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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.

Recent findings suggest additive effects of cerebrovascular disease and Alzheimer's disease (AD) on cognitive decline. MR imaging of cerebral blood flow holds great promise as an early dementia biomarker. Supply and demand of blood flow in the brain can be affected by, respectively, the loss of vascular health and the decrease of neuronal activity, as a consequence of AD. This study investigates to what extent vascular and AD components affect CBF and how they interact with each other.

__Objectives:__ The clinical applicability of magnetic resonance image−guided high-intensity focused ultrasound (MR-HIFU) treatment of uterine fibroids is often limited due to inaccessible fibroids or bowel interference. The aim of this study was to implement a newly developed 3-step modified manipulation protocol and to evaluate its influence on the number of eligible women and treatment fail

BACKGROUND: Ocular imaging receives much attention as a source of potential biomarkers for dementia. In the present study, we analyze these ocular biomarkers in cognitively impaired and healthy participants in a population aged over 90 years (= nonagenarian), and elucidate the effects of age on these biomarkers. METHODS: For this prospective cross-sectional study, we included individuals from the EMIF-AD 90+ study, consisting of a cognitively healthy (N = 67) and cognitively impaired group (N = 33), and the EMIF-AD PreclinAD study, consisting of cognitively healthy controls aged ≥60 (N = 198). Participants underwent Optical Coherence Tomography (OCT) and fundus photography of both eyes. OCT was used to asses total and individual inner retinal layer thickness in the macular region (Early Treatment Diabetic Retinopathy Study circles) as well as peripapillary retinal nerve fiber layer thickness, fundus images were analyzed with Singapore I Vessel Assessment to obtain 7 retinal vascular parameters. Values for both eyes were averaged. Differences in ocular biomarkers between the 2 nonagenarian groups were analyzed using linear regression, differences between the individual nonagenarian groups and controls were analyzed using generalized estimating equations. RESULTS: Ocular biomarkers did not differ between the healthy and cognitively impaired nonagenarian groups. 19 out of 22 ocular biomarkers assessed in this study differed between either nonagenarian group and the younger controls. CONCLUSION: The ocular biomarkers assessed in this study were not associated with cognitive impairment in nonagenarians, making their use as a screening tool for dementing disorders in this group limited. However, ocular biomarkers were significantly associated with chronological age, which were very similar to those ascribed to occur in Alzheimer's Disease.

The prevalence of brain pathologies increases with age and cognitive and physical functions worsen over the lifetime. It is unclear whether these processes show a similar increase with age. We studied the association of markers for brain pathology cognitive and physical functions with age in 288 cognitively normal individuals aged 60-102 years selected from the cross-sectional EMIF-AD PreclinAD and 90+ Study at the Amsterdam UMC. An abnormal score was consistent with a score below the 5th percentile in the 60- to 70-year-old individuals. Prevalence of abnormal scores was estimated using Generalized Estimating Equations (GEE) models. The prevalence of abnormal handgrip strength, the Digit Symbol Substitution Test, and hippocampal volume showed the fastest increase with age and abnormal MMSE score, muscle mass, and amyloid aggregation the lowest. The increase in prevalence of abnormal markers was partly dependent on sex, level of education, and amyloid aggregation. We did not find a consistent pattern in which markers of brain pathology cognitive and physical processes became abnormal with age.