Patients with Amyloid-Negative Mild Cognitive Impairment have Cortical Hypometabolism but the Hippocampus is Preserved

Abstract

Background: Patients with mild cognitive impairment (MCI) are at risk for Alzheimer’s dementia but the presence of amyloid (Aβ) strongly increases this risk. In clinical settings, when Aβ status is not available, different neurodegenerative markers are used to characterize MCI. The accuracy of these markers to discriminate between Aβ–and Aβ+ MCI is not yet determined.

Objective: To compare different markers of neurodegeneration in Aβ–and Aβ+ MCI, with an Aβ–elderly control (EC) group.

Methods: Patients with MCI (n = 39) and EC (n = 28) underwent MRI, ¹⁸F-FDG PET, and Aβ PET (¹⁸F-flutemetamol). We compared FDG and MRI biomarker values in cortical and hippocampal regions of interest, and using voxel-wise surface maps. We computed ROC curves discriminating between the three groups for each biomarker.

Results: All biomarker values were reduced in Aβ+ MCI compared to EC (p < 0.001). Aβ–MCI had low cortical metabolism (p = 0.002), but hippocampal volume, cortical thickness, and hippocampal metabolism were not significantly different between Aβ–MCI and EC (p > 0.40). Cortical metabolism best discriminated between MCI and EC (AUC = 0.92/0.86, Aβ+/Aβ–) while hippocampal volume best discriminated between Aβ–MCI and Aβ+ MCI (AUC = 0.79).

Conclusions: Cortical hypometabolism was observed in both Aβ–MCI and Aβ+ MCI whereas hippocampal atrophy was mostly found in Aβ+ MCI. For MCI patients without available Aβ information, hippocampal atrophy is thus more informative about Aβ status than cortical hypometabolism.

Keywords

Alzheimer’s disease amyloid FDG-PET hippocampus magnetic resonance imaging mild cognitive impairment neocortex PALZ score

INTRODUCTION

Amyloid-β (Aβ) and neurodegeneration (ND) biomarkers are widely used in clinical research to characterize patients with preclinical or prodromal stage of Alzheimer’s disease (AD) [1]. Prodromal AD is defined as patients with mild cognitive impairment(MCI) and evidence of either Aβ or ND [2]. Recent research using Aβ PET imaging [3] showed that patients with amyloid-positive (Aβ+) MCI were at higher risk of converting to AD dementia in the following years [4]. However, as Aβ PET imaging is not yet readily available in the clinical setting, ND markers are often used to ort the diagnosis of prodromal AD. The rate of conversion to AD dementia also depends on the MCI subtype: amnestic or non-amnestic MCI [5]. Therefore, it is important to test whether ND markers used to characterize MCI in memory clinics are informative about Aβ status, in both amnestic and non-amnestic MCI [6]. Hippocampal volume (HV) and cortical metabolism are two commonly used markers of ND in MCI [7, 8]. In the present study, we investigated these two ND biomarkers as well as cortical thickness and hippocampal metabolism in a population of MCI recruited at the Memory Clinic, compared to healthy elderly controls (EC).

METHODS

Protocol approvals

The study was conducted at the Memory Clinic of Saint-Luc University Hospital and was approved by the local Ethical Committee. It was conducted according to the recommendations of the International Conference on Harmonization (Good Clinical Practice) after receiving authorization from the national legally competent authority (Eudra-CT #: 2011-001756-12). All participants provided written informed consent before inclusion in the study.

Patients (see Table 1 for demographic and cognitive data)

The data analyzed in this report are part of a previously published [7] broader study, including patients without MCI that were not included here. Thirty-nine consecutive patients with MCI attending the Memory Clinic were analyzed in this report: 15 Aβ–MCI and 24 Aβ+ MCI. Patients were assessed clinically by one of two experienced investigators (BH/AI). Inclusion criteria included age older than 55, subjective complaints, and objective cognitive impairment. Evidence of cognitive decline was sought from a relative and obtained for all MCI patients. Dementia was excluded using DSM-IV-TR criteria and a Mini-Mental State Examination (MMSE) score of at least 24 was required [9]. Exclusion criteria were major depression or other psychiatric diseases, past or present neurological conditions, alcohol and/or drug abuse. Objective cognitive impairment was defined as a performance below the 10th percentile of an independent control group (z-score < –1.3) in any of the following cognitive domains: memory (Free and Cued Selective Reminding Test, FCSRT), language (Lexis naming test, the Letter Fluency test for ‘P’, and the Category Fluency test for animals), executive functions (Trail Making test A, B, and Luria’s graphic sequences), and visuospatial functions (Clock drawing test and the praxis part of the CERAD battery). Patients were considered as impaired in a domain if at least two out of three measures were impaired. Amnestic MCI were defined as patients with a z below –1.3 in at least 2 of the 3 following FCSRT measures: total free recall, total cued recall, and 20-min delayed recall. Thirty-one healthy EC recruited by advertisement also participated in the study. To avoid any Aβ effects within the control group, in the present report we only included the 28 EC who were Aβ–. EC did not complain about their memory and had an MMSE score of at least 27. Apolipoprotein E (APOE) genotype was conducted in all participants but two (Aβ+) MCI.

Brain imaging procedures

The brain imaging procedures were identical to those listed by Ivanoiu et al. [7].

Amyloid imaging

Flutemetamol is an investigational medicinal product being studied clinically as an amyloid imaging agent (now marketed by GE Healthcare under the brand name Vizamyl^®). Ninety min after intravenous injection of [¹⁸F]-flutemetamol (target activity 175–185 mega Becquerel [MBq], a 30-min list-mode PET/cortical thickness acquisition was performed on a Philips Gemini TF 16 slice (Philips Healthcare) and reconstructed as a dynamic scan of 6X5 min, with 2 mm isometric voxels including attenuation, scatter, decay corrections and time-of-flight information using the manufacturer’s standard reconstruction algorithm. Visual qualitative analysis (following criteria defined in a dedicated training program provided by GE) and quantitative analysis were performed. Quantitative standard uptake value ratios (SUVr) were computed using the region of interest defined in the MNI space and adapted to the subject’s anatomy after applying inverse transformation into the subject space using PVIEW and PFUS v3.5 software modules (Pmod Technologies Ltd Z, SPM, http://www.fil.ion.ucl.ac.uk/spm/software/spm5/). Patients were classified as Aβ+ when SUVr was greater than 1.56, as previously described [10]. Visual inspection by a trained nuclear physician (RL) confirmed Aβ status in all MCI patients. A single discordant result between visual reads and SUVR was observed for an EC (positive visual read and SUVR = 1.50). This EC was considered as positive and therefore not included in this study.

FDG-PET

All patients underwent a 7-min list-mode FDG-PET according to published guidelines [11] after intravenous injection of [¹⁸F]-fluorodeoxyglucose (FDG) (target activity 146–185 MBq). FDG-PET scans were post-processed using the PMOD Alzheimer’s discrimination analysis tool (PALZ v2.75), previously validated in a longitudinal study of an MCI population [12]. PALZ-AD scores were used as a measure of cortical metabolism. PALZ-AD scores are based on the count of abnormal voxels within a predefined mask (including major parts of the lateral temporal and parietal association cortices, the precuneus and posterior cingulate, as well as some lateral prefrontal areas). Hippocampal metabolism was determined using a 3D maximum probability atlas [13].

3D T1-weighted images were recorded at 3T (Achieva, Philips Healthcare) with a 32-channel phased-array head coil. One hundred and fifty sagittal slices were acquired using the following parameters: TR/TE/FA 9.1 ms/4.6 ms/8°; slice thickness 1 mm; resolution 0.81×0.95 mm² (acquisition) reconstructed in 0.75×0.75 mm²; FOV 220×197 mm²; acquisition matrix 296×247; SENSE factor 1.5 (parallel imaging). Whole brain was segmented by completing the FreeSurfer image analysis pipeline v5.3 [14]. A trained operator (LD), who was blinded to the PET results, performed visual assessment of the segmentation results and manual editing when needed. Subsequently, we normalized HV to the intracranial volume (ICV), using a residual correction approach (equation: normalized HV = raw HV –b * (ICV –mean ICV), where b is the regression coefficient between HV and ICV in the EC group; ICV, the actual ICV value of each participant; and mean ICV, the average ICV in the EC group).

We defined our measure of cortical thickness using an aggregate of the “AD-signature” regions, namely the entorhinal cortex, temporal pole, inferior temporal gyrus, inferior frontal sulcus, superior frontal, superior parietal, precuneus, supramarginal and angular gyrus [15]. White matter hyperintensities were evaluated in all participants from 4 mm thick axial FLAIR (FLuid Attenuated Inversion Recovery) MRI using the Manolio scale [16].

Statistics

To allow fair comparisons of the four biomarkers, all data were z-transformed using the mean and standard deviation of the EC group. Statistica v.12 (Statsoft, Tulsa, OK) was used for all statistical analyses, reporting two-tailed p-values.

We fitted General Linear Models (GLM) using biomarkers as dependent variables and group as a categorical predictor, covarying age, education, and sex. Post-hoc tests compared groups two-by-two, correcting for multiple comparisons using Dunnett’s procedure.

In order to avoid rejecting a difference that would exist (type 2 errors), we computed voxel-wise FDG and MRI surface maps, using a very liberal threshold (uncorrected p < 0.05). FDG maps were computed in PMOD 3.5, using 8 mm smoothing in all directions (x, y, z), after projecting the FDG maps onto the MNI atlas. MRI surface maps were computed in Freesurfer v5.3 using the QDEC tool.

We defined the pathological threshold as the 15th percentile of the value in the EC group for all biomarkers, computed the sensitivity of the markers to detect Aβ–and Aβ+ MCI, and compared these sensitivities using a chi-square test. We finally computed the areas under the ROC curves for each biomarker, adjusting for age, education, and sex.

RESULTS

Neuropsychological and APOE results

Twelve MCI were considered as non-amnestic (7 Aβ+) and 27 amnestic (17 Aβ+). Two non-amnestic (both Aβ+) and 15 amnestic (10 Aβ+) MCI were considered as multiple-domain. Ten non-amnestic (5 Aβ+) and 12 amnestic (7 Aβ+) MCI were considered as single-domain. Cognitive performances in Aβ–and Aβ+ MCI were not statistically different in any domain (see Table 1). So Aβ status could not have been inferred from cognitive testing in this sample. A chi-square test demonstrated that Aβ+ MCI were statistically more likely to be APOE4 carriers than Aβ–MCI (χ² = 85.3, p < 0.0001). Demographic, genetic, cognitive, vascular, and amyloid data for the different MCI subtypes is given in Supplementary Table 1.

Biomarker results using regions of interest (see Fig. 1)

We observed a group effect for all four biomarkers. Group effects were: F(2, 61) = 15.4, p < 0.0001 for HV; F = 20.6, p < 0.0001 for cortical metabolism; F = 8.5, p = 0.001 for cortical thickness; and F = 5.7, p = 0.005 for hippocampal metabolism. Post-hoc tests showed that Aβ+ MCI patients had significantly more ND than EC and Aβ–MCI patients as assessed by all biomarkers except cortical metabolism (trend level –see Table 2). Patients with Aβ–MCI had significantly lower cortical metabolism than the EC but the other measures of ND were not significantly different to those in the EC group.

Biomarker results using voxel-wise analyses (see Fig. 2)

Patients with Aβ+ MCI had cortical hypomet-abolism and cortical atrophy compared to the EC group. In contrast, Aβ–MCI patients had cortical hypometabolism but no significant atrophy, although we used a very liberal threshold (uncorrected p < 0.05). Direct comparison between the patients with Aβ+ and Aβ–MCI revealed that metabolism was not significantly different between the two groups but there was more cortical thinning in the Aβ+ than in the Aβ–MCI. There was no significant region of increased thickness or metabolism in either of the MCI groups compared to the EC.

Sensitivity of the markers to detect MCI according to Aβ status (see Table 3)

Using a threshold fixed at the 15th percentile of the value in the EC group (specificity = 85%), HV had the best sensitivity to discriminate Aβ+ MCI compared to Aβ–MCI, as shown by significantly higher proportions of Aβ+ MCI with hippocampal atrophy (p < 0.001). Although sample size was small, this result was confirmed for all subtypes of MCI, at the trend-level (p < 0.079). In contrast, cortical metabolism had high sensitivity for both Aβ–and Aβ+ MCI and could therefore less accurately discriminate between them (p = 0.117). In the whole MCI sample, cortical thickness and hippocampal metabolism had the same sensitivity, respectively 58% to detect Aβ+ MCI and 27% to detect Aβ–MCI. Both biomarkers tended to better detect Aβ+ than Aβ–MCI (p = 0.053). Cortical thickness better detected Aβ among the non-amnestic MCI while hippocampal metabolism better detected Aβ among the single-domain amnestic MCI.

Area under the ROC curves (AUC)

For discriminating between A β+ MCI and EC: AUC were: 0.86 (HV), 0.92 (cortical metabolism), 0.79 (cortical thickness), and 0.72 (hippocampal metabolism).

For discriminating between A β–MCI and EC: AUC were: 0.61 (HV), 0.86 (cortical metabolism), 0.50 (cortical thickness), and 0.56 (hippocampal metabolism).

For discriminating between A β+ and A β–MCI: AUC were: 0.79 (HV), 0.63 (cortical metabolism), 0.74 (cortical thickness), and 0.68 (hippocampal metabolism).

Cortical metabolism was thus the less accurate biomarker for discriminating between Aβ+ and Aβ–MCI, but the best one for discriminating between MCI and EC.

DISCUSSION

We studied four markers of ND using two imaging modalities: 3D-MRI and FDG-PET, and focusing on two regions: the hippocampus and the neocortex. We used diagnostic markers that were previously validated in longitudinal MCI studies: Freesurfer-defined HV [17] and hippocampal metabolism [18], the PALZ score (cortical metabolism) [12], and the AD cortical signature (cortical thickness) [19], and we directly compared these markers between Aβ–MCI and Aβ+ MCI. We observed that hippocampal volumetric and metabolic markers were only decreased in patients with Aβ+ MCI, whereas neocortical hypometabolism was also observed in Aβ–MCI, of both amnestic and non-amnestic subtypes. Below we discuss our findings in the context of other FDG and MRI studies in MCI.

Cortical hypometabolism in Aβ+ and in Aβ–MCI

In agreement with previous studies using the PALZ score, other neocortical aggregates, or voxel-wise analyses (among many others: [20 –22]), cortical metabolism was lower in MCI than in EC, demonstrating the good sensitivity of cortical metabolism to detect cognitive impairment. So far, most MCI studies combining FDG- and Aβ–PET have compared how the two PET signals related to cognitive status [23, 24] or clinical progression [25]. Few studies directly compared FDG in Aβ–and Aβ+ MCI and consistently found reduced cortical metabolism in Aβ+ MCI [26, 27] but were conflicting regarding Aβ–MCI: some found a typical AD pattern of hypometabolism [28], while others reported increased cortical metabolism in Aβ–MCI [29]. It has also been suggested that among Aβ–MCI patients, reduced cortical metabolism was a risk factor for conversion to dementia [8]. Hypermetabolism could occur at earlier stages, followed by hypometabolism, as suggested by compensatory theories [26]. From our results, we cannot exclude that some Aβ–MCI patients may have had increased cortical metabolism at an earlier stage as our sample already had significant cognitive impairment (mean MMSE < 27). However, samples differences across studies (e.g., proportion of non-amnestic MCI) might also explain discrepant results, as it appears from our results that patients with non-amnestic Aβ–MCI are less likely to have hypometabolism (see Table 3), confirming previous FDG studies in amnestic and non-amnestic MCI [30, 31].

The present results are also in line with a recently published ADNI study [32], demonstrating that Aβ–MCI with ND have worse memory than Aβ–MCI without ND. This latter study suggested that sub-threshold levels of Aβ were not responsible for ND in these Aβ–patients but cerebrovascular and other neurodegenerative diseases were likely involved. This study confirmed that sub-threshold Aβ was not present in Aβ–MCI although hypometabolism was. We did not find a significant increase in microvascular lesions in Aβ–MCI but it is possible that some of our Aβ–MCI have non-AD neurodegenerative disorders. The visual inspection of FDG scans by our trained nuclear physician (RL) revealed that one Aβ–MCI had a pronounced posterior pattern of hypometabolism, compatible with a posterior cortical atrophy; and another had a much more anterior FDG pattern, suggesting a frontotemporal dementia. Both patients were diagnosed clinically as amnestic MCI, multiple- and single-domain, respectively. While we cannot exclude that some Aβ–MCI actually had an amnestic form of frontotemporal dementia, posterior cortical atrophy seems less likely as a large study found that 96% of patients with this condition are Aβ+ [33]. Excluding subjects with atypical FDG scans did not change the results because two-thirds of Aβ–MCI had reduced cortical metabolism in our cohort. It is, however, important to acknowledge that the PALZ score includes some frontal regions, what could account for its high sensitivity to cognitive impairment but low specificity for Aβ. Larger samples, allowing further subgroup analyses, and longitudinal follow-up are necessary to better understand what leads to cortical metabolism reductions in MCI, but our data suggests that Aβ status in MCI cannot be inferred from the PALZ, score that is often used in clinical settings to determine FDG hypometabolism.

Hippocampal atrophy is most often observed in Aβ+than in Aβ–MCI

In contrast with FDG results, HV was only reduced in Aβ+ and not in Aβ–MCI, a finding consistent with other studies [34 –36] and with the association between low HV and the risk of converting to AD dementia [37, 38], although some have suggested that the volume of CA1, the most atrophic hippocampal subfield in MCI, was independent of the presence of Aβ [39]. In our study, the group-level analysis was confirmed by ROC curves, demonstrating that HV had the best discriminative power between Aβ+ and Aβ–MCI; in contrast with cortical metabolism that best discriminated between MCI and EC. Among the 26 MCI patients that had hippocampal atrophy, 21 (81%) were Aβ+; while among the 13 that had an HV above the threshold, only 3 (23%) were Aβ+, indicating that a low HV can support a diagnosis of prodromal AD in patients with MCI [2].

Cortical thinning in some Aβ+ MCI but in very few Aβ–MCI

As for HV, cortical thickness was not statistically lower in Aβ–MCI than in EC and was however lower in Aβ+than in Aβ–MCI. However, 10 out of 24 (42%) Aβ+ MCI had a cortical thickness above the 15th percentile of EC, making it difficult at the individual level to exclude a diagnosis of prodromal AD based on cortical thickness. Voxel-based morphometry, measuring cortical volume rather than thickness, could have yielded different results, although both techniques give highly correlated results and studies comparing both have found that cortical thinning was more sensitive to age-related changes [40].

Consistent with the present study, Ye and colleagues recently showed that cortical thinning in typical AD regions including the medial temporal lobe, precuneus, lateral temporal, and parietal regions were observed in Aβ+but not in Aβ–MCI [37].

Longitudinal studies in MCI have suggested that cortical thickness could be predictive of conversion to AD [41] and memory decline [42], although not as much as CSF Aβ [19]. Follow-up of our study patients is ongoing to determine the predictive power of each of these biomarkers, but from our cross-sectional results, cortical thickness does not appear as discriminant as HV to identify Aβ+ MCI.

Hippocampal metabolism is more decreased in Aβ+ than in Aβ–MCI

We observed lower hippocampal metabolism in patients with MCI than in EC, in accordance with several previous studies [43 –47]. Hippocampal metabolism was also significantly more reduced in Aβ+ than in Aβ–MCI. Previous research on hippocampal metabolism in MCI has been somewhat controversial as some research studies observed hippocampal hypometabolism [43 –47], whereas others did not [48 –50]. Aβ status could at least partially explain these discrepancies as the studies that did not find hippocampal metabolism reductions did not include Aβ imaging and could therefore have included a higher proportion of Aβ–MCI. Hippocampal metabolism has also been shown to be lower in MCI that will subsequently convert to AD dementia than in stable MCI [18]. These findings are consistent with ours, because Aβ+ MCI are more likely to evolve to AD than Aβ–MCI [4]. The absence of significant difference between Aβ–MCI and EC for both HV and hippocampal metabolism strongly suggest that ND in the hippocampus –when it occurs in MCI–is induced by Aβ and, therefore, could support a diagnosis of prodromal AD. However, HV seems to have a better accuracy than hippocampal metabolism to discriminate MCI from EC and to detect Aβ in MCI. With a clinical perspective, the higher specificity of hippocampal metabolism for Aβ+ MCI than cortical metabolism may help guide clinicians when interpreting an FDG-scan in the absence of other examinations as an MRI or an Aβ scan.

Conclusions

This study showed that hippocampal and cortical neurodegenerative biomarkers are significantly decreased in patients with Aβ+ MCI compared to EC. Although most Aβ–MCI had normal hippocampus, two-thirds of them had cortical hypometabolism. Further research should confirm these results, as the number of patients included was relatively small. Longitudinal follow-up is ongoing to determine which biomarker(s) best predict subsequent cognitive decline in this population. The rarity of hippocampal alterations in Aβ–MCI favors a higher specificity of hippocampal markers for prodromal AD. By contrast, the PALZ score measuring cortical metabolism is sensitive for cognitive impairment but not specific for Aβ pathology. For Memory Clinics that have no access to Aβ information, the present study recommends thus the use of HV rather than the use of the PALZ score to support the diagnosis of prodromal AD.

Footnotes

ACKNOWLEDGMENTS

This study was made possible thanks to the generous funding help of: the Belgian American Education Foundation, the Belgian Neurological Society and the Saint-Luc Foundation. We are also grateful to the firm GE Healthcare Ltd. for having supplied the [¹⁸F]-flutemetamol for the PET scan imaging according to an agreement with our Clinic.

Authors’ disclosures available online ().

Appendix

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