[¹⁸F]-Flutemetamol Uptake in Cortex and White Matter: Comparison with Cerebrospinal Fluid Biomarkers and [¹⁸F]-Fludeoxyglucose

Abstract

Flutemetamol (¹⁸F-Flut) is an [¹⁸F]-labelled amyloid PET tracer with increasing availability. The main objectives of this study were to investigate 1) cerebrospinal fluid (CSF) Aβ 1-42 (Aβ₄₂) concentrations associated with regional ¹⁸F-Flut uptake, 2) associations between cortical ¹⁸F-Flut and [¹⁸F]-fludeoxyglucose (¹⁸F-FDG)-PET, and 3) the potential use of ¹⁸F-Flut in WM pathology. Cognitively impaired, nondemented subjects were recruited (n = 44). CSF was drawn, and ¹⁸F-Flut-PET, ¹⁸F-FDG-PET, and MRI performed. Our main findings were: 1) Different Alzheimer’s disease predilection areas showed increased ¹⁸F-Flut retention at different CSF Aβ₄₂ concentrations (posterior regions were involved at higher concentrations). 2) There were strong negative correlations between regional cortical ¹⁸F-Flut and ¹⁸F-FDG uptake. 3) Increased ¹⁸F-Flut uptake were observed in multiple subcortical regions in amyloid positive subjects, including investigated reference regions. However, WM hyperintensity ¹⁸F-Flut standardized uptake value ratios (SUVr) were not significantly different, thus we cannot definitely conclude that the higher uptake in ¹⁸F-Flut(+) is due to amyloid deposition. In conclusion, our findings support clinical use of CSF Aβ₄₂, putatively relate decreasing CSF Aβ₄₂ concentrations to a sequence of regional amyloid deposition, and associate amyloid pathology to cortical hypometabolism. However, we cannot conclude that ¹⁸F-Flut-PET is a suitable marker for WM pathology due to high aberrant WM uptake.

INTRODUCTION

Aggregation and deposition of amyloid-β peptides (Aβ) into plaques is considered a key event in the evolution of Alzheimer’s disease (AD) [1]. There are two types of amyloid biomarkers: assays to measure cerebrospinal fluid (CSF) Aβ 1-42 (Aβ₄₂) and PET tracers binding amyloid. Both reliably identify amyloid plaque deposition [2] and show high concordance in diagnosing AD [3, 4]. They are thus considered equal markers of brain amyloidosis [5], but discrepancies occur [4, 6], demonstrated by, for example, abnormal CSF Aβ₄₂ and negative PET or vice versa. CSF Aβ₄₂ becomes decreased (or pathological) before amyloid PET, as PET tracers bind strongly to fibrillar Aβ and less strongly to diffuse Aβ plaques [7]. Other possible explanations are inconsistencies in amyloid PET rating, incorrect handling or between-laboratory and batch-to-batch CSF assay variability, a known issue for these analyses [8]. Hence standardization is important for correct detection of incipient AD. We have insufficient knowledge about at which CSF Aβ₄₂ concentrations amyloid PET becomes abnormal, and in which areas this happens first. CSF analyses are likely more available, and such knowledge may improve the diagnostic accuracy of CSF Aβ₄₂ also to predict regional deposition at different CSF Aβ₄₂ concentrations.

[¹¹C] Pittsburgh compound B (¹¹C-PiB) [9] is widely investigated, but ¹⁸F-labelled amyloid radiotracers (with increased availability due to longer half-life) have more recently been developed. Herein, we investigate [¹⁸F]-Flutemetamol (¹⁸F-Flut) [10] (a structural analogue to ¹¹C-PiB). ¹¹C-PiB and ¹⁸F-Flut are comparable with respect to cortical retention, but ¹⁸F-Flut typically presents with higher white matter (WM) uptake [11]. Possible explanations for the higher WM ¹⁸F-Flut uptake include non-specific uptake related to lipophilicity [12] in the high lipid content WM, and aberrant binding to the β-sheet structured myelin basic protein [13]. ¹⁸F-Flut clearance is also slower in WM compared to grey matter (GM) [14].

¹⁸F-Flut WM retention may pose challenges due to partial volume effects (PVE) (i.e., spill-over from areas of high uptake to adjacent areas of low uptake) [15]. It is not clear to which extent WM ¹⁸F-Flut uptake depends on pathology (e.g., WM amyloid load or pathology of the capillary bed) or is aberrant and unspecific, warranting further investigation of ¹⁸F-Flut for use in WM.

Applying PVE correction (PVC) enhances quantitative accuracy of ¹⁸F-Flut PET [15], and may also avoid bias in WM analyses. PVC may optimize PET images for studies of WM function and pathology, as we recently demonstrated with [¹⁸F]-fluorodeoxyglucose PET (FDG-PET) [16] (and also with diffusion tensor imaging [17]).

White matter hyperintensities of presumed vascular origin (WMHs) [18] (as visualized on T2-weighted MRI images) are thought to reflect underlying small-vessel cerebrovascular disease (CVD) [18]. There is strong evidence for an association between CVD and AD [19], but it is not certain whether there is a pathomechanistic interaction, or whether CVD is an independent process with manifestations like hippocampal atrophy [20] and synergistic effects on the clinical symptoms of AD. In both AD and CVD [21], there are microvascular alterations with capillary dysfunction, and the neurovascular unit is affected, impairing the blood-brain barrier and clearance of soluble substances [22, 23], and even amyloid deposits may be located at the outer basement membrane (cerebral amyloid angiopathy (CAA) type 1 [24]).

Establishing the potential contribution of amyloid pathology to vascular alterations (or vice versa) seen in AD may be of diagnostic, preventive and therapeutic value. To date, a handful studies have applied ¹¹C-PiB to analyses of WM, showing reduced retention in WMHs compared to normal-appearing WM (NAWM) [25–27]. ¹¹C-PiB is also shown to label vascular amyloid depositions [28, 29] as well as cortical deposits, a feature that may also be attributable to ¹⁸F-Flut given their structural similarities. WM ¹⁸F-Flut binding has been characterized [13], but to the best of our knowledge use of ¹⁸F-Flut in WMH has not been examined.

The objectives of the current study were therefore 1) to compare CSF Aβ₄₂ and ¹⁸F-Flut uptake to evaluate the different Aβ concentrations at which regional ¹⁸F-Flut uptake becomes abnormal, 2) to investigate associations between cortical ¹⁸F-Flut and ¹⁸F-FDG uptake, and 3) to investigate the potential use of ¹⁸F-Flut in analyses of WM pathology. For comparative purposes, we analyzed ¹⁸F-Flut uptake in different reference regions, regions not prone to plaque deposition, and also compared subcortical retention in subjects classified as ¹⁸F-Flut positive and ¹⁸F-Flut negative to investigate potential contribution of amyloid dysmetabolism to WM pathology in AD.

MATERIAL AND METHODS

Subjects

Forty-eight subjects were included from a longitudinal cohort followed at a university hospital from 2013 to 2016 (Fig. 1), of whom 29 are female. Inclusion criteria were age 40–80, complaints of cognitive decline relative to the previously normal state and Scandinavian first language. Exclusion criteria were established dementia, previous brain injury (including recognized stroke), neurodevelopmental disorder, and established serious psychiatric or somatic disorder, as well as drug use (including chemotherapy), which could significantly influence the cognitive function. All enrolled subjects had a standardized clinical assessment including physical examination, magnetic resonance imaging (MRI), lumbar puncture (n = 42), ¹⁸F-Flut PET (n = 48), and ¹⁸F-FDG PET (n = 34). All of the abovementioned procedures were performed within 3.5 months at the given assessment. The current data were collected either from time of inclusion (baseline) (n = 28), or at second assessment two years after inclusion (n = 20), given that they had not converted to dementia. The investigators were blinded to the subjects’ identities.

Fig.1.

Inclusions and exclusions in the cohort. Two subjects of <55 years of age were excluded to limit between-group age differences. PET, positron emission tomography; WMH, white matter hyperintensities; ¹⁸F-Flut, ¹⁸F-Flutemetamol.

Physicians trained in diagnosing cognitive disorders established the final diagnoses in consensus conferences. Almdahl et al. have described the diagnostic criteria, including the cognitive test battery, in detail [30]. Mild cognitive impairment (MCI, n = 15) was defined according to the core criteria in the recommendations from the National Institute on Aging/Alzheimer’s Association (NIA/AA) [31]. Subjective cognitive decline (SCD) (n = 29) was defined based on the recommendations by the Subjective Cognitive Decline Initiative Working Group [32]. All forty-eight subjects had preserved functions of daily living, with Clinical Dementia Rating score≤0.5 [33], i.e., not demented. Two subjects were classified as preclinical AD stage 2 according to recommendations from the NIA/AA [34], based on one positive amyloid marker (positive ¹⁸F-Flut PET) and evidence of neuronal injury (pathological CSF total and/or phosphorylated tau). Two additional subjects were classified as preclinical AD stage 3 according the NIA/AA criteria, based on positive ¹⁸F-Flut PET, evidence of neurodegeneration (pathological CSF tau), and with subtle cognitive decline.

One subject was excluded from the current study due to an incomplete PET scan (one frame only). One subject was excluded due to total WMH volume <350 mm³, as we considered this as a minimum volume for robust analyses. Between-group age differences were limited by excluding subjects aged <50 years (n = 2). The remaining forty-four subjects were dichotomized according to cortical ¹⁸F-Flut PET uptake and classified as positive (¹⁸F-Flut(+)) or negative (¹⁸F-Flut(–)), as determined by visual interpretation by trained readers blinded to any clinical information and CSF Aβ₄₂. The images were compared to a normal brain database supplied by the manufacturer of the tracer (CortexID, GE Healthcare, Chicago, IL, USA) and the subjects were classified as ¹⁸F-Flut(+) or ¹⁸F-Flut(–) according to the manufacturer’s guidelines.

Written informed consent was obtained from all participants prior to enrollment. The study was approved by the Regional Committee for Medical and Health Research Ethics, South East Norway (approval 2013/150) and conducted in accordance with the Helsinki Declaration.

MRI/PET Imaging acquisition

MRI was performed on a Philips Ingenia 3 Tesla system. A 3D T1-weighted turbo field echo sequence (TR/TE/TI/FA = 4.5 ms/2.2 ms/853 ms/8°, matrix = 256×213, 170 slices, thickness = 1.2 mm, in-plane resolution of 1 mm×1.2 mm) and a 3D axial fluid-attenuated inversion recovery (FLAIR) sequence (TR/TE/TI = 8000 ms/418 ms/2400 ms, slice thickness = 1.2 mm) were obtained.

¹⁸F-Flut and ¹⁸F-FDG PET were obtained from the same GE Discovery 690 PET/CT scanner on two separate days. Subjects fasted at least 6 hours in advance and blood glucose was routinely measured prior to ¹⁸F-FDG injection (subjects did not continue if blood glucose exceeded 8.0 mmol/l). For both the ¹⁸F-FDG and ¹⁸F-Flut scan sessions, a bolus injection of 185 MBq (5 mCi) was followed by rest before positioning the subject head-first supine in the scanner. Prior to PET acquisition, a low-dose CT scan for attenuation correction was acquired. PET scanning in 3D-mode started 45 minutes after injection of ¹⁸F-FDG and 90 minutes after ¹⁸F-Flut. PET data were acquired for 10 minutes for ¹⁸F-FDG (six frames) and for 20 minutes (four frames of five minutes) for ¹⁸F-Flut. Acquired data were corrected for random events, dead time, attenuation, scatter, and decay. PET volumes were reconstructed using an iterative algorithm (VUE Point FX SharpIR with six iterations, 24 subsets for ¹⁸F-FDG, four iterations, 16 subsets for ¹⁸F-Flut) and a post-reconstruction 3D Gaussian filter of 3 mm full-width half maximum was applied. The PET detector was cylindrical, 700 mm in the xy-plane and 153 mm in the z-plane, and there were 47 slices for both ¹⁸F-FDG and ¹⁸F-Flut. The matrix was 256×256 for ¹⁸F-FDG and 192×192 for ¹⁸F-Flut. Slice thickness was thus 3.27 mm for both ¹⁸F-FDG and ¹⁸F-Flut, original (xy plane) pixel size 3.27 mm×3.27 mm for ¹⁸F-FDG and 3.64 mm×3.64 mm for ¹⁸F-Flut.

Image processing

For the quantitative assessment, motion correction of the ¹⁸F-Flut PET was performed using frame-by-frame rigid registration, then the frames were summed to a single time-frame image and rigidly registered to the anatomical MRI volume using a 6-parameter rigid registration as implemented in the Spatial Parametrical Mapping (SPM 12, Wellcome Trust Centre for Neuroimaging, UCL, UK) toolbox.

Due to the relatively poor scanner resolution, PET images are susceptible to partial volume effects. Because these spill-over effects may impact the quantification of the regional uptake [15], we recently developed an algorithm to correct for partial volume effects [35]. This is a voxel-based algorithm that relies on MRI based segmentation of anatomical regions and accurate measurements of the effective point spread function of the PET imaging process. The PVC algorithm was applied to the ¹⁸F-Flut- and ¹⁸F-FDG PET images using an estimated PSF of 5.34×4.79×5.55 mm [36]. ¹⁸F-Flut- and ¹⁸F-FDG PET images and structural images were further co-registered to high-resolution 3D T1-weighted MRI scans for WMH segmentation and measurements. Registrations were manually inspected for accuracy.

An in-house developed object-based supervised machine-learning algorithm was used for automated segmentation and quantification of WMHs based on FLAIR image intensity and masks of tissue types [37]. For all segmentations, a single investigator (LFK) performed manual inspection of accuracy and edited when necessary. The reconstruction and segmentation of WM was performed with the FreeSurfer image analysis suite version 5.3.0 (http://surfer.nmr.mgh.harvard.edu/). This includes segmentation of the subcortical white-matter and deep grey-matter volumetric structures, and parcellation of the cortical surface. NAWM was defined by subtracting WMH from the total WM. Analyses and calculations were performed using The Oxford Centre for Functional MRI of the Brain (FMRIB) Software Library (FSL) version 5.0 [38, 39]. ¹⁸F-Flut and ¹⁸F-FDG standardized uptake value ratios (SUVr) were obtained by normalization to the cerebellar cortex. Prior to normalization, the cerebellar mask was eroded by 1 mm to avoid partial volume effects, inaccurate segmentation or co-registration. ¹⁸F-Flut and ¹⁸F-FDG uptake were analyzed in five pre-selected cortical regions of interest (ROIs), as defined by Desikan et al. [40], known to hold substantial amyloid plaques in AD: the precuneus and posterior cingulate combined, anterior cingulate, prefrontal, inferior parietal, and lateral temporal cortex [41]. These are also the regions used for clinical evaluation of ¹⁸F-Flut status according to the manufacturer’s guidelines [42]. We chose to only include early predilection sites for amyloid deposition in this subanalysis. We further calculated a composite SUVr by averaging the uptake in the above-mentioned regions. For comparative purposes, the ¹⁸F-Flut uptake was also analyzed in the hippocampus, parahippocampal gyrus, and entorhinal cortex. The ¹⁸F-Flut and ¹⁸F-FDG uptake for each region were averaged across the hemispheres, and the cerebellar cortex was used as the reference region.

Cerebrospinal fluid analyses

Lumbar puncture was performed consecutively after inclusion, between 8 a.m. and noon, at the L3/L4, L4/L5, or L5/S1 interspace. CSF was examined for the traditional CSF biomarkers Aβ₄₂, T-tau, and P-tau and quantified with commercially available ELISAs (Fujirebio Europe, Gent, Belgium). The analyses were carried out in accordance with the manufacturers’ instructions at the national reference laboratory for these tests at the Department of Interdisciplinary Laboratory Medicine and Medical Biochemistry, Akershus University Hospital. The laboratory recommends the following cut-off values for abnormality (modified from Sjögren et al. [43]); t-tau >300 pg/ml for age <50 years, >450 pg/ml for age 50–69 years, and >500 pg/ml for age≥70 years, p-tau≥80 pg/ml and Aβ₄₂ < 550 pg/ml. The national reference laboratory is part of the European multicenter project “Biomarkers for Alzheimer’s disease and Parkinson’s disease” (BIOMARKAPD), funded by EU Joint Programme-Neurodegenerative Disease Research (JPND), for standardization of analytical methods and laboratory procedures to increase its accuracy with regard to CSF biomarkers of neurodegenerative diseases.

Statistical analyses

Demographic and clinical features were assessed using chi-square tests for categorical data and independent-samples T-test or Mann-Whitney U test for continuous data. The WMH volume was logarithmically transformed prior to group-difference analyses.

Regional cortical ¹⁸F-Flut and CSF Aβ₄₂

To determine the CSF Aβ₄₂ concentrations associated with abnormal ¹⁸F-Flut uptake, slope and intercept values from regression equations representing associations between regional ¹⁸F-Flut and CSF Aβ₄₂ were used. Abnormal ¹⁸F-Flut SUVr was defined as SUVr > 1.46, as previously published [44]. Receiver operating characteristic (ROC) curves were used to determine the sensitivity and specificity of the different CSF Aβ₄₂ concentrations (as compared to ¹⁸F-Flut). All SUVr were PVE corrected. Linear regression was used to correct for age and sex, and the residuals were used for correlation analyses.

Associations between cortical ¹⁸F-Flut and ¹⁸F-FDG uptake

Spearman correlations were computed for assessment of associations between regional ¹⁸F-Flut SUVr and CSF Aβ₄₂, as well as correlations between cortical ¹⁸F-Flut and ¹⁸F-FDG SUVr. All SUVr were PVE corrected.

Regional ¹⁸F-Flutemetamol retention and relevance to white matter pathology

We investigated ¹⁸F-Flut uptake prior to normalization to reference regions, exploring a putative pathology-specific increased retention in regions commonly used as reference for intensity normalization. Such standardized uptake values (SUV) were also analyzed in cerebral WM, WMHs, and NAWM for comparative purposes. SUV was calculated by dividing the mean regional radioactivity by the injected dose and body weight. SUVr were analyzed for cortical regions. Hippocampal, parahippocampal and entorhinal SUVr were analyzed for comparative purposes. Ratios were calculated by dividing the uptake for each region in ¹⁸F-Flut(+) by ¹⁸F-Flut(–), and Independent Samples T-tests and Mann-Whitney U tests were conducted to test for significance. For SUVr age and total WMH volume were included as covariates.

To analyze the potential use of ¹⁸F-Flut as a marker of WM pathology, ¹⁸F-Flut uptake in WMH and NAWM were determined prior to and after normalization to reference region, and group differences (i.e., ¹⁸F-Flut(+) versus ¹⁸F-Flut(–)) were assessed with Independent-Samples T-Tests. ¹⁸F-Flut WMHs retention was logarithmically transformed prior to the analyses. Age and total WMH volume were included as covariates for analyses of SUVr, and the ensuing residuals were normally distributed. Retention in WMHs and NAWM were compared using Paired-Samples T test. For assessment of the WMHs binding of ¹⁸F-Flut, we investigated associations between the ¹⁸F-FDG SUVr and ¹⁸F-Flut SUVr in WMHs using Spearman correlation. Linear regression was used to correct for age and total WMH volume, and the resulting residuals were used for the correlation analyses. In addition, Spearman correlations were used to determine associations between ¹⁸F-Flut SUVr in WMHs and cortex, as well as associations between ¹⁸F-FDG SUVr in WMHs and cortical ¹⁸F-Flut SUVr.

All tests were performed with significance level set at p < 0.05. Statistical analyses were performed with SPSS version 23 (SPSS Inc., Chicago, IL, USA).

RESULTS

Demographic and clinical data are listed in Table 1. There were no significant group differences in sex, age, WMH volume, or Mini-Mental State Examination score. By design, ¹⁸F-Flut (+) presented with significantly lower CSF Aβ₄₂ and larger cortical SUVr of ¹⁸F-Flut when compared to ¹⁸F-Flut (–).

Table 1.

Characteristics of the cohort

	Total study cohort (n = 44)	18F-Flut (+) (n = 18)	18F-Flut (–) (n = 26)	p-value (18F-Flut(+) versus 18F-Flut(–))
Age, y	65.91 (6.88)	68.17 (4.80)	64.35 (7.72)	0.070a
Women, n (%)	25 (56.8)	10 (55.56)	15 (57.69)	0.890b
MMSE, points	28.53 (1.52)	28.06 (1.89)	28.85 (1.16)	0.208c
CSF Aβ42 (pg/ml)	873.57 (320.04)	567.19 (167.31)	1062.12 (232.67)	<0.001a
WMH volume (mm3)	5850.50 (7235.81)	7280.00 (9372.94)	4860.85 (5274.92)	0.448a,d

Values are given in mean (standard deviation). Statistically significant differences shown in bold. ^aIndependent-samples T-test. ^bPearson Chi-square test. ^cMann-Whitney U test. ^dp-value for log transformed WMH volume adjusted for age. ¹⁸F-Flut, ¹⁸F-Flutemetamol; MMSE, Mini-Mental State Examination; CSF, cerebrospinal fluid; Aβ, amyloid β-peptide; WMH, white matter hyperintensities.

Regional cortical ¹⁸F-Flut and CSF Aβ₄₂

We compared regional cortical SUVr of ¹⁸F-Flut and CSF Aβ₄₂. Figure 2 summarizes the results, and illustrates the CSF Aβ₄₂ concentration (vertical dotted line) at which abnormal ¹⁸F-Flut SUVr (>1.46, horizontal dotted line) would be identified for each AD-specific region (also the composite region). Corresponding CSF Aβ₄₂ levels ranged from 645 pg/ml in the anterior cingulate cortex to 762 pg/ml in the inferior parietal lobe, yielding sensitivities 87–93% and specificities 85–93% (Fig. 3). There were strong negative correlations between CSF Aβ₄₂ and ¹⁸F-Flut SUVr for all regions; precuneus and posterior cingulate cortex (r = –0.85, p < 0.001), anterior cingulate cortex (r = –0.76, p < 0.001), prefrontal lobe (r = –0.68, p < 0.001), inferior parietal lobe (r = –0.79, p < 0.001) and lateral temporal lobe (r = –0.72, p < 0.001).

Fig.2.

Associations between regional ¹⁸F-Flutemetamol SUVr and CSF Aβ₄₂. Based on the visual interpretation by a trained reader, the subjects are marked as ¹⁸F-Flut(+) (green dots) or ¹⁸F-Flut(–) (blue dots). Using an SUVr cut-off of 1.46 yields different CSF Aβ₄₂ cut-offs for each pre-selected region. SUVr, standardized uptake value ratios; CSF, cerebrospinal fluid; Aβ₄₂, amyloid-β 1-42.

Fig.3.

Receiver operating characteristic (ROC) curves for the accuracy of each CSF Aβ₄₂ cut-off to predict regional ¹⁸F-Flutemetamol abnormality.

Associations between cortical ¹⁸F-Flut and ¹⁸F-FDG uptake

There were strong negative Spearman correlations between regional cortical ¹⁸F-Flut and ¹⁸F-FDG uptake in ¹⁸F-Flut(+) subjects in the precuneus and posterior cingulate (r = –0.797, p = 0.002), anterior cingulate cortex (r = –0.699, p = 0.011), inferior parietal lobe (r = –0.727, p = 0.007), lateral temporal lobe (r = –0.622, p = 0.031), as well as the prefrontal lobe (r = –0.811, p = 0.001). No significant correlations were observed in ¹⁸F-Flut(–) subjects.

Regional ¹⁸F-Flutemetamol retention and relevance to white matter pathology

¹⁸F-Flut SUVs (i.e., prior to intensity normalization against a reference region) were analyzed in several regions, in both PVE corrected and uncorrected images, primarily to investigate the validity of typical reference regions used for further analyses. We found numerically higher retention in all regions in subjects classified as ¹⁸F-Flut(+) compared to ¹⁸F-Flut(–). Based on the comparison of the SUV values between possible reference regions, we used the cerebellum cortex as reference region to calculate SUVr values. Before PVE correction, significant differences were also observed in subcortical WM and NAWM. Cortical ROIs were analyzed using SUVr. As expected, highly significant differences were observed in cortical regions (p < 0.001) when comparing ¹⁸F-Flut(+) and ¹⁸F-Flut(–). Results are summarized in Table 2.

Table 2.

Regional ¹⁸F-Flutemetamol uptake in ¹⁸F-Flut(+) versus ¹⁸F-Flut(–)

PVE corrected				PVE uncorrected
	18F-Flut +	18F-Flut–	Ratio	p	18F-Flut +	18F-Flut –	Ratio	p
A. SUV
WMH	767.72 (193.92)	633.21 (173.71)	1.21	0.015a	700.66 (170.81)	575.13 (159.05)	1.22	0.019a
NAWM	1028.62 (190.89)	932.33 (240.24)	1.10	0.112a	978.49 (187.30)	848.64 (224.09)	1.15	0.035a
WM	1020.95 (195.67)	913.18 (242.43)	1.12	0.081a	918.69 (187.59)	749.75 (211.26)	1.23	0.007a
Cerebellum cortex	429.59 (109.05)	389.58 (142.53)	1.10	0.143a	429.13 (112.64)	391.34 (147.88)	1.10	0.169a
Cerebellum WM	921.76 (192.45)	830.06 (248.26)	1.11	0.121a	920.51 (192.29)	828.68 (248.16)	1.11	0.120a
Brainstem	1025.93 (233.15)	933.80 (305.42)	1.10	0.157a	1031.65 (232.99)	938.33 (307.75)	1.10	0.157a
B. SUVr
Precun + post.cing.	2.17 (0.54)	0.89 (0.14)	2.44	<0.001b	1.97 (0.31)	1.23 (0.14)	1.60	<0.001b
Anterior cingulate	1.94 (0.50)	0.78 (0.12)	2.49	<0.001b	1.88 (0.28)	1.20 (0.13)	1.57	<0.001b
Prefrontal	2.15 (0.53)	0.92 (0.11)	2.34	<0.001b	1.78 (0.28)	1.13 (0.10)	1.58	<0.001b
Inferior parietal	2.15 (0.50)	1.01 (0.11)	2.13	<0.001b	1.80 (0.27)	1.21 (0.11)	1.49	<0.001b
Lateral temporal	1.92 (0.43)	0.91 (0.12)	2.11	<0.001b	1.70 (0.23)	1.17 (0.10)	1.45	<0.001b
Hippocampus	1.07 (0.07)	1.05 (0.06)	1.02	0.274b	1.25 (0.10)	1.24 (0.12)	1.01	0.526b
Parahippocampal	1.33 (0.26)	0.76 (0.09)	1.75	<0.001b	1.35 (0.12)	1.07 (0.10)	1.26	<0.001b
Entorhinal	1.14 (0.24)	0.90 (0.09)	1.27	0.002b	1.19 (0.14)	1.04 (0.12)	1.14	0.003b

Values are given in standardized uptake value (SUV) in A and in standardized uptake value ratio (SUVr) in B. ^aCalculated using Independent-samples T test. ^bCalculated using Mann-Whitney U test. Ratio and p-values indicate ¹⁸F-Flut + versus ¹⁸F-Flut –. Statistically significant group differences are shown in bold. PVE, partial volume effects; WMH, white matter hyperintensities; NAWM, normal-appearing white matter; WM, white matter.

White matter hyperintensities. Before and after PVE corrections ¹⁸F-Flut SUVs were higher in WMHs in ¹⁸F-Flut(+) compared to ¹⁸F-Flut(–) (Table 2). No significant difference (p = 0.112) was observed in NAWM when comparing ¹⁸F-Flut(+) (mean = 1028.62, SD = 190.89) and ¹⁸F-Flut(–) (mean = 932.33, SD = 240.24). When normalizing against a reference region, corrected for age and total WMH volume, the group differences in WMHs were considerably reduced (p = 0.414; ¹⁸F-Flut(+) mean SUVr = 1.82, SD = 0.34; ¹⁸F-Flut(–) mean SUVr = 1.69, SD = 0.25) and not significant. No significance was observed in NAWM (p = 0.218; ¹⁸F-Flut(+) mean SUVr = 2.44, SD = 0.30; ¹⁸F-Flut(–) mean SUVr = 2.50, SD = 0.39). We found significantly reduced retention in WMHs compared to NAWM (Z = –5,78, p < 0,001), presenting with a reduction of 29.81%. We further assessed correlations between the ¹⁸F-Flut SUVr and ¹⁸F-FDG SUVr in WMHs and found strong positive correlations in ¹⁸F-Flut(+) subjects (r = 0.779, p = 0.003), while no significant correlation was found in ¹⁸F-Flut(–) subjects (r = 0.200, p = 0.371).

Associations between ¹⁸F-Flut SUVr in WMHs and GM, and between ¹⁸F-FDG SUVr in WMHs and cortical ¹⁸F-Flut SUVr were assessed (Table 3). ¹⁸F-FDG uptake in WMHs and cortical ¹⁸F-Flut SUVr in the AD-predilection cortical regions were negatively correlated. No significant correlations were found between ¹⁸F-Flut uptake in WMHs and cortex.

Table 3.

Associations between ¹⁸F-Flutemetamol and ¹⁸F-FDG SUVr in WMHs and cortical ¹⁸F-Flutemetamol

	18F-Flutemetamol in WMHs	18F-FDG in WMHs
18F-Flutemetamol in
Precuneus + post.cing	r = 0.068, p = 0.663	r  = –0.359, p = 0.037
Anterior cingulate	r = 0.046, p = 0.786	r  = –0.469, p = 0.005
Prefrontal	r = –0.018, p = 0.908	r  = –0.396, p = 0.020
Inferior parietal	r = 0.004, p = 0.978	r  = –0.422, p = 0.013
Lateral temporal	r = –0.064, p = 0.681	r  = –0.358, p = 0.037
Hippocampus	r = 0.009, p = 0.952	r = 0.077, p = 0.666
Parahippocampus	r = 0.139, p = 0.367	r = –0.246, p = 0.161
Entorhinal cortex	r = 0.086, p = 0.579	r = –0.267, p = 0.127

Significance shown in bold. SUVr, standardized uptake value ratios; FDG, fluorodeoxyglucose; WMHs, white matter hyperintensities.

DISCUSSION

In this study, we combined three AD biomarkers, CSF Aβ₄₂, ¹⁸F-Flut - and ¹⁸F-FDG PET, finding that CSF Aβ₄₂ concentrations associated with abnormal ¹⁸F-Flut uptake differ between predilection areas for AD pathology, posterior areas demonstrating a pathological high retention at higher CSF Aβ₄₂ levels. We showed strong negative correlations between cortical ¹⁸F-Flut and ¹⁸F-FDG uptake. To our knowledge, we are the first to evaluate the use of ¹⁸F-Flut in WMHs. We found generally higher ¹⁸F-Flut retention throughout the brain in ¹⁸F-Flut(+) subjects prior to normalization, including subcortical WM. We found significantly higher WMH ¹⁸F-Flut uptake in ¹⁸F-Flut(+) as compared to¹⁸F-Flut(–) without normalizing to a reference region, but after normalization the differences were noticeably reduced.

Brain amyloid pathology is a core diagnostic clinical criterion of AD, as evidenced by low CSF Aβ₄₂ or positive amyloid PET. CSF biomarkers have shown similar diagnostic accuracy as both regional and composite PET measures [5]. CSF was recently demonstrated to reliably predict Aβ accumulation, where a CSF Aβ₄₂ concentration of 647 pg/ml or less identified a positive composite ¹⁸F-Flut PET measure with high sensitivity and specificity [4]. However, regional differences in this aspect have not been investigated. In the current study, we find that regions known to harbor amyloid plaques in AD show abnormal ¹⁸F-Flut retention at different CSF Aβ₄₂ concentrations (the inferior parietal lobe and posterior cingulate and precuneus present with abnormal ¹⁸F-Flut SUVr at the highest concentrations, followed by the prefrontal cortex). There were strong negative correlations between CSF Aβ₄₂ and ¹⁸F-Flut SUVr in these regions, in line with previous findings [4], and our analyses yielded sensitivity and specificity around 90% for all investigated regions and CSF cut-offs (Fig. 3). The current findings are supported by the early involvement of these regions in the disease course [41] and local predilection for plaque deposition [45].

This gives additional valuable information for CSF as a clinical tool, also considering the ¹⁸F-Flut manufacturer’s guidelines stating that a positive ¹⁸F-Flut PET scan requires abnormal amyloid retention in at least one region [42]. This highlights the need for standardization of CSF biomarker analyses to define the optimal threshold for abnormal values. There is generally a large span in the CSF Aβ₄₂ cut-offs and cut-offs reported in the current study are slightly higher than what Palmqvist et al. found [4]. This may result from the known between-laboratory variability in CSF Aβ₄₂ analyses. There is a need for validation of these cut-offs to increase the diagnostic accuracy of CSF biomarkers, as CSF is an amenable clinical tool in many circumstances, and also to reduce the chances of false negatives.

Reduced cerebral glucose metabolism, as determined by ¹⁸F-FDG PET, is a well-established feature of AD [46]. Previous studies combining amyloid PET and ¹⁸F-FDG PET report conflicting results, some showing reduced glucose metabolism with increasing Aβ load [47, 48], while others do not [49, 50]. When associations were observed, they seemed to be region-specific, involving mostly posterior brain regions. This is consistent with our findings of significant cortical regional correlations, with the strongest correlations observed in the precuneus, posterior cingulate and inferior parietal lobe, and additional prefrontal regions. These predilection sites mimic early changes in ¹⁸F-Flut versus CSF (as described above). Significant associations were only found in ¹⁸F-Flut(+) subjects, which may not be surprising due to the by design low cortical uptake in ¹⁸F-Flut(–) subjects. However, it may also support previous hypotheses of toxic effects of Aβ on synaptic function in AD [47], as increased fibrillar Aβ (as is detected with amyloid PET) has been associated with higher levels of toxic oligomers [51].

We further investigated regional ¹⁸F-Flut retention (Table 2). As expected, significantly higher cortical uptake was observed in ¹⁸F-Flut(+) compared to ¹⁸F-Flut(–), using SUVr (Table 2 B). PET images are prone to PVE [52], primarily due to low spatial resolution, resulting in biased uptake measurements due to spill-over. For amyloid PET there may be an overestimation of cortical retention in healthy controls more than in AD due to relatively high WM retention, increasing the chances for false positives [53]. Therefore, correcting for PVE may improve statistical power [15]. This is illustrated by the higher cortical uptake in PVE uncorrected images compared to corrected images in ¹⁸F-Flut(–) subjects, likely due to spill-over from WM. Similarly, in ¹⁸F-Flut(+) subjects there may be a spill-over from GM to subcortical WM, illustrated by slightly larger ratios in PVE uncorrected compared to corrected images, causing a positive bias and overestimation of WM signal. This highlights the importance of such a correction, not only for accurate quantification of the cortical signal, but also for potential WM analyses.

SUV was used to investigate the validity of typical reference regions, for further analyses of WM (Table 2A). This was done because group differences in reference regions may potentially impact SUVr, especially in regions with more subtle differences in ¹⁸F-Flut uptake, e.g., WM. Higher SUV in ¹⁸F-Flut(+) was also observed in subcortical WM, with significant difference in WMHs, with and without PVE correction. Analyses with and without PVE correction were performed to investigate the potential impact of such correction also in WM analyses, as it is shown to increase the accuracy of analyses [15]. The high WM retention reported with ¹⁸F-Flut raises the possibility of ¹⁸F-Flut being a marker for WM pathology, though the binding characteristics of ¹⁸F-Flut in WM remain to be determined. We investigated subcortical retention in ¹⁸F-Flut(+) and ¹⁸F-Flut(–) to further explore the possible relationship between amyloid and WM pathology as this may improve the understanding of associations between CVD and AD. Investigating ¹⁸F-Flut retention in WMHs, we found significant group differences in WMH ¹⁸F-Flut uptake when comparing SUVs (i.e., prior to normalization) in ¹⁸F-Flut(+) and ¹⁸F-Flut(–), with the highest retention in ¹⁸F-Flut(+). The differences were noticeably reduced when normalizing the uptake to a reference region (SUVr) and no longer significant. There is generally a slightly higher ¹⁸F-Flut retention in subjects classified as ¹⁸F-Flut(+), and a possible explanation for the reduction in differences may be the observed higher aberrant uptake across the entire brain in ¹⁸F-Flut(+) subjects, including the typical reference regions cerebellum and brainstem in which specific binding is believed to be negligible. Furthermore, the findings must be interpreted with caution, as several factors are known to impact on SUV, and SUVr is considered the more precise and robust measure for amyloid PET.

High WM retention has been reported for several ¹⁸F-PET ligands [10, 54, 55]. The WM binding of ¹⁸F-Flut was recently investigated using spectral analysis and the authors concluded that WM binding was nonspecific, i.e., not related to amyloid binding [13]. This is in line with what has previously been reported with ¹⁸F-Florbetaben using kinetic model-based analyses [55]. This is supported by the current associations observed between ¹⁸F-Flut and ¹⁸F-FDG uptake in WMHs, as well as the lack of correlations between the ¹⁸F-Flut uptake in WMHs and cortex in the current study. Accumulating evidence links CVD and AD [19], and we have previously demonstrated impaired WMH microstructural integrity in Aβ positive subjects using diffusion tensor imaging [17]. Furthermore, we recently found reduced glucose metabolism in WMHs in the presence of amyloid dysmetabolism [16]. Increased fibrillar Aβ has been associated with higher levels of Aβ oligomers, suggested to be more neurotoxic and promote neurodegeneration [56]. Hence, the current findings of negative associations between the ¹⁸F-FDG uptake in WMHs and cortical ¹⁸F-Flut retention support our previous findings [16], connecting increasing Aβ pathology to glucose hypometabolism in WMHs.

Tracer tissue extraction and clearance is related to cerebral blood flow (CBF), and CBF decreases during AD progression [22]. WMHs are often considered to result from hypoperfusion [57], and previous studies using ¹¹C-PiB found reduced retention in WMHs compared to NAWM [25–27]. This has in addition been reported with another ¹⁸F-PET ligand, ¹⁸F-Florbetaben [58], and we observed the same with ¹⁸F-Flut in the current study. Hence one might expect lower ¹⁸F-Flut uptake in WMHs in ¹⁸F-Flut(+). However, SUVr do not distinguish between the contributions of CBF and clearance, and clearance also depends on the capillary integrity, known to be reduced in AD [22]. In CAA type 1, as opposed to CAA type 2 (involving larger vessels), the capillaries are affected, reflected by Aβ deposition in the outer basement membrane [24]. Similar to Aβ plaques, these vascular changes present with surrounding microglia activation and inflammatory responses [59]. The vascular bed in AD further presents with several alterations. The microvascular distortion and capillary dysfunction affects the neurovascular unit, thereby affecting the blood-brain barrier function [60] causing impaired clearance of soluble substances [22, 23]. Given little or absent specific ¹⁸F-Flut amyloid binding in WM, one possible explanation for the higher ¹⁸F-Flut retention observed in WMHs in ¹⁸F-Flut(+) subjects might be vascular damage present in AD impairing tracer clearance. The lack of difference observed in NAWM raises the possibility that these alterations are confined to WMHs. For other ¹⁸F-ligands, white matter retention has also been hypothesized to result from reduced WM blood perfusion compared to the cortex and thus slower clearance [61]. However, due to the aberrant uptake observed ¹⁸F-Flut (+), we cannot conclude that ¹⁸F-Flut is a suitable marker for WM pathology and the current findings must be interpreted with caution. Replications in larger cohorts are warranted for further assessment of the use of ¹⁸F-Flut in WM pathology.

The cross-sectional study design may be a limitation when considering regional comparisons of cortical ¹⁸F-Flut retention and CSF Aβ₄₂ in patients destined to develop dementia due to AD, and longitudinal studies should be conducted for further evaluations of the regional Aβ depositions related to CSF Aβ₄₂ cut-offs.

In conclusion, although the study design and the relatively small sample size are limitations and replications in larger cohorts are warranted, this study gives further evidence for CSF Aβ₄₂ as a clinical tool to predict regional cortical Aβ deposition. In addition, our findings support previous hypotheses of toxic effects of Aβ on synaptic function based on the strong correlations observed between cortical ¹⁸F-Flut and ¹⁸F-FDG uptake. Furthermore, we replicate our previous findings [16] linking amyloid dysmetabolism to glucose hypometabolism in WMHs. We also demonstrate generally higher ¹⁸F-Flut retention across the brain in subjects classified as ¹⁸F-Flut(+), including WM and WMHs, which may challenge the use of ¹⁸F-Flut for WM analyses. Hence replications in larger cohorts are warranted for further assessment of the use of ¹⁸F-Flut in WM pathology.