| 1. |
- Collij, Lyduine E., et al.
(author)
-
Quantification of [18F]florbetaben amyloid-PET imaging in a mixed memory clinic population : The ABIDE project
- 2023
-
In: Alzheimer's and Dementia. - : Wiley. - 1552-5260 .- 1552-5279. ; 19:6, s. 2397-2407
-
Journal article (peer-reviewed)abstract
- Introduction: We investigated amyloid-burden quantification in a mixed memory clinic population. Methods: [18F]Florbetaben amyloid-PET (positron emission tomography) scans of 348 patients were visually read and quantified using the Centiloid (CL) method. General linear models were used to assess CL differences across syndromic and etiological diagnosis. Linear mixed models were fitted to assess the predictive value of visual read (VR) and CL on longitudinal Mini-Mental Status Examination (MMSE). Results: CL was associated with syndromic (F = 4.42, p = 0.014) and etiological diagnosis (F = -12.66, p < 0.001), with Alzheimer's disease (AD) patients showing the highest amyloid burden (62.9 ± 27.5), followed by dementia with Lewy bodies (DLB) (25.3 ± 35.5) and cardiovascular disease (CVD) (16.7 ± 24.5), and finally frontotemporal lobe degeneration (FTLD) (5.0 ± 17.22, t = –12.66, p < 0.001). CL remained predictive of etiological diagnosis (t = –2.41, p = 0.017) within the VR+ population (N = 157). VR was not a significant predictor of MMSE (t = –1.53, p = 0.13) for the SCD population (N = 90), whereas CL was (t = -3.30, p = 0.001). Discussion: The extent of amyloid pathology through quantification holds clinical value, potentially in the context of differential diagnosis as well as prognosis.
|
|
| 2. |
- Laurie, Steven, et al.
(author)
-
The RD-Connect Genome-Phenome Analysis Platform : Accelerating diagnosis, research, and gene discovery for rare diseases
- 2022
-
In: Human Mutation. - : John Wiley & Sons. - 1059-7794 .- 1098-1004. ; 43:6, s. 717-733
-
Journal article (peer-reviewed)abstract
- Rare disease patients are more likely to receive a rapid molecular diagnosis nowadays thanks to the wide adoption of next-generation sequencing. However, many cases remain undiagnosed even after exome or genome analysis, because the methods used missed the molecular cause in a known gene, or a novel causative gene could not be identified and/or confirmed. To address these challenges, the RD-Connect Genome-Phenome Analysis Platform (GPAP) facilitates the collation, discovery, sharing, and analysis of standardized genome-phenome data within a collaborative environment. Authorized clinicians and researchers submit pseudonymised phenotypic profiles encoded using the Human Phenotype Ontology, and raw genomic data which is processed through a standardized pipeline. After an optional embargo period, the data are shared with other platform users, with the objective that similar cases in the system and queries from peers may help diagnose the case. Additionally, the platform enables bidirectional discovery of similar cases in other databases from the Matchmaker Exchange network. To facilitate genome-phenome analysis and interpretation by clinical researchers, the RD-Connect GPAP provides a powerful user-friendly interface and leverages tens of information sources. As a result, the resource has already helped diagnose hundreds of rare disease patients and discover new disease causing genes.
|
|
| 3. |
- Pemberton, Hugh G., et al.
(author)
-
Quantification of amyloid PET for future clinical use : a state-of-the-art review
- 2022
-
In: European Journal of Nuclear Medicine and Molecular Imaging. - : Springer Science and Business Media LLC. - 1619-7070 .- 1619-7089. ; 49:10, s. 3508-3528
-
Research review (peer-reviewed)abstract
- Amyloid-β (Aβ) pathology is one of the earliest detectable brain changes in Alzheimer’s disease (AD) pathogenesis. The overall load and spatial distribution of brain Aβ can be determined in vivo using positron emission tomography (PET), for which three fluorine-18 labelled radiotracers have been approved for clinical use. In clinical practice, trained readers will categorise scans as either Aβ positive or negative, based on visual inspection. Diagnostic decisions are often based on these reads and patient selection for clinical trials is increasingly guided by amyloid status. However, tracer deposition in the grey matter as a function of amyloid load is an inherently continuous process, which is not sufficiently appreciated through binary cut-offs alone. State-of-the-art methods for amyloid PET quantification can generate tracer-independent measures of Aβ burden. Recent research has shown the ability of these quantitative measures to highlight pathological changes at the earliest stages of the AD continuum and generate more sensitive thresholds, as well as improving diagnostic confidence around established binary cut-offs. With the recent FDA approval of aducanumab and more candidate drugs on the horizon, early identification of amyloid burden using quantitative measures is critical for enrolling appropriate subjects to help establish the optimal window for therapeutic intervention and secondary prevention. In addition, quantitative amyloid measurements are used for treatment response monitoring in clinical trials. In clinical settings, large multi-centre studies have shown that amyloid PET results change both diagnosis and patient management and that quantification can accurately predict rates of cognitive decline. Whether these changes in management reflect an improvement in clinical outcomes is yet to be determined and further validation work is required to establish the utility of quantification for supporting treatment endpoint decisions. In this state-of-the-art review, several tools and measures available for amyloid PET quantification are summarised and discussed. Use of these methods is growing both clinically and in the research domain. Concurrently, there is a duty of care to the wider dementia community to increase visibility and understanding of these methods.
|
|
