Alzheimer's biomarkers in daily practice (ABIDE) project: Rationale and design.

INTRODUCTION: The Alzheimer's biomarkers in daily practice (ABIDE) project is designed to translate knowledge on diagnostic tests (magnetic resonance imaging [MRI], cerebrospinal fluid [CSF], and amyloid positron emission tomography [PET]) to daily clinical practice with a focus on mild cognitive impairment (MCI). METHODS: ABIDE is a 3-year project with a multifaceted design and is structured into interconnected substudies using both quantitative and qualitative research methods. RESULTS: Based on retrospective data, we develop personalized risk estimates for MCI patients. Prospectively, we collect MRI and CSF data from 200 patients from local memory clinics and amyloid PET from 500 patients in a tertiary setting, to optimize application of these tests in daily practice. Furthermore, ABIDE will develop strategies for optimal patient-clinician conversations. DISCUSSION: Ultimately, this will result in a set of practical tools for clinicians to support the choice of diagnostic tests and facilitate the interpretation and communication of their results

Biomarker testing in MCI patients—deciding who to test

BACKGROUND: We aimed to derive an algorithm to define the optimal proportion of patients with mild cognitive impairment (MCI) in whom cerebrospinal fluid (CSF) testing is of added prognostic value. METHODS: MCI patients were selected from the Amsterdam Dementia Cohort (n = 402). Three-year progression probabilities to dementia were predicted using previously published models with and without CSF data (amyloid-beta1-42 (Abeta), phosphorylated tau (p-tau)). We incrementally augmented the proportion of patients undergoing CSF, starting with the 10% patients with prognostic probabilities based on clinical data around the median (percentile 45–55), until all patients received CSF. The optimal proportion was defined as the proportion where the stepwise algorithm showed similar prognostic discrimination (Harrell’s C) and accuracy (three-year Brier scores) compared to CSF testing of all patients. We used the BioFINDER study (n = 221) for validation. RESULTS: The optimal proportion of MCI patients to receive CSF testing selected by the stepwise approach was 50%. CSF testing in only this proportion improved the performance of the model with clinical data only from Harrell’s C = 0.60, Brier = 0.198 (Harrell’s C = 0.61, Brier = 0.197 if the information on magnetic resonance imaging was available) to Harrell’s C = 0.67 and Brier = 0.190, and performed similarly to a model in which all patients received CSF testing. Applying the stepwise approach in the BioFINDER study would again select half of the MCI patients and yielded robust results with respect to prognostic performance. INTERPRETATION: CSF biomarker testing adds prognostic value in half of the MCI patients. As such, we achieve a CSF saving recommendation while simultaneously retaining optimal prognostic accuracy

Personalized risk for clinical progression in cognitively normal subjects—the ABIDE project

Background: Biomarkers such as cerebrospinal fluid (CSF) and magnetic resonance imaging (MRI) have predictive value for progression to dementia in patients with mild cognitive impairment (MCI). The pre-dementia stage takes far longer, and the interpretation of biomarker findings is particular relevant for individuals who present at a memory clinic, but are deemed cognitively normal. The objective of the current study is to construct biomarker-based prognostic models for personalized risk of clinical progression in cognitively normal individuals presenting at a memory clinic. / Methods: We included 481 individuals with subjective cognitive decline (SCD) from the Amsterdam Dementia Cohort. Prognostic models were developed by Cox regression with patient characteristics, MRI, and/or CSF biomarkers to predict clinical progression to MCI or dementia. We estimated 5- and 3-year individualized risks based on patient-specific values. External validation was performed on Alzheimer’s Disease Neuroimaging Initiative (ADNI) and an European dataset. / Results: Based on demographics only (Harrell’s C = 0.70), 5- and 3-year progression risks varied from 6% [3–11] and 4% [2–8] (age 55, MMSE 30) to 38% [29–49] and 28% [21–37] (age 70, MMSE 27). Normal CSF biomarkers strongly decreased progression probabilities (Harrell’s C = 0.82). By contrast, abnormal CSF markedly increased risk (5 years, 96% [56–100]; 3 years, 89% [44–99]). The CSF model could reclassify 58% of the individuals with an “intermediate” risk (35–65%) based on the demographic model. MRI measures were not retained in the models. / Conclusion: The current study takes the first steps in a personalized approach for cognitively normal individuals by providing biomarker-based prognostic models

Biomarker-based prognosis for people with mild cognitive impairment (ABIDE): a modelling study

Background Biomarker-based risk predictions of dementia in people with mild cognitive impairment are highly relevant for care planning and to select patients for treatment when disease-modifying drugs become available. We aimed to establish robust prediction models of disease progression in people at risk of dementia. Methods In this modelling study, we included people with mild cognitive impairment (MCI) from single-centre and multicentre cohorts in Europe and North America: the European Medical Information Framework for Alzheimer's Disease (EMIF-AD; n=883), Alzheimer's Disease Neuroimaging Initiative (ADNI; n=829), Amsterdam Dementia Cohort (ADC; n=666), and the Swedish BioFINDER study (n=233). Inclusion criteria were a baseline diagnosis of MCI, at least 6 months of follow-up, and availability of a baseline Mini-Mental State Examination (MMSE) and MRI or CSF biomarker assessment. The primary endpoint was clinical progression to any type of dementia. We evaluated performance of previously developed risk prediction models—a demographics model, a hippocampal volume model, and a CSF biomarkers model—by evaluating them across cohorts, incorporating different biomarker measurement methods, and determining prognostic performance with Harrell's C statistic. We then updated the models by re-estimating parameters with and without centre-specific effects and evaluated model calibration by comparing observed and expected survival. Finally, we constructed a model combining markers for amyloid deposition, tauopathy, and neurodegeneration (ATN), in accordance with the National Institute on Aging and Alzheimer's Association research framework. Findings We included all 2611 individuals with MCI in the four cohorts, 1007 (39%) of whom progressed to dementia. The validated demographics model (Harrell's C 0·62, 95% CI 0·59–0·65), validated hippocampal volume model (0·67, 0·62–0·72), and updated CSF biomarkers model (0·72, 0·68–0·74) had adequate prognostic performance across cohorts and were well calibrated. The newly constructed ATN model had the highest performance (0·74, 0·71–0·76). Interpretation We generated risk models that are robust across cohorts, which adds to their potential clinical applicability. The models could aid clinicians in the interpretation of CSF biomarker and hippocampal volume results in individuals with MCI, and help research and clinical settings to prepare for a future of precision medicine in Alzheimer's disease. Future research should focus on the clinical utility of the models, particularly if their use affects participants' understanding, emotional wellbeing, and behaviour

Biomarker counseling, disclosure of diagnosis and follow-up in patients with mild cognitive impairment:A European Alzheimer's Disease Consortium survey

Objectives: Mild cognitive impairment (MCI) is associated with an increased risk of further cognitive decline, partly depending on demographics and biomarker status. The aim of the present study was to survey the clinical practices of physicians in terms of biomarker counseling, management, and follow-up in European expert centers diagnosing patients with MCI. Methods: An online email survey was distributed to physicians affiliated with European Alzheimer's disease Consortium centers (Northern Europe: 10 centers; Eastern and Central Europe: 9 centers; and Southern Europe: 15 centers) with questions on attitudes toward biomarkers and biomarker counseling in MCI and dementia. This included postbiomarker counseling and the process of diagnostic disclosure of MCI, as well as treatment and follow-up in MCI. Results: The response rate for the survey was 80.9% (34 of 42 centers) across 20 countries. A large majority of physicians had access to biomarkers and found them useful. Pre- and postbiomarker counseling varied across centers, as did practices for referral to support groups and advice on preventive strategies. Less than half reported discussing driving and advance care planning with patients with MCI. Conclusions: The variability in clinical practices across centers calls for better biomarker counseling and better training to improve communication skills. Future initiatives should address the importance of communicating preventive strategies and advance planning

The use of amyloid‐PET in memory clinic patients: AMYPAD Diagnostic and Patient Management Study

Abstract Background Amyloid‐PET allows the direct assessment of amyloid deposition, one of the main hallmarks of Alzheimer’s disease. However, this technique is currently not or incompletely reimbursed due to lack of randomized controlled studies demonstrating a clinical impact. Method AMYPAD‐DPMS is a prospective, multicenter, randomized controlled study. Patients with subjective cognitive decline plus (SCD+), mild cognitive impairment (MCI) or dementia from 8 European memory clinics were randomized into three study arms: ARM1, amyloid‐PET performed early in the diagnostic workup (within 1 month); ARM2, late in the diagnostic workup (after 8±2 months); or ARM3, if and when the managing physician chose to. The primary endpoint was the proportional difference in ARM1 and ARM2 participants in receiving an etiological diagnosis with very high diagnostic confidence (i.e. ≥ 90%) within 3 months. Secondary endpoints included changes in diagnosis and treatment plan. Result Participants were recruited from April 16th, 2018, to October 30th, 2020. There were 272 participants in ARM1, 260 ARM2, and 261 ARM3 that underwent both baseline and 3‐month visit. 88% of ARM3 participants underwent amyloid‐PET within 3 months, and the average time from baseline to prescribe amyloid‐PET was 46 (IQR=58) days. After 3 months, 40% (109/272) of ARM1 participants and 37% (97/261) of ARM3 had a diagnosis with very high confidence vs 11% (30/260) in ARM2 (p<0.001). This was consistent across clinical stages (SCD+: 30%, 22%, and 6%, p<0.05; MCI: 42%, 39%, and 9%, p<0.05; dementia: 49%, 49%, and 20%, p<0.05). Changes in diagnosis were more frequent in ARM1 (44%) versus ARM3 (29%, p=0.001) and ARM2 (11%, p<0.001), and in ARM3 versus ARM2 (p<0.001). In participants for whom an etiological diagnosis of AD or non‐AD was confirmed after 3 months, changes in diagnostic confidence were greater in ARM1 (AD: +14%; non‐AD: +12%) and ARM3 (+11%; +10%) vs ARM2 (+1%, +1%; p<0.05). Conclusion An amyloid‐PET performed in the early phases of a diagnostic workup is associated with a greater proportion of etiological diagnoses with very high confidence and more frequent changes in diagnosis and higher diagnostic confidence after 3 months. This evidence supports the implementation of this technique early in the diagnostic workup

Interpreting Biomarker Results in Individual Patients With Mild Cognitive Impairment in the Alzheimer's Biomarkers in Daily Practice (ABIDE) Project

Importance: Biomarkers do not determine conversion to Alzheimer disease (AD) perfectly, and criteria do not specify how to take patient characteristics into account. Consequently, biomarker use may be challenging for clinicians, especially in patients with mild cognitive impairment (MCI). Objective: To construct biomarker-based prognostic models that enable determination of future AD dementia in patients with MCI. Design, Setting, and Participants: This study is part of the Alzheimer's Biomarkers in Daily Practice (ABIDE) project. A total of 525 patients with MCI from the Amsterdam Dementia Cohort (longitudinal cohort, tertiary referral center) were studied. All patients had their baseline visit to a memory clinic from September 1, 1997, through August 31, 2014. Prognostic models were constructed by Cox proportional hazards regression with patient characteristics (age, sex, and Mini-Mental State Examination [MMSE] score), magnetic resonance imaging (MRI) biomarkers (hippocampal volume, normalized whole-brain volume), cerebrospinal fluid (CSF) biomarkers (amyloid-β1-42, tau), and combined biomarkers. Data were analyzed from November 1, 2015, to October 1, 2016. Main Outcomes and Measures: Clinical end points were AD dementia and any type of dementia after 1 and 3 years. Results: Of the 525 patients, 210 (40.0%) were female, and the mean (SD) age was 67.3 (8.4) years. On the basis of age, sex, and MMSE score only, the 3-year progression risk to AD dementia ranged from 26% (95% CI, 19%-34%) in younger men with MMSE scores of 29 to 76% (95% CI, 65%-84%) in older women with MMSE scores of 24 (1-year risk: 6% [95% CI, 4%-9%] to 24% [95% CI, 18%-32%]). Three- and 1-year progression risks were 86% (95% CI, 71%-95%) and 27% (95% CI, 17%-41%) when MRI results were abnormal, 82% (95% CI, 73%-89%) and 26% (95% CI, 20%-33%) when CSF test results were abnormal, and 89% (95% CI, 79%-95%) and 26% (95% CI, 18%-36%) when the results of both tests were abnormal. Conversely, 3- and 1-year progression risks were 18% (95% CI, 13%-27%) and 3% (95% CI, 2%-5%) after normal MRI results, 6% (95% CI, 3%-9%) and 1% (95% CI, 0.5%-2%) after normal CSF test results, and 4% (95% CI, 2%-7%) and 0.5% (95% CI, 0.2%-1%) after combined normal MRI and CSF test results. The prognostic value of models determining any type of dementia were in the same order of magnitude although somewhat lower. External validation in Alzheimer's Disease Neuroimaging Initiative 2 showed that our models were highly robust. Conclusions and Relevance: This study provides biomarker-based prognostic models that may help determine AD dementia and any type of dementia in patients with MCI at the individual level. This finding supports clinical decision making and application of biomarkers in daily practice