Detailed exploration of the endothelium: parameterization of flow-mediated dilation through principal component...

Endothelial dysfunction is associated with cardiovascular diseases and their risk factors (CVRF), and flow-mediated dilation (FMD) is increasingly used to explore it. In this test, artery diameter changes after post-ischaemic hyperaemia are classically quantified using maximum peak vasodilation (FMDc). To obtain more detailed descriptors of FMD we applied principal component analysis (PCA) to diameter-time curves (absolute), vasodilation-time curves (relative) and blood-velocity-time curves. Furthermore, combined PCA of vessel size and blood-velocity curves allowed exploring links between flow and dilation. Vessel diameter data for PCA (post-ischaemic: 140 s) were acquired from brachial ultrasound image sequences of 173 healthy male subjects using a computerized technique previously reported by our team based on image registration (Frangi et al 2003 IEEE Trans. Med. Imaging 22 1458). PCA provides a set of axes (called eigenmodes) that captures the underlying variation present in a database of waveforms so that the first few eigenmodes retain most of the variation. These eigenmodes can be used to synthesize each waveform analysed by means of only a few parameters, as well as potentially any signal of the same type derived from tests of new patients. The eigenmodes obtained seemed related to visual features of the waveform of the FMD process. Subsequently, we used eigenmodes to parameterize our data. Most of the main parameters (13 out of 15) correlated with FMDc. Furthermore, not all parameters correlated with the same CVRF tested, that is, serum lipids (i.e., high LDL-c associated with slow vessel return to a baseline, while low HDL-c associated with a lower vasodilation in response to similar velocity stimulus), thus suggesting that this parameterization allows a more detailed and factored description of the process than FMDc.