Early-stage Alzheimer disease: getting trial-ready

Cerebrospinal fluid VGF is associated with the onset and progression of Alzheimer's disease

BackgroundIt remains unclear whether cerebrospinal fluid (CSF) VGF (non-acronymic) is associated with the onset and progression of Alzheimer's disease (AD).ObjectiveTo assess the levels of CSF VGF throughout the AD continuum, and its association with primary AD pathology, cognition, brain atrophy, and brain metabolism.MethodsWe studied a total of 526 individuals including 377 amyloid-positive individuals (76 preclinical AD, 200 prodromal AD, and 101 AD dementia) and 149 amyloid-negative cognitively normal individuals. VGF peptide in CSF was analyzed using mass spectrometry.ResultsWe observed decreased CSF VGF in preclinical, prodromal, and AD dementia individuals than amyloid-negative cognitively normal individuals. Reduced CSF VGF was associated with cognitive decline, hippocampal atrophy, ventricle enlargement, and glucose hypometabolism at baseline, and it predicted a more marked deterioration over time.ConclusionsOur findings support the important contributions of VGF to disease pathogenesis and progression in the early stages of AD. Exploring the biologics modulating VGF might be a promising approach for AD prevention and early treatment.

Exploring shared therapeutic targets for Alzheimer's disease and glioblastoma using network pharmacology and protein-protein interaction approach

Background

Alzheimer's disease (AD) and glioblastoma (GBM) are complex neurological disorders with distinct pathologies but overlapping molecular mechanisms, including neuroinflammation, oxidative stress, and dysregulated signaling pathways. Despite significant advancements in research, effective therapies targeting both conditions remain elusive. Identifying shared molecular targets and potential therapeutic agents could offer novel treatment strategies for these disorders.

Methodology

The study employs an integrative network pharmacology approach to explore the therapeutic potential of bioactive compounds from Eclipta alba, a medicinal herb known for its neuroprotective and anti-inflammatory properties. A systematic methodology was adopted, starting with network pharmacology analysis using STRING and DisGeNET databases, which identified 617 common genes associated with AD and GBM. Among these, key hub genes-TP53, STAT3, AKT1, and IL6-were prioritized using Cytoscape for network visualization and analysis.

Results

Molecular docking studies were conducted using PyRx software to assess the binding interactions of 26 phytochemicals from Eclipta alba against the identified target genes. Luteolin exhibited the highest binding affinity to IL6 (-7.8 kcal/mol), forming stable hydrogen bonds and hydrophobic interactions. To further validate this interaction, molecular dynamics simulations (MDS) were performed using GROMACS, confirming the stability of the Luteolin-IL6 complex. Additionally, MM-PBSA binding energy calculations using AmberTools (-145.44 kJ/mol) provided further evidence of a strong and stable interaction. Pharmacokinetic and toxicity evaluations, conducted using SwissADME and pkCSM, highlighted luteolin's favorable drug-like properties, including good bioavailability and low toxicity. These findings suggest that luteolin may serve as a promising multi-target therapeutic agent for AD and GBM by modulating key pathological pathways.

Conclusion

The present study provides a strong computational foundation for further in vitro and in vivo validation. The results highlight the potential of luteolin in developing dual-target treatment strategies for neurodegenerative and oncological disorders, offering new avenues for therapeutic advancements.

Sequential Proteomic Analysis Reveals the Key APOE4-Induced Pathological and Molecular Features at the Presymptomatic Stage in Alzheimer's Disease Mice

AIMS

Alzheimer's disease (AD) involves a prolonged presymptomatic or preclinical stage with subtle pathological changes. Apolipoprotein E4 (APOE4) is a significant genetic risk factor for AD, yet its specific role at the presymptomatic stage is not fully understood. This study aimed to elucidate the cellular and molecular effects of APOE4 compared to APOE3 on AD progression during the presymptomatic stage.

METHODS

We generated 5xFAD AD mice carrying human APOE3 or APOE4 and their non-AD controls. Behavioral tests, immunostaining, quantitative proteomics and phosphoproteomics, Golgi staining, and Western blotting were conducted at 3 or 10 months of age, respectively. Cell culture experiments were performed to assess APOE4's direct impact on neuronal mitochondrial function.

RESULTS

APOE4 significantly increased β-amyloid (Aβ) deposition and microglial activation compared to APOE3 in 5xFAD mice at the presymptomatic stage, without aggravating the blood-brain barrier disruption. Proteomic and biochemical analysis revealed strong molecular features of synaptic degeneration and mitochondrial dysfunction associated with APOE4. Notably, APOE4 promoted mitochondrial fusion and mitophagy while inhibiting fission, leading to impaired neuronal energy supply and increased reactive oxygen species.

CONCLUSION

Our findings indicate that APOE4 accelerates AD pathologies at the presymptomatic stage by exacerbating Aβ deposition, neuroinflammation, and synaptic degeneration. The study highlights mitochondrial dysfunction as a critical mediator of APOE4-induced AD progression, providing potential targets for early intervention.

Association of subjective memory complaints with serum biomarkers of neurodegeneration and cognition: A population-based study

BACKGROUND

Subjective memory complaints (SMCs) refer to memory concerns reported by an individual, regardless of objective test impairment. We conducted a study to evaluate the association of SMCs with serum biomarkers of neurodegeneration, including neurofilament light chain (NfL), glial fibrillary acidic protein (GFAP), and total tau (t-tau). In addition, we evaluated the association of SMCs with the rate of cognitive decline.

METHODS

This study included 1096 individuals participating in the Chicago Health and Aging Project, with data on SMCs, neurodegenerative biomarkers (NfL, GFAP, and t-tau), and global cognitive scores. The SMC score ranges from 0 to 8 and higher scores reflect more memory complaints. Linear regression models were developed to investigate the association of SMCs with serum biomarkers, adjusted by age, sex, race, education, and APOE e4. Linear mixed-effects models were used to examine the independent association of SMCs with cognitive decline in a multivariable model that was additionally adjusted by biomarkers of neurodegeneration.

RESULTS

Of the 1096 individuals, 665 (60.7%) were female, 643 (58.7%) were African Americans, and the mean (SD) age at the baseline was 78.0 (5.8) years. Compared to individuals with fewer memory complaints (i.e., people in the first tertile of SMCs), those reporting more memory complaints (i.e., people in the third tertile of SMCs) had a 12.0% increase in serum concentrations of NfL and an 9.4% increase in GFAP. In addition, individuals reporting more memory complaints (i.e., those in the 3rd versus the 1st tertile of SMCs) had a faster rate of cognitive decline by 0.029 ( β -0.029, 95% CI -0.046 to -0.013) standardized units per year.

CONCLUSIONS

Individuals who reported more memory complaints had higher levels of biomarkers of neurodegeneration and a faster rate of cognitive decline, suggesting that SMCs may be valuable for identifying people at high risk of cognitive impairment.

Chromatin accessibility provides a window into the genetic etiology of human brain disease

Neuropsychiatric and neurodegenerative diseases have a significant genetic component. Risk variants often affect the noncoding genome, altering cis-regulatory elements (CREs) and chromatin structure, ultimately impacting gene expression. Chromatin accessibility profiling methods, especially assay for transposase-accessible chromatin with high-throughput sequencing (ATAC-seq), have been used to pinpoint disease-associated SNPs and link them to affected genes and cell types in the brain. The integration of single-cell technologies with genome-wide association studies (GWAS) and transcriptomic data has further advanced our understanding of cell-specific chromatin dynamics. This review discusses recent findings regarding the role played by chromatin accessibility in brain disease, highlighting the need for high-quality data and rigorous computational tools. Future directions include spatial chromatin studies and CRISPR-based functional validation to bridge genetic discovery and clinical applications, paving the way for targeted gene-regulatory therapies.

Enrichment for clinical trials of early AD: Combining genetic risk factors and plasma p-tau as screening instruments

INTRODUCTION

Identifying low-cost, minimally-invasive screening instruments for Alzheimer's disease (AD) trial enrichment will improve the efficiency of AD trials.

METHODS

A total of 685 cognitively normal (CN) individuals and individuals with mild cognitive impairment (MCI) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) were grouped according to cutoffs of genetic risk factor (G) polygenic hazard score (PHS) and tau pathology (T) plasma phosphorylated tau-181 (p-tau181) into four groups: G+T+, G-T-, G+T-, and G-T+. We assessed the associations between group level and longitudinal cognitive decline and AD conversion. Power analyses compared the estimated sample size required to detect differences in cognitive decline.

RESULTS

The G+T+ group was associated with faster cognitive decline and higher AD risk. Clinical trials enrolling G+T+ participants would benefit from significantly reduced sample sizes compared with similar trials using only single makers as an inclusion criterion.

DISCUSSION

The combination of two low-cost, minimally-invasive measures-genetics and plasma biomarkers-would be a promising screening procedure for clinical trial enrollment.

HIGHLIGHTS

Participants with unimpaired or mildly impaired cognition were grouped based on cutoffs on genetic risk factors (G: polygenic hazardous score [PHS]) and Alzheimer's pathology (T: baseline plasma phosphorylated tau-181 [p-tau181]). Participants with high PHSs and plasma p-tau181 levels (G+T+) were at risk of faster cognitive decline and AD progression. The combination of PHS and plasma p-tau181 could enhance clinical trial enrichment more effectively than using single biomarkers.

The changes of peripheral blood hub genes in 24-week-old APP/PS1/Tau triple transgenic mouse model based on weighted gene co-expression network analysis

Peripheral regulation emerges as a promising intervention in the early stages of Alzheimer's disease (AD). The hub genes in the peripheral blood of MCI patients from GEO database (GSE63060, GSE63061) were screened using weighted gene co-expression analysis (WGCNA). Meanwhile, behavioral tests, HE staining and Nissl staining were used to detect the memory impairment and histopathological changes in 24-week-old male 3×Tg-AD mice. Thioflavin-S and immunohistochemical staining were used to determine the Aβ deposition in both intracellular and extracellular neurons. Subsequently, the MCI-hub genes were verified by quantitative real-time PCR (qRT-PCR) in the peripheral blood of 3×Tg-AD mice. The research revealed ten hub genes associated with MCI were identified WGCNA. Short-term memory loss, intracellular Aβ deposition and limited of extracellular amyloid plaques in 3×Tg-AD mice. The qRT-PCR analysis of peripheral blood from these mice revealed significantly down-regulation in the expression levels of ATP5C1, ITGB2, EFTUD2 and RPS27A genes; whereas the expression level of VCP gene was significantly up-regulated. These findings confirmed that 24-week-old male 3×Tg-AD mice were a valuable animal model for simulating the early symptomatic stages of AD. Additionally, the peripheral blood MCI-hub genes related to immune response, energy metabolism and ribosomal coding efficiency provide potential biomarkers for this stage.

The ADNI4 Digital Study: A novel approach to recruitment, screening, and assessment of participants for AD clinical research

INTRODUCTION

We evaluated preliminary feasibility of a digital, culturally-informed approach to recruit and screen participants for the Alzheimer's Disease Neuroimaging Initiative (ADNI4).

METHODS

Participants were recruited using digital advertising and completed digital surveys (e.g., demographics, medical exclusion criteria, 12-item Everyday Cognition Scale [ECog-12]), Novoic Storyteller speech-based cognitive test). Completion rates and assessment performance were compared between underrepresented populations (URPs: individuals from ethnoculturally minoritized or low education backgrounds) and non-URPs.

RESULTS

Of 3099 participants who provided contact information, 654 enrolled in the cohort, and 595 completed at least one assessment. Two hundred forty-seven participants were from URPs. Of those enrolled, 465 met ADNI4 inclusion criteria and 237 evidenced possible cognitive impairment from ECog-12 or Storyteller performance. URPs had lower ECog and Storyteller completion rates. Scores varied by ethnocultural group and educational level.

DISCUSSION

Preliminary results demonstrate digital recruitment and screening assessment of an older diverse cohort, including those with possible cognitive impairment, are feasible. Improving engagement and achieving educational diversity are key challenges.

HIGHLIGHTS

A total of 654 participants enrolled in a digital cohort to facilitate ADNI4 recruitment. Culturally-informed digital ads aided enrollment of underrepresented populations. From those enrolled, 42% were from underrepresented ethnocultural and educational groups. Digital screening tools indicate > 50% of participants likely cognitively impaired. Completion rates and assessment performance vary by ethnocultural group and education.

Prognostic value of Alzheimer's disease plasma biomarkers in the oldest-old: a prospective primary care-based study

Background

Blood-based biomarkers offer a promising, less invasive, and more cost-effective alternative for Alzheimer's disease screening compared to cerebrospinal fluid or imaging biomarkers. However, they have been extensively studied only in memory clinic-based cohorts. We aimed to validate them in a more heterogeneous, older patient population from primary care.

Methods

We measured plasma Aβ42/Aβ40, P-tau181, NfL, and GFAP in 1007 individuals without dementia, aged 79-94 years, from the longitudinal, primary care-based German AgeCoDe study. We assessed the association with cognitive decline, disease progression, and the capacity to predict future dementia of the Alzheimer's type (DAT). We also evaluated biomarker dynamics in 305 individuals with a follow-up sample (∼8 years later).

Findings

Higher levels of P-tau181 (HR = 1.32 [95% CI: 1.17-1.51]), NfL (HR = 1.19 [95% CI: 1.03-1.36]), and GFAP (HR = 1.36 [95% CI: 1.22-1.52]), and a lower Aβ42/Aβ40 ratio (HR = 0.80 [95% CI: 0.68-0.95]) were associated with an increased risk of progressing to clinically-diagnosed DAT. Additionally, higher levels of P-tau181 (β = -0.49 [95% CI: -0.71 to 0.26]), NfL (β = -0.29 [95% CI: -0.52 to 0.06]), and GFAP (β = -0.60 [95% CI: -0.83 to 0.38]) were linked to faster cognitive decline. A two-step DAT prediction strategy combining initial MMSE with biomarkers improved the identification of individuals in the prodromal stage for potential treatment eligibility. Biomarker levels changed over time, with increases in P-tau181 (β = 0.19 [95% CI: 0.14-0.25]), NfL (β = 2.88 [95% CI: 2.18-3.59]), and GFAP (β = 8.23 [95% CI: 6.71-9.75]). NfL (β = 2.47 [95% CI: 1.04-3.89]) and GFAP (β = 4.45 [95% CI: 1.38-7.51]) exhibited a faster increase in individuals progressing to DAT.

Interpretation

Evaluating plasma biomarkers, alongside brief cognitive assessments, might enhance the precision of risk assessment for DAT progression in primary care.

Funding

Alzheimer Forschung Initiative, Bundesministerium für Bildung und Forschung.

Developing diagnostic biomarkers for Alzheimer's disease based on histone lactylation-related gene

Background

Research underscores the significant influence of histone lactylation pathways in the progression of Alzheimer's disease (AD), though the molecular mechanisms associated with histone lactylation-related genes (HLRGs) in AD are still insufficiently investigated.

Methods

This study employed datasets GSE85426 and GSE97760 to identify candidate genes by intersecting weighted gene co-expression network analysis (WGCNA) module genes with AD-control differentially expressed genes (DEGs). Subsequently, machine learning refined key genes, validated by receiver operating characteristic (ROC) curve performance. Gene-set enrichment analysis (GSEA) explored the molecular mechanisms of these diagnostic markers. Concurrently, the association between the diagnostic genes and both differential immune cells and immune responses was examined. Furthermore, a ceRNA and gene-drug network was developed. Finally, the expression of the selected genes was validated using brain tissues from AD model mice.

Results

This study identified five genes (ARID5B, NSMCE4A, SESN1, THADA, and XPA) with significant diagnostic utility, primarily enriched in olfactory transduction and N-glycan biosynthesis pathways. Correlation analysis demonstrated a strong positive association between all diagnostic genes and naive B cells. The ceRNA regulatory network comprised 7 miRNAs, 2 mRNAs, and 25 lncRNAs. Additionally, 33 drugs targeting the diagnostic genes were predicted. Following expression validation through training and validation sets, three genes (ARID5B, SESN1, XPA) were ultimately confirmed as biomarkers for this study. RT-qPCR and Western blot analyses revealed upregulated expression of ARID5B, SESN1, and XPA in the cerebral tissue of AD model mice.

Conclusion

Three histone lactylation-linked genes (ARID5B, SESN1, XPA) were identified as potential AD biomarkers, indicating a strong association with disease progression.

A machine learning-based prediction of tau load and distribution in Alzheimer's disease using plasma, MRI and clinical variables

Tau positron emission tomography (PET) is a reliable neuroimaging technique for assessing regional load of tau pathology in the brain, commonly used in Alzheimer's disease (AD) research and clinical trials. However, its routine clinical use is limited by cost and accessibility barriers. Here we explore using machine learning (ML) models to predict clinically useful tau-PET composites from low-cost and non-invasive features, e.g., basic clinical variables, plasma biomarkers, and structural magnetic resonance imaging (MRI). Results demonstrated that models including plasma biomarkers yielded the most accurate predictions of tau-PET burden (best model: R-squared=0.66-0.68), with especially high contribution from plasma P-tau217. In contrast, MRI variables stood out as best predictors (best model: R-squared=0.28-0.42) of asymmetric tau load between the two hemispheres (an example of clinically relevant spatial information). The models showed high generalizability to external test cohorts with data collected at multiple sites. Based on these results, we also propose a proof-of-concept two-step classification workflow, demonstrating how the ML models can be translated to a clinical setting. This study uncovers current potential in predicting tau-PET information from scalable cost-effective variables, which could improve diagnosis and prognosis of AD.

PM2.5 is linked to Alzheimer's syndrome and delirium: a mendelian randomization analysis

BACKGROUND

Increasing air pollution has drawn our attention to particulate matter (PM2.5), which has been shown to correlate significantly with respiratory and cardiovascular systems. However, whether PM2.5 is causally associated with Alzheimer's syndrome or delirium is unclear.

METHODS

We retrieved the genetic summary data of PM2.5 from genome-wide association studies (GWAS). The genetic information for Alzheimer's disease was obtained from the IEU OpenGWAS project, and that for delirium was obtained from FinnGen. We used two-sample Mendelian randomization analysis (MR) to associate PM2.5 with Alzheimer's disease or delirium.

RESULTS

The odds ratio (OR) for Alzheimer's disease was 0.996 with a p-value of 0.443 using the inverse variance weighted algorithm, and the OR associated with the outcome variable of delirium was 0.393 with a p-value of 0.343.

CONCLUSION

With the exclusion of confounding factors, our findings do not support a genetic association between PM2.5 and Alzheimer's disease or delirium. Further population-based and experimental studies are needed to dissect the complex correlation between PM2.5 and Alzheimer's disease or delirium.

Oxygen-enriching triphase platform for reliable sensing of femtomolar Alzheimer's neurofilament lights

Accurate quantification of neurofilament lights (NfLs), a prognostic blood biomarker, is highly required to predict neurodegeneration in the presymptomatic stages of Alzheimer's disease. Here, we report self-oxygen-enriching coral structures with triphase interfaces for the label-free photocathodic detection of NfLs in blood plasma with femtomolar sensitivities and high reliability. In conventional photocathodic immunoassays, the poor solubility and sluggish diffusion rate of the dissolved oxygen serving as electron acceptors have necessitated the incorporation of additional electron acceptors or aeration procedures. To address the challenge, we designed the coral-like copper bismuth oxides (CBO) with robust solid-liquid-air contact boundaries that enrich the interfacial oxygen levels without an external aeration source. By optimally assembling the perfluorododecyltrichlorosilane (FTCS) and platinum (Pt) co-catalysts into the silver-doped CBO (Ag:CBO), the stable solid-liquid-air contact boundaries were formed within the sensor interfaces, which allowed for the abundant supply of air phase oxygen through an air pocket connected to the atmosphere. The Pt/FTCS-Ag:CBO exhibited the stable background signals independent of the dissolved oxygen fluctuations and amplified photocurrent signals by 1.76-fold, which were attributed to the elevated interfacial oxygen levels and 11.15 times-lowered mass transport resistance. Under the illumination of white light-emitting diode, the oxygen-enriching photocathodic sensor composed of Pt/FTCS-Ag:CBO conjugated with NfLs-specific antibodies precisely quantified the NfLs in plasma with a low coefficient of variation (≤2.97%), a high degree of recovery (>97.0%), and a limit of detection of 40.38 fg/mL, which was 140 times lower than the typical photocathodic sensor with diphase interfaces.

Rationale and design of the BeyeOMARKER study: prospective evaluation of blood- and eye-based biomarkers for early detection of Alzheimer's disease pathology in the eye clinic

BACKGROUND

Alzheimer's disease (AD) is a common, complex and multifactorial disease that may require screening across multiple routes of referral to enable early detection and subsequent future implementation of tailored interventions. Blood- and eye-based biomarkers show promise as low-cost, scalable and patient-friendly tools for early AD detection given their ability to provide information on AD pathophysiological changes and manifestations in the retina, respectively. Eye clinics provide an intriguing real-world proof-of-concept setting to evaluate the performance of these potential AD screening tools given the intricate connections between the eye and brain, presumed enrichment for AD pathology in the aging population with eye disorders, and the potential for an accelerated diagnostic pathway for under-recognized patient groups.

METHODS

The BeyeOMARKER study is a prospective, observational, longitudinal cohort study aiming to include individuals visiting an eye-clinic. Inclusion criteria entail being ≥ 50 years old and having no prior dementia diagnosis. Excluded eye-conditions include traumatic insults, superficial inflammation, and conditions in surrounding structures of the eye that are not engaged in vision. The BeyeOMARKER cohort (n = 700) will undergo blood collection to assess plasma p-tau217 levels and a brief cognitive screening at the eye clinic. All participants will subsequently be invited for annual longitudinal follow-up including remotely administered cognitive screening and questionnaires. The BeyeOMARKER + cohort (n = 150), consisting of 100 plasma p-tau217 positive participants and 50 matched negative controls selected from the BeyeOMARKER cohort, will additionally undergo Aβ-PET and tau-PET, MRI, retinal imaging including hyperspectral imaging (primary), widefield imaging, optical coherence tomography (OCT) and OCT-Angiography (secondary), and cognitive and cortical vision assessments.

RESULTS

We aim to implement the current protocol between April 2024 until March 2027. Primary outcomes include the performance of plasma p-tau217 and hyperspectral retinal imaging to detect AD pathology (using Aβ- and tau-PET visual read as reference standard) and to detect cognitive decline. Initial follow-up is ~ 2 years but may be extended with additional funding.

CONCLUSIONS

We envision that the BeyeOMARKER study will demonstrate the feasibility of early AD detection based on blood- and eye-based biomarkers in alternative screening settings, and will improve our understanding of the eye-brain connection.

TRIAL REGISTRATION

The BeyeOMARKER study (Eudamed CIV ID: CIV-NL-23-09-044086; registration date: 19th of March 2024) is approved by the ethical review board of the Amsterdam UMC.

Latent diffusion model-based MRI superresolution enhances mild cognitive impairment prognostication and Alzheimer's disease classification

INTRODUCTION

Timely diagnosis and prognostication of Alzheimer's disease (AD) and mild cognitive impairment (MCI) are pivotal for effective intervention. Artificial intelligence (AI) in neuroradiology may aid in such appropriate diagnosis and prognostication. This study aimed to evaluate the potential of novel diffusion model-based AI for enhancing AD and MCI diagnosis through superresolution (SR) of brain magnetic resonance (MR) images.

METHODS

1.5T brain MR scans of patients with AD or MCI and healthy controls (NC) from Alzheimer's Disease Neuroimaging Initiative 1 (ADNI1) were superresolved to 3T using a novel diffusion model-based generative AI (d3T*) and a convolutional neural network-based model (c3T*). Comparisons of image quality to actual 1.5T and 3T MRI were conducted based on signal-to-noise ratio (SNR), naturalness image quality evaluator (NIQE), and Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE). Voxel-based volumetric analysis was then conducted to study whether 3T* images offered more accurate volumetry than 1.5T images. Binary and multiclass classifications of AD, MCI, and NC were conducted to evaluate whether 3T* images offered superior AD classification performance compared to actual 1.5T MRI. Moreover, CNN-based classifiers were used to predict conversion of MCI to AD, to evaluate the prognostication performance of 3T* images. The classification performances were evaluated using accuracy, sensitivity, specificity, F1 score, Matthews correlation coefficient (MCC), and area under the receiver-operating curves (AUROC).

RESULTS

Analysis of variance (ANOVA) detected significant differences in image quality among the 1.5T, c3T*, d3T*, and 3T groups across all metrics. Both c3T* and d3T* showed superior image quality compared to 1.5T MRI in NIQE and BRISQUE with statistical significance. While the hippocampal volumes measured in 3T* and 3T images were not significantly different, the hippocampal volume measured in 1.5T images showed significant difference. 3T*-based AD classifications showed superior performance across all performance metrics compared to 1.5T-based AD classification. Classification performance between d3T* and actual 3T was not significantly different. 3T* images offered superior accuracy in predicting the conversion of MCI to AD than 1.5T images did.

CONCLUSIONS

The diffusion model-based MRI SR enhances the resolution of brain MR images, significantly improving diagnostic and prognostic accuracy for AD and MCI. Superresolved 3T* images closely matched actual 3T MRIs in quality and volumetric accuracy, and notably improved the prediction performance of conversion from MCI to AD.

A generalizable data-driven model of atrophy heterogeneity and progression in memory clinic settings

Memory clinic patients are a heterogeneous population representing various aetiologies of pathological ageing. It is not known whether divergent spatiotemporal progression patterns of brain atrophy, as previously described in Alzheimer's disease patients, are prevalent and clinically meaningful in this group of older adults. To uncover distinct atrophy subtypes, we applied the Subtype and Stage Inference (SuStaIn) algorithm to baseline structural MRI data from 813 participants enrolled in the DELCODE cohort (mean ± standard deviation, age = 70.67 ± 6.07 years, 52% females). Participants were cognitively unimpaired (n = 285) or fulfilled diagnostic criteria for subjective cognitive decline (n = 342), mild cognitive impairment (n = 118) or dementia of the Alzheimer's type (n = 68). Atrophy subtypes were compared in baseline demographics, fluid Alzheimer's disease biomarker levels, the Preclinical Alzheimer Cognitive Composite (PACC-5) as well as episodic memory and executive functioning. PACC-5 trajectories over up to 240 weeks were examined. To test whether baseline atrophy subtype and stage predicted clinical trajectories before manifest cognitive impairment, we analysed PACC-5 trajectories and mild cognitive impairment conversion rates of cognitively unimpaired participants and those with subjective cognitive decline. Limbic-predominant and hippocampal-sparing atrophy subtypes were identified. Limbic-predominant atrophy initially affected the medial temporal lobes, followed by further temporal regions and, finally, the remaining cortical regions. At baseline, this subtype was related to older age, more pathological Alzheimer's disease biomarker levels, APOE ε4 carriership and an amnestic cognitive impairment. Hippocampal-sparing atrophy initially occurred outside the temporal lobe, with the medial temporal lobe spared up to advanced atrophy stages. This atrophy pattern also affected individuals with positive Alzheimer's disease biomarkers and was associated with more generalized cognitive impairment. Limbic-predominant atrophy, in all participants and in only unimpaired participants, was linked to more negative longitudinal PACC-5 slopes than observed in participants without or with hippocampal-sparing atrophy and increased the risk of mild cognitive impairment conversion. SuStaIn modelling was repeated in a sample from the Swedish BioFINDER-2 cohort. Highly similar atrophy progression patterns and associated cognitive profiles were identified. Cross-cohort model generalizability, at both the subject and the group level, was excellent, indicating reliable performance in previously unseen data. The proposed model is a promising tool for capturing heterogeneity among older adults at early at-risk states for Alzheimer's disease in applied settings. The implementation of atrophy subtype- and stage-specific end points might increase the statistical power of pharmacological trials targeting early Alzheimer's disease.

Polyoxometalates: metallodrug agents for combating amyloid aggregation

Alzheimer's disease (AD) is a devastating neurodegenerative disease that affects ∼50 million people globally. The accumulation of amyloid-β (Aβ) plaques, a predominant pathological feature of AD, plays a crucial role in AD pathogenesis. In this respect, Aβ has been regarded as a highly promising therapeutic target for AD treatment. Polyoxometalates (POMs) are a novel class of metallodrugs being developed as modulators of Aβ aggregation, owing to their negative charge, polarity, and three-dimensional structure. Unlike traditional discrete inorganic complexes, POMs contain tens to hundreds of metal atoms, showcasing remarkable tunability and diversity in nuclearities, sizes, and shapes. The easily adjustable and structurally variable nature of POMs allows for their favorable interactions with Aβ. This mini-review presents a balanced overview of recent progress in using POMs to mitigate amyloidosis. Clear correlations between anti-amyloid activities and structural features of POMs are also elaborated in detail. Finally, we discuss the current challenges and future prospects of POMs in combating AD.

Alzheimer's Disease and Small Vessel Disease Differentially Affect White Matter Microstructure

OBJECTIVE

Alzheimer's disease (AD) and cerebral small vessel disease (cSVD), the two most common causes of dementia, are characterized by white matter (WM) alterations diverging from the physiological changes occurring in healthy aging. Diffusion tensor imaging (DTI) is a valuable tool to quantify WM integrity non-invasively and identify the determinants of such alterations. Here, we investigated main effects and interactions of AD pathology, APOE-ε4, cSVD, and cardiovascular risk on spatial patterns of WM alterations in non-demented older adults.

METHODS

Within the prospective European Prevention of Alzheimer's Dementia study, we selected 606 participants (64.9 ± 7.2 years, 376 females) with baseline cerebrospinal fluid samples of amyloid β1-42 and p-Tau181 and MRI scans, including DTI scans. Longitudinal scans (mean follow-up time = 1.3 ± 0.5 years) were obtained in a subset (n = 223). WM integrity was assessed by extracting fractional anisotropy and mean diffusivity in relevant tracts. To identify the determinants of WM disruption, we performed a multimodel inference to identify the best linear mixed-effects model for each tract.

RESULTS

AD pathology, APOE-ε4, cSVD burden, and cardiovascular risk were all associated with WM integrity within several tracts. While limbic tracts were mainly impacted by AD pathology and APOE-ε4, commissural, associative, and projection tract integrity was more related to cSVD burden and cardiovascular risk. AD pathology and cSVD did not show any significant interaction effect.

INTERPRETATION

Our results suggest that AD pathology and cSVD exert independent and spatially different effects on WM microstructure, supporting the role of DTI in disease monitoring and suggesting independent targets for preventive medicine approaches.

Representational maps in the brain: concepts, approaches, and applications

Neural systems have evolved to process sensory stimuli in a way that allows for efficient and adaptive behavior in a complex environment. Recent technological advances enable us to investigate sensory processing in animal models by simultaneously recording the activity of large populations of neurons with single-cell resolution, yielding high-dimensional datasets. In this review, we discuss concepts and approaches for assessing the population-level representation of sensory stimuli in the form of a representational map. In such a map, not only are the identities of stimuli distinctly represented, but their relational similarity is also mapped onto the space of neuronal activity. We highlight example studies in which the structure of representational maps in the brain are estimated from recordings in humans as well as animals and compare their methodological approaches. Finally, we integrate these aspects and provide an outlook for how the concept of representational maps could be applied to various fields in basic and clinical neuroscience.

Alzheimer's Disease: A Suitable Case for Treatment with Precision Medicine?

Alzheimer's disease (AD) is the most common cause of neurodegenerative impairment in elderly people. Clinical characteristics include short-term memory loss, confusion, hallucination, agitation, and behavioural disturbance. Owing to evolving research in biomarkers AD can be discovered at early onset, but the disease is currently considered a continuum, which suggests that pharmacotherapy is most efficacious in the preclinical phase, possibly 15 - 20 years before discernible onset. Present developments in AD therapy aim to respond to this understanding and go beyond the drug families that relieve clinical symptoms. Another important factor in this development is the emergence of precision medicine that aims to tailor treatment to specific patients or patient subgroups. This relatively new platform would categorize AD patients on the basis of parameters like clinical aspects, brain imaging, genetic profiling, clinical genetics and epidemiological factors. This review enlarges on recent progress in the design and clinical use of antisense molecules, antibodies, antioxidants, small molecules and gene editing to stop AD progress and possibly reverse the disease on the basis of relevant biomarkers.

Machine Learning and Digital Biomarkers Can Detect Early Stages of Neurodegenerative Diseases

Neurodegenerative diseases (NDs) such as Alzheimer's Disease (AD) and Parkinson's Disease (PD) are devastating conditions that can develop without noticeable symptoms, causing irreversible damage to neurons before any signs become clinically evident. NDs are a major cause of disability and mortality worldwide. Currently, there are no cures or treatments to halt their progression. Therefore, the development of early detection methods is urgently needed to delay neuronal loss as soon as possible. Despite advancements in Medtech, the early diagnosis of NDs remains a challenge at the intersection of medical, IT, and regulatory fields. Thus, this review explores "digital biomarkers" (tools designed for remote neurocognitive data collection and AI analysis) as a potential solution. The review summarizes that recent studies combining AI with digital biomarkers suggest the possibility of identifying pre-symptomatic indicators of NDs. For instance, research utilizing convolutional neural networks for eye tracking has achieved significant diagnostic accuracies. ROC-AUC scores reached up to 0.88, indicating high model performance in differentiating between PD patients and healthy controls. Similarly, advancements in facial expression analysis through tools have demonstrated significant potential in detecting emotional changes in ND patients, with some models reaching an accuracy of 0.89 and a precision of 0.85. This review follows a structured approach to article selection, starting with a comprehensive database search and culminating in a rigorous quality assessment and meaning for NDs of the different methods. The process is visualized in 10 tables with 54 parameters describing different approaches and their consequences for understanding various mechanisms in ND changes. However, these methods also face challenges related to data accuracy and privacy concerns. To address these issues, this review proposes strategies that emphasize the need for rigorous validation and rapid integration into clinical practice. Such integration could transform ND diagnostics, making early detection tools more cost-effective and globally accessible. In conclusion, this review underscores the urgent need to incorporate validated digital health tools into mainstream medical practice. This integration could indicate a new era in the early diagnosis of neurodegenerative diseases, potentially altering the trajectory of these conditions for millions worldwide. Thus, by highlighting specific and statistically significant findings, this review demonstrates the current progress in this field and the potential impact of these advancements on the global management of NDs.

Spatial Neurolipidomics at the Single Amyloid-β Plaque Level in Postmortem Human Alzheimer's Disease Brain

Lipid dysregulations have been critically implicated in Alzheimer's disease (AD) pathology. Chemical analysis of amyloid-β (Aβ) plaque pathology in transgenic AD mouse models has demonstrated alterations in the microenvironment in the direct proximity of Aβ plaque pathology. In mouse studies, differences in lipid patterns linked to structural polymorphism among Aβ pathology, such as diffuse, immature, and mature fibrillary aggregates, have also been reported. To date, no comprehensive analysis of neuronal lipid microenvironment changes in human AD tissue has been performed. Here, for the first time, we leverage matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) through a high-speed and spatial resolution commercial time-of-light instrument, as well as a high-mass-resolution in-house-developed orbitrap system to characterize the lipid microenvironment in postmortem human brain tissue from AD patients carrying Presenilin 1 mutations (PSEN1) that lead to familial forms of AD (fAD). Interrogation of the spatially resolved MSI data on a single Aβ plaque allowed us to verify nearly 40 sphingolipid and phospholipid species from diverse subclasses being enriched and depleted, in relation to the Aβ deposits. This included monosialo-gangliosides (GM), ceramide monohexosides (HexCer), ceramide-1-phosphates (CerP), ceramide phosphoethanolamine conjugates (PE-Cer), sulfatides (ST), as well as phosphatidylinositols (PI), phosphatidylethanolamines (PE), and phosphatidic acid (PA) species (including Lyso-forms). Indeed, many of the sphingolipid species overlap with the species previously seen in transgenic AD mouse models. Interestingly, in comparison to the animal studies, we observed an increased level of localization of PE and PI species containing arachidonic acid (AA). These findings are highly relevant, demonstrating for the first time Aβ plaque pathology-related alteration in the lipid microenvironment in humans. They provide a basis for the development of potential lipid biomarkers for AD characterization and insight into human-specific molecular pathway alterations.

A multimodal deep learning approach for the prediction of cognitive decline and its effectiveness in clinical trials for Alzheimer's disease

Alzheimer's disease is one of the most important health-care challenges in the world. For decades, numerous efforts have been made to develop therapeutics for Alzheimer's disease, but most clinical trials have failed to show significant treatment effects on slowing or halting cognitive decline. Among several challenges in such trials, one recently noticed but unsolved is biased allocation of fast and slow cognitive decliners to treatment and placebo groups during randomization caused by the large individual variation in the speed of cognitive decline. This allocation bias directly results in either over- or underestimation of the treatment effect from the outcome of the trial. In this study, we propose a stratified randomization method using the degree of cognitive decline predicted by an artificial intelligence model as a stratification index to suppress the allocation bias in randomization and evaluate its effectiveness by simulation using ADNI data set.

Path integration deficits are associated with phosphorylated tau accumulation in the entorhinal cortex

Alzheimer's disease is a devastating disease that is accompanied by dementia, and its incidence increases with age. However, no interventions have exhibited clear therapeutic effects. We aimed to develop and characterize behavioural tasks that allow the earlier identification of signs preceding dementia that would facilitate the development of preventative and therapeutic interventions for Alzheimer's disease. To this end, we developed a 3D virtual reality task sensitive to the activity of grid cells in the entorhinal cortex, which is the region that first exhibits neurofibrillary tangles in Alzheimer's disease. We investigated path integration (assessed by error distance) in a spatial navigation task sensitive to grid cells in the entorhinal cortex in 177 volunteers, aged 20-89 years, who did not have self-reported dementia. While place memory was intact even in old age, path integration deteriorated with increasing age. To investigate the relationship between neurofibrillary tangles in the entorhinal cortex and path integration deficit, we examined a mouse model of tauopathy (P301S mutant tau-overexpressing mice; PS19 mice). At 6 months of age, PS19 mice showed a significant accumulation of phosphorylated tau only in the entorhinal cortex, associated with impaired path integration without impairments in spatial cognition. These data are consistent with the idea that path integration deficit is caused by the accumulation of phosphorylated tau in the entorhinal cortex. This method may allow the early identification of individuals likely to develop Alzheimer's disease.

Plasma pTau-217 and N-terminal tau (NTA) enhance sensitivity to identify tau PET positivity in amyloid-β positive individuals

INTRODUCTION

We set out to identify tau PET-positive (A+T+) individuals among amyloid-beta (Aβ) positive participants using plasma biomarkers.

METHODS

In this cross-sectional study we assessed 234 participants across the AD continuum who were evaluated by amyloid PET with [18 F]AZD4694 and tau-PET with [18 F]MK6240 and measured plasma levels of total tau, pTau-181, pTau-217, pTau-231, and N-terminal tau (NTA-tau). We evaluated the performances of plasma biomarkers to predict tau positivity in Aβ+ individuals.

RESULTS

Highest associations with tau positivity in Aβ+ individuals were found for plasma pTau-217 (AUC [CI95% ] = 0.89 [0.82, 0.96]) and NTA-tau (AUC [CI95% ] = 0.88 [0.91, 0.95]). Combining pTau-217 and NTA-tau resulted in the strongest agreement (Cohen's Kappa = 0.74, CI95%  = 0.57/0.90, sensitivity = 92%, specificity = 81%) with PET for classifying tau positivity.

DISCUSSION

The potential for identifying tau accumulation in later Braak stages will be useful for patient stratification and prognostication in treatment trials and in clinical practice.

HIGHLIGHTS

We found that in a cohort without pre-selection pTau-181, pTau-217, and NTA-tau showed the highest association with tau PET positivity. We found that in Aβ+ individuals pTau-217 and NTA-tau showed the highest association with tau PET positivity. Combining pTau-217 and NTA-tau resulted in the strongest agreement with the tau PET-based classification.

Mitophagy in Alzheimer's Disease: A Bibliometric Analysis from 2007 to 2022

Background

The investigation of mitophagy in Alzheimer's disease (AD) remains relatively underexplored in bibliometric analysis.

Objective

To delve into the progress of mitophagy, offering a comprehensive overview of research trends and frontiers for researchers.

Methods

Basic bibliometric information, targets, and target-drug-clinical trial-disease extracted from publications identified in the Web of Science Core Collection from 2007 to 2022 were assessed using bibliometric software.

Results

The study encompassed 5,146 publications, displaying a consistent 16-year upward trajectory. The United States emerged as the foremost contributor in publications, with the Journal of Alzheimer's Disease being the most prolific journal. P. Hemachandra Reddy, George Perry, and Xiongwei Zhu are the top 3 most prolific authors. PINK1 and Parkin exhibited an upward trend in the last 6 years. Keywords (e.g., insulin, aging, epilepsy, tauopathy, and mitochondrial quality control) have recently emerged as focal points of interest within the past 3 years. "Mitochondrial dysfunction" is among the top terms in disease clustering. The top 10 drugs/molecules (e.g., curcumin, insulin, and melatonin) were summarized, accompanied by their clinical trials and related targets.

Conclusions

This study presents a comprehensive overview of the mitophagy research landscape in AD over the past 16 years, underscoring mitophagy as an emerging molecular mechanism and a crucial focal point for potential drug in AD. This study pioneers the inclusion of targets and their correlations with drugs, clinical trials, and diseases in bibliometric analysis, providing valuable insights and inspiration for scholars and readers of JADR interested in understanding the potential mechanisms and clinical trials in AD.

The epidemiology of Parkinson's disease

The epidemiology of Parkinson's disease shows marked variations in time, geography, ethnicity, age, and sex. Internationally, prevalence has increased over and above demographic changes. There are several potential reasons for this increase, including the decline in other competing causes of death. Whether incidence is increasing, especially in women or in many low-income and middle-income countries where there is a shortage of high-quality data, is less certain. Parkinson's disease is more common in older people and men, and a variety of environmental factors have been suggested to explain why, including exposure to neurotoxic agents. Within countries, there appear to be ethnic differences in disease risk, although these differences might reflect differential access to health care. The cause of Parkinson's disease is multifactorial, and involves genetic and environmental factors. Both risk factors (eg, pesticides) and protective factors (eg, physical activity and tendency to smoke) have been postulated to have a role in Parkinson's disease, although elucidating causality is complicated by the long prodromal period. Following the establishment of public health strategies to prevent cardiovascular diseases and some cancers, chronic neurodegenerative diseases such as Parkinson's disease and dementia are gaining a deserved higher priority. Multipronged prevention strategies are required that tackle population-based primary prevention, high-risk targeted secondary prevention, and Parkinson's disease-modifying therapies for tertiary prevention. Future international collaborations will be required to triangulate evidence from basic, applied, and epidemiological research, thereby enhancing the understanding and prevention of Parkinson's disease at a global level.

Self- and interviewer-reported cognitive problems in relation to cognitive decline and dementia: results from two prospective studies

BACKGROUND

Little is known regarding the association of interviewer-reported cognitive problems (ICP) with age-related cognitive decline. We aimed to investigate the independent associations of ICP and the combined associations of ICP and self-reported cognitive problems (SCP) with subsequent cognitive decline and dementia in two prospective cohort studies.

METHODS

We included 10,976 Chinese (age = 57.7 ± 8.7) and 40,499 European (age = 64.6 ± 9.4) adults without dementia from the China Health and Retirement Longitudinal Study (CHARLS) and the Survey of Health, Ageing, and Retirement in Europe (SHARE). Self-rated memory (5-point scale) and interviewer-rated frequencies of asking for clarification (6-point scale) were used to define SCP and ICP (dichotomized). Outcomes included objective cognitive test scores (z-score transformation) and incident dementia. Generalized estimating equation models were performed to evaluate mean differences in objective cognitive decline. Logistic and Cox regression models were used to estimate the relative risk of dementia. Results from two cohorts were pooled using the random-effects models.

RESULTS

ICP was associated with faster cognitive decline in CHARLS (βCHARLS = -0.025 [-0.044, -0.006] z-score/year). ICP and SCP were also independently associated with higher risk of dementia in two cohorts (pooled relative risk for SCP = 1.73 [1.30, 2.29]; pooled relative risk for ICP = 1.40 [1.10, 1.79]). In the joint analysis, participants with coexistence of SCP and ICP had the fastest cognitive decline (βCHARLS = -0.051 [-0.080, -0.021]; βSHARE = -0.024 [-0.043, -0.004]; pooled β = -0.035 [-0.061, -0.009] z-score/year) and highest risk of dementia (ORCHARLS = 1.77 [1.42, 2.20]; HRSHARE = 2.94 [2.42, 3.59]; pooled relative risk = 2.29 [1.38, 3.77]).

CONCLUSIONS

The study suggested that interviewer-reported cognitive problems may be early indicators of cognitive decline and dementia in middle-aged and older adults. A combination of self- and interviewer-reported cognitive problems showed the strongest associations with cognitive decline and dementia.

Potential Impact of Slowing Disease Progression in Early Symptomatic Alzheimer's Disease on Patient Quality of Life, Caregiver Time, and Total Societal Costs: Estimates Based on Findings from GERAS-US Study

Background

Impact of Alzheimer's disease (AD) progression on patient health-related quality of life (HRQoL), caregiver time, and societal costs is not well characterized in early AD.

Objective

To assess the association of change in cognition with HRQoL, caregiver time, and societal costs over 36 months, and estimate the impact of slowing disease progression on these outcomes.

Methods

This post-hoc analysis included patients with amyloid-positive mild cognitive impairment (MCI) and mild AD dementia (MILD AD) from the 36-month GERAS-US study. Disease progression was assessed using the Mini-Mental State Examination score. Change in outcomes associated with slowing AD progression was estimated using coefficients from generalized linear models.

Results

At baseline, 300 patients had MCI and 317 had MILD AD. Observed natural progression over 36 months was associated with: 5.1 point decline in the Bath Assessment of Subjective Quality of Life in Dementia (BASQID) score (for HRQoL), increase in 1,050 hours of total caregiver time, and $8,504 total societal costs for MCI; 6.6 point decline in the BASQID score, increase in 1,929 hours of total caregiver time, and $12,795 total societal costs for MILD AD per person. Slowing AD progression by 30% could result in per person savings in HRQoL decline, total caregiver time, and total societal costs: for MCI: 1.5 points, 315 hours, and $2,638; for MILD AD: 2.0 points, 579 hours, and $3,974.

Conclusions

Slowing AD progression over 36 months could slow decline in HRQoL and save caregiver time and societal cost in patients with MCI and MILD AD.

Advances in Developing Small Molecule Drugs for Alzheimer's Disease

Alzheimer's disease (AD) is the most common type of dementia among middle-aged and elderly individuals. Accelerating the prevention and treatment of AD has become an urgent problem. New technology including Computer-aided drug design (CADD) can effectively reduce the medication cost for patients with AD, reduce the cost of living, and improve the quality of life of patients, providing new ideas for treating AD. This paper reviews the pathogenesis of AD, the latest developments in CADD and other small-molecule docking technologies for drug discovery and development; the current research status of small-molecule compounds for AD at home and abroad from the perspective of drug action targets; the future of AD drug development.

Predicting 5-year dementia conversion in veterans with mild cognitive impairment

INTRODUCTION

Identifying mild cognitive impairment (MCI) patients at risk for dementia could facilitate early interventions. Using electronic health records (EHRs), we developed a model to predict MCI to all-cause dementia (ACD) conversion at 5 years.

METHODS

Cox proportional hazards model was used to identify predictors of ACD conversion from EHR data in veterans with MCI. Model performance (area under the receiver operating characteristic curve [AUC] and Brier score) was evaluated on a held-out data subset.

RESULTS

Of 59,782 MCI patients, 15,420 (25.8%) converted to ACD. The model had good discriminative performance (AUC 0.73 [95% confidence interval (CI) 0.72-0.74]), and calibration (Brier score 0.18 [95% CI 0.17-0.18]). Age, stroke, cerebrovascular disease, myocardial infarction, hypertension, and diabetes were risk factors, while body mass index, alcohol abuse, and sleep apnea were protective factors.

DISCUSSION

EHR-based prediction model had good performance in identifying 5-year MCI to ACD conversion and has potential to assist triaging of at-risk patients.

Highlights

Of 59,782 veterans with mild cognitive impairment (MCI), 15,420 (25.8%) converted to all-cause dementia within 5 years.Electronic health record prediction models demonstrated good performance (area under the receiver operating characteristic curve 0.73; Brier 0.18).Age and vascular-related morbidities were predictors of dementia conversion.Synthetic data was comparable to real data in modeling MCI to dementia conversion.

Key Points

An electronic health record-based model using demographic and co-morbidity data had good performance in identifying veterans who convert from mild cognitive impairment (MCI) to all-cause dementia (ACD) within 5 years.Increased age, stroke, cerebrovascular disease, myocardial infarction, hypertension, and diabetes were risk factors for 5-year conversion from MCI to ACD.High body mass index, alcohol abuse, and sleep apnea were protective factors for 5-year conversion from MCI to ACD.Models using synthetic data, analogs of real patient data that retain the distribution, density, and covariance between variables of real patient data but are not attributable to any specific patient, performed just as well as models using real patient data. This could have significant implications in facilitating widely distributed computing of health-care data with minimized patient privacy concern that could accelerate scientific discoveries.

Pharmacologic and Nutritional Interventions for Early Alzheimer's Disease: A Systematic Review and Network Meta-Analysis of Randomized Controlled Trials

Background

Early intervention is essential for meaningful disease modification in Alzheimer's disease (AD).

Objective

We aimed to determine the efficacy and safety of pharmacologic and nutritional interventions for early AD.

Methods

PubMed, Embase, the Cochrane Library, and ClinicalTrials.gov were searched from database inception until 1 September 2023. We included randomized controlled trials that evaluated the efficacy of interventions in early AD. Only interventions that demonstrated efficacy compared to placebo were included in the network meta-analysis (NMA). Then we performed frequentist fixed-effects NMA to rank the interventions. GRADE criteria were used to evaluate the level of evidence.

Results

Fifty-eight trials including a total of 33,864 participants and 48 interventions were eligible for inclusion. Among the 48 interventions analyzed, only 6 (12.5%) treatments- ranging from low to high certainty- showed significant improvement in cognitive decline compared to placebo. High certainty evidence indicated that donanemab (standardized mean difference [SMD] -0.239, 95% confidence interval [CI] -0.343 to -0.134) and lecanemab (SMD -0.194, 95% CI -0.279 to -0.108) moderately slowed the clinical progression in patients with amyloid pathology. Additionally, methylphenidate, donepezil, LipiDiDiet, and aducanumab with low certainty showed significant improvement in cognitive decline compared to placebo. However, there was no significant difference in serious adverse events as reported between the six interventions and placebo.

Conclusions

Only 12.5% of interventions studied demonstrated efficacy in reducing cognitive impairment in early AD. Donanemab and lecanemab have the potential to moderately slow the clinical progression in patients with amyloid pathology. Further evidence is required for early intervention in AD.

A Strategy for Allowing Earlier Diagnosis and Rigorous Evaluation of BACE1 Inhibitors in Preclinical Alzheimer's Disease

Given continued failure of BACE1 inhibitor programs at symptomatic and prodromal stages of Alzheimer's disease (AD), clinical trials need to target the earlier preclinical stage. However, trial design is complex in this population with negative diagnosis of classical hippocampal amnesia on standard memory tests. Besides recent advances in brain imaging, electroencephalogram, and fluid-based biomarkers, new cognitive markers should be established for earlier diagnosis that can optimize recruitment to BACE1 inhibitor trials in presymptomatic AD. Notably, accelerated long-term forgetting (ALF) is emerging as a sensitive cognitive measure that can discriminate between asymptomatic individuals with high risks for developing AD and healthy controls. ALF is a form of declarative memory impairment characterized by increased forgetting rates over longer delays (days to months) despite normal storage within the standard delays of testing (20-60 min). Therefore, ALF may represent a harbinger of preclinical dementia and the impairment of systems memory consolidation, during which memory traces temporarily stored in the hippocampus become gradually integrated into cortical networks. This review provides an overview of the utility of ALF in a rational design of next-generation BACE1 inhibitor trials in preclinical AD. I explore potential mechanisms underlying ALF and relevant early-stage biomarkers useful for BACE1 inhibitor evaluation, including synaptic protein alterations, astrocytic dysregulation and neuron hyperactivity in the hippocampal-cortical network. Furthermore, given the physiological role of the isoform BACE2 as an AD-suppressor gene, I also discuss the possible association between the poor selectivity of BACE1 inhibitors and their side effects (e.g., cognitive worsening) in prior clinical trials.

Pre-Randomization Predictors of Study Discontinuation in a Preclinical Alzheimer's Disease Randomized Controlled Trial

BACKGROUND

Participant discontinuation from study treatment in a clinical trial can leave a trial underpowered, produce bias in statistical analysis, and limit interpretability of study results. Retaining participants in clinical trials for the full study duration is therefore as important as participant recruitment.

OBJECTIVE

This analysis aims to identify associations of pre-randomization characteristics of participants with premature discontinuation during the blinded phase of the Anti-Amyloid treatment in Asymptomatic AD (A4) Study.

DESIGN

All A4 trial randomized participants were classified as having prematurely discontinued study during the blinded period of the study for any reason (dropouts) or completed the blinded phase of the study on treatment (completers).

SETTING

The trial was conducted across 67 study sites in the United States, Canada, Japan and Australia through the global COVID-19 pandemic.

PARTICIPANTS

The sample consisted of all 1169 A4 trial randomized participants.

MEASUREMENTS

Pre-randomization demographic, clinical, amyloid PET and genetic predictors of study discontinuation were evaluated using a univariate generalized linear mixed model (GLMM), with discontinuation status as the binary outcome, each predictor as a fixed effect, and site as a random effect to account for differences among study sites in the trial. Characteristics significant at p<0.10 were then included in a multivariable GLMM.

RESULTS

Among randomized participants, 339 (29%) discontinued the study during the blinded period (median follow-up time in trial: 759 days). From the multivariable analysis, the two main predictors of study discontinuation were screening State-Trait Anxiety Inventory (STAI) scores (OR = 1.07 [95%CI = 1.02; 1.12]; p=0.002) and age (OR = 1.06 [95%CI = 1.03; 1.09]; p<0.001). Participants with a family history of dementia (OR = 0.75 [95%CI = 0.55; 1.01]; p=0.063) and APOE ε4 carriers (OR = 0.79 [95%CI = 0.6; 1.04]; p=0.094) were less likely to discontinue from the study, with the association being marginally significant. In these analyses, sex, race and ethnicity, cognitive scores and amyloid/tau PET scores were not associated with study dropout.

CONCLUSIONS

In the A4 trial, older participants and those with higher levels of anxiety at baseline as measured by the STAI were more likely to discontinue while those who had a family history of dementia or were APOE ε4 carriers were less likely to drop out. These findings have direct implications for future preclinical trial design and selection processes to identify those individuals at greatest risk of dropout and provide information to the study team to develop effective selection and retention strategies in AD prevention studies.

Precision Brain Morphometry Using Cluster Scanning

Measurement error limits the statistical power to detect group differences and longitudinal change in structural MRI morphometric measures (e.g., hippocampal volume, prefrontal thickness). Recent advances in scan acceleration enable extremely fast T1-weighted scans (~1 minute) to achieve morphometric errors that are close to the errors in longer traditional scans. As acceleration allows multiple scans to be acquired in rapid succession, it becomes possible to pool estimates to increase measurement precision, a strategy known as "cluster scanning." Here we explored brain morphometry using cluster scanning in a test-retest study of 40 individuals (12 younger adults, 18 cognitively unimpaired older adults, and 10 adults diagnosed with mild cognitive impairment or Alzheimer's Dementia). Morphometric errors from a single compressed sensing (CS) 1.0mm scan with 6x acceleration (CSx6) were, on average, 12% larger than a traditional scan using the Alzheimer's Disease Neuroimaging Initiative (ADNI) protocol. Pooled estimates from four clustered CSx6 acquisitions led to errors that were 34% smaller than ADNI despite having a shorter total acquisition time. Given a fixed amount of time, a gain in measurement precision can thus be achieved by acquiring multiple rapid scans instead of a single traditional scan. Errors were further reduced when estimates were pooled from eight CSx6 scans (51% smaller than ADNI). Neither pooling across a break nor pooling across multiple scan resolutions boosted this benefit. We discuss the potential of cluster scanning to improve morphometric precision, boost statistical power, and produce more sensitive disease progression biomarkers.

Amplified fluorogenic immunoassay for early diagnosis and monitoring of Alzheimer's disease from tear fluid

Accurate diagnosis of Alzheimer's disease (AD) in its earliest stage can prevent the disease and delay the symptoms. Therefore, more sensitive, non-invasive, and simple screening tools are required for the early diagnosis and monitoring of AD. Here, we design a self-assembled nanoparticle-mediated amplified fluorogenic immunoassay (SNAFIA) consisting of magnetic and fluorophore-loaded polymeric nanoparticles. Using a discovery cohort of 21 subjects, proteomic analysis identifies adenylyl cyclase-associated protein 1 (CAP1) as a potential tear biomarker. The SNAFIA demonstrates a low detection limit (236 aM), good reliability (R2 = 0.991), and a wide analytical range (0.320-1000 fM) for CAP1 in tear fluid. Crucially, in the verification phase with 39 subjects, SNAFIA discriminates AD patients from healthy controls with 90% sensitivity and 100% specificity in under an hour. Utilizing tear fluid as a liquid biopsy, SNAFIA could potentially aid in long-term care planning, improve clinical trial efficiency, and accelerate therapeutic development for AD.

Sex and Education Modify the Association Between Subjective Cognitive Decline and Amyloid Pathology

Background

Subjective cognitive decline (SCD) may be an early risk factor for dementia, particularly in highly educated individuals and women. This study examined the effect of education and sex on the association between SCD and Alzheimer's disease (AD) biomarkers in non-demented older adults.

Method

Vanderbilt Memory and Aging Project participants free of clinical dementia or stroke (n=156, 72±6 years, 37% mild cognitive impairment, 33% female) completed fasting lumbar puncture, SCD assessment, and Wide Range Achievement Test-III Reading subtest to assess reading level at baseline as a a proxy for educational quality. Cerebrospinal fluid (CSF) biomarkers for AD (β-amyloid 42 (Aβ42), Aβ42/40 ratio, phosphorylated tau (p-tau), tau, and neurofilament light (NfL)) were analyzed in batch. Linear mixed effects models related SCD to CSF AD biomarkers and follow-up models assessed SCD x sex, SCD x reading level , and SCD x education interactions on AD biomarkers.

Result

In main effect models, higher SCD was associated with lower Aβ42 and Aβ42/40 ratio (p-values<0.004). SCD was not associated with tau, p-tau, or NfL levels ( p- values>0.38). SCD score interacted with sex on Aβ42/40 ratio ( p =0.03) but no other biomarkers ( p -values>0.10). In stratified models, higher SCD was associated with lower Aβ42/40 ratio in men ( p =0.0003) but not in women ( p =0.48). SCD score interacted with education on Aβ42 ( p =0.005) and Aβ42/40 ratio ( p =0.001) such that higher education was associated with a stronger negative association between SCD and amyloid levels. No SCD score x reading level interaction was found (p-values> 0.51) though significant associations between SCD and amyloid markers were seen in the higher reading level group (p-values<0.004) but not the lower reading level group (p-values>0.12) when stratified by a median split in reading level.

Conclusion

Among community-dwelling older adults free of clinical dementia, higher SCD was associated with greater cerebral amyloid accumulation, one of the earliest pathological AD changes. SCD appears most useful in detecting early AD-related brain changes in men and individuals with higher quantity and quality of education. SCD was not associated with CSF markers of tau pathology or neurodegeneration. These findings suggest that considering sex and education is important when assessing SCD in older adults.

Recruitment of pre-dementia participants: main enrollment barriers in a longitudinal amyloid-PET study

BACKGROUND

The mismatch between the limited availability versus the high demand of participants who are in the pre-dementia phase of Alzheimer's disease (AD) is a bottleneck for clinical studies in AD. Nevertheless, potential enrollment barriers in the pre-dementia population are relatively under-reported. In a large European longitudinal biomarker study (the AMYPAD-PNHS), we investigated main enrollment barriers in individuals with no or mild symptoms recruited from research and clinical parent cohorts (PCs) of ongoing observational studies.

METHODS

Logistic regression was used to predict study refusal based on sex, age, education, global cognition (MMSE), family history of dementia, and number of prior study visits. Study refusal rates and categorized enrollment barriers were compared between PCs using chi-squared tests.

RESULTS

535/1856 (28.8%) of the participants recruited from ongoing studies declined participation in the AMYPAD-PNHS. Only for participants recruited from clinical PCs (n = 243), a higher MMSE-score (β =  - 0.22, OR = 0.80, p < .05), more prior study visits (β =  - 0.93, OR = 0.40, p < .001), and positive family history of dementia (β = 2.08, OR = 8.02, p < .01) resulted in lower odds on study refusal. General study burden was the main enrollment barrier (36.1%), followed by amyloid-PET related burden (PCresearch = 27.4%, PCclinical = 9.0%, X2 = 10.56, p = .001), and loss of research interest (PCclinical = 46.3%, PCresearch = 16.5%, X2 = 32.34, p < .001).

CONCLUSIONS

The enrollment rate for the AMYPAD-PNHS was relatively high, suggesting an advantage of recruitment via ongoing studies. In this observational cohort, study burden reduction and tailored strategies may potentially improve participant enrollment into trial readiness cohorts such as for phase-3 early anti-amyloid intervention trials. The AMYPAD-PNHS (EudraCT: 2018-002277-22) was approved by the ethical review board of the VU Medical Center (VUmc) as the Sponsor site and in every affiliated site.

Sensitive dual-mode sensing platform for Amyloid β detection: Combining dual Z-scheme heterojunction enhanced photoelectrochemistry analysis and dual-wavelength ratiometric electrochemiluminescence strategy

As a tumor biomarker, the accumulation of amyloid β oligomers (Aβo) in the brain has been suggested as a key feature in the pathogenesis and progression of Alzheimer's disease (AD). In this work, we designed a novel photoelectrochemical (PEC) and electrochemiluminescence resonance energy transfer (ECL-RET) dual-mode biosensor to achieve ultra-sensitive detection of Aβo. Specifically, the electrode surface modified Carbon Dots (C Dots) and the electrodeposited polyaniline (PANI) film formed a Z-scheme heterojunction reversing the photocurrent signal, and then the Aβo specific recognition peptide was attached to the surface via amide bonding between the amino group of PANI and carbonyl group of peptide. After that, in the presence of CdTe labeled specific recognition aptamer for Aβ (CdTe-Apt), Aβo was captured to construct a sandwich-type biosensor and exhibited a significantly enhanced cathodic photocurrent response because the formed dual Z-scheme heterojunction promoted charge separation efficiency. Interestingly, the proposed biosensor also caused a ratiometric change in the ECL intensity at 555 nm and 640 nm. Therefore, the developed biosensor achieved dual-mode detection of Aβo, where the PEC detection range of Aβo was from 10 fM to 0.1 μM (with a detection limit of 4.27 fM) and the ECL method provided a linear detection range of 10 fM to 10 nM (with a detection limit of 6.41 fM). The stability and reliability of the experimental results indicate that this has been a promising biosensing pattern and could be extended to the analysis of other biomarkers.

Helicobacter pylori infection contributes to the expression of Alzheimer's disease-associated risk factors and neuroinflammation

Over time, mounting evidence has demonstrated extra-gastric manifestations of Helicobacter pylori infection. As such, a number of studies demonstrated the potential contribution of H. pylori infection to the incidence and progression of Alzheimer's disease (AD). Considering unanswered questions regarding the effect of H. pylori infection on brain activity, we sought to investigate the impact of H. pylori infection on the expression of AD-associated risk factors. We used two H. pylori clinical strains obtained from two patients with peptic ulcer and evaluated their influence on the expression level of AD-associated genes (APP, ApoE2, ApoE4, ABCA7, BIN1, Clu, CD33) and genes for inflammatory markers (TLR-4, IL-8, TNF-α) by RT-qPCR in human glioblastoma (U87MG) and astrocyte (1321N1) cell lines. The expression of inflammatory cytokines was further assessed by ELISA assay. The exposure of U97MG and 1321N1 cells to H. pylori strains resulted in a significant enhancement in the expression level of the risk allele ApoE4, while reducing the expression of the protective allele ApoE2. H. pylori infection remarkably increased the expression level of main AD-associated risk genes, and also pro-inflammatory cytokines. Furthermore, we noticed a substantial elevation in the mRNA expression level of transmembrane receptor TLR-4 following H. pylori infection. Our findings presented the potential for H. pylori to stimulate the expression of AD-associated risk genes and trigger neuroinflammation in the brain tissue. This, in principle, leads to the recommendation that AD patients should perhaps test for H. pylori infection and receive treatments upon positive detection.

Neurophysiological alterations in mice and humans carrying mutations in APP and PSEN1 genes

BACKGROUND

Studies in animal models of Alzheimer's disease (AD) have provided valuable insights into the molecular and cellular processes underlying neuronal network dysfunction. Whether and how AD-related neurophysiological alterations translate between mice and humans remains however uncertain.

METHODS

We characterized neurophysiological alterations in mice and humans carrying AD mutations in the APP and/or PSEN1 genes, focusing on early pre-symptomatic changes. Longitudinal local field potential recordings were performed in APP/PS1 mice and cross-sectional magnetoencephalography recordings in human APP and/or PSEN1 mutation carriers. All recordings were acquired in the left frontal cortex, parietal cortex, and hippocampus. Spectral power and functional connectivity were analyzed and compared with wildtype control mice and healthy age-matched human subjects.

RESULTS

APP/PS1 mice showed increased absolute power, especially at higher frequencies (beta and gamma) and predominantly between 3 and 6 moa. Relative power showed an overall shift from lower to higher frequencies over almost the entire recording period and across all three brain regions. Human mutation carriers, on the other hand, did not show changes in power except for an increase in relative theta power in the hippocampus. Mouse parietal cortex and hippocampal power spectra showed a characteristic peak at around 8 Hz which was not significantly altered in transgenic mice. Human power spectra showed a characteristic peak at around 9 Hz, the frequency of which was significantly reduced in mutation carriers. Significant alterations in functional connectivity were detected in theta, alpha, beta, and gamma frequency bands, but the exact frequency range and direction of change differed for APP/PS1 mice and human mutation carriers.

CONCLUSIONS

Both mice and humans carrying APP and/or PSEN1 mutations show abnormal neurophysiological activity, but several measures do not translate one-to-one between species. Alterations in absolute and relative power in mice should be interpreted with care and may be due to overexpression of amyloid in combination with the absence of tau pathology and cholinergic degeneration. Future studies should explore whether changes in brain activity in other AD mouse models, for instance, those also including tau pathology, provide better translation to the human AD continuum.

Mild Behavioral Impairment in Psychogeriatric Patients: Clinical Features and Psychopathology Severity

The Mild Behavioral Impairment (MBI) concept was developed to determine whether late-onset persistent neuropsychiatric symptoms (NPSs) may be early manifestations of cognitive decline. Our study aims to investigate the prevalence and differentiating features of MBI with respect to major neurocognitive disorders (MNDs) and primary psychiatric disorders (PPDs). A total of 144 elderly patients who were referred to our psychogeriatric outpatient service were recruited. The severity of mental illness was evaluated by means of the Clinical Global Impression Severity scale, the severity of psychopathology was evaluated by means of the Brief Psychiatric Rating Scale (BPRS), and overall functioning was evaluated by means of the Global Assessment of Functioning scale. The sample included 73 (50.6%) patients with PPDs, 40 (27.8%) patients with MBI, and 31 (21.5%) patients with MNDs. Patients with MNDs reported the greatest severity of mental illness, the highest BPRS Total, Psychosis, Activation, and Negative Symptom scores, and the lowest functioning. Patients with MBI and PPDs had comparable levels of severity of mental illness and overall functioning, but MBI patients reported higher BPRS Total and Negative Symptom scores than PPD patients. Patients with MBI frequently reported specific clinical features, including a higher severity of apathy and motor retardation. These features merit further investigation since they may help the differential diagnosis between MBI and PPDs.

Donanemab in Early Symptomatic Alzheimer Disease: The TRAILBLAZER-ALZ 2 Randomized Clinical Trial

Importance

There are limited efficacious treatments for Alzheimer disease.

Objective

To assess efficacy and adverse events of donanemab, an antibody designed to clear brain amyloid plaque.

Design, Setting, and Participants

Multicenter (277 medical research centers/hospitals in 8 countries), randomized, double-blind, placebo-controlled, 18-month phase 3 trial that enrolled 1736 participants with early symptomatic Alzheimer disease (mild cognitive impairment/mild dementia) with amyloid and low/medium or high tau pathology based on positron emission tomography imaging from June 2020 to November 2021 (last patient visit for primary outcome in April 2023).

Interventions

Participants were randomized in a 1:1 ratio to receive donanemab (n = 860) or placebo (n = 876) intravenously every 4 weeks for 72 weeks. Participants in the donanemab group were switched to receive placebo in a blinded manner if dose completion criteria were met.

Main Outcomes and Measures

The primary outcome was change in integrated Alzheimer Disease Rating Scale (iADRS) score from baseline to 76 weeks (range, 0-144; lower scores indicate greater impairment). There were 24 gated outcomes (primary, secondary, and exploratory), including the secondary outcome of change in the sum of boxes of the Clinical Dementia Rating Scale (CDR-SB) score (range, 0-18; higher scores indicate greater impairment). Statistical testing allocated α of .04 to testing low/medium tau population outcomes, with the remainder (.01) for combined population outcomes.

Results

Among 1736 randomized participants (mean age, 73.0 years; 996 [57.4%] women; 1182 [68.1%] with low/medium tau pathology and 552 [31.8%] with high tau pathology), 1320 (76%) completed the trial. Of the 24 gated outcomes, 23 were statistically significant. The least-squares mean (LSM) change in iADRS score at 76 weeks was -6.02 (95% CI, -7.01 to -5.03) in the donanemab group and -9.27 (95% CI, -10.23 to -8.31) in the placebo group (difference, 3.25 [95% CI, 1.88-4.62]; P < .001) in the low/medium tau population and -10.2 (95% CI, -11.22 to -9.16) with donanemab and -13.1 (95% CI, -14.10 to -12.13) with placebo (difference, 2.92 [95% CI, 1.51-4.33]; P < .001) in the combined population. LSM change in CDR-SB score at 76 weeks was 1.20 (95% CI, 1.00-1.41) with donanemab and 1.88 (95% CI, 1.68-2.08) with placebo (difference, -0.67 [95% CI, -0.95 to -0.40]; P < .001) in the low/medium tau population and 1.72 (95% CI, 1.53-1.91) with donanemab and 2.42 (95% CI, 2.24-2.60) with placebo (difference, -0.7 [95% CI, -0.95 to -0.45]; P < .001) in the combined population. Amyloid-related imaging abnormalities of edema or effusion occurred in 205 participants (24.0%; 52 symptomatic) in the donanemab group and 18 (2.1%; 0 symptomatic during study) in the placebo group and infusion-related reactions occurred in 74 participants (8.7%) with donanemab and 4 (0.5%) with placebo. Three deaths in the donanemab group and 1 in the placebo group were considered treatment related.

Conclusions and Relevance

Among participants with early symptomatic Alzheimer disease and amyloid and tau pathology, donanemab significantly slowed clinical progression at 76 weeks in those with low/medium tau and in the combined low/medium and high tau pathology population.

Trial Registration

ClinicalTrials.gov Identifier: NCT04437511.

A FinnGen pilot clinical recall study for Alzheimer's disease

Successful development of novel therapies requires that clinical trials are conducted in patient cohorts with the highest benefit-to-risk ratio. Population-based biobanks with comprehensive health and genetic data from large numbers of individuals hold promise to facilitate identification of trial participants, particularly when interventions need to start while symptoms are still mild, such as for Alzheimer's disease (AD). This study describes a process for clinical recall studies from FinnGen. We demonstrate the feasibility to systematically ascertain customized clinical data from FinnGen participants with ICD10 diagnosis of AD or mild cognitive disorder (MCD) in a single-center cross-sectional study testing blood-based biomarkers and cognitive functioning in-person, computer-based and remote. As a result, 19% (27/140) of a pre-specified FinnGen subcohort were successfully recalled and completed the study. Hospital records largely validated registry entries. For 8/12 MCD patients, other reasons than AD were identified as underlying diagnosis. Cognitive measures correlated across platforms, with highest consistencies for dementia screening (r = 0.818) and semantic fluency (r = 0.764), respectively, for in-person versus telephone-administered tests. Glial fibrillary acidic protein (GFAP) (p < 0.002) and phosphorylated-tau 181 (pTau-181) (p < 0.020) most reliably differentiated AD from MCD participants. We conclude that informative, customized clinical recall studies from FinnGen are feasible.

Brain morphometry in older adults with and without dementia using extremely rapid structural scans

T1-weighted structural MRI is widely used to measure brain morphometry (e.g., cortical thickness and subcortical volumes). Accelerated scans as fast as one minute or less are now available but it is unclear if they are adequate for quantitative morphometry. Here we compared the measurement properties of a widely adopted 1.0 mm resolution scan from the Alzheimer's Disease Neuroimaging Initiative (ADNI = 5'12'') with two variants of highly accelerated 1.0 mm scans (compressed-sensing, CSx6 = 1'12''; and wave-controlled aliasing in parallel imaging, WAVEx9 = 1'09'') in a test-retest study of 37 older adults aged 54 to 86 (including 19 individuals diagnosed with a neurodegenerative dementia). Rapid scans produced highly reliable morphometric measures that largely matched the quality of morphometrics derived from the ADNI scan. Regions of lower reliability and relative divergence between ADNI and rapid scan alternatives tended to occur in midline regions and regions with susceptibility-induced artifacts. Critically, the rapid scans yielded morphometric measures similar to the ADNI scan in regions of high atrophy. The results converge to suggest that, for many current uses, extremely rapid scans can replace longer scans. As a final test, we explored the possibility of a 0'49'' 1.2 mm CSx6 structural scan, which also showed promise. Rapid structural scans may benefit MRI studies by shortening the scan session and reducing cost, minimizing opportunity for movement, creating room for additional scan sequences, and allowing for the repetition of structural scans to increase precision of the estimates.

Innovative Technologies in CNS Trials: Promises and Pitfalls for Recruitment, Retention, and Representativeness

Objective

Recruitment of a sufficiently large and representative patient sample and its retention during central nervous system (CNS) trials presents major challenges for study sponsors. Technological advances are reshaping clinical trial operations to meet these challenges, and the COVID-19 pandemic further accelerated this development.

Method of Research

The International Society for CNS Clinical Trials and Methodology (ISCTM; www.isctm.org) Innovative Technologies for CNS Trials Working Group surveyed the state of technological innovations for improved recruitment and retention and assessed their promises and pitfalls.

Results

Online advertisement and electronic patient registries can enhance recruitment, but challenges with sample representativeness, conversion rates from eligible prescreening to enrolled patients, data privacy and security, and patient identification remain hurdles for optimal use of these technologies. Electronic medical records (EMR) mining with artificial intelligence (AI)/machine learning (ML) methods is promising but awaits translation into trials. During the study treatment phase, technological innovations increasingly support participant retention, including adherence with the investigational treatment. Digital tools for adherence and retention support take many forms, including patient-centric communication channels between researchers and participants, real-time study reminders, and digital behavioral interventions to increase study compliance. However, such tools add technical complexities to trials, and their impact on the generalizability of results are largely unknown.

Conclusion

Overall, the group found a scarcity of systematic data directly assessing the impact of technological innovations on study recruitment and retention in CNS trials, even for strategies with already high adoption, such as online recruitment. Given the added complexity and costs associated with most technological innovations, such data is needed to fully harness technologies for CNS trials and drive further adoption.

Alterations to parvalbumin-expressing interneuron function and associated network oscillations in the hippocampal - medial prefrontal cortex circuit during natural sleep in AppNL-G-F/NL-G-F mice

In the early stages of Alzheimer's disease (AD), the accumulation of the peptide amyloid-β (Aβ) damages synapses and disrupts neuronal activity, leading to the disruption of neuronal oscillations associated with cognition. This is thought to be largely due to impairments in CNS synaptic inhibition, particularly via parvalbumin (PV)-expressing interneurons that are essential for generating several key oscillations. Research in this field has largely been conducted in mouse models that over-express humanised, mutated forms of AD-associated genes that produce exaggerated pathology. This has prompted the development and use of knock-in mouse lines that express these genes at an endogenous level, such as the App^{NL-G-F/NL-G-F} mouse model used in the present study. These mice appear to model the early stages of Aβ-induced network impairments, yet an in-depth characterisation of these impairments in currently lacking. Therefore, using 16 month-old App^{NL-G-F/NL-G-F} mice, we analysed neuronal oscillations found in the hippocampus and medial prefrontal cortex (mPFC) during awake behaviour, rapid eye movement (REM) and non-REM (NREM) sleep to assess the extent of network dysfunction. No alterations to gamma oscillations were found to occur in the hippocampus or mPFC during either awake behaviour, REM or NREM sleep. However, during NREM sleep an increase in the power of mPFC spindles and decrease in the power of hippocampal sharp-wave ripples was identified. The latter was accompanied by an increase in the synchronisation of PV-expressing interneuron activity, as measured using two-photon Ca²⁺ imaging, as well as a decrease in PV-expressing interneuron density. Furthermore, although changes were detected in local network function of mPFC and hippocampus, long-range communication between these regions appeared intact. Altogether, our results suggest that these NREM sleep-specific impairments represent the early stages of circuit breakdown in response to amyloidopathy.

Multifunctional DNA Tetrahedron for Alzheimer's Disease Mitochondria-Targeted Therapy by MicroRNA Regulation

The principal hallmark of Alzheimer's disease (AD) is neuron mitochondrial dysfunction, whereas mitochondrial miRNAs potentially play important roles. Nevertheless, efficacious mitochondria organelle therapeutic agents for treatment and management of AD are highly advisable. Herein, we report a multifunctional DNA tetrahedron-based mitochondria-targeted therapeutic platform, termed tetrahedral DNA framework-based nanoparticles (TDFNs), which was modified with triphenylphosphine (TPP) for mitochondria-targeting, cholesterol (Chol) for crossing the central nervous system, and functional antisense oligonucleotide (ASO) for both AD diagnosis and gene silencing therapy. After injecting intravenously through the tail vein of 3 × Tg-AD model mice, TDFNs can both easily cross the blood brain barrier and accurately arrive at the mitochondria. The functional ASO could not only be detected via the fluorescence signal for diagnosis but also mediate the apoptosis pathway through knocking miRNA-34a down, leading to recovery of the neuron cells. The superior performance of TDFNs suggests the great potential in mitochondria organelle therapeutics.

Detection and treatment of Alzheimer's disease in its preclinical stage

Longitudinal multimodal biomarker studies reveal that the continuum of Alzheimer's disease (AD) includes a long latent phase, referred to as preclinical AD, which precedes the onset of symptoms by decades. Treatment during the preclinical AD phase offers an optimal opportunity for slowing the progression of disease. However, trial design in this population is complex. In this Review, we discuss the recent advances in accurate plasma measurements, new recruitment approaches, sensitive cognitive instruments and self-reported outcomes that have facilitated the successful launch of multiple phase 3 trials for preclinical AD. The recent success of anti-amyloid immunotherapy trials in symptomatic AD has increased the enthusiasm for testing this strategy at the earliest feasible stage. We provide an outlook for standard screening of amyloid accumulation at the preclinical stage in clinically normal individuals, during which effective therapy to delay or prevent cognitive decline can be initiated.