Network meta-analysis (NMA) – a statistical technique that allows comparison of multiple treatments in the same meta-analysis simultaneously – has become increasingly popular in the medical literature in recent years. The statistical methodology underpinning this technique and software tools for implementing the methods are evolving. Both commercial and freely available statistical software packages have been developed to facilitate the statistical computations using NMA with varying degrees of functionality and ease of use. This paper aims to introduce the reader to three R packages, namely, gemtc, pcnetmeta, and netmeta, which are freely available software tools implemented in R. Each automates the process of performing NMA so that users can perform the analysis with minimal computational effort. We present, compare and contrast the availability and functionality of different important features of NMA in these three packages so that clinical investigators and researchers can determine which R packages to implement depending on their analysis needs. Four summary tables detailing (i) data input and network plotting, (ii) modeling options, (iii) assumption checking and diagnostic testing, and (iv) inference and reporting tools, are provided, along with an analysis of a previously published dataset to illustrate the outputs available from each package. We demonstrate that each of the three packages provides a useful set of tools, and combined provide users with nearly all functionality that might be desired when conducting a NMA.
Network meta-analysis (NMA) has emerged as a useful analytical tool allowing comparison of multiple treatments based on direct and indirect evidence. Commonly, a hierarchical Bayesian NMA model is used, which allows rank probabilities (the probability that each treatment is best, second best, and so on) to be calculated for decision making. However, the statistical properties of rank probabilities are not well understood. This study investigates how rank probabilities are affected by various factors such as unequal number of studies per comparison in the network, the sample size of individual studies, the network configuration, and effect sizes between treatments. In order to explore these factors, a simulation study of four treatments (three equally effective treatments and one less effective reference) was conducted. The simulation illustrated that estimates of rank probabilities are highly sensitive to both the number of studies per comparison and the overall network configuration. An unequal number of studies per comparison resulted in biased estimates of treatment rank probabilities for every network considered. The rank probability for the treatment that was included in the fewest number of studies was biased upward. Conversely, the rank of the treatment included in the most number of studies was consistently underestimated. When the simulation was altered to include three equally effective treatments and one superior treatment, the hierarchical Bayesian NMA model correctly identified the most effective treatment, regardless of all factors varied. The results of this study offer important insight into the ability of NMA models to rank treatments accurately under several scenarios. The authors recommend that health researchers use rank probabilities cautiously in making important decisions.
multiple treatment meta-analysis; mixed treatment comparison; ranking; network configuration
When participants drop out of randomised clinical trials, as frequently happens, the intention-to-treat (ITT) principle does not apply, potentially leading to attrition bias. Data lost from patient dropout/lack of follow-up are statistically addressed by imputing, a procedure prone to bias. Deviations from the original definition of ITT are referred to as modified intention-to-treat (mITT). As yet, the impact of the potential bias associated with mITT has not been assessed. Our objective is to investigate potential bias and disadvantages of performing mITT and evaluate possible concerns when executing different mITT approaches in meta-analyses.
Methods and analysis
Using meta-epidemiology on randomised trials considered less prone to bias (ie, good internal validity) and assessing biological or targeted agents in patients with rheumatoid arthritis, we will meta-analyse data from 10 biological and targeted drugs based on collections of trials that would correspond to 10 individual meta-analyses.
Ethics and dissemination
This study will enhance transparency for evaluating mITT treatment effects described in meta-analyses. The intended audience will include healthcare researchers, policymakers and clinicians. Results of the study will be disseminated by peer-review publication.
In PROSPERO CRD42013006702, 11. December 2013.
EPIDEMIOLOGY; RHEUMATOLOGY; CLINICAL PHARMACOLOGY
Objective was to determine whether prophylactic low level laser therapy (LLLT) reduces the risk of severe mucositis as compared to placebo or no therapy.
MEDLINE, EMBASE, and Cochrane Central Register of Controlled Trials were searched until February 2014 for randomized controlled trials (RCTs) comparing prophylactic LLLT with placebo or no therapy in patients with cancer or undergoing hematopoietic stem cell transplantation (HSCT). All analyses used random effects models.
Eighteen RCTs (1144 patients) were included. Prophylactic LLLT reduced the overall risk of severe mucositis (risk ratio (RR) 0.37, 95% confidence interval (CI) 0.20 to 0.67; P = 0.001). LLLT also reduced the following outcomes when compared to placebo/no therapy: severe mucositis at the time of anticipated maximal mucositis (RR 0.34, 95% CI 0.20 to 0.59), overall mean grade of mucositis (standardized mean difference −1.49, 95% CI −2.02 to −0.95), duration of severe mucositis (weighted mean difference −5.32, 95% CI −9.45 to −1.19) and incidence of severe pain (RR 0.26, 95% CI 0.18 to 0.37).
Prophylactic LLLT reduced severe mucositis and pain in patients with cancer and HSCT recipients. Future research should identify the optimal characteristics of LLLT and determine feasibility in the clinical setting.
Anemia in children continues to be a major public health challenge in most developing countries, particularly in Africa. Anemia in the early stages of life leads to severe negative consequences on the cognitive as well as the growth and development of children, which may persist even after treatment. We examine the prevalence of anemia in under-five children in the Ghanaian population to help inform and serve as a guide to health policies and possible interventions.
Data from the 2008 Ghana Demographic and Health Survey (GDHS) was used. Data consists of health, demographic and socio-economic factors. Anemia status was determined using hemoglobin level, and prevalence of childhood anemia along with 95% confidence intervals was provided. We also examined the distribution of prevalence across different age and socio-demographic groups as well as the different regions and sub-regions in Ghana.
The overall prevalence of anemia in under-five children in Ghana was 78.4% (N = 2168, 95% CI: 76.7-80.2), where 7.8% (N = 2168, 95% CI: 6.6-8.9) of the children had severe anemia, 48.0% (N = 2168, 95% CI: 45.9-50.2) moderate anemia and 22.6% (N = 2168, 95% CI: 20.8-24.4) had mild anemia. The highest prevalence regions were the Upper East, 88.9% (N = 158, 95% CI: 80.9-94.0), and Upper West 88.1% (N = 220, 95% CI: 76.4-94.6). The prevalence was also higher among children under 2 years of age, 85.1% (N = 781, 95% CI: 82.6-87.7) than children 2–5 years of age, 74.8% (N = 1387, 95% CI: 72.5-77.1). No significant difference in prevalence between boys and girls was observed.
Given the high prevalence of childhood anemia observed in Ghana, particularly among those less than 2 years old, and given the negative consequences on their cognitive and behavioral development even in later years, there is an urgent need for effective and efficient public health interventions.
Anemia; Prevalence; Micronutrient deficiency; Children; Ghana; Ghana demographic and health survey
Randomised controlled trials (RCTs) are often considered as the gold standard for assessing new health interventions. Patients are randomly assigned to receive an intervention or control. The effect of the intervention can be estimated by comparing outcomes between groups, whose prognostic factors are expected to balance by randomisation. However, patients’ non-compliance with their assigned treatment will undermine randomisation and potentially bias the estimate of treatment effect. Through simulation, we aim to compare common approaches in analysing non-compliant data under different non-compliant scenarios.
Based on a real study, we simulated hypothetical trials by varying three non-compliant factors: the type, randomness and degree of non-compliance. We compared the intention-to-treat (ITT), as-treated (AT), per-protocol (PP), instrumental variable (IV) and complier average casual effect (CACE) analyses to estimate large (50% improvement over the control), moderate (25% improvement) and null (same as the control) treatment effects. Different approaches were compared by the bias of estimate, mean square error (MSE) and 95% coverage of the true value.
For a large or moderate treatment effect, the ITT estimate was considerably biased in all scenarios. The AT, PP, IV and CACE estimates were unbiased when non-compliant behaviours were random. The IV estimate was unbiased when non-compliant behaviours were symmetrically dependent on patients’ conditions. The PP estimate was mostly unbiased when patients in the control group did not have access to the intervention. When the intervention was not different from the control, the ITT was less biased than the other approaches. Similar results were found when comparing the MSE and 95% coverage.
The standard ITT analysis under non-compliance is biased when the intervention has a moderate or large effect. Alternative analyses can provide unbiased or less biased estimates. Based on the results, we make some suggestions on choosing optimal approaches for analysing specific non-compliant scenarios.
STATISTICS & RESEARCH METHODS; RANDOMIZED CONTROLLED TRIAL; NON-COMPLIANCE
Our goal is to test the effect of both rare and common variants in a blood pressure study. We use a pathway-based approach, gene-set enrichment analysis, to search for related genes affecting 4 phenotypes: systolic blood pressure, diastolic blood pressure, the difference between each of them and mean arterial pressure, which is a weighted linear combination of systolic and diastolic blood pressure. Using the real Genetic Analysis Workshop 18 data, we consider both rare and common variants in our analysis and incorporate other covariates by using a recently proposed test statistic.
Our study identified a commonly enriched gene set/pathway for the two derived phenotypes we analyzed: the difference between systolic and diastolic blood pressure and mean arterial pressure, but none is identified with the individual blood pressure phenotypes. The gene CD47, in the enriched gene pathway/set, was reported in previous studies to be related to blood pressure.
The findings are not surprising because the sample size we use in our analysis is small, and hence power to detect small but important effects is likely inadequate.
Genetic variants that predispose adults and the elderly to high blood pressure are largely unknown. We used a bivariate linear mixed model approach to jointly test the associations of common single-nucleotide polymorphisms with systolic and diastolic blood pressure using data from a genome-wide association study consisting of genetic variants from chromosomes 3 and 9 and longitudinal measured phenotypes and environment variables from unrelated individuals of Mexican American ethnicity provided by the Genetic Analysis Workshop 18. Despite the small sample size of a maximum of 131 unrelated subjects, a few single-nucleotide polymorphisms appeared significant at the genome-wide level. Simulated data, which was also provided by Genetic Analysis Workshop 18 organizers, showed higher power of the bivariate approach over univariate analysis to detect the association of a selected single-nucleotide polymorphism with modest effect. This suggests that the bivariate approach to longitudinal data of jointly measured and correlated phenotypes can be a useful strategy to identify candidate single-nucleotide polymorphisms that deserve further investigation.
Many complex diseases are related to genetics, and it is of great interest to evaluate the association between single-nucleotide polymorphisms (SNPs) and disease outcome. The association of genetics with outcome can be modified by covariates such as age, sex, smoking status, and membership to the same pedigree. In this paper, we propose a block entropy method to separate two classes of SNPs, for which the association with hypertension is either sensitive or insensitive to the covariates. We also propose a consistency entropy method to further reduce the number of SNPs that might be associated with the outcome. Based on the data provided by the organizers of Genetic Analysis Workshop 18, we calculated the block entropies for six different blocking strategies. Using block entropy and consistency entropy, we identified 230 SNPs on chromosome 9 that are most likely to be associated with the outcome and whose associations with hypertension are sensitive to the covariates.
Background: Hypertension is a prevalent condition linked to major cardiovascular conditions and multiple other comorbidities. Genetic information can offer a deeper understanding about susceptibility and the underlying disease mechanisms. The Genetic Analysis Workshop 18 (GAW18) provides abundant genotype data to determine genetic associations for being hypertensive and for the underlying trait of systolic blood pressure (SBP). The high-dimensional nature of this data promotes dimension reduction techniques to remove excess noise and also synthesize genetic information for complex, polygenic traits. Methods: For both measured and simulated phenotype data from GAW18, we use sparse principal component analysis to obtain sparse genetic profiles that represent the underlying data structures. We then detect associations between the obtained sparse principal components (PCs) and SBP, a major indicator of hypertension, following up by investigating the sparse PCs for genetic structure to gain insight into new patterns. Results: After adjusting for multiple testing, 27 of 122 PCs were significantly associated with measured SBP, offering a large number of components to investigate. Considering the top 3 PCs, linked genetic regions have been identified; these may act in unison while associated with SBP. Simulated data offered similar results. Conclusions: Sparse PCs can offer a new data-driven approach to structuring genotype data and understanding the genetic mechanics behind complex, polygenic traits such as hypertension.
This article compares baseline, average, and longitudinal data analysis methods for identifying genetic variants in genome-wide association study using the Genetic Analysis Workshop 18 data. We apply methods that include (a) linear mixed models with baseline measures, (b) random intercept linear mixed models with mean measures outcome, and (c) random intercept linear mixed models with longitudinal measurements. In the linear mixed models, covariates are included as fixed effects, whereas relatedness among individuals is incorporated as the variance-covariance structure of the random effect for the individuals. The overall strategy of applying linear mixed models decorrelate the data is based on Aulchenko et al.'s GRAMMAR. By analyzing systolic and diastolic blood pressure, which are used separately as outcomes, we compare the 3 methods in identifying a known genetic variant that is associated with blood pressure from chromosome 3 and simulated phenotype data. We also analyze the real phenotype data to illustrate the methods. We conclude that the linear mixed model with longitudinal measurements of diastolic blood pressure is the most accurate at identifying the known single-nucleotide polymorphism among the methods, but linear mixed models with baseline measures perform best with systolic blood pressure as the outcome.
Systolic blood pressure and diastolic blood pressure are known risk factors for cardiovascular diseases and understanding their genetic basis will have important public health implications. For rare variants, it is extremely challenging to make statistical inference for single-maker tests. Therefore, joint analysis of a set of variants has been proposed. In this paper, we applied recently proposed methods "test for testing the effect of an optimally weighted combination of variants" and "variable weight-TOW" to determine genetic regions that are associated with blood pressure. Then least absolute shrinkage and selection operator, as well as sparse partial least square methods, were used to identify significant markers within a gene or in intergenic regions. We investigated the effect of rare variants and common variants, and their combined effect.
Genetic Analysis Workshop 18 provided a platform for developing and evaluating statistical methods to analyze whole-genome sequence data from a pedigree-based sample. In this article we present an overview of the data sets and the contributions that analyzed these data. The family data, donated by the Type 2 Diabetes Genetic Exploration by Next-Generation Sequencing in Ethnic Samples Consortium, included sequence-level genotypes based on sequencing and imputation, genome-wide association genotypes from prior genotyping arrays, and phenotypes from longitudinal assessments. The contributions from individual research groups were extensively discussed before, during, and after the workshop in theme-based discussion groups before being submitted for publication.
Evidence from controlled trials encourages the intake of dietary pulses (beans, chickpeas, lentils and peas) as a method of improving dyslipidemia, but heart health guidelines have stopped short of ascribing specific benefits to this type of intervention or have graded the beneficial evidence as low. We conducted a systematic review and meta-analysis of randomized controlled trials (RCTs) to assess the effect of dietary pulse intake on established therapeutic lipid targets for cardiovascular risk reduction.
We searched electronic databases and bibliographies of selected trials for relevant articles published through Feb. 5, 2014. We included RCTs of at least 3 weeks’ duration that compared a diet emphasizing dietary pulse intake with an isocaloric diet that did not include dietary pulses. The lipid targets investigated were low-density lipoprotein (LDL) cholesterol, apolipoprotein B and non–high-density lipoprotein (non-HDL) cholesterol. We pooled data using a random-effects model.
We identified 26 RCTs (n = 1037) that satisfied the inclusion criteria. Diets emphasizing dietary pulse intake at a median dose of 130 g/d (about 1 serving daily) significantly lowered LDL cholesterol levels compared with the control diets (mean difference −0.17 mmol/L, 95% confidence interval −0.25 to −0.09 mmol/L). Treatment effects on apolipoprotein B and non-HDL cholesterol were not observed.
Our findings suggest that dietary pulse intake significantly reduces LDL cholesterol levels. Trials of longer duration and higher quality are needed to verify these results. Trial registration: ClinicalTrials.gov, no. NCT01594567.
Sucrose has been demonstrated to provide analgesia for minor painful procedures in infants. However, results of trials investigating other sweet solutions for neonatal pain relief have not yet been synthesized.
To establish the efficacy of nonsucrose sweet-tasting solutions for pain relief during painful procedures in neonates.
The present article is a systematic review and meta-analyses of the literature. Standard methods of the Cochrane Neonatal Collaborative Review Group were used. Literature searches were reviewed for randomized controlled trials investigating the use of sweet solutions, except sucrose, for procedural pain management in neonates. Outcomes assessed included validated pain measures and behavioural and physiological indicators.
Thirty-eight studies (3785 neonates) were included, 35 of which investigated glucose. Heel lancing was performed in 21/38 studies and venipuncture in 11/38 studies. A 3.6-point reduction in Premature Infant Pain Profile scores during heel lances was observed in studies comparing 20% to 30% glucose with no intervention (two studies, 124 neonates; mean difference −3.6 [95% CI −4.6 to −2.6]; P<0.001; I2=54%). A significant reduction in the incidence of cry after venipuncture for infants receiving 25% to 30% glucose versus water or no intervention was observed (three studies, 130 infants; risk difference −0.18 [95% CI −0.31 to −0.05]; P=0.008, number needed to treat = 6 [95% CI 3 to 20]; I2=63%).
The present systematic review and meta-analyses demonstrate that glucose reduces pain scores and crying during single heel lances and venipunctures. Results indicate that 20% to 30% glucose solutions have analgesic effects and can be recommended as an alternative to sucrose for procedural pain reduction in healthy term and preterm neonates.
Analgesia; Glucose; Infant; Neonate; Pain; Sweet-tasting solution
Children with Down syndrome (DS) are at high risk of infectious toxicity when treated with acute lymphoblastic leukemia chemotherapy protocols optimized in children without DS. Our objective was to determine if children with DS and acute myeloid leukemia (AML) have a different risk of infection when treated with chemotherapy protocols developed for children with DS compared to AML treatment protocols developed for children without DS.
We conducted a retrospective, population-based cohort study that included DS children ≤ 18 years of age with de novo, non-M3 AML diagnosed between January 1995 and December 2004, and treated at 15 Canadian centers. Patients were monitored for infection from initiation of AML treatment until recovery from the last cycle of chemotherapy, conditioning for hematopoietic stem cell transplantation, relapse, persistent disease or death (whichever occurred first). Trained research associates abstracted all information from each site.
There were 31 children with DS included; median age was 1.7 (range 0.1-11.1) years. Eleven were treated according to a DS-specific protocol while 20 were treated with non-DS specific protocols. A total of 157 courses of chemotherapy were delivered. Microbiologically documented sterile site infection occurred in 11.9% and 14.3% of DS-specific and non-DS specific AML treatment courses respectively. Sepsis was rare and there were no infection-related deaths. In multiple regression, treatment with a DS-specific protocol was independently associated with a reduction in microbiologically documented sterile site infection (adjusted odds ratio (OR) 0.65, 95% confidence interval (CI) 0.42-0.99; P = 0.044), and clinically documented infection (adjusted OR 0.36, 95% CI 0.14-0.91; P = 0.031) but not bacteremia (adjusted OR 0.73, 95% CI 0.44-1.22; P = 0.231).
Our study suggests that children with DS do not experience excessive infectious toxicity during treatment for AML compared to children without DS. Incorporation of DS-specific AML treatment protocols is associated with a more favorable infection profile for children with DS-AML.
Down syndrome; Acute myeloid leukemia; Infection; Chemotherapy; Children
In multi-cohort genetic association studies or meta-analysis, associations of genetic variants with complex traits across cohorts may be heterogeneous because of genuine genetic diversity or differential biases or errors. To detect the associations of genes with heterogeneous associations across cohorts, new global fixed-effect (FE) and random-effects (RE) meta-analytic methods have been recently proposed. These global methods had improved power over both traditional FE and RE methods under heterogeneity in limited simulation scenarios and data application, but their usefulness in a wide range of practical situations is not clear. We assessed the performance of these methods for both binary and quantitative traits in extensive simulations and applied them to a multi-cohort association study. We found that these new approaches have higher power to detect mostly the very small to small associations of common genetic variants when associations are highly heterogeneous across cohorts. They worked well when both the underlying and assumed genetic models are either multiplicative or dominant. But, they offered no clear advantage for less common variants unless heterogeneity was substantial. In conclusion, these new meta-analytic methods can be used to detect the association of genetic variants with high heterogeneity, which can then be subjected to further exploration, in multi-cohort association studies and meta-analyses.
genome-wide and genetic association studies; single-nucleotide polymorphism; meta-analysis; study heterogeneity; statistical power; type I error rates
The value of integrated care through comprehensive, coordinated, and family-centered services has been increasingly recognized for improving health outcomes of children with special health care needs (CSHCN). In a randomized controlled trial (RCT), the integrated care provided through the Children’s Treatment Network (CTN) was compared with usual care in improving the psychosocial health of target CSHCN. In this paper, we aimed to estimate the effect of CTN care by conducting multiple analyses to handle noncompliance in the trial.
The trial recruited target children in Simcoe County and York Region, ON, Canada. Children were randomized to receive CTN or usual care and were followed for 2 years. The CTN group received integrated services through multiple providers to address their specific needs while the usual care group continued to receive care directed by their parents. The outcome was change in psychosocial quality of life at 2 years. We conducted intention-to-treat, as-treated, per-protocol, and instrumental variable analyses to analyze the outcome.
The trial randomized 445 children, with 229 in the intervention group and 216 in the control group. During follow-up, 52% of children in the intervention group did not receive complete CTN care for various reasons. At 2 years, we did not find a significant improvement in psychosocial quality of life among the children receiving CTN care compared with usual care (intention-to-treat mean difference 1.50, 95% confidence interval −1.49 to 4.50; P = 0.32). Other methods of analysis yielded similar results.
Although the effect of CTN care was not significant, there was evidence showing benefits of integrated care for CSHCN. More RCTs are needed to demonstrate the magnitude of such an effect. The CTN study highlights the key challenges in RCTs when assessing interventions involving integrated care, and informs further RCTs including similar evaluations.
children with special health care needs; chronically ill; family-centered care; randomized controlled trial; noncompliance
Tuberculosis (TB) disease affects survival among HIV co-infected patients on antiretroviral therapy (ART). Yet, the magnitude of TB disease on mortality is poorly understood.
Using a prospective cohort of 22,477 adult patients who initiated ART between August 2000 and June 2009 in Uganda, we assessed the effect of active pulmonary TB disease at the initiation of ART on all-cause mortality using a Cox proportional hazards model. Propensity score (PS) matching was used to control for potential confounding. Stratification and covariate adjustment for PS and not PS-based multivariable Cox models were also performed.
A total of 1,609 (7.52%) patients had active pulmonary TB at the start of ART. TB patients had higher proportions of being male, suffering from AIDS-defining illnesses, having World Health Organization (WHO) disease stage III or IV, and having lower CD4 cell counts at baseline (p < 0.001). The percentages of death during follow-up were 10.47% and 6.38% for patients with and without TB, respectively. The hazard ratio (HR) for mortality comparing TB to non-TB patients using 1,686 PS-matched pairs was 1.37 (95% confidence interval [CI]: 1.08 – 1.75), less marked than the crude estimate (HR = 1.74, 95% CI: 1.49 – 2.04). The other PS-based methods and not PS-based multivariable Cox model produced similar results.
After controlling for important confounding variables, HIV patients who had TB at the initiation of ART in Uganda had an approximate 37% increased hazard of overall mortality relative to non-TB patients.
Antiretroviral therapy; HIV; Tuberculosis; Propensity score methods; Uganda; Prospective cohort study
The standard approach to determine unique or shared genetic factors across populations is to identify risk alleles in one population and investigate replication in others. However, since populations differ in DNA sequence information, allele frequencies, effect sizes, and linkage disequilibrium patterns, SNP association using a uniform stringent threshold on p values may not be reproducible across populations. Here, we developed rank-based methods to investigate shared or population-specific loci and pathways for childhood asthma across individuals of diverse ancestry. We performed genome-wide association studies on 859,790 SNPs genotyped in 527 affected offspring trios of European, African, and Hispanic ancestry using publically available asthma database in the Genotypes and Phenotypes database.
Rank-based analyses showed that there are shared genetic factors for asthma across populations, more at the gene and pathway levels than at the SNP level. Although the top 1,000 SNPs were not shared, 11 genes (RYR2, PDE4D, CSMD1, CDH13, ROBO2, RBFOX1, PTPRD, NPAS3, PDE1C, SEMA5A, and CTNNA2) mapped by these SNPs were shared across populations. Ryanodine receptor 2 (RYR2, a statin response-related gene) showed the strongest association in European (p value = 2.55 × 10−7) and was replicated in African (2.57 × 10−4) and Hispanic (1.18 × 10−3) Americans. Imputation analyses based on the 1000 Genomes Project uncovered additional RYR2 variants associated with asthma. Network and functional ontology analyses revealed that RYR2 is an integral part of dermatological or allergic disorder biological networks, specifically in the functional classes involving inflammatory, eosinophilic, and respiratory diseases.
Our rank-based genome-wide analysis revealed for the first time an association of RYR2 variants with asthma and replicated previously discovered PDE4D asthma gene across human populations. The replication of top-ranked asthma genes across populations suggests that such loci are less likely to be false positives and could indicate true associations. Variants that are associated with asthma across populations could be used to identify individuals who are at high risk for asthma regardless of genetic ancestry.
Asthma; GWAS; Ancestry; Trans-ancestral analysis; Rank analysis; Imputation; dbGaP; 1000 Genomes project; Networks/pathways, RYR2
The effect of fructose on cardiometabolic risk in humans is controversial. We conducted a systematic review and meta-analysis of controlled feeding trials to clarify the effect of fructose on glycemic control in individuals with diabetes.
RESEARCH DESIGN AND METHODS
We searched MEDLINE, EMBASE, and the Cochrane Library (through 22 March 2012) for relevant trials lasting ≥7 days. Data were aggregated by the generic inverse variance method (random-effects models) and expressed as mean difference (MD) for fasting glucose and insulin and standardized MD (SMD) with 95% CI for glycated hemoglobin (HbA1c) and glycated albumin. Heterogeneity was assessed by the Cochran Q statistic and quantified by the I2 statistic. Trial quality was assessed by the Heyland methodological quality score (MQS).
Eighteen trials (n = 209) met the eligibility criteria. Isocaloric exchange of fructose for carbohydrate reduced glycated blood proteins (SMD −0.25 [95% CI −0.46 to −0.04]; P = 0.02) with significant intertrial heterogeneity (I2 = 63%; P = 0.001). This reduction is equivalent to a ∼0.53% reduction in HbA1c. Fructose consumption did not significantly affect fasting glucose or insulin. A priori subgroup analyses showed no evidence of effect modification on any end point.
Isocaloric exchange of fructose for other carbohydrate improves long-term glycemic control, as assessed by glycated blood proteins, without affecting insulin in people with diabetes. Generalizability may be limited because most of the trials were <12 weeks and had relatively low MQS (<8). To confirm these findings, larger and longer fructose feeding trials assessing both possible glycemic benefit and adverse metabolic effects are required.
It is not known whether children with acute promyelocytic leukemia (APL) have an infection risk similar to non- APL acute myeloid leukemia. The objective was to describe infectious risk in children with newly diagnosed APL and to describe factors associated with these infections.
We conducted a retrospective, population-based cohort study that included children ≤ 18 years of age with de novo APL treated at 15 Canadian centers. Thirty-three children with APL were included; 78.8% were treated with APL -specific protocols.
Bacterial sterile site infection occurred in 12 (36.4%) and fungal sterile site infection occurred in 2 (6.1%) children. Of the 127 chemotherapy courses, 101 (79.5%) were classified as intensive and among these, the proportion in which a sterile site microbiologically documented infection occurred was 14/101 (13.9%). There was one infection-related death.
One third of children with APL experienced at least one sterile site bacterial infection throughout treatment and 14% of intensive chemotherapy courses were associated with a microbiologically documented sterile site infection. Infection rates in pediatric APL may be lower compared to non- APL acute myeloid leukemia although these children may still benefit from aggressive supportive care during intensive chemotherapy.
Infection; Acute promyelocytic leukemia; Bacteremia; Sepsis; Acute myeloid leukemia
Basal-like breast cancers (BLBC) express a luminal progenitor gene signature. Notch receptor signaling promotes luminal cell fate specification in the mammary gland, while suppressing stem cell self-renewal. Here we show that deletion of Lfng, a sugar transferase that prevents Notch activation by Jagged ligands, enhances stem/progenitor cell proliferation. Mammary-specific deletion of Lfng induces basal-like and claudin-low tumors with accumulation of Notch intracellular domain fragments, increased expression of proliferation-associated Notch targets, amplification of the Met/Caveolin locus, and elevated Met and Igf-1R signaling. Human BL breast tumors, commonly associated with JAGGED expression, elevated MET signaling, and CAVEOLIN accumulation, express low levels of LFNG. Thus, reduced LFNG expression facilitates JAG/NOTCH luminal progenitor signaling and cooperates with MET/CAVEOLIN basal-type signaling to promote BLBC.
The objective of this simulation study is to compare the accuracy and efficiency of population-averaged (i.e. generalized estimating equations (GEE)) and cluster-specific (i.e. random-effects logistic regression (RELR)) models for analyzing data from cluster randomized trials (CRTs) with missing binary responses.
In this simulation study, clustered responses were generated from a beta-binomial distribution. The number of clusters per trial arm, the number of subjects per cluster, intra-cluster correlation coefficient, and the percentage of missing data were allowed to vary. Under the assumption of covariate dependent missingness, missing outcomes were handled by complete case analysis, standard multiple imputation (MI) and within-cluster MI strategies. Data were analyzed using GEE and RELR. Performance of the methods was assessed using standardized bias, empirical standard error, root mean squared error (RMSE), and coverage probability.
GEE performs well on all four measures — provided the downward bias of the standard error (when the number of clusters per arm is small) is adjusted appropriately — under the following scenarios: complete case analysis for CRTs with a small amount of missing data; standard MI for CRTs with variance inflation factor (VIF) <3; within-cluster MI for CRTs with VIF≥3 and cluster size>50. RELR performs well only when a small amount of data was missing, and complete case analysis was applied.
GEE performs well as long as appropriate missing data strategies are adopted based on the design of CRTs and the percentage of missing data. In contrast, RELR does not perform well when either standard or within-cluster MI strategy is applied prior to the analysis.
Marginal model; Population-averaged model; Cluster-specific model; Multiple imputation; Cluster randomized trial; Covariate dependent missingness; Generalized estimating equations; Random-effects logistic regression
To perform a meta-analysis of gene expression microarray data from animal studies of lung injury, and to identify an injury-specific gene expression signature capable of predicting the development of lung injury in humans.
We performed a microarray meta-analysis using 77 microarray chips across six platforms, two species and different animal lung injury models exposed to lung injury with or/and without mechanical ventilation. Individual gene chips were classified and grouped based on the strategy used to induce lung injury. Effect size (change in gene expression) was calculated between non-injurious and injurious conditions comparing two main strategies to pool chips: (1) one-hit and (2) two-hit lung injury models. A random effects model was used to integrate individual effect sizes calculated from each experiment. Classification models were built using the gene expression signatures generated by the meta-analysis to predict the development of lung injury in human lung transplant recipients.
Two injury-specific lists of differentially expressed genes generated from our meta-analysis of lung injury models were validated using external data sets and prospective data from animal models of ventilator-induced lung injury (VILI). Pathway analysis of gene sets revealed that both new and previously implicated VILI-related pathways are enriched with differentially regulated genes. Classification model based on gene expression signatures identified in animal models of lung injury predicted development of primary graft failure (PGF) in lung transplant recipients with larger than 80% accuracy based upon injury profiles from transplant donors. We also found that better classifier performance can be achieved by using meta-analysis to identify differentially-expressed genes than using single study-based differential analysis.
Taken together, our data suggests that microarray analysis of gene expression data allows for the detection of “injury" gene predictors that can classify lung injury samples and identify patients at risk for clinically relevant lung injury complications.