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Autosomal Dominantly Inherited Alzheimer Disease: Analysis of genetic subgroups by Machine Learning.

ABSTRACT: Despite subjects with Dominantly-Inherited Alzheimer's Disease (DIAD) represent less than 1% of all Alzheimer's Disease (AD) cases, the Dominantly Inherited Alzheimer Network (DIAN) initiative constitutes a strong impact in the understanding of AD disease course with special emphasis on the presyptomatic disease phase. Until now, the 3 genes involved in DIAD pathogenesis (PSEN1, PSEN2 and APP) have been commonly merged into one group (Mutation Carriers, MC) and studied using conventional statistical analysis. Comparisons between groups using null-hypothesis testing or longitudinal regression procedures, such as the linear-mixed-effects models, have been assessed in the extant literature. Within this context, the work presented here performs a comparison between different groups of subjects by considering the 3 genes, either jointly or separately, and using tools based on Machine Learning (ML). This involves a feature selection step which makes use of ANOVA followed by Principal Component Analysis (PCA) to determine which features would be realiable for further comparison purposes. Then, the selected predictors are classified using a Support-Vector-Machine (SVM) in a nested k-Fold cross-validation resulting in maximum classification rates of 72-74% using PiB PET features, specially when comparing asymptomatic Non-Carriers (NC) subjects with asymptomatic PSEN1 Mutation-Carriers (PSEN1-MC). Results obtained from these experiments led to the idea that PSEN1-MC might be considered as a mixture of two different subgroups including: a first group whose patterns were very close to NC subjects, and a second group much more different in terms of imaging patterns. Thus, using a k-Means clustering algorithm it was determined both subgroups and a new classification scenario was conducted to validate this process. The comparison between each subgroup vs. NC subjects resulted in classification rates around 80% underscoring the importance of considering DIAN as an heterogeneous entity.

SUBMITTER: Castillo-Barnes D

PROVIDER: S-EPMC7153760 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BACKGROUND:Autosomal dominant familial Alzheimer's disease (ADAD) is a rare disorder with non-amnestic neurological symptoms in some clinical presentations. We aimed to compile and compare data from symptomatic participants in the Dominantly Inherited Alzheimer Network observational study (DIAN-OBS) with those reported in the literature to estimate the prevalences of non-amnestic neurological symptoms in participants with ADAD. METHODS:We prospectively collected data from the DIAN-OBS database, which recruited participants from study centres in the USA, Europe, and Australia, between Feb 29, 2008, and July 1, 2014. We also did a systematic review of publications to extract individual-level clinical data for symptomatic participants with ADAD. We used data for age of onset (from first report of cognitive decline), disease course from onset to death, and the presence of 13 neurological findings that have been reported in association with ADAD. Using multivariable linear regression, we investigated the prevalences of various non-amnestic neurological symptoms and the contributions of age of onset and specific mutation type on symptoms. FINDINGS:The DIAN-OBS dataset included 107 individuals with detailed clinical data (forming the DIAN-OBS cohort). Our systematic review yielded 188 publications reporting on 1228 symptomatic individuals, with detailed neurological examination descriptions available for 753 individuals (forming the published data cohort). The most prevalent non-amnestic cognitive manifestations in participants in the DIAN-OBS cohort were those typical of mild to moderate Alzheimer's disease, including visual agnosia (55·1%, 95% CI 45·7-64·6), aphasia (57·9%, 48·6-67·3), and behavioural changes (61·7%, 51·5-70·0). Non-amnestic cognitive manifestations were less prevalent in the published data cohort (eg, visual agnosia [5·6%, 3·9-7·2], aphasia [23·0%, 20·0-26·0], and behavioural changes [31·7%, 28·4-35·1]). Prevalence of non-cognitive neurological manifestations in the DIAN-OBS cohort was low, including myoclonus and spasticity (9·3%, 95% CI 3·8-15·0), and seizures (2·8%, 0·5-5·9) and moderate for parkinsonism (11·2%, 5·3-17·1). By constrast, prevalence was higher in the published data cohort for myoclonus and spasticity (19·4%, 16·6-22·2 and 15·0%, 12·5-17·6, respectively), parkinsonism (12·5%, 10·1-15·0), and seizures (20·3%, 17·4-23·2). In an analysis of the published data cohort, ischaemic stroke was more prevalent at older ages of onset of symptoms of ADAD (odds ratio 1·09 per 1 year increase in age of onset, 95% CI 1·04-1·14, p=0·0003); and motor symptoms were more common at younger age of onset (myoclonus 0·93, 0·90-0·97, p=0·0007; seizures 0·95, 0·92-0·98, p=0·0018; corticobulbar deficits 0·91, 0·86-0·96, p=0·0012; and cerebellar ataxia 0·82, 0·74-0·91, p=0·0002). In the DIAN-OBS cohort, non-cognitive symptoms were more common at more severe stages of disease. INTERPRETATION:The non-cognitive clinical manifestations of Alzheimer's disease seem to affect a small proportion of participants with mild to moderate ADAD, and are probably influenced by disease severity, environmental, and genetic factors. When evaluating patients with potential ADAD, clinicians should note that cognitive symptoms typical of sporadic Alzheimer's disease are the most consistent finding, with some patients manifesting non-cognitive neurological symptoms. Future work is needed to determine the environmental and genetic factors that cause these neurological symptoms. FUNDING:National Institutes of Health and German Center for Neurodegenerative Diseases.

Project description:The order and magnitude of pathologic processes in Alzheimer's disease are not well understood, partly because the disease develops over many years. Autosomal dominant Alzheimer's disease has a predictable age at onset and provides an opportunity to determine the sequence and magnitude of pathologic changes that culminate in symptomatic disease.In this prospective, longitudinal study, we analyzed data from 128 participants who underwent baseline clinical and cognitive assessments, brain imaging, and cerebrospinal fluid (CSF) and blood tests. We used the participant's age at baseline assessment and the parent's age at the onset of symptoms of Alzheimer's disease to calculate the estimated years from expected symptom onset (age of the participant minus parent's age at symptom onset). We conducted cross-sectional analyses of baseline data in relation to estimated years from expected symptom onset in order to determine the relative order and magnitude of pathophysiological changes.Concentrations of amyloid-beta (A?)(42) in the CSF appeared to decline 25 years before expected symptom onset. A? deposition, as measured by positron-emission tomography with the use of Pittsburgh compound B, was detected 15 years before expected symptom onset. Increased concentrations of tau protein in the CSF and an increase in brain atrophy were detected 15 years before expected symptom onset. Cerebral hypometabolism and impaired episodic memory were observed 10 years before expected symptom onset. Global cognitive impairment, as measured by the Mini-Mental State Examination and the Clinical Dementia Rating scale, was detected 5 years before expected symptom onset, and patients met diagnostic criteria for dementia at an average of 3 years after expected symptom onset.We found that autosomal dominant Alzheimer's disease was associated with a series of pathophysiological changes over decades in CSF biochemical markers of Alzheimer's disease, brain amyloid deposition, and brain metabolism as well as progressive cognitive impairment. Our results require confirmation with the use of longitudinal data and may not apply to patients with sporadic Alzheimer's disease. (Funded by the National Institute on Aging and others; DIAN ClinicalTrials.gov number, NCT00869817.).

Project description:See Li and Donohue (doi:10.1093/brain/awy089) for a scientific commentary on this article.Dominantly-inherited Alzheimer's disease is widely hoped to hold the key to developing interventions for sporadic late onset Alzheimer's disease. We use emerging techniques in generative data-driven disease progression modelling to characterize dominantly-inherited Alzheimer's disease progression with unprecedented resolution, and without relying upon familial estimates of years until symptom onset. We retrospectively analysed biomarker data from the sixth data freeze of the Dominantly Inherited Alzheimer Network observational study, including measures of amyloid proteins and neurofibrillary tangles in the brain, regional brain volumes and cortical thicknesses, brain glucose hypometabolism, and cognitive performance from the Mini-Mental State Examination (all adjusted for age, years of education, sex, and head size, as appropriate). Data included 338 participants with known mutation status (211 mutation carriers in three subtypes: 163 PSEN1, 17 PSEN2, and 31 APP) and a baseline visit (age 19-66; up to four visits each, 1.1 ± 1.9 years in duration; spanning 30 years before, to 21 years after, parental age of symptom onset). We used an event-based model to estimate sequences of biomarker changes from baseline data across disease subtypes (mutation groups), and a differential equation model to estimate biomarker trajectories from longitudinal data (up to 66 mutation carriers, all subtypes combined). The two models concur that biomarker abnormality proceeds as follows: amyloid deposition in cortical then subcortical regions (∼24 ± 11 years before onset); phosphorylated tau (17 ± 8 years), tau and amyloid-β changes in cerebrospinal fluid; neurodegeneration first in the putamen and nucleus accumbens (up to 6 ± 2 years); then cognitive decline (7 ± 6 years), cerebral hypometabolism (4 ± 4 years), and further regional neurodegeneration. Our models predicted symptom onset more accurately than predictions that used familial estimates: root mean squared error of 1.35 years versus 5.54 years. The models reveal hidden detail on dominantly-inherited Alzheimer's disease progression, as well as providing data-driven systems for fine-grained patient staging and prediction of symptom onset with great potential utility in clinical trials.

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Autosomal Dominantly Inherited Alzheimer Disease: Analysis of genetic subgroups by Machine Learning.

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