757 research outputs found

    Obesity and the brain: a possible genetic link

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    Structural brain deficits have been repeatedly linked to body mass index and obesity, which itself is controlled by the effects of a number of independent genetic loci. One of the most consistently replicated of these putative obesity genes is fat mass and obesity-associated protein (FTO). A recent study by investigators from the Alzheimer's Disease Neuroimaging Initiative set out to assess whether polymorphisms in FTO are directly correlated with brain volume in a collection of over 200 healthy older individuals. The authors found a modest but significant reduction in brain volume in the frontal and occipital lobes exerted by the same FTO alleles that also predispose to obesity. Although potentially providing a novel genetic link between obesity and brain structure, the relevance of these findings for normal brain function and disease remains to be determined

    Is hyperkalaemia in heart failure a risk factor or a risk marker? Implications for renin–angiotensin–aldosterone system inhibitor use

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    Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/143644/1/ejhf1175.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/143644/2/ejhf1175_am.pd

    Towards Unveiling the Genetics of Neurodegenerative Diseases

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    Seven-CpG DNA Methylation Age Determined by Single Nucleotide Primer Extension and Illumina’s Infinium MethylationEPIC Array Provide Highly Comparable Results

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    DNA methylation age (DNAm age, epigenetic clock) is a novel and promising biomarker of aging. It is calculated from the methylation fraction of specific cytosine phosphate guanine sites (CpG sites) of genomic DNA. Several groups have proposed epigenetic clock algorithms and these differ mostly regarding the number and location of the CpG sites considered and the method used to assess the methylation status. Most epigenetic clocks are based on a large number of CpGs, e.g. as measured by DNAm microarrays. We have recently evaluated an epigenetic clock based on the methylation fraction of seven CpGs that were determined by methylation-sensitive single nucleotide primer extension (MS-SNuPE). This method is more cost-effective when compared to array-based technologies as only a few CpGs need to be examined. However, there is only little data on the correspondence in epigenetic age estimation using the 7-CpG clock and other algorithms. To bridge this gap, in this study we measured the 7-CpG DNAm age using two methods, via MS-SNuPE and via the MethylationEPIC array, in a sample of 1,058 participants of the Berlin Aging Study II (BASE-II), assessed as part of the GendAge study. On average, participants were 75.6 years old (SD: 3.7, age range: 64.9–90.0, 52.6% female). Agreement between methods was assessed by Bland-Altman plots. DNAm age was highly correlated between methods (Pearson’s r = 0.9) and Bland-Altman plots showed a difference of 3.1 years. DNAm age by the 7-CpG formula was 71.2 years (SD: 6.9 years, SNuPE) and 68.1 years (SD: 6.4 years, EPIC array). The mean of difference in methylation fraction between methods for the seven individual CpG sites was between 0.7 and 13 percent. To allow direct conversion of DNAm age obtained from both methods we developed an adjustment formula with a randomly selected training set of 529 participants using linear regression. After conversion of the Illumina data in a second and independent validation set, the adjusted DNAm age was 71.44 years (SD: 6.1 years, n = 529). In summary, we found the results of DNAm clocks to be highly comparable. Furthermore, we developed an adjustment formula that allows for direct conversion of DNAm age estimates between methods and enables one singular clock to be used in studies that employ either the Illumina or the SNuPE method

    Lower baseline performance but greater plasticity of working memory for carriers of the val allele of the COMT Val158Met polymorphism

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    Objective: Little is known about genetic contributions to individual differences in cognitive plasticity. Given that the neurotransmitter dopamine is critical for cognition and associated with cognitive plasticity, we investigated the effects of 3 polymorphisms of dopamine-related genes (LMX1A, DRD2, COMT) on baseline performance and plasticity of working memory (WM), perceptual speed, and reasoning. Method: One hundred one younger and 103 older adults underwent approximately 100 days of cognitive training, and extensive testing before and after training. We analyzed the baseline and posttest data using latent change score models. Results: For working memory, carriers of the val allele of the COMT polymorphism had lower baseline performance and larger performance gains from training than carriers of the met allele. There was no significant effect of the other genes or on other cognitive domains. Conclusions: We relate this result to available evidence indicating that met carriers perform better than val carriers in WM tasks taxing maintenance, whereas val carriers perform better at updating tasks. We suggest that val carriers may show larger training gains because updating operations carry greater potential for plasticity than maintenance operations. (DIPF/Orig.

    A QTL genome scan of the metabolic syndrome and its component traits

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    BACKGROUND: Because high blood pressure, altered lipid levels, obesity, and diabetes so frequently occur together, they are sometimes collectively referred to as the metabolic syndrome. While there have been many studies of each metabolic syndrome trait separately, few studies have attempted to analyze them combined, i.e., as one composite variable, in quantitative trait linkage or association analysis. We used genotype and phenotype data from the Framingham Heart Study to perform a full-genome scan for quantitative trait loci underlying the metabolic syndrome. RESULTS: Heritability estimates for all of the covariate-adjusted and age- and gender-standardized individual traits, and the composite metabolic syndrome trait, were all fairly high (0.39–0.62), and the composite trait was among the highest at 0.61. The composite trait yielded no regions with suggestive linkage by Lander and Kruglyak's criteria, although there were several noteworthy regions for individual traits, some of which were also observed for the composite variable. CONCLUSION: Despite its high heritability, the composite metabolic syndrome trait variable did not increase the power to detect or localize linkage peaks in this sample. However, this strategy and related methods of combining correlated individual traits deserve further investigation, particularly in settings with complex causal pathways

    Editorial

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    Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugänglich.This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.Human aging is characterized by large differences between and within older adults. Numerous factors are known to contribute to these differences, including genetic and immunological, somatic and medical, cognitive and behavioral, psychosocial and experiential, as well as socioeconomic and geospatial conditions. Continuing and expanding the scientific objectives of the Berlin Aging Study, the Berlin Aging Study II (BASE-II) seeks to comprehensively describe phenomena associated with aging and old age and to better understand the multiple different underlying factors and their interactions. To this end, BASE-II was established as a multi-institutional project combining and integrating interdisciplinary perspectives ranging from molecular genetics and immunology, geriatric medicine and psychology, to sociology and economics. In this Special Issue, we have compiled seven empirical analyses that feature examples of interdisciplinary insights that BASE-II provides by linking data across multiple levels of analyses at which human functioning and development occur in old age. Here, we provide an overview of the study, note commonalities between BASE-II and earlier studies, and highlight some of its unique qualities.BMBF, 01UW0808, Die Berliner Altersstudie (BASE): Fortführung und Erweiterung (BASE II)BMBF, 16SV5537, Berliner Altersstudie II - BASE II - ; Teilvorhaben: Survey Methodik und SozialwissenschaftBMBF, 16SV5837, Berliner Altersstudie II - BASE II - ; Teilvorhaben: Projektkoordination, Datenbank und PsychologieBMBF, 16SV5538, Berliner Altersstudie II - BASE II - ; Teilvorhaben: MolekulargenetikBMBF, 16SV5536K, Berliner Altersstudie II - BASE II - ; Teilvorhaben: Innere Medizin/Geriatri
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