October 2016
Volume 57, Issue 13
Open Access
Physiology and Pharmacology  |   October 2016
Factors Associated With Choroidal Blood Flow Regulation in Healthy Young Subjects
Author Affiliations & Notes
  • Doreen Schmidl
    Department of Clinical Pharmacology, Medical University of Vienna, Vienna, Austria
  • Leopold Schmetterer
    Department of Clinical Pharmacology, Medical University of Vienna, Vienna, Austria
    Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
    Singapore Eye Research Institute, Singapore
    Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore
  • Katarzyna J. Witkowska
    Department of Clinical Pharmacology, Medical University of Vienna, Vienna, Austria
  • Alexandra Rauch
    Department of Clinical Pharmacology, Medical University of Vienna, Vienna, Austria
  • René M. Werkmeister
    Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
  • Gerhard Garhöfer
    Department of Clinical Pharmacology, Medical University of Vienna, Vienna, Austria
  • Alina Popa-Cherecheanu
    Carol Davila University of Medicine and Pharmacy, Bucharest, Romania
    Department of Ophthalmology, Emergency University Hospital, Bucharest, Romania
  • Correspondence: Leopold Schmetterer, Department of Clinical Pharmacology, Medical University of Vienna, Währinger Gürtel 18-20, 1090 Vienna, Austria; Leopold.schmetterer@meduniwien.ac.at
Investigative Ophthalmology & Visual Science October 2016, Vol.57, 5705-5713. doi:10.1167/iovs.16-20225
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      Doreen Schmidl, Leopold Schmetterer, Katarzyna J. Witkowska, Alexandra Rauch, René M. Werkmeister, Gerhard Garhöfer, Alina Popa-Cherecheanu; Factors Associated With Choroidal Blood Flow Regulation in Healthy Young Subjects. Invest. Ophthalmol. Vis. Sci. 2016;57(13):5705-5713. doi: 10.1167/iovs.16-20225.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Purpose: To analyze regulation of subfoveal choroidal blood flow (FLOW) during isometric exercise in healthy subjects in dependence of intraocular pressure (IOP), mean arterial pressure (MAP), ocular perfusion pressure (OPP), age, sex, fasting glucose, cholesterol, triglycerides, creatinine, and C-reactive protein levels and hematocrit.

Methods: We retrospectively analyzed results obtained in 261 healthy subjects who underwent a period of 6 minutes of isometric exercise during which FLOW was measured continuously and MAP was measured every minute. From these data, OPP and choroidal pressure/flow curves were calculated. Subjects were grouped into tertiles with regard to the dependent variables, and pressure/flow relationships were compared.

Results: Choroidal blood flow started to increase at OPP values of approximately 65% dependent on the MAP/IOP tertile. A significant increase of FLOW from baseline was noted at 67.7 ± 2.1% in the lowest MAP tertile, at 67.7 ± 2.0% in the second MAP tertile, and at 61.8 ± 2.0% in the highest MAP tertile (P = 0.01). At the three IOP levels, FLOW started to increase at an OPP increase of 69.8 ± 2.1%, 70.1 ± 2.2%, and 65.4 ± 1.9% above baseline, respectively (P = 0.03). Choroidal pressure/flow curves were independent of the other variables.

Conclusions: The present analysis indicates that FLOW regulation during isometric exercise is dependent on absolute MAP as well as IOP levels. This indicates that regulation depends on pressure levels at both the arterial and the venous side of the choroidal circulation and highlights the complexity of FLOW regulation during changes in OPP that cannot be simply characterized by classical autoregulation models.

The choroid is a richly vascularized tissue that oxygenizes the outer retina including the photoreceptors.1,2 Regulation of blood flow in the choroid in response to changes in ocular perfusion pressure (OPP) is complex.3 For decades the choroidal vasculature was considered a passive vascular bed showing a linear pressure/flow relationship. In the early 1990s, however, a landmark paper proved that the pressure/flow relationship is nonlinear in the rabbit.4 This result was later verified in a wide variety of rabbit experiments.515 In humans, evidence for active choroidal blood flow (FLOW) regulation was obtained during an increase in OPP as well as during a decrease in OPP.1621 Alterations of FLOW regulation during changes in perfusion pressure were observed in patients with age-related macular degeneration,22 diabetes,23 central serous chorioretinopathy,24 glaucoma,25 and vasospasm.26,27 
We set out to analyze factors associated with the regulatory capacity of the choroid during isometric exercise–induced changes in OPP. For this purpose, we analyzed data that were previously collected in clinical studies in our department. Tested parameters included arterial blood pressure, intraocular pressure (IOP), OPP, heart rate, age, and sex as well as several factors measured in blood plasma and serum. 
Methods
Subjects
The present study analyzed data from previously published studies investigating FLOW during changes in perfusion pressure, as well as data from unpublished pilot experiments. In total, data from 261 subjects were included. To increase OPP, subjects performed isometric exercise for 6 minutes.24,2836 All study protocols were approved by the Ethics Committee of the Medical University of Vienna and followed the guidelines set forth in the Declaration of Helsinki. All subjects provided written informed consent prior to inclusion and passed a screening examination. All participating subjects were nonsmokers and had normal systemic and ocular findings. They were free of systemic or topical drug intake within the last 3 weeks before the experiments. Pregnancy and lactation were exclusion criteria in healthy female subjects. All participating volunteers were asked to abstain from beverages containing alcohol or caffeine for at least 12 hours before the study. 
Procedures and Interventions
Several of the studies were placebo-controlled trials. Only placebo days were used for analysis from these studies when subjects received intravenous saline solution. Some subjects participated in more than one of the above-mentioned clinical trials. In these subjects, only the first period of blood flow measurement during squatting was chosen for analysis. As part of the prescreening procedure, blood plasma levels of fasting glucose and hematocrit, as well as serum levels of cholesterol, triglycerides, creatinine, and C-reactive protein, were analyzed using routine laboratory techniques. 
All study days followed the same time schedule. Initially a resting period of at least 20 minutes was scheduled in order to achieve stable hemodynamic conditions. Thereafter baseline measurements of FLOW and blood pressure were registered. Choroidal blood flow was measured continuously for 3 minutes at baseline using laser Doppler flowmetry. Thereafter, subjects performed squatting for 6 minutes, and FLOW was measured continuously during the entire study period. Squatting was performed in a position in which the upper and the lower leg were as close as possible to a right angle. Systemic blood pressure and pulse rate were measured every minute during the squatting period. The IOP was measured at baseline and at end of the squatting period. Unpublished pilot data had shown that the change in IOP during isometric exercise is small and can adequately be estimated from one baseline measurement and one measurement of the end of the squatting cycle. 
Systemic Blood Pressure and Pulse Rate.
Systolic, diastolic, and mean arterial pressure (SBP, DBP, MAP) were measured on the upper arm by an automated oscillometric device. Pulse rate (PR) was automatically recorded from a finger pulse-oxymetric device (HP-CMS patient monitor; Hewlett Packard, Palo Alto, CA, USA). 
Intraocular Pressure and OPP.
IOP was measured with a Goldmann applanation tonometer mounted on a slit-lamp. Oxybuprocainhydrochloride was used for local anesthesia. Ocular perfusion pressure was calculated as OPP = 2/3 × MAP − IOP.37 This formula is based on the assumption that arterial blood pressure as measured on the upper arm equals blood pressure in the heart.38 The factor 2/3 accounts for the hydrostatic pressure difference between the eye and the upper arm in sitting position. In addition, IOP is assumed to equal pressure in choroidal veins, which is fulfilled over a wide range of pressures.39 
Laboratory Analysis.
Blood plasma levels of fasting glucose and hematocrit as well as serum levels of cholesterol, triglycerides, creatinine, and C-reactive protein were analyzed using routine techniques. 
Measurement of FLOW.
Continuous measurement of FLOW was performed with laser Doppler flowmetry (LDF) as described in detail previously.40 Briefly, the vascularized tissue is illuminated by coherent laser light, which is scattered by the tissue. When scattering occurs at moving red blood cells, the light undergoes a frequency shift attributed to the Doppler effect. In contrast, scattering at static tissue components does not change the light frequency, but leads to randomization of light directions. Due to multiple scattering, red blood cells receive light from numerous random directions. This leads to a broadening of the power spectrum, because the amount of frequency shift depends on the velocity of scatterers on the one hand and the angle of incidence on the other hand. From this spectrum, FLOW is calculated based on a theory of light scattering in tissue in arbitrary units (a.u.).41 
For all experiments, a compact portable laser Doppler flowmeter was used.42,43 The light source of this system is a single-mode laser diode (785 nm, 90 μW at the cornea), which illuminates the eye via a confocal optical system. The beam diameter at the fundus is nominally 12 μm. The light is collected by a bundle of six optical fibers with a core diameter of 110 μm, which are arranged on a circle with a diameter of 180 μm. All measurements were performed in the fovea by asking the subject to directly fixate at the beam, which appears to the subject as a small red dot. 
Data Analysis
For data description, % changes from baseline were calculated. A 1-way ANOVA model was used to study the time effect of FLOW and OPP during squatting. From the measurements of FLOW and OPP we calculated vascular resistance (RESIST) as Resist = FLOW/OPP according to Hagen-Poiseuille's law. Given that FLOW is measured in a.u. using LDF, RESIST is in a.u. as well. An ANCOVA model was used to study the influence of potential factors associated with FLOW regulation. In this model FLOW, OPP and RESIST were chosen as dependent variables and continuous predictors. MAP, IOP, age, blood plasma levels of fasting glucose and hematocrit as well as serum levels of cholesterol, triglycerides, creatinine and C-reactive protein were categorical predictors. In addition, linear regression analysis was done between continuous predictors at minute 6 of isometric exercise and categorical predictors. For an additional analysis subjects were grouped into tertiles with regard to age, MAP, IOP, OPP, blood plasma levels of fasting glucose, and hematocrit, as well as serum levels of cholesterol, triglycerides, creatinine, and C-reactive protein. In each of the tertiles, pressure–flow relationships were calculated. The OPP values were then sorted according to ascending OPP values and grouped into 12 intervals. For analysis with regard to IOP, OPP, and MAP, 87 subjects were distributed in each tertile. Given that six values were obtained in each subject during isometric exercise, this results in a total of 282 OPP/FLOW values. Hence, each of the 12 pooled data points in the pressure/flow relationship consisted of 43 or 44 individual data points. A statistically significant deviation from baseline flow was defined when the 95% confidence interval did not overlap with the baseline value any more. A P value < 0.05 was considered the level of significance. Statistical analysis was carried out using CSS Statistica for Windows (Version 6.0; Statsoft, Inc., Tulsa, OK, USA). 
Results
The characteristics of the study population are presented in the Table. Fasting glucose (n = 242: 4.9 ± 0.4 mM), cholesterol (n = 241: 6.1 ± 0.9 mM), triglycerides (n = 240: 2.1 ± 0.5 mM), creatinine (n = 242: 103 ± 22 μM), C-reactive protein plasma levels (n = 240: 3.4 ± 0.9 mg/L), and hematocrit (n = 243: 0.409 ± 0.049) were not available in all subjects. 
Table
 
Characteristics of Healthy Subjects Studied During Isometric Exercise (n = 261)
Table
 
Characteristics of Healthy Subjects Studied During Isometric Exercise (n = 261)
The time course of FLOW and OPP in response to isometric exercise is presented in Figure 1. The increase in FLOW and OPP was highly significant (P < 0.001 each). Given that the % change in FLOW was much smaller than the % change in OPP, these data clearly indicate choroidal autoregulation. 
Figure 1
 
Relative change of ocular perfusion pressure (OPP) and choroidal blood flow (FLOW) over the preexercise value during squatting. A significant increase was seen for both parameters over time. Data are presented as means ± SD (n = 261).
Figure 1
 
Relative change of ocular perfusion pressure (OPP) and choroidal blood flow (FLOW) over the preexercise value during squatting. A significant increase was seen for both parameters over time. Data are presented as means ± SD (n = 261).
Neither age nor sex had an influence on OPP, FLOW, or RESIST during isometric exercise in the ANCOVA model. Likewise, these parameters did not affect the pressure/flow relationship. In addition, none of the laboratory parameters affected the time course of OPP, FLOW, and RESIST (ANCOVA model) or the choroidal pressure/flow relationship (data not shown). 
Linear correlation between MAP at baseline and the change of OPP, FLOW, and RESIST during the last minute of isometric exercise is depicted in Figure 2. Significant negative association was observed between MAP and change in OPP (P = 0.035), and significant positive association was observed between MAP and change in FLOW (P = 0.041). The strongest correlation was found between MAP and change in RESIST (P < 0.001) with an approximately 2% increase in RESIST per 10 mm Hg. 
Figure 2
 
Correlation between baseline mean arterial pressure (MAP) and the change in ocular perfusion pressure (OPP, upper), in choroidal blood flow (Flow, middle), and in vascular resistance (Resist, lower). The correlation line and the 95% confidence interval are shown (n = 261).
Figure 2
 
Correlation between baseline mean arterial pressure (MAP) and the change in ocular perfusion pressure (OPP, upper), in choroidal blood flow (Flow, middle), and in vascular resistance (Resist, lower). The correlation line and the 95% confidence interval are shown (n = 261).
Figure 3 depicts the association between IOP at baseline and the change of OPP, FLOW, and RESIST during the last minute of isometric exercise. No association was found between MAP and change in OPP. By contrast, IOP was positively correlated with change in FLOW (P = 0.004) and negatively correlated with change in RESIST (P < 0.001). 
Figure 3
 
Correlation between baseline intraocular pressure (IOP) and the change in ocular perfusion pressure (OPP, upper), in choroidal blood flow (Flow, middle), and in vascular resistance (Resist, lower). The correlation line and the 95% confidence interval are shown (n = 261).
Figure 3
 
Correlation between baseline intraocular pressure (IOP) and the change in ocular perfusion pressure (OPP, upper), in choroidal blood flow (Flow, middle), and in vascular resistance (Resist, lower). The correlation line and the 95% confidence interval are shown (n = 261).
Analysis of covariance revealed that MAP at baseline was not associated with the time course of OPP or FLOW, although these effects were borderline significant at P = 0.07 and P = 0.09, respectively. Mean arterial pressure did, however, have a statistically significant influence on the time course of RESIST (P = 0.002). Intraocular pressure at baseline did not influence the time course of OPP, but determined isometric exercise–induced changes in FLOW (P = 0.007) and RESIST (P = 0.002). 
Results of pressure–flow relationship in dependence of MAP are presented in Figure 4. Grouping MAP values resulted in the following tertiles: group 1: 70.3 ± 3.1 mm Hg; group 2: 80.3 ± 2.4 mm Hg; group 3: 90.2 ± 3.0 mm Hg. A significant difference was noted between the three groups in terms of pressure–flow relationships. A significant increase of FLOW from baseline was noted at 67.7 ± 2.1% in the first tertile, at 67.7 ± 2.0% in the second tertile, and at 61.8 ± 2.0% in the third tertile. This effect reached the level of significance at P = 0.01 between groups. 
Figure 4
 
Pressure/flow relationship using the categorized ocular perfusion pressure (OPP) -choroidal blood flow (FLOW) data during isometric exercise. Relative data were sorted into 12 groups according to ascending OPP values. The data are analyzed according to baseline mean arterial pressure (MAP) values. The upper part of the figure shows the time course for subjects in the lowest MAP tertile, the middle part for subjects in the middle MAP tertile, and the lowest part for subjects in the highest MAP tertile. The means and the 95% confidence intervals are shown (n = 261).
Figure 4
 
Pressure/flow relationship using the categorized ocular perfusion pressure (OPP) -choroidal blood flow (FLOW) data during isometric exercise. Relative data were sorted into 12 groups according to ascending OPP values. The data are analyzed according to baseline mean arterial pressure (MAP) values. The upper part of the figure shows the time course for subjects in the lowest MAP tertile, the middle part for subjects in the middle MAP tertile, and the lowest part for subjects in the highest MAP tertile. The means and the 95% confidence intervals are shown (n = 261).
Results of pressure–flow relationship in dependence of MAP are presented in Figure 4. Grouping IOP values resulted in the following tertiles: group 1: 12.1 ± 0.9 mm Hg; group 2: 14.6 ± 0.7 mm Hg; group 3: 17.3 ± 0.9 mm Hg. The pressure/flow relationship was dependent on the level of IOP. At the lowest IOP level, FLOW started to increase at an OPP increase of 69.8 ± 2.1%; at the second IOP tertile, this increase was seen at 70.1 ± 2.2%, whereas at the third IOP tertile the increase occurred already at an OPP level of 65.4 ± 1.9% above baseline (Fig. 5, P = 0.03). 
Figure 5
 
Pressure/flow relationship using the categorized ocular perfusion pressure (OPP) -choroidal blood flow (FLOW) data during isometric exercise. Relative data were sorted into 12 groups according to ascending OPP values. The data are analyzed according to baseline intraocular pressure (IOP) values. The upper part of the figure shows the time course for subjects in the lowest IOP tertile, the middle part for subjects in the middle IOP tertile, and the lower part for subjects in the highest IOP tertile. The means and the 95% confidence intervals are shown (n = 261).
Figure 5
 
Pressure/flow relationship using the categorized ocular perfusion pressure (OPP) -choroidal blood flow (FLOW) data during isometric exercise. Relative data were sorted into 12 groups according to ascending OPP values. The data are analyzed according to baseline intraocular pressure (IOP) values. The upper part of the figure shows the time course for subjects in the lowest IOP tertile, the middle part for subjects in the middle IOP tertile, and the lower part for subjects in the highest IOP tertile. The means and the 95% confidence intervals are shown (n = 261).
Discussion
Regulation of FLOW during changes in OPP is a complex phenomenon that has not been fully elucidated.3 The present study adds to this knowledge by showing that in healthy young subjects, FLOW regulation depends on MAP and IOP at baseline. In both the highest tertile of MAP and the highest tertile of IOP, FLOW started to deviate from baseline earlier than in the other groups. This effect was seen although the participants all showed normal IOP levels and MAP levels that were either normal or only slightly elevated. 
In previous animal experiments in which the OPP was altered mechanically, the full pressure/flow relationship of the choroid could be investigated by setting all animals to the same OPP levels.4,4447 This is obviously not possible in humans, because different subjects start at different levels of MAP, OPP, and IOP. As such in human subjects, pressure/flow relationships can be calculated based on only relative changes over baseline. The pressure/flow relationship as obtained in the present analysis closely follows what has previously been reported by other groups using LDF16 or by our group using an alternative technology.19 These pressure/flow curves, however, cannot directly be compared to data as obtained in experimental animals. Whereas in the latter case OPP can be modified mechanically and thereby can be considered as autoregulation in its true sense,48 the former will significantly change neuronal input. Hence, the pressure/flow curves presented in Figures 2, 4, and 6 cannot be considered purely autoregulatory given that the choroid is richly innervated and that the choroidal vascular tone is under neural control.1,2,49,50 
Figure 6
 
Comparison of the relative change in ocular perfusion pressure (OPP) at which blood flow started to increase during isometric exercise. Data are extracted from the pressure/flow relationship (see Figs. 4 and 5). Data are presented for the tertiles of baseline MAP and the tertiles of baseline IOP. The means and the 95% confidence intervals are shown (n = 261).
Figure 6
 
Comparison of the relative change in ocular perfusion pressure (OPP) at which blood flow started to increase during isometric exercise. Data are extracted from the pressure/flow relationship (see Figs. 4 and 5). Data are presented for the tertiles of baseline MAP and the tertiles of baseline IOP. The means and the 95% confidence intervals are shown (n = 261).
The present study indicates that FLOW regulation in the eye is complex and does not depend only on the level of OPP, but also on the absolute values of MAP and IOP. This is in keeping with a variety of different animal experiments4,51,52 as well as our previous studies on choroidal and optic nerve head regulation.53,54 In both vascular beds we observed that the vasculature during combined isometric exercise– and suction cup–induced IOP increase regulates better when MAP is altered up than when IOP is altered up. It has previously been speculated that this is related to differences in the pressure gradient along the retinal vascular tree.3,4 As such, the choroid may regulate better during changes in MAP than during changes in IOP because the arterial pressure rather than the venous pressure is modified. Only the former will elicit a myogenic response because the veins contain no or very few smooth muscle cells.3,50 
Our results would be compatible with the following hypothesis: During an isometric exercise–induced increase in OPP, subjects with higher MAPs have less autoregulatory reserve, compatible with the theory of pressure autoregulation.2,3 In subjects with higher IOP, retinal venous pressure is higher than in subjects with lower IOPs. When MAP is increased in isometric exercise the pressure gradient in subjects with high IOP is therefore lower, leading to less autoregulatory response. Interestingly, we have previously observed that the autoregulatory plateau is increased after pharmacologic IOP lowering33 and that the pressure/flow relation in glaucoma patients normalizes after IOP lowering.55,56 This was observed with both timolol and dorzolamide, although only the latter increased blood flow.57 
When interpreting our results with regard to baseline MAP, it needs to be considered that subjects at higher MAP tended to have less increase in OPP. Hence, the upper limit of autoregulation may occur at the same absolute OPP values. As such, regulation at higher MAPs cannot be considered abnormal. It is, however, worth noting2,3 that this is still associated with a reduced autoregulatory reserve toward higher blood pressure values. This is important because it indicates that during diurnal changes in OPP, blood flow is more likely to deviate from the autoregulatory plateau. As such the present study indicates that in those subjects who had higher MAPs the choroidal vascular tone was in a state of vasoconstriction. 
The relation between IOP, blood pressure, OPP, and the prevalence, incidence, and progression of glaucoma is complicated. The fact that increased IOP is an important risk factor for glaucoma has been well established, and lowering IOP decreases the incidence and the progression of glaucoma.58 The relation with OPP is more controversial.38,5963 Many studies have reported that low OPP is associated with increased risk of glaucoma, but doubts have been raised because IOP is an essential part of how OPP is calculated.64 In the Early Manifest Glaucoma Trial (EMGT), low systolic blood pressure was, however, reported to be a risk factor for glaucoma,65 a result that is independent of the limitations mentioned above. Interestingly, a recent meta-analysis reported that glaucoma is also associated with systemic hypertension.66 In the present study reduced autoregulatory capacity was associated with both higher IOP and higher MAP levels. The latter result is in agreement with previous reports in the brain.67,68 
Fasting glucose, cholesterol, triglycerides, creatinine, C-reactive protein plasma levels, and hematocrit were not associated with the degree of FLOW regulation. In the present study only healthy subjects with laboratory values within relatively normal ranges were included, and we cannot exclude that FLOW regulation may be affected in patients with values far outside the normal range. Little is known about these factors for FLOW. There is evidence that some of these factors are associated with blood flow and its regulation in the retina.6979 These results may well be compatible with the general view that the retinal circulation is largely influenced by metabolic and hormonal factors, whereas the choroid is to a large degree under neural control.2,50 
The present analysis of previous data has several limitations. First of all, data do not stem from a prospective study, but include analysis of previously published data. As such, the subjects were not selected according to balanced sex or age distribution, or a wide range MAP or IOP, but according to the specific inclusion/exclusion criteria of these previous studies. As such the age range in the present analysis was narrow, and it may well be that FLOW regulation capacity may change in the elderly. In addition, blood pressure was not measured continuously as in a recent study,80 but was measured every minute only. Furthermore, OPP can only be estimated from measurements of blood pressure and IOP, and the limitations of this approach have been reviewed in some detail recently.63 It would be interesting to also analyze data when OPP is decreased. We have done some experiments during a suction cup–induced increase in IOP,33,34,53,81 but the number of subjects included in these previous studies is not sufficient to allow for the type of analysis applied in this report. Given that LDF was used in our experiments, measurements of blood flow are restricted to the subfoveal choroid. Whether other more peripheral areas of the choroid react in the same way is unknown. 
In conclusion, the present analysis of FLOW data during isometric exercise indicates that regulation of blood flow is altered in healthy subjects when they have either higher MAPs or higher IOPs. This result is compatible with the idea that already at slightly elevated MAP, choroidal resistance vessels are in a state of vasoconstriction due to myogenic response. 
Acknowledgments
Supported by Austrian Science Fund (Fonds zur Förderung der Wissenschaftlichen Forschung) Grants 21406 and KLIF340. 
Disclosure: D. Schmidl, None; L. Schmetterer, None; K.J. Witkowska, None; A. Rauch, None; R.M. Werkmeister, None; G. Garhöfer, None; A. Popa-Cherecheanu None 
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Figure 1
 
Relative change of ocular perfusion pressure (OPP) and choroidal blood flow (FLOW) over the preexercise value during squatting. A significant increase was seen for both parameters over time. Data are presented as means ± SD (n = 261).
Figure 1
 
Relative change of ocular perfusion pressure (OPP) and choroidal blood flow (FLOW) over the preexercise value during squatting. A significant increase was seen for both parameters over time. Data are presented as means ± SD (n = 261).
Figure 2
 
Correlation between baseline mean arterial pressure (MAP) and the change in ocular perfusion pressure (OPP, upper), in choroidal blood flow (Flow, middle), and in vascular resistance (Resist, lower). The correlation line and the 95% confidence interval are shown (n = 261).
Figure 2
 
Correlation between baseline mean arterial pressure (MAP) and the change in ocular perfusion pressure (OPP, upper), in choroidal blood flow (Flow, middle), and in vascular resistance (Resist, lower). The correlation line and the 95% confidence interval are shown (n = 261).
Figure 3
 
Correlation between baseline intraocular pressure (IOP) and the change in ocular perfusion pressure (OPP, upper), in choroidal blood flow (Flow, middle), and in vascular resistance (Resist, lower). The correlation line and the 95% confidence interval are shown (n = 261).
Figure 3
 
Correlation between baseline intraocular pressure (IOP) and the change in ocular perfusion pressure (OPP, upper), in choroidal blood flow (Flow, middle), and in vascular resistance (Resist, lower). The correlation line and the 95% confidence interval are shown (n = 261).
Figure 4
 
Pressure/flow relationship using the categorized ocular perfusion pressure (OPP) -choroidal blood flow (FLOW) data during isometric exercise. Relative data were sorted into 12 groups according to ascending OPP values. The data are analyzed according to baseline mean arterial pressure (MAP) values. The upper part of the figure shows the time course for subjects in the lowest MAP tertile, the middle part for subjects in the middle MAP tertile, and the lowest part for subjects in the highest MAP tertile. The means and the 95% confidence intervals are shown (n = 261).
Figure 4
 
Pressure/flow relationship using the categorized ocular perfusion pressure (OPP) -choroidal blood flow (FLOW) data during isometric exercise. Relative data were sorted into 12 groups according to ascending OPP values. The data are analyzed according to baseline mean arterial pressure (MAP) values. The upper part of the figure shows the time course for subjects in the lowest MAP tertile, the middle part for subjects in the middle MAP tertile, and the lowest part for subjects in the highest MAP tertile. The means and the 95% confidence intervals are shown (n = 261).
Figure 5
 
Pressure/flow relationship using the categorized ocular perfusion pressure (OPP) -choroidal blood flow (FLOW) data during isometric exercise. Relative data were sorted into 12 groups according to ascending OPP values. The data are analyzed according to baseline intraocular pressure (IOP) values. The upper part of the figure shows the time course for subjects in the lowest IOP tertile, the middle part for subjects in the middle IOP tertile, and the lower part for subjects in the highest IOP tertile. The means and the 95% confidence intervals are shown (n = 261).
Figure 5
 
Pressure/flow relationship using the categorized ocular perfusion pressure (OPP) -choroidal blood flow (FLOW) data during isometric exercise. Relative data were sorted into 12 groups according to ascending OPP values. The data are analyzed according to baseline intraocular pressure (IOP) values. The upper part of the figure shows the time course for subjects in the lowest IOP tertile, the middle part for subjects in the middle IOP tertile, and the lower part for subjects in the highest IOP tertile. The means and the 95% confidence intervals are shown (n = 261).
Figure 6
 
Comparison of the relative change in ocular perfusion pressure (OPP) at which blood flow started to increase during isometric exercise. Data are extracted from the pressure/flow relationship (see Figs. 4 and 5). Data are presented for the tertiles of baseline MAP and the tertiles of baseline IOP. The means and the 95% confidence intervals are shown (n = 261).
Figure 6
 
Comparison of the relative change in ocular perfusion pressure (OPP) at which blood flow started to increase during isometric exercise. Data are extracted from the pressure/flow relationship (see Figs. 4 and 5). Data are presented for the tertiles of baseline MAP and the tertiles of baseline IOP. The means and the 95% confidence intervals are shown (n = 261).
Table
 
Characteristics of Healthy Subjects Studied During Isometric Exercise (n = 261)
Table
 
Characteristics of Healthy Subjects Studied During Isometric Exercise (n = 261)
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