals, training it on 414 cognitively normal people and predicting cognitive assessment for all participants. In our analysis of accelerated cognitive decline GWAS, we identified two genome-wide significant loci: APOE locus (chromosome 19 p13.32) and rs144614292 (chromosome 11 p15.1). Variant rs144614292 (G>T) has not been reported in previous AD GWA studies. It is within the intronic region of NELL1, which is expressed in neuron and plays a role in controlling cell growth and differentiation. In addition, MUC7 and PROL1/OPRPNon chromosome 4 were significant at the gene level. The cell-type-specific enrichment analysis and functional enrichment of GWAS signals highlighted the microglia and immune-response pathways. Furthermore, we found that the cognitive decline slope GWAS was positively correlated with previous AD GWAS.

Conclusion

Our deep learning model was demonstrated effective on extracting relevant neuroimaging features and predicting individual cognitive decline. We reported a novel variant (rs144614292) within the NELL1 gene. Our approach has the potential to disentangle accelerated cognitive decline from the normal aging process and to determine its related genetic factors, leveraging opportunities for early intervention.

Does serum neurofilament light help predict accelerated cognitive ageing in unimpaired older adults?

Introduction

Neurofilament light (NfL) is a blood biomarker of neurodegeneration. While serum NfL levels have been demonstrated to increase with normal ageing, the relationship between serum NfL levels and normal age-related changes in cognitive functions is less well understood.

Methods

The current study investigated whether cross-sectional serum NfL levels measured by single molecule array technology (Simoa®) mediated the effect of age on cognition, measured by a battery of neuropsychological tests administered biannually for 8 years, in a cohort of 174 unimpaired older adults (≥50 years) from the Tasmanian Healthy Brain Project. Mediation analysis was conducted using latent variables representing cognitive test performance on three cognitive domains - episodic memory, executive function, and language (vocabulary, comprehension, naming). Cognitive test scores for the three domains were estimated for each participant, coincident with blood collection in 2018 using linear Bayesian hierarchical models.

Results

Higher serum NfL levels were significantly positively associated with age (p < 0.001 for all domains). Cognitive test scores were significantly negatively associated with age across the domains of executive function (p < 0.001), episodic memory (p < 0.001) and language (p < 0.05). However, serum NfL levels did not significantly mediate the relationship between age and cognitive test scores across any of the domains.

Discussion

This study adds to the literature on the relationship between serum NfL levels and cognition in unimpaired older adults and suggests that serum NfL is not a pre-clinical biomarker of ensuing cognitive decline in unimpaired older adults.

Investigating the relationship between allocentric spatial working memory and biomarker status in preclinical and prodromal Alzheimer's disease

The 4 Mountain Test (4MT) is a test of allocentric spatial working memory and has been proposed as an earlier marker of predementia Alzheimer's disease (AD) than episodic verbal memory. We here compare the 4MT to the CERAD word list memory recall in both cognitively normal (CN) and mild cognitive impairment (MCI) cases with or without cerebrospinal fluid markers (CSF) of Alzheimer's disease pathology. Linear regression was used to assess the influence of CSF determined Aβ-plaque (Aβ-/+) or neurofibrillary tau tangles (Tau-/+) on 4MT and CERAD recall performance. Analyses were performed in the full sample and the CN and MCI sub-samples. Pearson correlations were calculated to examine the relationship between 4MT and tests of psychomotor speed, verbal memory, cognitive flexibility, verbal fluency, and visuo-spatial perception. Analyses showed no significant differences in 4MT scores between Aβ-/Aβ+, nor Tau-/Tau + participants, irrespective of cognitive status. In contrast, CERAD recall scores were lower in both Aβ+ compared to Aβ- (p<.01), and Tau + compared to Tau- participants (p<.01) in the full sample analyses. There were no significant differences in CERAD recall performance between Aβ- vs. Aβ+ and Tau- vs. to Tau + in the in CN/MCI sub-samples. 4MT scores were significantly correlated with tests of psychomotor speed, cognitive flexibility, and visuo-spatial perception in the full sample analyses. In conclusion, the CERAD recall outperformed the 4MT as a cognitive marker of CSF determined AD pathology. This suggests that allocentric working memory, as measured by the 4MT, may not be used as an early marker of predementia AD.

The temporal event-based model: Learning event timelines in progressive diseases

Timelines of events, such as symptom appearance or a change in biomarker value, provide powerful signatures that characterise progressive diseases. Understanding and predicting the timing of events is important for clinical trials targeting individuals early in the disease course when putative treatments are likely to have the strongest effect. However, previous models of disease progression cannot estimate the time between events and provide only an ordering in which they change. Here, we introduce the temporal event-based model (TEBM), a new probabilistic model for inferring timelines of biomarker events from sparse and irregularly sampled datasets. We demonstrate the power of the TEBM in two neurodegenerative conditions: Alzheimer's disease (AD) and Huntington's disease (HD). In both diseases, the TEBM not only recapitulates current understanding of event orderings but also provides unique new ranges of timescales between consecutive events. We reproduce and validate these findings using external datasets in both diseases. We also demonstrate that the TEBM improves over current models; provides unique stratification capabilities; and enriches simulated clinical trials to achieve a power of 80 % with less than half the cohort size compared with random selection. The application of the TEBM naturally extends to a wide range of progressive conditions.

EVs-mediated delivery of CB2 receptor agonist for Alzheimer's disease therapy

Alzheimer's disease (AD) is a typical neurodegenerative disease that leads to irreversible neuronal degeneration, and effective treatment remains elusive due to the unclear mechanism. We utilized biocompatible mesenchymal stem cell-derived extracellular vesicles as carriers loaded with the CB2 target medicine AM1241 (EVs-AM1241) to protect against neurodegenerative progression and neuronal function in AD model mice. According to the results, EVs-AM1241 were successfully constructed and exhibited better bioavailability and therapeutic effects than bare AM1241. The Morris water maze (MWM) and fear conditioning tests revealed that the learning and memory of EVs-AM1241-treated model mice were significantly improved. In vivo electrophysiological recording of CA1 neurons indicated enhanced response to an auditory conditioned stimulus following fear learning. Immunostaining and Western blot analysis showed that amyloid plaque deposition and amyloid β (Aβ)-induced neuronal apoptosis were significantly suppressed by EVs-AM1241. Moreover, EVs-AM1241 increased the number of neurons and restored the neuronal cytoskeleton, indicating that they enhanced neuronal regeneration. RNA sequencing revealed that EVs-AM1241 facilitated Aβ phagocytosis, promoted neurogenesis and ultimately improved learning and memory through the calcium-Erk signaling pathway. Our study showed that EVs-AM1241 efficiently reversed neurodegenerative pathology and enhanced neurogenesis in model mice, indicating that they are very promising particles for treating AD.

Gut microbiome composition may be an indicator of preclinical Alzheimer's disease

Alzheimer's disease (AD) pathology is thought to progress from normal cognition through preclinical disease and ultimately to symptomatic AD with cognitive impairment. Recent work suggests that the gut microbiome of symptomatic patients with AD has an altered taxonomic composition compared with that of healthy, cognitively normal control individuals. However, knowledge about changes in the gut microbiome before the onset of symptomatic AD is limited. In this cross-sectional study that accounted for clinical covariates and dietary intake, we compared the taxonomic composition and gut microbial function in a cohort of 164 cognitively normal individuals, 49 of whom showed biomarker evidence of early preclinical AD. Gut microbial taxonomic profiles of individuals with preclinical AD were distinct from those of individuals without evidence of preclinical AD. The change in gut microbiome composition correlated with β-amyloid (Aβ) and tau pathological biomarkers but not with biomarkers of neurodegeneration, suggesting that the gut microbiome may change early in the disease process. We identified specific gut bacterial taxa associated with preclinical AD. Inclusion of these microbiome features improved the accuracy, sensitivity, and specificity of machine learning classifiers for predicting preclinical AD status when tested on a subset of the cohort (65 of the 164 participants). Gut microbiome correlates of preclinical AD neuropathology may improve our understanding of AD etiology and may help to identify gut-derived markers of AD risk.

A biomarker-validated time scale in years of disease progression has identified early- and late-onset subgroups in sporadic Alzheimer's disease

BACKGROUND

It is possible to calculate the number of years to the expected clinical onset (YECO) of autosomal-dominant Alzheimer's disease (adAD). A similar time scale is lacking for sporadic Alzheimer's disease (sAD). The purpose was to design and validate a time scale in YECO for patients with sAD in relation to CSF and PET biomarkers.

METHODS

Patients diagnosed with Alzheimer's disease (AD, n = 48) or mild cognitive impairment (MCI, n = 46) participated in the study. They underwent a standardized clinical examination at the Memory clinic, Karolinska University Hospital, Stockholm, Sweden, which included present and previous medical history, laboratory screening, cognitive assessment, CSF biomarkers (Aβ₄₂, total-tau, and p-tau), and an MRI of the brain. They were also assessed with two PET tracers, ¹¹C-Pittsburgh compound B and ¹⁸F-fluorodeoxyglucose. Assuming concordance of cognitive decline in sAD and adAD, YECO for these patients was calculated using equations for the relationship between cognitive performance, YECO, and years of education in adAD (Almkvist et al. J Int Neuropsychol Soc 23:195-203, 2017).

RESULTS

The mean current point of disease progression was 3.2 years after the estimated clinical onset in patients with sAD and 3.4 years prior to the estimated clinical onset in patients with MCI, as indicated by the median YECO from five cognitive tests. The associations between YECO and biomarkers were significant, while those between chronological age and biomarkers were nonsignificant. The estimated disease onset (chronological age minus YECO) followed a bimodal distribution with frequency maxima before (early-onset) and after (late-onset) 65 years of age. The early- and late-onset subgroups differed significantly in biomarkers and cognition, but after control for YECO, this difference disappeared for all except the APOE e4 gene (more frequent in early- than in late-onset).

CONCLUSIONS

A novel time scale in years of disease progression based on cognition was designed and validated in patients with AD using CSF and PET biomarkers. Two early- and late-disease onset subgroups were identified differing with respect to APOE e4.

Understanding Alzheimer's disease in the context of aging: Findings from applications of stochastic process models to the Health and Retirement Study

There is growing literature on applications of biodemographic models, including stochastic process models (SPM), to studying regularities of age dynamics of biological variables in relation to aging and disease development. Alzheimer's disease (AD) is especially good candidate for SPM applications because age is a major risk factor for this heterogeneous complex trait. However, such applications are largely lacking. This paper starts filling this gap and applies SPM to data on onset of AD and longitudinal trajectories of body mass index (BMI) constructed from the Health and Retirement Study surveys and Medicare-linked data. We found that APOE e4 carriers are less robust to deviations of trajectories of BMI from the optimal levels compared to non-carriers. We also observed age-related decline in adaptive response (resilience) related to deviations of BMI from optimal levels as well as APOE- and age-dependence in other components related to variability of BMI around the mean allostatic values and accumulation of allostatic load. SPM applications thus allow revealing novel connections between age, genetic factors and longitudinal trajectories of risk factors in the context of AD and aging creating new opportunities for understanding AD development, forecasting trends in AD incidence and prevalence in populations, and studying disparities in those.

How can secondary dementia prevention trials of Alzheimer's disease be clinically meaningful?

After clinical trial failures in symptomatic Alzheimer's disease (AD), our field has moved to earlier intervention in cognitively normal individuals with biomarker evidence of AD. This offers potential for dementia prevention, but mainly low and variable rates of progression to AD dementia reduce the usefulness of trials' data in decision making by potential prescribers. With results from several Phase 3 secondary prevention studies anticipated within the next few years and the Food and Drug Administration's recent endorsement of amyloid beta as a surrogate outcome biomarker for AD clinical trials, it is time to question the clinical significance of changes in biomarkers, adequacy of current trial durations, and criteria for treatment success if cognitively unimpaired patients and their doctors are to meaningfully evaluate the potential value of new agents. We argue for a change of direction toward trial designs that can unambiguously inform clinical decision making about dementia risk and progression.

Amyloid-beta misfolding and GFAP predict risk of clinical Alzheimer's disease diagnosis within 17 years

INTRODUCTION

Blood-based biomarkers for Alzheimer's disease (AD) are urgently needed. Here, four plasma biomarkers were measured at baseline in a community-based cohort followed over 17 years, and the association with clinical AD risk was determined.

METHODS

Amyloid beta (Aβ) misfolding status as a structure-based biomarker as well as phosphorylated tau 181 (P-tau181), glial fibrillary acidic protein (GFAP), and neurofilament light (NfL) concentration levels were determined at baseline in heparin plasma from 68 participants who were diagnosed with AD and 240 controls without dementia diagnosis throughout follow-up.

RESULTS

Aβ misfolding exhibited high disease prediction accuracy of AD diagnosis within 17 years. Among the concentration markers, GFAP showed the best performance, followed by NfL and P-tau181. The combination of Aβ misfolding and GFAP increased the accuracy.

DISCUSSION

Aβ misfolding and GFAP showed a strong ability to predict clinical AD risk and may be important early AD risk markers. Aβ misfolding illustrated its potential as a prescreening tool for AD risk stratification in older adults.

The dynamics of plasma biomarkers across the Alzheimer's continuum

BACKGROUND

Failures in drug trials strengthen the necessity to further determine the neuropathological events during the development of Alzheimer's disease (AD). We sought to investigate the dynamic changes and performance of plasma biomarkers across the entire Alzheimer's continuum in the Chinese population.

METHODS

Plasma amyloid-β (Αβ)42, Aβ40, Aβ42/Aβ40, phosphorylated tau (p-tau)181, neurofilament light (NfL), and glial fibrillary acidic protein (GFAP) were measured utilizing the ultrasensitive single-molecule array technology across the AD continuum (n=206), wherein Aβ status was defined by the values of cerebrospinal fluid (CSF) Aβ42 or Aβ positron emission tomography (PET). Their trajectories were compared with those of putative CSF biomarkers.

RESULTS

Plasma GFAP and p-tau181 increased only in Aβ-positive individuals throughout aging, whereas NfL increased with aging regardless of Aβ status. Among the plasma biomarkers studied, GFAP was the one that changed first. It had a prominent elevation early in the cognitively unimpaired (CU) A+T- phase (CU A+T- phase: 97.10±41.29 pg/ml; CU A-T- phase: 49.18±14.39 pg/ml; p<0.001). From preclinical to symptomatic stages of AD, plasma GFAP started to rise sharply as soon as CSF Aβ became abnormal and continued to increase until reaching its highest level during the AD dementia phase. The greatest slope of change was seen in plasma GFAP. This is followed by CSF p-tau181 and total-tau, and, to a lesser extent, then plasma p-tau181. In contrast, the changes in plasma NfL, Aβ42/Aβ40, Aβ42, and Aβ40 were less pronounced. Of note, these plasma biomarkers exhibited smaller dynamic ranges than their CSF counterparts, except for GFAP which was the opposite. Plasma GFAP and p-tau181 were tightly associated with AD pathologies and amyloid tracer uptake in widespread brain areas. Plasma GFAP could accurately identify CSF Aβ42 (area under the curve (AUC)=0.911) and Aβ PET (AUC=0.971) positivity. Plasma p-tau181 also performed well in discriminating Aβ PET status (AUC=0.916), whereas the discriminative accuracy was relatively low for other plasma biomarkers.

CONCLUSIONS

This study is the first to delineate the trajectories of plasma biomarkers throughout the Alzheimer's continuum in the Chinese population, providing important implications for future trials targeting plasma GFAP to facilitate AD prevention and treatment.

Plasma Amyloid-β, Total Tau, and Neurofilament Light Chain Across the Alzheimer's Disease Clinical Spectrum: A Population-Based Study

BACKGROUND

Plasma biomarkers have emerged as a promising approach for characterizing pathophysiology in mild cognitive impairment (MCI) and Alzheimer's disease (AD).

OBJECTIVE

We aimed to characterize plasma biomarkers for AD and neurodegeneration across the AD clinical continuum, and to assess their ability to differentiate between AD, MCI, and normal cognition.

METHODS

This population-based study engaged 1,446 rural-dwelling older adults (age ≥60 years, 61.0% women) derived from MIND-China; of these, 402 were defined with MCI and 142 with AD. Plasma amyloid-β (Aβ), total tau (t-tau), and neurofilament light chain (NfL) concentrations were analyzed using the Simoa platform. Data were analyzed using linear and logistic regression models, and receiver operating characteristic (ROC) analysis.

RESULTS

Across the AD clinical spectrum, plasma Aβ40 and NfL increased, whereas Aβ42/Aβ40 ratio decreased. Plasma t-tau was higher in people with AD dementia than those with MCI or normal cognition. Plasma NfL outperformed other biomarkers in differentiating AD from normal cognition (area under the ROC curve [AUC] = 0.75), but all plasma biomarkers performed poorly to distinguish MCI from normal cognition (AUC <0.60). Plasma NfL in combination with age, sex, education, and APOE genotype yielded the AUC of 0.87 for differentiating between AD and normal cognition, 0.79 between AD and MCI, and 0.64 between MCI and normal cognition.

CONCLUSIONS

In this Chinese population, AD plasma biomarkers vary by age, sex, and APOE genotype. Plasma Aβ, t-tau, and NfL differ across the AD clinical spectrum, and plasma NfL appears to be superior to plasma Aβ and t-tau for defining the clinical spectrum.

Loneliness and Cognitive Function in Older Adults Without Dementia: A Systematic Review and Meta-Analysis

BACKGROUND

Loneliness has been highlighted as a risk factor for dementia. However, the nature of the relationship between loneliness and cognitive function prior to onset of dementia is unclear.

OBJECTIVE

The aim of this systematic review and meta-analysis was to examine the relationship between loneliness and cognitive function in samples screened for dementia at study commencement.

METHODS

Five electronic databases (PubMed, PsycNET, Web of Science, EBSCOhost, Scopus) were searched from inception to August 31, 2021. A narrative review and random-effects meta-analysis were conducted on studies meeting search criteria. PROSPERO registration number: CRD42020155539.

RESULTS

The sixteen studies that met inclusion criteria involved 30,267 individuals, with mean age ranging from 63.0 to 84.9 years. Studies varied in dementia screening criteria, measurement of loneliness and cognitive function, and statistical modeling approach. The narrative review indicated that loneliness was associated with poorer global cognition, episodic memory, working memory, visuospatial function, processing speed, and semantic verbal fluency. Results of the meta-analysis indicated that loneliness was negatively associated with global cognitive function (overall r = -0.08; 95% CI = -0.14, -0.02; n = 6). Due to lack of sufficient data and heterogeneity between studies, we were unable to explore associations with other cognitive domains or longitudinal associations.

CONCLUSION

Loneliness is associated with subtle impairment across multiple cognitive domains in older adults who were screened for dementia. Better characterization of this relationship will provide important information about how loneliness contributes to the clinical and pathological sequalae of AD and be informative for risk reduction and early detection strategies.

Modelling Alzheimer's disease using human brain organoids: current progress and challenges

Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterised by gradual memory loss and declining cognitive and executive functions. AD is the most common cause of dementia, affecting more than 50 million people worldwide, and is a major health concern in society. Despite decades of research, the cause of AD is not well understood and there is no effective curative treatment so far. Therefore, there is an urgent need to increase understanding of AD pathophysiology in the hope of developing a much-needed cure. Dissecting the cellular and molecular mechanisms of AD pathogenesis has been challenging as the most commonly used model systems such as transgenic animals and two-dimensional neuronal culture do not fully recapitulate the pathological hallmarks of AD. The recent advent of three-dimensional human brain organoids confers unique opportunities to study AD in a humanised model system by encapsulating many aspects of AD pathology. In the present review, we summarise the studies of AD using human brain organoids that recapitulate the major pathological components of AD including amyloid-β and tau aggregation, neuroinflammation, mitochondrial dysfunction, oxidative stress and synaptic and circuitry dysregulation. Additionally, the current challenges and future directions of the brain organoids modelling system are discussed.

Population pharmacokinetic-pharmacodynamic analyses of amyloid positron emission tomography and plasma biomarkers for lecanemab in subjects with early Alzheimer's disease

Lecanemab is a humanized immunoglobulin G1 monoclonal antibody that selectively binds to soluble Aβ aggregate species, while demonstrating low affinity for Aβ monomer. This article describes the population pharmacokinetic (PK) and PK/pharmacodynamic (PD) analyses for amyloid plaques, as measured using positron emission tomography (PET), and biomarkers of amyloid pathology as evidenced by Aβ42/40 ratio and plasma p-tau181 following i.v. administration of lecanemab in subjects with early Alzheimer's disease. Lecanemab PKs were well-characterized with a two-compartment model with first-order elimination. Final PK model contained covariate effects of anti-drug antibody positive status, sex, body weight, and albumin on clearance. The time course of amyloid PET standard uptake ratio (SUVr), plasma Aβ42/40 ratio, and p-tau181 were described using indirect response models with lecanemab exposure as a maximum effect function stimulating the reduction of SUVr, and as a linear function increasing Aβ42/40 ratio and decreasing p-tau181 formation rates. PK/PD simulations show that 10 mg/kg biweekly dosing results in larger and faster decrease in SUVr and p-tau181 and increase in Aβ42/40 ratio as compared to 10 mg/kg monthly dose. Furthermore, the PK/PD simulations showed that after treatment discontinuation the brain amyloid re-accumulation to baseline levels is slow with a recovery half-life of ~4 years, whereas plasma Aβ42/40 ratio and p-tau181 return to baseline levels faster than amyloid. Given the relationship between changes in amyloid PET SUVr and soluble biomarkers, the developed PK/PD models can be used to inform lecanemab dose regimens in future clinical studies.

Current and Future Trends in Biomarkers for the Early Detection of Alzheimer's Disease in Asia: Expert Opinion

Alzheimer's disease (AD) poses a substantial healthcare burden in the rapidly aging Asian population. Early diagnosis of AD, by means of biomarkers, can lead to interventions that might alter the course of the disease. The amyloid, tau, and neurodegeneration (AT[N]) framework, which classifies biomarkers by their core pathophysiological features, is a biomarker measure of amyloid plaques and neurofibrillary tangles. Our current AD biomarker armamentarium, comprising neuroimaging biomarkers and cerebrospinal fluid biomarkers, while clinically useful, may be invasive and expensive and hence not readily available to patients. Several studies have also investigated the use of blood-based measures of established core markers for detection of AD, such as amyloid-β and phosphorylated tau. Furthermore, novel non-invasive peripheral biomarkers and digital biomarkers could potentially expand access to early AD diagnosis to patients in Asia. Despite the multiplicity of established and potential biomarkers in AD, a regional framework for their optimal use to guide early AD diagnosis remains lacking. A group of experts from five regions in Asia gathered at a meeting in March 2021 to review the current evidence on biomarkers in AD diagnosis and discuss best practice around their use, with the goal of developing practical guidance that can be implemented easily by clinicians in Asia to support the early diagnosis of AD. This article summarizes recent key evidence on AD biomarkers and consolidates the experts' insights into the current and future use of these biomarkers for the screening and early diagnosis of AD in Asia.

Prediction of amyloid PET positivity via machine learning algorithms trained with EDTA-based blood amyloid-β oligomerization data

Background

The tendency of amyloid-β to form oligomers in the blood as measured with Multimer Detection System-Oligomeric Amyloid-β (MDS-OAβ) is a valuable biomarker for Alzheimer’s disease and has been verified with heparin-based plasma. The objective of this study was to evaluate the performance of ethylenediaminetetraacetic acid (EDTA)-based MDS-OAβ and to develop machine learning algorithms to predict amyloid positron emission tomography (PET) positivity.

Methods

The performance of EDTA-based MDS-OAβ in predicting PET positivity was evaluated in 312 individuals with various machine learning models. The models with various combinations of features (i.e., MDS-OAβ level, age, apolipoprotein E4 alleles, and Mini-Mental Status Examination [MMSE] score) were tested 50 times on each dataset.

Results

The random forest model best-predicted amyloid PET positivity based on MDS-OAβ combined with other features with an accuracy of 77.14 ± 4.21% and an F1 of 85.44 ± 3.10%. The order of significance of predictive features was MDS-OAβ, MMSE, Age, and APOE. The Support Vector Machine using the MDS-OAβ value only showed an accuracy of 71.09 ± 3.27% and F−1 value of 80.18 ± 2.70%.

Conclusions

The Random Forest model using EDTA-based MDS-OAβ combined with the MMSE and apolipoprotein E status can be used to prescreen for amyloid PET positivity.

Role of micronutrients in Alzheimer's disease: Review of available evidence

Alzheimer's disease (AD) is one of the most common age-related neurodegenerative disorders that have been studied for more than 100 years. Although an increased level of amyloid precursor protein is considered a key contributor to the development of AD, the exact pathogenic mechanism remains known. Multiple factors are related to AD, such as genetic factors, aging, lifestyle, and nutrients. Both epidemiological and clinical evidence has shown that the levels of micronutrients, such as copper, zinc, and iron, are closely related to the development of AD. In this review, we summarize the roles of eight micronutrients, including copper, zinc, iron, selenium, silicon, manganese, arsenic, and vitamin D in AD based on recently published studies.

Single-Channel EEG Features Reveal an Association With Cognitive Decline in Seniors Performing Auditory Cognitive Assessment

Background

Cognitive decline remains highly underdiagnosed despite efforts to find novel cognitive biomarkers. Electroencephalography (EEG) features based on machine-learning (ML) may offer a non-invasive, low-cost approach for identifying cognitive decline. However, most studies use cumbersome multi-electrode systems. This study aims to evaluate the ability to assess cognitive states using machine learning (ML)-based EEG features extracted from a single-channel EEG with an auditory cognitive assessment.

Methods

This study included data collected from senior participants in different cognitive states (60) and healthy controls (22), performing an auditory cognitive assessment while being recorded with a single-channel EEG. Mini-Mental State Examination (MMSE) scores were used to designate groups, with cutoff scores of 24 and 27. EEG data processing included wavelet-packet decomposition and ML to extract EEG features. Data analysis included Pearson correlations and generalized linear mixed-models on several EEG variables: Delta and Theta frequency-bands and three ML-based EEG features: VC9, ST4, and A0, previously extracted from a different dataset and showed association with cognitive load.

Results

MMSE scores significantly correlated with reaction times and EEG features A0 and ST4. The features also showed significant separation between study groups: A0 separated between the MMSE < 24 and MMSE ≥ 28 groups, in addition to separating between young participants and senior groups. ST4 differentiated between the MMSE < 24 group and all other groups (MMSE 24–27, MMSE ≥ 28 and healthy young groups), showing sensitivity to subtle changes in cognitive states. EEG features Theta, Delta, A0, and VC9 showed increased activity with higher cognitive load levels, present only in the healthy young group, indicating different activity patterns between young and senior participants in different cognitive states. Consisted with previous reports, this association was most prominent for VC9 which significantly separated between all level of cognitive load.

Discussion

This study successfully demonstrated the ability to assess cognitive states with an easy-to-use single-channel EEG using an auditory cognitive assessment. The short set-up time and novel ML features enable objective and easy assessment of cognitive states. Future studies should explore the potential usefulness of this tool for characterizing changes in EEG patterns of cognitive decline over time, for detection of cognitive decline on a large scale in every clinic to potentially allow early intervention.

Trial Registration

NIH Clinical Trials Registry [https://clinicaltrials.gov/ct2/show/results/NCT04386902], identifier [NCT04386902]; Israeli Ministry of Health registry [https://my.health.gov.il/CliniTrials/Pages/MOH_2019-10-07_007352.aspx], identifier [007352].

Differential Abnormality in Functional Connectivity Density in Preclinical and Early-Stage Alzheimer's Disease

Background

Both subjective cognitive decline (SCD) and amnestic mild cognitive impairment (aMCI) have a high risk of progression to Alzheimer's disease (AD). While most of the available evidence described changes in functional connectivity (FC) in SCD and aMCI, there was no confirmation of changes in functional connectivity density (FCD) that have not been confirmed. Therefore, the purpose of this study was to investigate the specific alterations in resting-state FCD in SCD and aMCI and further assess the extent to which these changes can distinguish the preclinical and early-stage AD.

Methods

A total of 57 patients with SCD, 59 patients with aMCI, and 78 healthy controls (HC) were included. The global FCD, local FCD, and long-range FCD were calculated for each voxel to identify brain regions with significant FCD alterations. The brain regions with abnormal FCD were then used as regions of interest for FC analysis. In addition, we calculated correlations between neuroimaging alterations and cognitive function and performed receiver-operating characteristic analyses to assess the diagnostic effect of the FCD and FC alterations on SCD and aMCI.

Results

FCD mapping revealed significantly increased global FCD in the left parahippocampal gyrus (PHG.L) and increased long-range FCD in the left hippocampus for patients with SCD when compared to HCs. However, when compared to SCD, patients with aMCI showed significantly decreased global FCD and long-range FCD in the PHG.L. The follow-up FC analysis further revealed significant variations between the PHG.L and the occipital lobe in patients with SCD and aMCI. In addition, patients with SCD also presented significant changes in FC between the left hippocampus, the left cerebellum anterior lobe, and the inferior temporal gyrus. Moreover, changes in abnormal indicators in the SCD and aMCI groups were significantly associated with cognitive function. Finally, combining FCD and FC abnormalities allowed for a more precise differentiation of the clinical stages.

Conclusion

To our knowledge, this study is the first to investigate specific alterations in FCD and FC for both patients with SCD and aMCI and confirms differential abnormalities that can serve as potential imaging markers for preclinical and early-stage Alzheimer's disease (AD). Also, it adds a new dimension of understanding to the diagnosis of SCD and aMCI as well as the evaluation of disease progression.

Aβ oligomer concentration in mouse and human brain and its drug-induced reduction ex vivo

The elimination of amyloid beta (Aβ) oligomers is a promising strategy for therapeutic drug development of Alzheimer's disease (AD). AD mouse models that develop Aβ pathology have been used to demonstrate in vivo efficacy of compounds that later failed in clinical development. Here, we analyze the concentration and size distribution of Aβ oligomers in different transgenic mouse models of AD and in human brain samples by surface-based fluorescence intensity distribution analysis (sFIDA), a highly sensitive method for detecting and quantitating protein aggregates. We demonstrate dose- and time-dependent oligomer elimination by the compound RD2 in mouse and human AD brain homogenates as sources of native Aβ oligomers. Such ex vivo target engagement analyses with mouse- and human-brain-derived oligomers have the potential to enhance the translational value from pre-clinical proof-of-concept studies to clinical trials.

Natural Products from Plants and Algae for Treatment of Alzheimer’s Disease: A Review

Neurodegenerative disorders including Parkinson’s disease (PD), Huntington’s disease (HD) and the most frequent, Alzheimer’s disease (AD), represent one of the most urgent medical needs worldwide. Despite a significantly developed understanding of disease development and pathology, treatments that stop AD progression are not yet available. The recent approval of sodium oligomannate (GV-971) for AD treatment in China emphasized the potential value of natural products for the treatment of neurodegenerative disorders. Many current clinical studies include the administration of a natural compound as a single and combination treatment. The most prominent mechanisms of action are anti-inflammatory and anti-oxidative activities, thus preserving cellular survival. Here, we review current natural products that are either approved or are in testing for a treatment of neurodegeneration in AD. In addition to the most important compounds of plant origin, we also put special emphasis on compounds from algae, given their neuroprotective activity and their underlying mechanisms of neuroprotection.

Comparison and aggregation of event sequences across ten cohorts to describe the consensus biomarker evolution in Alzheimer's disease

BACKGROUND

Previous models of Alzheimer's disease (AD) progression were primarily hypothetical or based on data originating from single cohort studies. However, cohort datasets are subject to specific inclusion and exclusion criteria that influence the signals observed in their collected data. Furthermore, each study measures only a subset of AD-relevant variables. To gain a comprehensive understanding of AD progression, the heterogeneity and robustness of estimated progression patterns must be understood, and complementary information contained in cohort datasets be leveraged.

METHODS

We compared ten event-based models that we fit to ten independent AD cohort datasets. Additionally, we designed and applied a novel rank aggregation algorithm that combines partially overlapping, individual event sequences into a meta-sequence containing the complementary information from each cohort.

RESULTS

We observed overall consistency across the ten event-based model sequences (average pairwise Kendall's tau correlation coefficient of 0.69 ± 0.28), despite variance in the positioning of mainly imaging variables. The changes described in the aggregated meta-sequence are broadly consistent with the current understanding of AD progression, starting with cerebrospinal fluid amyloid beta, followed by tauopathy, memory impairment, FDG-PET, and ultimately brain deterioration and impairment of visual memory.

CONCLUSION

Overall, the event-based models demonstrated similar and robust disease cascades across independent AD cohorts. Aggregation of data-driven results can combine complementary strengths and information of patient-level datasets. Accordingly, the derived meta-sequence draws a more complete picture of AD pathology compared to models relying on single cohorts.

Using the Alzheimer's Disease Neuroimaging Initiative to improve early detection, diagnosis, and treatment of Alzheimer's disease

INTRODUCTION

The Alzheimer's Disease Neuroimaging Initiative (ADNI) has accumulated 15 years of clinical, neuroimaging, cognitive, biofluid biomarker and genetic data, and biofluid samples available to researchers, resulting in more than 3500 publications. This review covers studies from 2018 to 2020.

METHODS

We identified 1442 publications using ADNI data by conventional search methods and selected impactful studies for inclusion.

RESULTS

Disease progression studies supported pivotal roles for regional amyloid beta (Aβ) and tau deposition, and identified underlying genetic contributions to Alzheimer's disease (AD). Vascular disease, immune response, inflammation, resilience, and sex modulated disease course. Biologically coherent subgroups were identified at all clinical stages. Practical algorithms and methodological changes improved determination of Aβ status. Plasma Aβ, phosphorylated tau181, and neurofilament light were promising noninvasive biomarkers. Prognostic and diagnostic models were externally validated in ADNI but studies are limited by lack of ethnocultural cohort diversity.

DISCUSSION

ADNI has had a profound impact in improving clinical trials for AD.

18F-APN-1607 Tau Positron Emission Tomography Imaging for Evaluating Disease Progression in Alzheimer’s Disease

Purpose

¹⁸F-APN-1607 is a novel tau positron emission tomography (PET) tracer characterized with high binding affinity for 3− and 4-repeat tau deposits. The aim was to analyze the spatial distribution of ¹⁸F-APN-1607 PET imaging in Alzheimer’s disease (AD) subjects with different stages and to investigate the relationship between the change of tau deposition and overall disease progression.

Methods

We retrospectively analyzed the ¹⁸F-APN-1607 PET imaging of 31 subjects with clinically and imaging defined as AD. According to the Mini-Mental State Examination (MMSE) score, patients were divided into three groups, namely, mild (≥21, n = 7), moderate (10–20, n = 16), and severe (≤9, n = 8). PET imaging was segmented to 70 regions of interest (ROIs) and extracted the standard uptake value (SUV) of each ROI. SUV ratio (SUVR) was calculated from the ratio of SUV in different brain regions to the cerebellar cortex. The regions were defined as positive and negative with unsupervised cluster analysis according to SUVR. The SUVRs of each region were compared among groups with the one-way ANOVA or Kruskal–Wallis H test. Furthermore, the correlations between MMSE score and regional SUVR were calculated with Pearson or Spearman correlation analysis.

Results

There were no significant differences among groups in gender (χ² = 3.814, P = 0.161), age of onset (P = 0.170), age (P = 0.109), and education level (P = 0.065). With the disease progression, the ¹⁸F-APN-1607 PET imaging showed the spread of tau deposition from the hippocampus, posterior cingulate gyrus (PCG), and lateral temporal cortex (LTC) to the parietal and occipital lobes, and finally to the frontal lobe. Between the mild and moderate groups, the main brain areas with significant differences in ¹⁸F-APN-1607 uptake were supplementary motor area (SMA), cuneus, precuneus, occipital lobule, paracentral lobule, right angular gyrus, and parietal, which could be used for early disease progression assessment (P < 0.05). There were significant differences in the frontal lobe, right temporal lobe, and fusiform gyrus between the moderate and severe groups, which might be suitable for the late-stage disease progression assessment (P < 0.05).

Conclusion

¹⁸F-APN-1607 PET may serve as an effective imaging marker for visualizing the change pattern of tau protein deposition in AD patients, and its uptake level in certain brain regions is closely related to the severity of cognitive impairment. These indicate the potential of ¹⁸F-APN-1607 PET for the in vivo evaluation of the progression of AD.

Distinctive Temporal Trajectories of Alzheimer’s Disease Biomarkers According to Sex and APOE Genotype: Importance of Striatal Amyloid

Purpose

Previously, sex and apolipoprotein E (APOE) genotype had distinct effects on the cognitive trajectory across the Alzheimer’s disease (AD) continuum. We therefore aimed to investigate whether these trajectory curves including β-amyloid (Aβ) accumulation in the cortex and striatum, and tau accumulation would differ according to sex and APOE genotype.

Methods

We obtained 534 subjects for 18F-florbetapir (AV45) PET analysis and 163 subjects for 18F-flortaucipir (AV1451) PET analysis from the Alzheimer’s Disease Neuroimaging Initiative database. For cortical Aβ, striatal Aβ, and tau SUVR, we fitted penalized splines to model the slopes of SUVR value as a non-linear function of baseline SUVR value. By integrating the fitted splines, we obtained the predicted SUVR curves as a function of time.

Results

The time from initial SUVR to the cutoff values were 14.9 years for cortical Aβ, 18.2 years for striatal Aβ, and 22.7 years for tau. Although there was no difference in cortical Aβ accumulation rate between women and men, striatal Aβ accumulation was found to be faster in women than in men, and this temporal difference according to sex was more pronounced in tau accumulation. However, APOE ε4 carriers showed faster progression than non-carriers regardless of kinds of AD biomarkers’ trajectories.

Conclusion

Our temporal trajectory models illustrate that there is a distinct progression pattern of AD biomarkers depending on sex and APOE genotype. In this regard, our models will be able to contribute to designing personalized treatment and prevention strategies for AD in clinical practice.

Unraveling the heterogeneity in Alzheimer's disease progression across multiple cohorts and the implications for data-driven disease modeling

INTRODUCTION

Given study-specific inclusion and exclusion criteria, Alzheimer's disease (AD) cohort studies effectively sample from different statistical distributions. This heterogeneity can propagate into cohort-specific signals and subsequently bias data-driven investigations of disease progression patterns.

METHODS

We built multi-state models for six independent AD cohort datasets to statistically compare disease progression patterns across them. Additionally, we propose a novel method for clustering cohorts with regard to their progression signals.

RESULTS

We identified significant differences in progression patterns across cohorts. Models trained on cohort data learned cohort-specific effects that bias their estimations. We demonstrated how six cohorts relate to each other regarding their disease progression.

DISCUSSION

Heterogeneity in cohort datasets impedes the reproducibility of data-driven results and validation of progression models generated on single cohorts. To ensure robust scientific insights, it is advisable to externally validate results in independent cohort datasets. The proposed clustering assesses the comparability of cohorts in an unbiased, data-driven manner.

Traditional East Asian Herbal Medicine Treatment for Alzheimer's Disease: A Systematic Review and Meta-Analysis

Alzheimer's disease (AD) is a leading progressive neurodegenerative disease worldwide, and its treatment is a challenging clinical problem. This review was conducted to evaluate the efficacy and safety of herbal medicine for AD treatment. The PubMed, CENTRAL, EMBASE, CNKI, OASIS, KTKP, and CiNii databases were searched until June 2020 for randomized controlled trials (RCTs) on herbal medicine for AD, and a meta-analysis of 57 RCTs was conducted. For cognitive function, herbal medicine significantly improved the Mini-Mental State Examination (MMSE) and AD Assessment Scale-Cognitive Subscale (ADAS-cog) scores compared with conventional medicine. The MMSE scores showed no significant difference between the groups treated with herbal medicine and donepezil; however, herbal medicine significantly lowered the ADAS-cog score. Acori Graminei Rhizoma-containing and Cnidii Rhizoma-containing herbal medicine significantly improved the MMSE and ADAS-cog scores compared with conventional medicine. Ginseng Radix-containing herbal medicine showed a positive, but not statistically significant, tendency toward improving the MMSE score compared with conventional medicine. Herbal medicine with conventional medicine significantly improved the MMSE, ADAS-cog, and Montreal Cognitive Assessment (MoCA) scores compared with conventional medicine, and herbal medicine with donepezil also significantly improved these scores compared with donepezil. Acori Graminei Rhizoma or Cnidii Rhizoma-containing herbal medicine with conventional medicine significantly improved the MMSE and ADAS-cog scores compared with conventional medicine. Ginseng Radix-containing herbal medicine + conventional medicine significantly improved the MMSE score, but not the ADAS-cog score, compared with conventional medicine. For behavioral and psychological symptoms of dementia, the Neuropsychiatry Inventory (NPI) score was not significantly different between herbal and conventional medicines. Herbal medicine with conventional medicine significantly improved the NPI and Behavioral Pathology in Alzheimer's Disease Rating Scale scores compared with conventional medicine. The NPI score showed no significant difference between the groups treated with herbal medicine and placebo. Furthermore, herbal medicine with conventional medicine significantly lowered plasma amyloid beta levels compared with conventional medicine alone. Herbal medicine, whether used alone or as an adjuvant, may have beneficial effects on AD treatment. However, owing to the methodological limitations and high heterogeneity of the included studies, concrete conclusions cannot be made.

Neuronal-Derived EV Biomarkers Track Cognitive Decline in Alzheimer’s Disease

The hallmarks of Alzheimer’s disease (AD) pathology are senile plaques containing amyloid-beta (Aβ) and neurofibrillary tangles containing hyperphosphorylated tau. Additional pathologies often co-exist, whereas multiple pathogenic mechanisms are involved in AD, especially synaptic degeneration, which necessitate the need for synaptic integrity-related biomarkers alongside Aβ- and tau-related biomarkers. Plasma neuron-derived Extracellular Vesicles EVs (NDEVs) provide biomarkers related to Aβ and tau and synaptic degeneration. Here, to further establish the latter as a “liquid biopsy” for AD, we examined their relationship with ante-mortem cognition in pathologically-confirmed AD cases. We immunoprecipitated NDEVs by targeting neuronal marker L1CAM from ante-mortem plasma samples from 61 autopsy-confirmed cases of pure AD or AD with additional pathologies and measured Aβ₄₂, p181-Tau, total Tau, synaptophysin, synaptopodin and three canonical EV markers, CD63, CD81 and CD9. Higher NDEV Aβ₄₂ levels were consistently associated with better cognitive status, memory, fluency, working memory and executive function. Higher levels of NDEV synaptic integrity-related biomarkers were associated with better performance on executive function tasks. Our findings motivate the hypothesis that releasing Aβ₄₂-laden NDEVs may be an adaptive mechanism in AD.

Clinical Utility of Cerebrospinal Fluid Aβ42 and Tau Measures in Diagnosing Mild Cognitive Impairment in Early Onset Dementia

BACKGROUND

The differentiation of a preclinical or prodromal Alzheimer's disease (AD) is challenging particularly in patients with early onset Alzheimer's or related dementias (EOARD). We report our experience on diagnostic lumbar puncture to diagnose EOARD at a tertiary neurocognitive referral center in Nottingham, England from March 2018 to October 2020.

OBJECTIVE

To assess amyloid-β42 (Aβ42), total tau, and Thr181-phosphorylated tau (p-tau) measurements in the cerebrospinal fluid (CSF) in patients with mild cognitive impairment (MCI) and in relation to their follow-up cognitive performance.

METHODS

Thirty participants aged 32-68 years old (mean 59 years; 57% female) were included. Clinical diagnosis was based on clinical presentation, neurocognitive profile, neuroradiological features (MRI, FDG-PET CT) and CSF Aβ42, total tau, and p-tau measurements.

RESULTS

Patients with MCI who progressed to AD (prodromal AD) had significantly higher CSF total (797.63 pg/ml) and p-tau (82.31 pg/ml), and lower Aβ42 levels (398.94 pg/ml) in comparison to their counterparts with stable MCI (total tau 303.67 pg/ml, p-tau 43.56 pg/ml, Aβ42 873.44 pg/ml) (p < 0.01 for CSF total and p-tau measures and p < 0.0001 for CSF Aβ42 measures). None of the CSF biomarkers correlated with any of the cognitive performance measures. Principal component analysis confirmed that the clinical diagnosis of MCI secondary to AD, namely prodromal AD (as per NIA-AA criteria) in younger adults, was associated with decreased CSF Aβ42.

CONCLUSION

In early onset AD, low levels of CSF Aβ42 appear to be more sensitive than total and p-tau measures in differentiating AD MCI from other forms of dementia. Further work on larger samples of EOARD in clinical practice will address the cost effectiveness of making an earlier diagnosis.

A Machine Learning-Based Holistic Approach to Predict the Clinical Course of Patients within the Alzheimer's Disease Spectrum

BACKGROUND

Alzheimer's disease (AD) is a neurodegenerative condition driven by multifactorial etiology. Mild cognitive impairment (MCI) is a transitional condition between healthy aging and dementia. No reliable biomarkers are available to predict the conversion from MCI to AD.

OBJECTIVE

To evaluate the use of machine learning (ML) on a wealth of data offered by the Alzheimer's Disease Neuroimaging Initiative (ADNI) and Alzheimer's Disease Metabolomics Consortium (ADMC) database in the prediction of the MCI to AD conversion.

METHODS

We implemented an ML-based Random Forest (RF) algorithm to predict conversion from MCI to AD. Data related to the study population (587 MCI subjects) were analyzed by RF as separate or combined features and assessed for classification power. Four classes of variables were considered: neuropsychological test scores, AD-related cerebrospinal fluid (CSF) biomarkers, peripheral biomarkers, and structural magnetic resonance imaging (MRI) variables.

RESULTS

The ML-based algorithm exhibited 86% accuracy in predicting the AD conversion of MCI subjects. When assessing the features that helped the most, neuropsychological test scores, MRI data, and CSF biomarkers were the most relevant in the MCI to AD prediction. Peripheral parameters were effective when employed in association with neuropsychological test scores. Age and sex differences modulated the prediction accuracy. AD conversion was more effectively predicted in females and younger subjects.

CONCLUSION

Our findings support the notion that AD-related neurodegenerative processes result from the concerted activity of multiple pathological mechanisms and factors that act inside and outside the brain and are dynamically affected by age and sex.

The Amyloid-β Pathway in Alzheimer's Disease

Breakthroughs in molecular medicine have positioned the amyloid-β (Aβ) pathway at the center of Alzheimer's disease (AD) pathophysiology. While the detailed molecular mechanisms of the pathway and the spatial-temporal dynamics leading to synaptic failure, neurodegeneration, and clinical onset are still under intense investigation, the established biochemical alterations of the Aβ cycle remain the core biological hallmark of AD and are promising targets for the development of disease-modifying therapies. Here, we systematically review and update the vast state-of-the-art literature of Aβ science with evidence from basic research studies to human genetic and multi-modal biomarker investigations, which supports a crucial role of Aβ pathway dyshomeostasis in AD pathophysiological dynamics. We discuss the evidence highlighting a differentiated interaction of distinct Aβ species with other AD-related biological mechanisms, such as tau-mediated, neuroimmune and inflammatory changes, as well as a neurochemical imbalance. Through the lens of the latest development of multimodal in vivo biomarkers of AD, this cross-disciplinary review examines the compelling hypothesis- and data-driven rationale for Aβ-targeting therapeutic strategies in development for the early treatment of AD.

New Approaches to Profile the Microbiome for Treatment of Neurodegenerative Disease

Progressive neurodegenerative diseases represent some of the largest growing treatment challenges for public health in modern society. These diseases mainly progress due to aging and are driven by microglial surveillance and activation in response to changes occurring in the aging brain. The lack of efficacious treatment options for Alzheimer's disease (AD), as the focus of this review, and other neurodegenerative disorders has encouraged new approaches to address neuroinflammation for potential treatments. Here we will focus on the increasing evidence that dysbiosis of the gut microbiome is characterized by inflammation that may carry over to the central nervous system and into the brain. Neuroinflammation is the common thread associated with neurodegenerative diseases, but it is yet unknown at what point and how innate immune function turns pathogenic for an individual. This review will address extensive efforts to identify constituents of the gut microbiome and their neuroactive metabolites as a peripheral path to treatment. This approach is still in its infancy in substantive clinical trials and requires thorough human studies to elucidate the metabolic microbiome profile to design appropriate treatment strategies for early stages of neurodegenerative disease. We view that in order to address neurodegenerative mechanisms of the gut, microbiome and metabolite profiles must be determined to pre-screen AD subjects prior to the design of specific, chronic titrations of gut microbiota with low-dose antibiotics. This represents an exciting treatment strategy designed to balance inflammatory microglial involvement in disease progression with an individual's manifestation of AD as influenced by a coercive inflammatory gut.

Practical recommendations for timely, accurate diagnosis of symptomatic Alzheimer's disease (MCI and dementia) in primary care: a review and synthesis

The critical role of primary care clinicians (PCCs) in Alzheimer's disease (AD) prevention, diagnosis and management must evolve as new treatment paradigms and disease-modifying therapies (DMTs) emerge. Our understanding of AD has grown substantially: no longer conceptualized as a late-in-life syndrome of cognitive and functional impairments, we now recognize that AD pathology builds silently for decades before cognitive impairment is detectable. Clinically, AD first manifests subtly as mild cognitive impairment (MCI) due to AD before progressing to dementia. Emerging optimism for improved outcomes in AD stems from a focus on preventive interventions in midlife and timely, biomarker-confirmed diagnosis at early signs of cognitive deficits (i.e. MCI due to AD and mild AD dementia). A timely AD diagnosis is particularly important for optimizing patient care and enabling the appropriate use of anticipated DMTs. An accelerating challenge for PCCs and AD specialists will be to respond to innovations in diagnostics and therapy for AD in a system that is not currently well positioned to do so. To overcome these challenges, PCCs and AD specialists must collaborate closely to navigate and optimize dynamically evolving AD care in the face of new opportunities. In the spirit of this collaboration, we summarize here some prominent and influential models that inform our current understanding of AD. We also advocate for timely and accurate (i.e. biomarker-defined) diagnosis of early AD. In doing so, we consider evolving issues related to prevention, detecting emerging cognitive impairment and the role of biomarkers in the clinic.

Cerebrospinal fluid orexin in Alzheimer's disease: a systematic review and meta-analysis

OBJECTIVE/BACKGROUND

A growing body of evidence suggests that sleep and Alzheimer's disease (AD) have a bi-directional relationship. Emerging research also suggests that orexin, a key neurotransmitter involved in sleep-wake regulation, may be altered in persons with AD, however results have not been consistent across prior studies. This investigation was conducted to both evaluate the aggregate literature to minimize the risk of bias and identify potential factors associated with heterogeneity across studies.

METHODS

Systematic review identified relevant investigations that compared cerebrospinal fluid orexin in persons with AD and controls. Meta-analysis (random effects model) compared effect size (Hedge's g) for orexin between AD and controls. Meta-regression was additionally performed for key variables of interest to evaluate potential causes of heterogeneity among studies.

RESULTS

17 studies were identified that met inclusion/exclusion criteria. Evidence of publication bias was not identified. Non-significant increases in orexin were observed in AD relative to controls, with moderate to large heterogeneity among studies (Hedge's g = 0.20, p = 0.136, I² = 72.6%). Meta-regression demonstrated both year of publication (β = 0.055, p = 0.020) and effect size for phosphorylated tau in AD versus controls (β = 0.417, p = 0.031) were associated with differences in orexin.

CONCLUSIONS

Results do not support broad differences in orexin in AD compared to controls, however, evolving diagnostic criteria may have affected findings across studies. Future research that examines orexin in AD over the longitudinal course of the disorder and explores potential links between phosphorylated tau and orexin are indicated.

Amyloid-β positive individuals with subjective cognitive decline present increased CSF neurofilament light levels that relate to lower hippocampal volume

Neurofilament light chain (NfL) is an axonal protein that when measured in cerebrospinal fluid (CSF) serves as a biomarker of neurodegeneration. We aimed at investigating the association among CSF NfL, presence of Subjective Cognitive Decline (SCD) and hippocampal volume, and how CSF amyloid-β (Aβ) modifies these associations. We included 278 cognitively unimpaired participants from the Alfa+ cohort (78 SCD and 200 Controls). Linear models accounting for covariates (age, gender, and mood) were used to test the association between CSF NfL and SCD status, and between CSF NfL and bilateral hippocampal volumes. Interactions with Aβ were also explored. Individuals with SCD had higher CSF NfL and lower CSF Aβ42/40 than Controls. There was a significant interaction between SCD and CSF-Aβ42/40 levels. Stratified analyses showed a significant association between SCD and NfL only in Aβ+ individuals. Higher CSF NfL was significantly associated with lower hippocampal volume specifically in Aβ+ individuals with SCD. The presence of SCD in Aβ+ individuals may represent an early symptom in the Alzheimer's continuum related to incipient neurodegeneration.

Prognostic Role of CSF β-amyloid 1-42/1-40 Ratio in Patients Affected by Amyotrophic Lateral Sclerosis

The involvement of β-amyloid (Aβ) in the pathogenesis of amyotrophic lateral sclerosis (ALS) has been widely discussed and its role in the disease is still a matter of debate. Aβ accumulates in the cortex and the anterior horn neurons of ALS patients and seems to affect their survival. To clarify the role of cerebrospinal fluid (CSF) Aβ 1–42 and Aβ 42/40 ratios as a potential prognostic biomarker for ALS, we performed a retrospective observational study on a cohort of ALS patients who underwent a lumbar puncture at the time of the diagnosis. CSF Aβ 1–40 and Aβ 1–42 ratios were detected by chemiluminescence immunoassay and their values were correlated with clinical features. We found a significant correlation of the Aβ 42/40 ratio with age at onset and Mini Mental State Examination (MMSE) scores. No significant correlation of Aβ 1–42 or Aβ 42/40 ratios to the rate of progression of the disease were found. Furthermore, when we stratified patients according to Aβ 1–42 concentration and the Aβ 42/40 ratio, we found that patients with a lower Aβ 42/40 ratio showed a shorter survival. Our results support the hypothesis that Aβ 1–42 could be involved in some pathogenic mechanism of ALS and we suggest the Aβ 42/40 ratio as a potential prognostic biomarker.