Assessing the immune phenotype of cardiac syndrome x: a prospective study of biomarkers

Cardiac Syndrome X (CSX), the presence of angina pectoris with objective signs of myocardial ischaemia despite angiographically normal epicardial coronary arteries, appears to be due to coronary microvascular dysfunction and is known to be associated with an elevation of several inflammatory biomarkers, suggesting a possible role for inflammation in its pathogenesis. We aimed to further characterise this relationship by prospectively analysing a wide variety of molecular biomarkers in a cohort of CSX patients thereby charting the changes in biomarkers throughout the natural history of CSX from its initial diagnosis to eventual disease quiescence. We found that CSX patients, when compared to healthy controls, have a persistent low-grade systemic inflammatory response characterised by an elevation of Tumour Necrosis Factor and Interferon-gamma, regardless of the presence of contemporaneous signs or symptoms of disease activity. Interleukin-6 and C-reactive Protein (CRP) are only elevated when patients have clinical evidence of disease activity and may be state markers in CSX. Moreover, CRP levels appear to correlate with signals of disease severity such as the time taken to develop symptoms during exercise stress testing. We have also demonstrated that the enzyme Indoleamine-2,3- dioxygenase is upregulated in active disease thus providing a possible explanation for the increased burden of psychological disease encountered in CSX. Analysis of the microRNA transcriptome showed that miR-143 is significantly under-expressed in CSX patients. This could allow phenotype switching in vascular smooth muscle cells with the resultant vascular remodelling causing reduced vessel responsiveness to local rheological stimuli and reduced luminal diameter with consequent increased microvascular resistance during times of increased myocardial oxygen demand, thereby limiting maximal hyperaemia during exercise. Our findings corroborate many previous hypotheses regarding the role of inflammation in CSX, generate new insights into possible pathogenic mechanisms and offer new therapeutic targets for the future management of this important cardiological condition.

Personalized absolute benefit of statin treatment for primary or secondary prevention of vascular disease in individual elderly patients

Objective: To estimate the absolute treatment effect of statin therapy on major adverse cardiovascular events (MACE; myocardial infarction, stroke and vascular death) for the individual patient aged C70 years. Methods: Prediction models for MACE were derived in patients aged C70 years with (n = 2550) and without (n = 3253) vascular disease from the ‘‘PROspective Study of Pravastatin in Elderly at Risk’’ (PROSPER) trial and validated in the ‘‘Secondary Manifestations of ARTerial disease’’ (SMART) cohort study (n = 1442) and the ‘‘Anglo-Scandinavian Cardiac Outcomes Trial-Lipid Lowering Arm’’ (ASCOT-LLA) trial (n = 1893), respectively, using competing risk analysis. Prespecified predictors were various clinical characteristics including statin treatment. Individual absolute risk reductions (ARRs) for MACE in 5 and 10 years were estimated by subtracting ontreatment from off-treatment risk. Results: Individual ARRs were higher in elderly patients with vascular disease [5-year ARRs: median 5.1 %, interquartile range (IQR) 4.0–6.2 %, 10-year ARRs: median 7.8 %, IQR 6.8–8.6 %] than in patients without vascular disease (5-year ARRs: median 1.7 %, IQR 1.3–2.1 %, 10-year ARRs: 2.9 %, IQR 2.3–3.6 %). Ninetyeight percent of patients with vascular disease had a 5-year ARR C2.0 %, compared to 31 % of patients without vascular disease. Conclusions: With a multivariable prediction model the absolute treatment effect of a statin on MACE for individual elderly patients with and without vascular disease can be quantified. Because of high ARRs, treating all patients is more beneficial than prediction-based treatment for secondary prevention of MACE. For primary prevention of MACE, the prediction model can be used to identify those patients who benefit meaningfully from statin therapy.