Diagnosis and outcome of oesophageal Crohn's disease

BACKGROUND AND AIMS: Crohn's disease (CD) can involve any part of the gastrointestinal tract. We aimed to characterize clinical, endoscopic, histologic features and treatment outcomes of CD patients with oesophageal involvement. METHODS: We collected cases through a retrospective multicentre European Crohn's and Colitis Organisation CONFER [COllaborative Network For Exceptionally Rare case reports] project. Clinical data were recorded in a standardized case report form. RESULTS: A total of 40 patients were reported [22 males, mean (±SD, range) age at oesophageal CD diagnosis: 25 (±13.3, 10-71) years and mean time of follow-up: 67 (±68.1, 3-240) months]. Oesophageal involvement was established at CD diagnosis in 26 patients (65%) and during follow-up in 14. CD was exclusively located in the oesophagus in 2 patients. Thirteen patients (32.2%) were asymptomatic at oesophageal disease diagnosis. Oesophageal strictures were present in 5 patients and fistulizing oesophageal disease in one. Eight patients exhibited granulomas on biopsies. Proton-pump inhibitors (PPIs) were administered in 37 patients (92.5%). Three patients underwent endoscopic dilation for symptomatic strictures and none oesophageal-related surgery. Diagnosis in pre-established CD resulted in treatment modifications in 9/14 patients. Clinical remission of oesophageal disease was seen in 33/40 patients (82.5%) after a mean time of 7 (±5.6, 1-18) months. Follow-up endoscopy was performed in 29/40 patients and 26/29 (89.7%) achieved mucosal healing. CONCLUSION: In this case series the endoscopic and histologic characteristics of isolated oesophageal CD were similar to those reported in other sites of involvement. Treatment was primarily conservative, with PPIs administered in the majority of patients and modifications in pre-existing IBD-related therapy occurring in two thirds of them. Clinical and endoscopic remission was achieved in more than 80% of the patients.info:eu-repo/semantics/publishedVersio

Influences on Care Preferences of Older People with Advanced Illness: A Systematic Review and Thematic Synthesis.

OBJECTIVES: To determine and explore the influences on care preferences of older people with advanced illness and integrate our results into a model to guide practice and research. DESIGN: Systematic review using Medline, Embase, PsychINFO, Web of Science, and OpenGrey databases from inception to February 2017 and reference and citation list searching. Included articles investigated influences on care preference using qualitative or quantitative methodology. Thematic synthesis of qualitative articles and narrative synthesis of quantitative articles were undertaken. SETTING: Hospital and community care settings. PARTICIPANTS: Older adults with advanced illness, including people with specific illnesses and markers of advanced disease, populations identified as in the last year of life, or individuals receiving palliative care (N = 15,164). MEASUREMENTS: The QualSys criteria were used to assess study quality. RESULTS: Of 12,142 search results, 57 articles were included. Family and care context, illness, and individual factors interact to influence care preferences. Support from and burden on family and loved ones were prominent influences on care preferences. Mechanisms by which preferences are influenced include the process of trading-off between competing priorities, making choices based on expected outcome, level of engagement, and individual ability to form and express preferences. CONCLUSION: Family is particularly important as an influence on care preferences, which are influenced by complex interaction of family, individual, and illness factors. To support preferences, clinicians should consider older people with illnesses and their families together as a unit of care.Cicely Saunders International Atlantic Philanthropies. Grant Number: 24610 Collaboration for Leadership in Applied Health Research and Care, South London National Institute for Health Research (NIHR) King's Health Partners St. George's University London St George's Healthcare National Health Service (NHS) Trus

Measurement properties of the Inventory of Cognitive Bias in Medicine (ICBM)

© 2008 Sladek et al; licensee BioMed Central Ltd. This 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.Background Understanding how doctors think may inform both undergraduate and postgraduate medical education. Developing such an understanding requires valid and reliable measurement tools. We examined the measurement properties of the Inventory of Cognitive Bias in Medicine (ICBM), designed to tap this domain with specific reference to medicine, but with previously questionable measurement properties. Methods First year postgraduate entry medical students at Flinders University, and trainees (postgraduate doctors in any specialty) and consultants (N = 348) based at two teaching hospitals in Adelaide, Australia, completed the ICBM and a questionnaire measuring thinking styles (Rational Experiential Inventory). Results Questions with the lowest item-total correlation were deleted from the original 22 item ICBM, although the resultant 17 item scale only marginally improved internal consistency (Cronbach's α = 0.61 compared with 0.57). A factor analysis identified two scales, both achieving only α = 0.58. Construct validity was assessed by correlating Rational Experiential Inventory scores with the ICBM, with some positive correlations noted for students only, suggesting that those who are naïve to the knowledge base required to "successfully" respond to the ICBM may profit by a thinking style in tune with logical reasoning. Conclusion The ICBM failed to demonstrate adequate content validity, internal consistency and construct validity. It is unlikely that improvements can be achieved without considered attention to both the audience for which it is designed and its item content. The latter may need to involve both removal of some items deemed to measure multiple biases and the addition of new items in the attempt to survey the range of biases that may compromise medical decision making

Interrogating Type 2 Diabetes Genome-Wide Association Data Using a Biological Pathway-Based Approach

OBJECTIVE-Recent genome-wide association Studies have resulted in a dramatic increase in our knowledge of the genetic loci involved in type 2 diabetes. In a complementary approach to these single-marker studies, we attempted to identify biological pathways associated with type 2 diabetes. This approach could allow its to identify additional risk loci. RESEARCH DESIGN AND METHODS-We used individual level genotype data generated from the Wellcome Trust Case Control Consortium (WTCCC) type 2 diabetes study, consisting of 393,143 autosomal SNPs, genotyped across 1,924 case subjects and 2,938 control subjects. We sought additional evidence from summary level data available from the Diabetes Genetics Initiative (DGI) and the Finland-United States Investigation of NIDDM Genetics (FUSION) studies. Statistical analysis of pathways was performed using a modification of the Gene Set Enrichment Algorithm (GSEA). A total of 439 pathways were analyzed from the Kyoto Encyclopedia of Genes and Genomes, Gene Ontology, and BioCarta databases. RESULTS-After correcting for the number of pathways tested, we found no strong evidence for any pathway showing association with type 2 diabetes (top P-adj = 0.31). The candidate WNT-signaling pathway ranked top (nominal P = 0.0007, excluding TCF7L2; P = 0.002), containing a number of promising single gene associations. These include CCND2 (rs11833537; P = 0.003), SMAD3 (rs7178347; P = 0.0006), and PRICKLE1 (rs1796390; P = 0.001), all expressed in the pancreas. CONCLUSIONS-Common variants involved in type 2 diabetes risk are likely to occur in or near genes in multiple pathways. Pathway-based approaches to genome-wide association data may be more Successful for some complex traits than others, depending on the nature of the underlying disease physiology. Diabetes 58:1463-1467, 200

Type 2 Diabetes Risk Alleles Are Associated With Reduced Size at Birth

OBJECTIVE: Low birth weight is associated with an increased risk of type 2 diabetes. The mechanisms underlying this association are unknown and may represent intrauterine programming or two phenotypes of one genotype. The fetal insulin hypothesis proposes that common genetic variants that reduce insulin secretion or action may predispose to type 2 diabetes and also reduce birth weight, since insulin is a key fetal growth factor. We tested whether common genetic variants that predispose to type 2 diabetes also reduce birth weight. RESEARCH DESIGN AND METHODS: We genotyped single-nucleotide polymorphisms (SNPs) at five recently identified type 2 diabetes loci (CDKAL1, CDKN2A/B, HHEX-IDE, IGF2BP2, and SLC30A8) in 7,986 mothers and 19,200 offspring from four studies of white Europeans. We tested the association between maternal or fetal genotype at each locus and birth weight of the offspring. RESULTS: We found that type 2 diabetes risk alleles at the CDKAL1 and HHEX-IDE loci were associated with reduced birth weight when inherited by the fetus (21 g [95% CI 11-31], P = 2 x 10(-5), and 14 g [4-23], P = 0.004, lower birth weight per risk allele, respectively). The 4% of offspring carrying four risk alleles at these two loci were 80 g (95% CI 39-120) lighter at birth than the 8% carrying none (P(trend) = 5 x 10(-7)). There were no associations between birth weight and fetal genotypes at the three other loci or maternal genotypes at any locus. CONCLUSIONS: Our results are in keeping with the fetal insulin hypothesis and provide robust evidence that common disease-associated variants can alter size at birth directly through the fetal genotype

Characterizing Protein-Protein Interactions with the Fragment Molecular Orbital Method

Proteins are vital components of living systems, serving as building blocks, molecular machines, enzymes, receptors, ion channels, sensors, and transporters. Protein-protein interactions (PPIs) are a key part of their function. There are more than 645,000 reported disease-relevant PPIs in the human interactome, but drugs have been developed for only 2% of these targets. The advances in PPI-focused drug discovery are highly dependent on the availability of structural data and accurate computational tools for analysis of this data. Quantum mechanical approaches are often too expensive computationally, but the fragment molecular orbital (FMO) method offers an excellent solution that combines accuracy, speed and the ability to reveal key interactions that would otherwise be hard to detect. FMO provides essential information for PPI drug discovery, namely, identification of key interactions formed between residues of two proteins, including their strength (in kcal/mol) and their chemical nature (electrostatic or hydrophobic). In this chapter, we have demonstrated how three different FMO-based approaches (pair interaction energy analysis (PIE analysis), subsystem analysis (SA) and analysis of protein residue networks (PRNs)) have been applied to study PPI in three protein-protein complexes

PPARG, KCNJ11, CDKAL1, CDKN2A-CDKN2B, IDE-KIF11-HHEX, IGF2BP2 and SLC30A8 Are Associated with Type 2 Diabetes in a Chinese Population

Recent advance in genetic studies added the confirmed susceptible loci for type 2 diabetes to eighteen. In this study, we attempt to analyze the independent and joint effect of variants from these loci on type 2 diabetes and clinical phenotypes related to glucose metabolism.Twenty-one single nucleotide polymorphisms (SNPs) from fourteen loci were successfully genotyped in 1,849 subjects with type 2 diabetes and 1,785 subjects with normal glucose regulation. We analyzed the allele and genotype distribution between the cases and controls of these SNPs as well as the joint effects of the susceptible loci on type 2 diabetes risk. The associations between SNPs and type 2 diabetes were examined by logistic regression. The associations between SNPs and quantitative traits were examined by linear regression. The discriminative accuracy of the prediction models was assessed by area under the receiver operating characteristic curves. We confirmed the effects of SNPs from PPARG, KCNJ11, CDKAL1, CDKN2A-CDKN2B, IDE-KIF11-HHEX, IGF2BP2 and SLC30A8 on risk for type 2 diabetes, with odds ratios ranging from 1.114 to 1.406 (P value range from 0.0335 to 1.37E-12). But no significant association was detected between SNPs from WFS1, FTO, JAZF1, TSPAN8-LGR5, THADA, ADAMTS9, NOTCH2-ADAM30 and type 2 diabetes. Analyses on the quantitative traits in the control subjects showed that THADA SNP rs7578597 was association with 2-h insulin during oral glucose tolerance tests (P = 0.0005, empirical P = 0.0090). The joint effect analysis of SNPs from eleven loci showed the individual carrying more risk alleles had a significantly higher risk for type 2 diabetes. And the type 2 diabetes patients with more risk allele tended to have earlier diagnostic ages (P = 0.0006).The current study confirmed the association between PPARG, KCNJ11, CDKAL1, CDKN2A-CDKN2B, IDE-KIF11-HHEX, IGF2BP2 and SLC30A8 and type 2 diabetes. These type 2 diabetes risk loci contributed to the disease additively

Nuclear Receptor HNF4α Binding Sequences are Widespread in Alu Repeats

<p>Abstract</p> <p>Background</p> <p>Alu repeats, which account for ~10% of the human genome, were originally considered to be junk DNA. Recent studies, however, suggest that they may contain transcription factor binding sites and hence possibly play a role in regulating gene expression.</p> <p>Results</p> <p>Here, we show that binding sites for a highly conserved member of the nuclear receptor superfamily of ligand-dependent transcription factors, hepatocyte nuclear factor 4alpha (HNF4α, NR2A1), are highly prevalent in Alu repeats. We employ high throughput protein binding microarrays (PBMs) to show that HNF4α binds > 66 unique sequences in Alu repeats that are present in ~1.2 million locations in the human genome. We use chromatin immunoprecipitation (ChIP) to demonstrate that HNF4α binds Alu elements in the promoters of target genes (<it>ABCC3, APOA4, APOM, ATPIF1, CANX, FEMT1A, GSTM4, IL32, IP6K2, PRLR, PRODH2, SOCS2, TTR</it>) and luciferase assays to show that at least some of those Alu elements can modulate HNF4α-mediated transactivation <it>in vivo </it>(<it>APOM, PRODH2, TTR, APOA4</it>). HNF4α-Alu elements are enriched in promoters of genes involved in RNA processing and a sizeable fraction are in regions of accessible chromatin. Comparative genomics analysis suggests that there may have been a gain in HNF4α binding sites in Alu elements during evolution and that non Alu repeats, such as Tiggers, also contain HNF4α sites.</p> <p>Conclusions</p> <p>Our findings suggest that HNF4α, in addition to regulating gene expression via high affinity binding sites, may also modulate transcription via low affinity sites in Alu repeats.</p

Evaluating the association of common APOA2 variants with type 2 diabetes

<p>Abstract</p> <p>Background</p> <p><it>APOA2 </it>is a positional and biological candidate gene for type 2 diabetes at the chromosome 1q21-q24 susceptibility locus. The aim of this study was to examine if HapMap phase II tag SNPs in <it>APOA2 </it>are associated with type 2 diabetes and quantitative traits in French Caucasian subjects.</p> <p>Methods</p> <p>We genotyped the three HapMap phase II tagging SNPs (rs6413453, rs5085 and rs5082) required to capture the common variation spanning the <it>APOA2 </it>locus in our type 2 diabetes case-control cohort comprising 3,093 French Caucasian subjects. The association between these variants and quantitative traits was also examined in the normoglycaemic adults of the control cohort. In addition, meta-analysis of publicly available whole genome association data was performed.</p> <p>Results</p> <p>None of the <it>APOA2 </it>tag SNPs were associated with type 2 diabetes in the French Caucasian case-control cohort (rs6413453, <it>P </it>= 0.619; rs5085, <it>P </it>= 0.245; rs5082, <it>P </it>= 0.591). However, rs5082 was marginally associated with total cholesterol levels (<it>P </it>= 0.026) and waist-to-hip ratio (<it>P </it>= 0.029). The meta-analysis of data from 12,387 subjects confirmed our finding that common variation at the <it>APOA2 </it>locus is not associated with type 2 diabetes.</p> <p>Conclusion</p> <p>The available data does not support a role for common variants in <it>APOA2 </it>on type 2 diabetes susceptibility or related quantitative traits in Northern Europeans.</p

Common Variants at 10 Genomic Loci Influence Hemoglobin A(1C) Levels via Glycemic and Nonglycemic Pathways

OBJECTIVE-Glycated hemoglobin (HbA(1c)), used to monitor and diagnose diabetes, is influenced by average glycemia over a 2- to 3-month period. Genetic factors affecting expression, turnover, and abnormal glycation of hemoglobin could also be associated with increased levels of HbA(1c). We aimed to identify such genetic factors and investigate the extent to which they influence diabetes classification based on HbA(1c) levels.RESEARCH DESIGN AND METHODS-We studied associations with HbA(1c) in up to 46,368 nondiabetic adults of European descent from 23 genome-wide association studies (GWAS) and 8 cohorts with de novo genotyped single nucleotide polymorphisms (SNPs). We combined studies using inverse-variance meta-analysis and tested mediation by glycemia using conditional analyses. We estimated the global effect of HbA(1c) loci using a multilocus risk score, and used net reclassification to estimate genetic effects on diabetes screening.RESULTS-Ten loci reached genome-wide significant association with HbA(1c), including six new loci near FN3K (lead SNP/P value, rs1046896/P = 1.6 x 10(-26)), HFE (rs1800562/P = 2.6 x 10(-20)), TMPRSS6 (rs855791/P = 2.7 x 10(-14)), ANK1 (rs4737009/P = 6.1 x 10(-12)), SPTA1 (rs2779116/P = 2.8 x 10(-9)) and ATP11A/TUBGCP3 (rs7998202/P = 5.2 x 10(-9)), and four known HbA(1c) loci: HK1 (rs16926246/P = 3.1 x 10(-54)), MTNR1B (rs1387153/P = 4.0 X 10(-11)), GCK (rs1799884/P = 1.5 x 10(-20)) and G6PC2/ABCB11 (rs552976/P = 8.2 x 10(-18)). We show that associations with HbA(1c) are partly a function of hyperglycemia associated with 3 of the 10 loci (GCK, G6PC2 and MTNR1B). The seven nonglycemic loci accounted for a 0.19 (%HbA(1c)) difference between the extreme 10% tails of the risk score, and would reclassify similar to 2% of a general white population screened for diabetes with HbA(1c).CONCLUSIONS-GWAS identified 10 genetic loci reproducibly associated with HbA(1c). Six are novel and seven map to loci where rarer variants cause hereditary anemias and iron storage disorders. Common variants at these loci likely influence HbA(1c) levels via erythrocyte biology, and confer a small but detectable reclassification of diabetes diagnosis by HbA(1c) Diabetes 59: 3229-3239, 201