ATN profile classification across two independent prospective cohorts

BACKGROUND The ATN model represents a research framework used to describe in subjects the presence or absence of Alzheimer's disease (AD) pathology through biomarkers. The aim of this study was to describe the prevalence of different ATN profiles using quantitative imaging biomarkers in two independent cohorts, and to evaluate the pertinence of ATN biomarkers to identify comparable populations across independent cohorts. METHODS A total of 172 subjects from the Geneva Memory Clinic and 113 volunteers from a study on healthy aging at the University Hospital of Zurich underwent amyloid (A) and tau (T) PET, as well as T1-weigthed MRI scans using site-specific protocols. Subjects were classified by cognition (cognitively unimpaired, CU, or impaired, CI) based on clinical assessment by experts. Amyloid data converted into the standardized centiloid scale, tau PET data normalized to cerebellar uptake, and hippocampal volume expressed as a ratio over total intracranial volume ratio were considered as biomarkers for A, T, and neurodegeneration (N), respectively. Positivity for each biomarker was defined based on previously published thresholds. Subjects were then classified according to the ATN model. Differences among profiles were tested using Kruskal-Wallis ANOVA, and between cohorts using Wilcoxon tests. RESULTS Twenty-nine percent of subjects from the Geneva cohorts were classified with a normal (A-T-N-) profile, while the Zurich cohort included 64% of subjects in the same category. Meanwhile, 63% of the Geneva and 16% of the Zurich cohort were classified within the AD continuum (being A+ regardless of other biomarkers' statuses). Within cohorts, ATN profiles were significantly different for age and mini-mental state examination scores, but not for years of education. Age was not significantly different between cohorts. In general, imaging A and T biomarkers were significantly different between cohorts, but they were no longer significantly different when stratifying the cohorts by ATN profile. N was not significantly different between cohorts. CONCLUSION Stratifying subjects into ATN profiles provides comparable groups of subjects even when individual recruitment followed different criteria

Societal and equity challenges for Brain Health Services. A user manual for Brain Health Services-part 6 of 6.

Brain Health Services are a novel approach to the personalized prevention of dementia. In this paper, we consider how such services can best reflect their social, cultural, and economic context and, in doing so, deliver fair and equitable access to risk reduction. We present specific areas of challenge associated with the social context for dementia prevention. The first concentrates on how Brain Health Services engage with the "at-risk" individual, recognizing the range of factors that shape an individual's risk of dementia and the efficacy of risk reduction measures. The second emphasizes the social context of Brain Health Services themselves and their ability to provide equitable access to risk reduction. We then elaborate proposals for meeting or mitigating these challenges. We suggest that considering these challenges will enable Brain Health Services to address two fundamental questions: the balance between an individualized "high-risk" and population focus for public health prevention and the ability of services to meet ethical standards of justice and health equity

Combining Multi-Task Learning and Multi-Channel Variational Auto-Encoders to Exploit Datasets with Missing Observations -Application to Multi-Modal Neuroimaging Studies in Dementia

The joint modeling of neuroimaging data across multiple datasets requires to consistently analyze high-dimensional and heterogeneous information in presence of often non-overlapping sets of views across data samples (e.g. imaging data, clinical scores, biological measurements). This analysis is associated with the problem of missing information across datasets, which can happen in two forms: missing at random (MAR), when the absence of a view is unpredictable and does not depend on the dataset (e.g. due to data corruption); missing not at random (MNAR), when a specific view is absent by design for a specific dataset. In order to take advantage of the increased variability and sample size when pooling together observations from many cohorts and at the same time cope with the ubiquitous problem of missing information, we propose here a multi-task generative latent-variable model where the common variability across datasets stems from the estimation of a shared latent representation across views. Our formulation allows to retrieve a consistent latent representation common to all views and datasets, even in the presence of missing information. Simulations on synthetic data show that our method is able to identify a common latent representation of multi-view datasets, even when the compatibility across datasets is minimal. When jointly analyzing multi-modal neuroimaging and clinical data from real independent dementia studies, our model is able to mitigate the absence of modalities without having to discard any available information. Moreover, the common latent representation inferred with our model can be used to define robust classifiers gathering the combined information across different datasets. To conclude, both on synthetic and real data experiments, our model compared favorably to state of the art benchmark methods, providing a more powerful exploitation of multi-modal observations with missing views

Patterns of amyloid accumulation in amyloid-negative cases

Amyloid staging models showed that regional abnormality occurs before global positivity. Several studies assumed that the trajectory of amyloid spread is homogeneous, but clinical evidence suggests that it is highly heterogeneous. We tested whether different amyloid-β (Aβ) patterns exist by applying clustering on negative scans and investigating their demographics, clinical, cognitive, and biomarkers correlates, and cognitive trajectories. 151 individuals from Geneva and Zurich cohorts with T1-MRI, negative Aβ positron emission tomography (PET,centiloid<12) and clinical assessment were included. N=123 underwent tau PET, and N=65 follow-up neuropsychological assessment. We performed k-means clustering using 33 Aβ regional Standardized Uptake Vales ratio. Demographics, clinical, cognitive, and biomarkers differences were investigated. Longitudinal cognitive changes by baseline cluster status were estimated using a linear mixed model. The cluster analysis identified two clusters: temporal predominant (TP) and cingulate predominant (CP). TP tau deposition was higher than CP. A trend for a higher cognitive decline in TP compared to CP was observed. This study suggests the existence of two Aβ deposition patterns in the earliest phases of Aβ accumulation, differently prone to tau pathology and cognitive decline

SimulAD: A dynamical model for personalized simulation and disease staging in Alzheimer's disease

International audienceSimulAD is a computational disease progression model (DPM) originally developed on the ADNI database to simulate the evolution of clinical and imaging markers characteristic of AD, and to quantify the disease severity (DS) of a subject. In this work, we assessed the validity of this estimated DS, as well as the generalization of the DPM, by applying SimulAD on a new cohort from the Geneva Memory Center (GMC). The differences between the estimated DS of healthy, mild cognitive impairment and AD dementia groups were statistically significant (p-values < 0.05; d ≥ 0.8). DS correlated with MMSE (ρ =-0.55), hippocampal atrophy (ρ =-0.62), glucose hypometabolism (ρ =-0.67), amyloid burden (ρ = 0.31) and tau deposition (ρ = 0.62) (p-values < 0.01). Based on the dynamics estimated on the ADNI cohort, we simulated a DPM for the subjects of the GMC cohort. The difference between the temporal evolution of similar biomarkers simulated on the ADNI and GMC cohorts remained below 10%. This study illustrates the robustness and good generalization of SimulAD, highlighting its potential for clinical and pharmaceutical studies

Multidomain interventions: state-of-the-art and future directions for protocols to implement precision dementia risk reduction. A user manual for Brain Health Services-part 4 of 6.

Although prevention of dementia and late-life cognitive decline is a major public health priority, there are currently no generally established prevention strategies or operational models for implementing such strategies into practice. This article is a narrative review of available evidence from multidomain dementia prevention trials targeting several risk factors and disease mechanisms simultaneously, in individuals without dementia at baseline. Based on the findings, we formulate recommendations for implementing precision risk reduction strategies into new services called Brain Health Services. A literature search was conducted using medical databases (MEDLINE via PubMed and SCOPUS) to select relevant studies: non-pharmacological multidomain interventions (i.e., combining two or more intervention domains), target population including individuals without dementia, and primary outcomes including cognitive/functional performance changes and/or incident cognitive impairment or dementia. Further literature searches covered the following topics: sub-group analyses assessing potential modifiers for the intervention effect on cognition in the multidomain prevention trials, dementia risk scores used as surrogate outcomes in multidomain prevention trials, dementia risk scores in relation to brain pathology markers, and cardiovascular risk scores in relation to dementia. Multidomain intervention studies conducted so far appear to have mixed results and substantial variability in target populations, format and intensity of interventions, choice of control conditions, and outcome measures. Most trials were conducted in high-income countries. The differences in design between the larger, longer-term trials that met vs. did not meet their primary outcomes suggest that multidomain intervention effectiveness may be dependent on a precision prevention approach, i.e., successfully identifying the at-risk groups who are most likely to benefit. One such successful trial has already developed an operational model for implementing the intervention into practice. Evidence on the efficacy of risk reduction interventions is promising, but not yet conclusive. More long-term multidomain randomized controlled trials are needed to fill the current evidence gaps, especially concerning low- and middle-income countries and integration of dementia prevention with existing cerebrovascular prevention programs. A precision risk reduction approach may be most effective for dementia prevention. Such an approach could be implemented in Brain Health Services

Brain Health Services: organization, structure, and challenges for implementation. A user manual for Brain Health Services-part 1 of 6.

Dementia has a devastating impact on the quality of life of patients and families and comes with a huge cost to society. Dementia prevention is considered a public health priority by the World Health Organization. Delaying the onset of dementia by treating associated risk factors will bring huge individual and societal benefit. Empirical evidence suggests that, in higher-income countries, dementia incidence is decreasing as a result of healthier lifestyles. This observation supports the notion that preventing dementia is possible and that a certain degree of prevention is already in action. Further reduction of dementia incidence through deliberate prevention plans is needed to counteract its growing prevalence due to increasing life expectancy.An increasing number of individuals with normal cognitive performance seek help in the current memory clinics asking an evaluation of their dementia risk, preventive interventions, or interventions to ameliorate their cognitive performance. Consistent evidence suggests that some of these individuals are indeed at increased risk of dementia. This new health demand asks for a shift of target population, from patients with cognitive impairment to worried but cognitively unimpaired individuals. However, current memory clinics do not have the programs and protocols in place to deal with this new population.We envision the development of new services, henceforth called Brain Health Services, devoted to respond to demands from cognitively unimpaired individuals concerned about their risk of dementia. The missions of Brain Health Services will be (i) dementia risk profiling, (ii) dementia risk communication, (iii) dementia risk reduction, and (iv) cognitive enhancement. In this paper, we present the organizational and structural challenges associated with the set-up of Brain Health Services

Plasma biomarkers for Alzheimer’s disease: a field-test in a memory clinic

BACKGROUND: The key Alzheimer's disease (AD) biomarkers are traditionally measured with techniques/exams that are either expensive (amyloid-positron emission tomography (PET) and tau-PET), invasive (cerebrospinal fluid Aβ42 and p-tau181), or poorly specific (atrophy on MRI and hypometabolism on fluorodeoxyglucose-PET). Recently developed plasma biomarkers could significantly enhance the efficiency of the diagnostic pathway in memory clinics and improve patient care. This study aimed to: (1) confirm the correlations between plasma and traditional AD biomarkers, (2) assess the diagnostic accuracy of plasma biomarkers as compared with traditional biomarkers, and (3) estimate the proportion of traditional exams potentially saved thanks to the use of plasma biomarkers. METHODS: Participants were 200 patients with plasma biomarkers and at least one traditional biomarker collected within 12 months. RESULTS: Overall, plasma biomarkers significantly correlated with biomarkers assessed through traditional techniques: up to r=0.50 (p<0.001) among amyloid, r=0.43 (p=0.002) among tau, and r=-0.23 (p=0.001) among neurodegeneration biomarkers. Moreover, plasma biomarkers showed high accuracy in discriminating the biomarker status (normal or abnormal) determined by using traditional biomarkers: up to area under the curve (AUC)=0.87 for amyloid, AUC=0.82 for tau, and AUC=0.63 for neurodegeneration status. The use of plasma as a gateway to traditional biomarkers using cohort-specific thresholds (with 95% sensitivity and 95% specificity) could save up to 49% of amyloid, 38% of tau, and 16% of neurodegeneration biomarkers. CONCLUSION: The implementation of plasma biomarkers could save a remarkable proportion of more expensive traditional exams, making the diagnostic workup more cost-effective and improving patient care

Modifiable risk factors for dementia and dementia risk profiling. A user manual for Brain Health Services-part 2 of 6.

We envisage the development of new Brain Health Services to achieve primary and secondary dementia prevention. These services will complement existing memory clinics by targeting cognitively unimpaired individuals, where the focus is on risk profiling and personalized risk reduction interventions rather than diagnosing and treating late-stage disease. In this article, we review key potentially modifiable risk factors and genetic risk factors and discuss assessment of risk factors as well as additional fluid and imaging biomarkers that may enhance risk profiling. We then outline multidomain measures and risk profiling and provide practical guidelines for Brain Health Services, with consideration of outstanding uncertainties and challenges. Users of Brain Health Services should undergo risk profiling tailored to their age, level of risk, and availability of local resources. Initial risk assessment should incorporate a multidomain risk profiling measure. For users aged 39-64, we recommend the Cardiovascular Risk Factors, Aging, and Incidence of Dementia (CAIDE) Dementia Risk Score, whereas for users aged 65 and older, we recommend the Brief Dementia Screening Indicator (BDSI) and the Australian National University Alzheimer's Disease Risk Index (ANU-ADRI). The initial assessment should also include potentially modifiable risk factors including sociodemographic, lifestyle, and health factors. If resources allow, apolipoprotein E ɛ4 status testing and structural magnetic resonance imaging should be conducted. If this initial assessment indicates a low dementia risk, then low intensity interventions can be implemented. If the user has a high dementia risk, additional investigations should be considered if local resources allow. Common variant polygenic risk of late-onset AD can be tested in middle-aged or older adults. Rare variants should only be investigated in users with a family history of early-onset dementia in a first degree relative. Advanced imaging with 18-fluorodeoxyglucose positron emission tomography (FDG-PET) or amyloid PET may be informative in high risk users to clarify the nature and burden of their underlying pathologies. Cerebrospinal fluid biomarkers are not recommended for this setting, and blood-based biomarkers need further validation before clinical use. As new technologies become available, advances in artificial intelligence are likely to improve our ability to combine diverse data to further enhance risk profiling. Ultimately, Brain Health Services have the potential to reduce the future burden of dementia through risk profiling, risk communication, personalized risk reduction, and cognitive enhancement interventions