Significance

The rise in the prevalence of myopia, a significant worldwide public health concern, has been too rapid to be explained by genetic factors alone and thus suggests environmental influences.

Purpose

Relatively little attention has been paid to the possible role of nutrition in myopia. The availability of the large National Health and Nutrition Examination Survey data set, which includes results from vision examinations, offers the opportunity to investigate the relationship between several nutrition-related factors, including body metrics, and the presence and magnitude of myopia.

Methods

Cross-sectional survey data sets with vision examination, demographic, body metrics, and nutritional data, collected as part of the National Health and Nutrition Examination Survey over the years of 2003 to 2008, were extracted for analysis. Based on already published basic and epidemiological studies, the following parameters were selected for study: body height and body mass index, demographics, serum vitamin D and glucose/insulin levels, and caffeine intake, using multivariable models and objectively measured refractive errors as the main outcome measure.

Results

Data from a total of 6855 ethnically diverse Americans aged 12 to 25 years were analyzed. In final multivariate models, female sex and age were the most significant factors related to myopia status and refractive error. In general, body metrics (body mass index) or nutritional factors (serum vitamin D, glucose levels, and caffeine intake) were found to be associated with refractive error or myopia status; however, increased insulin levels were related to increased odds of having myopia.

Conclusions

These largely negative findings suggest that other environmental factors, such as those related to the visual environment, may contribute more to the development and/or progression of myopia and would argue for continued research in these areas in support of more evidence-based myopia clinical management.

Purpose

To examine the hypothesis that the spatial frequency spectra of urban and indoor environments differ from the natural environment in ways that may promote the development of myopia.

Methods

A total of 814 images were analyzed from three datasets; University of California Berkeley (UCB), University of Texas (UT), and Botswana (UPenn). Images were processed in Matlab (Mathworks Inc) to map the camera color characteristics to human cone sensitivities. From the photopic luminance images generated, two-dimensional spatial frequency (SF) spectra were calculated and converted to one-dimensional spectra by rotational averaging. The spatial filtering profile of a 0.4 Bangerter foil, which has been shown to induce myopia experimentally, was also determined.

Results

The SF slope for natural scenes followed the recognized 1/fα relationship with mean slopes of -1.08, -0.90, and -1.04 for the UCB, UT and UPenn image sets, respectively. Indoor scenes had a significantly steeper slope (-1.48, UCB; -1.52, UT; P < 0.0001). Urban environments showed an intermediate slope (-1.29, UCB; -1.22, UT) that was significantly different from the slopes derived from the natural scenes (P < 0.0001). The change in SF content between natural outdoor scenes and indoors was comparable to that induced by a 0.4 Bangerter foil, which reduced the SF slope of a natural scene from -0.88 to -1.47.

Conclusions

Compared to natural outdoor images, man-made outdoor and indoor environments have spatial frequency characteristics similar to those known to induce form-deprivation myopia in animal models. The spatial properties of the man-made environment may be one of the missing drivers of the human myopia epidemic.

Purpose

High-quality optical coherence tomography (OCT) macular scans make it possible to distinguish a range of normal and diseased states by characterising foveal pit shape. Existing mathematical models lack the flexibility to capture all known pit variations and thus characterise the pit with limited accuracy. This study aimed to develop a new model that provides a more robust characterisation of individual foveal pit variations.

Methods

A Sloped Piecemeal Gaussian (SPG) model, consisting of a linear combination of a tilted line and a piecemeal Gaussian function (two halves of a Gaussian connected by a separate straight line), was developed to fit retinal thickness data with the flexibility to characterise different degrees of pit asymmetry and pit bottom flatness. It fitted the raw pit data between the two rims of the fovea to improve accuracy. The model was tested on 3488 macular scans from both eyes of 581 young adults (376 myopes and 206 non-myopes, mean (S.D.) age 21.9 (1.4) years). Estimates for retinal thickness, wall height and slope, pit depth and width were derived from the best-fitting model curve. Ten variations of Gaussian and Difference of Gaussian models were fitted to the same scans and compared with the SPG model for goodness of fit (by Root mean square error, RMSE), model complexity (by the Bayesian Information Criteria) and model fidelity.

Results

The SPG model produced excellent goodness of fit (mean RMSE = 4.25 and 3.89 μm; 95% CI: 4.20, 4.30 and 3.86, 3.93 for fitting horizontal and vertical profiles respectively). The SPG model showed pit asymmetry, with average nasal walls 17.6 (11.6) μm higher and 0.96 (0.61)^° steeper than temporal walls and average superior walls 7.0 (12.2) μm higher and 0.41 (0.65)^° steeper than the inferior walls. The SPG model also revealed a continuum of human foveal shapes, from round bottoms to extended flat bottoms (up to 563 μm). 49.1% of foveal profiles were best fitted with a flat bottom >30 μm wide. Compared with the other tested models, the SPG was the preferred model overall based on the Bayesian Information Criteria.

Conclusions

The SPG is a new parsimonious mathematical model that improves upon other models by accounting for wall asymmetry and flat pit bottoms, providing an excellent fit and more faithful characterisation of typical foveal pit shapes and their known variations. This new model may be helpful in distinguishing normal foveal shape variations by refractive status as well by other characteristics such as sex, ethnicity and age.