Soucy, Jean‐Paul

Importance Tau positron emission tomography (PET) allows in vivo detection of neurofibrillary tangles, a core neuropathologic feature of Alzheimer disease (AD). Objective To provide estimates of the frequency of tau PET positivity and its associated risk of clinical outcomes. Design, Setting, and Participants Longitudinal study using data pooled from 21 cohorts, comprising a convenience sample of 6514 participants from 13 countries, collected between January 2013 and June 2024. Cognitively unimpaired individuals and patients with a clinical diagnosis of mild cognitive impairment (MCI), AD dementia, or other neurodegenerative disorders were included. Exposures Tau PET with flortaucipir F 18, amyloid-β (Aβ) PET, and clinical examinations. Tau PET scans were visually rated as positive according to a US Food and Drug Administration– and European Medicines Agency–approved method, designed to indicate the presence of advanced neurofibrillary tangle pathology (Braak stages V-VI). Main Outcomes and Measures Frequency of tau PET positivity and absolute risk of clinical progression (eg, progression to MCI or dementia). Results Among the 6514 participants (mean age, 69.5 years; 50.5% female), median follow-up time ranged from 1.5 to 4.0 years. Of 3487 cognitively unimpaired participants, 349 (9.8%) were tau PET positive; the estimated frequency of tau PET positivity was less than 1% in those aged younger than 50 years, and increased from 3% (95% CI, 2%-4%) at 60 years to 19% (95% CI, 16%-24%) at 90 years. Tau PET positivity frequency estimates increased across MCI and AD dementia clinical diagnoses (43% [95% CI, 41%-46%] and 79% [95% CI, 77%-82%] at 75 years, respectively). Most tau PET–positive individuals (92%) were also Aβ PET positive. Cognitively unimpaired participants who were positive for both Aβ PET and tau PET had a higher absolute risk of progression to MCI or dementia over the following 5 years (57% [95% CI, 45%-71%]) compared with both Aβ PET–positive/tau PET–negative (17% [95% CI, 13%-22%]) and Aβ PET–negative/tau PET–negative (6% [95% CI, 5%-8%]) individuals. Among participants with MCI at the time of the tau PET scan, an Aβ PET–positive/tau PET–positive profile was associated with a 5-year absolute risk of progression to dementia of 70% (95% CI, 59%-81%). Conclusions and Relevance In a large convenience sample, a positive tau PET scan occurred at a nonnegligible rate among cognitively unimpaired individuals, and the combination of Aβ PET positivity and tau PET positivity was associated with a high risk of clinical progression in both preclinical and symptomatic stages of AD. These findings underscore the potential of tau PET as a biomarker for staging AD pathology.

AbstractBackgroundAs the only optically accessible part of the central nervous system, the retina represents an intriguing opportunity for the detection of biomarkers for Alzheimer’s disease (AD). This study evaluated the performance of the Retinal Deep PhenotypingTM platform, a digital biomarker platform comprising a hyperspectral retinal camera and image analysis algorithms, for the detection of likely positron‐emission tomography (PET) amyloid status (negative or positive) in older adults. A set of phenotypic features that correlates with the cerebral amyloid status as determined by amyloid PET scan were identified and used to train a classifying algorithm.MethodHyperspectral retinal images acquired with a Mydriatic Hyperspectral Retinal Camera from 194 participants (age ≥ 50 years), including cognitively normal and cognitively impaired (mild cognitive impairment and dementia) across 5 imaging sites were processed in order to train the model. Of these 194 participants, 73 individuals (38%) were amyloid‐positive, as confirmed by unanimous readings of PET scans by a panel of 3 expert reviewers. The pre‐processed hyperspectral images were segmented into various anatomical sites, and a texture‐based approach was used to extract several thousands of spatial‐spectral features. The most relevant features for the classification task were selected using a minimum redundancy maximum relevance (MRMR) algorithm and used to train a linear support vector machine (SVM) classifier. A nested, cross‐validation technique was used to evaluate the performance of the classifier.ResultThe resulting model based on the 17 most significant features showed high performance to discriminate between amyloid positive and negative subjects with an area under the receiver operating curve (AUCROC) of 0.87 (95% CI: 0.83 – 0.92).ConclusionThe Retinal Deep PhenotypingTM platform shows promise for detecting the likely cerebral amyloid PET status in adults 50 years and older from a simple, non‐invasive retinal scan and could provide an accessible means to identify individuals with abnormal cerebral amyloid in a clinical or drug development context. This phenotyping platform provides a flexible approach that could also be used for the detection of multiple biomarkers involved in cognitive decline from the same hyperspectral images of the retina.

AbstractBackgroundTranslating recent advances in the diagnosis of Alzheimer’s disease (AD) and other dementias to clinical practice using biomarker‐based approaches requires accessible, affordable biomarkers. This protocol tested the performance of a Retinal Deep Phenotyping platform for the detection of likely positron‐emission tomography (PET) amyloid status (negative or positive) in human subjects. This platform generates data rich retinal reflectance images captured non‐invasively with a Metabolic Hyperspectral Retinal Camera (MHRC) yielding specific spatial‐spectral features which are analyzed by a machine learning algorithm relatively to a recognized gold standard biomarker (in this case PET amyloid imaging).Methods134 subjects (50 years and older) from 3 clinical sites were included, 94 with normal, 40 with abnormal cognition, imaged with the MHRC and amyloid PET (unanimous visual reads from a panel of 3 expert reviewers; 42 amyloid positive, 92 negative). Over 600 spatial‐spectral features were extracted from hyperspectral retinal images obtained at 450‐900 nm. After a feature selection based on F‐score ranking, a classifier was trained on the datasets from 87 patients (2/3 of the cohort) using the most discriminating features. The classification performance was then evaluated with the datasets from the remaining 47 patients.ResultsResubstituting the training set of 87 patients into the classifier yielded sensitivity and specificity values of 88% and 91%, respectively. The training misclassification rate was predicted using the k‐fold loss metric, with a predicted error rate of 19%. To verify this performance, the test set of 47 patients was classified by the model resulting in sensitivity and specificity values of 80% and 81%, respectively.ConclusionsThe Retinal Deep Phenotyping platform shows promise for detecting the likely cerebral amyloid PET status in human subjects and could serve as a screening tool to identify subjects in the AD continuum, for instance in a clinical or drug development context. The platform could also be used for the detection of other biomarkers involved in cognitive decline.