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).