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- Elvsashagen, T, et al.
(författare)
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The genetic architecture of human brainstem structures and their involvement in common brain disorders
- 2020
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Ingår i: Nature communications. - : Springer Science and Business Media LLC. - 2041-1723. ; 11:1, s. 4016-
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Tidskriftsartikel (refereegranskat)abstract
- Brainstem regions support vital bodily functions, yet their genetic architectures and involvement in common brain disorders remain understudied. Here, using imaging-genetics data from a discovery sample of 27,034 individuals, we identify 45 brainstem-associated genetic loci, including the first linked to midbrain, pons, and medulla oblongata volumes, and map them to 305 genes. In a replication sample of 7432 participants most of the loci show the same effect direction and are significant at a nominal threshold. We detect genetic overlap between brainstem volumes and eight psychiatric and neurological disorders. In additional clinical data from 5062 individuals with common brain disorders and 11,257 healthy controls, we observe differential volume alterations in schizophrenia, bipolar disorder, multiple sclerosis, mild cognitive impairment, dementia, and Parkinson’s disease, supporting the relevance of brainstem regions and their genetic architectures in common brain disorders.
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- Kurbatova, N., et al.
(författare)
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Urinary metabolic phenotyping for Alzheimer's disease
- 2020
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Ingår i: Scientific Reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 10:1
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Tidskriftsartikel (refereegranskat)abstract
- Finding early disease markers using non-invasive and widely available methods is essential to develop a successful therapy for Alzheimer's Disease. Few studies to date have examined urine, the most readily available biofluid. Here we report the largest study to date using comprehensive metabolic phenotyping platforms (NMR spectroscopy and UHPLC-MS) to probe the urinary metabolome in-depth in people with Alzheimer's Disease and Mild Cognitive Impairment. Feature reduction was performed using metabolomic Quantitative Trait Loci, resulting in the list of metabolites associated with the genetic variants. This approach helps accuracy in identification of disease states and provides a route to a plausible mechanistic link to pathological processes. Using these mQTLs we built a Random Forests model, which not only correctly discriminates between people with Alzheimer's Disease and age-matched controls, but also between individuals with Mild Cognitive Impairment who were later diagnosed with Alzheimer's Disease and those who were not. Further annotation of top-ranking metabolic features nominated by the trained model revealed the involvement of cholesterol-derived metabolites and small-molecules that were linked to Alzheimer's pathology in previous studies.
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- Visser, P. J., et al.
(författare)
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Development of screening guidelines and clinical criteria for predementia Alzheimer's disease
- 2008
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Ingår i: Neuroepidemiology. - : S. Karger AG. - 1423-0208 .- 0251-5350. ; 30:4, s. 254-265
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Tidskriftsartikel (refereegranskat)abstract
- Background: There is an urgent need to identify subjects with Alzheimer's disease (AD) in the predementia phase, but validated diagnostic approaches are currently lacking. In this paper, we present the background, design and methods of a study, which aims to develop clinical criteria for predementia AD. We also present baseline characteristics of the subjects included. The study was part of the multicentre DESCRIPA project, which is being conducted within the network of the European Alzheimer's Disease Consortium. Methods: Clinical criteria will be based on a prospective cohort study of non-demented subjects older than 55 years and referred to a memory clinic. At baseline, a number of markers and risk factors for AD were collected, including demographic variables, measures of performance in activities of daily living, cognitive, neuroimaging and genetic markers, and serum and cerebrospinal fluid markers. Subjects will be reassessed annually for 2 - 3 years, and we will evaluate which combination of variables best predicts AD-type dementia at follow-up. Results: Between 2003 and 2005, 881 subjects were included from 20 memory clinics. Subjects were on average 70.3 years old, and had 10.4 years of education. The average score on the Mini-Mental State Examination was 27.4.
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- Martensson, G., et al.
(författare)
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Stability of graph theoretical measures in structural brain networks in Alzheimer's disease
- 2018
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Ingår i: Scientific Reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 8
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Tidskriftsartikel (refereegranskat)abstract
- Graph analysis has become a popular approach to study structural brain networks in neurodegenerative disorders such as Alzheimer's disease (AD). However, reported results across similar studies are often not consistent. In this paper we investigated the stability of the graph analysis measures clustering, path length, global efficiency and transitivity in a cohort of AD (N = 293) and control subjects (N = 293). More specifically, we studied the effect that group size and composition, choice of neuroanatomical atlas, and choice of cortical measure (thickness or volume) have on binary and weighted network properties and relate them to the magnitude of the differences between groups of AD and control subjects. Our results showed that specific group composition heavily influenced the network properties, particularly for groups with less than 150 subjects. Weighted measures generally required fewer subjects to stabilize and all assessed measures showed robust significant differences, consistent across atlases and cortical measures. However, all these measures were driven by the average correlation strength, which implies a limitation of capturing more complex features in weighted networks. In binary graphs, significant differences were only found in the global efficiency and transitivity measures when using cortical thickness measures to define edges. The findings were consistent across the two atlases, but no differences were found when using cortical volumes. Our findings merits future investigations of weighted brain networks and suggest that cortical thickness measures should be preferred in future AD studies if using binary networks. Further, studying cortical networks in small cohorts should be complemented by analyzing smaller, subsampled groups to reduce the risk that findings are spurious.
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