Biomarkers associated with mortality in aortic stenosis: A systematic review and meta-analysis

The optimal timing of aortic valve replacement (AVR) remains controversial. Several biomarkers reflect the underlying pathophysiological processes in aortic stenosis (AS) and may be of use as mortality predictors. The aim of this systematic review and meta-analysis is to evaluate the blood biomarkers utilised in AS and assess whether they associate with mortality. PubMed and Embase were searched for studies reporting baseline biomarker level and mortality outcomes in patients with AS. A total of 83 studies met the inclusion criteria and were systematically reviewed. Of these, 21 reporting brain natriuretic peptide (BNP), N-terminal pro B-type natriuretic peptide (NT-proBNP), Troponin and Galectin-3 were meta-analysed. Pooled analysis demonstrated that all-cause mortality was significantly associated with elevated baseline levels of BNP (HR 2.59; 95% CI 1.95–3.44; p < 0.00001), NT-proBNP (HR 1.73; 95% CI 1.45–2.06; p = 0.00001), Troponin (HR 1.65; 95% CI 1.31–2.07; p < 0.0001) and Galectin-3 (HR 1.82; 95% CI 1.27–2.61; p < 0.001) compared to lower baseline biomarker levels. Elevated levels of baseline BNP, NT-proBNP, Troponin and Galectin-3 were associated with increased all-cause mortality in a population of patients with AS. Therefore, a change in biomarker level could be considered to refine optimal timing of intervention. The results of this meta-analysis highlight the importance of biomarkers in risk stratification of AS, regardless of symptom status

Measurement of bitumen viscosity in the room-temperature drop experiment: student education, public outreach and modern science in one

Slow flow of the viscous liquid is a thought-provoking experiment that challenges students, academics and public to think about some fundamental questions in modern science. In the Queensland demonstration, the world-longest running experiment earning the Ig Nobel prize, one drop of pitch takes about 10 years to fall, leading to problems of actually observing the drops. Here, we describe our recent demonstration of slowly-flowing bitumen where appreciable flow is observed on the time scale of months. The experiment is free from dissipative heating effects and has the potential to improve the accuracy of measurement. Bitumen viscosity was calculated by undergraduate students during the summer project. The worldwide access to the running experiment is provided by webcams uploading the images to a dedicated website, enhancing student education experience and promotion of science. This demonstration serves as an attractive student education exercise and stimulates the discussion of fundamental concepts and hotly debated ideas in modern physics research: difference between solids and liquids, the nature of liquid-glass transition, emergence of long time scales in a physical process, and the conflict between human intuition and physical reality

Meta-Analysis of Genome-Wide Scans for Human Adult Stature Identifies Novel Loci and Associations with Measures of Skeletal Frame Size

Recent genome-wide (GW) scans have identified several independent loci affecting human stature, but their contribution through the different skeletal components of height is still poorly understood. We carried out a genome-wide scan in 12,611 participants, followed by replication in an additional 7,187 individuals, and identified 17 genomic regions with GW-significant association with height. Of these, two are entirely novel (rs11809207 in CATSPER4, combined P-value = 6.1×10−8 and rs910316 in TMED10, P-value = 1.4×10−7) and two had previously been described with weak statistical support (rs10472828 in NPR3, P-value = 3×10−7 and rs849141 in JAZF1, P-value = 3.2×10−11). One locus (rs1182188 at GNA12) identifies the first height eQTL. We also assessed the contribution of height loci to the upper- (trunk) and lower-body (hip axis and femur) skeletal components of height. We find evidence for several loci associated with trunk length (including rs6570507 in GPR126, P-value = 4×10−5 and rs6817306 in LCORL, P-value = 4×10−4), hip axis length (including rs6830062 at LCORL, P-value = 4.8×10−4 and rs4911494 at UQCC, P-value = 1.9×10−4), and femur length (including rs710841 at PRKG2, P-value = 2.4×10−5 and rs10946808 at HIST1H1D, P-value = 6.4×10−6). Finally, we used conditional analyses to explore a possible differential contribution of the height loci to these different skeletal size measurements. In addition to validating four novel loci controlling adult stature, our study represents the first effort to assess the contribution of genetic loci to three skeletal components of height. Further statistical tests in larger numbers of individuals will be required to verify if the height loci affect height preferentially through these subcomponents of height

Genetic risk and a primary role for cell-mediated immune mechanisms in multiple sclerosis.

Multiple sclerosis is a common disease of the central nervous system in which the interplay between inflammatory and neurodegenerative processes typically results in intermittent neurological disturbance followed by progressive accumulation of disability. Epidemiological studies have shown that genetic factors are primarily responsible for the substantially increased frequency of the disease seen in the relatives of affected individuals, and systematic attempts to identify linkage in multiplex families have confirmed that variation within the major histocompatibility complex (MHC) exerts the greatest individual effect on risk. Modestly powered genome-wide association studies (GWAS) have enabled more than 20 additional risk loci to be identified and have shown that multiple variants exerting modest individual effects have a key role in disease susceptibility. Most of the genetic architecture underlying susceptibility to the disease remains to be defined and is anticipated to require the analysis of sample sizes that are beyond the numbers currently available to individual research groups. In a collaborative GWAS involving 9,772 cases of European descent collected by 23 research groups working in 15 different countries, we have replicated almost all of the previously suggested associations and identified at least a further 29 novel susceptibility loci. Within the MHC we have refined the identity of the HLA-DRB1 risk alleles and confirmed that variation in the HLA-A gene underlies the independent protective effect attributable to the class I region. Immunologically relevant genes are significantly overrepresented among those mapping close to the identified loci and particularly implicate T-helper-cell differentiation in the pathogenesis of multiple sclerosis

Large Scale Association Analysis of Novel Genetic Loci for Coronary Artery Disease

Background-Combined analysis of 2 genome-wide association studies in cases enriched for family history recently identified 7 loci (on 1p13.3, 1q41, 2q36.3, 6q25.1, 9p21, 10q11.21, and 15q22.33) that may affect risk of coronary artery disease (CAD). Apart from the 9p21 locus, the other loci await substantive replication. Furthermore, the effect of these loci on CAD risk in a broader range of individuals remains to be determined.Methods and Results-We undertook association analysis of single nucleotide polymorphisms at each locus with CAD risk in 11 550 cases and 11 205 controls from 9 European studies. The 9p21.3 locus showed unequivocal association (rs1333049, combined odds ratio [OR]=1.20, 95% CI [1.16 to 1.25], probability value=2.81x10(-21)). We also confirmed association signals at 1p13.3 (rs599839, OR=1.13 [1.08 to 1.19], P=1.44x10(-7)), 1q41 (rs3008621, OR=1.10 [1.04 to 1.17], P=1.02x10(-3)), and 10q11.21 (rs501120, OR=1.11 [1.05 to 1.18], P=4.34x10(-4)). The associations with 6q25.1 (rs6922269, P=0.020) and 2q36.3 (rs2943634, P=0.032) were borderline and not statistically significant after correction for multiple testing. The 15q22.33 locus did not replicate. The 10q11.21 locus showed a possible sex interaction (P = 0.015), with a significant effect in women (OR=1.29 [1.15 to 1.45], P=1.86x10(-5)) but not men (OR=1.03 [0.96 to 1.11], P=0.387). There were no other strong interactions of any of the loci with other traditional risk factors. The loci at 9p21, 1p13.3, 2q36.3, and 10q11.21 acted independently and cumulatively increased CAD risk by 15% (12% to 18%), per additional risk allele. ConclusionsThe findings provide strong evidence for association between at least 4 genetic loci and CAD risk. Cumulatively, these novel loci have a significant impact on risk of CAD at least in European populations. (Arterioscler Thromb Vasc Biol. 2009; 29: 774-780.

MiR-137-derived polygenic risk: effects on cognitive performance in patients with schizophrenia and controls

Variants at microRNA-137 (MIR137), one of the most strongly associated schizophrenia risk loci identified to date, have been associated with poorer cognitive performance. As microRNA-137 is known to regulate the expression of ~1900 other genes, including several that are independently associated with schizophrenia, we tested whether this gene set was also associated with variation in cognitive performance. Our analysis was based on an empirically derived list of genes whose expression was altered by manipulation of MIR137 expression. This list was cross-referenced with genome-wide schizophrenia association data to construct individual polygenic scores. We then tested, in a sample of 808 patients and 192 controls, whether these risk scores were associated with altered performance on cognitive functions known to be affected in schizophrenia. A subgroup of healthy participants also underwent functional imaging during memory (n=108) and face processing tasks (n=83). Increased polygenic risk within the empirically derived miR-137 regulated gene score was associated with significantly lower performance on intelligence quotient, working memory and episodic memory. These effects were observed most clearly at a polygenic threshold of P=0.05, although significant results were observed at all three thresholds analyzed. This association was found independently for the gene set as a whole, excluding the schizophrenia-associated MIR137 SNP itself. Analysis of the spatial working memory fMRI task further suggested that increased risk score (thresholded at P=10−5) was significantly associated with increased activation of the right inferior occipital gyrus. In conclusion, these data are consistent with emerging evidence that MIR137 associated risk for schizophrenia may relate to its broader downstream genetic effects

A "Candidate-Interactome" Aggregate Analysis of Genome-Wide Association Data in Multiple Sclerosis

Though difficult, the study of gene-environment interactions in multifactorial diseases is crucial for interpreting the relevance of non-heritable factors and prevents from overlooking genetic associations with small but measurable effects. We propose a “candidate interactome” (i.e. a group of genes whose products are known to physically interact with environmental factors that may be relevant for disease pathogenesis) analysis of genome-wide association data in multiple sclerosis. We looked for statistical enrichment of associations among interactomes that, at the current state of knowledge, may be representative of gene-environment interactions of potential, uncertain or unlikely relevance for multiple sclerosis pathogenesis: Epstein-Barr virus, human immunodeficiency virus, hepatitis B virus, hepatitis C virus, cytomegalovirus, HHV8-Kaposi sarcoma, H1N1-influenza, JC virus, human innate immunity interactome for type I interferon, autoimmune regulator, vitamin D receptor, aryl hydrocarbon receptor and a panel of proteins targeted by 70 innate immune-modulating viral open reading frames from 30 viral species. Interactomes were either obtained from the literature or were manually curated. The P values of all single nucleotide polymorphism mapping to a given interactome were obtained from the last genome-wide association study of the International Multiple Sclerosis Genetics Consortium & the Wellcome Trust Case Control Consortium, 2. The interaction between genotype and Epstein Barr virus emerges as relevant for multiple sclerosis etiology. However, in line with recent data on the coexistence of common and unique strategies used by viruses to perturb the human molecular system, also other viruses have a similar potential, though probably less relevant in epidemiological terms

Comparative performances of machine learning methods for classifying Crohn Disease patients using genome-wide genotyping data

Abstract: Crohn Disease (CD) is a complex genetic disorder for which more than 140 genes have been identified using genome wide association studies (GWAS). However, the genetic architecture of the trait remains largely unknown. The recent development of machine learning (ML) approaches incited us to apply them to classify healthy and diseased people according to their genomic information. The Immunochip dataset containing 18,227 CD patients and 34,050 healthy controls enrolled and genotyped by the international Inflammatory Bowel Disease genetic consortium (IIBDGC) has been re-analyzed using a set of ML methods: penalized logistic regression (LR), gradient boosted trees (GBT) and artificial neural networks (NN). The main score used to compare the methods was the Area Under the ROC Curve (AUC) statistics. The impact of quality control (QC), imputing and coding methods on LR results showed that QC methods and imputation of missing genotypes may artificially increase the scores. At the opposite, neither the patient/control ratio nor marker preselection or coding strategies significantly affected the results. LR methods, including Lasso, Ridge and ElasticNet provided similar results with a maximum AUC of 0.80. GBT methods like XGBoost, LightGBM and CatBoost, together with dense NN with one or more hidden layers, provided similar AUC values, suggesting limited epistatic effects in the genetic architecture of the trait. ML methods detected near all the genetic variants previously identified by GWAS among the best predictors plus additional predictors with lower effects. The robustness and complementarity of the different methods are also studied. Compared to LR, non-linear models such as GBT or NN may provide robust complementary approaches to identify and classify genetic markers