Determinants of angle width in Chinese Singaporeans

Abstract

Purpose: To investigate determinants of angle width and derive mathematic models to best predict angle width.

Design: Population-based, cross-sectional study.

Participants: A total of 1067 Chinese subjects aged ≥40 years.

Methods: Participants underwent gonioscopy, A-scan biometry, and imaging by anterior segment optical coherence tomography (ASOCT, Carl Zeiss Meditec, Dublin, CA). Customized software (Zhongshan Angle Assessment Program, Guangzhou, China) was used to measure ASOCT parameters. Linear regression modeling was performed with trabecular-iris space area at 750 μm (TISA750) and angle opening distance at 750 μm (AOD750) from the scleral spur as the 2 dependent angle width variables. By using a combination of ASOCT and biometric parameters, an optimal model that was predictive of angle width was determined by a forward selection regression algorithm. Validation of the results was performed in a separate set of community-based clinic study of 1293 persons aged ≥50 years.

Main outcome measures: Angle width and biometric parameters.

Results: The mean age (standard deviation) of the population-based subjects was 56.9 (8.5) years, and 50.2% were male. For TISA750, the strongest determinants among ASOCT and A-scan independent variables were anterior chamber volume (ACV, R(2)=0.51), followed by anterior chamber area (ACA, R(2)=0.49) and lens vault (LV, R(2)=0.47); for AOD750, these were LV (R(2)=0.56), ACA (R(2)=0.55), and ACV (R(2)=0.54). The R(2) values for anterior chamber depth and axial length were 0.39 and 0.27 for TISA750, respectively, and 0.46 and 0.30 for AOD750, respectively. An optimal model consisting of 6 variables (ACV, ACA, LV, anterior chamber width [ACW], iris thickness at 750 μm, and iris area) explained 81.4% of the variability in TISA750 and 85.5% of the variability in AOD750. The results of the population-based study were validated in the community-based clinic study, where the strongest determinants of angle width (ACA, ACV, and LV) and the optimal model with 6 variables were similar.

Conclusions: Angle width is largely dependent on variations in ACA, ACV, and LV. A predictive model comprising 6 quantitative ASOCT parameters explained more than 80% of the variability of angle width and may have implications for screening for angle closure.