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Background: CSF biomarkers are well characterised as surrogate markers for amyloid deposition and neurofibrillary tau tangles reflecting pathological features of Alzheimer's disease. Amyloid PET images provide a visual and quantitative assessment of brain amyloid burden. In diagnostic workup these biomarkers are considered interchangeable, however their relationship is not fully understood. Methods: Centiloid (CL) values for 387 [18F]flutemetamol images from the Swedish BioFINDER I study were derived following the methods of Klunk et.al. (Alzheimer's & Dementia, 2015). Subjects, comprising Controls, SCD and MCI (Table 1), were ranked by Centiloid values (lowest to highest) and the average for each consecutive group of 30 subjects calculated. Corresponding CSF biomarker values (Aβ42/40 and pTau181) were plotted for each group. Results: Subjects in groups averaging ‐7.7 CL to 29.0 CL had a mean(±SD) Aβ 42/40 ratio of 0.144 (±0.033, range 0.051 to 0.236, n = 240) with no statistically significant differences between groups (Fig. 1A). A statistically significant fall in CSF Aβ42/40 ratio between the 29.0 CL and 47.3 CL groups (from 0.134 (±0.041, 0.066 to 0.236) to 0.088(±0.031, 0.045 to 0.183), p<0.0001) was observed. The average CSF Aβ42/40 ratio value for all subjects ranging from 66.3 Cl to129.8 CL was 0.072 (±0.017, 0.030 to 0.127, n = 117) with no statistically significant difference in CSF levels across these CL bands. Subjects in the groups averaging ‐7.7 CL to 29.0 CL had a mean(±SD) pTau181 value of 48.9 pg/mL (±15.9, 22 to 138) with no statistically significant difference between groups (Fig. 1B). A statistically significant (p<0.05) increase between 29.0 CL (47.3±19.3, 24 to 116 pg/mL) and 47.3 CL groups (67.8±29.4, 24 to 175 pg/mL) was observed. For all Subjects from 66.3 CL to129.8 CL mean pTau181 levels were 79.6 (±30.3, 32 to 209 pg/mL) with no statistically significant difference across these CL bands. Conclusions: Comparison of [18F]flutemetamol image quantitation and CSF biomarkers indicates CSF Aβ42/40 and pTau181 starts to show amyloid positivity at approximately 30‐40 CL units. Recent comparisons between visual rating and Centiloids from PET imaging indicate visual positivity from approximately 20 CL units indicating sensitivity of amyloid PET imaging for the detection of early amyloid deposition. [ABSTRACT FROM AUTHOR]

Background: CSF biomarkers are well characterised as surrogate markers for amyloid deposition and neurofibrillary tau tangles reflecting pathological features of Alzheimer's disease. Amyloid PET images provide a visual and quantitative assessment of brain amyloid burden. In diagnostic workup these biomarkers are considered interchangeable, however their relationship is not fully understood. Method: Centiloid (CL) values for 387 [18F]flutemetamol images from the Swedish BioFINDER I study were derived following the methods of Klunk et.al. (Alzheimer's & Dementia, 2015). Subjects, comprising Controls, SCD and MCI (Table 1), were ranked by Centiloid values (lowest to highest) and the average for each consecutive group of 30 subjects calculated. Corresponding CSF biomarker values (Aß42/40 and pTau181) were plotted for each group. Result: Subjects in groups averaging ‐7.7 CL to 29.0 CL had a mean(±SD) Aß 42/40 ratio of 0.144 (±0.033, range 0.051 to 0.236, n = 240) with no statistically significant differences between groups (Fig. 1A). A statistically significant fall in CSF Aß42/40 ratio between the 29.0 CL and 47.3 CL groups (from 0.134 (+0.041, 0.066 to 0.236) to 0.088(+0.031, 0.045 to 0.183), p<0.0001) was observed. The average CSF Aß42/40 ratio value for all subjects ranging from 66.3 Cl to129.8 CL was 0.072 (±0.017, 0.030 to 0.127, n = 117) with no statistically significant difference in CSF levels across these CL bands. Subjects in the groups averaging ‐7.7 CL to 29.0 CL had a mean(±SD) pTau181 value of 48.9 pg/mL (±15.9, 22 to 138) with no statistically significant difference between groups (Fig. 1B). A statistically significant (p<0.05) increase between 29.0 CL (47.3±19.3, 24 to 116 pg/mL) and 47.3 CL groups (67.8±29.4, 24 to 175 pg/mL) was observed. For all Subjects from 66.3 CL to129.8 CL mean pTau181 levels were 79.6 (±30.3, 32 to 209 pg/mL) with no statistically significant difference across these CL bands. Conclusion: Comparison of [18F]flutemetamol image quantitation and CSF biomarkers indicates CSF Aß42/40 and pTau181 starts to show amyloid positivity at approximately 30‐40 CL units. Recent comparisons between visual rating and Centiloids from PET imaging indicate visual positivity from approximately 20 CL units indicating sensitivity of amyloid PET imaging for the detection of early amyloid deposition. [ABSTRACT FROM AUTHOR]

Background: The pathophysiological events associated with the earliest cerebral amyloid deposition are not fully understood. Here, we describe, in cognitively unimpaired individuals, the association between amyloid load and cerebrospinal fluid (CSF) biomarkers of the pathogenic events described in Alzheimer's disease (AD): core AD biomarkers (Aβ42/40 and pTau), synaptic dysfunction (neurogranin), neurodegeneration (neurofilament light [NfL]), microglial and astroglial‐related response (sTREM2, YKL‐40, GFAP, S100b), neuroinflammation (IL‐6) and α‐synuclein. Method: The first consecutive 293 participants of the ALFA+ study with both valid CSF biomarker determination and [18F]flutemetamol amyloid PET scans acquired within one year time were included. We measured the CSF biomarkers using the Roche NeuroToolkit robust prototype assays and the commercially available Elecsys® Phospho‐Tau CSF assay. Centiloid (CL) values and SUVr images were derived from [18F]flutemetamol scans using the whole cerebellum as reference region. We sought for correlations between CSF biomarkers and cerebral amyloid deposition both globally, using the Centiloids as outcome variable, and regionally, using a voxel‐wise approach. The final model used for both analyses included: main effects of all CSF biomarkers and also interaction effects between CSF biomarkers with age, sex and APOE‐ε4 status. Interactions were only included if they showed a trend effect (p<0.10) in the global analysis. Neurogranin and pTau were included in the model as a unique vector due to their high collinearity (r>0.9). Result: In the global analysis, pTau‐neurogranin, NfL, GFAP and YKL‐40 showed a positive correlation with global amyloid burden (Table 1). Significant interaction effects were found between: age*pTau‐neurogranin (older participants show higher amyloid deposition with higher pTau‐neurogranin); sex*sTREM2 (women with higher sTREM2 had higher amyloid deposition); APOE‐ε4*NfL (carriers with higher NfL had higher amyloid deposition) and APOE‐ε4*α‐synuclein (carriers with higher α‐synuclein had lower amyloid deposition), which are shown in Table 1 and Figure 1. Regional main effects are shown in Figure 2 while interactions are shown in Figure 3. Conclusion: Neurodegeneration and inflammation pathways are associated to early amyloid deposition. The fact that biomarkers of these pathways show interactive effects with the main AD risk factors (age, sex and APOE‐ε4) also suggests that they play different roles in the early stage of the Alzheimer's continuum. [ABSTRACT FROM AUTHOR]

Background: Visual assessment of [18F]flutemetamol amyloid PET images, performed regionally (frontal, lateral temporal, posterior cingulate/precuneus, temporo‐parietal, striatum), is recommended, with a positive (abnormal) classification if any one of these regions is clearly positive. Changes in the striatum, known to contain extensive fibrillar amyloid deposits in AD, could identify likely converters in Subjective Cognitive Decline (SDC) or Mild Cognitive Impairment (MCI) subjects. CSF biomarkers are proven to detect abnormal amyloid metabolism in early stages of AD, with Aβ42/40 more reliable than Aβ42 alone. We applied a novel regional Centiloid Atlas, based on standard Centiloid (CL) cortical VOIs with additional striatal , aiming to better stratify amyloid abnormalities. Correlation between CL and Aβ42/40 was also investigated. Methods: The regional Centiloid atlas, developed by overlaying standard CL cortical VOI on AAL atlas and incorporating additional striatal regions, was applied to 394 [18F]flutemetamol subjects from the Swedish BioFINDER I study following the methods of Klunk et.al. (Alzheimer's & Dementia, 2015). In total, 387 Subjects with CSF Aβ42/40 information from the ADx analysis platform available, were included for analysis. Results: Average Aβ42/40 and CL values are presented (Table 1). Statistically significant differences in Aβ42/40 between control and MCI (p=0.0004), and SCD and MCI (p=0.0031) were observed (Fig. 1). Statistically significant differences in CL were also found in the cortical (p=0.007 or less) and striatal (p=0.028 or less) regions across all groups. Correlation between CL and Aβ42/40 for controls was moderate in both cortical (R2=0.35, Fig. 1) and striatal (R2=0.33, Fig. 2) regions. Similar correlations were found for SCD (Cortical: R2=0.52, Striatal: R2=0.48) and MCI (Cortical: R2=0.52, Striatal: R2=0.54). Conclusions: Standard Centiloid PET quantification provides global values across the cortical region. Identifying combined striatal differences in [18F]flutemetamol retention may provide additional information, aiding. Detecting these abnormalities and understanding their relationship to CSF biomarker changes may be useful in identifying those subjects for progression to AD. Further work to identify differences between cortical and striatal uptake in within subjects is ongoing. [ABSTRACT FROM AUTHOR]

Background: The Centiloid method enables direct quantitative comparison of amyloid‐PET scans acquired with different amyloid tracers. However, it does not explicitly model differences in image resolution which may have an impact on quantification, particularly in multicenter studies. This study evaluated the stability of the Centiloid scale with or without post‐processing smoothing to simulate distinct reconstruction settings. In addition, the effect of reference region selection was assessed. Methods: A total of 318 [18F]flutemetamol PET scans from the ADC and EMIF‐AD Twin 60++ cohorts are included in this study (Table 1). Scans were reconstructed with LOR‐Ramla algorithm. Additionally, images were smoothed to get an effective resolution of 8mm. All images were quantified with a validated Centiloid (CL) pipeline using the GAAIN cortical composite and five different reference regions (whole cerebellum, cerebellum gray, pons, whole cerebellum+brainstem and eroded white‐matter). To assess the impact of reconstruction settings on CL values, we calculated the mean absolute difference between CL values of the original reconstructions vs the smoothed images. Differences above 5CL were considered as relevant. Finally, Bland‐Altman plots were used to assess whether any proportional bias was present between these outcome measures. Results: Figure 1 displays image quality differences between local reconstruction settings and post‐smoothed images. CL values were not significantly affected by reconstruction methods using either whole cerebellum, whole cerebellum+brainstem, or white‐matter as reference region (Table 2). However, for the images with a higher level of amyloid, ΔCL was higher than 5 using whole cerebellum. On the contrary, the CL was highly affected by image resolution when using the pons as reference region, introducing constant bias for all ranges of amyloid burden. Using cerebellum gray, ΔCL has an inverse linear relationship with amyloid burden, though it was below 5CL for subjects with low to moderate amyloid burden, and differences above 5 were observed for images with CL above 80 (Figure 2). Conclusion: Whole cerebellum and whole cerebellum+brainstem, the standard reference regions in CL quantification, are capable of providing stable CL values irrespective of image resolution for a wide range of amyloid burden. Other reference regions were less stable. [ABSTRACT FROM AUTHOR]

Background: Weight loss is common in Alzheimer's disease (AD) and may start before cognitive impairment, but little is known about the relationship between weight change and AD biomarkers in the preclinical Alzheimer's continuum. We aimed to assess the association between weight change and AD biomarkers and cognitive performance in cognitively unimpaired (CU) adults from the ALFA+ study. Method: We analyzed data from 295 CU middle‐aged adults with two consecutive (mean interval 3.7 years) weight measures and neuropsychological assessments (n=267). Preclinical Alzheimer's Cognitive Composite (PACC) was computed as an average of z‐scores of episodic memory, semantic fluency and processing speed measures. We measured cerebrospinal fluid (CSF) levels of Aβ42, Aβ40, p‐tau, t‐tau, neurofilament light (NfL) and neurogranin using the Roche NeuroToolKit and Elecsys® immunoassays (n=280), and performed a [18F]flutemetamol PET scan in the follow‐up visit (n=252). PET results were dichotomized (Aβ positive or negative) based on visual read. Using established CSF cutoffs, we grouped participants in three biomarker categories: A‐T‐ (both Aβ and tau negative), A+T‐ (Aβ positive only) and A+T+ (both Aβ and tau positive). Linear and logistic regression and Kernel‐weighted local polynomial smoothing were used for analyses, which were adjusted by age, sex, body mass index, APOE genotype and years of education, as appropriate. Result: Weight loss predicted amyloid PET positivity (odds ratio 1.1, p=0.028) and was associated with significantly higher CSF levels of p‐tau (p=0.005), t‐tau (p=0.005) and neurogranin (p=0.002) (Table 1). Weight loss ≥5% (a standard cutoff for clinically relevant weight change) yielded 87% specificity and 30% sensitivity for amyloid PET positivity. Participants from the A+T+ group experienced significantly higher weight loss compared to A+T‐ (p=0.0029) and A‐T‐ (p=0.0005) groups (Figure 1). Weight loss was not associated with longitudinal changes in cognitive performance and preceded cognitive decline when using Aβ42/40 as a proxy of AD change progression (Table 1, Figure 2). Conclusion: Weight loss predicts amyloid PET positivity in CU middle‐aged adults. Our results suggest that weight loss is mainly related with changes in tau‐related and synaptic dysfunction biomarkers and may precede cognitive decline in the preclinical stage of the Alzheimer's continuum. [ABSTRACT FROM AUTHOR]

Background: Amyloid pathology can be detected using cerebrospinal fluid (CSF) biomarkers and positron emission tomography (PET) imaging, even though they measure two distinct beta‐amyloid (Aβ) pools. While CSF Aβ concentrations reflect the clearance of soluble amyloid species from the brain, PET imaging informs on the presence of insoluble fibrillary plaques in the brain tissue. Many studies have shown the concordance between the two measures in individuals along the Alzheimer's continuum. Here, we assessed whether APOE‐ε4, the major genetic risk factor for Alzheimer's disease (AD), affects the pattern of cerebral Aβ load as a function of CSF Aβ levels. Method: The first consecutive 276 cognitively unimpaired (CDR = 0) individuals of the ALFA+ study, aged between 48 and 74, with valid structural MRI and [18F]flutemetamol (FTM) PET, APOE genotyping and CSF samples were included. Aβ40 and Aβ42 concentrations were determined with the Roche NeuroToolKit (Roche Diagnostics) and parametric standardized uptake value ratio images (SUVr) were calculated with the whole cerebellum as reference region. A voxel‐wise regression was performed to predict FTM uptake, with the following independent variables: age, sex, APOE‐ε4, and CSF Aβ42/40 ratio. Statistical threshold was set to p < 0.001 with a cluster extent correction of 100 voxels. Result: As expected, CSF Aβ42/40 ratio was negatively associated with cerebral FTM uptake (Figure 1a‐1b). Age and APOE‐ε4 gene dose mapped onto distinct spatial patterns of FTM‐binding across the whole sample (Figure 2b‐2c). Importantly, we found a significant interaction between CSF Aβ42/40 and APOE‐ε4 indicating that carriers had a negative association between fluid and imaging Aβ measures in the bilateral hippocampus, as well as the bilateral middle temporal and temporo‐parietal junction (Figure 3). Conclusion: Compared to non‐carriers, APOE‐ε4 carriers showed higher cerebral Ab load for any given level of CSF Aβ42/40 in areas that are not typically vulnerable to Aβ deposition yet undergoing neurodegeneration early in the Alzheimer's continuum, such as the hippocampus. This suggests that APOE‐ε4 may influence the biological mechanisms regulating the equilibrium between soluble and deposited amyloid, which may transcend the hindering effect caused by Aβ fibrillary plaques and render medial temporal lobe structures particularly vulnerable when Aβ metabolism becomes deficient. [ABSTRACT FROM AUTHOR]

Background: [18F]flutemetamol PET scanning provides information on brain amyloid load and has been approved for routine clinical use based upon visual interpretation of scans as either negative (equating to none or sparse amyloid plaques in pathology SoT), 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. Data was collected from multiple sources to assess comparability of the two methods of image interpretation. Method: 9 separate studies where visual reading of [18F]flutemetamol and quantitative analysis of images was available were combined to form a single cohort to measure the percentage agreement between the two methods of image interpretation. Scans covered a wide range of cases (eg from cognitively unimpaired subjects to patients attending the memory clinics see table). Methods of quantifying amyloid ranged from using CE marked/510K cleared software (eg Cortex ID), to other research tools (eg PMOD, CapAIBL) as well as newer methods such as AmyloidIQ. Concordance between visual and quantiative imaging used thresholds robustly established using pathology as the standard of truth Result: A total of 2,566 [18F] flutemetamol images were collected from 9 clinical studies, visually interpreted by readers using the approved reading methodology and compared to quantitative measures using thresholds which had a binary delineation of amyloid. The mean concordance between visual read and quantitation was 95.01% (SD 3.00%), the median was 95.0%, and the range of the 9 studies was from 89.7% to 99.0%. A total of 379 images were compared using CE marked/510k cleared amyloid quantitation software and the percent agreement in these studies was 98.8% to 99.0%. Conclusion: Given the high concordance of visual and quantitation from over 2500 scans, users are able apply quantitative amyloid PET software methods to supplement visual inspection. The decision to use these methods as an adjunct to visual reading is left to the choice of the physician as both methods of image interpretation are comparable. The European Medicines Agency (EMA) have agreed to update the prescribing instructions for [18F]flutemetamol (Vizamyl) based upon this analysis. [ABSTRACT FROM AUTHOR]

Background: Microglial activation plays a significant role, and could be quantified using PET tracers targeting TSPO. We evaluated the use of Flutriciclamide PET tracer in a cohort of subject diagnosed with Alzheimer's Disease (AD) or Mild Cognitive Impairment (MCI), using spectral analysis Method: Twenty‐three subjects with AD, fourteen with MCI and eleven healthy controls (HC) underwent 3T MRI and Flutriciclamide PET with arterial input. Spectral analysis was used to create parametric maps, and IRF at 90 minutes was sampled for region of interest analysis. IRF90 parametric maps were also used for voxel‐wise SPM group analysis. Result: Mean ages were 70.65±6.2‐ for AD, 70.3±6‐ for MCI and 63.4±1.4‐ for HCs. MCI and AD were age‐matched. Significant difference was found in the MMSE scores between AD and MCI (mean and SD respectively 21.8±3.7‐ and 26.8±3‐, p<0.001) and between AD and HC (mean and SD of HC 28.9±1.4‐, p<0.001). Significant difference was found in the hippocampal volumes between AD and MCI (mean 2711±477 on the left and 2750±455 on the right for AD, and 3942±284 and 4048±394 for MCI, p<0.01) and between AD and HC (mean 3942±284 on the left and 4048±394 on the right for HC, p<0.001).Significant difference was also found between MCI and HC (p=0.01 for left hippocampus, p=0.02 for the right one). When evaluating the IRF90 flutriciclamide uptake, significantly higher uptake was found in the AD group compared to HC in temporal (p<0.01), parietal (p=0.012), occipital (p=0.03) lobes and composite brain (p=0.01). In the AD group, IRF90 was higher in temporal lobe when compared with MCI group (p=0.04). SPM group wise analysis showed significantly higher uptake in part of temporal, parietal and occipital regions in AD compared to HCs, and a smaller cluster including part of temporal and occipital lobes when comparing AD and MCI. Conclusion: Our study, conducted in a population with a clinical diagnosis of AD or MCI demonstrated higher widespread flutriciclamide uptake in the AD group compared with HC, and more limited difference between AD and MCI, and can be used quantify neuroinflammation. [ABSTRACT FROM AUTHOR]