Risk Factors for Meibomian Gland Disease Assessed by Meibography.

Purpose: To elucidate risk factors for meibomian gland disease (MGD) and understand associated changes in meibography and in relation to ocular surface disease. Patients and Methods: As part of the standard workup for ocular surface disease at a tertiary academic center, 203 patients received an ocular history and lifestyle questionnaire. The questionnaire included detailed inquiries about ocular health and lifestyle, including makeup use, cosmetic eyelid procedures, screen time, and contact lens habits. Subjects also took the standardized patient evaluation of eye dryness (SPEED) II questionnaire. Meibomian gland (MG) dropout and structural changes were evaluated on meibography and scored by three independent graders using meiboscores. Statistical analysis was conducted to identify significant risk factors associated with MG loss. Results: This retrospective, cross-sectional study included 189 patients (378 eyes) with high-quality images for grading, and the average age was 67 years (77% female). Patients older than 45 years had significantly more dropout than younger patients (p < 0.01). Self-reported eye makeup use did not significantly impact MG loss. Patients with a history of blepharoplasty trended toward higher meiboscores, but the difference was not statistically significant. Self-reported screen time did not affect meiboscores. Contact lens use over 20 years was associated with significant MG loss (p < 0.05). SPEED II scores had no relationship to meiboscores (p = 0.75). Conclusion: Older age is a significant risk factor for MG loss. Any contact lens use over 20 years also impacted MG dropout. Highlighting the incongruence of symptoms to signs, SPEED II scores showed no relationship to the structural integrity of MGs.

A Novel Epidemiological Approach to Geographically Mapping Population Dry Eye Disease in the United States Through Google Trends.

PURPOSE: Our study fills the spatiotemporal gaps in dry eye disease (DED) epidemiology by using Google Trends as a novel epidemiological tool for geographically mapping DED in relation to environmental risk factors. METHODS: We used Google Trends to extract DED-related queries estimating users' intent from 2004 to 2019 in the United States. We incorporated national climate data to generate heat maps comparing geographic, temporal, and environmental relationships of DED. Multivariable regression models were constructed to generate quadratic forecasts predicting DED and control searches. RESULTS: Our results illustrated the upward trend, seasonal pattern, environmental influence, and spatial relationship of DED search volume across the US geography. Localized patches of DED interest were visualized in urban areas. There was no significant difference in DED queries across the US census regions (P = 0.3543). Regression model 1 predicted DED queries per state (R2 = 0.61), with the significant predictor being urban population [r = 0.56, adjusted (adj.) P < 0.001, n = 50]; model 2 predicted DED searches over time (R2 = 0.97), with significant predictors being control queries (r = 0.85, adj. P = 0.0169, n = 190), time (r = 0.96, adj. P < 0.001, n = 190), time2 (r = 0.97, adj. P < 0.001, n = 190), and seasonality (winter r = -0.04, adj. P = 0.0196, n = 190; spring r = 0.10, adj. P < 0.001, n = 190). CONCLUSIONS: Our study used Google Trends as a novel epidemiologic approach to geographically mapping the US DED. Importantly, urban population and seasonality were stronger risk factors of DED searches than temperature, humidity, sunshine, pollution, or region. Our work paves the way for future exploration of geographic information systems for locating DED and other diseases through online population metrics.

Quantifying Color Vision Changes Associated With Cataracts Using Cone Contrast Thresholds.

Purpose: To evaluate effects of age and simulated and real cataractous changes on color vision as measured by the high-definition cone contrast test (CCT). Methods: Twenty-four healthy volunteers from two cohort studies performed CCT using best-corrected visual acuity, filters, mydriasis, and pinhole correction. Retrospective cross-sectional study of patients seen in eye clinics evaluated the relationship between age and color vision, and age and lens status in 355 eyes. Last, 25 subjects underwent CCT before and after cataract surgery. Results: CCT scores were most reliable in the nonmydriatic condition without pinhole correction. Progressively dense brown filters produced small decreases in S-cone sensitivity. Linear regression analysis of phakic subjects showed a decline for all cone classes with age. Rate of decline was greater for S-cones (slope = -1.09; 95% confidence interval [CI], -1.30 to 0.86) than M-cones (slope = -0.80; 95% CI, -1.03 to -0.58) and L-cones (slope = -0.66; 95% CI, -0.88 to -0.44). CCT scores increased for S-cones but reduced for L- and M-cones in pseudophakic subjects compared with phakic patients. CCT scores after cataract surgery increased for S-cones, M-cones, and L-cones by 33.0 (95% CI, 8.6 to 57.4), 24.9 (95% CI, 3.8 to 46.0), and 22.0 (95% CI, -3.2 to 47.3), respectively. Conclusions: CCT assessment allows for clinically practical quantitation of color and contrast vision improvement after cataract surgery and aging patients who note poor vision despite good visual acuity. Translational Relevance: CCT testing, which quantifies hereditary and acquired color deficiency, can also quantify the degree of cataract severity and, combined with other parameters, can provide more precise guidance for cataract extraction to optimize patient care.