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Cross-sectional studies have indicated potential for positron emission tomography (PET) in imaging tau pathology in Alzheimer’s disease (AD); however, its prognostic utility remains unproven. In a longitudinal, multi-modal, prognostic study of cognitive decline, 20 patients with a clinical biomarker-based diagnosis in the AD spectrum (mild cognitive impairment or dementia and a positive amyloid-beta PET scan) were recruited from the Cognitive Clinic at Karolinska University Hospital. The participants underwent baseline neuropsychological assessment, PET imaging with [18F]THK5317, [11C]PIB and [18F]FDG, magnetic resonance imaging, and in a subgroup cerebrospinal fluid (CSF) sampling, with clinical follow-up after a median 48 months (interquartile range = 32:56). In total, 11 patients declined cognitively over time, while 9 remained cognitively stable. The accuracy of baseline [18F]THK5317 binding in temporal areas was excellent at predicting future cognitive decline (area under the receiver operating curve 0.84–1.00) and the biomarker levels were strongly associated with the rate of cognitive decline (β estimate −33.67 to −31.02, p < 0.05). The predictive accuracy of the other baseline biomarkers was poor (area under the receiver operating curve 0.58–0.77) and their levels were not associated with the rate of cognitive decline (β estimate −4.64 to 15.78, p > 0.05). Baseline [18F]THK5317 binding and CSF tau levels were more strongly associated with the MMSE score at follow-up than at baseline (p < 0.05). These findings support a temporal dissociation between tau deposition and cognitive impairment, and suggest that [18F]THK5317 predicts future cognitive decline better than other biomarkers. The use of imaging markers for tau pathology could prove useful for clinical prognostic assessment and screening before inclusion in relevant clinical trials.

Differential diagnosis of parkinsonism early upon symptom onset is often challenging for clinicians and stressful for patients. Several neuroimaging methods have been previously evaluated; however specific routines remain to be established. The aim of this study was to systematically assess the diagnostic accuracy of a previously developed 18 F-fluorodeoxyglucose positron emission tomography (FDG-PET) based automated algorithm in the diagnosis of parkinsonian syndromes, including unpublished data from a prospective cohort. A series of 35 patients prospectively recruited in a movement disorder clinic in Stockholm were assessed, followed by systematic literature review and meta-analysis. In our cohort, automated image-based classification method showed excellent sensitivity and specificity for Parkinson Disease (PD) vs. atypical parkinsonian syndromes (APS), in line with the results of the meta-analysis (pooled sensitivity and specificity 0.84; 95% CI 0.79–0.88 and 0.96; 95% CI 0.91 –0.98, respectively). In conclusion, FDG-PET automated analysis has an excellent potential to distinguish between PD and APS early in the disease course and may be a valuable tool in clinical routine as well as in research applications.

Background[18F]flutemetamol PET scanning provides information on brain amyloid load and has been approved for routine clinical use based upon visual interpretation as either negative (equating to none or sparse amyloid plaques) or amyloid positive (equating to moderate or frequent plaques). Quantitation is however fundamental to the practice of nuclear medicine and hence can be used to supplement amyloid reading methodology especially in unclear cases.MethodsA total of 2770 [18F]flutemetamol images were collected from 3 clinical studies and 6 research cohorts with available visual reading of [18F]flutemetamol and quantitative analysis of images. These were assessed further to examine both the discordance and concordance between visual and quantitative imaging primarily using thresholds robustly established using pathology as the standard of truth. Scans covered a wide range of cases (i.e. from cognitively unimpaired subjects to patients attending the memory clinics). Methods of quantifying amyloid ranged from using CE/510K cleared marked software (e.g. CortexID, Brass), to other research-based methods (e.g. PMOD, CapAIBL). Additionally, the clinical follow-up of two types of discordance between visual and quantitation (V+Q- and V-Q+) was examined with competing risk regression analysis to assess possible differences in prediction for progression to Alzheimer’s disease (AD) and other diagnoses (OD).ResultsWeighted mean concordance between visual and quantitation using the autopsy-derived threshold was 94% using pons as the reference region. Concordance from a sensitivity analysis which assessed the maximum agreement for each cohort using a range of cut-off values was also estimated at approximately 96% (weighted mean). Agreement was generally higher in clinical cases compared to research cases. V-Q+ discordant cases were 11% more likely to progress to AD than V+Q- for the SUVr with pons as reference region.ConclusionsQuantitation of amyloid PET shows a high agreement vs binary visual reading and also allows for a continuous measure that, in conjunction with possible discordant analysis, could be used in the future to identify possible earlier pathological deposition as well as monitor disease progression and treatment effectiveness.

Plasma biomarkers have shown promising performance in research cohorts in discriminating between different stages of Alzheimer’s disease (AD). Studies in clinical populations are necessary to provide insights on the clinical utility of plasma biomarkers before their implementation in real-world settings. Here we investigated plasma biomarkers (glial fibrillary acidic protein (GFAP), tau phosphorylated at 181 and 231 (pTau181, pTau231), amyloid β (Aβ) 42/40 ratio, neurofilament light) in 126 patients (age = 65 ± 8) who were admitted to the Clinic for Cognitive Disorders, at Karolinska University Hospital. After extensive clinical assessment (including CSF analysis), patients were classified as: mild cognitive impairment (MCI) (n = 75), AD (n = 25), non-AD dementia (n = 16), no dementia (n = 9). To refine the diagnosis, patients were examined with [18F]flutemetamol PET (Aβ-PET). Aβ-PET images were visually rated for positivity/negativity and quantified in Centiloid. Accordingly, 68 Aβ+ and 54 Aβ– patients were identified. Plasma biomarkers were measured using single molecule arrays (SIMOA). Receiver-operated curve (ROC) analyses were performed to detect Aβ-PET+ using the different biomarkers. In the whole cohort, the Aβ-PET centiloid values correlated positively with plasma GFAP, pTau231, pTau181, and negatively with Aβ42/40 ratio. While in the whole MCI group, only GFAP was associated with Aβ PET centiloid. In ROC analyses, among the standalone biomarkers, GFAP showed the highest area under the curve discriminating Aβ+ and Aβ– compared to other plasma biomarkers. The combination of plasma biomarkers via regression was the most predictive of Aβ-PET, especially in the MCI group (prior to PET, n = 75) (sensitivity = 100%, specificity = 82%, negative predictive value = 100%). In our cohort of memory clinic patients (mainly MCI), the combination of plasma biomarkers was sensitive in ruling out Aβ-PET negative individuals, thus suggesting a potential role as rule-out tool in clinical practice.

PurposeTo assess the clinical impact and incremental diagnostic value of 18F-fluorodeoxyglucose (FDG-PET) among memory clinic patients with uncertain diagnosis.MethodsThe study population consisted of 277 patients who, despite extensive baseline cognitive assessment, MRI, and CSF analyses, had an uncertain diagnosis of mild cognitive impairment (MCI) (n = 177) or dementia (n = 100). After baseline diagnosis, each patient underwent an FDG-PET, followed by a post-FDG-PET diagnosis formulation. We evaluated (i) the change in diagnosis (baseline vs. post-FDG-PET), (ii) the change in diagnostic accuracy when comparing each baseline and post-FDG-PET diagnosis to a long-term follow-up (3.6 ± 1.8 years) diagnosis used as reference, and (iii) comparative FDG-PET performance testing in MCI and dementia conditions.ResultsFDG-PET led to a change in diagnosis in 86 of 277 (31%) patients, in particular in 57 of 177 (32%) MCI and in 29 of 100 (29%) dementia patients. Diagnostic change was greater than two-fold in the sub-sample of cases with dementia “of unclear etiology” (change in diagnosis in 20 of 32 (63%) patients). In the dementia group, after results of FDG-PET, diagnostic accuracy improved from 77 to 90% in Alzheimer’s disease (AD) and from 85 to 94% in frontotemporal lobar degeneration (FTLD) patients (p < 0.01). FDG-PET performed better in dementia than in MCI (positive likelihood ratios >5 and < 5, respectively).ConclusionWithin a selected clinical population, FDG-PET has a significant clinical impact, both in early and differential diagnosis of uncertain dementia. FDG-PET provides significant incremental value to detect AD and FTLD over a clinical diagnosis of uncertain dementia.

Purpose Several radiotracers that bind to fibrillar amyloid-beta in the brain have been developed and used in various patient cohorts. This study aimed to investigate the comparability of two amyloid positron emission tomography (PET) tracers as well as examine how age affects the discriminative properties of amyloid PET imaging. Methods Fifty-one healthy controls (HCs), 72 patients with mild cognitive impairment (MCI) and 90 patients with Alzheimer’s disease (AD) from a European cohort were scanned with [11C]Pittsburgh compound-B (PIB) and compared with an age-, sex- and disease severity-matched population of 51 HC, 72 MCI and 84 AD patients from a North American cohort who were scanned with [18F]Florbetapir. An additional North American population of 246 HC, 342 MCI and 138 AD patients with a Florbetapir scan was split by age (55–75 vs 76–93 y) into groups matched for gender and disease severity. PET template-based analyses were used to quantify regional tracer uptake. Results The mean regional uptake patterns were similar and strong correlations were found between the two tracers across the regions of interest in HC (ρ = 0.671, p  = 0.02), amyloid-positive MCI (ρ = 0.902, p  < 0.001) and AD patients (ρ = 0.853, p  < 0.001). The application of the Florbetapir cut-off point resulted in a higher proportion of amyloid-positive HC and a lower proportion of amyloid-positive AD patients in the older group (28 and 30 %, respectively) than in the younger group (19 and 20 %, respectively). Conclusions These results illustrate the comparability of Florbetapir and PIB in unrelated but matched patient populations. The role of amyloid PET imaging becomes increasingly important with increasing age in the diagnostic assessment of clinically impaired patients.

β-amyloid (Aβ) pathology is not always coupled with Alzheimer’s disease (AD) relevant cognitive decline. We assessed the accuracy of tau PET to identify Aβ(+) individuals who show prospective disease progression. 396 cognitively unimpaired and impaired individuals with baseline Aβ and tau PET and a follow-up of ≥ 2 years were selected from the Alzheimer’s Disease Neuroimaging Initiative dataset. The participants were dichotomously grouped based on either clinical conversion (i.e., change of diagnosis) or cognitive deterioration (fast (FDs) vs. slow decliners (SDs)) using data-driven clustering of the individual annual rates of cognitive decline. To assess cognitive decline in individuals with isolated Aβ(+) or absence of both Aβ and tau (T) pathologies, we investigated the prevalence of non-AD comorbidities and FDG PET hypometabolism patterns suggestive of AD. Baseline tau PET uptake was higher in Aβ(+)FDs than in Aβ(-)FD/SDs and Aβ(+)SDs, independently of baseline cognitive status. Baseline tau PET uptake identified MCI Aβ(+) Converters and Aβ(+)FDs with an area under the curve of 0.85 and 0.87 (composite temporal region of interest) respectively, and was linearly related to the annual rate of cognitive decline in Aβ(+) individuals. The T(+) individuals constituted largely a subgroup of those being Aβ(+) and those clustered as FDs. The most common biomarker profiles in FDs ( n  = 70) were Aβ(+)T(+) ( n  = 34, 49%) and Aβ(+)T(-) ( n  = 19, 27%). Baseline Aβ load was higher in Aβ(+)T(+)FDs (M = 83.03 ± 31.42CL) than in Aβ(+)T(-)FDs (M = 63.67 ± 26.75CL) ( p -value = 0.038). Depression diagnosis was more prevalent in Aβ(+)T(-)FDs compared to Aβ(+)T(+)FDs (47% vs. 15%, p -value = 0.021), as were FDG PET hypometabolism pattern not suggestive of AD (86% vs. 50%, p -value = 0.039). Our findings suggest that high tau PET uptake is coupled with both Aβ pathology and accelerated cognitive decline. In cases of isolated Aβ(+), cognitive decline may be associated with changes within the AD spectrum in a multi-morbidity context, i.e., mixed AD.

PurposeTo investigate the impact of amyloid PET with [18F]flutemetamol on diagnosis and treatment management in a cohort of patients attending a tertiary memory clinic in whom, despite extensive cognitive assessment including neuropsychological testing, structural imaging, CSF biomarker analysis and in some cases [18F]FDG PET, the diagnosis remained unclear.MethodsThe study population consisted of 207 patients with a clinical diagnosis prior to [18F]flutemetamol PET including mild cognitive impairment (MCI; n = 131), Alzheimer’s disease (AD; n = 41), non-AD (n = 10), dementia not otherwise specified (dementia NOS; n = 20) and subjective cognitive decline (SCD; n = 5).ResultsAmyloid positivity was found in 53% of MCI, 68% of AD, 20% of non-AD, 20% of dementia NOS, and 60% of SCD patients. [18F]Flutemetamol PET led, overall, to a change in diagnosis in 92 of the 207 patients (44%). A high percentage of patients with a change in diagnosis was observed in the MCI group (n = 67, 51%) and in the dementia NOS group (n = 11; 55%), followed by the non-AD and AD (30% and 20%, respectively). A significant increase in cholinesterase inhibitor treatment was observed after [18F]flutemetamol PET (+218%, 34 patients before and 108 patients after).ConclusionThe present study lends support to the clinical value of amyloid PET in patients with an uncertain diagnosis in the tertiary memory clinic setting.

Purpose The aim of this study was to explore the cerebral distribution of the tau-specific PET tracer [ 18 F]THK5317 (also known as (S) -[ 18 F]THK5117) retention in different stages of Alzheimer’s disease; and study any associations with markers of hypometabolism and amyloid-beta deposition. Methods Thirty-three individuals were enrolled, including nine patients with Alzheimer’s disease dementia, thirteen with mild cognitive impairment (MCI), two with non-Alzheimer’s disease dementia, and nine healthy controls (five young and four elderly). In a multi-tracer PET design [ 18 F]THK5317, [ 11 C] Pittsburgh compound B ([ 11 C]PIB), and [ 18 F]FDG were used to assess tau pathology, amyloid-beta deposition and cerebral glucose metabolism, respectively. The MCI patients were further divided into MCI [ 11 C]PIB-positive ( n  = 11) and MCI [ 11 C]PIB-negative ( n  = 2) groups. Results Test-retest variability for [ 18 F]THK5317-PET was very low (1.17–3.81 %), as shown by retesting five patients. The patients with prodromal (MCI [ 11 C]PIB-positive) and dementia-stage Alzheimer’s disease had significantly higher [ 18 F]THK5317 retention than healthy controls ( p  = 0.002 and p  = 0.001, respectively) in areas exceeding limbic regions, and their discrimination from this control group (using the area under the curve) was >98 %. Focal negative correlations between [ 18 F]THK5317 retention and [ 18 F]FDG uptake were observed mainly in the frontal cortex, and focal positive correlations were found between [ 18 F]THK5317 and [ 11 C]PIB retentions isocortically. One patient with corticobasal degeneration syndrome and one with progressive supranuclear palsy showed no [ 11 C]PIB but high [ 18 F]THK5317 retentions with a different regional distribution from that in Alzheimer’s disease patients. Conclusions The tau-specific PET tracer [ 18 F]THK5317 images in vivo the expected regional distribution of tau pathology. This distribution contrasts with the different patterns of hypometabolism and amyloid-beta deposition.

Background Plasma biomarkers are potential candidates for screening patients for anti‐amyloid therapies. Although early changes have been reported, the duration of Aβ‐accumulation afetr which these changes become abnormal enough to identify Aβ+ individuals has yet to be determined. Method Data were acquired from a cohort assessed at the Memory Clinic, Karolinska University Hospital, Stockholm, Sweden (N = 132). Plasma biomarkers (pTau‐isoforms, GFAP) were analysed with NULISAseq™ CNS. Buildong up on the parameters of a model of Aβ‐accumulation estimated by Schindler et al., 2021, we calculated the time required (AmyloidTime) to reach a specific brain amyloid load on PET. We then estimated AmyloidTime linked with a threshold of biomarker becoming significantly abnormal to detect Aβ‐positivity with 90% sensitivity. By subtracting AmyloidTime from each patient's age, we estimated their Aβ‐accumulation onset ('age‐of‐Aβ‐onset'), identifying young accumulators as those with age‐of‐Aβ‐onset under 50 years, which would correspond to a minimum of 15 years of Aβ‐accumulation at age 65. Result The AmyloidTime corresponding to 90% sensitivity for detecting Aβ+ with the visual read was 2 years prior to the tipping point in Aβ‐accumulation. The AmyloidTime required for plasma biomarkers become abnormal enough to provide 90% sensitivity in identifying Aβ+ PET scans were: 6.7 years for pTau217, 8.4 – for pTau231,10 ‐ for GFAP, 10.6 ‐ for pTau181 (Figure 1). Plasma pTau217, pTau231, pTau181 levels, but not GFAP, were significantly higher in young Aβ‐accumulators, even after adjusting for the effect of Aβ‐load or ‐Time (Figure 2). Conclusion Plasma pTau217 enables 90% sensitivity in identifying Aβ+ individuals at 6.7 years after reaching the point of accelerated Aβ‐accumulation. Younger Aβ‐accumulators showed higher levels of pTau isoforms in plasma, suggesting an exacerbation of tau pathology in these individuals, which is consistent with a more aggressive pathology in this age group.