Factors associated with the mean LCT were assessed in the whole study subjects including both POAG (
n = 105) and control (
n = 23) groups (
Table 3). In univariate analysis, a worse visual field mean deviation (MD;
P = 0.044), a smaller global RNFL thickness (
P = 0.001), presence of glaucoma (
P < 0.001), lower scores on the MMSE (
P < 0.001), DST-B (
P < 0.001), CFT (
P = 0.010), mBNT (
P = 0.001), CPT (
P = 0.014), BVRT copy (
P < 0.001), WLMT (
P = 0.001), WLRT (
P < 0.001), CRT (
P = 0.003), and BVRT recall (
P < 0.001), and higher scores on the TMT-A (
P < 0.001) and TMT-B (
P < 0.001) were associated with a smaller measured LCT (
Table 3). The multivariate analysis revealed that a smaller global RNFL thickness (
P < 0.001), presence of glaucoma (
P = 0.006), and a lower score on the MMSE (
P < 0.001) were associated with a thinner LC (
Table 3).
Figure 2 shows the relationships between the LCT and the global RNFL thickness (
Figs. 2A,
2C) and MMSE score (
Figs. 2B,
2D) with the LCT set as an independent variable in the POAG (see
Figs. 2A,
2B) and control (see
Fig. 2C,
2D) groups. Despite of the difference in the LCT between groups, which was significantly smaller in the POAG group (
Table 1), the trend of cognitive impairment being associated with smaller LCT was observed in both groups. Although the LCT in the POAG group was significantly related to the MMSE score when assessed using a linear model, the associations appeared to be nonlinear in the scattergram (see
Fig. 2B), and so fractional polynomial analysis was performed using STATA (version 10.0; StataCorp, College Station, TX) to investigate whether the relationship was explained better by a nonlinear model.
30 This analysis revealed that a fractional polynomial model was better than a linear model for explaining the relationship of the LCT with the MMSE score (
R2 = 0.412 vs. 0.314
P = 0.001; see
Fig. 2D).