Spatial Profile of Macular Pigment and Its Relationship to Foveal Architecture

Nolan, John M.; Stringham, James M.; Beatty, Stephen; Snodderly, D. Max

doi:10.1167/iovs.07-0933

Cited by 85 publications

(112 citation statements)

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“…1. This method of calculating MP optical density Area is better suited to take account of the various different MP spatial profiles that may present (e.g., an MP spatial profile exhibiting a 'central dip'), since it does not involve making any prior assumption regarding the configuration of each individual spatial profile, as is the case with the 'Method of Integration' (Kirby et al, 2008;Nolan et al, 2008). This MP optical density Area gives a better measurement of the quantity of MP across the macula than an individual measurement at a single point of retinal eccentricity.…”

Section: Measurement Of Macular Pigment Optical Densitymentioning

confidence: 99%

The association between macular pigment optical density and CFH, ARMS2, C2/BF, and C3 genotype

Loane

Nolan

McKay

et al. 2011

Experimental Eye Research

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Keywords: age-related macular degeneration age-related maculopathy susceptibility 2 gene carotenoids Complement factor H lutein macular pigment zeaxanthin a b s t r a c t Age-related macular degeneration (AMD) is the most common cause of blindness in older people in developed countries, and risk for this condition may be classified as genetic or environmental, with an interaction between such factors predisposing to this disease. This study investigated the relationship between AMD risk genes, macular pigment optical density (MPOD), which may protect against AMD, and serum concentrations of the macular carotenoids, lutein (L) and zeaxanthin (Z). This was a cross-sectional study of 302 healthy adult subjects. Dietary intake of L and Z was assessed by food frequency questionnaire, and MPOD was measured by customized heterochromatic flicker photometry. We also calculated MPOD Area as the area of MP under the spatial profile curve, to reflect MP across the macula. Serum L and Z were measured by HPLC. Genotyping of tag SNPs in the genes CFH, ARMS2, C3, C2 and BF was undertaken with multiplex polymerase chain reaction (PCR) and primer extension methodology (ABI Snapshot, ABI Warrington UK) on DNA extracted from peripheral blood. The mean AE SD (range) age of the subjects in this study was 48 AE 11 (21e66) years. There was a statistically significant association between CFH genotype and family history of AMD, with subjects having two non-risk CFH haplotypes (n ¼ 35), or one non-risk and one protective CFH haplotype (n ¼ 33), being significantly more likely to have a negative family history of AMD (Pearson Chi square: p ¼ 0.001). There was no significant association between the AMD risk genes investigated and either MPOD (One way ANOVA: p > 0.05) or serum concentrations of L or Z (One way ANOVA: p > 0.05, for both). Subjects who were homozygous for risk alleles of both CFH and ARMS2 (n ¼ 4) had significantly lower MPOD at 0.5 and 1 retinal eccentricity (Independent samples t test: p < 0.05) and lower MPOD Area which approached statistical significance (Independent samples t test: p ¼ 0.058), compared to other subjects (n ¼ 291). In conclusion, this study did not detect an association between individual AMD risk genotypes and the putatively protective MP, or serum concentrations of its constituent carotenoids. However, the combination of homozygous risk alleles at both CFH and ARMS2 loci was associated with significantly lower MPOD centrally, despite comparable serum concentrations of the macular carotenoids. These findings suggest that the maculae of subjects at very high genetic risk of AMD represent a hostile environment for accumulation and/or stabilization of MP.

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Section: Measurement Of Macular Pigment Optical Densitymentioning

confidence: 99%

The association between macular pigment optical density and CFH, ARMS2, C2/BF, and C3 genotype

Loane

Nolan

McKay

et al. 2011

Experimental Eye Research

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“…2007; Nolan et al. 2008). Various approaches at averaging the MPOD across an area of the retina have been presented, as it has been suggested that a single central MPOD measurement is a poor predictor of the total amount of MP present (Robson et al.…”

Section: Introductionmentioning

confidence: 99%

“…Averaged MPOD calculated from MPOD measured at several eccentricities from 0.25° to 3° has been reported (Nolan et al. 2008). Alternatively, the area under the exponential curve fit to a subject's MP spatial distribution data has been calculated based on MPOD data obtained by heterochromatic flicker photometry, HFP (Nolan et al.…”

Section: Introductionmentioning

confidence: 99%

“…Alternatively, the area under the exponential curve fit to a subject's MP spatial distribution data has been calculated based on MPOD data obtained by heterochromatic flicker photometry, HFP (Nolan et al. 2008; Kirby et al. 2010) and by two‐wavelength FAF (Hammond et al.…”

Section: Introductionmentioning

confidence: 99%

“…If MP spatial profile classification is based on deviations from an exponential fit to the data (Berendschot & van Norren 2006; Nolan et al. 2008; Huntjens et al. 2014; Ctori & Huntjens 2017) or an increase relative to central MPOD (Nolan et al.…”

Section: Introductionmentioning

confidence: 99%

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Repeatability of the macular pigment spatial profile: A comparison of objective versus subjective classification

Ctori

Mahroo

Williams

et al. 2018

Acta Ophthalmologica

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PurposeClassification of macular pigment (MP) spatial profile phenotypes varies and is often based on subjective visualisation. We investigated repeatability of MP optical density (MPOD) comparing an objective versus subjective profiling system.MethodsThe coefficient of repeatability (CoR) was calculated for point MPOD values (0–3.8°) obtained by dual‐wavelength fundus autofluorescence (FAF) from two scans obtained in a single visit of 40 healthy individuals (39 ± 9 years). For each individual's dataset, the MP profile was classified as exponential, ring‐like or central dip using an objective method (based on deviations away from an exponential fit), as well as by subjective visual profiling. Existing FAF images of 88 monozygotic (MZ) and 69 dizygotic (DZ) twin pairs were reanalysed using the objective profiling method and concordance and heritability of ring‐like profiles determined.ResultsThe CoR was 0.23 at 0° and 0.06 at 0.8°. Agreement of objective profiling between scans was excellent (κ = 0.85, 95% CI 0.69 to 1.00; p < 0.0005). Subjective profiling showed moderate agreement between scans (κ = 0.48, 95% CI 0.23 to 0.73; p < 0.0005). Agreement between objective and subjective classification was low (κ = 0.23, 95% CI 0.04 to 0.42; p = 0.02). Concordance for the ring‐like profile using objective profiling was 0.74 for MZ compared to 0.36 for DZ twins. Heritability was calculated as 81.5% (95% confidence interval 61.1–93.1%).ConclusionCompared to visual assessment, objective MP profiling is a more reliable method and should be considered in future observational and interventional studies. In addition, MP profile phenotypes showed high heritability.

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Color appearance model incorporating contrast adaptation—Implications for individual differences in color vision

Smet¹,

Webster

Whitehead

2021

Color Research & Application

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Color appearance models are usually based on equations with fixed numerical coefficients that were predetermined to yield optimal agreement for a selected standard observer. Here we address the well‐known fact that, among color‐normal observers, there are significant differences of cone fundamentals, which often cause observer metamerism. This is not fully addressed by many appearance models. It might seem that observer metamerism could be accurately predicted by substituting an individual's color matching functions into an otherwise‐unchanged color appearance model, but this is not assured because the model's coefficients were not designed for the new observer. Here we explore a solution related to how the visual system tends to compensate for changes in color information arising from variations cone fundamentals. For this purpose, we developed a simple color appearance model that uses only a few numerical coefficients, yet accurately predicts the perceptual attributes of Munsell samples under a selected standard lighting condition. We then added a feedback loop to adjust the model coefficients automatically, in response to switching between the cone fundamentals for different observers. This feature is intended to model long term contrast adaptation in the vision system by maintaining the average long‐term levels of color contrast. Incorporating this adaptation principle into color appearance models could allow better assessments of displays and illumination systems, to help improve color appearances for most observers.

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Spatial Profile of Macular Pigment and Its Relationship to Foveal Architecture

Cited by 85 publications

References 23 publications

The association between macular pigment optical density and CFH, ARMS2, C2/BF, and C3 genotype

The association between macular pigment optical density and CFH, ARMS2, C2/BF, and C3 genotype

Repeatability of the macular pigment spatial profile: A comparison of objective versus subjective classification

Color appearance model incorporating contrast adaptation—Implications for individual differences in color vision

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