Crohn Disease (CD) is a complex genetic disorder for which more than 140 genes have been identified using genome wide association studies (GWAS). However, the genetic architecture of the trait remains largely unknown. The recent development of machine learning (ML) approaches incited us to apply them to classify healthy and diseased people according to their genomic information. The Immunochip dataset containing 18,227 CD patients and 34,050 healthy controls enrolled and genotyped by the international Inflammatory Bowel Disease genetic consortium (IIBDGC) has been re-analyzed using a set of ML methods: penalized logistic regression (LR), gradient boosted trees (GBT) and artificial neural networks (NN). The main score used to compare the methods was the Area Under the ROC Curve (AUC) statistics. The impact of quality control (QC), imputing and coding methods on LR results showed that QC methods and imputation of missing genotypes may artificially increase the scores. At the opposite, neither the patient/control ratio nor marker preselection or coding strategies significantly affected the results. LR methods, including Lasso, Ridge and ElasticNet provided similar results with a maximum AUC of 0.80. GBT methods like XGBoost, LightGBM and CatBoost, together with dense NN with one or more hidden layers, provided similar AUC values, suggesting limited epistatic effects in the genetic architecture of the trait. ML methods detected near all the genetic variants previously identified by GWAS among the best predictors plus additional predictors with lower effects. The robustness and complementarity of the different methods are also studied. Compared to LR, non-linear models such as GBT or NN may provide robust complementary approaches to identify and classify genetic markers.

Progression to B2/B3 disease and surgery is reduced by smoking cessation. All CD patients regardless of when they were diagnosed, or how many surgeries, should be strongly encouraged to cease smoking.

BackgroundPredicting risk of disease from genotypes is being increasingly proposed for a variety of diagnostic and prognostic purposes. Genome-wide association studies (GWAS) have identified a large number of genome-wide significant susceptibility loci for Crohn’s disease (CD) and ulcerative colitis (UC), two subtypes of inflammatory bowel disease (IBD). Recent studies have demonstrated that including only loci that are significantly associated with disease in the prediction model has low predictive power and that power can substantially be improved using a polygenic approach.MethodsWe performed a comprehensive analysis of risk prediction models using large case-control cohorts genotyped for 909,763 GWAS SNPs or 123,437 SNPs on the custom designed Immunochip using four prediction methods (polygenic score, best linear genomic prediction, elastic-net regularization and a Bayesian mixture model). We used the area under the curve (AUC) to assess prediction performance for discovery populations with different sample sizes and number of SNPs within cross-validation.ResultsOn average, the Bayesian mixture approach had the best prediction performance. Using cross-validation we found little differences in prediction performance between GWAS and Immunochip, despite the GWAS array providing a 10 times larger effective genome-wide coverage. The prediction performance using Immunochip is largely due to the power of the initial GWAS for its marker selection and its low cost that enabled larger sample sizes. The predictive ability of the genomic risk score based on Immunochip was replicated in external data, with AUC of 0.75 for CD and 0.70 for UC. CD patients with higher risk scores demonstrated clinical characteristics typically associated with a more severe disease course including ileal location and earlier age at diagnosis.ConclusionsOur analyses demonstrate that the power of genomic risk prediction for IBD is mainly due to strongly associated SNPs with considerable effect sizes. Additional SNPs that are only tagged by high-density GWAS arrays and low or rare-variants over-represented in the high-density region on the Immunochip contribute little to prediction accuracy. Although a quantitative assessment of IBD risk for an individual is not currently possible, we show sufficient power of genomic risk scores to stratify IBD risk among individuals at diagnosis.Electronic supplementary materialThe online version of this article (doi:10.1186/s12881-017-0451-2) contains supplementary material, which is available to authorized users.

In this large, real-life study, we demonstrate infliximab and adalimumab to have similar response characteristics. However, infliximab requires concomitant immunomodulator to achieve optimal maintenance of response comparable to adalimumab monotherapy. The results of this study will assist clinicians in further optimising patient care in their day-to-day clinical practice.

Gram-negative resistance is a serious global crisis putting the world on the cusp of 'pre-antibiotic era'. This serious crisis has been catalysed by the rapid increase in carbapenem-resistant Enterobacteriaceae (CRE). Spurge in colistin usage to combat CRE infections leads to the reports of (colistin and carbapenem resistant enterobacteriaceae) CCRE (resistance to colistin in isolates of CRE) infections further jeopardising our last defence. The antibacterial apocalypse imposed by global resistance crisis requires urgent alternative therapeutic options. Interest in the use of fosfomycin renewed recently for serious systemic infections caused by multidrug-resistant Enterobacteriaceae. This review aimed at analysing the recent evidence on intravenous fosfomycin to explore its hidden potential, especially when fosfomycin disodium is going to be available in India. Although a number of promising evidence are coming up for fosfomycin, there are still areas where more work is required to establish intravenous fosfomycin as the last resort antibacterial for severe Gram-negative infections.