Quantifying the causal impact of biological risk factors on healthcare costs

Understanding the causal impact that clinical risk factors have on healthcare-related costs is critical to evaluate healthcare interventions. Here, we used a genetically-informed design, Mendelian Randomization (MR), to infer the causal impact of 15 risk factors on annual total healthcare costs. We calculated healthcare costs for 373,160 participants from the FinnGen Study and replicated our results in 323,774 individuals from the United Kingdom and Netherlands. Robust causal effects were observed for waist circumference (WC), adult body mass index, and systolic blood pressure, in which a standard deviation increase corresponded to 22.78% [95% CI: 18.75-26.95], 13.64% [10.26-17.12], and 13.08% [8.84-17.48] increased healthcare costs, respectively. A lack of causal effects was observed for certain clinically relevant biomarkers, such as albumin, C-reactive protein, and vitamin D. Our results indicated that increased WC is a major contributor to annual total healthcare costs and more attention may be given to WC screening, surveillance, and mitigation

Stroke risk estimation across nine European countries in the MORGAM project.

Previous tools for stroke risk assessment have either been developed for specific populations or lack data on non-fatal events or uniform data collection. The purpose of this study was to develop a stepwise model for the estimation of 10 year risk of stroke in nine different countries across Europe.Using data from the MOnica Risk, Genetics, Archiving and Monograph (MORGAM) Project, sex-specific models estimating 10 year risk of stroke were developed using a Cox regression model stratified by country and including modelling of competing risks. Models were developed in a stepwise manner first using only data from questionnaires, and then adding data from physical examinations and finally data from blood samples.During 1,176,296 years of observation, 2928 incident fatal and non-fatal events of stroke were registered. The developed model showed good calibration and accuracy of prediction. The discrimination of the model varied between sex and country but increased with increasing number of variables used (area under the receiver operating characteristic curve between 0.77 and 0.79 in men and between 0.75 and 0.80 in women).The present study shows that using a large multicountry cohort from nine European countries it is possible to develop a stepwise risk estimation model for 10 year risk of stroke tailored to different availability of risk factors and still obtain valid measures of risk even in the simplest form of the model, with increasing performance of the model following increasing complexity. The methods chosen which separate this model from previous models (competing risk and stepwise approach) should be considered for future risk estimation models

Chromosome 1p13 genetic variants antagonize the risk of myocardial infarction associated with high ApoB serum levels

PMCID: PMC3480949This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Independent EEG Sources Are Dipolar

Independent component analysis (ICA) and blind source separation (BSS) methods are increasingly used to separate individual brain and non-brain source signals mixed by volume conduction in electroencephalographic (EEG) and other electrophysiological recordings. We compared results of decomposing thirteen 71-channel human scalp EEG datasets by 22 ICA and BSS algorithms, assessing the pairwise mutual information (PMI) in scalp channel pairs, the remaining PMI in component pairs, the overall mutual information reduction (MIR) effected by each decomposition, and decomposition ‘dipolarity’ defined as the number of component scalp maps matching the projection of a single equivalent dipole with less than a given residual variance. The least well-performing algorithm was principal component analysis (PCA); best performing were AMICA and other likelihood/mutual information based ICA methods. Though these and other commonly-used decomposition methods returned many similar components, across 18 ICA/BSS algorithms mean dipolarity varied linearly with both MIR and with PMI remaining between the resulting component time courses, a result compatible with an interpretation of many maximally independent EEG components as being volume-conducted projections of partially-synchronous local cortical field activity within single compact cortical domains. To encourage further method comparisons, the data and software used to prepare the results have been made available (http://sccn.ucsd.edu/wiki/BSSComparison)

Harmonising and linking biomedical and clinical data across disparate data archives to enable integrative cross-biobank research

A wealth of biospecimen samples are stored in modern globally distributed biobanks. Biomedical researchers worldwide need to be able to combine the available resources to improve the power of large-scale studies. A prerequisite for this effort is to be able to search and access phenotypic, clinical and other information about samples that are currently stored at biobanks in an integrated manner. However, privacy issues together with heterogeneous information systems and the lack of agreed-upon vocabularies have made specimen searching across multiple biobanks extremely challenging. We describe three case studies where we have linked samples and sample descriptions in order to facilitate global searching of available samples for research. The use cases include the ENGAGE (European Network for Genetic and Genomic Epidemiology) consortium comprising at least 39 cohorts, the SUMMIT (surrogate markers for micro- and macro-vascular hard endpoints for innovative diabetes tools) consortium and a pilot for data integration between a Swedish clinical health registry and a biobank. We used the Sample avAILability (SAIL) method for data linking: first, created harmonised variables and then annotated and made searchable information on the number of specimens available in individual biobanks for various phenotypic categories. By operating on this categorised availability data we sidestep many obstacles related to privacy that arise when handling real values and show that harmonised and annotated records about data availability across disparate biomedical archives provide a key methodological advance in pre-analysis exchange of information between biobanks, that is, during the project planning phase

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.

Genome-Wide Association Study for Incident Myocardial Infarction and Coronary Heart Disease in Prospective Cohort Studies: The CHARGE Consortium

Background Data are limited on genome-wide association studies (GWAS) for incident coronary heart disease (CHD). Moreover, it is not known whether genetic variants identified to date also associate with risk of CHD in a prospective setting. Methods We performed a two-stageGWAS analysis of incident myocardial infarction (MI) and CHD in a total of 64,297 individuals (including 3898MI cases, 5465 CHD cases). SNPs that passed an arbitrary threshold of 5×10-6 in Stage I were taken to Stage II for further discovery. Furthermore, in an analysis of prognosis, we studied whether known SNPs from former GWAS were associated with totalmortality in individuals who experienced MI during follow-up. Results In Stage I 15 loci passed the threshold of 5×10-6; 8 loci for MI and 8 loci for CHD, for which one locus overlapped and none were reported in previous GWAS meta-analyses. We took 60 SNPs representing these 15 loci to Stage II of discovery. Four SNPs near QKI showed nominally significant association with MI (p-value<8.8×10-3) and three exceeded the genome-wide significance threshold when Stage I and Stage II results were combined (top SNP rs6941513: p = 6.2×10-9). Despite excellent power, the 9p21 locus SNP (rs1333049) was only modestly associated with MI (HR = 1.09, p-value = 0.02) and marginally with CHD (HR = 1.06, p-value = 0.08). Among an inception cohort of those who experienced MI during follow-up, the risk allele of rs1333049 was associated with a decreased risk of subsequent mortality (HR = 0.90, p-value = 3.2×10-3). Conclusions QKI represents a novel locus that may serve as a predictor of incident CHD in prospective studies. The association of the 9p21 locus both with increased risk of first myocardial infarction and longer survival after MI highlights the importance of study design in investigating genetic determinants of complex disorders