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Broiler chickens are among the main livestock sectors worldwide. With individual treatments being inapplicable, contrary to many other animal species, the need for antimicrobial use (AMU) is relatively high. AMU in animals is known to drive the emergence and spread of antimicrobial resistance (AMR). High farm biosecurity is a cornerstone for animal health and welfare, as well as food safety, as it protects animals from the introduction and spread of pathogens and therefore the need for AMU. The goal of this study was to identify the main biosecurity practices associated with AMU in broiler farms and to develop a statistical model that produces customised recommendations as to which biosecurity measures could be implemented on a farm to reduce its AMU, including a cost-effectiveness analysis of the recommended measures. AMU and biosecurity data were obtained cross-sectionally in 2014 from 181 broiler farms across nine European countries (Belgium, Bulgaria, Denmark, France, Germany, Italy, the Netherlands, Poland and Spain). Using mixed-effects random forest analysis (Mix-RF), recursive feature elimination was implemented to determine the biosecurity measures that best predicted AMU at the farm level. Subsequently, an algorithm was developed to generate AMU reduction scenarios based on the implementation of these measures. In the final Mix-RF model, 21 factors were present: 10 about internal biosecurity, 8 about external biosecurity and 3 about farm size and productivity, with the latter showing the largest (Gini) importance. Other AMU predictors, in order of importance, were the number of depopulation steps, compliance with a vaccination protocol for non-officially controlled diseases, and requiring visitors to check in before entering the farm. K-means clustering on the proximity matrix of the final Mix-RF model revealed that several measures interacted with each other, indicating that high AMU levels can arise for various reasons depending on the situation. The algorithm utilised the AMU predictive power of biosecurity measures while accounting also for their interactions, representing a first step toward aiding the decision-making process of veterinarians and farmers who are in need of implementing on-farm biosecurity measures to reduce their AMU.

The emergence of antimicrobial resistance (AMR) is one of the biggest health threats globally. In addition, the use of antimicrobial drugs in humans and livestock is considered an important driver of antimicrobial resistance. The commensal microbiota, and especially the intestinal microbiota, has been shown to have an important role in the emergence of AMR. Mobile genetic elements (MGEs) also play a central role in facilitating the acquisition and spread of AMR genes. We isolated Escherichia coli (n = 627) from fecal samples in respectively 25 poultry, 28 swine, and 15 veal calf herds from 6 European countries to investigate the phylogeny of E. coli at country, animal host and farm levels. Furthermore, we examine the evolution of AMR in E. coli genomes including an association with virulence genes, plasmids and MGEs. We compared the abundance metrics retrieved from metagenomic sequencing and whole genome sequenced of E. coli isolates from the same fecal samples and farms. The E. coli isolates in this study indicated no clonality or clustering based on country of origin and genetic markers; AMR, and MGEs. Nonetheless, mobile genetic elements play a role in the acquisition of AMR and virulence genes. Additionally, an abundance of AMR was agreeable between metagenomic and whole genome sequencing analysis for several AMR classes in poultry fecal samples suggesting that metagenomics could be used as an indicator for surveillance of AMR in E. coli isolates and vice versa.

The aim of this article is to report on antimicrobial resistance (AMR) in commensal

The aim of this article is to report on antimicrobial resistance (AMR) in commensal Escherichia coli from livestock from several European countries. The relationships with antimicrobial usage (AMU) at country level and harmonized indicators to cover the most relevant AMR aspects for human health in animal production were also investigated. E. coli were isolated in faeces from broilers and fattening pigs (from nine countries), and fattening turkeys and veal calves (from three countries) and screened against a fixed antimicrobial panel. AMU data were collected at farm and average treatment incidences stratified by antimicrobial class, country and livestock species were calculated. Associations between AMR and AMU at country level were analysed. Independent of animal species, the highest resistance was observed for ampicillin, sulphamethoxazole, tetracycline and trimethoprim. E. coli from broilers showed the highest resistance level for (fluoro)quinolones, and multidrug resistance peaked in broilers and fattening turkeys. Colistin resistance was observed at very low levels with the exception of fattening turkeys. High resistance to third- and fourth-generation cephalosporins was detected in broilers and fattening turkeys. The lowest levels of resistance were for meropenem, azithromycin and tigecycline (

Objectives: To control the emerging threat of antimicrobial resistance, international policy appeals for appropriate monitoring of antimicrobial usage (AMU) at supranational, species and farm level. The aim of this study was to quantify AMU in broilers at farm and flock level in nine European countries. Methods: Antimicrobial treatment data of one flock and purchased antimicrobials over one year were collected at 181 European broiler farms. Afterwards AMU was quantified using treatment incidence (TI) per 100 days based on Defined Daily Dose (DDDvet), Defined Course Dose (DCDvet) or Used Daily Dose (UDDvet) values. Total AMU at flock level was obtained by summing the TIDDDvet of all treatments in the sampled flock (TIDDDvetFl*). Results: The median TIDDDvetFl* was 9.0 (95% CI 5.5-10.8), meaning that broilers were treated with antimicrobials during 9% of their rearing period. TIDDDvetFl* varied considerably within and between countries. However, in every country at least one untreated flock was present. Average TIDDDvetFl* at country level ranged from 3.3 to 36.7. Polymyxins, extended-spectrum aminopenicillins and fluoroquinolones were the most used antimicrobials, accounting for 26%, 26% and 18% of total AMU, respectively. Twenty-six percent of the farms started a treatment on day 1 of production, and 49% of overall AMU was administered within the first week. Conclusions: Results show that rearing broilers without AMU is feasible. However, a huge variation in AMU in terms of amount, moment of administration and antimicrobial classes was observed. This shows that there is still ground to be covered when it comes to AMU on broiler farms.

Identification of sequence variants robustly associated with predisposition to diabetic kidney disease (DKD) has the potential to provide insights into the pathophysiological mechanisms responsible. We conducted a genome-wide association study (GWAS) of DKD in type 2 diabetes (T2D) using eight complementary dichotomous and quantitative DKD phenotypes: the principal dichotomous analysis involved 5,717 T2D subjects, 3,345 with DKD. Promising association signals were evaluated in up to 26,827 subjects with T2D (12,710 with DKD). A combined T1D+T2D GWAS was performed using complementary data available for subjects with T1D, which, with replication samples, involved up to 40,340 subjects with diabetes (18,582 with DKD). Analysis of specific DKD phenotypes identified a novel signal near GABRR1 (rs9942471, P = 4.5 × 10-8) associated with microalbuminuria in European T2D case subjects. However, no replication of this signal was observed in Asian subjects with T2D or in the equivalent T1D analysis. There was only limited support, in this substantially enlarged analysis, for association at previously reported DKD signals, except for those at UMOD and PRKAG2, both associated with estimated glomerular filtration rate. We conclude that, despite challenges in addressing phenotypic heterogeneity, access to increased sample sizes will continue to provide more robust inference regarding risk variant discovery for DKD.