Kinetic differences between macro- and microvascular measures of reactive hyperemia

Abstract

Postischemia reperfusion kinetics are markedly dissociated when comparing the macro- versus microvasculature. We used Doppler ultrasound and near-infrared diffuse correlation spectroscopy (NIR-DCS), an emerging technique for continuously and noninvasively quantifying relative changes in skeletal muscle microvascular perfusion (i.e., blood flow index or BFI), to measure macro- and microvascular reactive hyperemia (RH) in the nondominant arm of 16 healthy young adults. First, we manipulated the duration of limb ischemia (3 vs. 6 min) with the limb at heart level (neutral, -N). Then, we reduced/increased forearm perfusion pressure (PP) by positioning the arm above (3 min-A, 60°) or below (3 min-B, 30°) the heart. The major novel findings were twofold: first, changes in the ischemic stimulus similarly affected peak macrovascular (i.e., conduit, mL/min) and microvascular (i.e., peak NIR-DCS-derived BFI) reperfusion during reactive hyperemia (6 min-N > 3 min-N, P < 0.05, both) but did not affect the rate at which microvascular reperfusion occurs (i.e., BFI slope). Second, changing forearm PP predictably affected both peak macro- and microvascular reperfusion during RH (3 min-B > N > A, P < 0.05, all), as well as the rate at which microvascular reperfusion occurred (BFI slope; 3 min-B >N > A, P < 0.05). Together, the data suggest that kinetic differences between macro- and microvascular reperfusion are largely determined by differences in fluid mechanical energy (i.e., pressure, gravitational, and kinetic energies) between the two compartments that work in tandem to restore pressure across the arterial tree following a period of tissue ischemia.

NEW & NOTEWORTHY We extend our understanding of macro- versus microvascular hemodynamics in humans, by using near-infrared diffuse correlation spectroscopy (micro-) and Doppler ultrasound (macro-) to characterize reperfusion hemodynamics following experimental manipulation of the ischemic stimulus and tissue perfusion pressure. Our results suggest kinetic differences between macro- and microvascular reperfusion are largely determined by differences in fluid mechanical energy (i.e., pressure, gravitational, and kinetic energies) between the two compartments, rather than inherent differences between the macro- and microvasculature.

INTRODUCTION

Ischemia-induced reactive hyperemia describes the acute augmentation in peripheral blood flow observed following a brief period of cuff occlusion. Notably, reactive hyperemia is predictive of all-cause and cardiovascular morbidity and mortality (1, 16, 18, 24, 27) and is widely regarded as a measure of “microvascular function” given its association with hypoxia-mediated vasodilation. In this context, the magnitude of reactive hyperemia (i.e., the fold change in blood flow above baseline) is assumed to follow Darcy’s law, whereby blood flow increases proportionally to the pressure difference that develops across the cuff between the systemic and ischemic arterial systems. Thus, for a given period of ischemia (i.e., typically 5 min), greater reactive hyperemia is assumed to reflect greater microvascular dilation and, by extension, greater microvascular function. However, several studies have shown that the magnitude of reactive hyperemia can be influenced by other factors. For example, we have shown that interindividual and cross-sectional differences in basal metabolic rate can influence the magnitude of the ischemic stimulus to vasodilate (31, 33), and therefore impact the magnitude of reactive hyperemia. Moreover, Jasperse et al. (19) have highlighted the impact of changing perfusion pressure on reactive hyperemia, by adjusting the position of the experimental limb relative to the heart, allowing gravity to assist/resist reperfusion.

To complicate matters further, the are several different techniques for measuring reactive hyperemia (32), which can complicate interpreting results across studies. Common techniques for measuring reactive hyperemia hemodynamics include Doppler ultrasound of an upstream conduit vessel, limb distension by venous occlusion plethysmography, and peripheral artery tonometry. Collectively, these “macrovascular” techniques have shown that reactive hyperemia is immediate and occurs within the first few cardiac cycles before returning to baseline within ∼30 s. In contrast to these macrovascular observations, however, studies that have measured microvascular hemodynamics during reactive hyperemia, such as arterial spin labeling MRI (12, 30), first-pass perfusion MRI (38), and positron emission tomography (37), have demonstrated markedly prolonged responses that peak ∼20–30 s after cuff release (a time when macrovascular hemodynamics are already returning to baseline). More recently, Didier et al. (10) used near-infrared diffuse correlation spectroscopy (NIR-DCS)—an optical imaging technique that permits direct assessment of microvascular perfusion—to examine differences in macro- and microvascular reactive hyperemia following several trials of brachial artery occlusion. Whereas brachial artery blood flow peaked almost instantaneously after cuff release, peak skeletal muscle microvascular reperfusion was once again significantly delayed (∼20 s). Together, these kinetic differences between macro- and microvascular reperfusions raise several methodological and physiological questions.

Consequently, the purpose of the present investigation was to extend our understanding of macro- versus microvascular hemodynamics, by using NIR-DCS in combination with Doppler ultrasound to compare and contrast reperfusion hemodynamics following experimental manipulation of the ischemic stimulus (i.e., time of arterial cuff occlusion) and perfusion pressure (i.e., limb position). We hypothesized that both the rate and the magnitude of microvascular reperfusion would increase with increasing duration of ischemia and increases in perfusion pressure.

METHODS

Ethical Statement

All experimental procedures were approved by the Institutional Review Board of the University of Texas at Arlington (IRB# 2020–0039) and conformed to the standards set forth by the Declaration of Helsinki, apart from registration in a database.

Experimental Design

To examine how changes in the ischemic stimulus to vasodilate and tissue perfusion pressure affect macro- and microvascular reperfusion hemodynamics, subjects completed four reactive hyperemia trials separated by at least 20 min of rest. To investigate the role of the ischemic stimulus to vasodilate, we manipulated the duration of limb ischemia (6 vs. 3 min) with the arm in a neutral (-N) position relative to that of the heart. To investigate the role of tissue perfusion pressure, we positioned the experimental arm above (-A, 60°) or below (-B, 30°) the heart and measured reactive hyperemia following 3 min of occlusion. The 6 min-N trial was always conducted first, followed by the 3 min-N, 3 min-A, and 3 min-B trials in a randomized, counterbalanced order.

Healthy young adults were recruited from the Dallas-Fort Worth, Texas, community by means of flyers and word of mouth. Potential participants were screened via phone call and excluded if they reported a history of overt cardiovascular, pulmonary, metabolic, or musculoskeletal disease. Subsequently, 16 healthy young adults were enrolled after they gave their written informed consent. Subjects arrived at the laboratory in a fasted state having abstained from alcohol, caffeine, and vigorous exercise for the 24 h preceding the visit. Premenopausal women were tested during the early follicular phase to reduce potential variability in vascular responsiveness.

Instrumentation and Measurements

Anthropometrics.

Following informed consent, the subjects’ height and weight were measured on an electrical stadiometer (Professional 500KL, Health-O-Meter, McCook, IL). Seated blood pressure was measured in triplicate using an automated blood pressure cuff (71WX-B Connex Spot Monitor; Welch Allyn Connex, Skaneateles Falls, NY). Skinfold thickness of the left medial forearm was measured in duplicate along three sites (proximal, midpoint, and distal), and maximal handgrip strength was measured using a Smedley handgrip dynamometer (Stoelting Corporation, Wood Dale, IL). Lastly, after all experimental procedures had been completed, body composition was measured by dual energy X-ray absorptiometry to determine whole body body-fat percentage, fat mass, and fat-free mass (DXA Lunar Prodigy; GE Healthcare, Little Chalfont, UK).

Experimental setup.

All tests were performed in a temperature-controlled (∼22°C) and ambient light-controlled room. Following collection of anthropometric characteristics, subjects were positioned supine on a bed and then instrumented for measurement of continuous fingertip blood pressure, heart rate, brachial artery blood flow, and forearm oxygen saturation/microvascular blood flow. Beat-by-beat blood pressure in the experimental limb was measured from a small finger-pressure cuff placed around the middle or index finger using photoplethysmography (Finometer PRO, Finapres Medical Systems, Arnhem, The Netherlands) that was calibrated using an automated brachial artery blood pressure cuff (71WX-B Connex Spot Monitor; Welch Allyn Connex, Skaneateles Falls, NY). Subsequently, mean fingertip pressure was calculated as the mean pressure observed across a continuous average of arterial waveforms and then converted into forearm perfusion pressure (FPP) by adjusting for changes in the height of the hydrostatic column. First, we calculated the change in perfusion pressure across the vertical distance separating the fingertip and heart (i.e., ΔmmHg/cm vertical displacement). Using this quotient, we then calculated FPP by adding (below trial) or subtracting (above trial) the ΔmmHg/cm of vertical displacement between the forearm and heart. Heart rate was measured via three-lead electrocardiography using standard CM5 electrode placement. Limb ischemia was achieved by inflating a cuff (220 mmHg) around the upper arm, proximal to the brachial artery blood flow measurements.

Doppler ultrasound.

Brachial artery mean blood velocity and vessel diameter were measured continuously on the upper left arm using a duplex ultrasound system (Vivid I; GE Healthcare). The ultrasound system consisted of a 12-MHz linear-array probe, positioned at least 2 cm proximal to the antecubital fossa with 60° of insonation. A validated Doppler audio transformer was used to convert the Doppler audio signal into real-time, continuous measurements of blood flow velocity (15), recorded using a Powerlab data acquisition system (ADInstruments Inc., Colorado Springs, CO). Brachial artery diameter was measured using B-mode ultrasound imaging and recorded using commercially available image-capturing software (Vascular Imager, Medical Imaging Applications LLC, Coralville, IA) at a frequency of 12 Hz. The recorded videos were then postprocessed using FMD Studio Cardiovascular Suite software (QUIPU, Pisa, Italy) for continuous measurement of brachial artery diameter 60 s before arterial cuff inflation and for 3 min following cuff deflation. Measurements of brachial artery diameter were then down-sampled to 1 Hz and exported to Excel for subsequent calculations of brachial artery blood flow.

Near-infrared DCS.

Forearm heme (i.e., hemoglobin + myoglobin) content, % tissue oxygenation (S_tO₂; the ratio of oxygenated heme divided by total heme), and microvascular blood flow index were measured continuously using a commercially available frequency-domain near-infrared diffuse correlation spectrometer (MetaOx; ISS Inc., Champaign, IL). The theoretical principles describing frequency domain multidistance (FDMD) near infrared spectroscopy (NIRS) (3, 13, 17) and diffuse correlation spectroscopy (5, 6, 11) have been described elsewhere. Briefly, FDMD-NIRS calculates optical tissue absorption (μ_a) and reduced scattering (μ’_s) coefficients by measuring the attenuation and phase shift of light as it travels through tissues from varying source-detector distances. The derived μ_a and μ’_s are then incorporated into the modified Beer–Lambert law to derive absolute concentrations of oxygenated and deoxygenated hemoglobin + myoglobin. An important limitation of NIRS is that it does not permit separation of the hemoglobin + myoglobin signals, which should be considered when interpreting changes in forearm heme content in NIRS studies. Alternatively, relative measures of microvascular blood flow (i.e., blood flow index or BFI) can be derived from DCS measurements of near-infrared photon intensity and speckle fluctuations, which are primarily driven by the movement of red blood cells.

All NIR-DCS measurements were collected at a sampling frequency of 5 Hz and then streamed into PowerLab (ADInstruments). A single NIR-DCS probe that contained multiple source-detector pairs for near-infrared and DCS measures was positioned longitudinally over the flexor digitorum profundus and secured in place using self-adhering wrap. The near-infrared component of the probe consisted of four sets of eight laser diodes operating at wavelengths of 670, 690, 700, 730, 750, 785, 808, and 830 nm separated from a single detector by distances of 2.0, 2.5, 3.5, and 4.5 cm. Only the three shortest distances were used during the present study. The DCS component of the probe consisted of four photon-counting detectors located 2.4 cm away from a long coherence laser operating at a wavelength of 850 nm. The depth of penetration was ∼1.2 cm. Throughout all testing procedures, the experimental limb was kept motionless to avoid inducing motion artifacts in the NIR-DCS BFI signal.

Data Processing and Analysis

Data acquisition from each of these measures was synchronized throughout all experimental procedures using a 16-channel data acquisition system (PowerLab 16/35; ADInstruments) and accompanying software (LabChart 8, ADInstruments). Throughout all trials, measures of fingertip pressure, heart rate, brachial artery blood flow velocity, and NIR-DCS BFI, Heme_Tot, and S_tO₂ were collected continuously for at least 60 s before arterial cuff inflation, for 3 or 6 min during cuff-induced ischemia, and for 3 min following cuff deflation. All data were down-sampled to 1 Hz and exported to Excel (Microsoft, Redmond, WA) for further postprocessing. Baseline measures were averaged over the entire 60-s resting period. Brachial artery blood flow (BABF) was calculated as:

$$\mathrm{BABF}\left(\mathrm{mL}·{\min }^{-1}\right)=\left(\mathrm{mBAV}·60\right)·\left(\pi r^2\right)$$ (1)

where mBAV is the 1-s average mean brachial artery velocity, in cm/s, multiplied by 60 to convert to cm/min, and r² is the brachial artery radius (in cm) squared. We then calculated shear rate according to Pyke et al. (28), as the quotient of mBAV divided by the brachial artery diameter (in cm).

The ischemic stimulus to vasodilate was calculated in two ways. First, we calculated the ΔS_tO₂ at the end of ischemia (End-Isch), relative to baseline (BL), as:

$$\Delta {\mathrm{StO}}_2\left(\%\right)=100·\left({\mathrm{StO}}_{2-\text{E}\text{n}\text{d}\text{I}\text{s}\text{c}\text{h}}/{\mathrm{StO}}_{2-\text{B}\text{L}}\right)$$ (2)

Second, we summed the area under the curve for S_tO₂ (i.e., S_tO₂·s) during ischemia using the Integrate function in the Origin Pro software package (Northampton, MA). The Integrate function was also used to quantify the magnitude of macro- and microvascular reactive hyperemia from the area under the curve over the first 60 s following cuff release.

Finally, we calculated time to peak responses for brachial artery blood flow, BFI, and Heme_Tot using a bilinear fitting procedure (20). The initial and mean rates of microvascular reperfusion (i.e., BFI slope) were then quantified using the slope function in Excel. Specifically, the initial rate of microvascular reperfusion was calculated as the peak BFI slope observed over a 5-s period within the first 6 s of cuff release, whereas the mean rate of microvascular reperfusion was calculated as the slope of BFI starting 1 s after cuff release and ending at the timepoint corresponding to the time to peak BFI calculated by the bilinear fitting procedure. In both calculations, the first 1–2 s of reactive hyperemia were excluded from BFI slope calculations to eliminate motion artifacts and non-perfusion-related changes in BFI that occur immediately at cuff release.

Statistics

All statistical analyses were conducted using SPSS v26.0 (IBM Statistics, Armonk, NY). Data are presented as means ± SD, with P values < 0.05 considered statistically significant. Prior to conducting any statistical comparisons, data were tested for normal distribution using the Shapiro–Wilk test. Normally distributed data were analyzed by paired t tests or repeated-measures ANOVAs. If a significant main effect was observed, post hoc comparisons were analyzed using the estimated marginal means. For non-normally distributed data, paired t tests were replaced by the nonparametric equivalent Wilcoxon signed rank test, and repeated-measures ANOVAs were replaced by the nonparametric Friedman’s tests. In the event of a significant Friedman’s test, post hoc comparisons were analyzed using individual Wilcoxon signed rank tests.

RESULTS

Descriptive data for the 16 young adults who completed the study are presented in Table 1. On average, the subjects reported engaging in mild-moderate physical activity for 30–40 min 3–5 days a week.

Table 1.

Participant characteristics

Measure	Means ± SD
Age	24 ± 4
Sex (M/F)	8/8
Height, cm	168.7 ± 8.9
Weight, kg	78.5 ± 17.4
BMI, kg/m2	27.6 ± 5.6
Body fat, %	34.9 ± 13.5
Handgrip strength, kg	36.4 ± 9.8
½ Skinfold thickness, mm
Proximal	4.0 ± 2.2
Medial	3.8 ± 1.9
Distal	3.1 ± 1.1

n = 16, M, male; F, female; BMI, body mass index (kg/m²).

Macro- versus Microvascular Reperfusion

Consistent with prior reports, we observed marked dissociations between macro- and microvascular hemodynamics during reactive hyperemia (Fig. 1). Across all four of the experimental trials/conditions studied during this investigation, the fold increase (as a % baseline) in macrovascular blood flow was significantly greater than that observed in the microvasculature (P < 0.01, all). Additionally, time to peak was always the fastest for Doppler measures of brachial artery blood flow, followed by near-infrared measures of Heme_Tot, and then DCS measures of BFI (Tables 3 and 5, P < 0.05, all).

Fig. 1.

Representative brachial artery blood flow (BABF), total heme (Heme_Tot), and near-infrared diffuse correlation spectroscopy (NIR-DCS) blood flow index (BFI) tracings from a single subject following 6 min of cuff-induced ischemia. The fold increase (i.e., normalized as a % change from baseline value) in Doppler-derived brachial artery blood flow (BABF, A) during reactive hyperemia was significantly greater than that of the NIR-DCS-derived blood flow index (BFI, C). Additionally, the time to peak response was always fastest for BABF, followed by Heme_Tot (THb; B) and BFI, respectively. Black lines denote the two linear equations derived during the bilinear fitting sequence to derive time to peak, and red lines denote their intersection (i.e., time to peak).

Table 3.

Ischemic stimulus time to peak responses

	6 min-N	3 min-N
Time to peak, s
BABF*	10.2 ± 6.2	5.4 ± 2.9
HemeTot*	23.4 ± 14.3†	14.8 ± 5.9†
BFI*	35.3 ± 11.7†‡	20.1 ± 6.4†‡

Data are presented as means ± SD. BFI, blood flow index.

^*Significant difference between the 3 min- and 6 min-N trials.

^†Significant difference from peak brachial artery blood flow (BABF) within the same trial.

^‡Significant difference from peak total heme (Heme_Tot) within the same trial.

Table 5.

Impact of forearm perfusion pressure on time to peak responses

	Neutral	Above	Below
Time to peak, s
BABF	4.9 ± 3.2	7.3 ± 2.7	6.2 ± 2.9
HemeTot*	15.3 ± 6.5†	21.6 ± 8.2†	10.2 ± 5.7†
BFI*	20.7 ± 7.2 †‡	28.4 ± 9.8†‡	15.4 ± 5.9†‡

Data are presented as means ± SD. BFI, blood flow index.

^*Significant main effect of position or perfusion pressure.

^†Significant difference from peak brachial artery blood flow (BABF) within the same trial.

^‡Significant difference from peak total heme (Heme_Tot) within the same trial.

Effect of Changing the Ischemic Stimulus

Technical difficulties prevented inclusion of Doppler data in five participants (3 males, 2 females) for the ischemic stimulus investigation. Baseline measures of blood flow and muscle oxygenation for the 6 min- and 3 min-N trials are described in Table 2. Brachial artery blood flow and O₂ saturation index were similar between trials (P > 0.05, both), but BFI, Heme_Tot, and FPP were all greater in the 3 min-N trial than in the 6 min-N trial (P < 0.05, all), suggesting there may have been an effect of trial order. Although the mean differences in baseline Heme_Tot (∼2.8%) and FPP (∼4.5%) were small enough to consider physiologically irrelevant, the mean difference in BFI (∼30%) was not. Consequently, NIR-DCS-derived BFI is reported in both absolute and normalized (% baseline BFI) terms.

Table 2.

Impact of the ischemic stimulus to vasodilate on measures of reactive hyperemia

	6 min-N	3 min-N
Baseline
BABF, mL/min	33.4 ± 12.7	30.9 ± 11.8
BFI, cm2·10−8*	0.70 ± 0.4	0.91 ± 0.4
HemeTot, μM*	61.7 ± 26.1	63.4 ± 26.1
StO2, %	68.4 ± 3.6	69.1 ± 4.3
FPP, mmHg*	93.5 ± 7.2	97.7 ± 6.4
Ischemic stimulus
StO2 AUC, %·s*	4209.1 ± 1654.4	1214 ± 463.5
ΔStO2 (% BL)*	31.0 ± 12.7	18.7 ± 6.8
Reactive hyperemia
Peak BABF, mL·min−1*	383.7 ± 110.0	308.9 ± 95.4
Peak BFI, cm2·10−8*	3.05 ± 1.2	2.6 ± 0.7
Peak BFI (%BL·100)*	487.3 ± 170.9	319 ± 124.6
60-s AUC BA-BF, (mL/min)·s*	12815.7 ± 5011.2	5901.0 ± 2714.5
60-s AUC BFI, (cm2·10−8)·s*	80.1 ± 37.9	58.0 ± 24.0
Mean BFI slope, cm2·10−8·s−1*	0.073 ± 0.03	0.103 ± 0.04
Mean BFI slope (% BL-BFI·s−1)	0.115 ± 0.05	0.128 ± 0.07

Data are presented as means ± SD. AUC, area under the curve; BABF, brachial artery blood flow; BFI, blood flow index; FPP, forearm perfusion pressure; S_tO₂, tissue oxygenation %.

^*Significant difference between the 3 min- and 6 min-N trials.

As expected, both the S_tO₂ area under the curve (4209.1 ± 1654.4 vs. 1214.0 ± 463.5 S_tO₂/s) and ΔS_tO₂ (31.0 ± 12.7 vs. 18.7 ± 6.8%Δ from baseline) were greater for the 6 min- than the 3 min-N trial (Fig. 2A; P < 0.05, both). Accordingly, the 6 min-N trial produced greater absolute measures of Doppler-derived brachial artery blood flow (383.7 ± 110.0 vs. 308.9 ± 95.4 mL/min; P = 0.001) and NIR-DCS-derived BFI (3.05 ± 1.15 vs. 2.60 ± 0.71 cm²·10⁻⁸; P = 0.046; Table 2). The difference in peak BFI persisted even after being normalized to baseline (478.3 ± 170.9 vs. 319 ± 124.6%BL·100; P = 0.001). However, normalizing measures of BFI did have an impact on the BFI slope. As shown in Fig. 2C,the mean BFI slope was greater during the 3 min- than the 6 min-N trial when measured from the absolute changes in BFI (0.103 ± 0.043 vs. 0.073 ± 0.033 cm²·10⁻⁸·s⁻¹; P = 0.015), but these differences were abolished after the absolute BFI values were normalized to baseline (0.128 ± 0.072 vs. 0.115 ± 0.049%·s⁻¹; P = 0.605; Fig. 2D and Table 2). The initial rate of microvascular reperfusion was also not different between the 6 min- and 3 min-N trials (0.148 ± 0.058 vs. 0.156 ± 0.087%·s⁻¹, respectively; P = 0.101). As described in Table 3, the ischemic stimulus also affected several time to peak responses, including time to peak brachial artery blood flow, BFI, and Heme_Tot, (3 min-N faster than 6 min-N, P < 0.05, all).

Fig. 2.

Impact of the ischemic stimulus to vasodilate on measures of macro- versus microvascular reactive hyperemia. A: summary data illustrating the group mean tissue saturation (S_tO₂, n = 16) response throughout the 3-min neutral (black lines) and 6-min neutral (blue lines) reactive hyperemia trials. B–D: summary data illustrating the group mean Doppler-derived brachial artery blood flow (BABF, B, n = 11) and NIR-DCS-derived blood flow index (BFI) expressed in absolute terms (C, n = 16) and normalized to baseline (D, normalized as a % change from baseline value, n = 16), during 3 min of reperfusion following 3 min and 6 min of cuff-induced ischemia. NIR-DCS, near-infrared diffuse correlation spectroscopy.

Manipulating the ischemic stimulus also caused a predictable change in shear stress and its associated influence of brachial artery diameter. Although peak shear stress was similar between the 6 min- and 3 min-N trials (236.2 ± 49.6 vs. 232.1 ± 58.1, P = 0.193), total shear stress over the first 60 s (area under the curve) was substantially greater in the 6 min-N trial compared with the 3 min-N trial (6422.2 ± 1927.7 vs. 3725.0 ± 1303.7, P = 0.032). Less predictable, however, was the similar pattern observed between brachial artery diameter and NIR-DCS-derived BFI (Fig. 3).

Fig. 3.

Temporal relationship between brachial artery diameter and BFI during reactive hyperemia. Summary data illustrating the composite mean brachial artery diameter (BA diam., A) and NIR-DCS-derived blood flow index (BFI, B) response during 3 min of reperfusion following 3 min (black line) and 6 min (blue lines) of cuff-induced ischemia. In both cases, the data are expressed as a percent change from baseline (i.e., normalized as a % change from baseline value). n = 11. BFI, blood flow index; NIR-DCS, near-infrared diffuse correlation spectroscopy.

Testing for an Order Effect

To test our suspicion of an order effect influencing baseline BFI on subsequent trials, a subset of five subjects (2 females) returned on a separate visit to undergo a series of three reactive hyperemia trials; two 3 min-N trials to examine test-retest characteristics, followed by a 6 min-N trial to reexamine the impact of the ischemic stimulus to vasodilate. Albeit a small effect, the results confirmed our suspicion of an order effect. Baseline BFI tended to be lowest during the first 3 min-N trial (0.80 ± 0.31) and higher during the second 3 min-N trial (0.94 ± 0.42) and 6 min-N trial (0.90 ± 0.36). We also noticed that the second 3 min-N trial resulted in a slightly greater (∼10%) peak absolute BFI response than the first 3 min-N trial (Fig. 4A). However, these differences were abolished after normalizing to baseline (Fig. 4B).

Fig. 4.

Impact of normalizing BFI to baseline during test-retest trials of reactive hyperemia. A subset of five subjects returned for a second visit to investigate the potential impact of order effects on NIR-DCS measures of blood flow index (BFI) during reactive hyperemia. A: when the data were analyzed in terms of absolute BFI (cm²·10⁻⁸), there was a slight potentiation of the peak BFI response following the second 3 min-neutral trial (red line) compared with the first 3 min-neutral trial (black line). B: normalizing the data to baseline BFI (i.e., normalized as a % change from baseline value) ameliorated differences between the two repeat 3-min-neutral trials, whereas the peak BFI response during the 6 min-neutral trial (blue line) remained elevated. NIR-DCS, near-infrared diffuse correlation spectroscopy.

Effect of Changing Forearm Perfusion Pressure

Technical difficulties prevented inclusion of Doppler data in five participants (3M, 2F) during the arm position manipulations, and FPP in two participants (1M, 1F). For FPP, all trials were significantly different from one another (A < N < B, P = 0.001, all). Baseline measures of blood flow and muscle oxygenation for the arm position and FPP trials are described in Table 4. There was a trend, but no main effect of trial, for brachial artery blood flow (P = 0.079) and Heme_Tot (P = 0.087) to differ at baseline, but BFI, S_tO₂, and FPP all exhibited main effects of trial (P < 0.05). For BFI, the above trial was lower than both the neutral and below trials (P = 0.01 and 0.011, respectively). For S_tO₂, the above trial was lower than both the neutral and below trials (P = 0.012 and 0.001, respectively).

Table 4.

Impact of perfusion pressure on measures of reactive hyperemia

	3 min-N	3 min-A	3 min-B
Baseline
BABF, mL·min−1	34.8 ± 17.6	25.3 ± 14.4	33.6 ± 11.0
BFI, cm2·10−8*	0.91 ± 0.39	0.67 ± 0.24†‡	0.95 ± 0.46
HemeTot, μM	63.4 ± 26.1	63.4 ± 27.4	66.0 ± 27.4
StO2, %*	69.1 ± 4.3	67.7 ± 3.6	69.4 ± 4.3
FPP, mmHg*	97.7 ± 6.4‡	77.9 ± 6.5†‡	107.2 ± 5.7
Reactive hyperemia
Peak BABF, mL·min−1*	311.8 ± 100.5‡	242.2 ± 73.0†‡	385.0 ± 130.0
Peak BFI, cm2·10−8*	2.60 ± 0.71‡	1.79 ± 0.55†‡	3.07 ± 0.93
60-s AUC BA-BF, (mL·min−1)·s	5839.5 ± 2643.8	5406.1 ± 2558.4	6533.7 ± 2418.4
60-s AUC BFI, (cm2·10−8)·s*	60.5 ± 22.7‡	35.4 ± 17.2†‡	74.1 ± 27.1
Mean BFI slope, cm2·10−8·s−1*	0.103 ± 0.043‡	0.055 ± 0.028†‡	0.168 ± 0.098
Mean BFI slope (% BL-BFI·s−1)*	0.128 ± 0.072‡	0.083 ± 0.037†‡	0.235 ± 0.204

Data are presented as means ± SD. AUC, area under the curve; BABF, brachial artery blood flow; BFI, blood flow index; FPP, forearm perfusion pressure; S_tO₂, tissue oxygenation %.

^*Significant main effect of position or perfusion pressure.

^†Significant difference from the neutral position.

^‡Significant difference from the below position.

During reactive hyperemia, a main effect of position was found for peak absolute measures of both brachial artery blood flow and BFI (Fig. 5A and B, P = 0.001, both). For peak brachial artery blood flow, all three positions were different from one another (A < N < B, 242.2 ± 73.0 vs. 311.8 ± 100.5 vs. 385.0 ± 130.0 mL/min; P < 0.05, all). Similarly, for peak BFI, all three positions were different from one another (A < N < B, 1.79 ± 0.55 vs. 2.60 ± 0.71 vs. 3.07 ± 0.93 cm²·10⁻⁸; P < 0.05, all). Arm position also affected the mean rate of microvascular reperfusion (main effect of position for mean BFI slope, P = 0.001), with all three positions being different from one another (A < N < B; 0.055 ± 0.028 vs. 0.103 ± 0.043 vs. 0.168 ± 0.098 cm²·10⁻⁸·s⁻¹; P < 0.05, all). A similar observation was observed for the initial rate of microvascular reperfusion (main effect P = 0.001), with all three positions being different from one another (A < N < B; 0.090 ± 0.039 vs. 0.156 ± 0.087 vs. 0.259 ± 0.162%·s⁻¹; P < 0.05, all). Time to peak BFI and Heme_Tot were also influenced by arm position (main effect P = 0.001, both) but not time to peak brachial artery blood flow (main effect P = 0.268). For time to peak BFI and Heme_Tot, the above trial always took the longest, followed by the neutral and below trials, respectively (A > N > B, P < 0.05, all) (Table 5).

Fig. 5.

Impact of forearm perfusion pressure on measures of macro- versus microvascular reactive hyperemia. A: summary data illustrating the group mean Doppler-derived brachial artery blood flow (BABF, mL·min⁻¹, n = 11) response to 3 min of cuff-induced ischemia with the arm in the neutral position (black line), raised above the heart (yellow line), or positioned below the heart (red line). B: summary data illustrating the group mean NIR-DCS-derived blood flow index (BFI, cm²·10⁻⁸, n = 16), during the same three arm positions. NIR-DCS, near-infrared diffuse correlation spectroscopy.

DISCUSSION

Postischemia reperfusion kinetics are markedly dissociated between the macro- and microvasculature. The present study used NIR-DCS and Doppler ultrasound to examine how changes in the ischemic stimulus to vasodilate and tissue perfusion pressure affect micro- and macrovascular reperfusion hemodynamics. The critical, novel findings were twofold: First, increasing the ischemic stimulus augments both peak macrovascular (i.e., conduit, mL/min) and microvascular (i.e., peak BFI) reperfusion but does not affect the rate at which microvascular reperfusion occurs (i.e., BFI slope). Second, increasing perfusion pressure augments peak macrovascular and microvascular reperfusion, as well as the rate at which microvascular reperfusion occurs (i.e., BFI slope). Taken together, the data suggest that kinetic differences between macro- and microvascular reperfusion are largely determined by differences in fluid mechanical energy, with peak microvascular reperfusion being delayed following cuff release due to the slow restoration of microvascular blood pressure.

Effect of the Ischemic Stimulus

That increasing the ischemic stimulus resulted in a marked increase in microvascular reperfusion extends prior investigations from our laboratory (31–33) and others (7, 19, 23), which have shown that the magnitude of macrovascular reactive hyperemia is dependent on the magnitude of the ischemic stimulus to vasodilate. Together, these data support the traditional notion that the absolute magnitude of reactive hyperemia is primarily driven by the extent of ischemia-mediated vasorelaxation, which causes expansion of the microvascular volume within the ischemic limb. This is not to say that the 6 min-N trial necessarily elicited maximal measures of reactive hyperemia. Indeed, although skeletal muscle is almost completely deoxygenated after ∼5–6 min of ischemia (14, 22), others have shown that macrovascular measures of reactive hyperemia can be increased further following more prolonged periods of ischemia (26). Nevertheless, that the magnitude of muscle deoxygenation was significantly different between the 6 min- and 3 min-N trials supports using this experimental approach to determine the impact of the ischemic stimulus to vasodilate on macro- (Doppler ultrasound) and microvascular (NIR-DCS) measures of reactive hyperemia.

We expected that the greater magnitude of brachial artery blood flow achieved during the 6-min trial would translate into a faster rate of microvascular reperfusion (i.e., BFI slope). However, in contrast to this prediction, the slope of microvascular reperfusion between the 6 min- and 3 min-N trials was nearly identical. The mechanical dynamics underlying the similarity between these BFI slopes are not immediately clear, but one potential explanation is that because the 6 min-N trial produced greater microvascular vasorelaxation, it also created a greater undistended volumetric void that must be filled before pressure can be equalized across the arterial vascular tree (i.e., systemic → brachial artery → forearm microvascular blood pressures). Put another way, vasorelaxation in the ischemic microvasculature reduces peripheral resistance by reducing microvessel constriction. Importantly, however, the change in transluminal pressure should be minimal because the volume of blood in the ischemic microvasculature remains constant. Thus, microvascular vasorelaxation produces an undistended volumetric void. Consequently, when the cuff is released, a portion of the kinetic fluid mechanical energy associated with macrovascular measures of reactive hyperemia (i.e., increased rates of brachial artery blood flow) is dissipated, or consumed, by the negative pressure mechanical energy of the undistended volumetric void, rather than being translated directly into changes in microvascular blood flow. Consistent with this notion, peak Heme_Tot always occurred just before attainment of peak BFI, suggesting that the microvasculature expanded (i.e., the volumetric void was filled) before microvascular pressure was equilibrated. This concept may offer a direct explanation for why macro- and microvascular hemodynamics are so temporally dissociated and suggests that blood flow during reactive hyperemia may be better explained by a more comprehensive model of fluids dynamics, such as Bernoulli’s principle of fluid mechanical energy, rather than traditional interpretations derived from Darcy’s law.

It is important to note, however, that although Bernoulli’s principle can help to explain fluid dynamics across a naïve system, in reality, the vascular system is not a rigid set of tubes but rather an integrative biologic system with complex vasoregulatory pathways. Accordingly, given the time course of microvascular reperfusion (peaking ∼30 s after cuff deflation), and what we know about mechanical vasodilation (29, 39), it is interesting to speculate that microvascular hemodynamics, and the time required to fill the volumetric void produced by microvascular relaxation, may also be affected by shear-mediated vasodilation. Indeed, consistent with the data presented here, Stoner et al. (34) reported that the shear rate and the magnitude of flow-mediated dilation are significantly affected by the duration of ischemia, and we also observed a close association between NIR-DCS-derived BFI and brachial artery diameter in both the 3- and 6-min neutral trials. This is particularly intriguing given that nitric oxide has historically been shown to have little to no impact on reactive hyperemia, at least when measured as peak macrovascular hyperemia immediately following cuff deflation (2, 8, 35). Inhibiting nitric oxide synthase does appear to blunt macrovascular reperfusion 30–60 s after cuff occlusion (8). Whether microvascular reperfusion hemodynamics are altered in individuals with impaired nitric oxide bioavailability and/or endothelial dysfunction is beyond the scope of this investigation but is an important aspect that needs to be addressed moving forward.

Effect of Arm Position and Arterial Perfusion Pressure

Manipulating arm position had a predictable effect on baseline measures of forearm perfusion pressure, increasing in the below position and decreasing in the above position. The ∼20 mmHg drop in FPP in the above position and ∼10 mmHg increase in the below position are similar to those reported previously by our laboratory (40) and others (4, 25). Importantly, consistent with the work of Jasperse et al. (19), we found that the magnitude of brachial artery blood flow during reactive hyperemia followed changes in FPP, being greatest in the below position, followed by the neutral and above positions, respectively. A key novel finding in the present study, however, is that both the magnitude (i.e., peak BFI) and rate (i.e., BFI slope) of microvascular reperfusion also follow changes in FPP, being greatest in the below position, followed by the neutral and above positions, respectively. Because the duration of ischemia was constant across all three positions (i.e., 3 min), the magnitude of microvascular vasorelaxation, as well as the magnitude of the volumetric void reducing arterial pressure within the ischemic microvasculature, should also have been equal across trials. Therefore, the rate of microvascular reperfusion appears to be most affected by factors independent of microvascular vasorelaxation.

Once again, these data strongly suggest that macro- and microvascular hemodynamics during reactive hyperemia are more complex than can be explained by Darcy’s law and support the use of more complex fluid mechanical energy models. For example, Bernoulli’s principle describes fluid mechanical energy as the sum of three components that were all affected by arm position: 1) pressure energy, which equates to arterial blood pressure, or FPP, which was different across all three trials; 2) potential energy, which is the product of blood mass, gravity, and tissue height relative to the position of the heart (different across all three trials); and 3) kinetic energy, which equates to the momentum energy of moving blood and is primarily a function of blood velocity. What remains unclear is which of these three components was responsible for affecting BFI slope. Given that we saw no difference in BFI slope between the 6 min- and 3 min-N trials, despite substantially different kinetic energies (i.e., peak brachial artery blood flow), it is unlikely that kinetic energy was responsible for the differences in BFI slope across arm positions. However, because changes in arm position have parallel effects on both pressure energy and potential energy (i.e., gravitational inertia), we are unable to partition which factor dominated in the present investigation. Regardless, these data highlight the importance of controlling for arterial blood pressure (e.g., hypertension) when assessing reactive hyperemia. That macrovascular measures do not allow for such partitioning may support broader use of microvascular techniques, like NIR-DCS, as they become more widely available.

Experimental Considerations

In the present study, the 6 min-N trial was always performed first. Consequently, we were curious as to whether order effects may have influenced the results for peak BFI and BFI slope during our investigation of the ischemic stimulus. Indeed, Didier et al. (10) recently reported that peak BFI increases after the first trial in a series of multiple reactive hyperemia experiments, but they did not normalize their data to baseline. Thus, a subset of participants returned on a separate visit to undergo a series of three reactive hyperemia trials: two 3 min-N trials to examine test-retest characteristics, followed by a 6 min-N trial to reexamine the impact of the ischemic stimulus to vasodilate. The results confirmed our suspicion of an order effect, with baseline BFI tending to be lower during the first 3 min-N trial than either the second 3 min-N trial or the subsequent 6 min-N trial. Consistent with Didier et al. (10), we also noticed that the second 3 min-N trial resulted in a slightly greater (∼10%) peak absolute BFI response than the first 3 min-N trial; however, this difference disappeared when BFI was normalized to baseline. Moreover, the BFI slopes were once again superimposable after normalizing BFI responses to baseline, and peak BFI slopes were similar across all three trials. These data suggest that additional work is necessary to better understand why NIR-DCS measures of baseline BFI and reactive hyperemia are affected during repeated test-retest experiments and whether or not these effects can be corrected in real time or posteriori.

Another important consideration is that the depth of penetration by our NIR-DCS probe is 1.2 cm (or ∼½ the source-detector distance). Given that previous work has shown that changes in skin blood flow can affect NIRS-measures of deoxy-heme, oxy-heme, and S_tO₂ (9, 21, 36), it is possible that changes in skin blood flow may have contributed to the NIR-DCS measures of skeletal muscle BFI and BFI slope. However, we do not think skin blood flow significantly affected BFI measurements in the present study because our microvascular reperfusion results are consistent with other noninvasive measurements of skeletal muscle reperfusion during reactive hyperemia (12, 30, 37, 38). Additionally, our results are also consistent with those of Didier et al. (10), who also used NIR-DCS to measure skeletal muscle BFI during reactive hyperemia, as well as laser-Doppler flowmetry to measure changes in skin blood flow. Their results suggest that skin blood flow peaks ∼10 s following cuff release, well before NIR-DCS BFI, suggesting that changes in skin blood flow during reactive hyperemia are likely insufficient to affect NIR-DCS BFI measures of skeletal muscle reperfusion. Nevertheless, the contribution of skin blood flow to NIR-DCS-derived measures of skeletal muscle perfusion is presently unknown and warrants direct investigation.

Conclusion

Postischemia reperfusion kinetics are markedly dissociated between the macro- and microvasculature. The present study used NIR-DCS to examine how changes in the ischemic stimulus to vasodilate and tissue perfusion pressure affect microvascular reperfusion hemodynamics. Taken together, the data suggest that kinetic differences between macro- and microvascular reperfusion are largely determined by differences in fluid mechanical energies between the two positions along the arterial tree and their complex interaction during the restoration of blood pressure across the arterial tree. Future studies are needed to determine what role, if any, vasoregulation plays in determining the rate and magnitude of microvascular reperfusion.

GRANTS

This work was supported by the generous contributions made by the Potratz Family, the Nagy Family Endowment, as well as the National Institutes of Health (R15HL140989).

DISCLOSURES

D.M. Hueber is employed by ISS Inc., the manufacturer of the near-infrared diffuse correlation spectroscopy device used in this investigation. No conflicts of interest, financial or otherwise, are declared by the other authors.

AUTHOR CONTRIBUTIONS

M.F.B. and M.D.N. conceived and designed research; M.F.B., A.O., M.F.J., and S.M. performed experiments; M.F.B. analyzed data; M.F.B., A.O., M.F.J., S.M., D.M.H., and M.D.N. interpreted results of experiments; M.F.B. prepared figures; M.F.B. and M.D.N. drafted manuscript; M.F.B., A.O., M.F.J., S.M., D.M.H., and M.D.N. edited and revised manuscript; M.F.B., A.O., M.F.J., S.M., D.M.H., and M.D.N. approved final version of manuscript.