Exposure to Domestic Abuse and the Subsequent Development of Atopic Disease in Women.

BACKGROUND: Exposure to domestic violence and abuse (DVA) is a global public health issue associated with substantial morbidity and mortality. There are few high-quality studies that assess the impact of DVA exposure on the development of atopic disease. OBJECTIVE: To examine the association between exposure to DVA and the subsequent development of atopy. METHODS: In this population-based, retrospective, open cohort study, we identified women with no history of atopic disease between January 1, 1995 and September 30, 2019 from IQVIA Medical Research Data, an anonymized UK primary care dataset. We used clinical codes to identify exposed patients (those with a code identifying exposure to DVA; n = 13,852) and unexposed patients (n = 49,036), who were matched by age and deprivation quintile. Cox proportional hazards regression was used to calculate hazard ratios (HRs) (with 95% CIs) of developing atopic disease: asthma, atopic eczema, or allergic rhinoconjunctivitis. RESULTS: During the study period, 967 exposed women (incidence rate, 20.10/1,000 person-years) developed atopic disease, compared with 2,607 unexposed women (incidence rate, 13.24/1,000 person-years). This translated to an adjusted HR of 1.52 (95% CI, 1.41-1.64) accounting for key confounders; asthma (adjusted HR = 1.69; 95% CI, 1.44-1.99), atopic eczema (adjusted HR = 1.40; 95% CI, 1.26-1.56), and allergic rhinoconjunctivitis (adjusted HR = 1.63; 95% CI, 1.45-1.84). CONCLUSIONS: Domestic violence and abuse is a significant global public health issue. These results demonstrate a significant associated risk for developing atopic disease. Public health approaches to the prevention and detection of DVA are necessary to reduce the associated ill health burden.

Impact of national COVID-19 restrictions on incidence of notifiable communicable diseases in England: an interrupted time series analysis.

BACKGROUND: Non-pharmaceutical interventions (NPIs), such as travel restrictions, social distancing and isolation policies, aimed at controlling the spread of COVID-19 may have reduced transmission of other endemic communicable diseases, such as measles, mumps and meningitis in England. METHODS: An interrupted time series analysis was conducted to examine whether NPIs was associated with trends in endemic communicable diseases, using weekly reported cases of seven notifiable communicable diseases (food poisoning, measles, meningitis, mumps, scarlet fever and pertussis) between 02/01/2017 to 02/01/2021 for England. RESULTS: Following the introduction of COVID-19 restrictions, there was an 81.1% (95% CI; 77.2-84.4) adjusted percentage reduction in the total number of notifiable diseases recorded per week in England. The greatest decrease was observed for measles, with a 90.5% percentage reduction (95% CI; 86.8-93.1) from 42 to 5 cases per week. The smallest decrease was observed for food poisoning, with a 56.4% (95%CI; 42.5-54.2) decrease from 191 to 83 cases per week. CONCLUSIONS: A total reduction in the incidence of endemic notifiable diseases was observed in England following the implementation of public health measures aimed at reducing transmission of SARS-COV-2 on March 23, 2020. The greatest reductions were observed in diseases most frequently observed during childhood that are transmitted via close human-to-human contact, such as measles and pertussis. A less substantive reduction was observed in reported cases of food poisoning, likely due to dining services (i.e., home deliveries and takeaways) remaining open and providing a potential route of transmission. This study provides further evidence of the effectiveness of non-pharmaceutical public health interventions in reducing the transmission of both respiratory and food-borne communicable diseases.

Association between childhood maltreatment and atopy in the UK: A population based retrospective cohort study.

Background: Childhood maltreatment affects over one in three children worldwide and is associated with a substantial disease burden. This study explores the association between childhood maltreatment and the development of atopic disease. Methods: We did a population-based retrospective matched open cohort study using participating general practices between 1st January 1995 and 30th September 2019. Read codes were utilised to identify patients exposed to childhood maltreatment (either suspected or confirmed) who were matched to up to four unexposed patients by age, sex, general practice, and Townsend deprivation quintile. Cox regression analysis was used to calculate adjusted (age, sex, Townsend deprivation quintile) hazard ratios (aHR) for development of atopy (asthma, atopic dermatitis, or allergic rhino conjunctivitis) during follow up in those without atopy at study entry. Results: 183,897 exposed patients were matched to 621,699 unexposed patients. During the follow up period, 18,555 patients (incidence rate (IR) 28.18 per 1000 person-years) in the exposed group developed atopic disease compared to the 68,368 (IR 23.58 per 1000 person-years) in the unexposed group, translating to an adjusted HR of 1.14 (95% CI 1.12-1.15). Notably, the risk of developing asthma was aHR 1.42 (95% CI 1.37-1.46). Associations were more pronounced in analyses restricted to females and confirmed cases of childhood maltreatment only. Interpretation: Considering the substantial health burden associated with childhood maltreatment, it is important to implement public health policies aimed at enhancing: 1) detection and primary prevention of childhood maltreatment, 2) secondary and tertiary prevention interventions to reduce the burden of ill health associated with exposure to maltreatment and 3) clinical awareness of such associations and subsequent knowledge of management. Funding: None.

Machine Learning-Based Identification of Colon Cancer Candidate Diagnostics Genes.

BACKGROUND: Colorectal cancer (CRC) is the third leading cause of cancer-related death and the fourth most commonly diagnosed cancer worldwide. Due to a lack of diagnostic biomarkers and understanding of the underlying molecular mechanisms, CRC's mortality rate continues to grow. CRC occurrence and progression are dynamic processes. The expression levels of specific molecules vary at various stages of CRC, rendering its early detection and diagnosis challenging and the need for identifying accurate and meaningful CRC biomarkers more pressing. The advances in high-throughput sequencing technologies have been used to explore novel gene expression, targeted treatments, and colon cancer pathogenesis. Such approaches are routinely being applied and result in large datasets whose analysis is increasingly becoming dependent on machine learning (ML) algorithms that have been demonstrated to be computationally efficient platforms for the identification of variables across such high-dimensional datasets. METHODS: We developed a novel ML-based experimental design to study CRC gene associations. Six different machine learning methods were employed as classifiers to identify genes that can be used as diagnostics for CRC using gene expression and clinical datasets. The accuracy, sensitivity, specificity, F1 score, and area under receiver operating characteristic (AUROC) curve were derived to explore the differentially expressed genes (DEGs) for CRC diagnosis. Gene ontology enrichment analyses of these DEGs were performed and predicted gene signatures were linked with miRNAs. RESULTS: We evaluated six machine learning classification methods (Adaboost, ExtraTrees, logistic regression, naïve Bayes classifier, random forest, and XGBoost) across different combinations of training and test datasets over GEO datasets. The accuracy and the AUROC of each combination of training and test data with different algorithms were used as comparison metrics. Random forest (RF) models consistently performed better than other models. In total, 34 genes were identified and used for pathway and gene set enrichment analysis. Further mapping of the 34 genes with miRNA identified interesting miRNA hubs genes. CONCLUSIONS: We identified 34 genes with high accuracy that can be used as a diagnostics panel for CRC.

CACONET: a novel classification framework for microbial correlation networks.

MOTIVATION: Existing microbiome-based disease prediction relies on the ability of machine learning methods to differentiate disease from healthy subjects based on the observed taxa abundance across samples. Despite numerous microbes have been implicated as potential biomarkers, challenges remain due to not only the statistical nature of microbiome data but also the lack of understanding of microbial interactions which can be indicative of the disease. RESULTS: We propose CACONET (classification of Compositional-Aware COrrelation NETworks), a computational framework that learns to classify microbial correlation networks and extracts potential signature interactions, taking as input taxa relative abundance across samples and their health status. By using Bayesian compositional-aware correlation inference, a collection of posterior correlation networks can be drawn and used for graph-level classification, thus incorporating uncertainty in the estimates. CACONET then employs a deep learning approach for graph classification, achieving excellent performance metrics by exploiting the correlation structure. We test the framework on both simulated data and a large real-world dataset pertaining to microbiome samples of colorectal cancer (CRC) and healthy subjects, and identify potential network substructure characteristic of CRC microbiota. CACONET is customizable and can be adapted to further improve its utility. AVAILABILITY AND IMPLEMENTATION: CACONET is available at https://github.com/yuanwxu/corr-net-classify. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Clinical and biochemical profile of 786 sequential episodes of diabetic ketoacidosis in adults with type 1 and type 2 diabetes mellitus.

INTRODUCTION: We explored the clinical and biochemical differences in demographics, presentation and management of diabetic ketoacidosis (DKA) in adults with type 1 and type 2 diabetes. RESEARCH DESIGN AND METHODS: This observational study included all episodes of DKA from April 2014 to September 2020 in a UK tertiary care hospital. Data were collected on diabetes type, demographics, biochemical and clinical features at presentation, and DKA management. RESULTS: From 786 consecutive DKA, 583 (75.9%) type 1 diabetes and 185 (24.1%) type 2 diabetes episodes were included in the final analysis. Those with type 2 diabetes were older and had more ethnic minority representation than those with type 1 diabetes. Intercurrent illness (39.8%) and suboptimal compliance (26.8%) were the two most common precipitating causes of DKA in both cohorts. Severity of DKA as assessed by pH, glucose and lactate at presentation was similar in both groups. Total insulin requirements and total DKA duration were the same (type 1 diabetes 13.9 units (9.1-21.9); type 2 diabetes 13.9 units (7.7-21.1); p=0.4638). However, people with type 2 diabetes had significantly longer hospital stay (type 1 diabetes: 3.0 days (1.7-6.1); type 2 diabetes: 11.0 days (5.0-23.1); p<0.0001). CONCLUSIONS: In this population, a quarter of DKA episodes occurred in people with type 2 diabetes. DKA in type 2 diabetes presents at an older age and with greater representation from ethnic minorities. However, severity of presentation and DKA duration are similar in both type 1 and type 2 diabetes, suggesting that the same clinical management protocol is equally effective. People with type 2 diabetes have longer hospital admission.

Immune infiltration and prognostic and diagnostic use of LGALS4 in colon adenocarcinoma and bladder urothelial carcinoma.

Colon adenocarcinoma (COAD) is a common tumor of the gastrointestinal tract with a high mortality rate. Current research has identified many genes associated with immune infiltration that play a vital role in the development of COAD. In this study, we analysed the prognostic and diagnostic features of such immune-related genes in the context of colonic adenocarcinoma (COAD). We analysed 17 overlapping gene expression profiles of COAD and healthy samples obtained from TCGA-COAD and public single-cell sequencing resources, to identify potential therapeutic COAD targets. We evaluated the abundance of immune infiltration with those genes using the TIMER (Tumor Immune Estimation Resource) deconvolution method. Subsequently, we developed predictive and survival models to assess the prognostic value of these genes. The LGALS4 (Galectin-4) gene was found to be significantly (P<0.05) downregulated in COAD and bladder urothelial carcinoma (BLCA) compared to healthy samples. We identified LGALS4 as a prognostic and diagnostic marker for multiple cancer types, including COAD and BLCA. Our analysis reveals a series of novel candidate drug targets, as well as candidate molecular markers, that may explain the pathogenesis of COAD and BLCA. LGALS4 gene is associated with multiple cancer types and is a possible prognostic, as well as diagnostic, marker of COAD and BLCA.

Integration of the Microbiome, Metabolome and Transcriptomics Data Identified Novel Metabolic Pathway Regulation in Colorectal Cancer.

Integrative multiomics data analysis provides a unique opportunity for the mechanistic understanding of colorectal cancer (CRC) in addition to the identification of potential novel therapeutic targets. In this study, we used public omics data sets to investigate potential associations between microbiome, metabolome, bulk transcriptomics and single cell RNA sequencing datasets. We identified multiple potential interactions, for example 5-aminovalerate interacting with Adlercreutzia; cholesteryl ester interacting with bacterial genera Staphylococcus, Blautia and Roseburia. Using public single cell and bulk RNA sequencing, we identified 17 overlapping genes involved in epithelial cell pathways, with particular significance of the oxidative phosphorylation pathway and the ACAT1 gene that indirectly regulates the esterification of cholesterol. These findings demonstrate that the integration of multiomics data sets from diverse populations can help us in untangling the colorectal cancer pathogenesis as well as postulate the disease pathology mechanisms and therapeutic targets.