Carotid Plaque Age Is a Feature of Plaque Stability Inversely Related to Levels of Plasma Insulin

C-declination curve (a result of the atomic bomb tests in the 1950s and 1960s) to determine the average biological age of carotid plaques.C content by accelerator mass spectrometry. The average plaque age (i.e. formation time) was 9.6±3.3 years. All but two plaques had formed within 5–15 years before surgery. Plaque age was not associated with the chronological ages of the patients but was inversely related to plasma insulin levels (p = 0.0014). Most plaques were echo-lucent rather than echo-rich (2.24±0.97, range 1–5). However, plaques in the lowest tercile of plaque age (most recently formed) were characterized by further instability with a higher content of lipids and macrophages (67.8±12.4 vs. 50.4±6.2, p = 0.00005; 57.6±26.1 vs. 39.8±25.7, p<0.0005, respectively), less collagen (45.3±6.1 vs. 51.1±9.8, p<0.05), and fewer smooth muscle cells (130±31 vs. 141±21, p<0.05) than plaques in the highest tercile. Microarray analysis of plaques in the lowest tercile also showed increased activity of genes involved in immune responses and oxidative phosphorylation.C, can improve our understanding of carotid plaque stability and therefore risk for clinical complications. Our results also suggest that levels of plasma insulin might be involved in determining carotid plaque age

Intima-media thickness at the near or far wall of the common carotid artery in cardiovascular risk assessment

Aims: Current guidelines recommend measuring carotid intima-media thickness (IMT) at the far wall of the common carotid artery (CCA). We aimed to precisely quantify associations of near vs. far wall CCA-IMT with the risk for atherosclerotic cardiovascular disease (CVD, defined as coronary heart disease or stroke) and their added predictive values. Methods and results: We analysed individual records of 41 941 participants from 16 prospective studies in the Proof-ATHERO consortium {mean age 61 years [standard deviation (SD) = 11]; 53% female; 16% prior CVD}. Mean baseline values of near and far wall CCA-IMT were 0.83 (SD = 0.28) and 0.82 (SD = 0.27) mm, differed by a mean of 0.02 mm (95% limits of agreement: −0.40 to 0.43), and were moderately correlated [r = 0.44; 95% confidence interval (CI): 0.39–0.49). Over a median follow-up of 9.3 years, we recorded 10 423 CVD events. We pooled study-specific hazard ratios for CVD using random-effects meta-analysis. Near and far wall CCA-IMT values were approximately linearly associated with CVD risk. The respective hazard ratios per SD higher value were 1.18 (95% CI: 1.14–1.22; I² = 30.7%) and 1.20 (1.18–1.23; I² = 5.3%) when adjusted for age, sex, and prior CVD and 1.09 (1.07–1.12; I² = 8.4%) and 1.14 (1.12–1.16; I²=1.3%) upon multivariable adjustment (all P < 0.001). Assessing CCA-IMT at both walls provided a greater C-index improvement than assessing CCA-IMT at one wall only [+0.0046 vs. +0.0023 for near (P < 0.001), +0.0037 for far wall (P = 0.006)]. Conclusions: The associations of near and far wall CCA-IMT with incident CVD were positive, approximately linear, and similarly strong. Improvement in risk discrimination was highest when CCA-IMT was measured at both walls

2016 ESC Guidelines for the management of atrial fibrillation developed in collaboration with EACTS.

Peer reviewe

Intima-media thickness at the near or far wall of the common carotid artery in cardiovascular risk assessment

Aims: Current guidelines recommend measuring carotid intima-media thickness (IMT) at the far wall of the common carotid artery (CCA). We aimed to precisely quantify associations of near vs. far wall CCA-IMT with the risk for atherosclerotic cardiovascular disease (CVD, defined as coronary heart disease or stroke) and their added predictive values. Methods and results: We analysed individual records of 41 941 participants from 16 prospective studies in the Proof-ATHERO consortium {mean age 61 years [standard deviation (SD) = 11]; 53% female; 16% prior CVD}. Mean baseline values of near and far wall CCA-IMT were 0.83 (SD = 0.28) and 0.82 (SD = 0.27) mm, differed by a mean of 0.02 mm (95% limits of agreement: -0.40 to 0.43), and were moderately correlated [r = 0.44; 95% confidence interval (CI): 0.39-0.49). Over a median follow-up of 9.3 years, we recorded 10 423 CVD events. We pooled study-specific hazard ratios for CVD using random-effects meta-analysis. Near and far wall CCA-IMT values were approximately linearly associated with CVD risk. The respective hazard ratios per SD higher value were 1.18 (95% CI: 1.14-1.22; I² = 30.7%) and 1.20 (1.18-1.23; I² = 5.3%) when adjusted for age, sex, and prior CVD and 1.09 (1.07-1.12; I² = 8.4%) and 1.14 (1.12-1.16; I²=1.3%) upon multivariable adjustment (all P < 0.001). Assessing CCA-IMT at both walls provided a greater C-index improvement than assessing CCA-IMT at one wall only [+0.0046 vs. +0.0023 for near (P < 0.001), +0.0037 for far wall (P = 0.006)]. Conclusions: The associations of near and far wall CCA-IMT with incident CVD were positive, approximately linear, and similarly strong. Improvement in risk discrimination was highest when CCA-IMT was measured at both walls

Progression of conventional cardiovascular risk factors and vascular disease risk in individuals: insights from the PROG-IMT consortium

Aims: Averaged measurements, but not the progression based on multiple assessments of carotid intima-media thickness, (cIMT) are predictive of cardiovascular disease (CVD) events in individuals. Whether this is true for conventional risk factors is unclear. Methods and results: An individual participant meta-analysis was used to associate the annualised progression of systolic blood pressure, total cholesterol, low-density lipoprotein cholesterol and high-density lipoprotein cholesterol with future cardiovascular disease risk in 13 prospective cohort studies of the PROG-IMT collaboration (n = 34,072). Follow-up data included information on a combined cardiovascular disease endpoint of myocardial infarction, stroke, or vascular death. In secondary analyses, annualised progression was replaced with average. Log hazard ratios per standard deviation difference were pooled across studies by a random effects meta-analysis. In primary analysis, the annualised progression of total cholesterol was marginally related to a higher cardiovascular disease risk (hazard ratio (HR) 1.04, 95% confidence interval (CI) 1.00 to 1.07). The annualised progression of systolic blood pressure, low-density lipoprotein cholesterol and high-density lipoprotein cholesterol was not associated with future cardiovascular disease risk. In secondary analysis, average systolic blood pressure (HR 1.20 95% CI 1.11 to 1.29) and low-density lipoprotein cholesterol (HR 1.09, 95% CI 1.02 to 1.16) were related to a greater, while high-density lipoprotein cholesterol (HR 0.92, 95% CI 0.88 to 0.97) was related to a lower risk of future cardiovascular disease events. Conclusion: Averaged measurements of systolic blood pressure, low-density lipoprotein cholesterol and high-density lipoprotein cholesterol displayed significant linear relationships with the risk of future cardiovascular disease events. However, there was no clear association between the annualised progression of these conventional risk factors in individuals with the risk of future clinical endpoints

Automatic identification of variables in epidemiological datasets using logic regression

textabstractBackground: For an individual participant data (IPD) meta-analysis, multiple datasets must be transformed in a consistent format, e.g. using uniform variable names. When large numbers of datasets have to be processed, this can be a time-consuming and error-prone task. Automated or semi-automated identification of variables can help to reduce the workload and improve the data quality. For semi-automation high sensitivity in the recognition of matching variables is particularly important, because it allows creating software which for a target variable presents a choice of source variables, from which a user can choose the matching one, with only low risk of having missed a correct source variable. Methods: For each variable in a set of target variables, a number of simple rules were manually created. With logic regression, an optimal Boolean combination of these rules was searched for every target variable, using a random subset of a large database of epidemiological and clinical cohort data (construction subset). In a second subset of this database (validation subset), this optimal combination rules were validated. Results: In the construction sample, 41 target variables were allocated on average with a positive predictive value (PPV) of 34%, and a negative predictive value (NPV) of 95%. In the validation sample, PPV was 33%, whereas NPV remained at 94%. In the construction sample, PPV was 50% or less in 63% of all variables, in the validation sample in 71% of all variables. Conclusions: We demonstrated that the application of logic regression in a complex data management task in large epidemiological IPD meta-analyses is feasible. However, the performance of the algorithm is poor, which may require backup strategies

Reasons for Disparity in Statin Adherence Rates between Clinical Trials and Real World Observations. A Review.

With statins, the reported rate of adverse events differs widely between randomized clinical trials (RCTs) and observations in clinical practice, the rates being 1-2% in RCTs versus 10-20% in the so-called real world. One possible explanation is the claim that RCTs mostly use a run-in period with a statin. This would exclude intolerant patients from being included into RCTs and therefore favor a bias towards lower rates of intolerance.We here review data from RCTs with more than 1000 participants with and without a run-in period, which were included in the Cholesterol Treatment Trialists collaboration (CTTC). Two major conclusions arise: 1) The majority of RCTs did not have a test dose of a statin in the run-in phase. 2) A test dose in the run-in phase was not associated with a significantly improved adherence rate within that trial when compared to trials without a test dose. Taken together, the RCTs of statins reviewed here do not suggest a bias towards an artificially higher adherence rate because of a run-in period with a test dose of the statin.Other possible explanations for the apparent disparity between RCTs and real world observations are also included in this review albeit mostly not supported by scientific data