Blood microcirculation plays an important role in the transport of nutriments, hormones, and oxygen and the discharge of metabolic waste. The significance of systemic vasculature, as well as associated thermoregulation and autoregulatory mechanisms, attracts a continuously growing interest in the understanding of blood microcirculation.

The morphological microvascular structure changes with pathological deviations and abnormalities in vascular tone regulation appearing.¹ Rheumatic diseases lead to microvascular morphological abnormalities and their reactive progression, including rarefication of the capillary network, capillaries asymmetry, and appearance of megacapillaries (with the diameter $\sim 50\mu \mathrm{m}$).² The functional disorder of microcirculation in patients with rheumatic diseases (PRD) has been observed during the temperature test.³

Spontaneous oscillations in the tone of skin blood vessel walls independent of heart beat (known as vasomotion) are used for functional studies of blood microcirculation. It has been shown that skin vasomotion depends on various physiological mechanisms that cause pulsations at different frequency bands.⁴ Oscillations of skin blood flow observed in the frequency intervals 0.6 to 1.6 Hz and 0.2 to 0.6 Hz are associated with heart and respiratory activities, respectively,^5–7 whereas oscillations in the 0.009- to 0.1-Hz frequency band are related to myogenic,^8–10 neurogenic,^8,11 and endothelial activities.^12–15

Recently, various optical diagnostic modalities, such as optical coherence tomography (OCT),¹⁶ Doppler OCT,¹⁷ correlation OCT,¹⁸ laser speckle imaging,¹⁹ Laser Doppler flowmetry (LDF),²⁰ have been developed and extensively used for functional studies of the skin microvascular system. Arguably, LDF is one of the most simple and popular approaches used in noninvasive diagnostics of blood flow in vivo.¹³ LDF is based on the extraction of a phase shift (Doppler shift) from the detected signal caused by the scattering of probing laser light on the moving red blood cells proportionally to their velocity. Despite strong scattering of light by biological tissues, the Doppler shift can be measured with a reasonably high resolution.²⁰

The current study applies the LDF approach²⁰ to the study of skin blood microflow oscillations to assess the nature of changes in vascular tone in 32 (16 male and 16 female) healthy volunteers (HV) (average age $22\pm 2\text{year}$) and 60 (12 male and 48 female) PRD (average age $55\pm 14\text{year}$) from the Rheumatology Department of the Orel Regional Clinical Hospital, Russia. The study was approved by the local Committee for Human Biomedical Research Ethics, and all subjects signed an informed consent.

The measurements of blood microcirculation were performed using the experimental systems “LAKK-OP” and “LAKK-M” (SPE “LAZMA” Ltd., Russia). All procedures and measurements were approved beforehand by the Ethical Committee of the Regional Clinical Hospital. The LDF probe was placed on a skin pad (palmar surface) of the right middle finger, as shown in Fig. 1.

Fig. 1

Schematic presentation of the LDF probe positioning on a finger: (1) LDF probe and (2) pulseoximetry sensor.

The LDF signal was collected during 5 min at normal conditions. During the cold pressor test (CPT), which was carried out for 5 min, patient’s arms were submerged in cold water with the temperature ($14\pm 0.8°\mathrm{C}$) controlled by a contactless digital thermometer (Sensitec NB401, the Netherlands). The LDF signal was measured again immediately after retrieving arms from the cold water and after a 20-min relaxation. The results of a typical set of three LDF measurements are shown in Fig. 2.

Fig. 2

(a) Example of measured LDF signals obtained at normal conditions, (b) immediately after cold exposure, and (c) 20 min after finishing CPT.

The results of LDF measurements, shown in Fig. 3, show that at normal conditions PRD has a slightly higher perfusion rate $21\pm 3\mathrm{p.u.}$ compared to the $\mathrm{HV}:18\pm 4\mathrm{p.u.}$ Significant variations occurred during the CPT: up to 7 p.u. for HV and 2 p.u. in the group of PRD.

Fig. 3

Box–Whisker diagram of averaged perfusion: black rectangles correspond to the group of HV and gray ones to the group of PRD. Numbers indicate time intervals: (1) before CPT, (2) immediately after, and (3) 20 min after CPT. Stars indicate significance in the difference: p < 0.05, p < 0.01, and p < 0.005.

The Mann–Whitney test was applied to quantitatively assess the difference. The obtained LDF signals were decomposed using a wavelet transform²¹

Being smoothed versions of Fourier transform, wavelets are known to be an effective tool for power spectral density estimation, especially for short nonstationary noisy data. Biomedical series are mainly short and nonstationary, and wavelet analysis is widely applied to trace energy distributions over frequencies in time.^{7,10,12,15,21}

The wavelet coefficients were counted for the frequency band 0.01 to 2 Hz with the logarithmic partitioning on 50 frequencies. $M(\nu )$ was calculated for three records of LDFs (see Fig. 2) for each patient. The integral wavelet spectra were averaged over the group of patients. The energy distribution for every frequency band has been calculated. The results of a comparison of the distributions obtained for HV and PRD are shown in Fig. 4. The data processing was performed by Mathmatica 8.0, Wolfram research.

Fig. 4

Box–Whisker diagrams for the power spectral density: (top) for 1 HV (black) and PRD (gray) at normal conditions; (middle) for HV only, taken at normal conditions (black) and after CPT (white); (bottom) for PRD only, at normal conditions (gray) and after CPT (white) at normal conditions. Solid stretches in the upper part of the plot indicate frequency bands where the difference between HV and PRD with pathology is significant. * indicates $p<0.05$ and ** for $p<0.01$.

Figure 4 shows the wavelet spectra averaged over groups of HV and PRD at normal conditions. Higher frequency pulsations of the blood flow have been observed in the group of PRD compared to HV. The difference is significant up to 0.24 Hz ($p<0.01$). At the same time, a trend to decrease an input of the low frequency oscillations is clearly indicated (see Fig. 4).

The analysis of averaged perfusion for HV and PRD showed significant differences before and after CPT (intervals 1 and 2) (see Fig. 3). The perfusion variation caused by cooling was more evident in the HV group. After 20 min of relaxation, the perfusion restored to the initial level. No significant difference was seen in the restore process (see Fig. 3, time interval 3), which agreed well with the results of alternative studies.²²

We assume that higher perfusion and weaker response to CPT in the PRD group is associated with the abnormal behavior of the peripheral vascular bed, which is manifest in aptitude to the angiospasm. The significantly different spectral properties can be caused by morphological and functional abnormalities of the microvascular structures. We assume that the presence in microvascular structures of mega-capillaries²³ and increased vessels stiffness decrease dumping properties of the circulation system with progression rheumatic pathology.

Finally, LDF has been successfully applied for the assessment of variations in blood microflows for PRD and HV. The obtained LDF signals have been analyzed utilizing a wavelet transform. The introduced approach provides an opportunity to obtain whole spectrum recordings of rhythmic processes in the microvasculature from pulse to circadian rhythms. Spectral properties of cutaneous blood flow vary due to the pathological process in the microcirculation system. The developed technique has a high potential in clinical application for functional evaluation of blood flow regulation and diagnosis of vascular abnormalities associated with rheumatic diseases.