Using Digital Tools to Advance Alzheimer's Drug Trials During a Pandemic: The EU/US CTAD Task Force.

The 2020 COVID-19 pandemic has disrupted Alzheimer's disease (AD) clinical studies worldwide. Digital technologies may help minimize disruptions by enabling remote assessment of subtle cognitive and functional changes over the course of the disease. The EU/US Clinical Trials in Alzheimer's Disease (CTAD) Task Force met virtually in November 2020 to explore the opportunities and challenges associated with the use of digital technologies in AD clinical research. While recognizing the potential of digital tools to accelerate clinical trials, improve the engagement of diverse populations, capture clinically meaningful data, and lower costs, questions remain regarding the stability, validity, generalizability, and reproducibility of digital data. Substantial concerns also exist regarding regulatory acceptance and privacy. Nonetheless, the Task Force supported further exploration of digital technologies through collaboration and data sharing, noting the need for standardization of digital readouts. They also concluded that while it may be premature to employ remote assessments for trials of novel experimental medications, remote studies of non-invasive, multi-domain approaches may be feasible at this time.

Plasma Biomarkers of AD Emerging as Essential Tools for Drug Development: An EU/US CTAD Task Force Report.

There is an urgent need to develop reliable and sensitive blood-based biomarkers of Alzheimer's disease (AD) that can be used for screening and to increase the efficiency of clinical trials. The European Union-North American Clinical Trials in Alzheimer's Disease Task Force (EU/US CTAD Task Force) discussed the current status of blood-based AD biomarker development at its 2018 annual meeting in Barcelona, Spain. Recent improvements in technologies to assess plasma levels of amyloid beta indicate that a single sample of blood could provide an accurate estimate of brain amyloid positivity. Plasma neurofilament light protein appears to provide a good marker of neurodegeneration, although not specific for AD. Plasma tau shows some promising results but weak or no correlation with CSF tau levels, which may reflect rapid clearance of tau in the bloodstream. Blood samples analyzed using -omics and other approaches are also in development and may provide important insight into disease mechanisms as well as biomarker profiles for disease prediction. To advance these technologies, international multidisciplinary, multi-stakeholder collaboration is essential.

Insulin signalling in Alzheimer's disease and diabetes: from epidemiology to molecular links.

As populations across the world both age and become more obese, the numbers of individuals with Alzheimer's disease and diabetes are increasing; posing enormous challenges for society and consequently becoming priorities for governments and global organizations. These issues, an ageing population at risk of neurodegenerative diseases such as Alzheimer's disease and an increasingly obese population at risk of metabolic alterations such as type 2 diabetes, are usually considered as independent conditions, but increasing evidence from both epidemiological and molecular studies link these disorders. The aim of this review was to highlight these multifactorial links. We will discuss the impact of direct links between insulin and IGF-1 signalling and the Alzheimer's disease-associated pathological events as well as the impact of other processes such as inflammation, oxidative stress and mitochondrial dysfunction either common to both conditions or perhaps responsible for a mechanistic link between metabolic and neurodegenerative disease. An understanding of such associations might be of importance not only in the understanding of disease mechanisms but also in the search for novel therapeutic options.

Blood metabolite markers of neocortical amyloid-ß burden: discovery and enrichment using candidate proteins.

We believe this is the first study to investigate associations between blood metabolites and neocortical amyloid burden (NAB) in the search for a blood-based biomarker for Alzheimer's disease (AD). Further, we present the first multi-modal analysis of blood markers in this field. We used blood plasma samples from 91 subjects enrolled in the University of California, San Francisco Alzheimer's Disease Research Centre. Non-targeted metabolomic analysis was used to look for associations with NAB using both single and multiple metabolic feature models. Five metabolic features identified subjects with high NAB, with 72% accuracy. We were able to putatively identify four metabolites from this panel and improve the model further by adding fibrinogen gamma chain protein measures (accuracy=79%). One of the five metabolic features was studied in the Alzheimer's Disease Neuroimaging Initiative cohort, but results were inconclusive. If replicated in larger, independent studies, these metabolic features and proteins could form the basis of a blood test with potential for enrichment of amyloid pathology in anti-amyloid trials.

Registries and Cohorts to Accelerate Early Phase Alzheimer's Trials. A Report from the E.U./U.S. Clinical Trials in Alzheimer's Disease Task Force.

The EU/US/CTAD Task Force, an international collaboration of AD investigators from industry and academia, met in Barcelona, Spain, on November 4th, 2015, to explore existing and planned patient registries and other clinical trial infrastructure meant to expedite recruitment of large numbers of participants into clinical trials and improve their productivity. The Task Force identified a number of approaches currently being tested around the world, including the use of predictive algorithms to identify individuals likely to have prodromal or preclinical AD, the establishment of clinical trial networks to streamline trials, and reforming the informed consent process to make it less burdensome to both investigators and trial participants. Multi-national systems such as the European Prevention of Alzheimer's Dementia (EPAD) and the Global Alzheimer's Platform (GAP) offer value for sponsors, trial sites, and patients by optimizing efforts to find effective disease-modifying and symptomatic treatments.

Plasma protein biomarkers of Alzheimer's disease endophenotypes in asymptomatic older twins: early cognitive decline and regional brain volumes.

There is great interest in blood-based markers of Alzheimer's disease (AD), especially in its pre-symptomatic stages. Therefore, we aimed to identify plasma proteins whose levels associate with potential markers of pre-symptomatic AD. We also aimed to characterise confounding by genetics and the effect of genetics on blood proteins in general. Panel-based proteomics was performed using SOMAscan on plasma samples from TwinsUK subjects who are asymptomatic for AD, measuring the level of 1129 proteins. Protein levels were compared with 10-year change in CANTAB-paired associates learning (PAL; n = 195), and regional brain volumes (n = 34). Replication of proteins associated with regional brain volumes was performed in 254 individuals from the AddNeuroMed cohort. Across all the proteins measured, genetic factors were found to explain ~26% of the variability in blood protein levels on average. The plasma level of the mitogen-activated protein kinase (MAPK) MAPKAPK5 protein was found to positively associate with the 10-year change in CANTAB-PAL in both the individual and twin difference context. The plasma level of protein MAP2K4 was found to suggestively associate negatively (Q < 0.1) with the volume of the left entorhinal cortex. Future studies will be needed to assess the specificity of MAPKAPK5 and MAP2K4 to eventual conversion to AD.

Practical cut-offs for visual rating scales of medial temporal, frontal and posterior atrophy in Alzheimer's disease and mild cognitive impairment.

BACKGROUND: Atrophy in the medial temporal lobe, frontal lobe and posterior cortex can be measured with visual rating scales such as the medial temporal atrophy (MTA), global cortical atrophy - frontal subscale (GCA-F) and posterior atrophy (PA) scales, respectively. However, practical cut-offs are urgently needed, especially now that different presentations of Alzheimer's disease (AD) are included in the revised diagnostic criteria. AIMS: The aim of this study was to generate a list of practical cut-offs for the MTA, GCA-F and PA scales, for both diagnosis of AD and determining prognosis in mild cognitive impairment (MCI), and to evaluate the influence of key demographic and clinical factors on these cut-offs. METHODS: AddNeuroMed and ADNI cohorts were combined giving a total of 1147 participants (322 patients with AD, 480 patients with MCI and 345 control subjects). The MTA, GCA-F and PA scales were applied and a broad range of cut-offs was evaluated. RESULTS: The MTA scale showed better diagnostic and predictive performances than the GCA-F and PA scales. Age, apolipoprotein E (ApoE) ε4 status and age at disease onset influenced all three scales. For the age ranges 45-64, 65-74, 75-84 and 85-94 years, the following cut-offs should be used. MTA: ≥1.5, ≥1.5, ≥2 and ≥2.5; GCA-F, ≥1, ≥1, ≥1 and ≥1; and PA, ≥1, ≥1, ≥1 and ≥1, respectively, with an adjustment for early-onset ApoE ε4 noncarrier AD patients (MTA: ≥2, ≥2, ≥3 and ≥3; and GCA-F: ≥1, ≥1, ≥2 and ≥2, respectively). CONCLUSIONS: If successfully validated in clinical settings, the list of practical cut-offs proposed here might be useful in clinical practice. Their use might also (i) promote research on atrophy subtypes, (ii) increase the understanding of different presentations of AD, (iii) improve diagnosis and prognosis and (iv) aid population selection and enrichment for clinical trials.

Plasma lipidomics analysis finds long chain cholesteryl esters to be associated with Alzheimer's disease.

There is an urgent need for the identification of Alzheimer's disease (AD) biomarkers. Studies have now suggested the promising use of associations with blood metabolites as functional intermediate phenotypes in biomedical and pharmaceutical research. The aim of this study was to use lipidomics to identify a battery of plasma metabolite molecules that could predict AD patients from controls. We performed a comprehensive untargeted lipidomic analysis, using ultra-performance liquid chromatography/mass spectrometry on plasma samples from 35 AD patients, 40 elderly controls and 48 individuals with mild cognitive impairment (MCI) and used multivariate analysis methods to identify metabolites associated with AD status. A combination of 10 metabolites could discriminate AD patients from controls with 79.2% accuracy (81.8% sensitivity, 76.9% specificity and an area under curve of 0.792) in a novel test set. Six of the metabolites were identified as long chain cholesteryl esters (ChEs) and were reduced in AD (ChE 32:0, odds ratio (OR)=0.237, 95% confidence interval (CI)=0.10-0.48, P=4.19E-04; ChE 34:0, OR=0.152, 95% CI=0.05-0.37, P=2.90E-04; ChE 34:6, OR=0.126, 95% CI=0.03-0.35, P=5.40E-04; ChE 32:4, OR=0.056, 95% CI=0.01-0.24, P=6.56E-04 and ChE 33:6, OR=0.205, 95% CI=0.06-0.50, P=2.21E-03, per (log2) metabolite unit). The levels of these metabolites followed the trend control>MCI>AD. We, additionally, found no association between cholesterol, the precursor of ChE and AD. This study identified new ChE molecules, involved in cholesterol metabolism, implicated in AD, which may help identify new therapeutic targets; although, these findings need to be replicated in larger well-phenotyped cohorts.

FASTKD2 is associated with memory and hippocampal structure in older adults.

Memory impairment is the cardinal early feature of Alzheimer's disease, a highly prevalent disorder whose causes remain only partially understood. To identify novel genetic predictors, we used an integrative genomics approach to perform the largest study to date of human memory (n=14 781). Using a genome-wide screen, we discovered a novel association of a polymorphism in the pro-apoptotic gene FASTKD2 (fas-activated serine/threonine kinase domains 2; rs7594645-G) with better memory performance and replicated this finding in independent samples. Consistent with a neuroprotective effect, rs7594645-G carriers exhibited increased hippocampal volume and gray matter density and decreased cerebrospinal fluid levels of apoptotic mediators. The MTOR (mechanistic target of rapamycin) gene and pathways related to endocytosis, cholinergic neurotransmission, epidermal growth factor receptor signaling and immune regulation, among others, also displayed association with memory. These findings nominate FASTKD2 as a target for modulating neurodegeneration and suggest potential mechanisms for therapies to combat memory loss in normal cognitive aging and dementia.

Random Forest ensembles for detection and prediction of Alzheimer's disease with a good between-cohort robustness.

Computer-aided diagnosis of Alzheimer's disease (AD) is a rapidly developing field of neuroimaging with strong potential to be used in practice. In this context, assessment of models' robustness to noise and imaging protocol differences together with post-processing and tuning strategies are key tasks to be addressed in order to move towards successful clinical applications. In this study, we investigated the efficacy of Random Forest classifiers trained using different structural MRI measures, with and without neuroanatomical constraints in the detection and prediction of AD in terms of accuracy and between-cohort robustness. From The ADNI database, 185 AD, and 225 healthy controls (HC) were randomly split into training and testing datasets. 165 subjects with mild cognitive impairment (MCI) were distributed according to the month of conversion to dementia (4-year follow-up). Structural 1.5-T MRI-scans were processed using Freesurfer segmentation and cortical reconstruction. Using the resulting output, AD/HC classifiers were trained. Training included model tuning and performance assessment using out-of-bag estimation. Subsequently the classifiers were validated on the AD/HC test set and for the ability to predict MCI-to-AD conversion. Models' between-cohort robustness was additionally assessed using the AddNeuroMed dataset acquired with harmonized clinical and imaging protocols. In the ADNI set, the best AD/HC sensitivity/specificity (88.6%/92.0% - test set) was achieved by combining cortical thickness and volumetric measures. The Random Forest model resulted in significantly higher accuracy compared to the reference classifier (linear Support Vector Machine). The models trained using parcelled and high-dimensional (HD) input demonstrated equivalent performance, but the former was more effective in terms of computation/memory and time costs. The sensitivity/specificity for detecting MCI-to-AD conversion (but not AD/HC classification performance) was further improved from 79.5%/75%-83.3%/81.3% by a combination of morphometric measurements with ApoE-genotype and demographics (age, sex, education). When applied to the independent AddNeuroMed cohort, the best ADNI models produced equivalent performance without substantial accuracy drop, suggesting good robustness sufficient for future clinical implementation.

Influence of age, disease onset and ApoE4 on visual medial temporal lobe atrophy cut-offs.

BACKGROUND: Visual assessment of medial temporal lobe atrophy (MTA; range 0-4, from no atrophy to increasing atrophy of the choroid fissure, temporal horns and hippocampus) is a sensitive radiological marker of Alzheimer's disease (AD). One of the critical elements for visual MTA assessment is the cut-off score that determines deviation from normality. METHODS: In this study, we assessed the sensitivity and specificity of different MTA cut-off scores to classify control subjects, individuals with mild cognitive impairment (MCI) and AD patients from two large independent cohorts, AddNeuroMed and Alzheimer's Disease Neuroimaging Initiative. Of note, we evaluated the effects of clinical, demographic and genetic variables on the classification performance according to the different cut-offs. RESULTS: A cut-off of ≥1.5 based on the mean MTA scores of both hemispheres showed higher sensitivity in classifying patients with AD (84.5%) and MCI subjects (75.8%) who converted to dementia compared to an age-dependent cut-off. The age-dependent cut-off showed higher specificity or ability to correctly identify control subjects (83.2%) and those with MCI who remained stable (65.5%). Increasing age, early-onset disease and absence of the ApoE ε4 allele had a stronger influence on classifications using the ≥1.5 cut-off. Above 75 years of age, an alternative cut-off of ≥2.0 should be applied to achieve a classification accuracy for both patients with AD and control subjects that is clinically useful. CONCLUSION: Clinical, demographic and genetic variables can influence the classification of MTA cut-off scores, leading to misdiagnosis in some cases. These variables, in addition to the differential sensitivity and specificity of each cut-off, should be carefully considered when performing visual MTA assessment.

Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer's disease.

Eleven susceptibility loci for late-onset Alzheimer's disease (LOAD) were identified by previous studies; however, a large portion of the genetic risk for this disease remains unexplained. We conducted a large, two-stage meta-analysis of genome-wide association studies (GWAS) in individuals of European ancestry. In stage 1, we used genotyped and imputed data (7,055,881 SNPs) to perform meta-analysis on 4 previously published GWAS data sets consisting of 17,008 Alzheimer's disease cases and 37,154 controls. In stage 2, 11,632 SNPs were genotyped and tested for association in an independent set of 8,572 Alzheimer's disease cases and 11,312 controls. In addition to the APOE locus (encoding apolipoprotein E), 19 loci reached genome-wide significance (P < 5 × 10(-8)) in the combined stage 1 and stage 2 analysis, of which 11 are newly associated with Alzheimer's disease.

Whole-exome sequencing and imaging genetics identify functional variants for rate of change in hippocampal volume in mild cognitive impairment.

Whole-exome sequencing of individuals with mild cognitive impairment, combined with genotype imputation, was used to identify coding variants other than the apolipoprotein E (APOE) ε4 allele associated with rate of hippocampal volume loss using an extreme trait design. Matched unrelated APOE ε3 homozygous male Caucasian participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) were selected at the extremes of the 2-year longitudinal change distribution of hippocampal volume (eight subjects with rapid rates of atrophy and eight with slow/stable rates of atrophy). We identified 57 non-synonymous single nucleotide variants (SNVs) which were found exclusively in at least 4 of 8 subjects in the rapid atrophy group, but not in any of the 8 subjects in the slow atrophy group. Among these SNVs, the variants that accounted for the greatest group difference and were predicted in silico as 'probably damaging' missense variants were rs9610775 (CARD10) and rs1136410 (PARP1). To further investigate and extend the exome findings in a larger sample, we conducted quantitative trait analysis including whole-brain search in the remaining ADNI APOE ε3/ε3 group (N=315). Genetic variation within PARP1 and CARD10 was associated with rate of hippocampal neurodegeneration in APOE ε3/ε3. Meta-analysis across five independent cross sectional cohorts indicated that rs1136410 is also significantly associated with hippocampal volume in APOE ε3/ε3 individuals (N=923). Larger sequencing studies and longitudinal follow-up are needed for confirmation. The combination of next-generation sequencing and quantitative imaging phenotypes holds significant promise for discovery of variants involved in neurodegeneration.

Classification and prediction of clinical diagnosis of Alzheimer's disease based on MRI and plasma measures of α-/γ-tocotrienols and γ-tocopherol.

BACKGROUND: The aim of this study was to evaluate the accuracy of combined structural magnetic resonance imaging (MRI) measures and plasma levels of vitamin E forms, including all eight natural vitamin E congeners (four tocopherols and four tocotrienols) and markers of vitamin E oxidative/nitrosative damage, in differentiating individuals with Alzheimer's disease (AD) and mild cognitive impairment (MCI) from cognitively intact control (CTL) subjects. METHODS: Overall, 81 patients with AD, 86 with MCI and 86 CTL individuals were enrolled from the longitudinal multicentre AddNeuroMed study. MRI and plasma vitamin E data were acquired at baseline. MRI scans were analysed using Freesurfer, an automated segmentation scheme which generates regional volume and cortical thickness measures. Orthogonal partial least squares to latent structures (OPLS), a multivariate data analysis technique, was used to analyse MRI and vitamin E measures in relation to AD and MCI diagnosis. RESULTS: The joint evaluation of MRI and plasma vitamin E measures enhanced the accuracy of differentiating individuals with AD and MCI from CTL subjects: 98.2% (sensitivity 98.8%, specificity 97.7%) for AD versus CTL, and 90.7% (sensitivity 91.8%, specificity 89.5%) for MCI versus CTL. This combination of measures also identified 85% of individuals with MCI who converted to clinical AD at follow-up after 1 year. CONCLUSIONS: Plasma levels of tocopherols and tocotrienols together with automated MRI measures can help to differentiate AD and MCI patients from CTL subjects, and to prospectively predict MCI conversion into AD. Our results suggest the potential role of nutritional biomarkers detected in plasma-tocopherols and tocotrienols-as indirect indicators of AD pathology, and the utility of a multimodality approach.

An MRI-based index to measure the severity of Alzheimer's disease-like structural pattern in subjects with mild cognitive impairment.

BACKGROUND: Structural magnetic resonance imaging (MRI) is sensitive to neurodegeneration and can be used to estimate the risk of converting to Alzheimer's disease (AD) in individuals with mild cognitive impairment (MCI). Brain changes in AD and prodromal AD involve a pattern of widespread atrophy. The use of multivariate analysis algorithms could enable the development of diagnostic tools based on structural MRI data. In this study, we investigated the possibility of combining multiple MRI features in the form of a severity index. METHODS: We used baseline MRI scans from two large multicentre cohorts (AddNeuroMed and ADNI). On the basis of volumetric and cortical thickness measures at baseline with AD cases and healthy control (CTL) subjects as training sets, we generated an MRI-based severity index using the method of orthogonal projection to latent structures (OPLS). The severity index tends to be close to 1 for AD patients and 0 for CTL subjects. Values above 0.5 indicate a more AD-like pattern. The index was then estimated for subjects with MCI, and the accuracy of classification was investigated. RESULTS: Based on the data at follow-up, 173 subjects converted to AD, of whom 112 (64.7%) were classified as AD-like and 61 (35.3%) as CTL-like. CONCLUSION: We found that joint evaluation of multiple brain regions provided accurate discrimination between progressive and stable MCI, with better performance than hippocampal volume alone, or a limited set of features. A major challenge is still to determine optimal cut-off points for such parameters and to compare their relative reliability.

Deletion of Crry, the murine ortholog of the sporadic Alzheimer's disease risk gene CR1, impacts tau phosphorylation and brain CFH.

Large-scale genome-wide SNP association studies have identified an association between variants of CR1, the gene encoding complement component receptor 1, and the sporadic form of Alzheimer's disease. The role of CR1 and the complement system in Alzheimer's disease remains far from clear. In rodents the closest ortholog of CR1 is the Crry gene (Cr1-related protein Y). To begin to explore its role in Alzheimer's disease we examined hippocampal lysates from Crry(-/-) mice and age matched controls by immunoblotting. We measured complement factor H, a component of the complement system and biomarker for Alzheimer's disease progression, and tau phosphorylation at the serine 235 site, hyperphosphorylated forms of tau being a defining neuropathological hallmark of the disease. We found that levels of CFH and of tau phosphorylation at serine 235 were strongly and significantly reduced in Crry(-/-) samples. These observations provide a starting point for further attempts to determine the role of CR1 in the neuropathological process driving Alzheimer's disease.

CERAD Neuropsychological Total Scores Reflect Cortical Thinning in Prodromal Alzheimer's Disease.

BACKGROUND: Sensitive cognitive global scores are beneficial in screening and monitoring for prodromal Alzheimer's disease (AD). Early cortical changes provide a novel opportunity for validating established cognitive total scores against the biological disease markers. METHODS: We examined how two different total scores of the Consortium to Establish a Registry for Alzheimer's Disease (CERAD) battery and the Mini-Mental State Examination (MMSE) are associated with cortical thickness (CTH) in mild cognitive impairment (MCI) and prodromal AD. Cognitive and magnetic resonance imaging (MRI) data of 22 progressive MCI, 78 stable MCI, and 98 control subjects, and MRI data of 103 AD patients of the prospective multicenter study were analyzed. RESULTS: CERAD total scores correlated with mean CTH more strongly (r = 0.34-0.38, p < 0.001) than did MMSE (r = 0.19, p = 0.01). Of those vertex clusters that showed thinning in progressive MCI, 60-75% related to the CERAD total scores and 3% to the MMSE. CONCLUSION: CERAD total scores are sensitive to the CTH signature of prodromal AD, which supports their biological validity in detecting early disease-related cognitive changes.

Genome-wide association study of Alzheimer's disease with psychotic symptoms.

Psychotic symptoms occur in ~40% of subjects with Alzheimer's disease (AD) and are associated with more rapid cognitive decline and increased functional deficits. They show heritability up to 61% and have been proposed as a marker for a disease subtype suitable for gene mapping efforts. We undertook a combined analysis of three genome-wide association studies (GWASs) to identify loci that (1) increase susceptibility to an AD and subsequent psychotic symptoms; or (2) modify risk of psychotic symptoms in the presence of neurodegeneration caused by AD. In all, 1299 AD cases with psychosis (AD+P), 735 AD cases without psychosis (AD-P) and 5659 controls were drawn from Genetic and Environmental Risk in AD Consortium 1 (GERAD1), the National Institute on Aging Late-Onset Alzheimer's Disease (NIA-LOAD) family study and the University of Pittsburgh Alzheimer Disease Research Center (ADRC) GWASs. Unobserved genotypes were imputed to provide data on >1.8 million single-nucleotide polymorphisms (SNPs). Analyses in each data set were completed comparing (1) AD+P to AD-P cases, and (2) AD+P cases with controls (GERAD1, ADRC only). Aside from the apolipoprotein E (APOE) locus, the strongest evidence for association was observed in an intergenic region on chromosome 4 (rs753129; 'AD+PvAD-P' P=2.85 × 10(-7); 'AD+PvControls' P=1.11 × 10(-4)). SNPs upstream of SLC2A9 (rs6834555, P=3.0 × 10(-7)) and within VSNL1 (rs4038131, P=5.9 × 10(-7)) showed strongest evidence for association with AD+P when compared with controls. These findings warrant further investigation in larger, appropriately powered samples in which the presence of psychotic symptoms in AD has been well characterized.