Circulating metabolites are associated with brain atrophy and white matter hyperintensities

Introduction: Our aim was to study whether systemic metabolites are associated with magnetic resonance imaging (MRI) measures of brain and hippocampal atrophy and white matter hyperintensities (WMH).Methods: We studied associations of 143 plasma-based metabolites with MRI measures of brain and hippocampal atrophy and WMH in three independent cohorts (n = 3962). We meta-analyzed the results of linear regression analyses to determine the association of metabolites with MRI measures.Results: Higher glucose levels and lower levels of three small high density lipoprotein (HDL) particles were associated with brain atrophy. Higher glucose levels were associated with WMH.Discussion: Glucose levels were associated with brain atrophy and WMH, and small HDL particle levels were associated with brain atrophy. Circulating metabolites may aid in developing future intervention trials.Analytical BioScience

Fat metabolism is associated with telomere length in six population-based studies

Telomeres are repetitive DNA sequences located at the end of chromosomes, which are associated to biological aging, cardiovascular disease, cancer and mortality. Lipid and fatty acid metabolism have been associated with telomere shortening. We have conducted an in-depth study investigating the association of metabolic biomarkers with telomere length (LTL). We performed an association analysis of 226 metabolic biomarkers with LTL using data from 11 775 individuals from six independent population-based cohorts (BBMRI-NL consortium). Metabolic biomarkers include lipoprotein lipids and subclasses, fatty acids, amino acids, glycolysis measures and ketone bodies. LTL was measured by quantitative polymerase chain reaction or F1owFISH. Linear regression analysis was performed adjusting for age, sex, lipid-lowering medication and cohort-specific covariates (model 1) and additionally for body mass index (BMI) and smoking (model 2), followed by inverse variance-weighted meta-analyses (significance threshold P me t a = 6.5 x 10(-4)). We identified four metabolic biomarkers positively associated with LTL, including two cholesterol to lipid ratios in small VLDL (S-VLDL-C % and S-VLDL-CE %) and two omega-6 fatty acid ratios (FAw6/FA and LA/FA). After additionally adjusting for BMI and smoking, these metabolic biomarkers remained associated with LTL with similar effect estimates. In addition, cholesterol esters in very small VLDL (XS-VLDL-CE) became significantly associated with LTL (P = 3.6 x 10(-4)). We replicated the association of FAw6/FA with LTL in an independent dataset of 7845 individuals (P = 1.9 x 10(-4)). To conclude, we identified multiple metabolic biomarkers involved in lipid and fatty acid metabolism that may be involved in LTL biology. Longitudinal studies are needed to exclude reversed causation

Fat metabolism is associated with telomere length in six population-based studies

Telomeres are repetitive DNA sequences located at the end of chromosomes, which are associated to biological aging, cardiovascular disease, cancer and mortality. Lipid and fatty acid metabolism have been associated with telomere shortening. We have conducted an in-depth study investigating the association of metabolic biomarkers with telomere length (LTL). We performed an association analysis of 226 metabolic biomarkers with LTL using data from 11 775 individuals from six independent population-based cohorts (BBMRI-NL consortium). Metabolic biomarkers include lipoprotein lipids and subclasses, fatty acids, amino acids, glycolysis measures and ketone bodies. LTL was measured by quantitative polymerase chain reaction or F1owFISH. Linear regression analysis was performed adjusting for age, sex, lipid-lowering medication and cohort-specific covariates (model 1) and additionally for body mass index (BMI) and smoking (model 2), followed by inverse variance-weighted meta-analyses (significance threshold P me t a = 6.5 x 10(-4)). We identified four metabolic biomarkers positively associated with LTL, including two cholesterol to lipid ratios in small VLDL (S-VLDL-C % and S-VLDL-CE %) and two omega-6 fatty acid ratios (FAw6/FA and LA/FA). After additionally adjusting for BMI and smoking, these metabolic biomarkers remained associated with LTL with similar effect estimates. In addition, cholesterol esters in very small VLDL (XS-VLDL-CE) became significantly associated with LTL (P = 3.6 x 10(-4)). We replicated the association of FAw6/FA with LTL in an independent dataset of 7845 individuals (P = 1.9 x 10(-4)). To conclude, we identified multiple metabolic biomarkers involved in lipid and fatty acid metabolism that may be involved in LTL biology. Longitudinal studies are needed to exclude reversed causation.Molecular Epidemiolog

Author Correction: Genetic meta-analysis of diagnosed Alzheimer’s disease identifies new risk loci and implicates Aβ, tau, immunity and lipid processing (Nature Genetics, (2019), 51, 3, (414-430), 10.1038/s41588-019-0358-2)

An amendment to this paper has been published and can be accessed via a link at the top of the paper

Author Correction: Genetic meta-analysis of diagnosed Alzheimer’s disease identifies new risk loci and implicates Aβ, tau, immunity and lipid processing (Nature Genetics, (2019), 51, 3, (414-430), 10.1038/s41588-019-0358-2)

An amendment to this paper has been published and can be accessed via a link at the top of the paper

Author Correction: Genetic meta-analysis of diagnosed Alzheimer\u2019s disease identifies new risk loci and implicates A\u3b2, tau, immunity and lipid processing (Nature Genetics, (2019), 51, 3, (414-430), 10.1038/s41588-019-0358-2)

An amendment to this paper has been published and can be accessed via a link at the top of the paper

Genetic annotation of gain-of-function screens using RNA interference and in situ hybridization of candidate genes in the Drosophila wing

Gain-of-function screens in Drosophila are an effective method with which to identify genes that affect the development of particular structures or cell types. It has been found that a fraction of 2-10% of the genes tested, depending on the particularities of the screen, results in a discernible phenotype when overexpressed. However, it is not clear to what extent a gain-of-function phenotype generated by overexpression is informative about the normal function of the gene. Thus, very few reports attempt to correlate the loss- and overexpression phenotype for collections of genes identified in gain-of-function screens. In this work we use RNA interference and in situ hybridization to annotate a collection of 123 P-GS insertions that in combination with different Gal4 drivers affect the size and/or patterning of the wing. We identify the gene causing the overexpression phenotype by expressing, in a background of overexpression, RNA interference for the genes affected by each P-GS insertion. Then, we compare the loss and gainof-function phenotypes obtained for each gene and relate them to its expression pattern in the wing disc. We find that 52% of genes identified by their overexpression phenotype are required during normal development. However, only in 9% of the cases analyzed was there some complementarity between the gain- and loss-of-function phenotype, suggesting that, in general, the overexpression phenotypes would not be indicative of the normal requirements of the gene. © 2012 by the Genetics Society of America.Peer Reviewe

Genetic meta-analysis of diagnosed Alzheimer’s disease identifies new risk loci and implicates Aβ, tau, immunity and lipid processing

Risk for late-onset Alzheimer’s disease (LOAD), the most prevalent dementia, is partially driven by genetics. To identify LOAD risk loci, we performed a large genome-wide association meta-analysis of clinically diagnosed LOAD (94,437 individuals). We confirm 20 previous LOAD risk loci and identify five new genome-wide loci (IQCK, ACE, ADAM10, ADAMTS1, and WWOX), two of which (ADAM10, ACE) were identified in a recent genome-wide association (GWAS)-by-familial-proxy of Alzheimer’s or dementia. Fine-mapping of the human leukocyte antigen (HLA) region confirms the neurological and immune-mediated disease haplotype HLA-DR15 as a risk factor for LOAD. Pathway analysis implicates immunity, lipid metabolism, tau binding proteins, and amyloid precursor protein (APP) metabolism, showing that genetic variants affecting APP and Aβ processing are associated not only with early-onset autosomal dominant Alzheimer’s disease but also with LOAD. Analyses of risk genes and pathways show enrichment for rare variants (P = 1.32 × 10−7), indicating that additional rare variants remain to be identified. We also identify important genetic correlations between LOAD and traits such as family history of dementia and education