Designing the next-generation clinical care pathway for Alzheimer's disease

Radiomics evaluation for the early detection of Alzheimer's dementia using T1-weighted MRI

INTRODUCTION

Alzheimer's disease (AD), the most common cause of dementia, presents a global health crisis with its prevalence expected to triple worldwide by 2050, emphasizing the urgent need for early diagnosis to delay progression and improve patient quality of life. Our project aims to detect AD in its early phase by identifying subtle neuroanatomical changes with Radiomics features, offering a more accurate diagnosis.

METHODS

The AssemblyNet segmentation model was used to analyze brain changes by employing anonymized T1 MRI scans from 416 patients. For each segmented label we extracted Radiomic features. After preprocessing of Radiomic features we trained four models, Gradient Booster, Random Forest, Support Vector Classifier, and XGBoost, in a 70%/20%/10% train, validation and test split. All models were hyperparameter tuned with GridSearch, Cross validation and evaluated with accuracy on the test data.

RESULTS

208 T1-weighted MRI scans were segmented, with 132 segmentation labels per patient, 1130 Radiomic features per segmentation, totalling in over 31 million features. For all four models we achieved accuracies between 0.71 and 0.86, and the machine learning model with highest accuracy were XGBoost, achieving an accuracy at 0.86 on the segmentation of the left inferior lateral ventricle.

CONCLUSION

Our study's use of segmentation on T1-weighted MRI scans resulted promising accuracies for early AD diagnosis with the machine learning model XGBoost, peaking at 0.86 accuracy. Future research should aim to expand datasets and refine methodologies for broader applicability.

IMPLICATION FOR PRACTICE

Implementing Radiomics for early AD detection using T1-weighted MRI scans could substantially improve diagnostic accuracy, enabling earlier interventions that may delay disease progression and improve outcomes, thereby requiring radiographers to adopt more advanced imaging techniques and analysis tools, as well as additional training to effectively interpret complex Radiomic data.

Structure-activity relationship of dual inhibitors containing maleimide and imidazole motifs against glutaminyl cyclase and glycogen synthase kinase-3β

Alzheimer's disease (AD) is a major cause of dementia and one of the most common chronic diseases affecting the aging population. Because AD is considered a public health priority, there is a critical need to discover novel and effective agents for the treatment of this condition. In view of the known contribution of up-regulated glutaminyl cyclase (QC) and glycogen synthase kinase-3β (GSK-3β) to the initiation of AD, we previously evaluated a series of dual inhibitors containing maleimide and imidazole motifs as potential anti-AD agents. Here, we assessed another series of hybrids containing maleimide and imidazole motifs to gain an in-depth understanding of the structure-activity relationship (SAR). Based on the primary screening, the introduction of 5-methyl imidazole at one side of the molecule did not enhance the QC-specific inhibitory activity of these hybrids (2, IC50 = 1.22 μM), although the potency was increased by 2' substitution on the maleimide motif at the other side of the molecule. Interestingly, compounds containing 5-methyl imidazole exhibited stronger GSK-3β-specific inhibitory activity (2, IC50 = 0.0021 μM), and the electron-withdrawing group and 2' and 3' substitution were favorable. Further investigation of substitutions on the maleimide motif in compounds 14-35 revealed that QC-specific inhibition in the presence of piperidine was improved by introduction of a methoxy group (R2). Increasing the linker length and introduction of a methoxy group (R2) also increased the GSK-3β-specific inhibitory potency. These findings were further confirmed by molecular docking analysis of 33 and 24 with QC and GSK-3β. Overall, these hybrids exhibited enhanced inhibitory potency against both QC and GSK-3β, highlighting an important strategy for improving the potency of hybrids as dual-targeting anti-AD agents.

Apolipoprotein E in Alzheimer's disease trajectories and the next-generation clinical care pathway

Alzheimer's disease (AD) is a complex, progressive primary neurodegenerative disease. Since pivotal genetic studies in 1993, the ε4 allele of the apolipoprotein E gene (APOE ε4) has remained the strongest single genome-wide associated risk variant in AD. Scientific advances in APOE biology, AD pathophysiology and ApoE-targeted therapies have brought APOE to the forefront of research, with potential translation into routine AD clinical care. This contemporary Review will merge APOE research with the emerging AD clinical care pathway and discuss APOE genetic risk as a conduit to genomic-based precision medicine in AD, including ApoE's influence in the ATX(N) biomarker framework of AD. We summarize the evidence for APOE as an important modifier of AD clinical-biological trajectories. We then illustrate the utility of APOE testing and the future of ApoE-targeted therapies in the next-generation AD clinical-diagnostic pathway. With the emergence of new AD therapies, understanding how APOE modulates AD pathophysiology will become critical for personalized AD patient care.

Advances in blood biomarkers for Alzheimer disease (AD): A review

Alzheimer disease (AD) and Alzheimer Disease and Related Dementias (AD/ADRD) are growing public health challenges globally affecting millions of older adults, necessitating concerted efforts to advance our understanding and management of these conditions. AD is a progressive neurodegenerative disorder characterized pathologically by amyloid plaques and tau neurofibrillary tangles that are the primary cause of dementia in older individuals. Early and accurate diagnosis of AD dementia is crucial for effective intervention and treatment but has proven challenging to accomplish. Although testing for AD brain pathology with cerebrospinal fluid (CSF) or positron emission tomography (PET) has been available for over 2 decades, most patients never underwent this testing because of inaccessibility, high out-of-pocket costs, perceived risks, and the lack of AD-specific treatments. However, in recent years, rapid progress has been made in developing blood biomarkers for AD/ADRD. Consequently, blood biomarkers have emerged as promising tools for non-invasive and cost-effective diagnosis, prognosis, and monitoring of AD progression. This review presents the evolving landscape of blood biomarkers in AD/ADRD and explores their potential applications in clinical practice for early detection, prognosis, and therapeutic interventions. It covers recent advances in blood biomarkers, including amyloid beta (Aβ) peptides, tau protein, neurofilament light chain (NfL), and glial fibrillary acidic protein (GFAP). It also discusses their diagnostic and prognostic utility while addressing associated challenges and limitations. Future research directions in this rapidly evolving field are also proposed.

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.

ArcheD, a residual neural network for prediction of cerebrospinal fluid amyloid-beta from amyloid PET images

Detection and measurement of amyloid-beta (Aβ) in the brain is a key factor for early identification and diagnosis of Alzheimer's disease (AD). We aimed to develop a deep learning model to predict Aβ cerebrospinal fluid (CSF) concentration directly from amyloid PET images, independent of tracers, brain reference regions or preselected regions of interest. We used 1870 Aβ PET images and CSF measurements to train and validate a convolutional neural network ("ArcheD"). We evaluated the ArcheD performance in relation to episodic memory and the standardized uptake value ratio (SUVR) of cortical Aβ. We also compared the brain region's relevance for the model's CSF prediction within clinical-based and biological-based classifications. ArcheD-predicted Aβ CSF values correlated with measured Aβ CSF values (r = 0.92; q < 0.01), SUVR (rAV45 = -0.64, rFBB = -0.69; q < 0.01) and episodic memory measures (0.33 < r < 0.44; q < 0.01). For both classifications, cerebral white matter significantly contributed to CSF prediction (q < 0.01), specifically in non-symptomatic and early stages of AD. However, in late-stage disease, the brain stem, subcortical areas, cortical lobes, limbic lobe and basal forebrain made more significant contributions (q < 0.01). Considering cortical grey matter separately, the parietal lobe was the strongest predictor of CSF amyloid levels in those with prodromal or early AD, while the temporal lobe played a more crucial role for those with AD. In summary, ArcheD reliably predicted Aβ CSF concentration from Aβ PET scans, offering potential clinical utility for Aβ level determination.

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 outcomes 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 highly 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 reveals current potential in predicting tau-PET information from scalable cost-effective variables, which could improve diagnosis and prognosis of AD.

Serum albumin-embedding copper nanoclusters inhibit Alzheimer's β-amyloid fibrillogenesis and neuroinflammation

Increasing evidence suggests that the accumulations of reactive oxygen species (ROS), β-amyloid (Aβ), and neuroinflammation are crucial pathological hallmarks for the onset of Alzheimer's disease (AD), yet there are few effective treatment strategies. Therefore, design of nanomaterials capable of simultaneously elimination of ROS and inhibition of Aβ aggregation and neuroinflammation is urgently needed for AD treatment. Herein, we designed human serum albumin (HSA)-embedded ultrasmall copper nanoclusters (CuNCs@HSA) via an HSA-mediated fabrication strategy. The as-prepared CuNCs@HSA exhibited outstanding multiple enzyme-like properties, including superoxide dismutase (>5000 U/mg), catalase, and glutathione peroxidase activities as well as hydroxyl radicals scavenging ability. Besides, CuNCs@HSA prominently inhibited Aβ fibrillization, and its inhibitory potency was 2.5-fold higher than native HSA. Moreover, CuNCs@HSA could significantly increase the viability of Aβ-treated cells from 60 % to over 96 % at 40 μg/mL and mitigate Aβ-induced oxidative stresses. The secretion of neuroinflammatory cytokines by lipopolysaccharide-induced BV-2 cells, including tumor necrosis factor-α and interleukin-6, was alleviated by CuNCs@HSA. In vivo studies manifested that CuNCs@HSA effectively suppressed the formation of plaques in transgenic C. elegans, reduced ROS levels, and extended C. elegans lifespan by 5 d. This work, using HSA as a template to mediate the fabrication of copper nanoclusters with robust ROS scavenging capability, exhibited promising potentials in inhibiting Aβ aggregation and neuroinflammation for AD treatment.

Clinical application of plasma P-tau217 to assess eligibility for amyloid-lowering immunotherapy in memory clinic patients with early Alzheimer's disease

Background

With the approval of disease-modifying treatments (DMTs) for early Alzheimer's disease (AD), there is an increased need for efficient and non-invasive detection methods for cerebral amyloid-β (Aβ) pathology. Current methods, including positron emission tomography (PET) and cerebrospinal fluid (CSF) analysis, are costly and invasive methods that may limit access to new treatments. Plasma tau phosphorylated at threonine-217 (P-tau217) presents a promising alternative, yet optimal cutoffs for treatment eligibility with DMTs like aducanumab require further investigation. This study evaluates the efficacy of one- and two-cutoff strategies for determining DMT eligibility at the Butler Hospital Memory & Aging Program (MAP).

Methods

In this retrospective, cross-sectional diagnostic cohort study, we first developed P-tau217 cutoffs using site-specific training data and BioFINDER-2, which were then tested in potential DMT candidates from Butler MAP (total n = 150). ROC analysis was used to calculate the area under the curve (AUC) and accuracy of P-tau217 interpretation strategies, using Aβ-PET/CSF testing as the standard of truth.

Results

Potential DMT candidates at Butler MAP (n = 50), primarily diagnosed with mild cognitive impairment (n = 29 [58%]) or mild dementia (21 [42%]), were predominantly Aβ-positive (38 [76%]), and half (25 [50%]) were subsequently treated with aducanumab. Elevated P-tau217 predicted cerebral Aβ positivity in potential DMT candidates (AUC = 0.97 [0.92-1]), with diagnostic accuracy ranging from 0.88 (0.76-0.95, p = 0.028) to 0.96 (0.86-1, p < .001). When using site-specific cutoffs, a subset of DMT candidates (10%) exhibited borderline P-tau217 (between 0.273 and 0.399 pg/mL) that would have potentially required from confirmatory testing.

Conclusions

This study, which included participants treated with aducanumab, confirms the utility of one- and two-cutoff strategies for interpreting plasma P-tau217 in assessing DMT eligibility. Using P-tau217 could potentially replace more invasive diagnostic methods, and all aducanumab-treated participants would have been deemed eligible based on P-tau217. However, false positives remain a concern, particularly when applying externally derived cutoffs that exhibited lower specificity which could have led to inappropriate treatment of Aβ-negative participants. Future research should focus on prospective validation of P-tau217 cutoffs to enhance their generalizability and inform standardized treatment decision-making across diverse populations.

Computerized decision support is an effective approach to select memory clinic patients for amyloid-PET

BACKGROUND

The use of amyloid-PET in dementia workup is upcoming. At the same time, amyloid-PET is costly and limitedly available. While the appropriate use criteria (AUC) aim for optimal use of amyloid-PET, their limited sensitivity hinders the translation to clinical practice. Therefore, there is a need for tools that guide selection of patients for whom amyloid-PET has the most clinical utility. We aimed to develop a computerized decision support approach to select patients for amyloid-PET.

METHODS

We included 286 subjects (135 controls, 108 Alzheimer's disease dementia, 33 frontotemporal lobe dementia, and 10 vascular dementia) from the Amsterdam Dementia Cohort, with available neuropsychology, APOE, MRI and [18F]florbetaben amyloid-PET. In our computerized decision support approach, using supervised machine learning based on the DSI classifier, we first classified the subjects using only neuropsychology, APOE, and quantified MRI. Then, for subjects with uncertain classification (probability of correct class (PCC) < 0.75) we enriched classification by adding (hypothetical) amyloid positive (AD-like) and negative (normal) PET visual read results and assessed whether the diagnosis became more certain in at least one scenario (PPC≥0.75). If this was the case, the actual visual read result was used in the final classification. We compared the proportion of PET scans and patients diagnosed with sufficient certainty in the computerized approach with three scenarios: 1) without amyloid-PET, 2) amyloid-PET according to the AUC, and 3) amyloid-PET for all patients.

RESULTS

The computerized approach advised PET in n = 60(21%) patients, leading to a diagnosis with sufficient certainty in n = 188(66%) patients. This approach was more efficient than the other three scenarios: 1) without amyloid-PET, diagnostic classification was obtained in n = 155(54%), 2) applying the AUC resulted in amyloid-PET in n = 113(40%) and diagnostic classification in n = 156(55%), and 3) performing amyloid-PET in all resulted in diagnostic classification in n = 154(54%).

CONCLUSION

Our computerized data-driven approach selected 21% of memory clinic patients for amyloid-PET, without compromising diagnostic performance. Our work contributes to a cost-effective implementation and could support clinicians in making a balanced decision in ordering additional amyloid PET during the dementia workup.

Future perspective and clinical applicability of the combined use of plasma phosphorylated tau 181 and neurofilament light chain in Subjective Cognitive Decline and Mild Cognitive Impairment

We aimed to assess diagnostic accuracy of plasma p-tau181 and NfL separately and in combination in discriminating Subjective Cognitive Decline (SCD) and Mild Cognitive Impairment (MCI) patients carrying Alzheimer's Disease (AD) pathology from non-carriers; to propose a flowchart for the interpretation of the results of plasma p-tau181 and NfL. We included 43 SCD, 41 MCI and 21 AD-demented (AD-d) patients, who underwent plasma p-tau181 and NfL analysis. Twenty-eight SCD, 41 MCI and 21 AD-d patients underwent CSF biomarkers analysis (Aβ1-42, Aβ1-42/1-40, p-tau, t-tau) and were classified as carriers of AD pathology (AP+) it they were A+/T+ , or non-carriers (AP-) when they were A-, A+/T-/N-, or A+/T-/N+ according to the A/T(N) system. Plasma p-tau181 and NfL separately showed a good accuracy (AUC = 0.88), while the combined model (NfL + p-tau181) showed an excellent accuracy (AUC = 0.92) in discriminating AP+ from AP- patients. Plasma p-tau181 and NfL results were moderately concordant (Coehn's k = 0.50, p < 0.001). Based on a logistic regression model, we estimated the risk of AD pathology considering the two biomarkers: 10.91% if both p-tau181 and NfL were negative; 41.10 and 76.49% if only one biomarker was positive (respectively p-tau18 and NfL); 94.88% if both p-tau181 and NfL were positive. Considering the moderate concordance and the risk of presenting an underlying AD pathology according to the positivity of plasma p-tau181 and NfL, we proposed a flow chart to guide the combined use of plasma p-tau181 and NfL and the interpretation of biomarker results to detect AD pathology.

Personalized medicine in old age psychiatry and Alzheimer's disease

Elderly patients show us unfolded lives with unique individual characteristics. An increasing life span is associated with increasing physical and mental disease burden. Alzheimer's disease (AD) is an increasing challenge in old age. AD cannot be cured but it can be treated. The complexity of old age and AD offer targets for personalized medicine (PM). Targets for stratification of patients, detection of patients at risk for AD or for future targeted therapy are plentiful and can be found in several omic-levels.

Repurposing non-pharmacological interventions for Alzheimer's disease through link prediction on biomedical literature

Non-pharmaceutical interventions (NPI) have great potential to improve cognitive function but limited investigation to discover NPI repurposing for Alzheimer's Disease (AD). This is the first study to develop an innovative framework to extract and represent NPI information from biomedical literature in a knowledge graph (KG), and train link prediction models to repurpose novel NPIs for AD prevention. We constructed a comprehensive KG, called ADInt, by extracting NPI information from biomedical literature. We used the previously-created SuppKG and NPI lexicon to identify NPI entities. Four KG embedding models (i.e., TransE, RotatE, DistMult and ComplEX) and two novel graph convolutional network models (i.e., R-GCN and CompGCN) were trained and compared to learn the representation of ADInt. Models were evaluated and compared on two test sets (time slice and clinical trial ground truth) and the best performing model was used to predict novel NPIs for AD. Discovery patterns were applied to generate mechanistic pathways for high scoring candidates. The ADInt has 162,212 nodes and 1,017,284 edges. R-GCN performed best in time slice (MR = 5.2054, Hits@10 = 0.8496) and clinical trial ground truth (MR = 3.4996, Hits@10 = 0.9192) test sets. After evaluation by domain experts, 10 novel dietary supplements and 10 complementary and integrative health were proposed from the score table calculated by R-GCN. Among proposed novel NPIs, we found plausible mechanistic pathways for photodynamic therapy and Choerospondias axillaris to prevent AD, and validated psychotherapy and manual therapy techniques using real-world data analysis. The proposed framework shows potential for discovering new NPIs for AD prevention and understanding their mechanistic pathways.

Use of a digital tool to support the diagnostic process in memory clinics-a usability study

BACKGROUND

Both memory clinic professionals and patients see value in digital tools, yet these hardly find their way to clinical practice. We explored the usability of a digital tool to support the diagnostic work-up in daily memory clinic practice. We evaluated four modules that integrate multi-modal patient data (1.cognitive test; cCOG, and 2. MRI quantification; cMRI) into useful diagnostic information for clinicians (3. cDSI) and understandable and personalized information for patients (4. patient report).

METHODS

We conducted a mixed-methods study in five Dutch memory clinics. Fourteen clinicians (11 geriatric specialists/residents, two neurologists, one nurse practitioner) were invited to integrate the tool into routine care with 43 new memory clinic patients. We evaluated usability and user experiences through quantitative data from questionnaires (patients, care partners, clinicians), enriched with thematically analyzed qualitative data from interviews (clinicians).

RESULTS

We observed wide variation in tool use among clinicians. Our core findings were that clinicians: 1) were mainly positive about the patient report, since it contributes to patient-centered and personalized communication. This was endorsed by patients and care partners, who indicated that the patient report was useful and understandable and helped them to better understand their diagnosis, 2) considered the tool acceptable in addition to their own clinical competence, 3) indicated that the usefulness of the tool depended on the patient population and purpose of the diagnostic process, 4) addressed facilitators (ease of use, practice makes perfect) and barriers (high workload, lack of experience, data unavailability).

CONCLUSION

This multicenter usability study revealed a willingness to adopt a digital tool to support the diagnostic process in memory clinics. Clinicians, patients, and care partners appreciated the personalized diagnostic report. More attention to education and training of clinicians is needed to utilize the full functionality of the tool and foster implementation in actual daily practice. These findings provide an important step towards a lasting adoption of digital tools in memory clinic practice.

Digging into the intrinsic capacity concept: Can it be applied to Alzheimer's disease?

Historically, aging research has largely centered on disease pathology rather than promoting healthy aging. The World Health Organization's (WHO) policy framework (2015-2030) underscores the significance of fostering the contributions of older individuals to their families, communities, and economies. The WHO has introduced the concept of intrinsic capacity (IC) as a key metric for healthy aging, encompassing five primary domains: locomotion, vitality, sensory, cognitive, and psychological. Past AD research, constrained by methodological limitations, has focused on single outcome measures, sidelining the complexity of the disease. Our current scientific milieu, however, is primed to adopt the IC concept. This is due to three critical considerations: (I) the decline in IC is linked to neurocognitive disorders, including AD, (II) cognition, a key component of IC, is deeply affected in AD, and (III) the cognitive decline associated with AD involves multiple factors and pathophysiological pathways. Our study explores the application of the IC concept to AD patients, offering a comprehensive model that could revolutionize the disease's diagnosis and prognosis. There is a dearth of information on the biological characteristics of IC, which are a result of complex interactions within biological systems. Employing a systems biology approach, integrating omics technologies, could aid in unraveling these interactions and understanding IC from a holistic viewpoint. This comprehensive analysis of IC could be leveraged in clinical settings, equipping healthcare providers to assess AD patients' health status more effectively and devise personalized therapeutic interventions in accordance with the precision medicine paradigm. We aimed to determine whether the IC concept could be extended from older individuals to patients with AD, thereby presenting a model that could significantly enhance the diagnosis and prognosis of this disease.

Predicting clinical progression trajectories of early Alzheimer's disease patients

BACKGROUND

Models for forecasting individual clinical progression trajectories in early Alzheimer's disease (AD) are needed for optimizing clinical studies and patient monitoring.

METHODS

Prediction models were constructed using a clinical trial training cohort (TC; n = 934) via a gradient boosting algorithm and then evaluated in two validation cohorts (VC 1, n = 235; VC 2, n = 421). Model inputs included baseline clinical features (cognitive function assessments, APOE ε4 status, and demographics) and brain magnetic resonance imaging (MRI) measures.

RESULTS

The model using clinical features achieved R2 of 0.21 and 0.31 for predicting 2-year cognitive decline in VC 1 and VC 2, respectively. Adding MRI features improved the R2 to 0.29 in VC 1, which employed the same preprocessing pipeline as the TC. Utilizing these model-based predictions for clinical trial enrichment reduced the required sample size by 20% to 49%.

DISCUSSION

Our validated prediction models enable baseline prediction of clinical progression trajectories in early AD, benefiting clinical trial enrichment and various applications.

Lithium and disease modification: A systematic review and meta-analysis in Alzheimer's and Parkinson's disease

The role of lithium as a possible therapeutic strategy for neurodegenerative diseases has generated scientific interest. We systematically reviewed and meta-analyzed pre-clinical and clinical studies that evidenced the neuroprotective effects of lithium in Alzheimer's (AD) and Parkinson's disease (PD). We followed the PRISMA guidelines and performed the systematic literature search using PubMed, EMBASE, Web of Science, and Cochrane Library. A total of 32 articles were identified. Twenty-nine studies were performed in animal models and 3 studies were performed on human samples of AD. A total of 17 preclinical studies were included in the meta-analysis. Our analysis showed that lithium treatment has neuroprotective effects in diseases. Lithium treatment reduced amyloid-β and tau levels and significantly improved cognitive behavior in animal models of AD. Lithium increased the tyrosine hydroxylase levels and improved motor behavior in the PD model. Despite fewer clinical studies on these aspects, we evidenced the positive effects of lithium in AD patients. This study lends further support to the idea of lithium's therapeutic potential in neurodegenerative diseases.

New Multitarget Rivastigmine-Indole Hybrids as Potential Drug Candidates for Alzheimer's Disease

Alzheimer's disease (AD) is the most common form of dementia with no cure so far, probably due to the complexity of this multifactorial disease with diverse processes associated with its origin and progress. Several neuropathological hallmarks have been identified that encourage the search for new multitarget drugs. Therefore, following a multitarget approach, nine rivastigmine-indole (RIV-IND) hybrids (5a1-3, 5b1-3, 5c1-3) were designed, synthesized and evaluated for their multiple biological properties and free radical scavenging activity, as potential multitarget anti-AD drugs. The molecular docking studies of these hybrids on the active center of acetylcholinesterase (AChE) and butyrylcholinesterase (BChE) suggest their capacity to act as dual enzyme inhibitors with probable greater disease-modifying impact relative to AChE-selective FDA-approved drugs. Compounds 5a3 (IC50 = 10.9 µM) and 5c3 (IC50 = 26.8 µM) revealed higher AChE inhibition than the parent RIV drug. Radical scavenging assays demonstrated that all the hybrids containing a hydroxyl substituent in the IND moiety (5a2-3, 5b2-3, 5c2-3) have good antioxidant activity (EC50 7.8-20.7 µM). The most effective inhibitors of Aβ42 self-aggregation are 5a3, 5b3 and 5c3 (47.8-55.5%), and compounds 5b2 and 5c2 can prevent the toxicity induced by Aβ1-42 to cells. The in silico evaluation of the drug-likeness of the hybrids also showed that all the compounds seem to have potential oral availability. Overall, within this class of RIV-IND hybrids, 5a3 and 5c3 appear as lead compounds for anti-AD drug candidates, deserving further investigation.

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.

Mannose-Integrated Nanoparticle Hitchhike Glucose Transporter 1 Recycling to Overcome Various Barriers of Oral Delivery for Alzheimer's Disease Therapy

A brain-targeting nanodelivery system has been a hot topic and has undergone rapid progression. However, due to various obstacles such as the intestinal epithelial barrier (IEB) and the blood-brain barrier (BBB), few nanocarriers can achieve brain-targeting through oral administration. Herein, an intelligent oral brain-targeting nanoparticle (FTY@Man NP) constructed from a PLGA-PEG skeleton loaded with fingolimod (FTY) and externally modified with mannose was designed in combination with a glucose control strategy for the multitarget treatment of Alzheimer's disease (AD). The hydrophilic and electronegative properties of the nanoparticle facilitated its facile penetration through the mucus barrier, while the mannose ligand conferred IEB targeting abilities to the nanoparticle. Subsequently, glycemic control allowed the mannose-integrated nanoparticle to hitchhike the glucose transporter 1 (GLUT1) circulation across the BBB. Finally, the released FTY modulated the polarity of microglia from pro-inflammatory M1 to anti-inflammatory M2 and normalized the activated astrocyte, enhancing the clearance of toxic protein Amyloid-β (Aβ) while alleviating oxidative stress and neuroinflammation. Notably, both in vitro and in vivo results have consistently demonstrated that the oral administration of FTY@Man NP could effectively traverse the multiple barriers, thereby exerting significant therapeutic effects. This breakthrough holds the promise of realizing a highly effective orally administered treatment for AD.

Natural Polyphenolic Nanodots for Alzheimer's Disease Treatment

The abnormal amyloid-β accumulation is essential and obbligato in Alzheimer's disease pathogenesis and natural polyphenols exhibit great potential as amyloid aggregation inhibitors. However, the poor metabolic stability, low bioavailability, and weak blood-brain barrier crossing ability of natural polyphenol molecules fail to meet clinical needs. Here, a universal protocol to prepare natural polyphenolic nanodots is developed by heating in aqueous solution without unacceptable additives. The nanodots are able to not only inhibit amyloid-β fibrillization and trigger the fibril disaggregation, but mitigate the amyloid-β-plaque-induced cascade impairments including normalizing oxidative microenvironment, altering microglial polarization, and rescuing neuronal death and synaptic loss, which results in significant improvements in recognition and cognition deficits in transgenic mice. More importantly, natural polyphenolic nanodots possess stronger antiamyloidogenic performance compared with small molecule, as well as penetrate the blood-brain barrier. The excellent biocompatibility further guarantees the potential of natural polyphenolic nanodots for clinical applications. It is expected that natural polyphenolic nanodots provide an attractive paradigm to support the development of the therapeutics for Alzheimer's disease.

Dementia Ideal Care: Ecosystem Map of Best Practices and Care Pathways Enhanced by Technology and Community

Background

Globally, much work has been done by nonprofit, private, and academic groups to develop best practices for the care of people living with dementia (PLWD), including Alzheimer's disease. However, these best practices reside in disparate repositories and tend to focus on one phase of the patient journey or one relevant group.

Objective

To fill this gap, we developed a Dementia Ideal Care Map that everyone in the dementia ecosystem can use as an actionable tool for awareness, policy development, funding, research, training, service delivery, and technology design. The intended audience includes (and not limited to) policymakers, academia, industry, technology developers, health system leaders, clinicians, social service providers, patient advocates, PLWD, their families, and communities at large.

Methods

A search was conducted for published dementia care best practices and quality measures, which were then summarized in a visual diagram. The draft diagram was analyzed to identify barriers to ideal care. Then, additional processes, services, technologies, and quality measures to overcome those challenges were brainstormed. Feedback was then obtained from experts.

Results

The Dementia Ideal Care Map summarizes the ecosystem of over 200 best practices, nearly 100 technology enablers, other infrastructure, and enhanced care pathways in one comprehensive diagram. It includes psychosocial interventions, care partner support, community-based organizations; awareness, risk reduction; initial detection, diagnosis, ongoing medical care; governments, payers, health systems, businesses, data, research, and training.

Conclusions

Dementia Ideal Care Map is a practical tool for planning and coordinating dementia care. This visualized ecosystem approach can be applied to other conditions.

Identifying the joint signature of brain atrophy and gene variant scores in Alzheimer's Disease

The joint modeling of genetic data and brain imaging information allows for determining the pathophysiological pathways of neurodegenerative diseases such as Alzheimer's disease (AD). This task has typically been approached using mass-univariate methods that rely on a complete set of Single Nucleotide Polymorphisms (SNPs) to assess their association with selected image-derived phenotypes (IDPs). However, such methods are prone to multiple comparisons bias and, most importantly, fail to account for potential cross-feature interactions, resulting in insufficient detection of significant associations. Ways to overcome these limitations while reducing the number of traits aim at conveying genetic information at the gene level and capturing the integrated genetic effects of a set of genetic variants, rather than looking at each SNP individually. Their associations with brain IDPs are still largely unexplored in the current literature, though they can uncover new potential genetic determinants for brain modulations in the AD continuum. In this work, we explored an explainable multivariate model to analyze the genetic basis of the grey matter modulations, relying on the AD Neuroimaging Initiative (ADNI) phase 3 dataset. Cortical thicknesses and subcortical volumes derived from T1-weighted Magnetic Resonance were considered to describe the imaging phenotypes. At the same time the genetic counterpart was represented by gene variant scores extracted by the Sequence Kernel Association Test (SKAT) filtering model. Moreover, transcriptomic analysis was carried on to assess the expression of the resulting genes in the main brain structures as a form of validation. Results highlighted meaningful genotype-phenotype interactionsas defined by three latent components showing a significant difference in the projection scores between patients and controls. Among the significant associations, the model highlighted EPHX1 and BCAS1 gene variant scores involved in neurodegenerative and myelination processes, hence relevant for AD. In particular, the first was associated with decreased subcortical volumes and the second with decreasedtemporal lobe thickness. Noteworthy, BCAS1 is particularly expressed in the dentate gyrus. Overall, the proposed approach allowed capturing genotype-phenotype interactions in a restricted study cohort that was confirmed by transcriptomic analysis, offering insights into the underlying mechanisms of neurodegeneration in AD in line with previous findings and suggesting new potential disease biomarkers.

The age-specific comorbidity burden of mild cognitive impairment: a US claims database study

BACKGROUND

Identifying individuals with mild cognitive impairment (MCI) who are likely to progress to Alzheimer's disease and related dementia disorders (ADRD) would facilitate the development of individualized prevention plans. We investigated the association between MCI and comorbidities of ADRD. We examined the predictive potential of these comorbidities for MCI risk determination using a machine learning algorithm.

METHODS

Using a retrospective matched case-control design, 5185 MCI and 15,555 non-MCI individuals aged ≥50 years were identified from MarketScan databases. Predictive models included ADRD comorbidities, age, and sex.

RESULTS

Associations between 25 ADRD comorbidities and MCI were significant but weakened with increasing age groups. The odds ratios (MCI vs non-MCI) in 50-64, 65-79, and ≥ 80 years, respectively, for depression (4.4, 3.1, 2.9) and stroke/transient ischemic attack (6.4, 3.0, 2.1). The predictive potential decreased with older age groups, with ROC-AUCs 0.75, 0.70, and 0.66 respectively. Certain comorbidities were age-specific predictors.

CONCLUSIONS

The comorbidity burden of MCI relative to non-MCI is age-dependent. A model based on comorbidities alone predicted an MCI diagnosis with reasonable accuracy.

Contextualizing the Role of Osteopontin in the Inflammatory Responses of Alzheimer's Disease

Alzheimer's disease (AD) is characterized by progressive accumulations of extracellular amyloid-beta (Aβ) aggregates from soluble oligomers to insoluble plaques and hyperphosphorylated intraneuronal tau, also from soluble oligomers to insoluble neurofibrillary tangles (NFTs). Tau and Aβ complexes spread from the entorhinal cortex of the brain to interconnected regions, where they bind pattern recognition receptors on microglia and astroglia to trigger inflammation and neurotoxicity that ultimately lead to neurodegeneration and clinical AD. Systemic inflammation is initiated by Aβ's egress into the circulation, which may be secondary to microglial activation and can confer both destructive and reparative actions. Microglial activation pathways and downstream drivers of Aβ/NFT neurotoxicity, including inflammatory regulators, are primary targets for AD therapy. Osteopontin (OPN), an inflammatory cytokine and biomarker of AD, is implicated in Aβ clearance and toxicity, microglial activation, and inflammation, and is considered to be a potential therapeutic target. Here, using the most relevant works from the literature, we review and contextualize the evidence for a central role of OPN and associated inflammation in AD.

Online clinical tools to support the use of new plasma biomarker diagnostic technology in the assessment of Alzheimer's disease: a narrative review

Recent advances in new diagnostic technologies for Alzheimer's disease have improved the speed and precision of diagnosis. However, accessing the potential benefits of this technology poses challenges for clinicians, such as deciding whether it is clinically appropriate to order a diagnostic test, which specific test or tests to order and how to interpret test results and communicate these to the patient and their caregiver. Tools to support decision-making could provide additional structure and information to the clinical assessment process. These tools could be accessed online, and such 'e-tools' can provide an interactive interface to support patients and clinicians in the use of new diagnostic technologies for Alzheimer's disease. We performed a narrative review of the literature to synthesize information available on this research topic. Relevant studies that provide an understanding of how these online tools could be used to optimize the clinical utility of diagnostic technology were identified. Based on these, we discuss the ways in which e-tools have been used to assist in the diagnosis of Alzheimer's disease and propose recommendations for future research to aid further development.

A review of the flortaucipir literature for positron emission tomography imaging of tau neurofibrillary tangles

Alzheimer's disease is defined by the presence of β-amyloid plaques and neurofibrillary tau tangles potentially preceding clinical symptoms by many years. Previously only detectable post-mortem, these pathological hallmarks are now identifiable using biomarkers, permitting an in vivo definitive diagnosis of Alzheimer's disease. 18F-flortaucipir (previously known as 18F-T807; 18F-AV-1451) was the first tau positron emission tomography tracer to be introduced and is the only Food and Drug Administration-approved tau positron emission tomography tracer (Tauvid™). It has been widely adopted and validated in a number of independent research and clinical settings. In this review, we present an overview of the published literature on flortaucipir for positron emission tomography imaging of neurofibrillary tau tangles. We considered all accessible peer-reviewed literature pertaining to flortaucipir through 30 April 2022. We found 474 relevant peer-reviewed publications, which were organized into the following categories based on their primary focus: typical Alzheimer's disease, mild cognitive impairment and pre-symptomatic populations; atypical Alzheimer's disease; non-Alzheimer's disease neurodegenerative conditions; head-to-head comparisons with other Tau positron emission tomography tracers; and technical considerations. The available flortaucipir literature provides substantial evidence for the use of this positron emission tomography tracer in assessing neurofibrillary tau tangles in Alzheimer's disease and limited support for its use in other neurodegenerative disorders. Visual interpretation and quantitation approaches, although heterogeneous, mostly converge and demonstrate the high diagnostic and prognostic value of flortaucipir in Alzheimer's disease.

ArcheD, a residual neural network for prediction of cerebrospinal fluid amyloid-beta from amyloid PET images

Detection and measurement of amyloid-beta (Aβ) aggregation in the brain is a key factor for early identification and diagnosis of Alzheimer's disease (AD). We aimed to develop a deep learning model to predict Aβ cerebrospinal fluid (CSF) concentration directly from amyloid PET images, independent of tracers, brain reference regions or preselected regions of interest. We used 1870 Aβ PET images and CSF measurements to train and validate a convolutional neural network ("ArcheD"). We evaluated the ArcheD performance in relation to episodic memory and the standardized uptake value ratio (SUVR) of cortical Aβ. We also compared the brain region's relevance for the model's CSF prediction within clinical-based and biological-based classifications. ArcheD-predicted Aβ CSF values correlated strongly with measured Aβ CSF values ( r =0.81; p <0.001) and showed correlations with SUVR and episodic memory measures in all participants except in those with AD. For both clinical and biological classifications, cerebral white matter significantly contributed to CSF prediction ( q <0.01), specifically in non-symptomatic and early stages of AD. However, in late-stage disease, brain stem, subcortical areas, cortical lobes, limbic lobe, and basal forebrain made more significant contributions (q<0.01). Considering cortical gray matter separately, the parietal lobe was the strongest predictor of CSF amyloid levels in those with prodromal or early AD, while the temporal lobe played a more crucial role for those with AD. In summary, ArcheD reliably predicted Aβ CSF concentration from Aβ PET scans, offering potential clinical utility for Aβ level determination and early AD detection.

Mechanistic Intimate Insights into the Role of Hydrogen Sulfide in Alzheimer's Disease: A Recent Systematic Review

In the rapidly evolving field of Alzheimer's Disease (AD) research, the intricate role of Hydrogen Sulfide (H2S) has garnered critical attention for its diverse involvement in both pathological substrates and prospective therapeutic paradigms. While conventional pathophysiological models of AD have primarily emphasized the significance of amyloid-beta (Aβ) deposition and tau protein hyperphosphorylation, this targeted systematic review meticulously aggregates and rigorously appraises seminal contributions from the past year elucidating the complex mechanisms of H2S in AD pathogenesis. Current scholarly literature accentuates H2S's dual role, delineating its regulatory functions in critical cellular processes-such as neurotransmission, inflammation, and oxidative stress homeostasis-while concurrently highlighting its disruptive impact on quintessential AD biomarkers. Moreover, this review illuminates the nuanced mechanistic intimate interactions of H2S in cerebrovascular and cardiovascular pathology associated with AD, thereby exploring avant-garde therapeutic modalities, including sulfurous mineral water inhalations and mud therapy. By emphasizing the potential for therapeutic modulation of H2S via both donors and inhibitors, this review accentuates the imperative for future research endeavors to deepen our understanding, thereby potentially advancing novel diagnostic and therapeutic strategies in AD.

Blood-based biomarkers for Alzheimer's disease: Current state and future use in a transformed global healthcare landscape

Timely detection of the pathophysiological changes and cognitive impairment caused by Alzheimer's disease (AD) is increasingly pressing because of the advent of biomarker-guided targeted therapies that may be most effective when provided early in the disease. Currently, diagnosis and management of early AD are largely guided by clinical symptoms. FDA-approved neuroimaging and cerebrospinal fluid biomarkers can aid detection and diagnosis, but the clinical implementation of these testing modalities is limited because of availability, cost, and perceived invasiveness. Blood-based biomarkers (BBBMs) may enable earlier and faster diagnoses as well as aid in risk assessment, early detection, prognosis, and management. Herein, we review data on BBBMs that are closest to clinical implementation, particularly those based on measures of amyloid-β peptides and phosphorylated tau species. We discuss key parameters and considerations for the development and potential deployment of these BBBMs under different contexts of use and highlight challenges at the methodological, clinical, and regulatory levels.

Treatment of Alzheimer's Disease: Beyond Symptomatic Therapies

In an ever-increasing aged world, Alzheimer's disease (AD) represents the first cause of dementia and one of the first chronic diseases in elderly people. With 55 million people affected, the WHO considers AD to be a disease with public priority. Unfortunately, there are no final cures for this pathology. Treatment strategies are aimed to mitigate symptoms, i.e., acetylcholinesterase inhibitors (AChEI) and the N-Methyl-D-aspartate (NMDA) antagonist Memantine. At present, the best approaches for managing the disease seem to combine pharmacological and non-pharmacological therapies to stimulate cognitive reserve. Over the last twenty years, a number of drugs have been discovered acting on the well-established biological hallmarks of AD, deposition of β-amyloid aggregates and accumulation of hyperphosphorylated tau protein in cells. Although previous efforts disappointed expectations, a new era in treating AD has been working its way recently. The Food and Drug Administration (FDA) gave conditional approval of the first disease-modifying therapy (DMT) for the treatment of AD, aducanumab, a monoclonal antibody (mAb) designed against Aβ plaques and oligomers in 2021, and in January 2023, the FDA granted accelerated approval for a second monoclonal antibody, Lecanemab. This review describes ongoing clinical trials with DMTs and non-pharmacological therapies. We will also present a future scenario based on new biomarkers that can detect AD in preclinical or prodromal stages, identify people at risk of developing AD, and allow an early and curative treatment.

Rethinking the detection and diagnosis of Alzheimer's disease: Outcomes of a European Brain Council project

Alzheimer's disease (AD), the most common form of dementia, is a progressive and debilitating neurodegenerative condition which robs people of their memory, their independence, their relationships and, ultimately, their lives. It affects close to 7 million people in the European Union (EU) alone. The detection and diagnosis of AD relies on a system that remains focused on the late stage of the disease, despite a better understanding of the disease progression. Clinical practice and healthcare systems' readiness to detect, diagnose and treat the disease effectively are still lagging. The use of biomarkers (cerebrospinal fluid tests (CSF) and positron emission tomography scans (PET)), which are central to a diagnostic assessment for people with AD symptoms, as well as relevant diagnostic facilities are under-utilised. PET imaging is expensive and of limited availability, and CSF sampling may be considered invasive. The European Brain Council's 'Rethinking Alzheimer's disease: Detection and diagnosis' White Paper has looked at the barriers to early diagnosis and how the healthcare systems infrastructure for detection and diagnosis of AD need to be transformed in order for people with AD to benefit from innovative solutions once they become approved for use.

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.

Emerging nanotechnology for Alzheimer's disease: From detection to treatment

Alzheimer's disease (AD), one of the most common chronic neurodegenerative diseases, is characterized by memory impairment, synaptic dysfunction, and character mutations. The pathological features of AD are Aβ accumulation, tau protein enrichment, oxidative stress, and immune inflammation. Since the pathogenesis of AD is complicated and ambiguous, it is still challenging to achieve early detection and timely treatment of AD. Due to the unique physical, electrical, magnetic, and optical properties of nanoparticles (NPs), nanotechnology has shown great potential for detecting and treating AD. This review provides an overview of the latest developments in AD detection via nanotechnology based on NPs with electrochemical sensing, optical sensing, and imaging techniques. Meanwhile, we highlight the important advances in nanotechnology-based AD treatment through targeting disease biomarkers, stem-cell therapy and immunotherapy. Furthermore, we summarize the current challenges and present a promising prospect for nanotechnology-based AD diagnosis and intervention.

A Systematic Review of Clinical Practice Guidelines for Alzheimer's Disease and Strategies for Future Advancements

INTRODUCTION

Alzheimer's disease (AD) is a disease continuum from pathophysiologic, biomarker and clinical perspectives. With the advent of advanced technologies, diagnosing and managing patients is evolving.

METHODS

A systematic literature review (SLR) of practice guidelines for mild cognitive impairment (MCI) and AD dementia was performed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). This systematic literature review (SLR) aimed to summarize current clinical practice guidelines for screening, testing, diagnosis, treatment and monitoring in the AD continuum. The results of this SLR were used to propose a way forward for practice guidelines given the possible introduction of biomarker-guided technology using blood- or plasma-based assays and disease-modifying treatments (DMTs) targeted for early disease.

RESULTS

53 clinical practice guidelines were identified, 15 of which were published since 2018. Screening for asymptomatic populations was not recommended. Biomarker testing was not included in routine diagnostic practice. There was no consensus on which neurocognitive tests to use to diagnose and monitor MCI or AD dementia. Pharmacologic therapies were not recommended for MCI, while cholinesterase inhibitors and memantine were recommended for AD treatment.

DISCUSSION

The pre-2018 and post-2018 practice guidelines share similar recommendations for screening, diagnosis and treatment. However, once DMTs are approved, clinicians will require guidance on the appropriate use of DMTs in a clinical setting. This guidance should include strategies for identifying eligible patients and evaluating the DMT benefit-to-risk profile to facilitate shared decision-making among physicians, patients and care partners.

CONCLUSION

Regular evidence-based updates of existing guidelines for the AD continuum are required over the coming decades to integrate rapidly evolving technologic and medical scientific advances and bring emerging approaches for management of early disease into clinical practice. This will pave the way toward biomarker-guided identification and targeted treatment and the realization of precision medicine for AD.

A task force for diagnosis and treatment of people with Alzheimer's disease in Latin America

Alzheimer's disease (AD) represents a substantial burden to patients, their caregivers, health systems, and society in Latin America and the Caribbean (LAC). This impact is exacerbated by limited access to diagnosis, specialized care, and therapies for AD within and among nations. The region has varied geographic, ethnic, cultural, and economic conditions, which create unique challenges to AD diagnosis and management. To address these issues, the Americas Health Foundation convened a panel of eight neurologists, geriatricians, and psychiatrists from Argentina, Brazil, Colombia, Ecuador, Guatemala, Mexico, and Peru who are experts in AD for a three-day virtual meeting to discuss best practices for AD diagnosis and treatment in LAC and create a manuscript offering recommendations to address identified barriers. In LAC, several barriers hamper diagnosing and treating people with dementia. These barriers include access to healthcare, fragmented healthcare systems, limited research funding, unstandardized diagnosis and treatment, genetic heterogeneity, and varying social determinants of health. Additional training for physicians and other healthcare workers at the primary care level, region-specific or adequately adapted cognitive tests, increased public healthcare insurance coverage of testing and treatment, and dedicated search strategies to detect populations with gene variants associated with AD are among the recommendations to improve the landscape of AD.

Current Diagnostic Pathways for Alzheimer's Disease: A Cross-Sectional Real-World Study Across Six Countries

Background

Diagnostic pathways for patients presenting with cognitive complaints may vary across geographies.

Objective

To describe diagnostic pathways of patients presenting with cognitive complaints across 6 countries.

Methods

This real-world, cross-sectional study analyzed chart-extracted data from healthcare providers (HCPs) for 6,744 patients across China, France, Germany, Spain, UK, and the US.

Results

Most common symptoms at presentation were cognitive (memory/amnestic; 89.86%), followed by physical/behavioral (87.13%). Clinical/cognitive tests were used in > 95%, with Mini-Mental State Examination being the most common cognitive test (79.0%). Blood tests for APOE ɛ4/other mutations, or to rule out treatable causes, were used in half of the patients. Clinical and cognitive tests were used at higher frequency at earlier visits, and amyloid PET/CSF biomarker testing at higher frequency at later visits. The latter were ordered at low rates even by specialists (across countries, 5.7% to 28.7% for amyloid PET and 5.0% to 27.3% for CSF testing). Approximately half the patients received a diagnosis (52.1% of which were Alzheimer's disease [AD]). Factors that influenced risk of not receiving a diagnosis were HCP type (higher for primary care physicians versus specialists) and region (highest in China and Germany).

Conclusion

These data highlight variability in AD diagnostic pathways across countries and provider types. About 45% of patients are referred/told to 'watch and wait'. Improvements can be made in the use of amyloid PET and CSF testing. Efforts should focus on further defining biomarkers for those at risk for AD, and on dismantling barriers such low testing capacity and reimbursement challenges.

Pathological BBB Crossing Melanin-Like Nanoparticles as Metal-Ion Chelators and Neuroinflammation Regulators against Alzheimer's Disease

Inflammatory responses, manifested in excessive oxidative stress and microglia overactivation, together with metal ion-triggered amyloid-beta (Aβ) deposition, are critical hallmarks of Alzheimer's disease (AD). The intricate pathogenesis causes severe impairment of neurons, which, in turn, exacerbates Aβ aggregation and facilitates AD progression. Herein, multifunctional melanin-like metal ion chelators and neuroinflammation regulators (named PDA@K) were constructed for targeted treatment of AD. In this platform, intrinsically bioactive material polydopamine nanoparticles (PDA) with potent metal ion chelating and ROS scavenging effects were decorated with the KLVFF peptide, endowing the system with the capacity of enhanced pathological blood-brain barrier (BBB) crossing and lesion site accumulation via Aβ hitchhiking. In vitro and in vivo experiment revealed that PDA@K had high affinity toward Aβ and were able to hitch a ride on Aβ to achieve increased pathological BBB crossing. The engineered PDA@K effectively mitigated Aβ aggregate and alleviated neuroinflammation. The modulated inflammatory microenvironment by PDA@K promoted microglial polarization toward the M2-like phenotype, which restored their critical functions for neuron care and plaque removal. After 3-week treatment of PDA@K, spatial learning and memory deficit as well as neurologic changes of FAD^4T transgenic mice were largely rescued. Transcriptomics analysis further revealed the therapeutic mechanism of PDA@K. Our study provided an appealing paradigm for directly utilizing intrinsic properties of nanomaterials as therapeutics for AD instead of just using them as nanocarriers, which largely widen the application of nanomaterials in AD therapy.

Personalising Alzheimer's Disease progression using brain atrophy markers

INTRODUCTION

Neuroanatomical normative modelling can capture individual variability in Alzheimer's Disease (AD). We used neuroanatomical normative modelling to track individuals' disease progression in people with mild cognitive impairment (MCI) and patients with AD.

METHODS

Cortical thickness and subcortical volume neuroanatomical normative models were generated using healthy controls (n~58k). These models were used to calculate regional Z-scores in 4361 T1-weighted MRI time-series scans. Regions with Z-scores <-1.96 were classified as outliers and mapped on the brain, and also summarised by total outlier count (tOC).

RESULTS

Rate of change in tOC increased in AD and in people with MCI who converted to AD and correlated with multiple non-imaging markers. Moreover, a higher annual rate of change in tOC increased the risk of MCI progression to AD. Brain Z-score maps showed that the hippocampus had the highest rate of atrophy change.

CONCLUSIONS

Individual-level atrophy rates can be tracked by using regional outlier maps and tOC.

Treatment of women with BRCA mutation

The cumulative risk for breast and ovarian cancer is high in BRCA1 or BRCA2 mutation carriers. The lifetime risk of breast cancer by the age of 80 years is respectively up to 72% and 69% in BRCA1 and BRCA2 mutation carriers. The risk of ovarian cancer is higher (44%) in BRCA1 than in BRCA2 (17%) mutation carriers. Breast and ovarian cancers tend to arise earlier in BRCA1 mutation carriers. Breast cancers in BRCA1 mutation carriers are more frequently (up to 70%) triple negative while the majority (up to 80%) of breast cancers in BRCA2 mutation carriers are hormone sensitive. Many issues remain to be resolved. In daily practice we are often confronted with patients having BRCA mutations classified as variants of unknown significance, who do have breast cancer personally or have a strong family history of breast cancer. On the other hand, 30-40% of mutation carriers will not develop breast cancer. Moreover, it is very difficult to predict the age at which cancer will arise. In a multidisciplinary setting we need to offer BRCA and other mutation carriers a wide range of information, advice and support.

Understanding Barriers Along the Patient Journey in Alzheimer's Disease Using Social Media Data

INTRODUCTION

We speculated that social media data from Alzheimer's disease (AD) stakeholders (patients, caregivers, and clinicians) could identify barriers along the patient journey in AD, and that insights gained may help devise strategies to remove barriers, and ultimately improve the patient journey.

METHODS

Our sample was drawn from a repository of social media posts extracted from 112 public sources between January 1998 and December 2021 using natural language processing text-mining algorithms. The patient journey was classified into three phases: (1) early signs/experiences (Early Signs); (2) screening/assessment/diagnosis (Screening); and (3) treatment/management (Treatment). In the Early Signs phase, issues/challenges derived from a conceptual AD identification framework (ADIF) were examined. In subsequent phases, behavioral/psychiatric challenges, access/barriers to health care, screening/diagnostic methods, and symptomatic treatments for AD were identified. Posts were classified by AD stakeholder type or disease stage, if possible.

RESULTS

We identified 225,977 AD patient journey-related social media posts. Anxiety was a predominant issue/challenge in all patient journey phases. In the Screening and Treatment phases combined, access/barriers to care were described in 16% of posts; unwillingness/resistance to seeking care was a major barrier (≥ 75% of access-related posts across all stakeholders). Commonly identified structural barriers (e.g., affordability/cost, geography/transportation/distance) were more common in patient/caregiver posts than clinician posts. Among Screening-related posts, imaging/scans were commonly mentioned by all stakeholders; biomarkers were more commonly mentioned by patients than clinicians. Treatment-related concerns were identified in 17% of stakeholder-specified posts that named pharmacological agents/classes for the symptomatic management of AD.

CONCLUSION

This descriptive analysis of out-of-clinic experiences reflected in AD social media posts found that unwillingness/resistance to seeking care was a key barrier, followed by structural barriers to health care, such as affordability/cost. Insights from the lived experiences of AD stakeholders are valuable and highlight the need to improve the patient journey in AD and ease patient and caregiver burden.

Repurposing Non-pharmacological Interventions for Alzheimer's Diseases through Link Prediction on Biomedical Literature

Recently, computational drug repurposing has emerged as a promising method for identifying new pharmaceutical interventions (PI) for Alzheimer's Disease (AD). Non-pharmaceutical interventions (NPI), such as Vitamin E and Music therapy, have great potential to improve cognitive function and slow the progression of AD, but have largely been unexplored. This study predicts novel NPIs for AD through link prediction on our developed biomedical knowledge graph. We constructed a comprehensive knowledge graph containing AD concepts and various potential interventions, called ADInt, by integrating a dietary supplement domain knowledge graph, SuppKG, with semantic relations from SemMedDB database. Four knowledge graph embedding models (TransE, RotatE, DistMult and ComplEX) and two graph convolutional network models (R-GCN and CompGCN) were compared to learn the representation of ADInt. R-GCN outperformed other models by evaluating on the time slice test set and the clinical trial test set and was used to generate the score tables of the link prediction task. Discovery patterns were applied to generate mechanism pathways for high scoring triples. Our ADInt had 162,213 nodes and 1,017,319 edges. The graph convolutional network model, R-GCN, performed best in both the Time Slicing test set (MR = 7.099, MRR = 0.5007, Hits@1 = 0.4112, Hits@3 = 0.5058, Hits@10 = 0.6804) and the Clinical Trials test set (MR = 1.731, MRR = 0.8582, Hits@1 = 0.7906, Hits@3 = 0.9033, Hits@10 = 0.9848). Among high scoring triples in the link prediction results, we found the plausible mechanism pathways of (Photodynamic therapy, PREVENTS, Alzheimer's Disease) and (Choerospondias axillaris, PREVENTS, Alzheimer's Disease) by discovery patterns and discussed them further. In conclusion, we presented a novel methodology to extend an existing knowledge graph and discover NPIs (dietary supplements (DS) and complementary and integrative health (CIH)) for AD. We used discovery patterns to find mechanisms for predicted triples to solve the poor interpretability of artificial neural networks. Our method can potentially be applied to other clinical problems, such as discovering drug adverse reactions and drug-drug interactions.

Anti-Amyloid Monoclonal Antibodies are Transformative Treatments that Redefine Alzheimer's Disease Therapeutics

Two anti-amyloid monoclonal antibodies (MABs)-lecanemab (Leqembi®) and aducanumab (Aduhelm®)-have been approved in the USA for the treatment of Alzheimer's disease (AD). Anti-amyloid monoclonal antibodies are the first disease-modifying therapies for AD that achieve slowing of clinical decline by intervening in the basic biological processes of the disease. These are breakthrough agents that can slow the inevitable progression of AD into more severe cognitive impairment. The results of trials of anti-amyloid MABs support the amyloid hypothesis and amyloid as a target for AD drug development. The success of MABs reflects a relentless application of neuroscience knowledge to solving major challenges facing humankind. The success of these transformative agents will foster the development of more anti-amyloid MABs, other types of anti-amyloid therapies, treatments of other targets of AD biology, and new approaches to therapies for an array of neurodegenerative disorders. Monoclonal antibodies have side effects and, during the period of treatment initiation, patients must be closely monitored for the occurrence of amyloid-related imaging abnormalities (ARIA) and infusion reactions. A successful first step in the development of disease-modifying therapy for AD defines desirable features for the next phase of therapeutic development including less frequent ARIA, more convenient administration, and greater efficacy. Unprecedented agents make new demands on patients and care partners, clinicians, payers, and health care systems. Collaboration among stakeholders is essential to take advantage of the therapeutic benefits offered by these agents and to make them widely available. Monoclonal antibodies usher in a new era in AD therapy and define a new landscape of what is possible for therapeutic development for neurodegenerative disorders.

Current Advances of Plant-Based Vaccines for Neurodegenerative Diseases

Neurodegenerative diseases (NDDs) are characterized by the progressive degeneration and/or loss of neurons belonging to the central nervous system, and represent one of the major global health issues. Therefore, a number of immunotherapeutic approaches targeting the non-functional or toxic proteins that induce neurodegeneration in NDDs have been designed in the last decades. In this context, due to unprecedented advances in genetic engineering techniques and molecular farming technology, pioneering plant-based immunogenic antigen expression systems have been developed aiming to offer reliable alternatives to deal with important NDDs, including Alzheimer's disease, Parkinson's disease, and multiple sclerosis. Diverse reports have evidenced that plant-made vaccines trigger significant immune responses in model animals, supported by the production of antibodies against the aberrant proteins expressed in the aforementioned NDDs. Moreover, these immunogenic tools have various advantages that make them a viable alternative for preventing and treating NDDs, such as high scalability, no risk of contamination with human pathogens, cold chain free production, and lower production costs. Hence, this article presents an overview of the current progress on plant-manufactured vaccines for NDDs and discusses its future prospects.

Current understanding of metal-dependent amyloid-β aggregation and toxicity

The discovery of effective therapeutics targeting amyloid-β (Aβ) aggregates for Alzheimer's disease (AD) has been very challenging, which suggests its complicated etiology associated with multiple pathogenic elements. In AD-affected brains, highly concentrated metals, such as copper and zinc, are found in senile plaques mainly composed of Aβ aggregates. These metal ions are coordinated to Aβ and affect its aggregation and toxicity profiles. In this review, we illustrate the current view on molecular insights into the assembly of Aβ peptides in the absence and presence of metal ions as well as the effect of metal ions on their toxicity.

The Future of Cognitive Screening in Neurodegenerative Diseases

Cognitive screening instruments (CSI) have variable sensitivity and specificity to the cognitive changes associated with dementia syndromes, and the most recent systematic review found insufficient evidence to support the benefit of cognitive screening tools in older adults residing within the community. Consequently, there is a critical need to improve CSI methods, which have not yet incorporated advances in psychometrics, neuroscience, and technology. The primary goal of this article is to provide a framework for transitioning from legacy CSIs to advanced dementia screening measurement. In line with ongoing efforts in neuropsychology and the call for next-generation digital assessment for early detection of AD, we propose a psychometrically advanced (including application of item response theory methods), automated selective assessment model that provides a framework to help propel an assessment revolution. Further, we present a three-phase model for modernizing CSIs and discuss critical diversity and inclusion issues, current challenges in differentiating normal from pathological aging, and ethical considerations.