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Background: Alzheimer's disease (AD) is a neurodegenerative condition for which there is currently no available medication that can stop its progression. Previous studies suggest that mild cognitive impairment (MCI) is a phase that precedes the disease. Therefore, a better understanding of the molecular mechanisms behind MCI conversion to AD is needed. Method: Here, we propose a machine learning-based approach to detect the key metabolites and proteins involved in MCI progression to AD using data from the European Medical Information Framework for Alzheimer's Disease Multimodal Biomarker Discovery Study. Proteins and metabolites were evaluated separately in multiclass models (controls, MCI and AD) and together in MCI conversion models (MCI stable vs converter). Only features selected as relevant by 3/4 algorithms proposed were kept for downstream analysis. Results: Multiclass models of metabolites highlighted nine features further validated in an independent cohort (0.726 mean balanced accuracy). Among these features, one metabolite, oleamide, was selected by all the algorithms. Further in-vitro experiments in rodents showed that disease-associated microglia excreted oleamide in vesicles. Multiclass models of proteins stood out with nine features, validated in an independent cohort (0.720 mean balanced accuracy). However, none of the proteins was selected by all the algorithms. Besides, to distinguish between MCI stable and converters, 14 key features were selected (0.872 AUC), including tTau, alpha-synuclein (SNCA), junctophilin-3 (JPH3), properdin (CFP) and peptidase inhibitor 15 (PI15) among others. Conclusions: This omics integration approach highlighted a set of molecules associated with MCI conversion important in neuronal and glia inflammation pathways.

Background Biomarkers of Alzheimer’s disease (AD) pathology and progression have now been identified across various modalities. The aims of the two studies presented (ANM-MMB and EMIF-AD biomarker discovery) are to discover and replicate previously identified biomarkers of disease pathology and progression, and moreover to determine whether a multimodal biomarker signature may add value in comparison to a biomarker of a single modality. Methods The ANM-MMB cohort is comprised of 718 AD, MCI converters and non-converters, and control subjects selected from the AddNeuroMed, Alzheimer’s research trust and Dementia case register cohorts. Cognitive measures, serum and urine metabolomics, structural MRI, genomics, whole blood transcriptomics and plasma proteomics data was available. The EMIF-AD biomarker discovery cohort consists of 1221 AD, MCI and control subjects, selected from the EMIF catalogue. All subjects had existing amyloid measures (CSF Aβ or amyloid-PET), structural MRI and clinical data, and furthermore plasma proteomics (targeted and untargeted), CSF proteomics (targeted), metabolomics, genomics and epigenetics data were generated. For both studies univariate and multivariate statistics were utilised to identify candidate biomarkers of AD pathology (neurodegeneration and/or brain amyloid burden), rates of cognitive decline, and MCI progression to dementia. pQTL-eQTL-mQTL analyses, network/pathway analysis, and multimodal classifiers were employed to detect multimodal signatures. Results Initial analyses indicate that in the ANM-MMB study a serum and urine derived 15 metabolite classifier predicts MCI progression to AD with 72% accuracy, and the biological significance of the metabolites included in the biomarker panel was identified. Further analyses will examine whether a multimodal classifier is able to predict with even greater accuracy. We will then seek to replicate this in the EMIF-AD biomarker discovery study. Further analyses will also examine single

Contains fulltext : 167931.pdf (publisher's version ) (Open Access), Longitudinal designs are widely used in medical studies as a means of observing within-subject changes over time in groups of subjects, thereby aiming to improve sensitivity for detecting disease effects. Paralleling an increased use of such studies in neuroimaging has been the adoption of pattern recognition algorithms for making individualized predictions of disease. However, at present few pattern recognition methods exist to make full use of neuroimaging data that have been collected longitudinally, with most methods relying instead on cross-sectional style analysis. This article presents a principal component analysis-based feature construction method that uses longitudinal high-dimensional data to improve predictive performance of pattern recognition algorithms. The method can be applied to data from a wide range of longitudinal study designs and permits an arbitrary number of time-points per subject. We apply the method to two longitudinal datasets, one containing subjects with mild cognitive impairment along with healthy controls, the other with early dementia subjects and healthy controls. Across both datasets, we show improvements in predictive accuracy relative to cross-sectional classifiers for discriminating disease subjects from healthy controls on the basis of whole-brain structural magnetic resonance image-based voxels. In addition, we can transfer longitudinal information from one set of subjects to make disease predictions in another set of subjects. The proposed method is simple and, as a feature construction method, flexible with respect to the choice of classifier and image registration algorithm. Hum Brain Mapp 37:4385-4404, 2016. (c) 2016 Wiley Periodicals, Inc.

Contains fulltext : 153311.pdf (publisher's version ) (Closed access), INTRODUCTION: There is a need for prodromal markers to diagnose Parkinson's disease (PD) as early as possible. Knowing that most patients with overt PD have abnormal nocturnal movement patterns, we hypothesised that such changes might occur already in non-PD individuals with a potentially high risk for future development of the disease. METHODS: Eleven patients with early PD (Hoehn & Yahr stage

Neurodegenerative tauopathies may be inherited as autosomal-dominant disorders with variable clinicopathological phenotypes, and causative mutations in the microtubule-associated protein tau (MAPT) gene are not regularly seen. Herein, we describe a patient with clinically typical and autopsy-proven corticobasal degeneration (CBD). Her mother was diagnosed to have Parkinson's disease, but autopsy showed CBD pathology as in the index patient. The sister of the index patient had the clinical symptoms of primary progressive aphasia (PPA), but no pathology was available to date. Molecular analysis did not reveal any mutation in the MAPT or progranulin (GRN) genes. Our findings illustrate that CBD, progressive supranuclear palsy and PPA may be overlapping diseases with a common pathological basis rather than distinct entities. Clinical presentation and course might be determined by additional, yet unknown, genetic modifying factors.

BACKGROUND: Alzheimer's disease (AD) and prion diseases such as sporadic Creutzfeldt-Jakob disease (sCJD) share common features concerning their molecular pathogenesis and neuropathological presentation and the coexistence of AD and CJD in patients suggest an association between the deposition of the proteolytically processed form of the amyloid precursor protein, beta-amyloid (Abeta), which deposits in AD, and the abnormal form of the prion protein, PrP(Sc), which deposits in sCJD. METHODS: We have characterized sCJD patients (n = 14), AD patients (n = 5) and nondemented controls (n = 5) with respect to the deposition of PrP(Sc) and Abeta morphologically, biochemically and genetically and correlated these findings to clinical data. RESULTS: sCJD-diseased individuals with abundant deposits of Abeta present with a specific clinicopathological profile, defined by higher age at disease onset, long disease duration, a genetic profile and only minimal amounts of PrP(Sc) in the cerebellum. CONCLUSION: The co-occurrence of pathological changes typical for sCJD and AD in combination with the inverse association between accumulation of Abeta and PrP(Sc) in a subgroup of sCJD patients is indicative of common pathways involved in the generation or clearance of Abeta and PrP(Sc) in a subgroup of sCJD patients.

Presenilin 1 (PSEN1) mutations cause autosomal dominant familial Alzheimer's disease (FAD). PSEN1 mutation carriers undergo the course of cognitive deterioration, which is typical for sporadic Alzheimer's disease but disease onset is earlier and disease progression is faster. Here, we sought to detect signs of FAD in presymptomatic carriers of the PSEN1 mutation (C410Y) by use of a neuropsychological examination, functional MRI during learning and memory tasks and MRI volumetry. We examined five non-demented members of a FAD family and 21 non-related controls. Two of the five family members were carrying the mutation; one was 20 years old and the other 45 years old. The age of clinical manifestation of FAD in the family studied here is approximately 48 years. Neuropsychological assessments suggested subtle problems with episodic memory in the 20-year-old mutation carrier. The middle-aged mutation carrier fulfilled criteria for amnestic mild cognitive impairment. The 20-year-old mutation carrier exhibited increased, while the middle-aged mutation carrier exhibited decreased brain activity compared to controls within memory-related neural networks during episodic learning and retrieval, but not during a working-memory task. The increased memory-related brain activity in the young mutation carrier might reflect a compensatory effort to overcome preclinical neural dysfunction caused by first pathological changes. The activity reductions in the middle-aged mutation carrier might reflect gross neural dysfunction in a more advanced stage of neuropathology. These data suggest that functional neuroimaging along with tasks that challenge specifically those brain areas which are initial targets of Alzheimer's disease pathology may reveal activity alterations on a single-subject level decades before the clinical manifestation of Alzheimer's disease.

Alzheimer's disease (AD) is the most common cause of dementia. It is characterized by beta-amyloid (A beta) plaques, neurofibrillary tangles and the degeneration of specifically vulnerable brain neurons. We observed high expression of the cholesterol 25-hydroxylase (CH25H) gene in specifically vulnerable brain regions of AD patients. CH25H maps to a region within 10q23 that has been previously linked to sporadic AD. Sequencing of the 5' region of CH25H revealed three common haplotypes, CH25Hchi2, CH25Hchi3 and CH25Hchi4; CSF levels of the cholesterol precursor lathosterol were higher in carriers of the CH25Hchi4 haplotype. In 1,282 patients with AD and 1,312 healthy control subjects from five independent populations, a common variation in the vicinity of CH25H was significantly associated with the risk for sporadic AD (p = 0.006). Quantitative neuropathology of brains from elderly non-demented subjects showed brain A beta deposits in carriers of CH25Hchi4 and CH25Hchi3 haplotypes, whereas no A beta deposits were present in CH25Hchi2 carriers. Together, these results are compatible with a role of CH25Hchi4 as a putative susceptibility factor for sporadic AD; they may explain part of the linkage of chromosome 10 markers with sporadic AD, and they suggest the possibility that CH25H polymorphisms are associated with different rates of brain A beta deposition.