AD Course Map charts Alzheimer’s disease progression

Koval, Igor; Bône, Alexandre; Louis, Maxime; Lartigue, Thomas; Bottani, Simona; Marcoux, Arnaud; Samper‐Gonzàlez, Jorge; Burgos, Ninon; Charlier, Benjamin; Bertrand, Anne; Epelbaum, Stéphane; Colliot, Olivier; Allassonnière, Stéphanie; Durrleman, Stanley

doi:10.1038/s41598-021-87434-1

Cited by 48 publications

(60 citation statements)

References 77 publications

(33 reference statements)

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“…The funnel plot for cortical surface area demonstrated a slight departure from symmetry, but all other funnel plots were symmetrical about the IVW estimate (Supplementary Figs 7-12). The MR-Egger intercept test did not show evidence of directional pleiotropy in any of the analyses (Supplementary Table 3), and there was no distortion in any of the leave-one-out plots (Supplementary Figs [13][14][15][16][17][18]. The proportion of education-associated SNPs that explained more variance in the imaging phenotype than in educational attainment ranged from 14% for intracellular volume fraction to 30% for white matter hyperintensities volume (Supplementary Table 4).…”

Section: Association Of Genetically-predicted Educational Attainment ...mentioning

confidence: 96%

“…2; Supplementary Methods), most of which reflect global brain measures previously shown to be associated phenotypically with educational attainment and/or Alzheimer's disease. 12,14,[16][17][18][19][24][25][26][27][28][31][32][33] We also chose to study the total volume of white matter hyperintensities as a metric closely linked to age-related cognitive decline and thus a putative marker of structural brain health. [60][61][62][63] In order to generate genetic variant association estimates with the whole-brain cortical macro-structure (surface area, volume, cortical thickness, intrinsic curvature, mean curvature, local gyrification index) and micro-structure phenotypes (mean diffusivity, fractional anisotropy, intracellular volume fraction, and orientation dispersion index), we conducted an in-house GWAS using the latest UK Biobank data release (Supplementary Methods).…”

Section: Data Sourcesmentioning

confidence: 99%

“…5 Brain reserve refers to individual differences in the anatomical and structural characteristics of the brain that enable some individuals to preserve their cognitive and functional status despite neuropathology. [9][10][11] The progression of amyloid-β and tau pathology in Alzheimer's disease is associated with several structural alterations in the brain, including progressive cortical thinning, [12][13][14][15] widespread grey matter atrophy in cortical and sub-cortical regions, 16,17 and damage to the integrity and organization of white matter tracts. [18][19][20][21] Many of these structural characteristics can be measured in-vivo using structural or diffusion MRI, and thus their premorbid levels may serve as a useful proxy for the structural basis of brain reserve capacity.…”

Section: Introductionmentioning

confidence: 99%

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Educational attainment, structural brain reserve, and Alzheimer’s disease: a Mendelian randomization analysis

Seyedsalehi

Warrier

Bethlehem

et al. 2022

Preprint

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Higher educational attainment is observationally associated with lower risk of Alzheimer’s disease. However, the biological mechanisms underpinning this association remain unclear. The protective effect of education on Alzheimer’s disease may be mediated via increased brain reserve. We used two-sample Mendelian randomization to explore putative causal relationships between educational attainment, structural brain reserve as proxied by MRI phenotypes, and Alzheimer’s disease.Summary statistics were obtained from genome-wide association studies of educational attainment (n=1,131,881), late-onset Alzheimer’s disease (35,274 cases, 59,163 controls), and 15 measures of grey or white matter macro- or micro-structure derived from structural or diffusion MRI (nmax=33,211). We conducted univariable Mendelian randomization analyses to investigate bidirectional associations between (i) educational attainment and Alzheimer’s disease, (ii) educational attainment and imaging-derived phenotypes, (iii) imaging-derived phenotypes and Alzheimer’s disease. Multivariable Mendelian randomization was used to assess whether brain structure phenotypes mediated the protective effect of education on Alzheimer’s disease risk.Genetically-proxied educational attainment was inversely associated with Alzheimer’s disease (odds ratio per standard deviation increase in genetically-predicted years of schooling = 0.70, 95% confidence interval [CI]: 0.60, 0.80). There was also evidence for positive associations between genetically-predicted educational attainment and four cortical macro-structure metrics (surface area: β=0.30, 95% CI: 0.20, 0.40; volume: β=0.29, 95% CI: 0.20, 0.37; intrinsic curvature: β=0.18, 95% CI: 0.11, 0.25; local gyrification index: β=0.21, 95% CI: 0.11, 0.31), as well as inverse associations with cortical intracellular volume fraction (β=-0.09, 95% CI: − 0.15, −0.03) and white matter hyperintensities volume (β=-0.14, 95% CI: −0.23, −0.05).Genetically-proxied levels of three cortical macro-structure metrics were positively associated with years of education (surface area: β=0.13, 95% CI: 0.10, 0.16; volume: β=0.15, 95% CI: 0.11, 0.19; intrinsic curvature: β=0.12, 95% CI: 0.04, 0.19). We found no evidence of associations between genetically-predicted imaging-derived phenotypes and Alzheimer’s disease. The inverse association of genetically-predicted education with Alzheimer’s disease did not attenuate after adjusting for imaging-derived phenotypes in multivariable analyses.Our results provide support for a protective causal effect of educational attainment on Alzheimer’s disease risk, as well as bi-directional causal associations between education and several brain macro- and micro-structure metrics. However, we did not find evidence that these structural markers affect risk of Alzheimer’s disease. The protective effect of education on Alzheimer’s disease may be mediated via other measures of brain reserve not included in the present study, or by alternative mechanisms.

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Section: Association Of Genetically-predicted Educational Attainment ...mentioning

confidence: 96%

Section: Data Sourcesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Educational attainment, structural brain reserve, and Alzheimer’s disease: a Mendelian randomization analysis

Seyedsalehi

Warrier

Bethlehem

et al. 2022

Preprint

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“…Continuous aggregation of patient data in registries could offer an efficient way of enriching well-phenotyped patients for genotyping. This type of approach has been developed for Alzheimer's disease [34] and would certainly offer a useful aid for accelerated patient screening for precision-medicine clinical trials in PD. Consumer genomics tools such as home test kits, fast genotyping/sequencing services, and existing databases with genetic profiling data could also significantly facilitate implementation of precision medicine trials in PD.…”

Section: Challenges and Opportunities Of Genetically Targeted Clinical Trials In Pdmentioning

confidence: 99%

Genetically Targeted Clinical Trials in Parkinson’s Disease: Learning from the Successes Made in Oncology

Sjögren

Huttunen

Svenningsson

et al. 2021

Genes

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Clinical trials in neurodegenerative disorders have been associated with high rate of failures, while in oncology, the implementation of precision medicine and focus on genetically defined subtypes of disease and targets for drug development have seen an unprecedented success. With more than 20 genes associated with Parkinson’s disease (PD), most of which are highly penetrant and often cause early onset or atypical signs and symptoms, and an increasing understanding of the associated pathophysiology culminating in dopaminergic neurodegeneration, applying the technologies and designs into the field of neurodegeneration seems a logical step. This review describes some of the methods used in oncology clinical trials and some attempts in Parkinson’s disease and the potential of further implementing genetics, biomarkers and smart clinical trial designs in this disease area.

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“…Alzheimer’s disease (AD) and dementia research has progressed considerably thanks to the increased availability of patient-level cohort datasets [ 1 ]. Cohort data have, among others, laid the foundation to discover novel biomarkers [ 2 ], investigate disease progression [ 3 ], and identify disease subtypes [ 4 ]. To ensure the robustness and reproducibility of results achieved in such data-driven analyses, they must be externally validated in independent cohort datasets [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

ADataViewer: exploring semantically harmonized Alzheimer’s disease cohort datasets

et al. 2022

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Background Currently, Alzheimer’s disease (AD) cohort datasets are difficult to find and lack across-cohort interoperability, and the actual content of publicly available datasets often only becomes clear to third-party researchers once data access has been granted. These aspects severely hinder the advancement of AD research through emerging data-driven approaches such as machine learning and artificial intelligence and bias current data-driven findings towards the few commonly used, well-explored AD cohorts. To achieve robust and generalizable results, validation across multiple datasets is crucial. Methods We accessed and systematically investigated the content of 20 major AD cohort datasets at the data level. Both, a medical professional and a data specialist, manually curated and semantically harmonized the acquired datasets. Finally, we developed a platform that displays vital information about the available datasets. Results Here, we present ADataViewer, an interactive platform that facilitates the exploration of 20 cohort datasets with respect to longitudinal follow-up, demographics, ethnoracial diversity, measured modalities, and statistical properties of individual variables. It allows researchers to quickly identify AD cohorts that meet user-specified requirements for discovery and validation studies regarding available variables, sample sizes, and longitudinal follow-up. Additionally, we publish the underlying variable mapping catalog that harmonizes 1196 unique variables across the 20 cohorts and paves the way for interoperable AD datasets. Conclusions In conclusion, ADataViewer facilitates fast, robust data-driven research by transparently displaying cohort dataset content and supporting researchers in selecting datasets that are suited for their envisioned study. The platform is available at https://adata.scai.fraunhofer.de/.

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AD Course Map charts Alzheimer’s disease progression

Cited by 48 publications

References 77 publications

Educational attainment, structural brain reserve, and Alzheimer’s disease: a Mendelian randomization analysis

Educational attainment, structural brain reserve, and Alzheimer’s disease: a Mendelian randomization analysis

Genetically Targeted Clinical Trials in Parkinson’s Disease: Learning from the Successes Made in Oncology

ADataViewer: exploring semantically harmonized Alzheimer’s disease cohort datasets

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