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The association between dynamical and averaging characterization of LDF skin blood flow: An integrated approach
Microvascular Research 89 (2013) 159–160
Contents lists available at ScienceDirect
Microvascular Research journal homepage: www.elsevier.com/locate/ymvre
Letter to the Editor The association between dynamical and averaging characterization of LDF skin blood flow: An integrated approach Dear Editor: Two approaches for characterizing microvascular blood flow by Laser Doppler Flowmetry (LDF) have been recently discussed (Cracowski and Roustit, 2012; Stefanovska et al., 2011). On one hand, Stefanovska et al. (2011) advocated the analysis of the distinct frequency components of skin LDF data, in view of the inherent oscillatory behavior of blood flow. Likewise, they questioned the validity of time averages for characterizing microvascular blood flow arguing a substantial loss of relevant physiological information. On the other hand, Cracowski and Roustit (2012) supported the interest of time-averaged LDF data, especially when large increments in skin blood flow are induced. According to these methodological questions that are relevant to the future use of LDF in clinical contexts, we would like to contribute to the discussion. Dynamical characterization of skin blood flow (i.e. the periodic oscillations that compose the LDF signal) allows the study of simultaneous mechanisms regulating the microcirculation. The amplitude of these oscillations is specifically influenced by several tests such as local heating, post-occlusive reactive hyperemia, and transdermally delivered vasoactive substances (e.g. acetylcholine, sodium nitroprusside, insulin) and their effects are altered under pathological conditions (Newman et al., 2009), thus having a promising potential as a diagnostic tool. For instance, transdermal delivery of insulin specifically increased the amplitude of the myogenic oscillation, and concomitantly produced more than a 300% increment of time-averaged LDF skin blood flow in healthy subjects (Rossi et al., 2005). Conversely, induced insulin resistance blocked the increased myogenic oscillation and reduced the LDF signal in response to insulin in mice (Newman et al., 2009). Consequently, we suggest that dynamic and time-averaged LDF approaches may be related to each other, and the study of their associations could favor a more precise interpretation of LDF microcirculatory results. To illustrate these points, Fig. 1 shows both the dynamic and time-averaging approaches to the analysis of the LDF skin blood flow response to insulin iontophoresis in severely obese adolescents (data not published), a good model of early insulin resistance state. Area under the curve (AUC) and common time-averaged parameters such as peak (PK), peak minus baseline (PK − BL), and peak as a percentage increase of baseline (PK%BL) were used to characterize skin blood flow. Moreover, a spectral analysis method previously used in an insulin iontophoresis investigation (Rossi et al., 2005) is applied for dynamical characterization. Furthermore, Table 1 shows the results
0026-2862/$ – see front matter © 2013 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.mvr.2013.05.001
of multiple regression analyses conducted to assess the independent contribution of different periodic oscillations to skin blood flow during insulin iontophoresis in severely obese adolescents. The myogenic oscillation is the primary determinant of insulininduced skin blood flow, particularly for AUC data expression, and also for time-averaged parameters as PK and PK − BL in which the myogenic oscillation shares some predictive power with the endothelial oscillation (Table 1). However, when LDF data are expressed as PK%BL, its only independent determinant turns out to be the sympathetic oscillation (Table 1). This a priori unexpected result is likely explained by the well-established increase in sympathetic activity, and the associated decrease in baseline forearm skin blood flow reported in obesity (Doupis et al., 2010). Therefore, taking into account the dynamic analysis to interpret LDF skin blood flow reinforces the physiological relevance of absolute time-averaged parameters (in PU or PU · s) rather than PK%BL, which is noticed as a potentially biased parameter of insulin action in the microcirculation of severely obese adolescents. In conclusion, we suggest to associate dynamic and time-averaging approaches when assessing LDF skin measurements, which may help to step forward in the process of obtaining more conclusive results in the field of non-invasive microvascular research. References Cracowski, J.L., Roustit, M., 2012. Reproducibility of LDF blood flow measurements: dynamical characterization versus averaging. A response to the letter from Stefanovska. Microvasc. Res. 83, 97. Doupis, J., et al., 2010. Effects of diabetes and obesity on vascular reactivity, inflammatory cytokines, and growth factors. Obesity 19 (4), 729–735 (Silver Spring). Newman, J.M., et al., 2009. Decreased microvascular vasomotion and myogenic response in rat skeletal muscle in association with acute insulin resistance. J. Physiol. 587, 2579–2588. Rossi, M., et al., 2005. Skin blood flowmotion response to insulin iontophoresis in normal subjects. Microvasc. Res. 70, 17–22. Stefanovska, A., et al., 2011. Reproducibility of LDF blood flow measurements: dynamical characterization versus averaging. Microvasc. Res. 82, 274–276.
David Montero⁎ Guillaume Walther Agnès Vinet University of Avignon, Laboratoire Pharm-Ecologie Cardiovasculaire EA4278, F-84000 Avignon, France ⁎Corresponding author at: Laboratoire Pharm-Ecologie Cardiovasculaire (LaPEC), Faculté des Sciences, 33 rue Louis Pasteur, 84000 AVIGNON, France. Fax: +33 4 90 16 29 01. E-mail address: [email protected]. (D. Montero).
160
Letter to the Editor
Fig. 1. (a) An example of LDF skin blood flow recorded from the volar side of the forearm during baseline (5 min) and insulin iontophoresis (22 min; 12 pulses of 0.2 mA for 20 s each, with a 90 s interval between each pulse) in a severely obese adolescent placed supine in a temperature-controlled room (24 °C). (b) Three-dimensional plot of the spectral Fourier analysis of the LDF signal during insulin iontophoresis in that same subject. The frequency band of each periodic oscillation is: 0.005–0.02 Hz for endothelial activity, 0.02–0.06 Hz for sympathetic activity, 0.06–0.2 Hz for myogenic activity, 0.2–0.6 Hz for respiratory activity, and 0.6–1.6 Hz for cardiac activity. AUC, area under the curve; BL, baseline value; BZ, biological zero; LDF, Laser Doppler Flowmetry; PK, peak skin blood flow; PK − BL, peak minus baseline; PK%BL, peak as a percentage increase of baseline; PU, perfusion units.
Table 1 Independent dynamic determinants for skin blood flow in response to insulin iontophoresis in severely obese adolescents (n = 28). Skin blood flow
AUC
Independent dynamic determinants
57,910 (29,374)
PK
59 (43)
PK − BL
54 (41)
PK%BL
1107 (537)
Endothelial
Sympathetic
Myogenic
Respiratory
Cardiac
–
–
–
–
rp = .440 P = .025 rp = .434 P = .027 –
–
rp = .832 P b .001 rp = .497 P = .009 rp = .498 P = .009 –
–
–
–
–
–
–
– rp = .592 P = .001
Skin blood flow is expressed as median (ITQ, interquartile). AUC, area under the curve (in PU · s); PK, peak skin blood flow (in PU); PK − BL, peak minus baseline (in PU); PK%BL, peak as a percentage increase of baseline; PU, perfusion units; rp, partial r.
Contents lists available at ScienceDirect
Microvascular Research journal homepage: www.elsevier.com/locate/ymvre
Letter to the Editor The association between dynamical and averaging characterization of LDF skin blood flow: An integrated approach Dear Editor: Two approaches for characterizing microvascular blood flow by Laser Doppler Flowmetry (LDF) have been recently discussed (Cracowski and Roustit, 2012; Stefanovska et al., 2011). On one hand, Stefanovska et al. (2011) advocated the analysis of the distinct frequency components of skin LDF data, in view of the inherent oscillatory behavior of blood flow. Likewise, they questioned the validity of time averages for characterizing microvascular blood flow arguing a substantial loss of relevant physiological information. On the other hand, Cracowski and Roustit (2012) supported the interest of time-averaged LDF data, especially when large increments in skin blood flow are induced. According to these methodological questions that are relevant to the future use of LDF in clinical contexts, we would like to contribute to the discussion. Dynamical characterization of skin blood flow (i.e. the periodic oscillations that compose the LDF signal) allows the study of simultaneous mechanisms regulating the microcirculation. The amplitude of these oscillations is specifically influenced by several tests such as local heating, post-occlusive reactive hyperemia, and transdermally delivered vasoactive substances (e.g. acetylcholine, sodium nitroprusside, insulin) and their effects are altered under pathological conditions (Newman et al., 2009), thus having a promising potential as a diagnostic tool. For instance, transdermal delivery of insulin specifically increased the amplitude of the myogenic oscillation, and concomitantly produced more than a 300% increment of time-averaged LDF skin blood flow in healthy subjects (Rossi et al., 2005). Conversely, induced insulin resistance blocked the increased myogenic oscillation and reduced the LDF signal in response to insulin in mice (Newman et al., 2009). Consequently, we suggest that dynamic and time-averaged LDF approaches may be related to each other, and the study of their associations could favor a more precise interpretation of LDF microcirculatory results. To illustrate these points, Fig. 1 shows both the dynamic and time-averaging approaches to the analysis of the LDF skin blood flow response to insulin iontophoresis in severely obese adolescents (data not published), a good model of early insulin resistance state. Area under the curve (AUC) and common time-averaged parameters such as peak (PK), peak minus baseline (PK − BL), and peak as a percentage increase of baseline (PK%BL) were used to characterize skin blood flow. Moreover, a spectral analysis method previously used in an insulin iontophoresis investigation (Rossi et al., 2005) is applied for dynamical characterization. Furthermore, Table 1 shows the results
0026-2862/$ – see front matter © 2013 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.mvr.2013.05.001
of multiple regression analyses conducted to assess the independent contribution of different periodic oscillations to skin blood flow during insulin iontophoresis in severely obese adolescents. The myogenic oscillation is the primary determinant of insulininduced skin blood flow, particularly for AUC data expression, and also for time-averaged parameters as PK and PK − BL in which the myogenic oscillation shares some predictive power with the endothelial oscillation (Table 1). However, when LDF data are expressed as PK%BL, its only independent determinant turns out to be the sympathetic oscillation (Table 1). This a priori unexpected result is likely explained by the well-established increase in sympathetic activity, and the associated decrease in baseline forearm skin blood flow reported in obesity (Doupis et al., 2010). Therefore, taking into account the dynamic analysis to interpret LDF skin blood flow reinforces the physiological relevance of absolute time-averaged parameters (in PU or PU · s) rather than PK%BL, which is noticed as a potentially biased parameter of insulin action in the microcirculation of severely obese adolescents. In conclusion, we suggest to associate dynamic and time-averaging approaches when assessing LDF skin measurements, which may help to step forward in the process of obtaining more conclusive results in the field of non-invasive microvascular research. References Cracowski, J.L., Roustit, M., 2012. Reproducibility of LDF blood flow measurements: dynamical characterization versus averaging. A response to the letter from Stefanovska. Microvasc. Res. 83, 97. Doupis, J., et al., 2010. Effects of diabetes and obesity on vascular reactivity, inflammatory cytokines, and growth factors. Obesity 19 (4), 729–735 (Silver Spring). Newman, J.M., et al., 2009. Decreased microvascular vasomotion and myogenic response in rat skeletal muscle in association with acute insulin resistance. J. Physiol. 587, 2579–2588. Rossi, M., et al., 2005. Skin blood flowmotion response to insulin iontophoresis in normal subjects. Microvasc. Res. 70, 17–22. Stefanovska, A., et al., 2011. Reproducibility of LDF blood flow measurements: dynamical characterization versus averaging. Microvasc. Res. 82, 274–276.
David Montero⁎ Guillaume Walther Agnès Vinet University of Avignon, Laboratoire Pharm-Ecologie Cardiovasculaire EA4278, F-84000 Avignon, France ⁎Corresponding author at: Laboratoire Pharm-Ecologie Cardiovasculaire (LaPEC), Faculté des Sciences, 33 rue Louis Pasteur, 84000 AVIGNON, France. Fax: +33 4 90 16 29 01. E-mail address: [email protected]. (D. Montero).
160
Letter to the Editor
Fig. 1. (a) An example of LDF skin blood flow recorded from the volar side of the forearm during baseline (5 min) and insulin iontophoresis (22 min; 12 pulses of 0.2 mA for 20 s each, with a 90 s interval between each pulse) in a severely obese adolescent placed supine in a temperature-controlled room (24 °C). (b) Three-dimensional plot of the spectral Fourier analysis of the LDF signal during insulin iontophoresis in that same subject. The frequency band of each periodic oscillation is: 0.005–0.02 Hz for endothelial activity, 0.02–0.06 Hz for sympathetic activity, 0.06–0.2 Hz for myogenic activity, 0.2–0.6 Hz for respiratory activity, and 0.6–1.6 Hz for cardiac activity. AUC, area under the curve; BL, baseline value; BZ, biological zero; LDF, Laser Doppler Flowmetry; PK, peak skin blood flow; PK − BL, peak minus baseline; PK%BL, peak as a percentage increase of baseline; PU, perfusion units.
Table 1 Independent dynamic determinants for skin blood flow in response to insulin iontophoresis in severely obese adolescents (n = 28). Skin blood flow
AUC
Independent dynamic determinants
57,910 (29,374)
PK
59 (43)
PK − BL
54 (41)
PK%BL
1107 (537)
Endothelial
Sympathetic
Myogenic
Respiratory
Cardiac
–
–
–
–
rp = .440 P = .025 rp = .434 P = .027 –
–
rp = .832 P b .001 rp = .497 P = .009 rp = .498 P = .009 –
–
–
–
–
–
–
– rp = .592 P = .001
Skin blood flow is expressed as median (ITQ, interquartile). AUC, area under the curve (in PU · s); PK, peak skin blood flow (in PU); PK − BL, peak minus baseline (in PU); PK%BL, peak as a percentage increase of baseline; PU, perfusion units; rp, partial r.