Daniel R Machin1,2, Phillip E Gates1,2, Hans Vink1,2, Tracy M Frech1,2, Anthony J Donato3,4. 1. From the University of Utah, Department of Internal Medicine, and the Department of Exercise and Sport Science, and the Department of Biochemistry; VA Salt Lake City, Geriatric Research, Education, and Clinical Center (GRECC), Salt Lake City, Utah, USA; Maastricht University, Department of Physiology, Maastricht, the Netherlands; MicroVascular Health Solutions LLC, Alpine, Utah, USA. 2. D.R. Machin, PhD, University of Utah, Department of Internal Medicine, and VA Salt Lake City, GRECC; P.E. Gates, PhD, University of Utah, Department of Internal Medicine; H. Vink, PhD, Maastricht University, Department of Physiology, and MicroVascular Health Solutions LLC; T.M. Frech, MD, University of Utah, Department of Internal Medicine; A.J. Donato, PhD, University of Utah, Department of Internal Medicine, Department of Exercise and Sport Science, Department of Biochemistry, and VA Salt Lake City, GRECC. 3. From the University of Utah, Department of Internal Medicine, and the Department of Exercise and Sport Science, and the Department of Biochemistry; VA Salt Lake City, Geriatric Research, Education, and Clinical Center (GRECC), Salt Lake City, Utah, USA; Maastricht University, Department of Physiology, Maastricht, the Netherlands; MicroVascular Health Solutions LLC, Alpine, Utah, USA. tony.donato@utah.edu. 4. D.R. Machin, PhD, University of Utah, Department of Internal Medicine, and VA Salt Lake City, GRECC; P.E. Gates, PhD, University of Utah, Department of Internal Medicine; H. Vink, PhD, Maastricht University, Department of Physiology, and MicroVascular Health Solutions LLC; T.M. Frech, MD, University of Utah, Department of Internal Medicine; A.J. Donato, PhD, University of Utah, Department of Internal Medicine, Department of Exercise and Sport Science, Department of Biochemistry, and VA Salt Lake City, GRECC. tony.donato@utah.edu.
Abstract
OBJECTIVE: The aim of our study was to determine whether an automated capture and analysis system could detect differences in structure and function of sublingual microvessels in patients with systemic sclerosis (SSc) compared to healthy controls. METHODS: Intravital microscopy of the sublingual microcirculation was automatically captured and analyzed in 40 patients with SSc and 10 age-matched healthy controls. RESULTS: Total and perfused microvascular density were lower in patients with SSc compared with controls (total microvascular density: 2471 ± 134 µm/mm2 vs 3067 ± 197 µm/mm2, p = 0.020; perfused microvascular density: 1708 ± 92 µm/mm2 vs 2192 ± 144 µm/mm2, p = 0.009). However, the relative percentage of perfused to total microvascular density was similar between SSc and controls (72 ± 2% vs 71 ± 2%, respectively, p = 0.429). Mean red blood cell (RBC) fraction, which indicates the longitudinal tube hematocrit of microvessel segments, was lower in patients with SSc compared with controls (69 ± 1% vs 77 ± 1%, respectively, p < 0.001). Perfused boundary region (PBR), a marker of endothelial glycocalyx barrier properties, was higher in patients with SSc compared with controls (2.1 ± 0.0 µm vs 1.9 ± 0.0 µm, respectively, p = 0.012), suggestive of a dysfunctional glycocalyx. There was an inverse association of PBR with perfused microvascular density (r = -0.40, p = 0.004) and RBC fraction (r = -0.80, p < 0.001). CONCLUSION: Our results indicate that automated capture and analysis of sublingual microvessel segments produces detailed, objective microvascular structural and functional data that have allowed us to distinguish patients with SSc from controls. These data suggest that microvascular structural and functional abnormalities present in patients with SSc could be at least partly due to a dysfunctional glycocalyx.
OBJECTIVE: The aim of our study was to determine whether an automated capture and analysis system could detect differences in structure and function of sublingual microvessels in patients with systemic sclerosis (SSc) compared to healthy controls. METHODS: Intravital microscopy of the sublingual microcirculation was automatically captured and analyzed in 40 patients with SSc and 10 age-matched healthy controls. RESULTS: Total and perfused microvascular density were lower in patients with SSc compared with controls (total microvascular density: 2471 ± 134 µm/mm2 vs 3067 ± 197 µm/mm2, p = 0.020; perfused microvascular density: 1708 ± 92 µm/mm2 vs 2192 ± 144 µm/mm2, p = 0.009). However, the relative percentage of perfused to total microvascular density was similar between SSc and controls (72 ± 2% vs 71 ± 2%, respectively, p = 0.429). Mean red blood cell (RBC) fraction, which indicates the longitudinal tube hematocrit of microvessel segments, was lower in patients with SSc compared with controls (69 ± 1% vs 77 ± 1%, respectively, p < 0.001). Perfused boundary region (PBR), a marker of endothelial glycocalyx barrier properties, was higher in patients with SSc compared with controls (2.1 ± 0.0 µm vs 1.9 ± 0.0 µm, respectively, p = 0.012), suggestive of a dysfunctional glycocalyx. There was an inverse association of PBR with perfused microvascular density (r = -0.40, p = 0.004) and RBC fraction (r = -0.80, p < 0.001). CONCLUSION: Our results indicate that automated capture and analysis of sublingual microvessel segments produces detailed, objective microvascular structural and functional data that have allowed us to distinguish patients with SSc from controls. These data suggest that microvascular structural and functional abnormalities present in patients with SSc could be at least partly due to a dysfunctional glycocalyx.
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