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- de Rojas, I., et al.
(författare)
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Common variants in Alzheimer’s disease and risk stratification by polygenic risk scores
- 2021
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Ingår i: Nature Communications. - : Springer Science and Business Media LLC. - 2041-1723. ; 12:1
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Tidskriftsartikel (refereegranskat)abstract
- Genetic discoveries of Alzheimer’s disease are the drivers of our understanding, and together with polygenetic risk stratification can contribute towards planning of feasible and efficient preventive and curative clinical trials. We first perform a large genetic association study by merging all available case-control datasets and by-proxy study results (discovery n = 409,435 and validation size n = 58,190). Here, we add six variants associated with Alzheimer’s disease risk (near APP, CHRNE, PRKD3/NDUFAF7, PLCG2 and two exonic variants in the SHARPIN gene). Assessment of the polygenic risk score and stratifying by APOE reveal a 4 to 5.5 years difference in median age at onset of Alzheimer’s disease patients in APOE ɛ4 carriers. Because of this study, the underlying mechanisms of APP can be studied to refine the amyloid cascade and the polygenic risk score provides a tool to select individuals at high risk of Alzheimer’s disease. © 2021, The Author(s).
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- Malzbender, K., et al.
(författare)
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Validation, Deployment, and Real-World Implementation of a Modular Toolbox for Alzheimer’s Disease Detection and Dementia Risk Reduction: The AD-RIDDLE Project
- 2024
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Ingår i: Journal of Prevention of Alzheimer's Disease. - 2274-5807 .- 2426-0266. ; 11:2, s. 329-338
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Tidskriftsartikel (refereegranskat)abstract
- The Real-World Implementation, Deployment, and Validation of Early Detection Tools and Lifestyle Enhancement (AD-RIDDLE) project, recently launched with the support of the EU Innovative Health Initiative (IHI) public-private partnership and UK Research and Innovation (UKRI), aims to develop, test, and deploy a modular toolbox platform that can reduce existing barriers to the timely detection, and therapeutic approaches in Alzheimer’s disease (AD), thus accelerating AD innovation. By focusing on health system and health worker practices, AD-RIDDLE seeks to improve and smooth AD management at and between each key step of the clinical pathway and across the disease continuum, from at-risk asymptomatic stages to early symptomatic ones. This includes innovation and improvement in AD awareness, risk reduction and prevention, detection, diagnosis, and intervention. The 24 partners in the AD-RIDDLE interdisciplinary consortium will develop and test the AD-RIDDLE toolbox platform and its components individually and in combination in six European countries. Expected results from this cross-sectoral research collaboration include tools for earlier detection and accurate diagnosis; validated, novel digital cognitive and blood-based biomarkers; and improved access to individualized preventative interventions (including multimodal interventions and symptomatic/disease-modifying therapies) across diverse populations, within the framework of precision medicine. Overall, AD-RIDDLE toolbox platform will advance management of AD, improving outcomes for patients and their families, and reducing costs.
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- Kurbatova, N., et al.
(författare)
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Urinary metabolic phenotyping for Alzheimer's disease
- 2020
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Ingår i: Scientific Reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 10:1
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Tidskriftsartikel (refereegranskat)abstract
- Finding early disease markers using non-invasive and widely available methods is essential to develop a successful therapy for Alzheimer's Disease. Few studies to date have examined urine, the most readily available biofluid. Here we report the largest study to date using comprehensive metabolic phenotyping platforms (NMR spectroscopy and UHPLC-MS) to probe the urinary metabolome in-depth in people with Alzheimer's Disease and Mild Cognitive Impairment. Feature reduction was performed using metabolomic Quantitative Trait Loci, resulting in the list of metabolites associated with the genetic variants. This approach helps accuracy in identification of disease states and provides a route to a plausible mechanistic link to pathological processes. Using these mQTLs we built a Random Forests model, which not only correctly discriminates between people with Alzheimer's Disease and age-matched controls, but also between individuals with Mild Cognitive Impairment who were later diagnosed with Alzheimer's Disease and those who were not. Further annotation of top-ranking metabolic features nominated by the trained model revealed the involvement of cholesterol-derived metabolites and small-molecules that were linked to Alzheimer's pathology in previous studies.
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| 8. |
- Martensson, G., et al.
(författare)
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Stability of graph theoretical measures in structural brain networks in Alzheimer's disease
- 2018
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Ingår i: Scientific Reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 8
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Tidskriftsartikel (refereegranskat)abstract
- Graph analysis has become a popular approach to study structural brain networks in neurodegenerative disorders such as Alzheimer's disease (AD). However, reported results across similar studies are often not consistent. In this paper we investigated the stability of the graph analysis measures clustering, path length, global efficiency and transitivity in a cohort of AD (N = 293) and control subjects (N = 293). More specifically, we studied the effect that group size and composition, choice of neuroanatomical atlas, and choice of cortical measure (thickness or volume) have on binary and weighted network properties and relate them to the magnitude of the differences between groups of AD and control subjects. Our results showed that specific group composition heavily influenced the network properties, particularly for groups with less than 150 subjects. Weighted measures generally required fewer subjects to stabilize and all assessed measures showed robust significant differences, consistent across atlases and cortical measures. However, all these measures were driven by the average correlation strength, which implies a limitation of capturing more complex features in weighted networks. In binary graphs, significant differences were only found in the global efficiency and transitivity measures when using cortical thickness measures to define edges. The findings were consistent across the two atlases, but no differences were found when using cortical volumes. Our findings merits future investigations of weighted brain networks and suggest that cortical thickness measures should be preferred in future AD studies if using binary networks. Further, studying cortical networks in small cohorts should be complemented by analyzing smaller, subsampled groups to reduce the risk that findings are spurious.
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