Macular Pigment Assessment in Indian Population Using Degree of Polarization Threshold: Impact of Diet on Macular Pigment Density.

Purpose: To determine macular pigment (MP) density scores in healthy Indians and examine correlations with demographic and lifestyle variables. Methods: We observed 484 Indians without an ocular pathology. Body mass index (BMI) and self-reported lifestyle factors (sunglasses usage, physical activity, and smoking) were noted. MP density was assessed as the threshold of perception of the shadow of their macular pigments on their retina using a new MP assessment tool (MP-eye). Lutein and zeaxanthin intake was assessed using a prevalidated questionnaire regionally designed for the Indian diet. Clusters of participants were created for statistical analysis based on MP-eye scores secondarily to detect any relevant effects in very low, low, medium, and high ranges of MPs. Results: Data analyzed included 235 males and 249 females with mean age of 36.1 ± 12.9 years (range, 14-72). The median MP-eye score was 6 (range, 0-10, with 10 being high). Most were non-smokers (413, 85.3%) and did not use sunglasses (438, 90.5%), and 314 (64.9%) had low physical activity. Diabetes was present in 62 participants (12.8%) and hypertension in 53 (10.9%). Advancing age (r = -0.209; P < 0.000) and BMI (r = -0.094; P = 0.038) had weak negative correlation with MP-eye scores. Hypertension was less prevalent (7/88) in the cluster with the highest median MP-eye score (P = 0.033). Dietary intake of MPs and other lifestyle factors did not correlate significantly with MP-eye score overall or when analyzed in clusters. Conclusions: MP-eye scores of an Indian population were normally distributed. Higher age, high BMI, and presence of hypertension were weakly associated with lower MP-eye scores. The impact of diet on MPs requires further evaluation. Translational Relevance: This normative regional database enables risk stratification of macular degeneration.

Myopia progression risk assessment score (MPRAS): a promising new tool for risk stratification.

Timely identification of individuals "at-risk" for myopia progression is the leading requisite for myopia practice as it aids in the decision of appropriate management. This study aimed to develop 'myopia progression risk assessment score' (MPRAS) based on multiple risk factors (10) to determine whether a myope is "at-risk" or "low-risk" for myopia progression. Two risk-score models (model-1: non-weightage, model-2: weightage) were developed. Ability of MPRAS to diagnose individual "at-risk" for myopia progression was compared against decision of five clinicians in 149 myopes, aged 6-29 years. Using model-1 (no-weightage), further 7 sub-models were created with varying number of risk factors in decreasing step-wise manner (1a: 10 factors to 1g: 4 factors). In random eye analysis for model-1, the highest Youden's J-index (0.63-0.65) led to the MPRAS cut-off score of 41.50-43.50 for 5 clinicians with a sensitivity ranging from 78 to 85% and specificity ranging from 79 to 87%. For this cut-off score, the mean area under the curve (AUC) between clinicians and the MPRAS model ranged from 0.89 to 0.90. Model-2 (weighted for few risk-factors) provided similar sensitivity, specificity, and AUC. Sub-model analysis revealed greater AUC with high sensitivity (89%) and specificity (94%) in model-1g that has 4 risk factors compared to other sub-models (1a-1f). All the MPRAS models showed good agreement with the clinician's decision in identifying individuals "at-risk" for myopia progression.

Ambient light level varies with different locations and environmental conditions: Potential to impact myopia.

PURPOSE: Considering that time spent outdoors is protective for myopia, we investigated how ambient light levels reaching the eye varies across 9 outdoor and 4 indoor locations in 5 different environmental conditions. METHODS: Illuminance (lux) was recorded using a lux meter under conditions of weather (sunny/cloudy), time of a day (7:00,10:00,13:00, and 16:00 hours), seasons (summer/winter), and sun protection (hat and cap) in outdoor and indoor locations. Nine outdoor locations were "open playground", "under a translucent artificial-shade", "under a porch facing east", "under a porch facing south", "under a big tree", "between three buildings", "within 4 buildings", and "canopy". As a ninth outdoor location, "Under a glass bowl" in the outdoor location was used as a simulation for "glass classroom model" and measurement was taken at the floor level only to determine in overall the illuminance conditions with glass covered on all sides. The 4 indoor locations included "room with multiple large windows", "room with combination light source", "room with multiple artificial lights", and "room with single artificial light". RESULTS: The overall median illuminance level (median; Q1-Q3) recorded in 9 outdoor locations was 8 times higher than that of all indoor locations (1175;197-5400 lux vs. 179;50-333 lux). Highest illuminance in outdoor locations was recorded in "open playground" (9300;4100-16825 lux), followed by "under a translucent artificial shade (8180;4200-13300 lux) and the lowest in "within 4 buildings" (11;6-20 lux). Illuminance under 'Canopy', 'between three buildings' and 'within four buildings' was similar to that of indoor locations (<1000 lux). Time of the day, weather, season, sensor position and using sun protection did not alter illuminance to change from high to low level (>1000 to <1000 lux). Among indoor locations, illuminance in "room with multiple large windows" crossed 1000 lux at a specific time points on both sunny and cloudy days. CONCLUSIONS: Illuminance levels in outdoors and indoors varied with location type, but not with other conditions. Given the variation in illuminance in different locations, and the impact it may have on myopia control, appropriate detailed recommendations seems necessary while suggesting time outdoors as an anti-myopia strategy to ensure desired outcomes.