Differential Calcium Signaling by Cone Specific Guanylate Cyclase-Activating Proteins from the Zebrafish Retina

Zebrafish express in their retina a higher number of guanylate cyclase-activating proteins (zGCAPs) than mammalians pointing to more complex guanylate cyclase signaling systems. All six zGCAP isoforms show distinct and partial overlapping expression profiles in rods and cones. We determined critical Ca2+-dependent parameters of their functional properties using purified zGCAPs after heterologous expression in E.coli. Isoforms 1–4 were strong, 5 and 7 were weak activators of membrane bound guanylate cyclase. They further displayed different Ca2+-sensitivities of guanylate cyclase activation, which is half maximal either at a free Ca2+ around 30 nM (zGCAP1, 2 and 3) or around 400 nM (zGCAP4, 5 and 7). Zebrafish GCAP isoforms showed also differences in their Ca2+/Mg2+-dependent conformational changes and in the Ca2+-dependent monomer-dimer equilibrium. Direct Ca2+-binding revealed that all zGCAPs bound at least three Ca2+. The corresponding apparent affinity constants reflect binding of Ca2+ with high (≤100 nM), medium (0.1–5 µM) and/or low (≥5 µM) affinity, but were unique for each zGCAP isoform. Our data indicate a Ca2+-sensor system in zebrafish rod and cone cells supporting a Ca2+-relay model of differential zGCAP operation in these cells

Preventable clinical and psychosocial factors predicted two out of three recurrent cardiovascular events in a coronary population

Background The relative importance of lifestyle, medical and psychosocial factors on the risk of recurrent major cardiovascular (CV) events (MACE) in coronary patients’ needs to be identified. The main objective of this study is to estimate the association between potentially preventable factors on MACE in an outpatient coronary population from routine clinical practice. Methods This prospective follow-up study of recurrent MACE, determine the predictive impact of risk factors and a wide range of relevant co-factors recorded at baseline. The baseline study included 1127 consecutive patients 2–36 months after myocardial infarction (MI) and/or revascularization procedure. The primary composite endpoint of recurrent MACE defined as CV death, hospitalization due to MI, revascularization, stroke/transitory ischemic attacks or heart failure was obtained from hospital records. Data were analysed using cox proportional hazard regression, stratified by prior coronary events before the index event. Results During a mean follow-up of 4.2 years from study inclusion (mean time from index event to end of study 5.7 years), 364 MACE occurred in 240 patients (21, 95% confidence interval: 19 to 24%), of which 39 were CV deaths. In multi-adjusted analyses, the strongest predictor of MACE was not taking statins (Relative risk [RR] 2.13), succeeded by physical inactivity (RR 1.73), peripheral artery disease (RR 1.73), chronic kidney failure (RR 1.52), former smoking (RR 1.46) and higher Hospital Anxiety and Depression Scale-Depression subscale score (RR 1.04 per unit increase). Preventable and potentially modifiable factors addressed accounted for 66% (95% confidence interval: 49 to 77%) of the risk for recurrent events. The major contributions were smoking, low physical activity, not taking statins, not participating in cardiac rehabilitation and diabetes. Conclusions Coronary patients were at high risk of recurrent MACE. Potentially preventable clinical and psychosocial factors predicted two out of three MACE, which is why these factors should be targeted in coronary populations. Trial registration Registered at ClinicalTrials.gov: NCT02309255. Registered at December 5th, 2014, registered retrospectively

Making robust decisions about the impact of health education programs: Psychometric evaluation of the Health Education Impact Questionnaire (heiQ) in diverse patient groups in Norway

OBJECTIVE: To undertake a rigorous psychometric evaluation of the widely used eight-scale heiQ version 2.0 (evaluating immediate effects of self-management interventions) in diverse patient groups in Norway. METHODS: Cross-sectional survey data were collected from 1019 Norwegians. Data were extracted from studies among people with musculoskeletal disorders (n=516), psoriasis (n=254), heart disease (n=97), and Type 2 diabetes (n=152). To investigate the factorial validity of the Norwegian heiQ, confirmatory factor analyses (CFA) were carried out using Mplus. RESULTS: One-factor model fit, without modifications, was acceptable for the Emotional distress scale. Only one correlated residual was required to be fitted in each of the other scales to achieve satisfactory model fit. The postulated highly restricted full eight-factor model (no cross-loadings, no correlated residuals) showed good fit to the data. Internal consistency was acceptable for most scales (0.72-0.90) but low for Self-monitoring and insight. CONCLUSION: This study of the Norwegian heiQ replicates the factor structure of the original Australian heiQ, using robust and highly restricted CFA procedures, demonstrating a clean independent clusters model structure. PRACTICE IMPLICATIONS: Researchers, program implementers and policymakers could use the Norwegian heiQ with confidence to generate reliable information on program outcomes and support quality improvement activities

Plasma concentration of atorvastatin metabolites correlates with low‐density lipoprotein cholesterol reduction in patients with coronary heart disease

Abstract In this exploratory study from a randomized double‐blinded crossover trial including 70 patients with coronary heart disease and self‐perceived muscular side effects of statins, we aimed to determine the relationship between low‐density lipoprotein cholesterol (LDL‐C) reduction and atorvastatin metabolite plasma concentrations. All patients underwent a 7 weeks treatment period with atorvastatin 40 mg/day and a 7 weeks placebo period in random order. Nonlinear regression with a three‐parameter equation explored the relationship between percentage LDL‐C reduction (statin vs. placebo) and the pharmacokinetic variables. Mean LDL‐C reduction was 49% (range 12% to 71%). The sum of 4‐OH‐atorvastatin acid and lactone correlated moderately with the LDL‐C response (Spearman ρ 0.27, 95% confidence interval [CI]: 0.03 to 0.48). Accordingly, nonlinear regression showed R2 of 0.14 (95% CI: 0.03 to 0.37, R2 adjusted equaled 0.11). Even a perfect underlying correlation of 1.0 showed R2 = 0.32 by simulation, using historical intra‐individual LDL‐C variation (8.5%). The 90% inhibitory concentration was 2.1 nmol/L, and the 4‐OH‐metabolite sum exceeded this threshold in 34% of the patients. In conclusion, trough plasma concentrations of 4‐OH‐atorvastatin metabolites correlated moderately to the LDL‐C reduction. A plateau LDL‐C response was observed above a pharmacokinetic threshold, below which the response was highly variable. The usefulness of monitoring concentrations of atorvastatin metabolites to optimize the individual dosage have limitations, but its supportive potential may be pursued in relevant patient subsets to achieve adequate efficacy at the lowest possible dose. The results add knowledge to the overall understanding of the variable LDL‐C response mediated by atorvastatin