Traumatic brain injury (TBI) can lead to lasting neurological and emotional effects. Latent Toxoplasma gondii (T. gondii) infection, prevalent worldwide, may exacerbate these outcomes by altering immune and neurochemical pathways. This cross-sectional observational study investigated whether chronic T. gondii infection is associated with structural brain differences and long-term outcomes in survivors of moderate-to-severe TBI ≥ 10 years post-injury. 89 TBI survivors (≥10 years post-injury) were recruited from a tertiary-centre database; 35 (39%) tested positive for latent T. gondii via plasma IgG. Thirty-two age- and sex-matched controls without TBI were included (25% T. gondii-seropositive). Nonparametric regression analyses adjusted for age, sex, and intracranial volume, with false discovery rate corrections applied. Primary outcomes were MRI-based measures of white matter microstructure, cortical thickness, and subcortical volumes. Secondary outcomes included neuropsychological assessments of anxiety, depression, cognition, and TBI blood biomarkers. Within the TBI group, T. gondii-positive individuals had reduced fibre density and increased diffusivity in the posterior corpus callosum, increased cortical thickness (insula, cuneus), and reduced brainstem volume. They also reported higher anxiety and mediation analysis showed brainstem volume partially mediated the link between infection and anxiety. Within controls, T. gondii infection was not significantly associated with anxiety, cognition, white matter microstructure, or blood biomarkers. However, controls with T. gondii infection did have increased cortical thickness in the left inferior temporal gyrus and reduced volume in the left caudate compared to their uninfected counterparts. Taken together, latent T. gondii infection may alter brain structure and exacerbate anxiety in chronic TBI. These findings support considering infection status in TBI prognostics and call for further research into its mechanistic and clinical implications.

In cognitively unimpaired (CU) individuals, the PACC is widely used as a cognitive outcome measure and endpoint in observational studies and clinical trials. However, it has drawn criticism for being heavily weighted towards memory. Increasing evidence indicates a decline spanning multiple cognitive domains in CU individuals. Therefore, using principal component analysis (PCA), we derived data-driven domain-specific cognitive composites. And subsequently, compared them against their summed z-score counterparts in predicting progression to mild cognitive impairment (MCI). Baseline cognitive, demographic, and genotype data of 2,853 CU older adults (aged 41.6 to 98.3) was obtained from the Alzheimer’s Dementia Onset and Progression in International Cohorts (ADOPIC) Consortium. Using varimax-rotated PCA, tests significantly loading (≥ 0.5) onto each principal component were extracted to derive domain-specific cognitive composites. The resulting domain scores were normalised to a mean of 0 and SD of 1, with a higher score indicating better cognition. Cox regression was used to assess the association between progression to MCI and baseline demographics, cognition, and APOE ε4 carriage. Akaike information criteria (AIC) was used to compare the fitness of PCA-derived composites against the zPACC and z-score domain-specific composites. Baseline cohort characteristics are described in Table 1. PCA explained 68% of the variance and resulted in four independent cognitive composites (Figure 1): memory; executive function; attention and processing speed; and global cognition. At 15 years from baseline, 309 participants progressed to MCI, while 2,544 remained CU. Cox regression showed that the four cognitive composites, age and APOE genotype significantly predicted progression to MCI (Concordance = 77%, p < 0.001, AIC = 4105, Table 1). Additionally, the PCA-derived composites performed comparably, if not better than the summed z-score counterparts, the PACC (Concordance = 74%, p < 0.001, AIC = 4163) and domain-specific composites (Concordance = 75%, p < 0.001, AIC = 4142). Baseline older age, APOE ε4 carriage in a dose-dependent manner (Figure 2) and poorer cognition for each PCA-composite were independently associated with progression to MCI. Together with APOE ε4 carriage, our PCA-derived domain-specific composites performed better than their summed z-score counterparts at predicting progression to MCI 15 years before symptom onset.

Recent advances in immunoassays have enabled sensitive detection of Aβ42/40 and pTau217 in plasma, components of Alzheimer’s disease (AD) neuropathological markers. Further characterization of increased diagnostic accuracy with PET Amyloid-β (Aβ) across the AD continuum is needed for clinical application. Participants from the Australian Imaging, Biomarkers and Lifestyle (AIBL) study of ageing (N=197) representing a cross-sectional population of four clinical and PET-Aβ subgroups: cognitive unimpaired (CU) Aβ- (n=75), CU Aβ+ (n=48), mild cognitive impairment (MCI) Aβ+ (n=26), and AD Aβ+ (n=48). EDTA plasma was analyzed with Aβ42/40 (Quanterix Simoa & Fujirebio Lumipulse) and pTau217 (ALZpath Simoa & Lumipulse). Data were investigated using Cohen’s D for effect size, Receiver Operating Characteristic (ROC) analyses to define AUC values for PET-Aβ positivity and Spearman’s Rho for correlation between biomarkers and Centiloid. Lower Lumipulse and Simoa plasma Aβ42/40 ratios were observed in Aβ+ vs Aβ- groups (p<0.0001; with effect size Cohen’s D indices = 1.39 and 1.07, respectively). Aβ42/40 ratios initially decreased with increasing amyloid PET Centiloid levels through the positivity threshold (25CL), levelling off with increasing Centiloid values across the disease continuum (Figure 1A). Highest effects sizes were seen for pTau217 when comparing Aβ+ vs Aβ- groups (p<0.0001; Cohen’s D: 1.54 [Simoa] and 1.49 [Lumipulse]), with stepwise increases per Centiloid group (Figure 1B). Biomarker AUC values to predict PET-Aβ were highest for pTau217 in the complete sample compared with the CU sample (Simoa AUC: 0.947/0.906, Lumipulse AUC: 0.941/0.906). For the Aβ42/40 ratio, AUC values were higher in the CU group as compared with the complete sample (Lumipulse AUC: 0.896/0.889, Simoa AUC: 0.885/0.849 (Figure 1C). Adding in age, gender, and APOE ε4 allele status improved prediction of PET-Aβ, with AUC values reaching 0.97 for pTau217 (Figure 1C). Correlations between biomarker and Centiloid were higher for pTau217 (Simoa: r=0.744 (Figure 1D), Lumipulse r=0.732) as compared with the Aβ42/40 ratio (Lumipulse r=-0.509, Simoa r=-0.462). Sensitive assays for the Aβ42/40 ratio may be more appropriate to detect Aβ burden in CU participants while p-Tau217 appears to be more accurate and has the highest effect size and AUC values to predict Aβ-PET across the AD continuum.

Tau PET is instrumental in tracking the longitudinal progression of Alzheimer's disease (AD). 18 F-MK6240 is a high affinity tracer targeting the 3R/4R paired helical filaments of tau in AD. We aimed to evaluate the early phase of the natural progression of tau accumulation using 18 F-MK6240. 231 participants: 100 cognitively unimpaired (CU) Aβ− (Centiloid<25CL), 58 CU Aβ+, 73 cognitively impaired Aβ+ (41 with mild cognitive impairment (MCI) and 32 with dementia) from the AIBL cohort were followed-up with 18 F-MK6240 PET over one to four years (median 2.2years). Meta-Temporal CenTauR (CTR) were generated using CapAIBL and CU CL<15, N=120 and AD (typical AD tau pattern, MMSE>24, CL>50 & age<75, N=39) as 0 and 100CTR anchored points. Abnormal level of tau was defined at 2 standard deviations above the CU Aβ- (13CTR). Linear ordinary differential equations (ODE) were employed to model the mean natural history of CTR based on tau accumulators (CTR>13 or CTR rate>0) only. Given the limited numbers of individuals with CTR>100, our analysis concentrated on the early phase of tau accumulation (CTR<100, N=204). Figure 1A illustrates a linear relationship between the average and the rate of CTR, with a R of 0.72. Tau accumulation spanned from 0.5CTR/yr at 13CTR to 12.2CTR/yr at 100CTR, with a standard deviation of the residuals at 2.4CTR/yr. Figure 1B displays the individual’s trajectories projected on the ODE model. We estimated that, on average it takes 15.1 (CI:[12.6-17.9]) years for an individual crossing 13CTR to reach 100CTR. Longitudinal 18 F-MK6240 is a robust tool for estimating natural progression of tau accumulation. Our findings indicate that it typically takes around 15 years to reach the tau levels associated with mild AD once tau starts aggregating in the neocortex. These findings shed light into the initial stages of cortical tau accumulation, relevant for early diagnosis and therapeutic interventions in AD.

Abstract Background Plasma phospho-tau biomarkers, such as p217+tau, excel at identifying Alzheimer’s disease (AD) neuropathology. However, their ability to substitute for tau PET to identify AD biological stage is unclear. Methods Participants included 248 cognitively unimpaired (CU) and 227 cognitively impaired (CI) individuals, with Janssen plasma p217+tau Simoa® assay, 18F-NAV4694 Aβ-PET (A) and 18F-MK6240 tau-PET (T) data. Biological PET stages were defined according to the Revised Criteria for Diagnosis and Staging of Alzheimer’s Disease (2024): Initial (A + T-), Early (A + TMTL + ), Intermediate (A + TMOD + ), and Advanced (A + THIGH + ). The threshold for A+ was 25 Centiloid and for THIGH + , the 75th percentile SUVRtemporo-parietal in A + CI. Sixty percent were A + , 36% Intermediate/Advanced, and 9% Advanced. The performance of p217+tau in discriminating AD stages was assessed using Receiver Operating Characteristic (ROC) analysis and logistic regression. Results Plasma p217+tau concentrations increase across the AD biological PET stages, except between Initial and Early stages. Screening for all AD stages (vs. A-T-), combined Intermediate/Advanced stages, or Advanced stage yields AUC of 0.92, 0.92, and 0.91, respectively (CI only: AUC 0.93, 0.89, 0.83). Plasma p217+tau Youden threshold provides sensitivity of 0.77 [0.73–0.90], specificity 0.91 [0.80–0.95], PPV 0.84 [0.71–0.89], and NPV 0.88 [0.85–0.93] for combined Intermediate/Advanced stages. For the Advanced stage alone, sensitivity is 0.89 [0.79–0.97], specificity 0.82 [0.75–0.9], NPV 0.99 [0.98–1.0], but PPV is only 0.33 [0.25–0.47]. Conclusions In addition to accurately screening for A+ individuals, plasma p217+tau is useful for identifying a combined Intermediate/Advanced stage AD cohort or pre-screening to reduce the tau-PET required to identify Advanced stage AD individuals.

Plasma phospho-tau biomarkers, such as p217+tau, excel at identifying Alzheimer’s Disease (AD) neuropathology. However, questions remain regarding their capacity to inform AD biological PET stages at group level and maintain the same precision at individual patient level. Participants included 248 cognitively unimpaired (CU) and 227 cognitively impaired (CI) individuals, with Janssen plasma p217+tau Simoa® assay, 18 F-NAV4694 Aβ PET (A) and 18 F-MK6240 tau PET (T) data. Biological PET stages were defined based on the draft NIA-AA Revised Criteria (July 2023): Initial (A+T-), Early (A+T MTL +), Intermediate (A+T MOD +), and Advanced (A+T HIGH +). We used thresholds for A+ of 25 Centiloid and for T HIGH of 80 Centaur (2.68 SUVR temporo-parietal ). Adding an A-T- stage for comparison, we assessed the performance of p217+tau in discriminating between these stages at the group level using Receiver Operating Characteristic (ROC) analysis and at the individual level using logistic regression. Plasma p217+tau concentrations increased across the stages, with significant differences between them, except for the Initial and Early stages. Screening for Advanced (vs. lower stages), combined Intermediate/Advanced (vs. lower stages), or all stages (vs. A-T-), p217+tau showed good group-level discriminations (AUC 0.91, 0.92 and 0.92; CI only: AUC 0.83, 0.89, 0.93, respectively). At the individual level, the likelihood of PET stage vs. p217+tau level showed good discrimination of A-T- vs any A+ stage and of combined Intermediate/Advanced disease stage vs lower stages in the CI. In addition to accurately screening for A+ individuals, plasma p217+tau shows promise for separating persons with either Intermediate or Advanced stage AD from those at a lower stage, providing prognostic information and informing better selection for trials and disease modifying therapies.

INTRODUCTION:For a blood-based biomarker to be considered a confirmatory test for the detection of abnormal amyloid beta (Aβ) levels, the sensitivity and specificity must be equivalent to that of current cerebrospinal fluid tests. METHODS:In the current study we assessed the ability of phosphorylated tau (p-tau)217 and Aβ42/40 from the Lumipulse G p-tau217 and β-amyloid ratio (1-42/1-40) tests, individually and combined, to predict Aβ positron emission tomography status in two sub-cohorts from the Australian Imaging, Biomarkers, and Lifestyle Study of Ageing. RESULTS:Testing an Alzheimer's disease continuum cohort, the area under the curve (AUC), sensitivity, specificity, and accuracy for the p-tau217/Aβ42 ratio reached 0.961, 93%, 92%, and 93%, respectively. Validation in an intention-to-treat cohort demonstrated similar AUC (0.959), with increased sensitivity (99%), decreased specificity (87%), and increased accuracy (95%). Dual cut-offs generating balanced 95% sensitivity/specificity result in 93% accuracy. DISCUSSION:Combinations of plasma p-tau217 and Aβ42 demonstrate recommended performance, confirming the presence of Aβ positivity prior to selection for disease-modifying therapies. HIGHLIGHTS:The phosphorylated tau (p-tau)217/amyloid beta (Aβ)42 ratio had high performance to detect Aβ positron emission tomography (PET) status, with > 90% sensitivity, specificity, and accuracy. p-tau217/Aβ42 ratio dual cut-offs set at 95% sensitivity and specificity found 10% to 15% of participants in the intermediate zone. Cut-offs derived for the intention-to-treat cohort meet confirmatory assay criteria for a disease-modifying therapy and can be used in clinical settings.

BACKGROUND:New amyloid-targeting monoclonal antibody (mAb) therapies for Alzheimer disease (AD) are currently under review by the Therapeutic Goods Administration for use in Australia. AIMS:To determine the infrastructure, workforce and training needs of Australian memory and cognition clinics in order to characterise health system preparedness for these therapies. METHODS:A national, cross-sectional online survey of medical specialists. RESULTS:Thirty medical specialists (geriatricians, n = 23; psychiatrists, n = 4; neurologists, n = 3) from 30 different clinics participated (public, 76.7%; private, 23.3%), including from metropolitan (73.3%), regional (20.0%) and rural (6.7%) areas. On average, clinics reported assessing 5.4 (SD = 3.2) new patients per week, of which 2.4 (range: 0-5) were considered to have mild cognitive impairment (MCI). Only 40% of clinics use biomarkers to assess whether patients with MCI have AD, and 45% have intravenous infusion capability. While the majority of clinicians were confident in their knowledge of mAbs, only 33% felt confident in using these. Identified impediments to clinical implementation included (i) lack of real-world experience, (ii) lack of current Models of Care and appropriate use guidelines, (iii) current clinic set-up and (iv) information about safety. CONCLUSIONS:Australia's health system preparedness for amyloid-targeting mAb therapies will require further investment in infrastructure, equity of access, clinician training and support. Long wait times already impact access to clinics, and with the forecast rise in MCI and dementia cases, services will need to be expanded, and appropriate Models of Care and clear and efficient inter-sector health pathways will be needed to prepare for the use of mAbs.

BACKGROUND:Dietary nitrate, as a nitric oxide (NO) precursor, may support brain health and protect against dementia. OBJECTIVE:Our primary aim was to investigate whether dietary nitrate is associated with neuroimaging markers of brain health linked with Alzheimer's disease (AD). PARTICIPANTS:Study participants were cognitively unimpaired individuals from the Australian Imaging, Biomarkers and Lifestyle Study of Ageing (AIBL) who had β-amyloid positron emission tomography (PET) scans (n = 554) and magnetic resonance imaging (MRI) scans (n = 335) and had completed a Food Frequency Questionnaire at baseline. METHODS:Source-specific nitrate intakes were estimated using comprehensive nitrate food composition databases. Rates of cerebral β-amyloid (Aβ) deposition, measured using PET, and rates of brain atrophy, measured using MRI, were assessed between baseline and 126-months follow-up, at intervals of 18 months. Multivariable-adjusted linear mixed effect models were used to examine associations between baseline source-specific nitrate intake and rates of (i) cerebral Aβ deposition and (ii) brain atrophy, over the 126 months of follow-up. Analyses were carried out following stratification of the sample by established dementia Alzheimer's disease (AD) risk factors including sex and presence or absence of the apolipoprotein E (APOE) ε4 allele. RESULTS:In women carriers of the APOE ε4 allele, higher plant sourced nitrate intake (median intake 121 mg/day), was associated with a slower rate of cerebral Aβ deposition [β: 4.47 versus 8.99 Centiloid (CL) /18 months, p < 0.05] and right hippocampal atrophy [-0.01 versus -0.03 mm3 /18 months, p < 0.01], after multivariable adjustments. Moderate intake showed protective associations in men carriers and in both men and women non-carriers of APOE ε4. CONCLUSIONS:Associations were observed between plant-derived nitrate intake and cerebral Aβ deposition, particularly in high-risk populations (women and APOE ε4 carriers). Associations were also observed for brain volume atrophy, however these exhibited subgroup variability without clear patterns relative to sex and APOE ε4 allele carriage. These findings suggest a potential link between plant-sourced nitrate and AD related neuroimaging markers of brain health improved brain health, but further validation in larger studies is required.

Sex differences in cognitive reserve might contribute to females being disproportionately affected by Alzheimer's disease (AD). We investigated sex differences in the protective effects of cognitive reserve, and whether brain beta-amyloid accounts for differences. Older adults (n = 997 from the Australian Imaging, Biomarkers and Lifestyle Study of Ageing) diagnosed as Cognitively Normal, Mild Cognitive Impairment, or AD at baseline were assessed every 18 months for up to a maximum of seven visits. Cognitive reserve was calculated from the variance in episodic memory not explained by demographic or brain measures. Executive functioning (EF) intercept and slope were regressed onto the main and interaction effects of cognitive reserve x brain integrity x sex, plus covariates (age, number of APOE ε4 alleles). A three-way interaction was observed between cognitive reserve, brain integrity, and sex on the EF slope. Females benefitted more than males from the protective effects of cognitive reserve at low levels of brain integrity. Sex differences in the protective effect of cognitive reserve were not moderated by brain beta-amyloid burden.

Genome-wide association studies (GWAS) have identified numerous genetic variants associated with Alzheimer’s disease (AD) risk, but genetic variation in the onset and progression of AD pathology is less understood. Accumulation of amyloid-β (Aβ) in the brain is a key pathological hallmark of AD beginning 10 – 20 years prior to cognitive symptoms. We investigated the genetic basis of variation in age at onset (AAO) of brain Aβ by comparing the performance of polygenic scores (PGSs) based on AD risk and resilience with a Aβ-AAO trait-specific PGS. 1122 participants from the Alzheimer’s Dementia Onset and Progression in International Cohorts (ADOPIC) study underwent genome-wide SNP genotyping and assessment of brain Aβ using positron emission tomography (PET) imaging at two or more timepoints. AAO was the age at which participants were estimated to have crossed the 20 centiloid (CL) threshold for high Aβ. We utilised AD risk and resilience GWAS summary statistics and conducted a GWAS for AAO using a cross-validation approach (10 test-validation folds). We used PRSice to identify optimal PGSs for Aβ-AAO for risk (PGS Risk ), resilience (PGS Resilience ) and Aβ-AAO (PGS AAO ). PGS Risk and PGS Resilience were both significantly associated with Aβ-AAO, such that higher PGS Risk and lower PGS Resilience were associated with an earlier Aβ-AAO. PGS Risk showed the strongest association and explained more variance in Aβ-AAO than did PGS AAO . When stratified by APOE ε4 carriage, the strongest genetic risk factor for AD, the association of PGS Risk with Aβ-AAO was stronger among ε4 non-carriers, whilst PGS Resilience , was more strongly associated with Aβ-AAO in ε4 carriers. PGS based on genetic risk and resilience for AD are both significant predictors of the age at which people are estimated to cross the threshold for high brain Aβ burden. Predicting the age at which a person will pass this threshold would enable treatment at an earlier stage, when it may more effectively delay or prevent symptom onset.

Diagnostic and prognostic decisions about Alzheimer’s disease (AD) are more accurate when based on large data sets. We developed and validated a machine learning (ML) data harmonization tool for aggregation of prospective data from neuropsychological tests applied to study AD. The online ML-combine application (OML-combine app) allows researchers to utilize the ML-harmonization method for harmonization of their own data with that from other large available data bases (e.g. AIBL) to enable development of their own neuropsychological models of AD. The OML-Combine application implements an established neuropsychological test data harmonization method 1 based on non-parametric multivariate imputation using random forests (missForest) 2 . Test data not included in a cohort is classified as missing and imputed using known data from the cohort based on information known from other studies 1 . A web-based R-Shiny application was developed to facilitate harmonization of data from different cohorts and visualise outcomes. OML-combine also calculates percentages of missing values for each test score across the pooled dataset allowing decisions about the validity of harmonized data. OML-combine also allows harmonization of multiple datasets simultaneously. The R Shiny package was used to produce an interactive data harmonization tool. Figure 1 displays the interface, showing results from an example harmonization and validation step using simulated data from AIBL (N=1813) and ADNI (N=1945). In the validation tab, users are provided with a figure of the distributions of both raw and harmonized datasets, including predicted test scores and an accuracy measurement for each score. These can be used to validate the outcomes and compare them to known relationships established from the raw data for each dataset. OML-Combine facilitates the harmonization of neuropsychological test data from established AD cohort studies. Visualization of predicted test scores and the original data sets can thereby assist with decisions about accuracy of harmonized data and can provide a basis for adjustment of inputs to optimize models. This allows researchers to combine their own data with that from other currently available studies to improve diagnostic and prognostic models of AD. References: 1 doi: 10.1002/alz.044302 2 doi:10.1093/bioinformatics/btr597

BACKGROUND:Healthy lifestyle factors, including diet, may affect brain amyloid beta (Aβ) load. This study examines dietary patterns as moderators of the relationships among symptoms of depression, anxiety, and brain Aβ load. METHOD:A cross-sectional study of cognitively unimpaired older adults (n = 524) from the Australian Imaging, Biomarkers, and Lifestyle study assessed dietary patterns, depressive and anxiety symptoms, and brain Aβ load. Moderation and simple slope analyses were conducted. RESULTS:The Dietary Approaches to Stop Hypertension (DASH) diet moderated the relationship between depressive and anxiety symptoms and brain Aβ load. Higher symptoms were associated with greater Aβ load in individuals with lower DASH adherence. This effect was also observed for anxiety symptoms in apolipoprotein E ε4 carriers. The Mediterranean and Western diets did not moderate these relationships. CONCLUSION:The DASH diet adherence may mitigate the impact of depressive and anxiety symptoms on brain Aβ load, supporting genotype-specific dietary interventions in mental and brain health. HIGHLIGHTS:The Dietary Approaches to Stop Hypertension (DASH) diet moderates the links among depression, anxiety, and brain amyloid load. Higher symptoms were linked to greater amyloid load in those with low DASH adherence. This effect was observed for anxiety symptoms in apolipoprotein E ε4 allele carriers. Mediterranean and Western diets did not moderate these relationships. Findings support genotype-specific dietary interventions for brain and mental health.

An accurate prediction of Alzheimer’s disease (AD) progression is important for patient management and optimization of participant selection for trials. Here, we compared and combined plasma p-tau217 and tau-PET measures for predicting longitudinal cognitive decline and clinical progression in cognitively unimpaired participants. We included 982 participants from six independent cohorts (AiBL, BioFINDER-1, BioFINDER-2, TRIAD, PREVENT-AD and WRAP; Table 1) with available plasma p-tau217 and tau-PET measures (measured less than one-year apart), being either amyloid-positive or amyloid-negative. Biomarker and cognitive data were z-scored by cohort using cognitively unimpaired CSF/PET amyloid-negative participants as reference for a unified analysis. We performed linear mixed models (for predicting cognitive decline on the mPACC and MMSE) and Cox-proportional hazards models (to assess progression to MCI or dementia), testing among both amyloid-negative and amyloid-positive individuals. We entered baseline plasma p-tau217, and tau-PET uptake in the medial temporal lobe (MTL) or in the temporal neocortex individually, and also performed combined plasma/PET models. Age, sex, years of education and cohort were used as covariates. All individual tau biomarkers significantly predicted cognitive decline for both mPACC (R 2 p-tau217 =0.27, R 2 MTL-tau =0.31, R 2 neotemporal-tau =0.28, Figure 1) and MMSE (R 2 p-tau217 =0.12, R 2 MTL-tau =0.16, R 2 neotemporal-tau =0.19, Figure 2). The best model for predicting mPACC change included plasma p-tau217 and MTL tau-uptake (R 2 =0.32, p comparison <0.001), while for MMSE change included plasma p-tau217 and tau-uptake in the temporal neocortex (R 2 =0.20, p comparison ≤0.007). Progression to MCI or dementia was also best predicted when including both plasma p-tau217 (HR[95%CI]=1.29[1.14,1.47], p<0.001) and MTL tau-uptake (HR[95%CI]=1.39[1.25,1.54], p<0.001, c-index=0.83). Analyses by individual cohorts showed similar trends. Our data suggest that plasma p-tau217 is a suitable screening method for clinical trials in CU populations given its logistic advantages. In scenarios where a more refined prediction of cognitive decline is mandated, Tau-PET (preferably in a combined algorithm with plasma p-tau217) would be the methodology of choice.