Performance of a [18F]Flortaucipir PET Visual Read Method Across the Alzheimer Disease Continuum and in Dementia With Lewy Bodies

Lewy Body Dementia

OBJECTIVE

Lewy body dementia (LBD) is an umbrella term describing two closely related conditions: Parkinson disease dementia (PDD) and dementia with Lewy bodies (DLB). LBD is the second most common cause of neurodegenerative dementia but is often underrecognized in clinical practice. This review covers the key epidemiologic, clinical, cognitive, behavioral, and biomarker features of LBD and discusses current treatment options.

LATEST DEVELOPMENTS

Indicative biomarkers of LBD improve the ability to make a diagnosis and include single-photon emission computed tomography (SPECT) of the dopamine system (brain) and the noradrenergic system (cardiac), and polysomnography. α-Synuclein-specific biomarkers in spinal fluid, skin, plasma, and brain imaging are in active development with some available for clinical use. Prodromal stages of PDD and DLB have been contextualized, and diagnostic criteria have been published. An emerging theme is whether an integrated staging system focusing on protein aggregation, rather than clinical symptoms, may advance research efforts.

ESSENTIAL POINTS

LBD is a common cause of cognitive impairment in older adults but is often subject to significant delays in diagnosis and treatment, increasing the burden on patients and family care partners. Understanding key features of disease and the use of biomarkers will improve recognition. Earlier detection may also facilitate the development of new therapeutics and enrollment in clinical trials.

The impact of tau-PET in a selected memory clinic cohort: rationale and design of the TAP-TAU study

BACKGROUND

Tau-PET is a diagnostic tool with high sensitivity and specificity for discriminating Alzheimer's disease (AD) dementia from other neurodegenerative disorders in well-controlled research environments. The role of tau-PET in real-world clinical practice, however, remains to be established. The aim of the TAP-TAU study is therefore to investigate the impact of tau-PET in clinical practice.

METHODS

TAP-TAU is a prospective, longitudinal multi-center study in 300 patients (≥ 50 years old) with mild cognitive impairment or mild dementia across five Dutch memory clinics. Patients are eligible if diagnostic certainty is < 85% after routine dementia screening and if the differential diagnosis includes AD. More specifically, we will include patients who (i) are suspected of having mixed pathology (e.g., AD and vascular pathology), (ii) have an atypical clinical presentation, and/or (iii) show conflicting or inconclusive outcomes on other tests (e.g., magnetic resonance imaging or cerebrospinal fluid). Participants will undergo a [18F]flortaucipir tau-PET scan, blood-based biomarker sampling, and fill out questionnaires on patient reported outcomes and experiences. The primary outcomes are change (pre- versus post- tau-PET) in diagnosis, diagnostic certainty, patient management and patient anxiety and uncertainty. Secondary outcome measures are head-to-head comparisons between tau-PET and less invasive and lower cost diagnostic tools such as novel blood-based biomarkers and artificial intelligence-based classifiers.

RESULTS

TAP-TAU has been approved by the Medical Ethics Committee of the Amsterdam UMC. The first participant is expected to be included in October 2024.

CONCLUSIONS

In TAP-TAU, we will investigate the added clinical value of tau-PET in a real-world clinical setting, including memory clinic patients with diagnostic uncertainty after routine work-up. Findings of our study may contribute to recommendations regarding which patients would benefit most from assessment with tau-PET. This study is timely in the dawning era of disease modifying treatments as an accurate etiological diagnosis becomes increasingly important.

TRIAL REGISTRATION

This trial is registered and authorized on December 21st, 2023 in EU Clinical Trials with registration number 2023-505430-10-00.

Investigating the feasibility of 18F-flortaucipir PET imaging in the antemortem diagnosis of primary age-related tauopathy (PART): An observational imaging-pathological study

INTRODUCTION

Primary age-related tauopathy (PART) is characterized by neurofibrillary tangles and minimal β-amyloid deposition, diagnosed postmortem. This study investigates 18F-flortaucipir (FTP) PET imaging for antemortem PART diagnosis.

METHODS

We analyzed FTP PET scans from 50 autopsy-confirmed PART and 13 control subjects. Temporal lobe uptake was assessed both qualitatively and quantitatively. Demographic and clinicopathological characteristics and voxel-level uptake using SPM12 were compared between FTP-positive and FTP-negative cases. Intra-reader reproducibility was evaluated with Krippendorff's alpha.

RESULTS

Minimal/mild and moderate FTP uptake was seen in 32% of PART cases and 62% of controls, primarily in the left inferior temporal lobe. No demographic or clinicopathological differences were found between FTP-positive and FTP-negative cases. High intra-reader reproducibility (α = 0.83) was noted.

DISCUSSION

FTP PET imaging did not show a specific uptake pattern for PART diagnosis, indicating that in vivo PART identification using FTP PET is challenging. Similar uptake in controls suggests non-specific uptake in PART.

HIGHLIGHTS

18F-flortaucipir (FTP) PET scans were analyzed for diagnosing PART antemortem. 32% of PART cases had minimal/mild FTP uptake in the left inferior temporal lobe. Similar to PART FTP uptake was found in 62% of control subjects. No specific uptake pattern was found, challenging in vivo PART diagnosis.

A comparison of visual assessment and semi-quantification for the diagnostic and prognostic use of [18F]flortaucipir PET in a memory clinic cohort

PURPOSE

[18F]Flortaucipir PET is a powerful diagnostic and prognostic tool for Alzheimer's disease (AD). Tau status definition is mainly based in the literature on semi-quantitative measures while in clinical settings visual assessment is usually preferred. We compared visual assessment with established semi-quantitative measures to classify subjects and predict the risk of cognitive decline in a memory clinic population.

METHODS

We included 245 individuals from the Geneva Memory Clinic who underwent [18F]flortaucipir PET. Amyloid status was available for 207 individuals and clinical follow-up for 135. All scans were blindly evaluated by three independent raters who visually classified the scans according to Braak stages. Standardized uptake value ratio (SUVR) values were obtained from a global meta-ROI to define tau positivity, and the Simplified Temporo-Occipital Classification (STOC) was applied to obtain semi-quantitatively tau stages. The agreement between measures was tested using Cohen's kappa (k). ROC analysis and linear mixed-effects models were applied to test the diagnostic and prognostic values of tau status and stages obtained with the visual and semi-quantitative approaches.

RESULTS

We found good inter-rater reliability in the visual interpretation of tau Braak stages, independently from the rater's expertise (k>0.68, p<0.01). A good agreement was equally found between visual and SUVR-based classifications for tau status (k=0.67, p<0.01). All tau-assessment modalities significantly discriminated amyloid-positive MCI and demented subjects from others (AUC>0.80) and amyloid-positive from negative subjects (AUC>0.85). Linear mixed-effect models showed that tau-positive individuals presented a significantly faster cognitive decline than the tau-negative group (p<0.01), independently from the classification method.

CONCLUSION

Our results show that visual assessment is reliable for defining tau status and stages in a memory clinic population. The high inter-rater reliability, the substantial agreement, and the similar diagnostic and prognostic performance of visual rating and semi-quantitative methods demonstrate that [18F]flortaucipir PET can be robustly assessed visually in clinical practice.

Multiomics Blood-Based Biomarkers Predict Alzheimer's Predementia with High Specificity in a Multicentric Cohort Study

BACKGROUND

The primary criteria for diagnosing mild cognitive impairment (MCI) due to Alzheimer's Disease (AD) or probable mild AD dementia rely partly on cognitive assessments and the presence of amyloid plaques. Although these criteria exhibit high sensitivity in predicting AD among cognitively impaired patients, their specificity remains limited. Notably, up to 25% of non-demented patients with amyloid plaques may be misdiagnosed with MCI due to AD, when in fact they suffer from a different brain disorder. The introduction of anti-amyloid antibodies complicates this scenario. Physicians must prioritize which amyloid-positive MCI patients receive these treatments, as not all are suitable candidates. Specifically, those with non-AD amyloid pathologies are not primary targets for amyloid-modifying therapies. Consequently, there is an escalating medical necessity for highly specific blood biomarkers that can accurately detect pre-dementia AD, thus optimizing amyloid antibody prescription.

OBJECTIVES

The objective of this study was to evaluate a predictive model based on peripheral biomarkers to identify MCI and mild dementia patients who will develop AD dementia symptoms in cognitively impaired population with high specificity.

DESIGN

Peripheral biomarkers were identified in a gene transfer-based animal model of AD and then validated during a retrospective multi-center clinical study.

SETTING

Participants from 7 retrospective cohorts (US, EU and Australia).

PARTICIPANTS

This study followed 345 cognitively impaired individuals over up to 13 years, including 193 with MCI and 152 with mild dementia, starting from their initial visits. The final diagnoses, established during their last assessments, classified 249 participants as AD patients and 96 as having non-AD brain disorders, based on the specific diagnostic criteria for each disorder subtype. Amyloid status, assessed at baseline, was available for 82.9% of the participants, with 61.9% testing positive for amyloid. Both amyloid-positive and negative individuals were represented in each clinical group. Some of the AD patients had co-morbidities such as metabolic disorders, chronic diseases, or cardiovascular pathologies.

MEASUREMENTS

We developed targeted mass spectrometry assays for 81 blood-based biomarkers, encompassing 45 proteins and 36 metabolites previously identified in AAV-AD rats.

METHODS

We analyzed blood samples from study participants for the 81 biomarkers. The B-HEALED test, a machine learning-based diagnostic tool, was developed to differentiate AD patients, including 123 with Prodromal AD and 126 with mild AD dementia, from 96 individuals with non-AD brain disorders. The model was trained using 70% of the data, selecting relevant biomarkers, calibrating the algorithm, and establishing cutoff values. The remaining 30% served as an external test dataset for blind validation of the predictive accuracy.

RESULTS

Integrating a combination of 19 blood biomarkers and participant age, the B-HEALED model successfully distinguished participants that will develop AD dementia symptoms (82 with Prodromal AD and 83 with AD dementia) from non-AD subjects (71 individuals) with a specificity of 93.0% and sensitivity of 65.4% (AUROC=81.9%, p<0.001) during internal validation. When the amyloid status (derived from CSF or PET scans) and the B-HEALED model were applied in association, with individuals being categorized as AD if they tested positive in both tests, we achieved 100% specificity and 52.8% sensitivity. This performance was consistent in blind external validation, underscoring the model's reliability on independent datasets.

CONCLUSIONS

The B-HEALED test, utilizing multiomics blood-based biomarkers, demonstrates high predictive specificity in identifying AD patients within the cognitively impaired population, minimizing false positives. When used alongside amyloid screening, it effectively identifies a nearly pure prodromal AD cohort. These results bear significant implications for refining clinical trial inclusion criteria, facilitating drug development and validation, and accurately identifying patients who will benefit the most from disease-modifying AD treatments.