Madeiros, Marina Scop

Abstract Background Quantifying tau aggregates in the human brain can be achieved using Positron Emission Tomography (PET) techniques, which can potentially be affected by binding competition due to medication use. Patients with dementia often have high rates of comorbidities and polypharmacy. Therefore, this study aims to investigate the potential influence of multiple medications on the uptake of the tau tracers MK6240 (MK) and Flortaucipir (FTP). Method Five classes of medications were evaluated: Anti‐Hypertensives, Statins, Anti‐Diabetics, Psychoactive drugs, and NSAIDs (Table 1). We included 292 individuals [170 cognitively unimpaired (CU) Aβ‐negative and 122 cognitively impaired (CI) Aβ‐positive] from the HEAD study (Table 2). We compared MK and FTP SUVR in the Medial Temporal Lobe (MTL) and Neotemporal Cortex (NTC) in individuals on and off medications. The linear regressions that tested associations were corrected for confounding factors, including age, sex, education, and MoCA score. Correction for multiple comparisons was applied using the Bonferroni method (adjusted p ‐value at 0.00125). Result Among CI Aβ‐positive individuals, Anti‐Diabetics were associated with lower SUVR in the NTC for both FTP and MK. However, these associations did not remain significant after correction for multiple comparisons. (Table 3). Conclusion Our findings indicate that there are no significant associations between the use of the medications studied and MK or FTP uptake when accounting for covariates and applying multiple comparison corrections.

Abstract Background Tau phosphorylation and aggregation are hallmark features of Alzheimer's disease pathology(AD). Previous studies have shown that plasma phosphorylated tau( p ‐tau) at threonine 217 can detect AD‐related tau tangle pathology. However, the head‐to‐head association between p ‐tau217 and different tau PET tracers remains unexplored. Here, we conducted a head‐to‐head comparison of the association between plasma p ‐tau217 and four distinct tau PET tracers[MK6240, Flortaucipir(FTP), PI2620, RO948]. Method We studied 338 individuals across the AD continuum from the HEAD study[190 cognitively unimpaired elderly(CU), 109 with mild cognitive impairment(MCI), and 39 with dementia] with available FTP, MK6240, and plasma p ‐tau217 measured by ALZpath assay. A subset of 64 individuals(28 CU, 25 MCI, and 11 dementia) also had PI2620 and RO948, and p ‐tau217 measured by Lumipulse. The strength of the association was determined using t‐value maps from regression models testing the association between each tau PET tracer and plasma p ‐tau217, and the extent was measured by the percentage of significant voxels(t‐value>3.2). R‐square metric was used to determine how much of the variance in tau PET tracer estimates was explained by plasma p ‐tau217 concentrations. Associations between p ‐tau217 with tau PET were evaluated using the Spearman test. Result We found that in CU, both the extent and magnitude of the association with plasma p ‐tau217 were higher for MK6240 compared to FTP (Figures 1A‐D). Plasma p ‐tau217 explained a greater proportion of the variance in MK6240 than in FTP (Figures 1E‐F). In CI, the extent and magnitude of the association were similar for MK6240 and FTP. Additionally, plasma p ‐tau217 explained a similar proportion of the variance in MK6240 and FTP. Using the subset of individuals who had all tau tracers, MK6240 showed a slightly higher extent of association with p ‐tau217, followed by FTP, PI2620, and RO948 (Figures 2A‐B). The magnitude of association was stronger for MK6240, followed by PI2620, RO948, and FTP (Figures 2C‐D). Plasma p ‐tau217 explained a greater proportion of the variance in MK6240, followed by FTP, RO948, and PI2620 (Figures 2E‐F). We compared the associations between different p ‐tau217 assays, and both assays presented similar associations with all tau PET tracers (Figure 3). Conclusion In summary, our results indicate that the four tau PET tracers exhibit robust associations with plasma p ‐tau217 in similar topographical regions.

Abstract Background Many risk factors can contribute to the occurrence of Alzheimer`s Disease (AD). However, little is known about the impact of dementia risk factors to the uptake of tau‐PET tracers. Therefore, in this work we aim to investigate the influence of dementia risk factors and comorbidities on 18 F‐Flortaucipir (FTP) and 18 F‐MK6240 (MK) tau‐PET tracers’ uptake. Additionally, we will assess how these factors impact the association of tau‐PET and cognition. Method We accessed 436 individuals across the aging and AD spectrum (251 amyloid negative and 185 amyloid positive) from the HEAD study, with available Aβ‐PET, FTP, MK, and clinical assessments. Linear regression models corrected for age, sex, clinical diagnosis, and study site tested the association of factors with tau‐PET tracers in the medial temporal lobe (MTL). A tau‐PET × risk factor term was added to test the influence of risk factors to the association of tau with cognition. Result In amyloid‐β negative individuals, high BMI were positively associated with the uptake of both FTP and MK, whereas hearing loss were positively associated only with MK in the MTL (Figure 1A). In amyloid‐β positive individuals, high body mass index (BMI), hearing loss and sleep disorders were negatively associated with the uptake of both tau‐PET tracers in the MTL. On the other hand, hypertension showed negative association only with MK uptake (Figure 1B). Using Mini‐Mental State Examination (MMSE) scores as outcome, we observed that amyloid‐β negative individuals with high BMI showed worse cognitive performance as a function of both MK and FTP in the MTL, whereas individuals with vision impairment and hearing loss showed worse cognitive performance as a function of MK only (Figure 2A). Amyloid‐β positive individuals with hypercholesterolemia and hypertension presented worse cognitive performance as a function of both MK and FTP in the MTL (Figure 2B). Conclusion In this preliminary analysis, sleep disorders, hypertension, and high BMI were independently associated with tau‐PET tracer uptake, with the effects varying according to amyloid‐β pathology. These prevalent factors in the elderly also changed the association between tau‐PET and cognition, underscoring the need for further studies to better understand their role in modulating this relationship.

Abstract Background The Alzheimer's disease (AD) Braak staging is a key framework for classifying tau pathology progression in AD based on histopathological post‐mortem brain examinations. However, adapting it to PET imaging can be challenging due to differences in tracer uptake patterns and binding properties, which affect sensitivity, specificity, and regional staging. This study compares Braak staging across four tau PET tracers: Flortaucipir, MK6240, PI2620, and RO948. Methods We assessed 90 participants across the AD spectrum (46 CU, 31 MCI, 13 dementia; mean age 66.1 ± 7.8) using Aβ PET and four tau PET tracers: (Flortaucipir, MK6240, PI2620, and RO948). Braak positivity was defined based on Aβ− CU individuals (mean +2.5 SD, SUVR). To evaluate systematic bias and agreement between tracers, we computed pairwise differences at corresponding Braak stage estimates and applied the Bland‐Altman method to assess mean bias and limits of agreement. Additionally, Tau PET Braak region trajectories were modeled as functions of Aβ burden (Centiloid scale) using the Lowess method. Results Braak stage trajectories as a function of Aβ differ depending on the tracer and the sequential order of abnormality is highly variable. For instance, while for MK6240, RO948 and PI2620, Braak I is the first region to became abnormal, for Flortaucipir the earliest region to became abnormal is Braak IV (Figure 1). This variable pattern of abnormality impacts in the concordance of Braak staging between tracers with the highest Braak staging agreement resulting in concordance levels of approximately 70%. The highest levels of agreement between tracers usually happen at Braak 0 or Braak IV‐V, with intermediate stages showing very low concordance (Figure 2). The Bland‐Altman analysis identified wide limits of agreement, suggesting high variability and high tracer‐specific differences (Figure 3). On the other hand, it also identified that mean differences between tracers were small, indicating minimal systematic bias. Conclusion These preliminary findings reveal discrepancies in Braak staging when comparing Flortaucipir, MK6240, PI2620 and RO948. These findings suggest that while the tracers provide comparable stages on average, they may not be fully interchangeable in individual cases.

AbstractBackgroundTau PET provides continuous measurements of tau tangle pathology in the human brain. However, establishing cutoffs is crucial for selecting individuals for treatment in clinical trials or practice. In the absence of postmortem data, PET cutoffs must be established using statistical methods based on what is considered normal tracer uptake. In this study, we tested the impact of various methods to determine tau positivity using two different tau PET tracers in individuals scanned head‐to‐head.MethodsWe studied 147 individuals from Head‐to‐Head Harmonization of Tau Tracers in Alzheimer's Disease (HEAD) study with tau tangle PET scans with [18F]Flortaucipir and [18F]MK‐6240, and amyloid‐β (Aβ) PET. Tau deposition was measured with the standardized uptake value ratio (SUVR) of each agent in the Medial Temporal Lobe (MTL) and the Entorhinal Cortex (EC). To determine Tau positivity three different methods were used: >2.5 standard deviations (SD) than the mean of the young, >1.5 SD mean of the cognitively unimpaired (CU) and >1.5 SD mean of CUAβ‐.ResultsDemographic characteristics of the study population are reported in Table 1. Using the cutoff >2.5 SD mean of young, [18F]Flortaucipir was positive in 35 (23.8%) and 69 (46.9%) individuals in the EC and MTL, respectively. [18F]MK‐6240 was positive in 49 (33.3%) and 58 (39.5%) individuals in EC and in MTL. Using >1.5 SD mean of CUAβ‐, [18F]Flortaucipir was positive in 38 (25.9%) and 45 (30.6%) individuals in EC and MTL, while [18F]MK‐6240 was positive in 51 (34.7%) and 50 (34.0%) in EC and in MTL (Figure 1).ConclusionsOur findings indicate variations in tau positivity when employing different methods based on either the young or CUAβ‐. [18F]Flortaucipir exhibited a higher rate of positive results when the method based on young individuals was applied in the MTL. Conversely, [18F]MK‐6240 showed more consistent and generally higher positivity when other methods were used for cutoff determination and/or in the EC region. Further research with a larger sample size is required to gain a better understanding of the optimal cutoff determination methods for these tracers.

AbstractBackgroundUtilizing PET amyloid‐beta (Aβ) and tau for staging Alzheimer’s Disease (AD) has demonstrated potential in identifying individuals with varying degrees of disease severity, applicable to both clinical trials and practice. However, the diverse binding characteristics of tau tracers pose challenges to the application of this staging across different ligands. In this study, we evaluate a novel staging framework proposed by the AA working group, employing Aβ PET and either [18F]MK6240 or [18F]Flortaucipir in individuals participating in a head‐to‐head study of tau PET tracers. This will provide insights into the robustness of this framework when applied across different tau ligands.MethodWe evaluated 154 people (89 CU and 65 CI), with MK6240, Flortaucipir, Aβ‐PET, MRI. SUVRs used inferior cerebellar gray as reference. Participants were categorized in Aβ positivity(A), using visual read, and tau positivity (T) in medial temporal lobe(MTL) and neocortex(NEO). Tau MTL(TMTL) and NEO positivity were defined as average +3 SD of youngs. NEO moderate(TMOD) and high(THIGH) was defined as below and above median SUVR of NEO‐positive individuals. Individuals were grouped as: A‐T‐, A+T‐, A+TMTL+(early), A+TMOD+(intermediary), and A+THIGH+(advanced).ResultsWe found that the distribution of the number of individuals across the five AD stages was comparable when using both MK6240 and Flortaucipir tracers (Figure 1). However, upon examining the concordance within each group, we found a low concordance of 30% (3/10) between tracers in the early tau stage (A+TMTL+). Concordance increased in the intermediary stage A+TMOD+, with a rate of 65% (9/14), and further increased to 79% (11/14) in the advanced stage (A+THIGH+). The A+T‐ group exhibited the highest concordance between tracer results, at 95% (21/22).ConclusionOur findings indicate that the application of this framework across different populations using different tau tracers can lead to a compatible distribution among groups. However, in a direct comparison, we noted that this did not necessarily result in high concordance between groups, as group shifts were observed that depended on the cutoffs used (data not shown). These results emphasize the necessity for harmonization of tau PET tracers prior to their utilization for stage determination.

AbstractBackgroundTau PET provides continuous measurements of tau tangle pathology in the human brain. However, establishing cutoffs is crucial for selecting individuals for treatment in clinical trials or practice. In the absence of postmortem data, PET cutoffs must be established using statistical methods based on what is considered normal tracer uptake. In this study, we tested the impact of various methods to determine tau positivity using two different tau PET tracers in individuals scanned head‐to‐head.MethodsWe studied 147 individuals from Head‐to‐Head Harmonization of Tau Tracers in Alzheimer's Disease (HEAD) study with tau tangle PET scans with [18F]Flortaucipir and [18F]MK‐6240, and amyloid‐ß (Aß) PET. Tau deposition was measured with the standardized uptake value ratio (SUVR) of each agent in the Medial Temporal Lobe (MTL) and the Entorhinal Cortex (EC). To determine Tau positivity three different methods were used: >2.5 standard deviations (SD) than the mean of the young, >1.5 SD mean of the cognitively unimpaired (CU) and >1.5 SD mean of CUAß‐.ResultsDemographic characteristics of the study population are reported in Table 1. Using the cutoff >2.5 SD mean of young, [18F]Flortaucipir was positive in 35 (23.8%) and 69 (46.9%) individuals in the EC and MTL, respectively. [18F]MK‐6240 was positive in 49 (33.3%) and 58 (39.5%) individuals in EC and in MTL. Using >1.5 SD mean of CUAß‐, [18F]Flortaucipir was positive in 38 (25.9%) and 45 (30.6%) individuals in EC and MTL, while [18F]MK‐6240 was positive in 51 (34.7%) and 50 (34.0%) in EC and in MTL (Figure 1).ConclusionsOur findings indicate variations in tau positivity when employing different methods based on either the young or CUAß‐. [18F]Flortaucipir exhibited a higher rate of positive results when the method based on young individuals was applied in the MTL. Conversely, [18F]MK‐6240 showed more consistent and generally higher positivity when other methods were used for cutoff determination and/or in the EC region. Further research with a larger sample size is required to gain a better understanding of the optimal cutoff determination methods for these tracers.

AbstractBackgroundUtilizing PET amyloid‐beta (Aß) and tau for staging Alzheimer’s Disease (AD) has demonstrated potential in identifying individuals with varying degrees of disease severity, applicable to both clinical trials and practice. However, the diverse binding characteristics of tau tracers pose challenges to the application of this staging across different ligands. In this study, we evaluate a novel staging framework proposed by the AA working group, employing Aß PET and either [18F]MK6240 or [18F]Flortaucipir in individuals participating in a head‐to‐head study of tau PET tracers. This will provide insights into the robustness of this framework when applied across different tau ligands.MethodWe evaluated 154 people (89 CU and 65 CI), with MK6240, Flortaucipir, Aß‐PET, MRI. SUVRs used inferior cerebellar gray as reference. Participants were categorized in Aß positivity(A), using visual read, and tau positivity (T) in medial temporal lobe(MTL) and neocortex(NEO). Tau MTL(TMTL) and NEO positivity were defined as average +3 SD of youngs. NEO moderate(TMOD) and high(THIGH) was defined as below and above median SUVR of NEO‐positive individuals. Individuals were grouped as: A‐T‐, A+T‐, A+TMTL+(early), A+TMOD+(intermediary), and A+THIGH+(advanced).ResultsWe found that the distribution of the number of individuals across the five AD stages was comparable when using both MK6240 and Flortaucipir tracers (Figure 1). However, upon examining the concordance within each group, we found a low concordance of 30% (3/10) between tracers in the early tau stage (A+TMTL+). Concordance increased in the intermediary stage A+TMOD+, with a rate of 65% (9/14), and further increased to 79% (11/14) in the advanced stage (A+THIGH+). The A+T‐ group exhibited the highest concordance between tracer results, at 95% (21/22).ConclusionOur findings indicate that the application of this framework across different populations using different tau tracers can lead to a compatible distribution among groups. However, in a direct comparison, we noted that this did not necessarily result in high concordance between groups, as group shifts were observed that depended on the cutoffs used (data not shown). These results emphasize the necessity for harmonization of tau PET tracers prior to their utilization for stage determination.

AbstractBackgroundThe newly proposed criteria by the AA working group incorporates both biological and clinical stages to characterize the progression of AD. In this study, we aim to evaluate the agreement between these two complementary systems.MethodsUsing 188 participants from McGill TRIAD and 139 from the HEAD cohorts, we categorized participants into biological (0‐4) and clinical (0‐4) stages using amyloid PET, tau PET(MK‐6240), and clinical measures as described by the working group. Participants were then stratified into three categories: congruent (clinical = biological), higher in the biological stage (clinical < biological), and higher in the clinical stage (clinical > biological). In the TRIAD cohort, we further compared these groups for age, sex, years of education, vascular burden (WMH), microglial activation (PBR scan), Astrocyte reactivity (GFAP), amyloid load, tau load, and NPI‐Q scores using Welch two‐sided t‐test with FDR multiple comparison correction.ResultsIn the TRIAD cohort, 34% of participants were congruent, 24.5% had a higher clinical stage, and 41.5% had a higher biological stage. Meanwhile in the HEAD cohort 68.2% were congruent, 20.7% had a higher clinical stage, and 17% had a higher biological stage. TRIAD participants with a higher clinical stage had lower education (P = 0.02), more neuropsychiatric symptoms (P = 0.03), and a higher vascular burden (P = 0.03) (Table 1). As expected, people with higher biological stage had more amyloid(P<0.001), tau(P<0.001), and astrocyte reactivity(P<0.001) (Table 1, Figure 2).ConclusionsOur findings highlight important discordance between clinical and biological stages, which could be partially explained by cognitive reserve. This was supported by the protective effects of educational attainment in participants with a higher biological stage. Vascular burden played a major role in the cognitive impairment of individuals with higher clinical stages. Future studies should replicate these findings in larger more representative population‐based cohorts.