Aims
Coronary microvascular dysfunction (CMD) is a major cause of angina and impaired outcome. Protein biomarkers could simplify patient selection for assessment and help uncover pathophysiologic pathways.

Methods and results
We quantified 184 protein biomarkers in 1471 women with angina and no obstructive coronary artery disease on angiography characterized for CMD by coronary flow velocity reserve (CFVR) by Doppler Echocardiography. Sixty-one biomarkers were significantly associated with CFVR (Figure 1). The strongest correlations were seen for renin, growth differentiation factor 15 (GDF15), brain natriuretic protein (BNP), NT-proBNP and adrenomedullin (ADM) (all p<1e-06). To identify pathophysiological patterns, we applied principal components (PC) analyses and weighted protein co-abundance network analyses. Two PCs with the highest loading on BNP/ NTproBNP and interleukin 6 (IL-6), respectively, were strongly associated with low CFVR. The weighted protein co-abundance network analyses identified two clusters associated with low CFVR reflecting involvement of hypertension/vascular function and immune modulation. For prediction of CMD (CFVR <2.25, n=646), data was split into model and validation cohorts. The best model using only clinical data had area under the receiver operating characteristic curve (ROC-AUC) of 0.61 (95% CI 0.56–0.66). ROC-AUC improved to 0.66 (95% CI: 0.62–0.71) with addition of biomarkers. Stringent two-layer cross-validated machine learning models had ROC-AUC ranging from 0.58 to 0.66 (Figure 2); the most predictive biomarkers were renin, BNP, NT-proBNP, GDF15 and ADM.

Conclusion
CMD was associated with biological pathways, particularly involving inflammation (IL-6), blood pressure (renin, ADM) and ventricular remodelling (BNP/NTproBNP). The identified biomarker pathways may prove potential treatment targets for CMD. Diagnostic models improved significantly when adding protein biomarkers to clinical information.