CORP: Ultrasound assessment of vascular function with the passive leg movement technique

Abstract

As dysfunction of the vascular system is an early, modifiable step in the progression of many cardiovascular diseases, there is demand for methods to monitor the health of the vascular system noninvasively in clinical and research settings. Validated by very good agreement with more technical assessments of vascular function, like intra-arterial drug infusions and flow-mediated dilation, the passive leg movement (PLM) technique has emerged as a powerful, yet relatively simple, test of peripheral vascular function. In the PLM technique, the change in leg blood flow elicited by the passive movement of the leg through a 90° range of motion is quantified with Doppler ultrasound. This relatively easy-to-learn test has proven to be ≤80% dependent on nitric oxide bioavailability and is especially adept at determining peripheral vascular function across the spectrum of cardiovascular health. Indeed, multiple reports have documented that individuals with decreased cardiovascular health such as the elderly and those with heart failure tend to exhibit a substantially blunted PLM-induced hyperemic response (~50 and ~85% reduction, respectively) compared with populations with good cardiovascular health such as young individuals. As specific guidelines have not yet been put forth, the purpose of this Cores of Reproducibility in Physiology (CORP) article is to provide a comprehensive reference for the assessment and interpretation of vascular function with PLM with the aim to increase reproducibility and consistency among studies and facilitate the use of PLM as a research tool with clinical relevance.

Keywords: cardiovascular health; endothelial function; guidelines; nitric oxide.

Figures

Fig. 1.

Passive leg movement (PLM)-induced hyperemia in groups typified by differing levels of cardiovascular health. YA, Young Physically Active (n = 5; J. R. Gifford, R. Broxterman, and R. S. Richardson, unpublished observations). YS, Young Sedentary (n = 12; Ref. 22). OA, Old Physically Active (n = 10; Ref. 22). OS, Old Sedentary (n = 12; Ref. 22). HF, Heart Failure (n = 14; Ref. 81). AUC, area under the curve.

Fig. 2.

Effect of nitic oxide (NO) synthase inhibition with l-NMMA on passive leg movement (PLM)-induced hyperemia in young, healthy individuals. These data from Trinity et al. (71) indicate that NO is responsible for up to 80% of the overall PLM-induced hyperemic response. Other studies have verified this observation, supporting the role of NO in the PLM response (24, 45, 70). Note that passive leg movement begins at 0 s. AUC, area under the curve. *Significantly different from control.

Fig. 3.

Schematic illustrating the appropriate steps involved in performing a passive leg movement (PLM) test of vascular function. Note that details about each step are presented in the order indicated in the boxes.

Fig. 4.

Illustration of the passive leg movement (PLM) test of vascular function. In the PLM test, the participant sits in a chair while his or her leg is passively moved through a 90° range of motion at 1 Hz by a member of the research team. Simultaneously, Doppler ultrasound is used to quantify the change in blood flow through the common femoral artery.

Fig. 5.

Effect of insonation angle on measurements of leg blood flow during passive leg movement (PLM). A: representative image of Doppler ultrasound with the angle correction cursor (solid line) acceptably aligned with the artery at an insonation angle of 60° from the Doppler beam or out of alignment with the artery walls at either 50 or 70° from the Doppler beam. The cross hairs on artery wall represent location of the media layer where diameter measurements were made. B: effect of error in insonation angle (i.e., misalignment of angle correction cursor with the artery wall) on resting blood flow and peak blood flow during PLM (n = 5). Note that the acceptable error condition was taken as an accurately determined insonation angle of 60° with the angle correction cursor parallel to the artery. Dashed lines represent mean value at the acceptable error associated with this 60° insonation angle for each condition. *Significantly different from the acceptable error associated with an insonation angle of 60°.

Fig. 6.

An illustration of the calculations to assess a passive leg movement (PLM) test of vascular function. Blood flow during the initial baseline period represents the average resting leg blood flow for the ≥60 s immediately before leg movement. Peak blood flow represents the greatest blood flow measured during PLM. Δ Peak blood flow represents the change from baseline to peak blood flow. Area under the curve (AUC) represents the overall PLM-induced hyperemia above baseline.

Fig. 7.

Evidence of the reproducibility of passive leg movement (PLM)-induced hyperemia. In these examples, PLM-induced hyperemia was measured on 2 individuals (1 young and 1 old) on 3 separate days.

Fig. 8.

Theoretical effect of small errors in the measurement of artery diameter on previously reported values for flow-mediated dilation (FMD) and passive leg movement (PLM) of healthy controls (Con) in relation to patients with heart failure. A: effect of 0.1-mm error (Con – 0.1 mm) in the measurement of brachial artery diameter on the FMD of healthy controls in comparison with the FMD of patients with heart failure (80). Note that the left vertical axis represents FMD as %dilation, whereas the right vertical axis represents the FMD as the absolute change in artery diameter. B: effect of 0.1-mm error (Con – 0.1 mm) in the measurement of common femoral artery diameter on peak hyperemia during PLM of healthy controls in comparison with that of patients with heart failure (81). C: %change in FMD or peak PLM response with a 0.1-mm error in the diameter measurement in healthy controls. *Note that Con and Heart Failure data are derived from previously published studies (80, 81), whereas Con – 0.1 mm and Con – 3.0 mm are the theoretical values that would have been reported if such errors occurred in the measurement of the Con data.