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- Kivipelto, Miia, et al.
(författare)
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World-Wide FINGERS Network : A global approach to risk reduction and prevention of dementia
- 2020
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Ingår i: Alzheimer's & Dementia. - : Wiley. - 1552-5260 .- 1552-5279. ; 16:7, s. 1078-1094
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Tidskriftsartikel (refereegranskat)abstract
- Reducing the risk of dementia can halt the worldwide increase of affected people. The multifactorial and heterogeneous nature of late-onset dementia, including Alzheimer's disease (AD), indicates a potential impact of multidomain lifestyle interventions on risk reduction. The positive results of the landmark multidomain Finnish Geriatric Intervention Study to Prevent Cognitive Impairment and Disability (FINGER) support such an approach. The World-Wide FINGERS (WW-FINGERS), launched in 2017 and including over 25 countries, is the first global network of multidomain lifestyle intervention trials for dementia risk reduction and prevention. WW-FINGERS aims to adapt, test, and optimize the FINGER model to reduce risk across the spectrum of cognitive decline-from at-risk asymptomatic states to early symptomatic stages-in different geographical, cultural, and economic settings. WW-FINGERS aims to harmonize and adapt multidomain interventions across various countries and settings, to facilitate data sharing and analysis across studies, and to promote international joint initiatives to identify globally implementable and effective preventive strategies.
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| 2. |
- 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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| 6. |
- 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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