Serge Gauthier

Neurofibrillary tangles (NFTs) progressively damage gray matter in Alzheimer disease (AD). Resulting cortical microstructural alterations might not be detectable using macrostructural metrics but may be studied using isotropic water diffusion, as it reflects extracellular free water content. The aim of this study was to examine the effect of NFTs on cortical microstructure by investigating whether cortical free water increases as a function of tau load. We also investigated whether phosphorylated tau in blood plasma also indicated cortical microstructural abnormalities. For this cross-sectional study, we sampled participants with T1 MRI, multishell diffusion-weighted MRI, amyloid PET ([¹⁸F]AZD4694), tau PET, and plasma phosphorylated tau 217+ (p-tau217) from the Translational biomarkers in Aging and Dementia cohort at McGill University; participants were recruited between 2017 and 2024. We used the Neurite Orientation Dispersion and Density Imaging algorithm to calculate isotropic free water images ("free water"). FreeSurfer was used to calculate cortical thickness in the entorhinal, fusiform, inferior temporal, and middle temporal gyri regions of interest ("meta-ROI"); Automatic Segmentation of Hippocampal Subfields was used to calculate hippocampal volumes. We grouped participants by amyloid PET positivity (A), plasma p-tau217 positivity (T1), and tau PET positivity (T2). We performed voxel-wise correlation analyses between free water and these proteinopathy markers, as well as ROI-based analyses in the meta-ROI. A total of 303 participants (mean age 67 years, 58.7% female) were included in this study (168 cognitively normal individuals, 43 with mild cognitive impairment, 23 with AD dementia, 68 not diagnosed). Tau PET was positively correlated with free water in gray matter predominantly in the temporal lobe (partial R² = 0.39, p < 0.001), and the correlation of p-tau217 with the meta-ROI free water was entirely mediated by tau PET (p < 0.001). In addition, medial temporal and hippocampal free water was negatively correlated with Montreal Cognitive Assessment scores in the A-T₁+ and A+T₂+ groups. The strongest ROI-based multilinear models for predicting temporal gray matter and hippocampal tau PET burden used both cortical thickness and free water as predictors (temporal gray matter partial R² = 0.62; hippocampal partial R² = 0.64). In AD-relevant regions, increased free water correlates with tau load independently of macrostructural metrics or amyloid load. Free water may serve as an imaging marker for microstructural changes in gray matter resulting from NFT accumulation, complementary to macrostructural metrics.

Tau-PET is increasingly used in Alzheimer disease (AD) clinical trials, both for participant selection based on disease stage and as an outcome measure. However, the longitudinal patterns of tau accumulation according to AD stages remain unclear. We aimed to examine the patterns of tau-PET accumulation in different AD stages defined by the Alzheimer's Association Workgroup to inform trial design. This was a retrospective analysis of data collected from participants in 2 longitudinal observational cohort studies: the Translational Biomarkers in Aging and Dementia (TRIAD) study from Montreal, Canada, and Alzheimer's Disease Neuroimaging Initiative (ADNI), a multisite study in North America. Amyloid-PET-positive participants were biologically staged as one of the following based on tau-PET uptake: A+T₂- (initial), A+T_2MTL+ (early), A+T_2MOD+ (intermediate), and A+T_2HIGH+ (advanced). Amyloid-PET-negative participants were included as non-AD controls. All participants underwent amyloid-PET and tau-PET imaging, with longitudinal follow-up (mean: 2.96 ± 1.35 years). Linear mixed-effects models evaluated stage-specific regional longitudinal tau-PET changes; statistical power analyses estimated the sample sizes needed to detect differences in tau-PET accumulation for future AD trials. The study included 542 participants (mean age: 67.9 ± 15.3 years; 56.3% female), comprising 321 non-AD controls and 221 individuals with AD. The baseline AD stage determined both the regional distribution and magnitude of tau accumulation. No significant accumulation occurred in A+T₂- individuals over 4-6 years. In those with early-stage AD, tau accumulation was localized to early affected regions (TRIAD: β = 0.15, 95% CI 0.09-0.21, p < 0.001; ADNI: β = 0.21, 95% CI 0.03-0.40, p = 0.03). Individuals with intermediate-stage AD showed accumulation in intermediate regions (TRIAD: β = 0.16, 95% CI 0.10-0.22, p < 0.001; ADNI: β = 0.37, 95% CI 0.15-0.59, p = 0.001) while those with advanced-stage AD exhibited accumulation in later affected regions (TRIAD: β = 0.45, 95% CI 0.39-0.50, p < 0.001; ADNI: β = 0.31, 95% CI 0.14-0.49, p < 0.001). Stage-specific tau-PET region-of-interest selection reduced required sample sizes by 30%-93% for detecting hypothetical disease-modifying drug effects. Stage-specific patterns of tau-PET change highlighted the importance of baseline biological AD staging for selecting outcome regions of interest. Statistical power analyses indicated that aligning outcome regions with disease stage reduced sample size estimates, suggesting potential gains in trial efficiency. While findings were consistent across 2 independent cohorts, differences in tracers and demographic composition represent key limitations.

Background: Regulatory approval of new investigational Alzheimer's disease (AD) therapies could be accelerated if reasonably likely surrogate endpoints could be used. Neurofilament light chain (NfL) has potential utility as a prognostic biomarker of neurodegeneration in AD. Objective: To synthesize available evidence on the relationship between baseline NfL levels and longitudinal clinical decline. Methods: A systematic literature review identified 19 eligible studies, contributing 37 longitudinal statistical models evaluating the association between baseline NfL (plasma or cerebrospinal fluid [CSF]) with subsequent clinical decline based on validated clinical scales including Mini-Mental State Examination (MMSE), Alzheimer's Disease Assessment Scale-Cognitive Subscale, and Clinical Dementia Rating. Results were pooled via meta-analysis, using partial correlation coefficients (PCC), separately for patient sub-groups (mild cognitive impairment, AD or combined). Results: Across the AD continuum, higher baseline NfL levels were consistently associated with greater cognitive and global clinical decline in most analyses. This pattern was consistent for both plasma (pooled PCC = −0.17 [95% CI = −0.22, −0.12] for MMSE, any AD population) and CSF NfL (pooled PCC = −0.14 [95% CI = −0.24, −0.04] for MMSE, any AD population). The strength of association across multiple clinical endpoints and populations, measured by absolute value of pooled PCC, ranged from 0.13 to 0.25. Conclusions: The results support the utility of NfL as a predictive biomarker for progression of clinical decline in AD patients.

Background: Amyloid-β imaging through positron emission tomography (PET) has significantly transformed Alzheimer’s disease (AD) research. [¹¹C]PiB has been widely used for imaging β-amyloid plaques due to its high affinity and selectivity for amyloid deposits. [¹⁸F]AZD4694 is a more recently developed amyloid-PET imaging agent, which structurally resembles PiB and has less non-specific binding in the white matter than other ¹⁸F-labeled compounds. The purpose of this study is to compare the in vitro binding properties of the amyloid-PET radiotracers [¹¹C]PiB and [¹⁸F]AZD4694 in post-mortem human brain tissue. Total binding was assessed by autoradiography in prefrontal, inferior parietal, posterior cingulate cortices and hippocampal sections of healthy control (HC) and AD autopsy-confirmed brain tissues. Furthermore, the displacement of [¹⁸F]AZD4694 by unlabeled PiB was evaluated in the above-mentioned sections of AD brain tissues. Results: For both radiotracers, we found significant differences (p < 0.0001) between HC and AD tissues binding in the prefrontal cortex ([¹¹C]PiB Cohen’s d = 3.424, [¹⁸F]AZD4694 Cohen’s d = 5.070), inferior parietal cortex ([¹¹C]PiB Cohen’s d = 3.156, [¹⁸F]AZD4694 Cohen’s d = 3.959), posterior cingulate cortex ([¹¹C]PiB Cohen’s d = 1.781, [¹⁸F]AZD4694 Cohen’s d = 3.434), and hippocampus ([¹¹C]PiB Cohen’s d = 1.320, [¹⁸F]AZD4694 Cohen’s d = 3.696). Higher binding was detected for [¹⁸F]AZD4694 compared to [¹¹C]PiB in AD prefrontal, inferior parietal and posterior cingulate cortices, while binding in the hippocampus was comparable for both radioligands. Strong correlations between [¹⁸]AZD4694 and [¹¹C]PiB were found in the prefrontal (R = 0.959, p < 0.0001), inferior parietal (R = 0.893, p < 0.0001), posterior cingulate (R = 0.838, p = 0.0006) cortices and hippocampus (R = 0.750, p < 0.0001). Bland–Altman analyses revealed strong agreement between [¹¹C]PiB and [¹⁸F]AZD4694 in the prefrontal, inferior parietal, and posterior cingulate cortices, but lower agreement in the hippocampus. Displacement studies confirmed high binding affinity of PiB in all tissues, indicating that both amyloid-PET agents compete for the same binding sites. Conclusions: This head-to-head study provides evidence that while [¹⁸F]AZD4694 and [¹¹C]PiB bindings are highly correlated with both tracers competing for the same binding sites, [¹⁸F]AZD4694 has a slightly higher effect size when comparing between neuropathologically-confirmed AD and HC brain tissues.

To compare the diagnostic performance of an immunoassay for plasma concentrations of phosphorylated tau (p-tau) 217 with visual assessments of fluorine-18 fluorodeoxyglucose [¹⁸F]FDG-PET in individuals who meet appropriate use criteria for Alzheimer dementia (AD) biomarker assessments. We performed a retrospective analysis of individuals with early-onset (age 18F]FDG-PET scans, amyloid-PET scans, and plasma p-tau217 quantifications. To determine whether the [¹⁸F]FDG-PET images were compatible with AD, images were visually rated by 2 nuclear medicine experts. Using a contingency analysis, we evaluated the accuracy of [¹⁸F]FDG-PET scan interpretation and plasma p-tau217 for an AD biomarker profile in CSF and for amyloid-PET positivity. A total of 81 individuals with early onset and/or atypical dementia were included in this study (mean age = 65 years; 48/81 female (59%). Both [¹⁸F]FDG-PET and plasma p-tau217 showed high levels of agreement with reference standard AD biomarkers ([¹⁸F]FDG-PET area under the curve [AUC]: 71%; plasma p-tau217 AUC: 81%). Although both biomarkers had similar specificity for AD [¹⁸F]FDG-PET: 70%, CI: 0.56-0.81; plasma p-tau217: 70%, CI: 0.56-0.81), plasma p-tau217 had higher sensitivity for AD (plasma p-tau217: 97%, CI: 0.85-0.99 vs [¹⁸F]FDG-PET: 73%, CI: 0.57-0.85) (p = 0.01). Overall accuracy was also higher for plasma p-tau217 (AUC = 84%, CI: 0.75-0.93 vs 72%, CI: 0.60-0.83 of [¹⁸F]FDG-PET) (p = 0.02). The same pattern of results was observed when using amyloid-PET as the reference standard. Our study provides evidence that plasma p-tau217 has strong discriminative accuracy for AD among patients with early-onset and/or atypical dementia assessed in specialized settings. Future work should replicate these findings in secondary care settings.

Early detection of atypical dementia remains difficult partly because of the absence of specific cognitive screening tools. This creates undue delays in diagnosis and management. The Dépistage Cognitif de Québec (DCQ; dcqtest.org) was developed in French and later validated in participants with atypical syndromes. We report the validation of the English version. This multicentre prospective validation study was conducted in 10 centers across Canada and the United States on 260 English-speaking participants aged over 50. We translated and modified the original French DCQ to add targeted stimuli to the Visusopatial Index and social cognition vignettes to the Behavioral Index. A backward translation was performed and equivalence between languages was assessed by administering both tests to 30 bilingual participants. Mean DCQ total score (out of 100) was 95.0 (SD = 3.6). Spearman’s correlation coefficient showed a strong and significant correlation (r = 0.49, p < .001) with the Montreal Cognitive Assessment. Test–retest reliability was good (Spearman’s coefficient = 0.72, p < .001) and interrater reliability, excellent (intraclass correlation = 0.97, p < .001). Normative data shown in percentiles were stratified by age and education for a population-based sample of 260 English-speaking controls aged between 50 and 87 years old. Similar to the French version, the English DCQ proved to be a valid cognitive screening test. The original version was very sensitive to detect atypical dementias such as primary progressive aphasias, Alzheimer’s disease’ variants and syndromes along the frontotemporolobar degeneration spectrum. This 20-min test can be administered à la carte and offers an alternative to detailed comprehensive neuropsychological evaluations.

Updates in Alzheimer disease (AD) diagnostic guidelines by the National Institute on Aging-Alzheimer's Association (NIA-AA) and the International Working Group (IWG) over the past 11 years may affect clinical diagnoses. We assessed how these guidelines affect clinical AD diagnosis in a cohort of cognitively unimpaired (CU) and cognitively impaired (CI) individuals. We applied clinical and biomarker data in algorithms to classify individuals from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort according to the following diagnostic guidelines for AD: 2011 NIA-AA, 2016 IWG-2, 2018 NIA-AA, and 2021 IWG-3, assigning the following generic diagnostic labels: (1) not AD (nAD), (2) increased risk of developing AD (irAD), and (3) AD. Diagnostic labels were compared according to their frequency, convergence across guidelines, biomarker profiles, and prognostic value. We also evaluated the diagnostic discordance among the criteria. A total of 1,195 individuals (mean age 73.2 ± 7.2 years, mean education 16.1 ± 2.7, 44.0% female) presented different repartitions of diagnostic labels according to the 2011 NIA-AA (nAD = 37.8%, irAD = 23.0%, AD = 39.2%), 2016 IWG-2 (nAD = 37.7%, irAD = 28.7%, AD = 33.6%), 2018 NIA-AA (nAD = 40.7%, irAD = 9.3%, AD = 50.0%), and 2021 IWG-3 (nAD = 51.2%, irAD = 8.4%, AD = 48.3%) frameworks. Discordant diagnoses across all guidelines were found in 512 participants (42.8%) (138 [91.4%] occurring in only β-amyloid [CU 65.4%, CI 34.6%] and 191 [78.6%] in only tau-positive [CU 71.7%, CI 28.3%] individuals). Differences in predicting cognitive impairment between nAD and irAD groups were observed with the 2011 NIA-AA (hazard ratio [HR] 2.21, 95% CI 1.34-3.65, p = 0.002), 2016 IWG-2 (HR 2.81, 95% CI 1.59-4.96, p < 0.000), and 2021 IWG-3 (HR 3.61, 95% CI 2.09-6.23, p < 0.000), but not with 2018 NIA-AA (HR 1.69, 95% CI 0.87-3.28, p = 0.115). Over 42% of the studied population presented discordant diagnoses when using the different examined AD criteria, mostly in individuals with a single positive biomarker. Except for 2018 NIA-AA, all guidelines identified asymptomatic individuals at risk of cognitive impairment. Our findings highlight important differences between the guidelines, emphasizing the necessity for updated criteria with enhanced staging metrics, considering clinical, research, therapeutic, and trial design aspects.