Increased levels of microparticles originating from endothelial cells, platelets and erythrocytes in subjects with metabolic syndrome: relationship with oxidative stress

Background and aims The Metabolic Syndrome (MetS) is associated with increased cardiovascular risk. Circulating microparticles (MP) are involved in the pathogenesis of atherothrombotic disorders and are raised in individual with CVD. We measured their level and cellular origin in subjects with MetS and analyzed their associations with 1/anthropometric and biological parameters of MetS, 2/inflammation and oxidative stress markers. Methods and results Eighty-eight subjects with the MetS according to the NCEP-ATPIII definition were enrolled in a bicentric study and compared to 27 healthy controls. AnnexinV-positive MP (TMP), MP derived from platelets (PMP), erythrocytes (ErMP), endothelial cells (EMP), leukocytes (LMP) and granulocytes (PNMP) were determined by flow cytometry. MetS subjects had significantly higher counts/μl of TMP (730.6 ± 49.7 vs 352.8 ± 35.6), PMP (416.0 ± 43.8 vs 250.5 ± 23.5), ErMP (243.8 ± 22.1 vs 73.6 ± 19.6) and EMP (7.8 ± 0.8 vs 4.0 ± 1.0) compared with controls. LMP and PNMP were not statistically different between groups. Multivariate analysis demonstrated that each criterion for the MetS influenced the number of TMP. Waist girth was a significant determinant of PMP and EMP level and blood pressure was correlated with EMP level. Glycemia positively correlated with PMP level whereas dyslipidemia influenced EMP and ErMP levels. Interestingly, the oxidative stress markers, plasma glutathione peroxydase and urinary 8-iso-prostaglandin F2 α, independently influenced TMP and PMP levels whereas inflammatory markers did not, irrespective of MP type. Conclusion Increased levels of TMP, PMP, ErMP and EMP are associated with individual metabolic abnormalities of MetS and oxidative stress. Whether MP assessment may represent a marker for risk stratification or a target for pharmacological intervention deserves further investigation.

Targeting medication non-adherence behavior in selected autoimmune diseases: a systematic approach to digital health program development

Background 29 autoimmune diseases, including Rheumatoid Arthritis, gout, Crohn’s Disease, and Systematic Lupus Erythematosus affect 7.6-9.4% of the population. While effective therapy is available, many patients do not follow treatment or use medications as directed. Digital health and Web 2.0 interventions have demonstrated much promise in increasing medication and treatment adherence, but to date many Internet tools have proven disappointing. In fact, most digital interventions continue to suffer from high attrition in patient populations, are burdensome for healthcare professionals, and have relatively short life spans. Objective Digital health tools have traditionally centered on the transformation of existing interventions (such as diaries, trackers, stage-based or cognitive behavioral therapy programs, coupons, or symptom checklists) to electronic format. Advanced digital interventions have also incorporated attributes of Web 2.0 such as social networking, text messaging, and the use of video. Despite these efforts, there has not been little measurable impact in non-adherence for illnesses that require medical interventions, and research must look to other strategies or development methodologies. As a first step in investigating the feasibility of developing such a tool, the objective of the current study is to systematically rate factors of non-adherence that have been reported in past research studies. Methods Grounded Theory, recognized as a rigorous method that facilitates the emergence of new themes through systematic analysis, data collection and coding, was used to analyze quantitative, qualitative and mixed method studies addressing the following autoimmune diseases: Rheumatoid Arthritis, gout, Crohn’s Disease, Systematic Lupus Erythematosus, and inflammatory bowel disease. Studies were only included if they contained primary data addressing the relationship with non-adherence. Results Out of the 27 studies, four non-modifiable and 11 modifiable risk factors were discovered. Over one third of articles identified the following risk factors as common contributors to medication non-adherence (percent of studies reporting): patients not understanding treatment (44%), side effects (41%), age (37%), dose regimen (33%), and perceived medication ineffectiveness (33%). An unanticipated finding that emerged was the need for risk stratification tools (81%) with patient-centric approaches (67%). Conclusions This study systematically identifies and categorizes medication non-adherence risk factors in select autoimmune diseases. Findings indicate that patients understanding of their disease and the role of medication are paramount. An unexpected finding was that the majority of research articles called for the creation of tailored, patient-centric interventions that dispel personal misconceptions about disease, pharmacotherapy, and how the body responds to treatment. To our knowledge, these interventions do not yet exist in digital format. Rather than adopting a systems level approach, digital health programs should focus on cohorts with heterogeneous needs, and develop tailored interventions based on individual non-adherence patterns.