Oxidative Stress Biomarkers and Peripheral Endothelial Dysfunction in Rheumatoid Arthritis: A Monocentric Cross-Sectional Case-Control Study

Previous studies have suggested that oxidative stress may heighten atherosclerotic burden in rheumatoid arthritis (RA), but direct evidence is lacking. Objective: To evaluate the relationship between established plasma oxidative stress biomarkers and peripheral endothelial dysfunction (ED), a marker of early atherosclerosis, in RA. Methods: Paroxonase-1 (PON-1), protein-SH (PSH), and malondialdehyde (MDA) were measured in 164 RA patient s and 100 age- and sex-matched healthy controls without previous cardiovascular events. Peripheral ED, evaluated by flow-mediated pulse amplitude tonometry, was defined by log-transformed reactive hyperemia index (Ln-RHI) values < 0.51. Results: PON-1 activity and PSH concentrations were significantly reduced in RA patients compared to controls. In regression analysis, increased plasma MDA levels were significantly associated with reduced Ln-RHI [B coefficient (95% CI) = −0.003 (−0.005 to −0.0008), p = 0.008] and the presence of peripheral ED (OR (95% CI) = 1.75 (1.06–2.88), p = 0.028). Contrary to our expectations, increased PON-1 activity was significantly associated, albeit weakly, with the presence of ED (OR (95% CI) = 1.00 (1.00–1.01), p = 0.017). Conclusions: In this first evidence of a link between oxidative stress and markers of atherosclerosis, MDA and PON-1 showed opposite associations with peripheral vasodilatory capacity and the presence of ED in RA. Further studies are needed to determine whether this association predicts atherosclerotic events in the RA population

Genetic characterization and implications for conservation of the last autochthonous Mouflon population in Europe

Population genetic studies provide accurate information on population structure, connectivity, and hybridization. These are key elements to identify units for conservation and define wildlife management strategies aimed to maintain and restore biodiversity. The Mediterranean island of Sardinia hosts one of the last autochthonous mouflon populations, descending from the wild Neolithic ancestor. The first mouflon arrived in Sardinia ~ 7000 years ago and thrived across the island until the twentieth century, when anthropogenic factors led to population fragmentation. We analysed the three main allopatric Sardinian mouflon sub-populations, namely: the native sub-populations of Montes Forest and Mount Tonneri, and the reintroduced sub-population of Mount Lerno. We investigated the spatial genetic structure of the Sardinian mouflon based on the parallel analysis of 14 highly polymorphic microsatellite loci and mitochondrial D-loop sequences. The Montes Forest sub-population was found to harbour the ancestral haplotype in the phylogeny of European mouflon. We detected high levels of relatedness in all the sub-populations and a mitochondrial signature of hybridization between the Mount Lerno sub-population and domestic sheep. Our findings provide useful insights to protect such an invaluable genetic heritage from the risk of genetic depletion by promoting controlled inter-population exchange and drawing informed repopulation plans sourcing from genetically pure mouflon stocks

Comprehensive arginine metabolomics and peripheral vasodilatory capacity in rheumatoid arthritis: A monocentric cross-sectional study

Background: The relationship between plasma arginine metabolites influencing vascular homeostasis and peripheral vasodilatory capacity in rheumatoid arthritis (RA) patients is not known. Methods: L-arginine (Arg), monomethyl-L-arginine (MMA), L-homoarginine (hArg), asymmetric dimethyl-L-arginine (ADMA), symmetric dimethyl-L-arginine, and L-citrulline (Cit) were measured by liquid chromatography tandem mass spectrometry (LC-MS/MS) in 164 RA patients and 100 age- and sex-matched healthy controls without previous cardiovascular events. Log-transformed reactive hyperemia index (Ln-RHI) evaluated by flow-mediated pulse amplitude tonometry (PAT, EndoPAT2000 device) was assessed as surrogate measure of peripheral vasodilatory capacity in RA patients. Ln-RHI values <0.51 indicated peripheral endothelial dysfunction (ED). The relationship between plasma arginine metabolite concentrations, RA descriptors and peripheral vasodilatory capacity was evaluated by bivariate correlation and regression analyses. Results: Plasma ADMA concentrations were significantly higher, and plasma hArg concentrations significantly lower, in RA patients than in controls (0.53 ± 0.09 vs 0.465 ± 0.07 μmol/L and 1.50 ± 0.60 vs 1.924 ± 0.78 μmol/L, respectively; p < 0.001 for both comparisons). Bivariate correlation analysis demonstrated no significant correlation between arginine metabolites and disease descriptors. In regression analysis in RA patients, higher plasma ADMA concentrations were independently associated with presence of ED [OR(95% CI) = 77.3(1.478–4050.005), p = 0.031] and lower Ln-RHI [B coefficient(95% CI) = −0.57(−1.09 to −0.05), p = 0.032]. Conclusions: ADMA was significantly, albeit weakly, associated with impaired microcirculatory vasodilatory capacity and peripheral endothelial dysfunction in RA. This suggests an important pathophysiological role of this metabolite in the vascular alterations observed in this patient group

Methotrexate and vasculoprotection: Mechanistic insights and potential therapeutic applications in old age

Increasing age is a strong, independent risk factor for atherosclerosis and cardiovascular disease. Key abnormalities driving cardiovascular risk in old age include endothelial dysfunction, increased arterial stiffness, blood pressure, and the pro-atherosclerotic effects of chronic, low-grade, inflammation. The identification of novel therapies that comprehensively target these alterations might lead to a major breakthrough in cardiovascular risk management in the older population. Systematic reviews and meta-analyses of observational studies have shown that methotrexate, a first-line synthetic disease-modifying anti-rheumatic drug, significantly reduces cardiovascular morbidity and mortality in patients with rheumatoid arthritis, a human model of systemic inflammation, premature atherosclerosis, and vascular aging. We reviewed in vitro and in vivo studies investigating the effects of methotrexate on endothelial function, arterial stiffness, and blood pressure, and the potential mechanisms of action involved. The available evidence suggests that methotrexate might have beneficial effects on vascular homeostasis and blood pressure control by targeting specific inflammatory pathways, adenosine metabolism, and 5' adenosine monophosphate-activated protein kinase. Such effects might be biologically and clinically relevant not only in patients with rheumatoid arthritis but also in older adults with high cardiovascular risk. Therefore, methotrexate has the potential to be repurposed for cardiovascular risk management in old age because of its putative pharmacological effects on inflammation, vascular homeostasis, and blood pressure. However, further study and confirmation of these effects are essential in order to adequately design intervention studies of methotrexate in the older population

Soil Organic Carbon and Nitrogen Feedbacks on Crop Yields under Climate Change

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Comparing correction methods of RCM outputs for improving crop impact projections in the Iberian Peninsula for 21st century

Assessment of climate change impacts on crops in regions of complex orography such as the Iberian Peninsula (IP) requires climate model output which is able to describe accurately the observed climate. The high resolution of output provided by Regional Climate Models (RCMs) is expected to be a suitable tool to describe regional and local climatic features, although their simulation results may still present biases. For these reasons, we compared several post-processing methods to correct or reduce the biases of RCM simulations from the ENSEMBLES project for the IP. The bias-corrected datasets were also evaluated in terms of their applicability and consequences in improving the results of a crop model to simulate maize growth and development at two IP locations, using this crop as a reference for summer cropping systems in the region. The use of bias-corrected climate runs improved crop phenology and yield simulation overall and reduced the inter-model variability and thus the uncertainty. The number of observational stations underlying each reference observational dataset used to correct the bias affected the correction performance. Although no single technique showed to be the best one, some methods proved to be more adequate for small initial biases, while others were useful when initial biases were so large as to prevent data application for impact studies. An initial evaluation of the climate data, the bias correction/reduction method and the consequences for impact assessment would be needed to design the most robust, reduced uncertainty ensemble for a specific combination of location, crop, and crop management

Statistical Analysis of Large Simulated Yield Datasets for Studying Climate Effects

Many studies have been carried out during the last decade to study the effect of climate change on crop yields and other key crop characteristics. In these studies, one or several crop models were used to simulate crop growth and development for different climate scenarios that correspond to different projections of atmospheric CO2 concentration, temperature, and rainfall changes (Semenov et al., 1996; Tubiello and Ewert, 2002; White et al., 2011). The Agricultural Model Intercomparison and Improvement Project (AgMIP; Rosenzweig et al., 2013) builds on these studies with the goal of using an ensemble of multiple crop models in order to assess effects of climate change scenarios for several crops in contrasting environments. These studies generate large datasets, including thousands of simulated crop yield data. They include series of yield values obtained by combining several crop models with different climate scenarios that are defined by several climatic variables (temperature, CO2, rainfall, etc.). Such datasets potentially provide useful information on the possible effects of different climate change scenarios on crop yields. However, it is sometimes difficult to analyze these datasets and to summarize them in a useful way due to their structural complexity; simulated yield data can differ among contrasting climate scenarios, sites, and crop models. Another issue is that it is not straightforward to extrapolate the results obtained for the scenarios to alternative climate change scenarios not initially included in the simulation protocols. Additional dynamic crop model simulations for new climate change scenarios are an option but this approach is costly, especially when a large number of crop models are used to generate the simulated data, as in AgMIP. Statistical models have been used to analyze responses of measured yield data to climate variables in past studies (Lobell et al., 2011), but the use of a statistical model to analyze yields simulated by complex process-based crop models is a rather new idea. We demonstrate herewith that statistical methods can play an important role in analyzing simulated yield data sets obtained from the ensembles of process-based crop models. Formal statistical analysis is helpful to estimate the effects of different climatic variables on yield, and to describe the between-model variability of these effects