Predictive models of aging of the human eye based on ocular anterior segment morphology.

Aging is a major risk factor for various eye diseases, such as cataract, glaucoma, and age-related macular degeneration. Age-related changes are observed in almost all structures of the human eye. Considerable individual variations exist within a group of similarly aged individuals, indicating the need for more informative biomarkers for assessing the aging of the eyes. The morphology of the ocular anterior segment has been reported to vary across age groups, focusing on only a few corneal parameters, such as keratometry and thickness of the cornea, which could not provide accurate estimation of age. Thus, the association between eye aging and the morphology of the anterior segment remains elusive. In this study, we aimed to develop a predictive model of age based on a large number of anterior segment morphology-related features, measured via the high-resolution ocular anterior segment analysis system (Pentacam). This approach allows for an integrated assessment of age-related changes in corneal morphology, and the identification of important morphological features associated with different eye aging patterns. Three machine learning methods (neural networks, Lasso regression and extreme gradient boosting) were employed to build predictive models using 276 anterior segment features of 63,753 participants from 10 ophthalmic centers in 10 different cities of China. The best performing age prediction model achieved a median absolute error of 2.80 years and a mean absolute error of 3.89 years in the validation set. An external cohort of 100 volunteers was used to test the performance of the prediction model. The developed neural network model achieved a median absolute error of 3.03 years and a mean absolute error of 3.40 years in the external cohort. In summary, our study revealed that the anterior segment morphology of the human eye may be an informative and non-invasive indicator of eye aging. This could prompt doctors to focus on age-related medical interventions on ocular health.

Associations Between Regional Environment and Cornea-Related Morphology of the Eye in Young Adults: A Large-Scale Multicenter Cross-Sectional Study.

Purpose: To investigate environmental factors associated with corneal morphologic changes. Methods: A cross-sectional study was conducted, which enrolled adults of the Han ethnicity aged 18 to 44 years from 20 cities. The cornea-related morphology was measured using an ocular anterior segment analysis system. The geographic indexes of each city and meteorological indexes of daily city-level data from the past 40 years (1980-2019) were obtained. Correlation analyses at the city level and multilevel model analyses at the eye level were performed. Results: In total, 114,067 eyes were used for analysis. In the correlation analyses at the city level, the corneal thickness was positively correlated with the mean values of precipitation (highest r [correlation coefficient]: >0.700), temperature, and relative humidity (RH), as well as the amount of annual variation in precipitation (r: 0.548 to 0.721), and negatively correlated with the mean daily difference in the temperature (DIF T), duration of sunshine, and variance in RH (r: -0.694 to 0.495). In contrast, the anterior chamber (AC) volume was negatively correlated with the mean values of precipitation, temperature, RH, and the amount of annual variation in precipitation (r: -0.672 to -0.448), and positively associated with the mean DIF T (r = 0.570) and variance in temperature (r = 0.507). In total 19,988 eyes were analyzed at the eye level. After adjusting for age, precipitation was the major explanatory factor among the environmental factors for the variability in corneal thickness and AC volume. Conclusions: Individuals who were raised in warm and wet environments had thicker corneas and smaller AC volumes than those from cold and dry ambient environments. Our findings demonstrate the role of local environmental factors in corneal-related morphology.