Phenotypic Similarities Between Late-Onset Autosomal Dominant and Sporadic Alzheimer Disease

Day, Gregory S.; Musiek, Erik S.; Roe, Catherine M.; Norton, Joanne; Goate, Alison M.; Cruchaga, Carlos; Cairns, Nigel J.; Morris, John C.

doi:10.1001/jamaneurol.2016.1236

Cited by 18 publications

(20 citation statements)

References 51 publications

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“…This allowed extracting AD‐specific brain changes that occur early in the disease course. We acknowledge differences between ADAD and sporadic AD including earlier symptom onset, higher likelihood of non‐memory symptoms, 40 and higher prevalence of psychiatric comorbidities 41 in ADAD versus sporadic AD. However, both sporadic AD and ADAD share core neuropathological features and biomarker abnormalities including amyloid and tau accumulation, FDG‐PET hypometabolism, and neurodegeneration 28,41 .…”

Section: Discussionmentioning

confidence: 99%

Predicting sporadic Alzheimer's disease progression via inherited Alzheimer's disease‐informed machine‐learning

Franzmeier

Koutsouleris

Benzinger

et al. 2020

Alzheimer's & Dementia

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Introduction Developing cross‐validated multi‐biomarker models for the prediction of the rate of cognitive decline in Alzheimer's disease (AD) is a critical yet unmet clinical challenge. Methods We applied support vector regression to AD biomarkers derived from cerebrospinal fluid, structural magnetic resonance imaging (MRI), amyloid‐PET and fluorodeoxyglucose positron‐emission tomography (FDG‐PET) to predict rates of cognitive decline. Prediction models were trained in autosomal‐dominant Alzheimer's disease (ADAD, n = 121) and subsequently cross‐validated in sporadic prodromal AD (n = 216). The sample size needed to detect treatment effects when using model‐based risk enrichment was estimated. Results A model combining all biomarker modalities and established in ADAD predicted the 4‐year rate of decline in global cognition (R2 = 24%) and memory (R2 = 25%) in sporadic AD. Model‐based risk‐enrichment reduced the sample size required for detecting simulated intervention effects by 50%–75%. Discussion Our independently validated machine‐learning model predicted cognitive decline in sporadic prodromal AD and may substantially reduce sample size needed in clinical trials in AD.

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Section: Discussionmentioning

confidence: 99%

Predicting sporadic Alzheimer's disease progression via inherited Alzheimer's disease‐informed machine‐learning

Franzmeier

Koutsouleris

Benzinger

et al. 2020

Alzheimer's & Dementia

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“…( Fig 1B , Fam #6). There are reported cases in which autosomal dominant AD variants present with a later AAO and clinically mimic LOAD [ 91 ]. The observed genetic overlap of AD with other neurodegenerative diseases suggests that future studies focused on gene discovery for AD using late onset families should not rely solely on segregation patterns; additional methods that take into account endophenotypes, gene-networks and pathway analysis need to be developed and implemented in routine analysis.…”

Section: Discussionmentioning

confidence: 99%

“…This limits the power of this study to establish causality of the detected pathogenic variants and it could as well be limiting our statistical power and enrichment. Second, sporadic and familial AD are categorically different entities, despite the fact that we believe the genetic architecture and molecular load are largely similar [ 91 ]. However, the complex and heterogeneous presentation of AD makes it difficult to recruit large cohorts with good phenotypic characterization.…”

Section: Discussionmentioning

confidence: 99%

Analysis of neurodegenerative Mendelian genes in clinically diagnosed Alzheimer Disease

et al. 2017

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Alzheimer disease (AD), Frontotemporal lobar degeneration (FTD), Amyotrophic lateral sclerosis (ALS) and Parkinson disease (PD) have a certain degree of clinical, pathological and molecular overlap. Previous studies indicate that causative mutations in AD and FTD/ALS genes can be found in clinical familial AD. We examined the presence of causative and low frequency coding variants in the AD, FTD, ALS and PD Mendelian genes, in over 450 families with clinical history of AD and over 11,710 sporadic cases and cognitive normal participants from North America. Known pathogenic mutations were found in 1.05% of the sporadic cases, in 0.69% of the cognitively normal participants and in 4.22% of the families. A trend towards enrichment, albeit non-significant, was observed for most AD, FTD and PD genes. Only PSEN1 and PINK1 showed consistent association with AD cases when we used ExAC as the control population. These results suggest that current study designs may contain heterogeneity and contamination of the control population, and that current statistical methods for the discovery of novel genes with real pathogenic variants in complex late onset diseases may be inadequate or underpowered to identify genes carrying pathogenic mutations.

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“…Others have shown distinctly different cognitive [ 198 , 227 , 229 , 247 ], neurological [ 227 ], metabolic [ 183 ] and biochemical [ 11 , 200 ] presentations between autosomal dominant AD, early-onset AD (in general) and late-onset AD [ 58 ] (reviewed in [ 227 , 247 , 262 ]). Trisomy 21, too, once provided an important pillar of support for the amyloid hypothesis, but several valid findings question its relevance to all AD (refer to Supplementary Material 1.5 for extended discussion).…”

Section: Question 5: How Widely Applicable Are Findings From Autosomamentioning

confidence: 99%

Questions concerning the role of amyloid-β in the definition, aetiology and diagnosis of Alzheimer’s disease

2018

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The dominant hypothesis of Alzheimer’s disease (AD) aetiology, the neuropathological guidelines for diagnosing AD and the majority of high-profile therapeutic efforts, in both research and in clinical practice, have been built around one possible causal factor, amyloid-β (Aβ). However, the causal link between Aβ and AD remains unproven. Here, in the context of a detailed assessment of historical and contemporary studies, we raise critical questions regarding the role of Aβ in the definition, diagnosis and aetiology of AD. We illustrate that a holistic view of the available data does not support an unequivocal conclusion that Aβ has a central or unique role in AD. Instead, the data suggest alternative views of AD aetiology are potentially valid, at this time. We propose that an unbiased way forward for the field, beyond the current Aβ-centric approach, without excluding a role for Aβ, is required to come to an accurate understanding of AD dementia and, ultimately, an effective treatment.Electronic supplementary materialThe online version of this article (10.1007/s00401-018-1918-8) contains supplementary material, which is available to authorized users.

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Phenotypic Similarities Between Late-Onset Autosomal Dominant and Sporadic Alzheimer Disease

Cited by 18 publications

References 51 publications

Predicting sporadic Alzheimer's disease progression via inherited Alzheimer's disease‐informed machine‐learning

Predicting sporadic Alzheimer's disease progression via inherited Alzheimer's disease‐informed machine‐learning

Analysis of neurodegenerative Mendelian genes in clinically diagnosed Alzheimer Disease

Questions concerning the role of amyloid-β in the definition, aetiology and diagnosis of Alzheimer’s disease

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