Life-Time Covariation of Major Cardiovascular Diseases: A 40-Year Longitudinal Study and Genetic Studies

BACKGROUND: It is known that certain cardiovascular diseases (CVD) are associated, like atrial fibrillation and stroke. However, for other CVDs, the links and temporal trends are less studied. In this longitudinal study, we have investigated temporal epidemiological and genetic associations between different CVDs. METHODS: The ULSAM (Uppsala Longitudinal Study of Adult Men; 2322 men aged 50 years) has been followed for 40 years regarding 4 major CVDs (incident myocardial infarction, ischemic stroke, heart failure, and atrial fibrillation). For the genetic analyses, publicly available data were used. RESULTS: Using multistate modeling, significant relationships were seen between pairs of all of the 4 investigated CVDs. However, the risk of obtaining one additional CVD differed substantially both between different CVDs and between their temporal order. The relationship between heart failure and atrial fibrillation showed a high risk ratio (risk ratios, 24-26) regardless of the temporal order. A consistent association was seen also for myocardial infarction and atrial fibrillation but with a lower relative risk (risk ratios, 4-5). In contrast, the risk of receiving a diagnosis of heart failure following a myocardial infarction was almost twice as high as for the reverse temporal order (risk ratios, 16 versus 9). Genetic loci linked to traditional risk factors could partly explain the observed associations between the CVDs, but pathway analyses disclosed also other pathophysiological links. CONCLUSIONS: During 40 years, all of the 4 investigated CVDs were pairwise associated with each other regardless of the temporal order of occurrence, but the risk magnitude differed between different CVDs and their temporal order. Genetic analyses disclosed new pathophysiological links between CVDs

Using Genetic Variants to Assess the Relationship Between Circulating Lipids and Type 2 Diabetes

Journal ArticleResearch Support, Non-U.S. Gov'tCopyright © 2015 by the American Diabetes Association.This article contains Supplementary Data online at http://diabetes.diabetesjournals.org/lookup/suppl/doi:10.2337/db14-1710/-/DC1.The effects of dyslipidemia on the risk of type 2 diabetes (T2D) and related traits are not clear. We used regression models and 140 lipid-associated genetic variants to estimate associations between circulating HDL cholesterol (HDL-C), LDL cholesterol (LDL-C), and triglycerides and T2D and related traits. Each genetic test was corrected for effects of variants on the other two lipid types and surrogates of adiposity. We used the largest data sets available: 34,840 T2D case and 114,981 control subjects from the DIAGRAM (DIAbetes Genetics Replication And Meta-analysis) consortium and up to 133,010 individuals without diabetes for insulin secretion and sensitivity from the MAGIC (Meta-Analyses of Glucose and Insulin-related traits Consortium) and GENESIS (GENEticS of Insulin Sensitivity) studies. Eight of 21 associations between groups of variants and diabetes traits were significant at the nominal level, including those between genetically determined lower HDL-C (β = -0.12, P = 0.03) and T2D and genetically determined lower LDL-C (β = -0.21, P = 5 × 10(-6)) and T2D. Although some of these may represent causal associations, we discuss why caution must be used when using Mendelian randomization in the context of circulating lipid levels and diabetes traits. In conclusion, we found evidence of links between genetic variants associated with lipids and T2D, but deeper knowledge of the underlying genetic mechanisms of specific lipid variants is needed before drawing definite conclusions about causality based on Mendelian randomization methodology.Knut and Alice Wallenberg FoundationERCSwedish Research CouncilFredrik och Ingrid Thurings StiftelseSwedish Heart-Lung Foundationacknowledges Sydvästra Skånes DiabetesföreningNovo Nordisk FoundationUniversity of TartuEuropean Foundation for the Study of Diabetes New HorizonsAmerican Heart Associatio

Sensitivity Analyses for Robust Causal Inference from Mendelian Randomization Analyses with Multiple Genetic Variants.

Mendelian randomization investigations are becoming more powerful and simpler to perform, due to the increasing size and coverage of genome-wide association studies and the increasing availability of summarized data on genetic associations with risk factors and disease outcomes. However, when using multiple genetic variants from different gene regions in a Mendelian randomization analysis, it is highly implausible that all the genetic variants satisfy the instrumental variable assumptions. This means that a simple instrumental variable analysis alone should not be relied on to give a causal conclusion. In this article, we discuss a range of sensitivity analyses that will either support or question the validity of causal inference from a Mendelian randomization analysis with multiple genetic variants. We focus on sensitivity analyses of greatest practical relevance for ensuring robust causal inferences, and those that can be undertaken using summarized data. Aside from cases in which the justification of the instrumental variable assumptions is supported by strong biological understanding, a Mendelian randomization analysis in which no assessment of the robustness of the findings to violations of the instrumental variable assumptions has been made should be viewed as speculative and incomplete. In particular, Mendelian randomization investigations with large numbers of genetic variants without such sensitivity analyses should be treated with skepticism.Stephen Burgess is funded by a fellowship from the Wellcome Trust (100114). Jack Bowden is supported by a Methodology Research Fellowship from the UK Medical Research Council (grant number MR/N501906/1). Simon G. Thompson is supported by the British Heart Foundation (grant number CH/12/2/29428)

Non-targeted urine metabolomics and associations with prevalent and incident type 2 diabetes

Better risk prediction and new molecular targets are key priorities in type 2 diabetes (T2D) research. Little is known about the role of the urine metabolome in predicting the risk of T2D. We aimed to use non-targeted urine metabolomics to discover biomarkers and improve risk prediction for T2D. Urine samples from two community cohorts of 1,424 adults were analyzed by ultra-performance liquid chromatography/mass spectrometry (UPLC-MS). In a discovery/replication design, three out of 62 annotated metabolites were associated with prevalent T2D, notably lower urine levels of 3-hydroxyundecanoyl-carnitine. In participants without diabetes at baseline, LASSO regression in the training set selected six metabolites that improved prediction of T2D beyond established risk factors risk over up to 12 years' follow-up in the test sample, from C-statistic 0.866 to 0.892. Our results in one of the largest non-targeted urinary metabolomics study to date demonstrate the role of the urine metabolome in identifying at-risk persons for T2D and suggest urine 3-hydroxyundecanoyl-carnitine as a biomarker candidate.Peer reviewe

The Alzheimer's Disease-Associated Amyloid β-Protein Is an Antimicrobial Peptide

Background: The amyloid $\beta$-protein (A$\beta$) is believed to be the key mediator of Alzheimer's disease (AD) pathology. A$\beta$ is most often characterized as an incidental catabolic byproduct that lacks a normal physiological role. However, A$\beta$ has been shown to be a specific ligand for a number of different receptors and other molecules, transported by complex trafficking pathways, modulated in response to a variety of environmental stressors, and able to induce pro-inflammatory activities. Methodology/Principal Findings: Here, we provide data supporting an in vivo function for A$\beta$ as an antimicrobial peptide (AMP). Experiments used established in vitro assays to compare antimicrobial activities of A$\beta$ and LL-37, an archetypical human AMP. Findings reveal that A$\beta$ exerts antimicrobial activity against eight common and clinically relevant microorganisms with a potency equivalent to, and in some cases greater than, LL-37. Furthermore, we show that AD whole brain homogenates have significantly higher antimicrobial activity than aged matched non-AD samples and that AMP action correlates with tissue A$\beta$ levels. Consistent with A$\beta$-mediated activity, the increased antimicrobial action was ablated by immunodepletion of AD brain homogenates with anti-A$\beta$ antibodies. Conclusions/Significance: Our findings suggest A$\beta$ is a hitherto unrecognized AMP that may normally function in the innate immune system. This finding stands in stark contrast to current models of A$\beta$-mediated pathology and has important implications for ongoing and future AD treatment strategies

Маркетинг инноваций как инструмент активизации трансфера знаний

Модель «Тройная спираль» (Triple Helix Model (THM)), основанная на исследовании сложного взаимодействия университетов, бизнеса и власти, является современной моделью развития инновационных систем. В модели ТНМ ведущее значение отводится университетам, которые превращаются в предпринимательские университеты и через собственные каналы для трансфера знаний применяют знания на практике и вкладывают результаты в новые образовательные дисциплины. Университеты все чаще становятся залогом успешного экономического развития региона

Identification of rare-disease genes using blood transcriptome sequencing and large control cohorts.

It is estimated that 350 million individuals worldwide suffer from rare diseases, which are predominantly caused by mutation in a single gene1. The current molecular diagnostic rate is estimated at 50%, with whole-exome sequencing (WES) among the most successful approaches2-5. For patients in whom WES is uninformative, RNA sequencing (RNA-seq) has shown diagnostic utility in specific tissues and diseases6-8. This includes muscle biopsies from patients with undiagnosed rare muscle disorders6,9, and cultured fibroblasts from patients with mitochondrial disorders7. However, for many individuals, biopsies are not performed for clinical care, and tissues are difficult to access. We sought to assess the utility of RNA-seq from blood as a diagnostic tool for rare diseases of different pathophysiologies. We generated whole-blood RNA-seq from 94 individuals with undiagnosed rare diseases spanning 16 diverse disease categories. We developed a robust approach to compare data from these individuals with large sets of RNA-seq data for controls (n = 1,594 unrelated controls and n = 49 family members) and demonstrated the impacts of expression, splicing, gene and variant filtering strategies on disease gene identification. Across our cohort, we observed that RNA-seq yields a 7.5% diagnostic rate, and an additional 16.7% with improved candidate gene resolution