George Davey Smith

Bio: George Davey Smith is an academic researcher from University of Bristol. The author has contributed to research in topics: Population & Mendelian randomization. The author has an hindex of 224, co-authored 2540 publications receiving 248373 citations. Previous affiliations of George Davey Smith include Keele University & Western Infirmary.

Income inequality and mortality: why are they related?

Abstract: The long held belief that household income is an important indicator of risk of death has recently received strong support from a series of large prospective studies.1 2 Income inequality within a population has also been suggested to be an important determinant of population mortality. In a cross national comparison, Rodgers found associations between income inequality and three mortality indicators—infant mortality, life expectancy at birth, and life expectancy at age 5—after taking overall gross national product into account.3 Several replications of this, across both a wide range of countries and within industrialised nations alone, using a variety of health indicators, have appeared.4 5 6 7 These studies have related income inequality to infant mortality,4 life expectancy,5 height,6 and morbidity,7 with a consistent finding that the less equitable the income distribution in a country, the less favourable the health outcome. In this week's issue of the BMJ, two studies relate income inequality between states in the United States to mortality rates within these states. Kennedy and colleagues (p 1004) show that greater income inequality is associated with higher mortality from several broad causes of death, although taking levels of poverty and smoking prevalence into account attenuates these associations.8 Kaplan et al find associations between level of inequality and mortality in both 1980 and 1990 (p 999), with trends in mortality differences between states over this decade being inconsistently related to changes in income inequality.9 In Britain, reliable data on income inequality by area are not readily available. Also in this issue, Ben-Shlomo et al (p 1013) have used the variation in small area deprivation scores within local authority areas in Britain as their …

Body Mass Index and Ischemic and Hemorrhagic Stroke A Prospective Study in Korean Men

Abstract: Background and Purpose— The association between obesity and stroke remains controversial, with earlier studies suggesting that differences might stem from heterogeneous stroke subtype compositions. The association between body mass index (BMI) and stroke subtypes was examined prospectively in a large cohort study. Methods— A total of 234 863 Korean men aged 40 to 64 years without substantial weight loss over 4 years after baseline examination in 1986 were divided into 8 categories of BMI and were followed up between 1991 and 2000 for fatal and nonfatal stroke events. Results— There was a positive association across the whole range of BMI and ischemic stroke, with a confounder-adjusted hazard of 11% (95% CI, 1.09 to 1.12) for 1 kg/m2 higher BMI. A J-shaped association was observed between BMI and hemorrhagic stroke; groups with a higher BMI than the reference category (22 to 23 kg/m2) had significantly increased risks. Full adjustment for confounders and variables potentially on the causal pathway (ie, blo...

Phenotypic manifestation of genetic risk for schizophrenia during adolescence in the general population

Abstract: Importance Schizophrenia is a highly heritable, polygenic condition characterized by a relatively diverse phenotype and frequent comorbid conditions, such as anxiety and depression. At present, limited evidence explains how genetic risk for schizophrenia is manifest in the general population. Objective To investigate the extent to which genetic risk for schizophrenia is associated with different phenotypes during adolescence in a population-based birth cohort. Design, Setting, and Participants This cohort study used data from the Avon Longitudinal Study of Parents and Children (ALSPAC). Of 14 062 children in the birth cohort, genetic data were available for 9912 adolescents. Data were collected periodically from September 6, 1990, and collection is ongoing. Data were analyzed from March 4 to August 13, 2015. Exposures Polygenic risk scores (PRSs) for schizophrenia generated for individuals in the ALSPAC cohort using results of the second Psychiatric Genomics Consortium Schizophrenia genome-wide association study as a training set. Main Outcomes and Measures Logistic regression was used to assess associations between the schizophrenia PRS and (1) psychotic experiences (Psychosis-Like Symptom Interview at 12 and 18 years of age), (2) negative symptoms (Community Assessment of Psychic Experiences at 16.5 years of age), (3) depressive disorder (Development and Well-Being Assessment at 15.5 years of age), and (4) anxiety disorder (Development and Well-Being Assessment at 15.5 years of age) in adolescence. Results Of the 8230 ALSPAC participants whose genetic data passed quality control checks (51.2% male, 48.8% female), 3676 to 5444 participated in assessments from 12 to 18 years of age. The PRSs created using single-nucleotide polymorphisms with a training-set P ≤ .05 threshold were associated with negative symptoms (odds ratio [OR] per SD increase in PRS, 1.21; 95% CI, 1.08-1.36; R2 = 0.007) and anxiety disorder (OR per SD increase in PRS, 1.17; 95% CI, 1.06- 1.29; R2 = 0.005). No evidence was found of an association between schizophrenia PRS and psychotic experiences (OR per SD increase in PRS, 1.08; 95% CI, 0.98-1.19; R2 = 0.001) or depressive disorder (OR per SD increase in PRS, 1.02; 95% CI, 0.91-1.13; R2 = 0.00005). Results were mostly consistent across different training-set P value thresholds and using different cutoffs and measures of the psychopathological outcomes. Conclusions and Relevance This study demonstrates polygenic overlaps between common genetic polymorphisms associated with schizophrenia and negative symptoms and anxiety disorder but not with psychotic experiences or depression. Because the genetic risk for schizophrenia appears to be manifest as anxiety and negative symptoms during adolescence, a greater focus on these phenotypes rather than on psychotic experiences might be required for prediction of transition in at-risk samples.

CYT387, a selective JAK1/JAK2 inhibitor: in vitro assessment of kinase selectivity and preclinical studies using cell lines and primary cells from polycythemia vera patients

Abstract: Somatic mutations in Janus kinase 2 (JAK2), including JAK2V617F, result in dysregulated JAK-signal transducer and activator transcription (STAT) signaling, which is implicated in myeloproliferative neoplasm (MPN) pathogenesis. CYT387 is an ATP-competitive small molecule that potently inhibits JAK1/JAK2 kinases (IC(50)=11 and 18 nM, respectively), with significantly less activity against other kinases, including JAK3 (IC(50)=155 nM). CYT387 inhibits growth of Ba/F3-JAK2V617F and human erythroleukemia (HEL) cells (IC(50) approximately 1500 nM) or Ba/F3-MPLW515L cells (IC(50)=200 nM), but has considerably less activity against BCR-ABL harboring K562 cells (IC=58 000 nM). Cell lines harboring mutated JAK2 alleles (CHRF-288-11 or Ba/F3-TEL-JAK2) were inhibited more potently than the corresponding pair harboring mutated JAK3 alleles (CMK or Ba/F3-TEL-JAK3), and STAT-5 phosphorylation was inhibited in HEL cells with an IC(50)=400 nM. Furthermore, CYT387 selectively suppressed the in vitro growth of erythroid colonies harboring JAK2V617F from polycythemia vera (PV) patients, an effect that was attenuated by exogenous erythropoietin. Overall, our data indicate that the JAK1/JAK2 selective inhibitor CYT387 has potential for efficacious treatment of MPN harboring mutated JAK2 and MPL alleles.

Measuring inconsistency in meta-analyses

Abstract: Cochrane Reviews have recently started including the quantity I 2 to help readers assess the consistency of the results of studies in meta-analyses. What does this new quantity mean, and why is assessment of heterogeneity so important to clinical practice? Systematic reviews and meta-analyses can provide convincing and reliable evidence relevant to many aspects of medicine and health care.1 Their value is especially clear when the results of the studies they include show clinically important effects of similar magnitude. However, the conclusions are less clear when the included studies have differing results. In an attempt to establish whether studies are consistent, reports of meta-analyses commonly present a statistical test of heterogeneity. The test seeks to determine whether there are genuine differences underlying the results of the studies (heterogeneity), or whether the variation in findings is compatible with chance alone (homogeneity). However, the test is susceptible to the number of trials included in the meta-analysis. We have developed a new quantity, I 2, which we believe gives a better measure of the consistency between trials in a meta-analysis. Assessment of the consistency of effects across studies is an essential part of meta-analysis. Unless we know how consistent the results of studies are, we cannot determine the generalisability of the findings of the meta-analysis. Indeed, several hierarchical systems for grading evidence state that the results of studies must be consistent or homogeneous to obtain the highest grading.2–4 Tests for heterogeneity are commonly used to decide on methods for combining studies and for concluding consistency or inconsistency of findings.5 6 But what does the test achieve in practice, and how should the resulting P values be interpreted? A test for heterogeneity examines the null hypothesis that all studies are evaluating the same effect. The usual test statistic …

Bias in meta-analysis detected by a simple, graphical test

Abstract: Objective: Funnel plots (plots of effect estimates against sample size) may be useful to detect bias in meta-analyses that were later contradicted by large trials. We examined whether a simple test of asymmetry of funnel plots predicts discordance of results when meta-analyses are compared to large trials, and we assessed the prevalence of bias in published meta-analyses. Design: Medline search to identify pairs consisting of a meta-analysis and a single large trial (concordance of results was assumed if effects were in the same direction and the meta-analytic estimate was within 30% of the trial); analysis of funnel plots from 37 meta-analyses identified from a hand search of four leading general medicine journals 1993-6 and 38 meta-analyses from the second 1996 issue of the Cochrane Database of Systematic Reviews . Main outcome measure: Degree of funnel plot asymmetry as measured by the intercept from regression of standard normal deviates against precision. Results: In the eight pairs of meta-analysis and large trial that were identified (five from cardiovascular medicine, one from diabetic medicine, one from geriatric medicine, one from perinatal medicine) there were four concordant and four discordant pairs. In all cases discordance was due to meta-analyses showing larger effects. Funnel plot asymmetry was present in three out of four discordant pairs but in none of concordant pairs. In 14 (38%) journal meta-analyses and 5 (13%) Cochrane reviews, funnel plot asymmetry indicated that there was bias. Conclusions: A simple analysis of funnel plots provides a useful test for the likely presence of bias in meta-analyses, but as the capacity to detect bias will be limited when meta-analyses are based on a limited number of small trials the results from such analyses should be treated with considerable caution. Key messages Systematic reviews of randomised trials are the best strategy for appraising evidence; however, the findings of some meta-analyses were later contradicted by large trials Funnel plots, plots of the trials9 effect estimates against sample size, are skewed and asymmetrical in the presence of publication bias and other biases Funnel plot asymmetry, measured by regression analysis, predicts discordance of results when meta-analyses are compared with single large trials Funnel plot asymmetry was found in 38% of meta-analyses published in leading general medicine journals and in 13% of reviews from the Cochrane Database of Systematic Reviews Critical examination of systematic reviews for publication and related biases should be considered a routine procedure

The PRISMA Statement for Reporting Systematic Reviews and Meta-Analyses of Studies That Evaluate Health Care Interventions: Explanation and Elaboration

Abstract: Systematic reviews and meta-analyses are essential to summarize evidence relating to efficacy and safety of health care interventions accurately and reliably. The clarity and transparency of these reports, however, is not optimal. Poor reporting of systematic reviews diminishes their value to clinicians, policy makers, and other users. Since the development of the QUOROM (QUality Of Reporting Of Meta-analysis) Statement—a reporting guideline published in 1999—there have been several conceptual, methodological, and practical advances regarding the conduct and reporting of systematic reviews and meta-analyses. Also, reviews of published systematic reviews have found that key information about these studies is often poorly reported. Realizing these issues, an international group that included experienced authors and methodologists developed PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) as an evolution of the original QUOROM guideline for systematic reviews and meta-analyses of evaluations of health care interventions. The PRISMA Statement consists of a 27-item checklist and a four-phase flow diagram. The checklist includes items deemed essential for transparent reporting of a systematic review. In this Explanation and Elaboration document, we explain the meaning and rationale for each checklist item. For each item, we include an example of good reporting and, where possible, references to relevant empirical studies and methodological literature. The PRISMA Statement, this document, and the associated Web site (http://www.prisma-statement.org/) should be helpful resources to improve reporting of systematic reviews and meta-analyses.

The Cochrane Collaboration’s tool for assessing risk of bias in randomised trials

Abstract: Flaws in the design, conduct, analysis, and reporting of randomised trials can cause the effect of an intervention to be underestimated or overestimated. The Cochrane Collaboration’s tool for assessing risk of bias aims to make the process clearer and more accurate