Preclinical diagnosis of Alzheimer’s disease (AD) is one of the major challenges for the prevention of AD. AD biomarkers are needed not only to reveal preclinical pathologic changes, but also to monitor progression and therapeutics. PET neuroimaging can reliably assess aspects of the molecular biology and neuropathology of AD. The aim of this article is to review the use of FDG-PET and amyloid PET imaging in the early detection of AD.
To estimate a regional progression pattern of amyloid deposition from cross-sectional amyloid-sensitive PET data and evaluate its potential for in vivo staging of an individual's amyloid pathology.
Abstract Multifactorial mechanisms underlying late-onset Alzheimer’s disease (LOAD) are poorly characterized from an integrative perspective. Here spatiotemporal alterations in brain amyloid-β deposition, metabolism, vascular, functional activity at rest, structural properties, cognitive integrity and peripheral proteins levels are characterized in relation to LOAD progression. We analyse over 7,700 brain images and tens of plasma and cerebrospinal fluid biomarkers from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Through a multifactorial data-driven analysis, we obtain dynamic LOAD–abnormality indices for all biomarkers, and a tentative temporal ordering of disease progression. Imaging results suggest that intra-brain vascular dysregulation is an early pathological event during disease development. Cognitive decline is noticeable from initial LOAD stages, suggesting early memory deficit associated with the primary disease factors. High abnormality levels are also observed for specific proteins associated with the vascular system’s integrity. Although still subjected to the sensitivity of the algorithms and biomarkers employed, our results might contribute to the development of preventive therapeutic interventions.
Approximately 5% of dementia patients develop symptoms before the age of 65 (EO). EO Alzheimer's Disease (EOAD) is believed to have a more aggressive disease course than late onset AD (LOAD). We analyzed the available ADNI MRI and FDG-PET data of 99 amyloid-positive amnestic EO (44 MCI and 55 dementia), 186 amyloid-positive amnestic LO (109 MCI and 77 dementia) and 212 amyloid-negative cognitively normal (CN) subjects (Table 1). Amyloid positive status was determined using an 18F-AV-45 amyloid PET standard uptake value ratio (SUVR) ≥ 1.17 normalized to whole cerebellum. In order to study the extent of disease involvement, we compared the MRI, FDG-PET and amyloid PET of EO- and LO- MCI and DEM to the CN group using linear regression in SPM8 correcting for age, gender, and education. Additionally, in the MRI analyses, we controlled for intracranial volume and scan type. FWE correction for multiple comparisons was applied. Demographic and amyloid burden comparisons of the diagnostic groups to CN can be seen in Tables 1 and 2. Direct comparisons of EO and LO showed the expected significant difference in age and age at symptom onset in the MCI and DEM stages (p<0.001 both). LO-MCI had shorter disease duration (p<0.001) and were more likely to be male (p=0.032) compared to EO-MCI. EO and LO subjects had comparable MMSE and CDR-SOB. Amyloid load appeared to be greater in LO compared to EO subjects regardless of disease stage (see significance and β-coefficient maps in Figure 1). EO-MCI showed more extensive hypometabolism vs. atrophy in the medial and inferior temporal, as well as the temporoparietal regions. LO-MCI showed additional effects in the frontal lobes (Figures 2 and 3). In the dementia stage, the extent of MRI and FDG abnormalities were comparable in EO and LO and also involved the frontal lobes. β-coefficient maps showed larger effect sizes in EO-MCI and EO-DEM subjects (Figures 1 and 2).
Clinical diagnosis of Alzheimer's disease (AD) can be challenging as numerous diseases mimic the characteristics of AD. In this light, recent guidelines developed by different associations and working groups point out the need for biomarkers to support AD diagnosis. This paper discusses 18F-labeled radiotracers (which are indicated for PET imaging of the brain) and ongoing clinical studies that aim to generate new evidence for the usage of amyloid imaging. In addition to their relatively long half-life, these agents are known for their high sensitivity and high negative predictive values for detection of neuritic Aβ plaques. Comparisons with other biomarkers are provided and implications of diagnostic disclosures discussed. Finally, recent data from clinical trials underscore the importance of amyloid PET for detecting, quantifying and monitoring Aβ plaque deposits.
We utilized visually rated amyloid PET scans to determine the quantitative threshold of amyloid positivity for [F-18]Florbetaben PET scans and to determine the inter-relationships among amyloid load, age, ethnicity, gender, severity of cognitive impairment and ApolipoproteinE (APOE) genotype. Two experienced raters, blinded to clinical and genetic status of the subjects, conducted visual ratings on PET scans (inter-rater reliability was 100% for amyloid positive and 93% for amyloid negative) for 152 participants (mean age 71.1 ±7.6 yrs) who were Cognitively Normal (n=46), Mild Cognitive Impairment (n=68) and Mild Dementia (n=38). Quantitative amyloid load was measured with Standardized Uptake Value Ratios (SUVRs), referenced to the cerebellar gray matter and averaged for five cortical regions (frontal, temporal, parietal, anterior cingulate and posterior cingulate). By visual rating, 10% of CN, 40% of MCI and 69% of Dementia subjects were amyloid positive. Mean SUVRs were 1.22 ± 0.16 for CNs, 1.41 ± 0.27 for MCI, and 1.59 for Dementia. Cognition assessed with the MoCA was strongly related to SUVR (p < .001). Mean SUVR was 1.56 ± 1.32 for APOEε4 genotype carriers (E4+) and 1.30 ±0.23 for non-carriers (E4-) (p< .001). There was no significant main effect for age, gender or ethnicity on SUVR. However, the following interactions were present: (1) SUVR for E4+ White non-Hispanics (WNHs) was higher than E4+ Hispanics, but E4- WNHs had lower SUVR than E4- Hispanics; (2) E4+ males had higher SUVR than E4+ females. The optimal SUVR threshold for amyloid positivity among all subjects (n=152), using ROC analyses and Youden's criteria, was 1.42 (sensitivity =94%; specificity = 92.5 %). For E4- subjects this threshold was 1.33 and 1.45 for E4+ subjects. Amyloid load was not significantly influenced by age, gender, ethnicity and cognitive status, other than the interactions between E4 status, ethnicity and gender, but was higher among E4+ than E4- subjects, as was the optimal SUVR threshold for amyloid positivity. Research studies in which quantitative thresholds for amyloid positivity are used should consider the impact of E4+ and E4- status, and the resulting clinical progression rates.
Abstract Background A pathology‐proven cutoff for NeuraCeq ( 18 F‐florbetaben) was previously developed in end‐of‐life cases to discriminate Alzheimer’s disease (AD) patients from elderly Aβ‐negative non‐demented controls (NDC). However, these cutoffs could be of limited use in early stages of AD. Here, we developed and validated 18 F‐florbetaben cutoffs to detect low Aβ deposition and established amyloid pathology. Method The low Aβ deposition threshold was derived from a sample of young healthy controls (YHC) (n=70, 27.6±5.1 y), as 2 standard deviations above the mean. The established pathology cutoff was derived using ROC curve analysis on a sample of visually determined Aβ‐negative NDC (n=62, 67.8±6.8 y) and Aβ‐positive AD dementia patients (n=62, 70.6±8.0 y). Validation was based on longitudinal samples: (1) subjective cognitive decline (SCD) volunteers (n=166; 64.9±7.2 y) and (2) mild cognitively impaired (MCI) subjects (n=45, 72.7 ± 6.5 y). The standard centiloid (CL)methodology was applied for quantification. Result The low Aβ‐deposition detection cutoff was 13.5 CL, and the threshold for established Aβ pathology was 35.7 CL. In the validation samples, the rate of Aβ accumulation in the “low amyloid load” interval (13.5‐35.7 CL) was significantly different from zero (p<0.05), (2.1±2.1 %/year (SCD), 2.6±1.5 %/year (MCI)), which was also observed for “established Aβ deposition” (>35.7 CL) (3.4±3.2 %/y (SCD), 1.4±1.8 %/y (MCI), (p<0.05)). The accumulation rate for Aβ‐negative subjects (<13.5 CL) was not significantly different from zero (‐0.1±1.1 %/year (SCD), 0.1±1.6 %/year (MCI)). Conclusion An interval between 13.5 and 35.7 CL is optimal for the detection of early Aβ deposition and to identify subjects that are likely accumulating Aβ. Further validation tests using additional samples are ongoing.
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