Table 4 gives regression coefficients and probabilities of the null hypothesis for regression models of single anthropomorphic, socioeconomic, systemic, and ocular factors on IOP estimates. Nine factors were significantly associated with IOP: age (
P < 0.001), sBP (
P < 0.001), dBP (
P < 0.001), height (
P = 0.004), individual income (
P = 0.015), corneal thickness (
P < 0.001), quadrants of PAS (
P < 0.001), width of the drainage angle (
P = 0.024), and previous glaucoma surgery (
P < 0.001). A multiple regression model was then calculated including all nine variables, in which age (
P = 0.795), income (
P = 0.849), diastolic BP (
P = 0.969), height (
P = 0.401), and previous glaucoma surgery (
P = 0.393) were not statistically significant. A final regression model was calculated using sBP, CCT, quadrants with any PAS, and angle width. In this model, a 10-μm increase in CCT would be associated with a 0.15 mm Hg increase in measured IOP (
P < 0.001). A 10-mm Hg increase in systolic BP would be associated with a 0.3-mm Hg increase in IOP. A mean increase in IOP of 0.6 mm Hg would be expected per quadrant of drainage angle with any PAS. A very small increase in IOP was associated with narrowing angle width. This remained significant even after controlling for manifestations of angle-closure (presence of PAS). The magnitude of the difference would be on the order of 0.2 mm Hg per 10° change in width in all four quadrants (
P = 0.049). The final regression model accounted for 8.6% of all variation in IOP in this data set (adjusted
R 2 = 0.086). Very similar results were obtained when the analysis was repeated for left eyes.
Table 5 summarizes the multiple regression models. The absolute magnitude of the standardized regression coefficient indicates the relative importance of a variable as a determinant of IOP. sBP appears to be the most important variable in our models.