The association between psychiatric diagnosis and violent re-offending in adult offenders in the community.

BACKGROUND: High rates of repeat offending are common across nations that are socially and culturally different. Although psychiatric disorders are believed to be risk factors for violent reoffending, the available evidence is sparse and liable to bias. METHOD: We conducted a historical cohort study in Sweden of a selected sample of 4828 offenders given community sentences who were assessed by a psychiatrist during 1988-2001, and followed up for an average of 5 years for first violent offence, death, or emigration, using information from national registers. Hazard ratios for violent offending were calculated by Cox regression models. RESULTS: Nearly a third of the sample (n = 1506 or 31.3%) offended violently during follow-up (mean duration: 4.8 years). After adjustment for socio-demographic and criminal history variables, substance use disorders (hazard ratio 1.97, 95% CI, 1.40-2.77) and personality disorders (hazard ratio 1.71, 1.20-2.44) were significantly associated with an increased risk of violent offending. No other diagnoses were related to recidivism risk. Adding information on diagnoses of substance use and personality disorders to data recorded on age, sex, and criminal history improved only minimally the prediction of violent offending. CONCLUSION: Diagnoses of substance use and personality disorders are associated with the risk of subsequent violent offending in community offenders about as strongly as are its better documented demographic and criminal history risk factors. Despite this, assessment of such disorders in addition to demographic and criminal history factors enhances only minimally the prediction of violent offending in the community.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are

Passive Energy Recapture in Jellyfish Contributes to Propulsive Advantage over other Metazoans

Gelatinous zooplankton populations are well known for their ability to take over perturbed ecosystems. The ability of these animals to outcompete and functionally replace fish that exhibit an effective visual predatory mode is counterintuitive because jellyfish are described as inefficient swimmers that must rely on direct contact with prey to feed. We show that jellyfish exhibit a unique mechanism of passive energy recapture, which is exploited to allow them to travel 30% further each swimming cycle, thereby reducing metabolic energy demand by swimming muscles. By accounting for large interspecific differences in net metabolic rates, we demonstrate, contrary to prevailing views, that the jellyfish (Aurelia aurita) is one of the most energetically efficient propulsors on the planet, exhibiting a cost of transport (joules per kilogram per meter) lower than other metazoans. We estimate that reduced metabolic demand by passive energy recapture improves the cost of transport by 48%, allowing jellyfish to achieve the large sizes required for sufficient prey encounters. Pressure calculations, using both computational fluid dynamics and a newly developed method from empirical velocity field measurements, demonstrate that this extra thrust results from positive pressure created by a vortex ring underneath the bell during the refilling phase of swimming. These results demonstrate a physical basis for the ecological success of medusan swimmers despite their simple body plan. Results from this study also have implications for bioinspired design, where low-energy propulsion is required

Alcohol intake in relation to fatal and non-fatal incident coronary heart disease and stroke in the EPIC-CVD study.

ABSTRACT Objective To investigate the association between alcohol consumption (at baseline and over lifetime) and non-fatal and fatal coronary heart disease (CHD) and stroke. Design Multicentre case-cohort study. Setting A study of cardiovascular disease (CVD) aetiology (EPIC-CVD) within the European Prospective Investigation into Cancer and nutrition cohort from 8 European countries. Participants A case-cohort study of 32 549 participants without baseline CVD, comprised of incident CVD cases and a subcohort for comparison. Main outcome measure Non-fatal and fatal CHD and stroke (including ischaemic and haemorrhagic). Results There were 9307 non-fatal CHD, 1699 fatal CHD, 5855 non-fatal stroke and 733 fatal stroke events. Baseline alcohol intake was inversely associated with non-fatal CHD, with a hazard ratio of 0.94 (95% confidence interval 0.92 to 0.96) per 12 g/day higher intake. There was a J-shaped association between baseline alcohol intake and risk of fatal CHD (hazard ratios=0.83 [95% confidence interval 0.70 to 0.98], 0.65 [0.53 to 0.81], and 0.82 [0.65 to 1.03] for categories 5.0-14.9 g/day, 15.0-29.9 g/day, and 30.0-59.9 g/day, respectively, compared with 0.1-4.9 g/day. In contrast, hazard ratios for non-fatal and fatal stroke risk were 1.04 (95% confidence interval 1.02 to 1.07), and 1.05 (0.98 to 1.13) per 12 g/day increase in baseline alcohol intake, respectively, including broadly similar findings for ischaemic and haemorrhagic stroke. Associations with cardiovascular outcomes were broadly similar with average lifetime alcohol consumption as for baseline alcohol intake, and across the eight countries we studied. There was no strong evidence for interactions of alcohol consumption with smoking status on the risk of CVD events. Conclusions Alcohol intake was inversely associated with non-fatal CHD risk but positively associated with risk of different stroke subtypes, highlighting the opposing associations of alcohol intake with different cardiovascular disease types and strengthening the evidence for policies to reduce alcohol consumption.This work was supported by the Direction Générale de la Santé (French Ministry of Health) (grant GR-IARC-2003-09-12-01). EPIC-CVD has been supported by the European Union Framework 7 (HEALTH-F2-2012-279233), the European Research Council (268834), the UK Medical Research Council (G0800270 and MR/L003120/1), the British Heart Foundation (SP/09/002 and RG/08/014 and RG13/13/30194), and the UK National Institute of Health Research. The establishment of the random subcohort was supported by the EU Sixth Framework Programme (FP6) (grant LSHM_CT_2006_037197 to the InterAct project) and the Medical Research Council Epidemiology Unit (grants MC_UU_12015/1 and MC_UU_12015/5)

Statistical methods for the time-to-event analysis of individual participant data from multiple epidemiological studies

Background Meta-analysis of individual participant time-to-event data from multiple prospective epidemiological studies enables detailed investigation of exposure–risk relationships, but involves a number of analytical challenges. Methods This article describes statistical approaches adopted in the Emerging Risk Factors Collaboration, in which primary data from more than 1 million participants in more than 100 prospective studies have been collated to enable detailed analyses of various risk markers in relation to incident cardiovascular disease outcomes. Results Analyses have been principally based on Cox proportional hazards regression models stratified by sex, undertaken in each study separately. Estimates of exposure–risk relationships, initially unadjusted and then adjusted for several confounders, have been combined over studies using meta-analysis. Methods for assessing the shape of exposure–risk associations and the proportional hazards assumption have been developed. Estimates of interactions have also been combined using meta-analysis, keeping separate within- and between-study information. Regression dilution bias caused by measurement error and within-person variation in exposures and confounders has been addressed through the analysis of repeat measurements to estimate corrected regression coefficients. These methods are exemplified by analysis of plasma fibrinogen and risk of coronary heart disease, and Stata code is made available. Conclusion Increasing numbers of meta-analyses of individual participant data from observational data are being conducted to enhance the statistical power and detail of epidemiological studies. The statistical methods developed here can be used to address the needs of such analyses

Serum Uric Acid and Coronary Heart Disease in 9,458 Incident Cases and 155,084 Controls: Prospective Study and Meta-Analysis

BACKGROUND: It has been suggested throughout the past fifty years that serum uric acid concentrations can help predict the future risk of coronary heart disease (CHD), but the epidemiological evidence is uncertain. METHODS AND FINDINGS: We report a “nested” case-control comparison within a prospective study in Reykjavik, Iceland, using baseline values of serum uric acid in 2,456 incident CHD cases and in 3,962 age- and sex-matched controls, plus paired serum uric acid measurements taken at baseline and, on average, 12 y later in 379 participants. In addition, we conducted a meta-analysis of 15 other prospective studies in eight countries conducted in essentially general populations. Compared with individuals in the bottom third of baseline measurements of serum uric acid in the Reykjavik study, those in the top third had an age- and sex-adjusted odds ratio for CHD of 1.39 (95% confidence interval [CI], 1.23–1.58) which fell to 1.12 (CI, 0.97–1.30) after adjustment for smoking and other established risk factors. Overall, in a combined analysis of 9,458 cases and 155,084 controls in all 16 relevant prospective studies, the odds ratio was 1.13 (CI, 1.07–1.20), but it was only 1.02 (CI, 0.91–1.14) in the eight studies with more complete adjustment for possible confounders. CONCLUSIONS: Measurement of serum uric acid levels is unlikely to enhance usefully the prediction of CHD, and this factor is unlikely to be a major determinant of the disease in general populations

PhenoScanner V2: an expanded tool for searching human genotype-phenotype associations.

SUMMARY: PhenoScanner is a curated database of publicly available results from large-scale genetic association studies in humans. This online tool facilitates 'phenome scans', where genetic variants are cross-referenced for association with many phenotypes of different types. Here we present a major update of PhenoScanner ('PhenoScanner V2'), including over 150 million genetic variants and more than 65 billion associations (compared to 350 million associations in PhenoScanner V1) with diseases and traits, gene expression, metabolite and protein levels, and epigenetic markers. The query options have been extended to include searches by genes, genomic regions and phenotypes, as well as for genetic variants. All variants are positionally annotated using the Variant Effect Predictor and the phenotypes are mapped to Experimental Factor Ontology terms. Linkage disequilibrium statistics from the 1000 Genomes project can be used to search for phenotype associations with proxy variants. AVAILABILITY AND IMPLEMENTATION: PhenoScanner V2 is available at www.phenoscanner.medschl.cam.ac.uk.This work was supported by the UK Medical Research Council [G0800270; MR/L003120/1], the British Heart Foundation [SP/09/002; RG/13/13/30194; RG/18/13/33946], Pfizer [G73632], the European Research Council [268834], the European Commission Framework Programme 7 [HEALTH-F2-2012-279233], the National Institute for Health Research and Health Data Research UK (*). *The views expressed are those of the authors and not necessarily those of the NHS or the NIHR

A CD4+ T cell antagonist epitope down-regulates activating signaling proteins, up-regulates inhibitory signaling proteins and abrogates HIV-specific T cell function

BACKGROUND: CD4(+) T cells are critically important in HIV infection, being both the primary cells infected by HIV and likely playing a direct or indirect role in helping control virus replication. Key areas of interest in HIV vaccine research are mechanisms of viral escape from the immune response. Interestingly, in HIV infection it has been shown that peptide sequence variation can reduce CD4(+) T cell responses to the virus, and small changes to peptide sequences can transform agonist peptides into antagonist peptides. RESULTS: We describe, at a molecular level, the consequences of antagonism of HIV p24-specific CD4(+) T cells. Antagonist peptide exposure in the presence of agonist peptide caused a global suppression of agonist-induced gene expression and signaling molecule phosphorylation. In addition to down-regulation of factors associated with T cell activation, a smaller subset of genes associated with negative regulation of cell activation was up-regulated, including KFL-2, SOCS-1, and SPDEY9P. Finally, antagonist peptide in the absence of agonist peptide also delivered a negative signal to T cells. CONCLUSIONS: Small changes in p24-specific peptides can result in T cell antagonism and reductions of both T cell receptor signaling and activation. These changes are at least in part mediated by a dominant negative signal delivered by antagonist peptide, as evidenced by up-regulation of negative regulatory genes in the presence of agonist plus antagonist stimulation. Antagonism can have dramatic effects on CD4(+) T cell function and presents a potential obstacle to HIV vaccine development

Reproducible disease phenotyping at scale: Example of coronary artery disease in UK Biobank

IMPORTANCE: A lack of internationally agreed standards for combining available data sources at scale risks inconsistent disease phenotyping limiting research reproducibility. OBJECTIVE: To develop and then evaluate if a rules-based algorithm can identify coronary artery disease (CAD) sub-phenotypes using electronic health records (EHR) and questionnaire data from UK Biobank (UKB). DESIGN: Case-control and cohort study. SETTING: Prospective cohort study of 502K individuals aged 40-69 years recruited between 2006-2010 into the UK Biobank with linked hospitalization and mortality data and genotyping. PARTICIPANTS: We included all individuals for phenotyping into 6 predefined CAD phenotypes using hospital admission and procedure codes, mortality records and baseline survey data. Of these, 408,470 unrelated individuals of European descent had a polygenic risk score (PRS) for CAD estimated. EXPOSURE: CAD Phenotypes. MAIN OUTCOMES AND MEASURES: Association with baseline risk factors, mortality (n = 14,419 over 7.8 years median f/u), and a PRS for CAD. RESULTS: The algorithm classified individuals with CAD into prevalent MI (n = 4,900); incident MI (n = 4,621), prevalent CAD without MI (n = 10,910), incident CAD without MI (n = 8,668), prevalent self-reported MI (n = 2,754); prevalent self-reported CAD without MI (n = 5,623), yielding 37,476 individuals with any type of CAD. Risk factors were similar across the six CAD phenotypes, except for fewer men in the self-reported CAD without MI group (46.7% v 70.1% for the overall group). In age- and sex- adjusted survival analyses, mortality was highest following incident MI (HR 6.66, 95% CI 6.07-7.31) and lowest for prevalent self-reported CAD without MI at baseline (HR 1.31, 95% CI 1.15-1.50) compared to disease-free controls. There were similar graded associations across the six phenotypes per SD increase in PRS, with the strongest association for prevalent MI (OR 1.50, 95% CI 1.46-1.55) and the weakest for prevalent self-reported CAD without MI (OR 1.08, 95% CI 1.05-1.12). The algorithm is available in the open phenotype HDR UK phenotype library (https://portal.caliberresearch.org/). CONCLUSIONS: An algorithmic, EHR-based approach distinguished six phenotypes of CAD with distinct survival and PRS associations, supporting adoption of open approaches to help standardize CAD phenotyping and its wider potential value for reproducible research in other conditions