The end-of-treatment ribavirin concentration predicts hepatitis C virus relapse.

BACKGROUND: The optimization of combination therapy with ribavirin (RBV) and pegylated interferon alpha has substantially improved sustained virologic response (SVR) rates and lowered virologic relapse rates in patients infected with hepatitis C virus (HCV). In this study, we performed an analysis of the relationship between the end-of-treatment plasma RBV concentration and virologic relapse. METHODS: Thirty-four patients with HCV treated with pegylated interferon/RBV and with an end-of-treatment response were assayed for plasma RBV concentration using liquid chromatography assay coupled to tandem mass-spectrometric detection on the last day of the treatment. Clinical data and the concentration of RBV were compared between patients classified as either relapsers or nonrelapsers. RESULTS: Eleven patients (32.4%) relapsed and 23 patients (67.6%) achieved an SVR. The mean plasma RBV concentration on the last day of treatment was 1380 ± 312 ng/mL for relapsers and 2278 ± 569 ng/mL for SVR patients (P < 0.0001). A receiver operating characteristic analysis showed that a threshold of 1960 ng/mL was associated with the greatest sensitivity and specificity (100% and 83%, respectively, with an area under the curve of 0.94; P < 0.0001) for discriminating between patients who relapsed and those who did not. A univariate logistic regression analysis indicated that a plasma RBV concentration of <1960 ng/mL at the end of the treatment was strongly associated with relapse (odds ratio, 55; 95% confidence interval, 7.24-∞; P = 0.0001) independently of age, body weight, RBV dose, baseline viral load, the interleukin-28B genotype, and response to previous courses of treatment. CONCLUSIONS: Our study results highlight the relevance of measuring plasma RBV concentrations during and at the end of HCV treatment, with a view to avoiding virologic relapse.

The TRACTOR Project: TRACking and MoniToring Occupational Risks in Agriculture Using French Insurance Health Data (MSA).

OBJECTIVES: A vast data mining project called 'TRACking and moniToring Occupational Risks in agriculture' (TRACTOR) was initiated in 2017 to investigate work-related health events among the entire French agricultural workforce. The goal of this work is to present the TRACTOR project, the challenges faced during its implementation, to discuss its strengths and limitations and to address its potential impact for health surveillance. METHODS: Three routinely collected administrative health databases from the National Health Insurance Fund for Agricultural Workers and Farmers (MSA) were made available for the TRACTOR project. Data management was required to properly clean and prepare the data before linking together all available databases. RESULTS: After removing few missing and aberrant data (4.6% values), all available databases were fully linked together. The TRACTOR project is an exhaustive database of agricultural workforce (active and retired) from 2002 to 2016, with around 10.5 million individuals including seasonal workers and farm managers. From 2012 to 2016, a total of 6 906 290 individuals were recorded. Half of these individuals were active and 46% had at least one health event (e.g. declared chronic disease, reimbursed drug prescription) during this 5-year period. CONCLUSIONS: The assembled MSA databases available in the TRACTOR project are regularly updated and represent a promising and unprecedent dataset for data mining analysis dedicated to the early identification of current and emerging work-related illnesses and hypothesis generation. As a result, this project could help building a prospective integrated health surveillance system for the benefit of agricultural workers.

Guanidino compounds as cause of cardiovascular damage in chronic kidney disease: an in vitro evaluation.

Chronic kidney disease is considered a major cause of cardiovascular risk and non-traditional risk factors remain largely unknown. The in vitro toxicity of 10 guanidino compounds (GCs) was evaluated via a standardized approach on different cell systems of relevance in cardiovascular disease. The parameters evaluated were production of reactive oxygen species, expression of surface molecules, cell proliferation, cytotoxicity and calcification. Several GCs had a stimulatory effect on monocytes and granulocytes (SDMA, creatine and guanidinobutyric acid (GBA)). Some GCs (guandine (G), guanidinosuccinic acid (GSA) and SDMA) inhibited endothelial cell proliferation or reduced calcification in osteoblast-like human VSMC (ADMA, GSA and SDMA). Stimulation of osteoclastogenesis could be demonstrated for ADMA, G, guanidinoacetic acid and GBA in a RAW264.7 cell line. No compounds were cytotoxic to AoSMC or endothelial cells, nor influenced their viability. GCs, especially SDMA, likely contribute to cardiovascular complications in uremia, mainly those related to microinflammation and leukocyte activation.

Medico-administrative data combined with agricultural practices data to retrospectively estimate pesticide use by agricultural workers.

This work is part of a global project aiming to use medico-administrative big data from the whole French agricultural population (~3 millions), collected through their mandatory health insurance system (Mutualité Sociale Agricole), to highlight associations between chronic diseases and agricultural activities. At the request of the French Agency for Food, Environmental and Occupational Health & Safety (ANSES), our objective was to estimate which pesticides were probably used by each agricultural worker, in order to include this information in our analyses and search for association with diseases. We selected five databases to achieve this objective: the Graphical Land Parcel Registration (RPG), the French Agricultural Census, "Cultivation Practice" surveys from the Agriculture ministry, the MATPHYTO crop-exposure matrix and the Compilation of Phytosanitary Indexes from the French Public Health Agency. A geographical grid was designed to use geographical location while maintaining worker anonymity, dividing France into square tracts of variable surface each containing a minimum of 1500 agricultural workers. We developed an automated algorithm to predict each individual potential exposure by crossing her/his occupational activity, the geographical grid and the RPG to deduce cultivation practices and use it as a gateway to estimate pesticides use. This approach allowed drawing, from administrative data, a list of substances potentially used by each agricultural worker throughout France. Results of the algorithm are illustrated at collective level (descriptive statistics for the whole population), as well as at individual level (some workers taken as examples). The generalization of this method in other national contexts is discussed. By linking this information with the health insurance databases, this approach could contribute to the agricultural workers health surveillance.