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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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- Hong, S. J., et al.
(författare)
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TMEM106B and CPOX are genetic determinants of cerebrospinal fluid Alzheimer's disease biomarker levels
- 2021
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Ingår i: Alzheimers & Dementia. - : Wiley. - 1552-5260 .- 1552-5279. ; 17:10, s. 1628-1640
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Tidskriftsartikel (refereegranskat)abstract
- Introduction Neurofilament light (NfL), chitinase-3-like protein 1 (YKL-40), and neurogranin (Ng) are biomarkers for Alzheimer's disease (AD) to monitor axonal damage, astroglial activation, and synaptic degeneration, respectively. Methods We performed genome-wide association studies (GWAS) using DNA and cerebrospinal fluid (CSF) samples from the EMIF-AD Multimodal Biomarker Discovery study for discovery, and the Alzheimer's Disease Neuroimaging Initiative study for validation analyses. GWAS were performed for all three CSF biomarkers using linear regression models adjusting for relevant covariates. Results We identify novel genome-wide significant associations between DNA variants in TMEM106B and CSF levels of NfL, and between CPOX and YKL-40. We confirm previous work suggesting that YKL-40 levels are associated with DNA variants in CHI3L1. Discussion Our study provides important new insights into the genetic architecture underlying interindividual variation in three AD-related CSF biomarkers. In particular, our data shed light on the sequence of events regarding the initiation and progression of neuropathological processes relevant in AD.
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- Manera, AL, et al.
(författare)
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MRI data-driven algorithm for the diagnosis of behavioural variant frontotemporal dementia
- 2021
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Ingår i: Journal of neurology, neurosurgery, and psychiatry. - : BMJ. - 1468-330X .- 0022-3050. ; 92:6, s. 608-616
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Tidskriftsartikel (refereegranskat)abstract
- Structural brain imaging is paramount for the diagnosis of behavioural variant of frontotemporal dementia (bvFTD), but it has low sensitivity leading to erroneous or late diagnosis.MethodsA total of 515 subjects from two different bvFTD cohorts (training and independent validation cohorts) were used to perform voxel-wise morphometric analysis to identify regions with significant differences between bvFTD and controls. A random forest classifier was used to individually predict bvFTD from deformation-based morphometry differences in isolation and together with semantic fluency. Tenfold cross validation was used to assess the performance of the classifier within the training cohort. A second held-out cohort of genetically confirmed bvFTD cases was used for additional validation.ResultsAverage 10-fold cross-validation accuracy was 89% (82% sensitivity, 93% specificity) using only MRI and 94% (89% sensitivity, 98% specificity) with the addition of semantic fluency. In the separate validation cohort of definite bvFTD, accuracy was 88% (81% sensitivity, 92% specificity) with MRI and 91% (79% sensitivity, 96% specificity) with added semantic fluency scores.ConclusionOur results show that structural MRI and semantic fluency can accurately predict bvFTD at the individual subject level within a completely independent validation cohort coming from a different and independent database.
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