The aim of this study was to compare and externally validate risk scores developed to predict incident colorectal cancer (CRC) that include common genetic variants (single nucleotide polymorphisms, SNPs), with or without established lifestyle/environmental (questionnairebased/classical/phenotypic) risk factors. We externally validated 23 risk models from a previous systematic review in 443,888 participants aged 37-73 from the UK Biobank cohort who had 6 year prospective follow-up, no prior history of CRC and data for incidence of CRC through linkage to national cancer registries. There were 2,679 (0.6%) cases of incident CRC. We assessed model discrimination using the Area Under the Curve (AUC) and relative risk calibration. The AUC of models including only SNPs increased with the number of included SNPs and was similar in men and women: the model by Huyghe with 120 SNPs had the highest AUC of 0.62(95%CI 0.59-0.64) in women and 0.64(95%CI 0.61-0.66) in men.Adding phenotypic risk factors without age improved discrimination in men but not in women. Adding phenotypic risk factors and age increased discrimination in all cases (p<0.05), with the best performing models including SNPs, phenotypic risk factors and age having AUCs between 0.64-0.67 in women and 0.67-0.71 in men. Relative risk calibration varied substantially across the models. Among middle-aged people in the UK, existing polygenic risk scores discriminate moderately well between those who do and do not develop colorectal cancer over six years. Consideration should be given to exploring the feasibility of incorporating genetic and lifestyle/environmental information in any future stratified CRC screening program.

Colorectal cancer screening reduces colorectal cancer incidence and mortality. Risk models based on phenotypic variables have relatively good discrimination in external validation and may improve efficiency of screening. Models incorporating genetic variables may perform better. In this review, we updated our previous review by searching Medline and EMBASE from the end date of that review (January 2014) to February 2019 to identify models incorporating at least one SNP and applicable to asymptomatic individuals in the general population. We identified 23 new models, giving a total of 29. Of those in which the SNP selection was on the basis of published genome-wide association studies, in external or split-sample validation the AUROC was 0.56 to 0.57 for models that included SNPs alone, 0.61 to 0.63 for SNPs in combination with other risk factors, and 0.56 to 0.70 when age was included. Calibration was only reported for four. The addition of SNPs to other risk factors increases discrimination by 0.01 to 0.06. Public health modeling studies suggest that, if determined by risk models, the range of starting ages for screening would be several years greater than using family history alone. Further validation and calibration studies are needed alongside modeling studies to assess the population-level impact of introducing genetic risk-based screening programs.

Respiratory tract infections (RTIs) are extremely common and can cause gastrointestinal tract symptoms and changes to the gut microbiota, yet these effects are poorly understood. We conducted a systematic review to evaluate the reported evidence of gut microbiome alterations in patients with a RTI compared to healthy controls (PROSPERO: CRD42019138853). We systematically searched Medline, Embase, Web of Science, Cochrane and the Clinical Trial Database for studies published between January 2015 and June 2021. Studies were eligible for inclusion if they were human cohorts describing the gut microbiome in patients with an RTI compared to healthy controls and the infection was caused by a viral or bacterial pathogen. Dual data screening and extraction with narrative synthesis was performed. We identified 1,593 articles and assessed 11 full texts for inclusion. Included studies (some nested) reported gut microbiome changes in the context of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) (n = 5), influenza (H1N1 and H7N9) (n = 2), Tuberculosis (TB) (n = 4), Community-Acquired Pneumonia CAP (n = 2) and recurrent RTIs (rRTI) (n = 1) infections. We found studies of patients with an RTI compared to controls reported a decrease in gut microbiome diversity (Shannon) of 1.45 units (95% CI, 0.15–2.50 [p, <0.0001]) and a lower abundance of taxa (p, 0.0086). Meta-analysis of the Shannon value showed considerable heterogeneity between studies (I2, 94.42). Unbiased analysis displayed as a funnel plot revealed a depletion of Lachnospiraceae, Ruminococcaceae and Ruminococcus and enrichment of Enterococcus. There was an important absence in the lack of cohort studies reporting gut microbiome changes and high heterogeneity between studies may be explained by variations in microbiome methods and confounder effects. Further human cohort studies are needed to understand RTI-induced gut microbiome changes to better understand interplay between microbes and respiratory health.

Congenital heart defects remain the most common congenital malformation in live births and are the leading cause of infant mortality in the developed world. Major developments in the management over the last decade have led to significant improvements in survival of infants with congenital heart defects. Early diagnosis and timely appropriate management of critical and serious CHDs is essential to improve outcome. Feat anomaly screening is being used detect the critical and significant congenital heart defects prenatally between 18+0 weeks and 20+6 weeks of pregnancy. This helps professional and parents in critical decision making regarding the pregnancy, planning for delivery and preparing the parents for the anticipated problems. Prenatal diagnosis of critical congenital heart conditions has shown to improve the outcome in infants with critical congenital heart defects.

BackgroundAn increased risk of intussusception has been reported following rotavirus vaccination. We sought to determine whether introduction of rotavirus vaccination in England in July 2013 was associated with a change in the burden of total and age group-specific childhood hospital admissions for intussusception. MethodsWe identified all children aged 0-36 months admitted to hospitals in England with intussusception using the Hospital Episode Statistics dataset. We performed a retrospective ecological analysis comparing hospital admission rates for intussusception during the