Prediction of dementia in MCI patients based on core diagnostic markers for Alzheimer disease

PET Center, Section of Nuclear Medicine & PET, Department of Radiology, Oncology and Radiation Sciences (A.E.W.), Uppsala University, Uppsala
Neurology (Impact Factor: 8.29). 02/2013; 80(11). DOI: 10.1212/WNL.0b013e3182872830
Source: PubMed


The current model of Alzheimer disease (AD) stipulates that brain amyloidosis biomarkers turn abnormal earliest, followed by cortical hypometabolism, and finally brain atrophy ones. The aim of this study is to provide clinical evidence of the model in patients with mild cognitive impairment (MCI).

A total of 73 patients with MCI from 3 European memory clinics were included. Brain amyloidosis was assessed by CSF Aβ42 concentration, cortical metabolism by an index of temporoparietal hypometabolism on FDG-PET, and brain atrophy by automated hippocampal volume. Patients were divided into groups based on biomarker positivity: 1) Aβ42- FDG-PET- Hippo-, 2) Aβ42+ FDG-PET- Hippo-, 3) Aβ42 + FDG-PET + Hippo-, 4) Aβ42 + FDG-PET+ Hippo+, and 5) any other combination not in line with the model. Measures of validity were prevalence of group 5, increasing incidence of progression to dementia with increasing biological severity, and decreasing conversion time.

When patients with MCI underwent clinical follow-up, 29 progressed to dementia, while 44 remained stable. A total of 26% of patients were in group 5. Incident dementia was increasing with greater biological severity in groups 1 to 5 from 4% to 27%, 64%, and 100% (p for trend < 0.0001), and occurred increasingly earlier (p for trend = 0.024).

The core biomarker pattern is in line with the current pathophysiologic model of AD. Fully normal and fully abnormal pattern is associated with exceptional and universal development of dementia. Cases not in line might be due to atypical neurobiology or inaccurate thresholds for biomarker (ab)normality.

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Available from: Anna Caroli, Oct 01, 2015
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    • "Vice versa we also observed 5 patients with normal CSF A but positive amyloid PET and pathological CSF tau. This inverse constellation was also reported by others [10] and can barely be interpreted in terms of modern pathogenetic models [32]. Cases with a typical AD clinical phenotype together with pathological CSF tau and non-pathological A markers could be regarded as neurofibrillary tangle predominant dementia, in which neuropathology reveals abundant neurofibrillary pathology and absence or scarcity of neuritic plaques and other A deposits [33]. "
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    ABSTRACT: Background: Biomarkers of neuronal injury and amyloid pathology play a pivotal role in the diagnosis of Alzheimer's disease (AD). The degree of AD biomarker congruence is still unclear in clinical practice. Objective: Diagnosis of AD with regard to the congruence of the clinical diagnosis and different biomarkers. Methods: In this prospective cross-sectional observational study, 54 patients with mild cognitive impairment or dementia due to AD or not due to AD were investigated. Biomarkers of neuronal injury were medial temporal lobe atrophy (MTA) on magnetic resonance imaging (MRI) and tau concentration in the cerebrospinal fluid (CSF). CSF Aβ1-42 and amyloid-targeting positron emission tomography (PET) were considered as biomarkers of amyloid pathology. Results: Forty cases were diagnosed as AD and 14 cases were diagnosed as non-AD based on clinical and routine MRI assessment. AD cases had higher MTA scores, higher levels of CSF tau and lower levels of CSF Aβ1 - 42, and higher amyloid load on PET compared to the non-AD group. In the AD group, completely consistently pathological biomarkers were found in 32.5% , non-pathological in 5% . In 62.5% the findings were inconsistent. Congruence of biomarkers was 67.5% for neuronal injury and for amyloid dysfunction, respectively. In two patients, clinical diagnosis switched to non-AD due to completely consistent non-pathological biomarker findings. The criteria of the international working group were met in 75.0% . Conclusion: Surprisingly, the number of completely congruent biomarkers was relatively low. Interpretation of AD biomarkers is complicated by multiple biomarker constellations. However, the level of biomarker consistency required to reliably diagnose AD remains uncertain.
    Journal of Alzheimer's disease: JAD 09/2015; 48(2):425-432. DOI:10.3233/JAD-150229 · 4.15 Impact Factor
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    • "In the working model for Alzheimer's Disease (AD) progression, a cascade of events starts with the buildup of amyloid plaque, followed by tau-mediated neuronal injury, and then by memory loss and finally clinical diagnosis of AD [1]. Recently, Prestia et al. [2] have provided clinical evidence that the core biomarker patterns are c 1 onsistent with this model. Specifically, the model predicts that tracer retention on amyloid PET imaging and low A-42 concentration in the cerebral spinal fluid (CSF) should become abnormal earlier in the disease course, followed by cortical hypometabolism on F18-FDG-PET, and finally brain atrophy in structural MRI. "
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    • "Similarly, the IWG criteria for prodromal AD require the positivity of biomarkers, in association with the presence of hippocampal-type memory dysfunction (Dubois et al., 2014). [ 18 F]FDG-PET has been recognized as a crucial diagnostic marker in dementia since the early disease phases, predicting the possible progression to AD in MCI subjects (Anchisi et al., 2005; Chételat et al., 2005; Mosconi, 2005; Mosconi et al., 2008; Fouquet et al., 2009; Patterson II et al., 2010; Brück et al., 2013; Dukart et al., 2013; Hatashita & Yamasaki, 2013; Prestia et al., 2013), and allowing the exclusion of AD pathology (Silverman et al., 2008; Ossenkoppele et al., 2013). The typical AD metabolic pattern was shown even years before the disease onset, as proven in dominantly inherited AD (Bateman et al., 2012) and in familial sporadic cases (Mosconi et al., 2014). "
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    ABSTRACT: [(18)F]FDG-PET imaging has been recognized as a crucial diagnostic marker in Mild Cognitive Impairment (MCI), supporting the presence or the exclusion of Alzheimer's Disease (AD) pathology. A clinical heterogeneity, however, underlies MCI definition. In this study, we aimed to evaluate the predictive role of single-subject voxel-based maps of [(18)F]FDG distribution generated through statistical parametric mapping (SPM) in the progression to different dementia subtypes in a sample of 45 MCI. Their scans were compared to a large normal reference dataset developed and validated for comparison at single-subject level. Additionally, Aβ42 and Tau CSF values were available in 34 MCI subjects. Clinical follow-up (mean 28.5 ± 7.8 months) assessed subsequent progression to AD or non-AD dementias. The SPM analysis showed: 1) normal brain metabolism in 14 MCI cases, none of them progressing to dementia; 2) the typical temporo-parietal pattern suggestive for prodromal AD in 15 cases, 11 of them progressing to AD; 3) brain hypometabolism suggestive of frontotemporal lobar degeneration (FTLD) subtypes in 7 and dementia with Lewy bodies (DLB) in 2 subjects (all fulfilled FTLD or DLB clinical criteria at follow-up); and 4) 7 MCI cases showed a selective unilateral or bilateral temporo-medial hypometabolism without the typical AD pattern, and they all remained stable. In our sample, objective voxel-based analysis of [(18)F]FDG-PET scans showed high predictive prognostic value, by identifying either normal brain metabolism or hypometabolic patterns suggestive of different underlying pathologies, as confirmed by progression at follow-up. These data support the potential usefulness of this SPM [(18)F]FDG PET analysis in the early dementia diagnosis and for improving subject selection in clinical trials based on MCI definition.
    Clinical neuroimaging 01/2015; 7:187-94. DOI:10.1016/j.nicl.2014.12.004 · 2.53 Impact Factor
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