Determinants of coronary flow reserve in non-diabetic patients with chest pain without myocardial perfusion defects

<div><p>Background</p><p>Microvascular dysfunction could be responsible for chest pain in patients without myocardial perfusion defects. We evaluated microvascular function using ultrasound-assessed coronary flow reserve (CFR) in patients with chest pain and normal myocardial perfusion scintigram. Secondly, we investigated association between cardiovascular parameters and decreased CFR in a sex specific manner.</p><p>Methods</p><p>A total of 202 (128 women) non-diabetic patients with chest pain and suspected myocardial ischemia, but without myocardial perfusion defects on myocardial perfusion scintigram, were enrolled and underwent CFR examination and blood sampling. All patients were followed-up for cardiovascular events. We used a supervised principal component analysis including 66 variables such as clinical parameters, ongoing medication, coronary artery disease history, lipids, metabolic parameters, inflammatory and other cardiovascular parameters.</p><p>Results</p><p>During a median follow-up time of 5.4 years, 25 cardiovascular events occurred; (men;18, women;7). Average CFR of the study cohort was 2.7±1.2 and 14% showed impaired CFR<2.0. In an adjusted Cox regression analysis, CFR<2.0 independently predicted event-free survival (HR:2.5, p = 0.033). In the supervised principal component analysis high insulin resistance assessed by Homeostatic model assessment for insulin resistance was the strongest biochemical marker associated with decreased CFR. Interestingly, upon sex specific multivariable linear regression analysis, the association was only significant in men (β = -0.132, p = 0.041) while systolic blood pressure remained an independent predictor in women (β = -0.009, p = 0.011).</p><p>Conclusions</p><p>In non-diabetic patients with chest pain without myocardial perfusion defects, low CFR has prognostic value for future cardiovascular events. Insulin resistance appears to be a marker for decreased CFR in men. Indeed, in the context of contribution of traditional risk factors in this patient population, the value of systolic blood pressure seems to be important in the women.</p></div

Patient recruitment flowchart over the study population.

<p>Flow chart for overview of patient recruitment in the current study. The study was performed at Sahlgrenska University Hospital, Gothenburg Sweden, during the years 2006–2008. CFR = coronary flow reserve.</p

OPLS discriminant analysis illustrating the major differences between men and women.

<p>(A) Shows the scores (t, to) of men (black circles) and women (grey circles) based on the loadings (p) and orthogonal loadings (po) of the variables shown in (B). The distribution of loadings along the x-axis ranks the best descriptors for sex differences, whereas the position along the Y-axis ranks the orthogonal loadings i.e. the variablility that is independent of the sex difference. The most pronounced variables (HDL, LVEF, ApoA1, Chol, MYO and Hb) are thus represented by large loading values (in either direction) combined with small orthogonal loading values, and the most outstanding individuals in this comparison is woman #220 and man #119, having the smallest and highest t(1) scores, respectively. The ellipse shown in (A) represents the confidence level (p = 0.05) of the model (n = 202). For explanation of abbreviations, see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0176511#pone.0176511.t003" target="_blank">Table 3</a>. OPLS = orthogonal projection to latent structures by partial least square analysis.</p

Sex specific linear regression analysis.

<p>Most associated variables to coronary flow reserve in women and men.</p

The contribution of variables for explaining CFR-determination.

<p>(A) Shows the loadings of the OPLS describing the relationship between the 66 variables and CFR in both sexes (n = 202). Each variable generated a loading value, p(1), along the principal component describing CFR. A positive loading value means that the variable is positively correlated to CFR and vice versa for a negative value. The error bars represent the 95% CI for the variable, and loadings representing variables of which variability is within the 95% CI are highlighted in grey. Dark grey represents plasma blood biomarkers (HOMA-IR, insulin, hyperlipidemia, HbA_1c, glucose, osteopontin and eosinophil counts). (B) Shows the distribution of men and women along the principal component describing CFR. A high score represents an individual with high CFR and vice versa. The box represents the median and the 25 to 75th percentiles and the whiskers min and max. Each individual is represented by black circle. CFR = coronary flow reserve, HbA_1c = glycosylated hemoglobin, HOMA-IR = homeostasic model assessment of insulin resistance, OPLS = orthogonal projection to latent structures by partial least square analysis.</p

Cox regression analysis.

<p>Univariate and multivariable predictors of major adverse cardiovascular events in whole study population and in a subgroup of patients without previous history of coronary artery disease.</p

Coronary flow reserve predicts major cardiovascular events in non-diabetic patients with chest pain without myocardial perfusion defects.

<p>(A) In a Cox regression analysis, coronary flow reserve below the pathological cut of <2.0 provides an independent prognostic value predicting long-term cardiovascular events in a study population of non-diabetic patients with chest pain without myocardial perfusion defects (n = 202). (B) The prognostic value remains when excluding patients with previously known CAD (n = 168). CAD = coronary artery disease.</p

Prospective evaluation of coronary FLOW reserve and molecular biomarkers in patients with established coronary artery disease the PROFLOW-trial : Cross-sectional evaluation of coronary flow reserve

Background: Survivors of myocardial infarction (MI) are at high risk of new major adverse cardiovascular events (MACE). Coronary flow reserve (CFR) is a strong and independent predictor of MACE. Understanding the prevalence of impaired CFR in this patient group and identifying risk markers for impaired CFR are important steps in the development of personalized and targeted treatment for high-risk individuals with prior MI. Methods: PROFLOW is a prospective, exploratory, cross-sectional open study. We used information from the SCAAR (Swedish Coronary Angiography and Angioplasty Registry) to identify high-risk patients with a history of type-1 MI. We measured CFR non-invasively in a left anterior descending artery (LAD) using transthoracic Doppler echocardiography. Coronary flow velocity was measured at rest and at maximal flow after induction of hyperemia by intravenous infusion of adenosine (140 μg/kg/min). Independent predictors of CFR were assessed with multiple linear regression. Results: We included 619 patients. The median age was 69 (IQR 65–73), and 114 (18.4%) were women. Almost one-half of the patients, 285 (46.0%) had the multi-vessel disease, and 147 (23.7%) were incompletely revascularized. The majority were on optimal standard treatment eg ASA (93.1%), statins (90.0%), ACEI/ARB (82.6%) and beta-blockers (80.8%). The majority, 547 (88.4%) had no angina pectoris, and 572 (92.2%) were in NYHA class I. Evaluation of CFR was possible in 611 (98.7%) patients. Mean CFR was 2.74 (±0.79 (mean ± SD)). A substantial number of patients (39.7%) had CFR ≤2.5. In a multiple linear regression model age, dyslipidemia, smoking, hypertension, body mass index, incomplete revascularization, and treatment with angio-tensin receptor blockers were independent predictors of CFR. Conclusion: In this high-risk group of patients with prior MI, the prevalence of impaired CFR was high. Further risk stratification with CFR in addition to traditional cardiovascular risk factors may improve predictive accuracy for future MACE in this patient population