Smoking cessation is associated with lower disease activity and predicts cardiovascular risk reduction in rheumatoid arthritis patients

Objectives: Smoking is a major risk factor for the development of both cardiovascular disease (CVD) and RA and may cause attenuated responses to anti-rheumatic treatments. Our aim was to compare disease activity, CVD risk factors and CVD event rates across smoking status in RA patients. Methods: Disease characteristics, CVD risk factors and relevant medications were recorded in RA patients without prior CVD from 10 countries (Norway, UK, Netherlands, USA, Sweden, Greece, South Africa, Spain, Canada and Mexico). Information on CVD events was collected. Adjusted analysis of variance, logistic regression and Cox models were applied to compare RA disease activity (DAS28), CVD risk factors and event rates across categories of smoking status. Results: Of the 3311 RA patients (1012 former, 887 current and 1412 never smokers), 235 experienced CVD events during a median follow-up of 3.5 years (interquartile range 2.5-6.1). At enrolment, current smokers were more likely to have moderate or high disease activity compared with former and never smokers (P < 0.001 for both). There was a gradient of worsening CVD risk factor profiles (lipoproteins and blood pressure) from never to former to current smokers. Furthermore, former and never smokers had significantly lower CVD event rates compared with current smokers [hazard ratio 0.70 (95% CI 0.51, 0.95), P = 0.02 and 0.48 (0.34, 0.69), P < 0.001, respectively]. The CVD event rates for former and never smokers were comparable. Conclusion: Smoking cessation in patients with RA was associated with lower disease activity and improved lipid profiles and was a predictor of reduced rates of CVD events

Prediction of cardiovascular events in rheumatoid arthritis using risk age calculations: evaluation of concordance across risk age models

Background: In younger individuals, low absolute risk of cardiovascular disease (CVD) may conceal an increased risk age and relative risk of CVD. Calculation of risk age is proposed as an adjuvant to absolute CVD risk estimation in European guidelines. We aimed to compare the discriminative ability of available risk age models in prediction of CVD in rheumatoid arthritis (RA). Secondly, we also evaluated the performance of risk age models in subgroups based on RA disease characteristics. Methods: RA patients aged 30?70 years were included from an international consortium named A Trans-Atlantic Cardiovascular Consortium for Rheumatoid Arthritis (ATACC-RA). Prior CVD and diabetes mellitus were exclusión criteria. The discriminatory ability of specific risk age models was evaluated using c-statistics and their standard errors after calculating time until fatal or non-fatal CVD or last follow-up. Results: A total of 1974 patients were included in the main analyses, and 144 events were observed during followup, the median follow-up being 5.0 years. The risk age models gave highly correlated results, demonstrating R2 values ranging from 0.87 to 0.97. However, risk age estimations differed > 5 years in 15?32% of patients. C-statistics ranged 0.68?0.72 with standard errors of approximately 0.03. Despite certain RA characteristics being associated with low c-indices, standard errors were high. Restricting analysis to European RA patients yielded similar results. Conclusions: The cardiovascular risk age and vascular age models have comparable performance in predicting CVD in RA patients. The influence of RA disease characteristics on the predictive ability of these prediction models remains inconclusive

Prediction of cardiovascular events in rheumatoid arthritis using risk age calculations: evaluation of concordance across risk age models

Background In younger individuals, low absolute risk of cardiovascular disease (CVD) may conceal an increased risk age and relative risk of CVD. Calculation of risk age is proposed as an adjuvant to absolute CVD risk estimation in European guidelines. We aimed to compare the discriminative ability of available risk age models in prediction of CVD in rheumatoid arthritis (RA). Secondly, we also evaluated the performance of risk age models in subgroups based on RA disease characteristics. Methods RA patients aged 30–70 years were included from an international consortium named A Trans-Atlantic Cardiovascular Consortium for Rheumatoid Arthritis (ATACC-RA). Prior CVD and diabetes mellitus were exclusion criteria. The discriminatory ability of specific risk age models was evaluated using c-statistics and their standard errors after calculating time until fatal or non-fatal CVD or last follow-up. Results A total of 1974 patients were included in the main analyses, and 144 events were observed during follow-up, the median follow-up being 5.0 years. The risk age models gave highly correlated results, demonstrating R 2 values ranging from 0.87 to 0.97. However, risk age estimations differed > 5 years in 15–32% of patients. C-statistics ranged 0.68–0.72 with standard errors of approximately 0.03. Despite certain RA characteristics being associated with low c-indices, standard errors were high. Restricting analysis to European RA patients yielded similar results. Conclusions The cardiovascular risk age and vascular age models have comparable performance in predicting CVD in RA patients. The influence of RA disease characteristics on the predictive ability of these prediction models remains inconclusive

Iron Behaving Badly: Inappropriate Iron Chelation as a Major Contributor to the Aetiology of Vascular and Other Progressive Inflammatory and Degenerative Diseases

The production of peroxide and superoxide is an inevitable consequence of aerobic metabolism, and while these particular "reactive oxygen species" (ROSs) can exhibit a number of biological effects, they are not of themselves excessively reactive and thus they are not especially damaging at physiological concentrations. However, their reactions with poorly liganded iron species can lead to the catalytic production of the very reactive and dangerous hydroxyl radical, which is exceptionally damaging, and a major cause of chronic inflammation. We review the considerable and wide-ranging evidence for the involvement of this combination of (su)peroxide and poorly liganded iron in a large number of physiological and indeed pathological processes and inflammatory disorders, especially those involving the progressive degradation of cellular and organismal performance. These diseases share a great many similarities and thus might be considered to have a common cause (i.e. iron-catalysed free radical and especially hydroxyl radical generation). The studies reviewed include those focused on a series of cardiovascular, metabolic and neurological diseases, where iron can be found at the sites of plaques and lesions, as well as studies showing the significance of iron to aging and longevity. The effective chelation of iron by natural or synthetic ligands is thus of major physiological (and potentially therapeutic) importance. As systems properties, we need to recognise that physiological observables have multiple molecular causes, and studying them in isolation leads to inconsistent patterns of apparent causality when it is the simultaneous combination of multiple factors that is responsible. This explains, for instance, the decidedly mixed effects of antioxidants that have been observed, etc...Comment: 159 pages, including 9 Figs and 2184 reference

Fiscal Year 2009

Progress in MEMS fabrication has enabled a wide variety of force and displacement sensing devices to be constructed. One device under intense development at Sandia is a passive shock switch, described elsewhere (Mitchell 2008). A goal of all MEMS devices, including the shock switch, is to achieve a high degree of reliability. This, in turn, requires systematic methods for validating device performance during each iteration of design. Once a design is finalized, suitable tools are needed to provide quality assurance for manufactured devices. To ensure device performance, measurements on these devices must be traceable to NIST standards. In addition, accurate metrology of MEMS components is needed to validate mechanical models that are used to design devices to accelerate development and meet emerging needs. Progress towards a NIST-traceable calibration method is described for a next-generation, 2D Interfacial Force Microscope (IFM) for applications in MEMS metrology and qualification. Discussed are the results of screening several suitable calibration methods and the known sources of uncertainty in each method

Calibration of an interfacial force microscope for MEMS metrology : FY08-09 activities.

Progress in MEMS fabrication has enabled a wide variety of force and displacement sensing devices to be constructed. One device under intense development at Sandia is a passive shock switch, described elsewhere (Mitchell 2008). A goal of all MEMS devices, including the shock switch, is to achieve a high degree of reliability. This, in turn, requires systematic methods for validating device performance during each iteration of design. Once a design is finalized, suitable tools are needed to provide quality assurance for manufactured devices. To ensure device performance, measurements on these devices must be traceable to NIST standards. In addition, accurate metrology of MEMS components is needed to validate mechanical models that are used to design devices to accelerate development and meet emerging needs. Progress towards a NIST-traceable calibration method is described for a next-generation, 2D Interfacial Force Microscope (IFM) for applications in MEMS metrology and qualification. Discussed are the results of screening several suitable calibration methods and the known sources of uncertainty in each method

Fiscal Year 2007

This report describes activities conducted in FY07 to mature the MEMS passive shock sensor. The first chapter of the report provides motivation and background on activities that are described in detail in later chapters. The second chapter discusses concepts that are important for integrating the MEMS passive shock sensor into a system. Following these two introductory chapters, the report details modeling and design efforts, packaging, failure analysis and testing and validation. At the end of FY07, the MEMS passive shock sensor was at TRL 4

Rheumatoid arthritis-specific cardiovascular risk scores are not superior to general risk scores: a validation analysis of patients from seven countries

ObjectivesCardiovascular disease (CVD) risk calculators developed for the general population do not accurately predict CVD events in patients with RA. We sought to externally validate risk calculators recommended for use in patients with RA including the EULAR 1.5 multiplier, the Expanded Cardiovascular Risk Prediction Score for RA (ERS-RA) and QRISK2.MethodsSeven RA cohorts from UK, Norway, Netherlands, USA, South Africa, Canada and Mexico were combined. Data on baseline CVD risk factors, RA characteristics and CVD outcomes (including myocardial infarction, ischaemic stroke and cardiovascular death) were collected using standardized definitions. Performance of QRISK2, EULAR multiplier and ERS-RA was compared with other risk calculators [American College of Cardiology/American Heart Association (ACC/AHA), Framingham Adult Treatment Panel III Framingham risk score-Adult Treatment Panel (FRS-ATP) and Reynolds Risk Score] using c-statistics and net reclassification index.ResultsAmong 1796 RA patients without prior CVD [mean ( s . d .) age: 54.0 (14.0) years, 74% female], 100 developed CVD events during a mean follow-up of 6.9 years (12430 person-years). Estimated CVD risk by ERS-RA [mean ( s . d .) 8.8% (9.8%)] was comparable to FRS-ATP [mean ( s . d .) 9.1% (8.3%)] and Reynolds [mean ( s . d .) 9.2% (12.2%)], but lower than ACC/AHA [mean ( s . d .) 9.8% (12.1%)]. QRISK2 substantially overestimated risk [mean ( s . d .) 15.5% (13.9%)]. Discrimination was not improved for ERS-RA (c-statistic = 0.69), QRISK2 or EULAR multiplier applied to ACC/AHA compared with ACC/AHA (c-statistic = 0.72 for all) or for FRS-ATP (c-statistic = 0.75). The net reclassification index for ERS-RA was low (-0.8% vs ACC/AHA and 2.3% vs FRS-ATP).ConclusionThe QRISK2, EULAR multiplier and ERS-RA algorithms did not predict CVD risk more accurately in patients with RA than CVD risk calculators developed for the general population

Prediction of cardiovascular events in rheumatoid arthritis using risk age calculations: Evaluation of concordance across risk age models

Background: In younger individuals, low absolute risk of cardiovascular disease (CVD) may conceal an increased risk age and relative risk of CVD. Calculation of risk age is proposed as an adjuvant to absolute CVD risk estimation in European guidelines. We aimed to compare the discriminative ability of available risk age models in prediction of CVD in rheumatoid arthritis (RA). Secondly, we also evaluated the performance of risk age models in subgroups based on RA disease characteristics. Methods: RA patients aged 30-70 years were included from an international consortium named A Trans-Atlantic Cardiovascular Consortium for Rheumatoid Arthritis (ATACC-RA). Prior CVD and diabetes mellitus were exclusion criteria. The discriminatory ability of specific risk age models was evaluated using c-statistics and their standard errors after calculating time until fatal or non-fatal CVD or last follow-up. Results: A total of 1974 patients were included in the main analyses, and 144 events were observed during follow-up, the median follow-up being 5.0 years. The risk age models gave highly correlated results, demonstrating R 2 values ranging from 0.87 to 0.97. However, risk age estimations differed &amp;gt; 5 years in 15-32% of patients. C-statistics ranged 0.68-0.72 with standard errors of approximately 0.03. Despite certain RA characteristics being associated with low c-indices, standard errors were high. Restricting analysis to European RA patients yielded similar results. Conclusions: The cardiovascular risk age and vascular age models have comparable performance in predicting CVD in RA patients. The influence of RA disease characteristics on the predictive ability of these prediction models remains inconclusive. © 2020 The Author(s)