Association of Inhaled Corticosteroids with Incident Pneumonia and Mortality in COPD Patients; Systematic Review and Meta-Analysis

<p><i>Background:</i> Inhaled corticosteroids are commonly prescribed for patients with severe COPD. They have been associated with increased risk of pneumonia but not with increased pneumonia-associated or overall mortality. <i>Methods:</i> To further examine the effects of inhaled corticosteroids on pneumonia incidence, and mortality in COPD patients, we searched for potentially relevant articles in PubMed, Medline, CENTRAL, EMBASE, Scopus, Web of Science and manufacturers' web clinical trial registries from 1994 to February 4, 2014. Additionally, we checked the included and excluded studies' bibliographies. We subsequently performed systematic review and meta-analysis of included randomized controlled trials and observational studies on the topic. <i>Results:</i> We identified 38 studies: 29 randomized controlled trials and nine observational studies. The estimated unadjusted risk of pneumonia was increased in randomized trials: RR 1.61; 95% CI 1.35–1.93, <i>p</i> < 0.001; as well as in observational studies: OR 1.89; 95% CI 1.39–2.58, <i>p</i> < 0·001. Six randomized trials and seven observational studies were useful in estimating unadjusted risk of pneumonia ­case-fatality: RR 0.91; 95% CI 0.52–1.59, <i>p</i> = 0.74; and OR 0.72; 95% CI 0.59–0.88, <i>p</i> = 0.001, respectively. Twenty-nine randomized trials and six observational studies allowed estimation of unadjusted risk of overall mortality: RR 0.95; 95% CI 0.85–1.05, <i>p</i> = 0.31; and OR 0.79; 95% CI 0.65–0.97, <i>p</i> = 0.02, respectively. <i>Conclusions:</i> Despite a substantial and significant increase in unadjusted risk of pneumonia associated with inhaled corticosteroid use, pneumonia fatality and overall mortality were found not to be increased in randomized controlled trials and were decreased in observational studies.</p

Association of Inhaled Corticosteroids with Incident Pneumonia and Mortality in COPD Patients; Systematic Review and Meta-Analysis

<div><p></p><p><i>Background:</i> Inhaled corticosteroids are commonly prescribed for patients with severe COPD. They have been associated with increased risk of pneumonia but not with increased pneumonia-associated or overall mortality. <i>Methods:</i> To further examine the effects of inhaled corticosteroids on pneumonia incidence, and mortality in COPD patients, we searched for potentially relevant articles in PubMed, Medline, CENTRAL, EMBASE, Scopus, Web of Science and manufacturers' web clinical trial registries from 1994 to February 4, 2014. Additionally, we checked the included and excluded studies' bibliographies. We subsequently performed systematic review and meta-analysis of included randomized controlled trials and observational studies on the topic. <i>Results:</i> We identified 38 studies: 29 randomized controlled trials and nine observational studies. The estimated unadjusted risk of pneumonia was increased in randomized trials: RR 1.61; 95% CI 1.35–1.93, <i>p</i> < 0.001; as well as in observational studies: OR 1.89; 95% CI 1.39–2.58, <i>p</i> < 0·001. Six randomized trials and seven observational studies were useful in estimating unadjusted risk of pneumonia ­case-fatality: RR 0.91; 95% CI 0.52–1.59, <i>p</i> = 0.74; and OR 0.72; 95% CI 0.59–0.88, <i>p</i> = 0.001, respectively. Twenty-nine randomized trials and six observational studies allowed estimation of unadjusted risk of overall mortality: RR 0.95; 95% CI 0.85–1.05, <i>p</i> = 0.31; and OR 0.79; 95% CI 0.65–0.97, <i>p</i> = 0.02, respectively. <i>Conclusions:</i> Despite a substantial and significant increase in unadjusted risk of pneumonia associated with inhaled corticosteroid use, pneumonia fatality and overall mortality were found not to be increased in randomized controlled trials and were decreased in observational studies.</p></div

DataSheet_1_Whole-exome sequencing in familial type 2 diabetes identifies an atypical missense variant in the RyR2 gene.pdf

Genome-wide association studies have identified several hundred loci associated with type 2 diabetes mellitus (T2DM). Additionally, pathogenic variants in several genes are known to cause monogenic diabetes that overlaps clinically with T2DM. Whole-exome sequencing of related individuals with T2DM is a powerful approach to identify novel high-penetrance disease variants in coding regions of the genome. We performed whole-exome sequencing on four related individuals with T2DM – including one individual diagnosed at the age of 33 years. The individuals were negative for mutations in monogenic diabetes genes, had a strong family history of T2DM, and presented with several characteristics of metabolic syndrome. A missense variant (p.N2291D) in the type 2 ryanodine receptor (RyR2) gene was one of eight rare coding variants shared by all individuals. The variant was absent in large population databases and affects a highly conserved amino acid located in a mutational hotspot for pathogenic variants in Catecholaminergic polymorphic ventricular tachycardia (CPVT). Electrocardiogram data did not reveal any cardiac abnormalities except a lower-than-normal resting heart rate (2+ release contributes to glucose-mediated insulin secretion and pathogenic RyR2 mutations cause glucose intolerance in humans and mice. Analysis of glucose tolerance testing data revealed that missense mutations in a CPVT mutation hotspot region – overlapping the p.N2291D variant – are associated with complete penetrance for glucose intolerance. In conclusion, we have identified an atypical missense variant in the RyR2 gene that co-segregates with diabetes in the absence of overt CPVT.</p

Targeted resequencing-based CNV calls in TOF patients.

<p>Targeted resequencing-based CNV calls in TOF patients.</p

Number and quality of 36-end reads obtained from targeted resequencing in TOF patients using Illumina’s Genome Analyzer IIx platform.

<p>Number and quality of 36-end reads obtained from targeted resequencing in TOF patients using Illumina’s Genome Analyzer IIx platform.</p

CNVs in TOF patients.

<p>(A) CNVs detected in <i>PRODH</i> by CoNIFER and our outlier-based CNV calling method. The duplications are depicted in the UCSC Genome Browser as blue bars. The positions of the two quantitative real-time PCR products selected for validation are shown as light and dark grey bars, respectively. (B) Quantitative real-time PCR validation of <i>PRODH</i> copy number gains. Measurement was performed at two different positions (light and dark grey bars, respectively) and normalized to the <i>RPPH1</i> gene. The HapMap individual NA10851 was used as a reference. The plot shows a representative of two independent measurements, which were each performed in triplicates. (C–D) Validation of copy number gains in <i>ISL1</i> and <i>NOTCH1</i>, respectively, that were only identified by our outlier-based CNV calling method.</p

Base qualities versus coverage values.

<p>Scatterplot indicates the average base qualities (Phred scores) and depths of coverage for samples targeted resequenced by Illumina’s Genome Analyzer IIx platform (36 bp paired-end reads).</p

Outlier-based CNV calling method.

<p>(A) Read mapping and calculation of copy number value per window. Reads are mapped to extended targeted regions, which are then joined chromosome-wise. mrCaNaVaR is used to split the joined regions into windows. For each window, its copy number value is calculated by mrCaNaVaR, where represents the value for window W in sample S. (B) Dixon’s Q test is applied for each window over all samples to identify outliers. Here, sample 1 represents an outlier (loss, L) for the first, second, third and fifth window, while sample 2 represents an outlier (gain, G) for the fourth window. (C) Assessment of outliers using a Hidden Markov Model (HMM). In the given example, the fourth window of sample 1 is considered as normal (N). After applying the HMM, it will also be considered as a loss. Similarly, the fourth window of sample 2 is considered as normal after applying the HMM. A region is called as a copy number alteration, if at least five continuous windows show the same kind of change, i.e. either gain or loss.</p

Additional file 1: Table S1. of Spectrum of mutations in monogenic diabetes genes identified from high-throughput DNA sequencing of 6888 individuals

List of 22 genes associated with monogenic forms of diabetes that were analyzed in this paper. Table S2. Criteria used to select genes for targeted sequencing. Table S3. Summary of samples sequenced in Stages 1, 2, and 3, and the coding variants identified in each stage. Table S4. Clinical data of the cases and controls for type 2 diabetes sequenced in this study. Table S5. List of all protein truncating mutations identified in the 22 monogenic diabetes genes. Table S6. Rare missense mutations in the HNF1A, HNF4A, HNF1B, ABCC8, and KCNJ11 genes predicted to be deleterious by PolyPhen2, SIFT, and MutationTaster. Table S7. Number of individuals with protein truncating variants and previously reported pathogenic missense variants in MODY genes. Table S8. List of exons with low sequence coverage in data from Stage 1 and 2 pools. Figure S1. Minor allele frequency distribution of variants identified from sequencing of pools in Stages 1 and 2. Figure S2. Pooled sequencing design of the study. Figure S3. Comparison of sequence coverage between cases and controls. (PDF 700 kb

Additional file 1: Table S1. of Spectrum of mutations in monogenic diabetes genes identified from high-throughput DNA sequencing of 6888 individuals

List of 22 genes associated with monogenic forms of diabetes that were analyzed in this paper. Table S2. Criteria used to select genes for targeted sequencing. Table S3. Summary of samples sequenced in Stages 1, 2, and 3, and the coding variants identified in each stage. Table S4. Clinical data of the cases and controls for type 2 diabetes sequenced in this study. Table S5. List of all protein truncating mutations identified in the 22 monogenic diabetes genes. Table S6. Rare missense mutations in the HNF1A, HNF4A, HNF1B, ABCC8, and KCNJ11 genes predicted to be deleterious by PolyPhen2, SIFT, and MutationTaster. Table S7. Number of individuals with protein truncating variants and previously reported pathogenic missense variants in MODY genes. Table S8. List of exons with low sequence coverage in data from Stage 1 and 2 pools. Figure S1. Minor allele frequency distribution of variants identified from sequencing of pools in Stages 1 and 2. Figure S2. Pooled sequencing design of the study. Figure S3. Comparison of sequence coverage between cases and controls. (PDF 700 kb