| Literature DB >> 21072124 |
Nicholas W Witt1, Neil Chapman, Simon A McG Thom, Alice V Stanton, Kim H Parker, Alun D Hughes.
Abstract
Conventionally, the relationship between parent and daughter vessels at vascular bifurcations has been expressed by the junction exponent (x), and deviations of this parameter from the optimal conditions predicted by Murray's law (x = 3) have been shown to be associated with vascular disease. However, the junction exponent is normally calculated iteratively from diameter measurements, and Monte-Carlo simulation studies show the junction exponent to be biased in the presence of measurement noise.We present an alternative parameter, referred to as optimality ratio, that is simpler to compute and also more robust in the presence of noise.To demonstrate the sensitivity of the optimality ratio to alterations in topography of the retinal vascular network, we analysed the effect of inducing endothelial dysfunction by infusion of NG-monomethyl-l-arginine (l-NMMA), a nitric oxide synthase inhibitor, compared to placebo in a double-blind crossover study. The optimality ratio showed a significant increase (p = 0.03) during infusion of l-NMMA compared to placebo.We propose that a measure of the extent of departure of optimality ratio from its optimal value of 2(-1/3) may be a useful indicator of microvascular endothelial dysfunction in vivo.Entities:
Year: 2010 PMID: 21072124 PMCID: PMC2954284 DOI: 10.1016/j.artres.2010.06.003
Source DB: PubMed Journal: Artery Res ISSN: 1872-9312 Impact factor: 0.597
Figure 1Junction exponent as a function of non-dimensional daughter diameters ζ1 and ζ2.
Figure 2Dependence of correction factor on asymmetry.
Figure 3Junction exponent against optimality ratio.
Effect of measurement noise on junction exponent and optimality ratio.
| SD of measurement noise (% of vessel diameter) | Mean bias (SD) | |
|---|---|---|
| Units of optimality ratio | ||
| From junction exponent | From optimality ratio | |
| 5 | 0.019 (0.087) | 0.0030 (0.049) |
| 10 | 0.060 (0.23) | 0.0089 (0.10) |
| 15 | 0.042 (0.32) | 0.024 (0.16) |
Results of Monte-Carlo simulation of the effect of measurement noise at different asymmetry factors (α) on optimality ratio (Γratio).
| Mean bias (SD) | |||||
|---|---|---|---|---|---|
| 1.0 | 0.008 (0.075) | 0.009 (0.089) | 0.014 (0.106) | 0.014 (0.114) | 0.018 (0.128) |
| 0.8 | 0.008 (0.077) | 0.010 (0.090) | 0.017 (0.104) | 0.011 (0.113) | 0.017 (0.126) |
| 0.6 | 0.008 (0.077) | 0.009 (0.093) | 0.012 (0.102) | 0.011 (0.119) | 0.015 (0.130) |
| 0.4 | 0.011 (0.082) | 0.009 (0.094) | 0.009 (0.103) | 0.015 (0.121) | 0.016 (0.135) |
Baseline measurements in placebo and l-NMMA phases.
| Placebo | ||
|---|---|---|
| Systolic BP, mmHg | 129 (11) | 127 (14) |
| Diastolic BP, mmHg | 69 (9) | 72 (9) |
| Heart rate, bpm | 68 (7) | 68 (11) |
| Diameter | 28.3 (3.1) | 27.9 (3.8) |
| Bifurcation angle, degrees | 77 (10) | 79 (9) |
| Optimality ratios | 0.784 (0.006) | 0.795 (0.021) |
Data are means (SD).
Figure 4Change in optimality ratio (mean (SD)) during active and placebo phases of trial.
Effect on measured variables of l-NMMA compared with placebo.
| Marginal effects (placebo– | ||
|---|---|---|
| Systolic BP, mmHg | 9.0 (4.1, 13.8) | < 0.001 |
| Diastolic BP, mmHg | 4.5 (2.3, 6.6) | < 0.001 |
| Heart rate, bpm | −1.2 (−3.6, 1.3) | 0.4 |
| Diameter | −0.72 (−1.38, −0.06) | 0.03 |
| Bifurcation angle, degrees | −1.1 (2.8, 0.7) | 0.2 |
| Optimality ratios | 0.022 (0.002, 0.043) | 0.03 |
Data are marginal means (95% confidence intervals).