Macular Pigment and Its Contribution to Visual Performance in the Older Human Eye

Macular pigment and its contribution to visual performance in the older human eye

A thesis submitted to the University of Manchester

for the degree of a Doctor of Philosophy

in the Faculty of Life Sciences

2014

Laura Patryas

Optometry Contents

1 Introduction 19

1.1 Impact of ageing on the anatomy and physiology of the visual system . 20 1.1.1 The pupil ...... 22 1.1.2 The crystalline lens ...... 23 1.1.3 The retina ...... 24 1.1.4 Rods and cones ...... 26 1.1.5 Retinal pigment epithelium ...... 28 1.1.6 The interphotoreceptor matrix ...... 29 1.1.7 Bruch’s membrane ...... 30 1.1.8 Choriocapillaris and choroid ...... 30 1.2 Psychophysical function in ageing ...... 31 1.2.1 Spatial vision ...... 31 1.2.1.1 Visual acuity ...... 31 1.2.1.2 Contrast sensitivity ...... 32 1.2.2 Temporal vision ...... 33 1.2.3 Visual adaptation ...... 33 1.2.3.1 Dark adaptation ...... 33 1.2.3.2 Glare recovery ...... 36 1.3 Environmental factors in ageing ...... 37 1.3.1 Smoking ...... 38 1.3.2 Alcohol ...... 39 1.3.3 Radiation exposure ...... 39 1.3.4 Physical activity ...... 41 1.4 Ageing and age-related macular degeneration ...... 41

2 The macular pigment 43

2.1 Bio- and stereochemistry of macular pigment ...... 43 2.2 Location of macular pigment ...... 44

2 2.3 The function of macular pigment ...... 46 2.3.1 The potential role of macular pigment in normal healthy eyes . 49 2.4 Characteristics of macular pigment ...... 50 2.4.1 Composition ...... 50 2.4.2 Distribution ...... 50 2.4.3 Spatial profile ...... 52 2.4.4 Density and assimilation ...... 53 2.5 Factors affecting macular pigment density ...... 54 2.5.1 Age ...... 54 2.5.2 Gender ...... 54 2.5.3 Ocular factors ...... 55 2.5.3.1 Iris pigmentation ...... 55 2.5.3.2 Crystalline lens optical density ...... 55 2.5.4 Non-ocular factors ...... 55 2.5.4.1 Nutrition ...... 55 2.5.4.2 Smoking ...... 58 2.5.4.3 Obesity ...... 58 2.5.4.4 Light exposure ...... 58 2.5.4.5 Heredity ...... 59 2.5.4.6 Cultural differences in nutrition ...... 59 2.5.4.7 Ethnicity ...... 59 2.6 From plant to supplement ...... 59 2.6.1 Xanthophyll extraction ...... 59 2.6.2 Supplement preparation ...... 60 2.6.3 Recommended dosage ...... 60 2.6.4 Supplement safety ...... 60 2.7 Nutritional augmentation of macular pigment ...... 61 2.7.1 Supplementation studies in healthy eyes ...... 61 2.7.1.1 Effect on visual function ...... 62

3 2.7.2 Supplementation studies in ocular disease ...... 65 2.8 Bioavailability of macular pigment ...... 68 2.8.1 Dietary factors ...... 68 2.8.2 Supplement type ...... 69 2.8.3 Non-dietary factors ...... 69 2.9 Measurement of macular pigment ...... 71

3 Development of methods 73

3.1 Dark adaptation ...... 73 3.1.1 Introduction ...... 73 3.1.1.1 Retinal disease and dark adaptation ...... 76 3.1.1.2 General health and dark adaptation ...... 77 3.1.1.3 Photochemistry ...... 79 3.1.2 Study apparatus ...... 82 3.1.3 Calculating the percentage bleach ...... 83 3.1.4 Procedure ...... 84 3.1.5 Correcting data for pre-retinal absorption ...... 86 3.1.6 Modeling of dark adaptation data ...... 87 3.2 Factors aﬀecting dark adaptation parameters: experimental data . . . . 89 3.2.1 Bleach intensity ...... 89 3.2.2 Bleach frequency ...... 91 3.2.3 Pupil dilation ...... 92 3.2.4 Flash position ...... 94 3.2.5 Stimulus size in the periphery: rod spatial summation ...... 95 3.2.6 Stimulus location ...... 97 3.2.7 Summary ...... 98 3.3 Macular pigment apparatus ...... 99 3.3.1 Procedure ...... 99 3.3.2 MPOD calculation ...... 101

4 3.3.3 Repeatability ...... 102 3.4 Study design and protocol ...... 102 3.4.1 Pilot study ...... 102 3.4.1.1 Study design ...... 102 3.4.1.2 Study protocol ...... 103 3.4.1.3 Objectives ...... 103 3.4.1.4 Selection of study population ...... 103 3.4.1.5 Inclusion criteria ...... 103 3.4.1.6 Exclusion criteria ...... 104 3.4.1.7 Ethical conduct of the study ...... 105 3.4.1.8 Patient information sheet ...... 105 3.4.1.9 Study procedure and visit schedule ...... 106 3.4.1.10 Statistical analysis ...... 106 3.4.2 Supplementation study ...... 106 3.4.2.1 Study design ...... 106 3.4.2.2 Study protocol ...... 107 3.4.2.3 Objectives ...... 107 3.4.2.4 Selection of study population ...... 107 3.4.2.5 Inclusion criteria ...... 108 3.4.2.6 Exclusion criteria ...... 108 3.4.2.7 Ethical conduct of the study ...... 109 3.4.2.8 Patient information sheet ...... 109 3.4.2.9 Study procedure and visit schedule ...... 109 3.4.2.10 Study formulation and randomisation ...... 110 3.4.2.11 Active and placebo tablet formulation ...... 111 3.4.2.12 Dispensation and randomisation ...... 113 3.4.2.13 Directions for use ...... 113 3.4.2.14 Compliance ...... 113 3.4.2.15 Sample size calculation ...... 114 3.4.2.16 Statistical analysis ...... 115 3.4.2.17 Study protocol alterations ...... 115

5 4 Assessment of age changes and repeatability for computer based rod dark adaptation 116

4.1 Abstract ...... 117 4.1.1 Purpose...... 117 4.1.2 Methods...... 117 4.1.3 Results...... 117 4.1.4 Conclusions...... 117 4.2 Introduction ...... 118 4.3 Methods ...... 119 4.3.1 Subjects ...... 119 4.3.2 Procedure ...... 120 4.3.3 Data analysis ...... 122 4.4 Results ...... 123 4.4.1 Preliminary data ...... 123 4.4.2 Repeatability ...... 124 4.4.3 Dark adaptation in older and younger eyes ...... 125 4.5 Discussion ...... 127 4.6 References ...... 129

5 The association between dark adaptation and macular pigment optical density in healthy subjects 132

5.1 Abstract ...... 133 5.1.1 Purpose ...... 133 5.1.2 Methods ...... 133 5.1.3 Results ...... 133 5.1.4 Conclusions ...... 133 5.2 Introduction ...... 134 5.3 Methods ...... 135 5.3.1 Subjects ...... 135

6 5.3.2 Procedure ...... 136 5.3.3 Data analysis ...... 137 5.4 Results ...... 137 5.5 Discussion ...... 140 5.6 References ...... 142

6 Statin use may be associated with reduced retinal carotenoids and visual function in normal ageing 148

6.1 Abstract ...... 149 6.1.1 Purpose ...... 149 6.1.2 Methods ...... 149 6.1.3 Results ...... 149 6.1.4 Conclusions ...... 149 6.2 Introduction ...... 150 6.3 Methods ...... 151 6.3.1 Subjects ...... 151 6.3.2 Procedures ...... 152 6.3.2.1 Visual acuity ...... 152 6.3.2.2 Macular pigment optical density ...... 152 6.3.2.3 Contrast sensitivity ...... 152 6.3.2.4 Photostress recovery time ...... 152 6.3.2.5 Dark adaptation ...... 153 6.3.2.6 Statistical analysis ...... 154 6.4 Results ...... 155 6.5 Discussion ...... 157 6.6 References ...... 159

7 7 Photopic and scotopic visual function in the ageing eye: relationship to macular pigment, lifestyle factors and health status 163

7.1 Abstract ...... 164 7.1.1 Purpose ...... 164 7.1.2 Methods ...... 164 7.1.3 Results ...... 164 7.1.4 Conclusions ...... 164 7.2 Introduction ...... 165 7.3 Methods ...... 166 7.3.1 Subjects ...... 166 7.3.2 Visual function assessment ...... 167 7.3.2.1 Visual acuity ...... 167 7.3.2.2 Macular pigment optical density ...... 167 7.3.2.3 Dark adaptation ...... 167 7.3.2.4 Contrast sensitivity ...... 168 7.3.2.5 Photostress recovery time ...... 168 7.3.3 Statistical analysis ...... 169 7.4 Results ...... 170 7.4.1 Age and visual function ...... 172 7.4.2 Macular pigment optical density ...... 172 7.4.3 Lifestyle factors (smoking, nutrition and alcohol) ...... 172 7.4.4 Health factors (cardiovascular health and BMI) ...... 173 7.5 Discussion ...... 174 7.6 References ...... 175

8 Eﬀect of lutein and co-antioxidant supplementation on macular pigment and visual function in ageing 181

8.1 Abstract ...... 182 8.1.1 Purpose ...... 182

8 8.1.2 Methods ...... 182 8.1.3 Results ...... 182 8.1.4 Conclusions ...... 182 8.2 Introduction ...... 183 8.3 Methods ...... 184 8.3.1 Study design ...... 184 8.3.2 Subjects ...... 185 8.3.3 Supplement speciﬁcations and randomisation ...... 185 8.3.4 Visual function tests ...... 186 8.3.4.1 Best-corrected visual acuity ...... 187 8.3.4.2 Contrast sensitivity ...... 187 8.3.4.3 Photostress recovery time ...... 188 8.3.4.4 Resolution limit of gratings ...... 189 8.3.4.5 Macular pigment optical density ...... 189 8.3.4.6 Dark adaptation ...... 189 8.3.4.7 Statistical analysis ...... 190 8.4 Results ...... 191 8.5 Discussion ...... 194 8.6 References ...... 198

9 Conclusions and future work 203

9.1 Summary of key ﬁndings ...... 203 9.2 Suggestions for future work ...... 206

Final word count: 55, 631

9 List of Figures

1.1 The electromagnetic spectrum ...... 21 1.2 Luminous efficiency function ...... 22 1.3 Lens optical density as a function of age ...... 23 1.4 Layers of the retina ...... 25 1.5 Anatomy of the central retina ...... 26 1.6 Rhodopsin absorption spectrum ...... 27 1.7 Distribution of photoreceptors ...... 27 1.8 Typical dark adaptation curve ...... 34 1.9 Dark adaptation as a function of age ...... 36 1.10 Ageing and age-related macular degeneration ...... 42 2.1 Chemical structure of lutein, zeaxanthin and meso-zeaxanthin . . . . . 44 2.2 Location of macular pigment ...... 45 2.3 Macular pigment absorption spectrum ...... 47 2.4 Xanthophyll absorption spectra ...... 48 2.5 Relationship of rod:cone to lutein:zeaxanthin ratios ...... 50 2.6 Macular pigment spatial profile ...... 52 2.7 Bioavailability of lutein from various sources ...... 69 3.1 Factors affecting dark adaptation kinetics ...... 74 3.2 Dark adaptation recovery following a variety of bleaches ...... 75 3.3 Dark adaptation in early age-related macular degeneration ...... 77 3.4 G-protein cascade of phototransduction ...... 80 3.5 The visual cycle ...... 82 3.6 Monitor and semi-silvered mirror calibration ...... 83 3.7 Dark adaptation set up ...... 85 3.8 Dark adapted rod thresholds as a function of age ...... 87 3.9 Typical dark adaptation curve ...... 88 3.10 Effect of flash intensity on dark adaptation ...... 90

10 3.11 Effect of repeated bleach on dark adaptation ...... 91 3.12 Effect of pupil dilation on dark adaptation ...... 93 3.13 Effect of flash position on dark adaptation ...... 95 3.14 Effect of stimulus size on dark adaptation ...... 96 3.15 Effect of stimulus location on dark adaptation ...... 97 3.16 MPS 9000 test screen and method ...... 100 3.17 Algorithm of MPS 9000 ...... 101 3.18 Lens opacities classification system ...... 104 3.19 Study label ...... 111 4.1 Dark adaptation set up ...... 121 4.2 Typical dark adaptation data ...... 123 4.3 Dark adaptation repeatability ...... 125 4.4 Group data ...... 126 4.5 Older vs younger eyes ...... 127 5.1 Typical dark adaptation data ...... 138 5.2 Relationship between MPOD and cone dark adaptation parameters . . 139 5.3 Relationship between MPOD and rod dark adaptation parameters . . . 140 6.1 Typical dark adaptation curve ...... 154 6.2 Box and whisker plots for MPOD and PSRT for statin and non-statin users ...... 156 6.3 Box and whisker plots for dark adaptation parameters for statin and non-statin users ...... 157 7.1 Typical dark adaptation data ...... 169 7.2 The relationship between MPOD and BMI ...... 173 8.1 Example contrast sensitivity stimulus ...... 187 8.2 Typical dark adaptation data ...... 191 8.3 Baseline and final visit MPOD for active and placebo groups ...... 194

11 List of Tables

2.1 Retinal carotenoid levels in human ocular tissues ...... 46 2.2 Quantity of lutein and zeaxanthin in foodstuffs ...... 57 2.3 Macular pigment and visual performance in healthy eyes ...... 64 2.4 Macular pigment and visual performance in diseased eyes ...... 67 3.1 Percentage bleach ...... 84 3.2 Dark adaptation curve parameters for data presented in Figure 3.11 . . 92 3.3 Dark adaptation curve parameters for data presented in Figure 3.12 . . 93 3.4 Dark adaptation curve parameters for data presented in Figure 3.13 . . 95 3.5 Dark adaptation curve parameters for data presented in Figure 3.15 . . 97 3.6 Macular grading system ...... 105 3.7 Pilot study flow chart ...... 106 3.8 Supplementation study flow chart ...... 110 3.9 Active tablet formulation ...... 112 3.10 Placebo tablet formulation ...... 113 4.1 Summary of statistical comparisons: older vs younger group ...... 124 5.1 Summary of group dark adaptation parameter ...... 138 5.2 Summary of correlation coefficients between MPOD and dark adaptation parameters ...... 139 6.1 Participant anthropometric, medical and lifestyle data ...... 155 6.2 Summary of group means ...... 156 7.1 Participant anthropometric, lifestyle and medical data ...... 171 7.2 Visual function group means ...... 172 8.1 Active supplements study formulation ...... 186 8.2 Test parameter means, SDs and statistical comparisons ...... 193

12 Abbreviations

AF - Autofluorescence spectrometry AMD - Age-related macular degeneration ANOVA - Analysis of variance ARM - Age-related maculopathy BMI - Body mass index cAMP - Cyclic adenosine monophosphate CFF - Critical flicker frequency cGMP - Cyclic guanosine monophosphate CIE - Commission Internationale de l’Éclairage CoR - Coefficient of repeatability cpd - Cycles per degree CRALBP - Cellular retinaldehyde binding protein CRBP-I - Cellular retinal-binding protein CRT - Cathode ray tube CS - Contrast sensitivity CVD - Cardiovascular disease DU - Density units ERG - Electroretinogram ETDRS - Early treatment diabetic retinopathy study GPLRs - G-protein linked receptors HDL - High-density lipoprotein HFP - Heterochromatic flicker photometry HPLC - High performance liquid chromatography Hz - Hertz IPM - Interphotoreceptor matrix L - Lutein LAC - Longitudinal chromatic aberration LDL - Low-density lipoprotein

13 LED - Light emitting diode logMAR - Logarithm of the minimum angle of resolution LRAT - Lecithin-retinol acyltransferase MANOVA - Multivariate analysis of variance MP - Macular pigment MPOD - Macular pigment optical density MZ - Meso-zeaxanthin ND - Neutral density nm - Nanometers PSRT - Photostress recovery time RBP - Retinol-binding protein RCB - Rod-cone break REH - Retinyl ester hydrolase RM ANOVA - Repeated measures analysis of variance RP - Retinitis pigmentosa RPE - Retinal pigment epithelium RRB - Rod-rod break S2 - Second component of rod recovery S3 - Third component of rod recovery SD - Standard deviation SLO - Scanning laser ophthalmoscopy SSE - Summed squared error

T30 - Threshold 30 minutes after the onset of bleach UK - United Kingdom UoM - University of Manchester UV - Ultraviolet VA - Visual acuity Z - Zeaxanthin 11cROLDH - 11-cis retinol dehydrogenase

14 Abstract Visual function degrades with increasing age, in absence of frank disease, and affects both photopic and scotopic sensitivity. The mechanisms underlying these impairments may be related to biological (e.g., neural, optical) and environmental (e.g., smoking, dietary) factors. Recent evidence suggests that visual function may be improved following retinal carotenoid supplementation, both, in healthy and diseased eyes. Reti- nal carotenoids accumulate within the retina to form the macular pigment (MP) - a biomarker of antioxidant status of the eye and retinal disease risk. The objectives of this thesis were manyfold. First, the extent of vision loss (particularly scotopic sensitivity) in healthy ageing was examined. The results of this investigation showed that dark adaptation recovery slows with increasing age despite no significant change in visual acuity or fundus appearance. The technique described had excellent repeatability and correlated well with previous research. The potential link between MP and dark adaptation was also examined. The results showed that macular pigment optical density (MPOD) was correlated with a specific parameter of dark adaptation (S2) - a sensitive marker of functional degradation in normal ageing and retinal disease. The main part of this thesis sought to investigate the effect of MP augmentation on visual function in a large group of observers aged between 50 and 90 years old. The baseline data from this clinical trial revealed very interesting findings with regards to unhealthy lifestyle behaviours, health status and statin use. Subjects taking statins were identified (n = 25) and matched with 25 participants not using statins for age and body mass index. It was found that statin users had a higher proportion of males, higher prevalence of current smoking status and poorer general health (e.g. hypertension, high cholesterol and heart disease). Statin users also had significantly reduced MPOD, prolonged photostress recovery time, and deficits in a number of dark adaptation parameters. In a separate analysis of the whole group (n = 74, mean age 65.51), smokers were found to have reduced MPOD, slower S2, higher prevalence of high cholesterol and lower fruit and vegetable intake. MPOD was also reduced among obese subjects. The impact of MP augmentation on visual function in normal older subjects was assessed (n = 74, mean age 65.51) in a 12 month, randomized, double-blind, placebo- controlled study. Active formulation consisted of 20 mg lutein combined with vitamins and minerals. Data were collected at baseline, 6 months and 12 months. The results showed that, despite a 24% MPOD increase in the active group, there were no significant differences between the two groups over the three visits for any of the visual parameters. Given the increasing size of the older adult population in developed countries, research aimed at slowing or reversing age-related declines in vision is much needed both from an economical and psycho-social perspective. The results of the studies presented in this thesis show that lifestyle, health status and certain medications can adversely affect visual function in normal ageing. MP augmentation, however, had no effect on visual function. Further research is warranted, particularly paying close attention to subjects engaging in several unhealthy lifestyle/dietary behaviours, statin users and those with low MPOD and suboptimal visual function.

15 Declaration

No portion of the work referred to in the thesis has been submitted in support of an application for another degree or qualiﬁcation of this or any other university or other institute of learning. Thesis Format

This thesis is presented in ‘Alternative Format’. The decision to present the thesis this way was taken as several of the chapters featured here had already been either published, or prepared for submission to peer-reviewed journals. Where manuscripts based on these chapters have been published, or submitted for publication in a refereed journal it is indicated on the first page of the chapter. The author’s contribution to the work presented in each chapter is also identified on the first page of each chapter. Copyright Statement i. The author of this thesis (including any appendices and/or schedules to this thesis) owns certain copyright or related rights in it (the “Copyright”) and s/he has given The University of Manchester certain rights to use such Copyright, including for adminis- trative purposes. ii. Copies of this thesis, either in full or in extracts and whether in hard or electronic copy, may be made only in accordance with the Copyright, Designs and Patents Act 1988 (as amended) and regulations issued under it or, where appropriate, in accordance with licensing agreements which the University has from time to time. This page must form part of any such copies made. iii. The ownership of certain Copyright, patents, designs, trade marks and other intellectual property (the “Intellectual Property”) and any reproductions of copyright works in the thesis, for example graphs and tables (“Reproductions”), which may be described in this thesis, may not be owned by the author and may be owned by third parties. Such Intellectual Property and Reproductions cannot and must not be made available for use without the prior written permission of the owner(s) of the relevant Intellectual Property and/or Reproductions. iv. Further information on the conditions under which disclosure, publication and commercialisation of this thesis, the Copyright and any Intellectual Property and/or Reproductions described in it may take place is available in the University IP Policy (see http://www.campus.manchester.ac.uk/ medialibrary/policies/intellectual- property.pdf), in any relevant Thesis restriction declarations deposited in the University Library, The

16 University Library’s regulations (see http://www.manchester.ac.uk/library/aboutus /regulations) and in The University’s policy on presentation of Theses. Acknowledgments

I would like to thank my supervisor, Ian Murray, who has been a great source of support and guidance, and much appreciated laughter. Thanks to Neil Parry for his software which was used to run dark adaptation. Thanks to my co-authors for help with manuscript preparation. Thanks also to the eagle-eyed Dave Carden, Maria Makridaki, Thanasis Panorgias and Mike Kelly for their useful comments, and Michael Read and Ben Packer for their assistance. Thanks to all my PhD colleagues. Thanks to Hillary and all the reception staﬀ who have helped with participant recruitment. A huge thank you to all the subjects who participated in this research. Finally, my deepest gratitude goes to Daniel Baker who participated in many insightful discussions and wrote the Matlab code for dark adaptation data analysis. This research was funded by the BBSRC and Vitabiotics.

17 To Pavol, Squirrel, Fluﬀy, Flossy and the Little One

18 1 Introduction

It is well known that normal ageing, free from ocular disease, has a negative impact on visual performance as a result of senile miosis, media opacities and neuronal degeneration. Photoreceptor degeneration is an unavoidable consequence of ageing and contributes to the pathogenesis of some eye diseases such as age-related macular degeneration (AMD) (Curcio et al., 1996), as well as reduced visual function in older eyes (Jackson et al., 1998, 1999, 2002). Since the elderly population is rapidly growing, it is vital to study the mechanisms of photoreceptor degeneration and the possible beneficial effects of nutritional supplementation. Whilst cone-driven photopic function (e.g. visual acuity; VA) remains fairly well preserved in healthy ageing and in AMD, rods (responsible for scotopic vision) are particularly susceptible to degeneration in both states. Photooxidative damage is a major contributing factor towards rod photoreceptor degeneration on which the cones depend on for survival (Curcio et al., 2000, Mohand-Said et al., 2001, Fintz et al., 2003, Léveillard et al., 2004b,a, Yang et al., 2009). As such, the emphasis of this thesis will be geared towards scotopic function (measured by dark adaptometry) and rod anatomy/physiology, both in healthy ageing and disease. A growing body of evidence exists to support the beneficial effects of the retinal carotenoids (lutein, zeaxanthin and meso-zeaxanthin) on visual and retinal function in normal and diseased eyes (Stringham and Hammond, 2007, 2008, Loughman et al., 2007, 2010, Seddon et al., 1994, Snodderly, 1995, Beatty et al., 1999, Loughman et al., 2012, Murray et al., 2013). Lutein (L), zeaxanthin (Z) and meso-zeaxanthin (MZ), collectively known as the macular pigment (MP), are hypothesized to play two important roles in human vision: filtration of high energy blue light and protection of the retina from oxidative stress (Ahmed et al., 2005). Studies have demonstrated a clear link between carotenoid supplementation and a) increase in L and Z blood serum level (Johnson et al., 2000, Bone et al., 2003), b) increase in macular pigment density (MPOD) (Landrum et al., 1997, Hammond et al., 1997a, Bone et al., 2003, 2007), and c) improvement in AMD (Richer, 1999, Richer et al., 2002, 2004, Berendschot et al., 2011, Murray et al., 2013) and retinitis pigmentosa (Dagnelie et al., 2000, Aleman et al., 2001, Berson et al., 2010). Furthermore, a low carotenoid status (serum levels and MPOD) has been linked to an increased risk of AMD (Seddon et al., 1994, Landrum et al., 1996, Gale et al., 2003). These studies suggest that MPOD can be used as a biomarker of macular health and, as such, research into the benefits of retinal carotenoid supplementation in normal human eyes should be pursued.

19 The main bulk of research has focused on the eﬀects of MP on central visual function as served by the cones and tested under photopic conditions, i.e. VA. The association between MP and rod function, as demonstrated during psychophysical dark adaptation, has largely been ignored despite the fact that between 10 and 25% of the total retinal carotenoids are found in the membranes of rod outer segments (Sommerburg et al., 1999, Rapp et al., 2000). In investigating visual function in ageing, the standard VA testing is redundant as it only tests the high contrast and high luminance spectrum of the visual scene. One only has to attempt reading a gray newspaper in a dimly lit room, or a road sign on a foggy, downcast day to realize that the visual scene is seldom composed of such parameters. In fact, the visual scene is a rich medley of varying spatial and temporal frequency, colour, contrast and luminance. Furthermore, visual function based on VA measurement alone appears to be little affected by the ageing process (Haegerstrom-Portnoy et al., 1999). The same is not true for contrast sensitivity (CS) and dark adaptation. This introduction serves to provide a relevant background to this research area, particularly paying close attention to the psychophysical tests rarely performed in practice and clinical research (such as dark adaptation) and the associated ocular anatomy.

1.1 Impact of ageing on the anatomy and physiology of the visual system

Age-related degradations in visual function have been documented for many years and affect both photopic and scotopic vision (Marshall, 1987). Ageing affects all components of the eye in different ways, however in this section only the most pertinent structures to the production of vision will be reviewed. Visual performance is affected by the amount of illumination available to the eye. The visible light that evokes a behavioural and neuronal response in the human observer typically ranges from ∼400 nm to ∼700 nm within the electromagnetic spectrum (see Figure 1.1), although wavelengths of up to 1400 nm are able to penetrate to the retina (Ham et al., 1980). The majority of ultraviolet C (UVC) light (<295 nm) is absorbed by the cornea and wavelengths above that in the UVB (280-315 nm) and UVA (315-400 nm) range are screened by the crystalline lens. Aphakic and pseudophakic eyes are more susceptible to photochemical retinal light damage, particularly from blue light as these wavelengths (∼320 nm) are able to penetrate to the the retina (Algvere et al., 2006). This leads to increased risk of AMD (Wang et al., 2003).

20 Figure 1.1: The electromagnetic spectrum (Solanki, 2007).

The human eye is able to perceive light over a wide range of intensities from 10−4 to 106 cd.m-2 (a luminance range of about 10 log units) (Zhou et al., 2009). This task is shared between the two retinal photoreceptor classes, rods and cones, each functioning over a range of about 5 log units. This extraordinary ability allows us to adapt to most lighting environments within seconds. However, under scotopic (rod-mediated) conditions adaptation can take tens of minutes (discussed later). The average visual sensitivity of a standard human observer can be represented by a luminous eﬃciency function which was ﬁrst described by the Commission Internationale de l’Éclairage (CIE) in 1932 for the photopic curve Vλ (CIE, 1932) and in 1951 for the scotopic curve V´λ (CIE, 1951).

21 5 5 5

1 . 2 5 0 7 5 5 5 y c

n S c o t o p i c e

i P h o t o p i c c i f f

e 0 . 8

s u o n i V ' λ V λ m u l

0 . 4 e v i t a l e R 0 . 0 4 0 0 5 0 0 6 0 0 7 0 0 W a v e l e n g t h ( n m )

Figure 1.2: Luminous eﬃciency functions for the standard human observer plotted using CIE speciﬁed numerical values for scotopic (dotted line) and photopic (dashed line) vision (CIE, 1932, 1951).

Under scotopic conditions the sensitivity peaks at around 507 nm (blue-green) and under photopic conditions at around 555 nm (yellow-green) (see Figure 1.2). The scotopic sensitivity curve, subserved by the rods, is shifted towards the shorter wavelength (blue) of the spectrum. This shift from maximum sensitivity under photopic conditions to maximum sensitivity under scotopic conditions is called the Purkinje shift (Hess et al., 1990) and underpins the duplicity theory of vision (Schultze, 1866).

1.1.1 The pupil

The visual system is able to control the amount of light entering the eye via the pupil aperture controlled by the iris. The average pupil diameter, in young adults, varies from 2 to 8 mm (Oyster, 1999) and reduces linearly with increasing age. This physiological phenomenon is termed senile miosis (Weale, 1963) and is responsible for a reduction in the total retinal illumination with increasing age, which corresponds with elevated photopic and scotopic thresholds. Senile miosis is reported to elevate thresholds by 0.05 log units per decade (Birren and Shock, 1950, Pulos, 1989, Sturr et al., 1997). The mechanisms underlying senile miosis are unclear, however increased rigidity and hyaline deposition, and loss of autonomic innervation have been proposed (Korczyn et al., 1976, Larsson and Österlind, 2009).

22 1.1.2 The crystalline lens

The crystalline lens is a transparent biconvex body which affects the propagation of light more than the other structures in the anterior segment, markedly degrading the visual performance in older age (Klein et al., 1996). It is divided into three parts: the capsule (an elastic membrane forming the outermost layer of the lens), the epithelium (source of new lens cell) and the lens fibres which form the bulk of the lens. The oldest lens fibres form the nucleus (the innermost layer of the lens), and the younger fibres form the lens cortex, thus the lens is not optically homogeneous. The majority of the lens is composed of crystallins, the water-soluble proteins which are responsible for lens transparency. The zonular fibres (from which the lens is suspended in place) and the ciliary body muscles act synergistically to alter the curvature of the lens and bring about accommodation. In a young, healthy eye the transparent crystalline lens absorbs highly in the UVB and UVA range up to approximately 400 nm and transmits short wavelength light (400-500 nm). With increasing age the lens thickness increases by approximately 0.02 mm per year (Brown, 1976). The optical density of the lens increases approximately linearly with age (Werner, 1982, Weale, 1988, 1991). Pokorny et al. (1987) demonstrated that the increase in lens optical density with age follows a bi-linear function and that the senile lens absorbs strongly in the short wavelength region (see Figure 1.3).

1 . 2 4 5 0 n m y t i 1 . 0 5 5 0 n m s n

e 0 . 8 d

l a c

i 0 . 6 t p o 0 . 4 s n e

L 0 . 2

0 . 0 2 0 4 0 6 0 8 0 1 0 0 A g e ( y e a r s )

Figure 1.3: Lens optical density as a function of age and wavelength. Plotted using data from Pokorny et al. (1987).

With age tryptophan-based pigments accumulate in the lens cells rendering the lens

23 yellowish in colour. The yellowing of the lens filters and protects the retina from UV radiation and blue light and is thought to be a result of glycosylation of lens proteins and tryptophan oxidation products (Algvere et al., 2006). Photo-induced oxidation of lens proteins has been linked to formation of cataracts (Alves-Rodrigues and Shao, 2004). The presence of nuclear opacities is associated with a reduced risk of AMD, whereas cataract extraction increases the risk of AMD and progression (Sperduto et al., 1981, Klein et al., 1998). By the seventh decade the lens filters up to 90% of the harmful light, which is coupled with a 75% reduction in scotopic vision (Algvere et al., 2006). The reduction in light sensitivity in the presence of cataract (irrespective of density) does not significantly affect MP measurement based on heterochromatic flicker photometry (HFP) (Ciulla et al., 2001, Makridaki et al., 2009). Like the macula, the crystalline lens accumulates L and Z to the exclusion of other carotenoids (Yeum et al., 1995). Although, their concentration is much lower than in the macula, they are hypothesized to play the same protective role (Alves-Rodrigues and Shao, 2004). Consequently, a diet deficient in L and Z, coupled with low L and Z plasma levels has been linked to the development of age-related cataracts (Brown et al., 1999, Chasan-Taber et al., 1999). L supplementation has also been found to improve VA and glare sensitivity in patients with existing age-related cataracts, although this may be due to retinal changes rather than the cataract itself (Olmedilla et al., 2001).

1.1.3 The retina

The retina is analogous to a postage stamp in terms of its dimensions with a total surface area of approximately 1095 mm² and thickness of 0.25 mm (Wilkinson, 1997). The human retina is composed of many layers (see Figure 1.4) including a complex neural tissue containing specialized cells, collectively driven for one purpose - transduction from light energy to neural activity. The outer nuclear layer contains rods and cones, the inner nuclear layer contains bipolar, horizontal and amacrine cells, and the ganglion cell layer contains the ganglion cells and displaced amacrine cells.

24 a b

Figure 1.4: a) Layers of the human retina. Modiﬁed from Cunningham (1981), b) The anatomy of the outer retina, retinal pigment epithelium (RPE) and the photoreceptors. (A) Schematic of a mammalian rod photoreceptor shown in relation to the outer retina in (B). (B) Light micrograph of a rhesus monkey outer retina showing the choriocapillaris (CH), Bruch’s membrane, (BM), RPE, outer segment (OS), inner segment (IS), outer limiting membrane (OLM), outer nuclear layer (ONL), (C) Schematic of the choriocapillaris, Bruch’s membrane, retinal pigment epithelium and rod outer segments, (D) Schematic of an amphibian rod. Arrows illustrate the circulating current. (Lamb and Pugh, 2004).

The retina is generally divided into two portions: the central retina (the macula which contains the fovea and foveola), and the peripheral retina (Polyak, 1941). The macula is an area ∼6 mm in diameter (∼4% of the total retina area) and ∼21.5° of visual angle, and is specialized for high contrast detail and colour vision under photopic conditions (Curcio et al., 2000, Loughman et al., 2007). Subdivision of the macula, according to Provis (2005), is illustrated in Figure 1.5.

25 Figure 1.5: Anatomy of the central retina. OD = optic disk (Provis, 2005).

The retina is thought to have a higher oxygen consumption, per gram, than the brain (Lamb and Pugh, 2004). Its main vascular supply stems from both the central retinal artery and the choriocapillaris (Bernstein and Hollenberg, 1965).

1.1.4 Rods and cones

The retina contains two diﬀerent classes of photoreceptor cells, rods and cones, situated in the outermost layer, next to the retinal pigment epithelium (RPE). These cells contain photolabile pigments which absorb incident photons resulting in their structural alteration. Cones contain the photopigment iodopsin and operate under photopic conditions. They are responsible for ﬁne spatial detail and colour vision. The highest concentration of cones is within the central area of 1.5° in diameter (with a peak at the fovea centralis). This central 350 μm area (termed foveola) is completely devoid of rods (Hecht, 1937). Humans normally have three types of cones: long wavelength, medium wavelength, and short wavelength which selectively absorb in the red (564 nm), green (534 nm) and blue (420 nm) regions of the visible light spectrum, respectively. The rod photopigment is rhodopsin which exhibits a peak absorption at around 498 nm (Figure 1.6). Rods specialize in the transduction of single photon absorptions and are therefore specialized for seeing in low light conditions (scotopic vision). Their number increases towards the periphery with a peak at 5 mm from the centre of the fovea (∼20° visual angle) (Curcio et al., 1990, Osterberg, 1935). The topographical distribution of rods and cones is illustrated in Figure 1.7.

26 1 0 0 e

c 8 0 n a b r 6 0 o s b a

4 0 e v i t a

l 2 0 e R 0

4 0 0 4 5 0 5 0 0 5 5 0 6 0 0 6 5 0 W a v e l e n g t h ( n m )

Figure 1.6: Rhodopsin absorption spectrum. Plotted using data from Bowmaker and Dartnall (1980).

In the entire retina, there are approximately 120 million rods and 6.5 million cones, thus the rods outnumber the cones by approximately 20:1 (Curcio et al., 2000). Age- related distortion of the rod outer segment results in increased outer segment length and diameter which may be correlated with increased rhodopsin density (Liem et al., 1991).

) 1 8 0 3

0 R o d s

1 1 6 0 C o n e s x

2 1 4 0 -

m 1 2 0 m ( 1 0 0 y t i

s 8 0 n c s i e d

d 6 0 c

i r t p o

t 4 0 O p

e 2 0 c e 0 R 6 0 4 0 2 0 0 2 0 4 0 6 0 8 0 T e m p o r a l F o v e a N a s a l E c c e n t r i c i t y ( d e g r e e s )

Figure 1.7: Distribution of photoreceptors. Plotted using data from Osterberg (1935).

The rod outer segment is believed to be particularly vulnerable to damage from oxidative stress (Davies and Morland, 2004) due to its high concentration of polyunsaturated fatty acids (Farnsworth and Dratz, 1976) and rhodopsin (Demontis et al., 2002).

27 The proportion of unbleached rhodopsin governs the extent and severity of blue light- induced retinal damage. This is because the unbleached rhodopsin is actively absorbing blue light, whereas bleaching the photo-pigments inactivates light absorption until the photo-pigment is regenerated. Evidence for this has been shown through research on rhodopsin-deficient mice who do not exhibit light-induced RPE cell apoptosis and photoreceptor damage (Algvere et al., 2006). About 10% of the rod outer segment discs are shed every day (Young, 1971). Both types of photoreceptors are affected by the ageing process, with the rods to a much greater extent than the cones (Gao and Hollyfield, 1992, Curcio et al., 1993). Annually the rods diminish by 970 cells/mm2, and by the fourth decade about 50% of the rods are lost (Gao and Hollyfield, 1992). The preferential site of rod loss is at the parafovea (Curcio et al., 1993). With progressing age, there is a small increase in rhodopsin density, and a decrease in rod and cone regeneration rate (increased time constant) which may be a result of are-related changes in the RPE and the choroid (Liem et al., 1991). With age, the rod density decreases considerably in the peri-macular area (Curcio et al., 2000), and much less in the periphery. By contrast, the absolute scotopic sensitivity loss in older adults is exaggerated in the periphery (where there is little rod loss) (Jackson et al., 1998). This suggests that rod loss plays a small part in the sensitivity reduction in an ageing eye. This is because the potentially reduced quantum-catching ability of rod density decrease may be compensated by the increase in the diameter of the rod inner segments as well as enlargement of rod spatial summation (Schefrin et al., 1998).

1.1.5 Retinal pigment epithelium

The RPE is situated between the Bruch’s membrane and the outer rod and cone segments, as illustrated in Figure 1.4 (C). It forms the blood-retina barrier (an extension of the blood-brain barrier) which shields the retina from the choroidal circulation. Its strategic anatomical position forms a functional unit with the photoreceptors thus providing active transport of nutrients (such as vitamin A) and metabolites, absorption of stray light by the melanosomes, synthesis of 11-cis retinal and rod outer segment phagocytosis and renewal (Bok and Heller, 1976). Numerous microvilli project from the surface of the RPE greatly increasing the apical surface area (which plays a part in phagocytosis) and interlink with the photoreceptor outer segments (Bonilha et al., 2004). These microvilli are thought to be intimately

28 involved in the visual cycle (reviewed in Section 3.1.1.3) (Lamb and Pugh, 2004) and are also found to decrease in their number and length with age (Weisse, 1995). Degeneration of the RPE is characterized by accumulation of lipofuscin which is highly indicative of retinal ageing (Guymer et al., 1999). These ﬂuorescent granules, produced as a by-product of the visual cycle, accumulate in the RPE cells (due to the close proximity of this layer to the outer segments) and by the eighth decade make up 20% of the cells’ cytoplasmic volume (Bellmann and Sahel, 2008). Accumulation of lipofuscin within the RPE has been linked to impairment of the cells’ ability to phagocytose rod and cone outer segments (Sundelin et al., 1998) leading to photoreceptor cell damage and loss (Katz, 2002). Photoreceptor cell death can also result from disruption in the ability of the RPE cells to synthesize 11-cis retinal (Katz, 2002). With age the RPE basement membrane becomes thickened and the number of basal convolutions reduce. These changes, combined, are indicative of RPE cellular overload (Guymer et al., 1999). It is thought that RPE cell loss with age occurs as a result of apoptosis (Del Priore et al., 2002) and their numbers decrease by approximately 0.3% per year (Panda-Jonas et al., 1996). Del Priore et al. (2002) found a much higher proportion of apoptotic RPE cells in the older eye compared to a younger eye. Furthermore, the highest proportion of the apoptotic cells was found in the macular region which may play a role in the pathogenesis of degenerative eye diseases such as AMD.

1.1.6 The interphotoreceptor matrix

The interphotoreceptor matrix is located between the apical surfaces of the neural retina (the photoreceptor outer segments) and the RPE. Its main roles are to assist the transfer of nutrients and metabolites from the choriocapillaris to the neural retina, photoreceptor alignment, and retinal adhesion to the RPE (Hollyﬁeld, 1999). The interphotoreceptor binding protein is a major component of the interphotoreceptor matrix. The biological function of this protein is not fully understood although it has been hypothesized that it is involved in the transport of retinol (a vitamin A derivative) across the RPE-photoreceptor interface (Johnson et al., 1986). Consequently, it may be responsible for the rate of recovery of 11-cis retinal and rhodopsin regeneration following bleaching. It may also be responsible for photoreceptor survival (Noy, 2000). Animal studies have demonstrated a correlation between interphotoreceptor binding protein changes and photoreceptor degeneration. Degeneration has also been reported to occur in the distribution of proteoglycans (Johnson et al., 1986).

29 1.1.7 Bruch’s membrane

Bruch’s membrane is situated between the RPE and the choriocapillaris from which it receives the majority of its nutrients. It forms a semipermeable membrane allowing nutrient transfer from the choriocapillaris to the RPE and photoreceptors and diffusion of waste material in the opposite direction (Guymer et al., 1999). Basal deposits, drusen and esterified cholesterol all accumulate with age, effectively forming a hydrophobic layer of debris which impedes diffusion of materials (Bok, 1985). This in turn affects the normal function of the retinal photoreceptors. The formation of drusen may partly be due to chronic inflammatory response to the malfunctioning RPE cell debris (Bellmann and Sahel, 2008). Impairment in Bruch’s membrane functioning may be involved in the formation of pigment epithelial detachments and geographic atrophy; both are features of AMD (Guymer et al., 1999). Material deposition causes diffuse thickening of Bruch’s membrane and renders it brittle leading to loss of elasticity thus compromising material transfer between the RPE and the choroid (Bird, 1992, Guymer et al., 1999). By the tenth decade the membrane thickness increases by 135% (Ramrattan et al., 1994) which reduces the transfer of vitamin A to the photoreceptors. Vitamin A plays an important role in the biochemistry of the visual cycle which underpins the process of dark adaptation (both are discussed in detail in later sections). A reduction of this nutrient leads to disruption of the visual cycle, the rhodopsin regeneration rate and the speed of dark adaptation recovery (Guymer et al., 1999, Lamb and Pugh, 2004).

1.1.8 Choriocapillaris and choroid

The choriocapillaris consists of a rich network of capillaries which form the inner vascular layer of the choroid. It is a nutrient and oxygen source for the photoreceptors, and is involved in the clearance of waste material diffused from the RPE through Bruch’s membrane into the choroidal circulation. The choriocapillaris has a high blood flow which results in high oxygen tension. This mechanism is thought to protect the retina from light induced thermal damage by stabilizing the temperature (Lamb and Pugh, 2004). Photoreceptor dark current is sensitive to temperature changes (Lamb, 1981) therefore high blood flow may play a role in maintaining it during the dark adapted state (Guymer et al., 1999). Unlike the central retinal artery, the choriocapillaris has a marked attenuation of the endothelial cytoplasm which gives it the fenestrated (porous) appearance (Bernstein

30 and Hollenberg, 1965). In a young, healthy eye the capillary bed is sinusoidal, however with age it becomes tubular (Ramrattan et al., 1994). Ageing decreases the number and diameter of the choriocapillaris which leads to atrophy and may play a role in AMD pathogenesis (Gass, 1967). Ramrattan et al. (1994) found a decrease of 45% in the choriocapillaris density with age in some subjects but not all. They also found a decrease of 34% in the choriocapillaris diameter. The choroid (a vascular layer that supports the RPE) thickens with age from 2 µm in younger eyes to 6 µm in older eyes (Bellmann and Sahel, 2008).

1.2 Psychophysical function in ageing

Visual psychophysics quantitatively investigates the relationship between visual stimuli and the perceptions they evoke as measured by the subject’s response. The results obtained provide information about the visual system integrity and conﬁrm and complement neurophysiological ﬁndings. In this section the most commonly employed psychophysical tests in practice and research will be reviewed.

1.2.1 Spatial vision

Spatial vision refers to the ability of the visual system to resolve or discriminate spatially deﬁned patterns. The two primary measures of spatial vision are VA and CS.

1.2.1.1 Visual acuity

VA is the spatial resolving capacity of the visual system most commonly tested in ophthalmic practice using a Snellen chart (Cole, 2014). The Snellen VA notation expresses the angular size of an optotype as a fraction, with the test distance as the numerator and the distance at which the just resolvable optotype subtends an angle of 5 minutes of arc as the denominator. However, given the inherent design-related ﬂaws with this type of chart, many studies investigating visual function have used the Early Treatment Diabetic Retinopathy Study (ETDRS) chart. ETDRS chart expresses the VA as the common logarithm of the minimum angle of resolution (logMAR) (Cole, 2014) . Most commonly, VA is performed under conditions of high contrast and high luminance which are seldom representative of the targets experienced in the daily environment, thus possibly over-estimating visual capability. A signiﬁcant proportion of the visual scene is made up of large, low contrast patterns such as faces and nearby objects (Rubin et al., 1994). Furthermore, a clinically normal VA may not necessarily signify a healthy

31 retina since good VA has been demonstrated in patients with retinal disease (Owsley, 2011). On the contrary, deﬁcits under low contrast conditions do appear to be more sensitive to retinal ageing and disease (Schneck et al., 2004). VA decline with age has been demonstrated in both cross-sectional and longitudinal studies (reviewed in Jackson and Owsley (2003)). For high contrast acuity (measured on a Bailey-Lovie logMAR letter chart), the ageing-related loss is found to be 5.5 letters per decade (equivalent to 0.11 logMAR units) (Haegerstrom-Portnoy et al., 1999). Additionally, the amount of contrast required to perceive a grating pattern of 0.5 cycles per degree (cpd) under photopic conditions is equal for younger and older adults, however under scotopic conditions older adults require approximately threefold greater contrast to detect the same target compared with young adults (Owsley, 2011). The ageing-related decline in acuity is largely related to a combination of ‘normal ageing’ processes such as reduced retinal illuminance (e.g. pupil miosis and increased ocular media). This is further exacerbated by disease-related processes such as the onset of cataract and AMD (Owsley, 2011).

1.2.1.2 Contrast sensitivity

The contrast of a target, for instance a sinusoidal grating, refers to the intensity diﬀer- ence between the light and dark bars. The amount of contrast required to see a target reliably is known as contrast threshold, which is clinically expressed as CS, where sensitivity is the reciprocal of threshold (Pelli and Bex, 2013). Typically, for periodic patterns such as sinusoidal gratings, contrast is speciﬁed as the Michelson contrast:

Lmax−Lmin Lmax+Lmin

Unlike the standard test of VA, CS is more indicative of the ability to carry out every day visual tasks such as reading and driving (Rohaly and Owsley, 1993). Older adults display reduced CS under photopic conditions with the greatest negative eﬀect towards the high spatial frequency patterns (Owsley, 2011). Research shows that CS (measured using letters) deteriorates ∼12 years before high contrast VA (Haegerstrom-Portnoy et al., 1999). Compared to younger adults, older observers require higher contrast to adequately perceive everyday images such as faces and traﬃc signs (Rubin et al., 1994). The majority of research has attributed spatial CS losses under photopic viewing conditions to changes in optical characteristic in ageing (Owsley, 2011), although neural factors have also been suggested (Elliott et al., 2009). On the contrary, spatial vision

32 loss under mesopic and scotopic conditions is largely attributed to neural factors. Sev- eral mechanisms have been proposed for this loss including decreased rod and ganglion cell density (Gao and Hollyﬁeld, 1992).

1.2.2 Temporal vision

Temporal vision deals with how the visual system perceives changes in luminance over time. Temporal function is typically assessed using light stimuli with a luminance profile that changes sinusoidally over time and perceptually appears as a flickering light. The human eye is capable of perceiving flicker rates of up to 60-80 Hertz (Hz). Temporal sensitivity to flicker has been shown to reduce with increasing age, particularly as the temporal frequency increases (Owsley, 2011). The loss of sensitivity at higher frequencies is due to, both, optical changes (senile miosis) and neural losses with increasing age. Additionally, it may be related to reduced photoreceptor response time with increasing age (Wooten et al., 2010). Both, in healthy and diseased eyes, a flickering light stimulus is more sensitive to early functional changes than static stimuli (Neelam et al., 2009). Critical flicker frequency (CFF) threshold represents the highest resolvable flicker frequency. In healthy eyes, typical values found in the centre of the retina are around 45 cycles per second, with a slow decline towards the periphery (Wolf and Schraffa, 1964). With increasing age, however, CFF has been shown to decline both in the central and peripheral retina (Bernardi et al., 2007, Haegerstrom-Portnoy et al., 1999). Haegerstrom-Portnoy et al. (1999) attributed the 0.5 log units loss in modulation transfer over the age range tested to pupillary changes with age and neural losses in the retina.

1.2.3 Visual adaptation

1.2.3.1 Dark adaptation

Dark adaptation refers to the recovery of sensitivity of the visual system following exposure to intense illumination (Hecht, 1937) and reveals the duplex nature of the retina. Perceptually, the observer experiences a greatly elevated visual threshold (over 5 log units) which takes tens of minutes to recover (Lamb and Pugh, 2004). The slowness of recovery is due to the biochemical processes which underpin dark adaptation and enable the rods to be highly sensitive to light. In a typical dark adaptation experiment a subject is placed in a dark room and exposed to a brief ﬂash of light which “bleaches” the photoreceptor pigment. The thresholds (least amount of light intensity just detectable by the observer) are then measured for

33 around 30 minutes, although recovery following a total bleach of rhodopsin can take as long as 50 minutes (Hecht et al., 1937). The data are usually obtained using a method of adjustment, whereby the intensity of the stimulus is continually adjusted by the experimenter until it is barely perceived by the subject. Example dark adaptation data is presented in Figure 1.8. The curve is composed of two phases, each subserved by either the cones or the rods. The cone phase is much faster than the rod phase and ends at the rod-cone break (RCB). At this point the rods become more sensitive than the cones and the curve is further subdivided into two components: S2 and S3. The transition point between S2 and S3 is the rod-rod break

(RRB). The ﬁnal threshold of the function in this thesis will be referred to as T30, as it is the threshold 30 minutes after the onset of bleach.

) 2

- 0 C o n e t i m e c o n s t a n t m . d

c - 1 R o d - C o n e b r e a k g o l

( - 2 S 2 d l C o n e t h r e s h o l d o T h - 3 3 0 s e r

h - 4 T R o d - R o d b r e a k S 3 - 5 0 5 1 0 1 5 2 0 2 5 3 0 3 5 T i m e ( m i n u t e s )

Figure 1.8: Typical dark adaptation curve for a young, healthy observer (LP).

Normal ageing, in absence of disease, impacts negatively on scotopic vision and dark adaptation (Birren and Shock, 1950, McFarland et al., 1960, Gunkel and Gouras, 1963, Pitts, 1982). Age-related changes occur both in the cone and rod-mediated portions of the dark adaptation curve (Coile and Baker, 1992, Jackson et al., 1999). It is reported that the absolute dark adaptation threshold in an older human eye is elevated by around 1 log unit (Sturr et al., 1997, Jackson et al., 1998, 1999, 2002). This may contribute to the visual impairment experienced by the elderly under low light levels which carries a higher risk of road traffic accidents and falls, and impedes enjoyment of vision-oriented activities (Jackson et al., 1999, Jackson and Owsley, 2000). Sturr et al. (1997) found 0.39 log unit threshold elevation in older observers after correcting for pre-retinal factors and criterion effects. The negative age effect on rod sensitivity, however, was not found by McFarland and Fisher (1955) (using Hecht-Shlaer

34 adaptometer) and Pulos (1989) who, after correcting for pre-retinal factors, found no significant correlation with age. Methodology related problems adopted in older studies may account for these negative findings (Jackson et al., 1999). Pupillary miosis and increased lens density account for approximately half of the scotopic sensitivity loss with age. The other half may be attributed to neural factors such as slower visual cycle (rhodopsin regeneration) with age (Liem et al., 1991, Jackson et al., 1999), ganglion and rod cell loss (Gao and Hollyfield, 1992, Curcio and Drucker, 1993, Curcio et al., 2000), structural changes in the retina (Bird, 1992, Guymer et al., 1999, Katz, 2002, Lamb and Pugh, 2004) and age-related alterations of the retinal and cortical gain control mechanisms (Jackson et al., 1999). Jackson et al. (1999) studied the kinetics of dark adaptation (following a 98% bleach) as a function of age in a group of normal (free from ocular disease) subjects ranging in age 20 to 80 years old. The results are presented in Figure 1.9. The rightward shift of each function (from 20s to 80s) demonstrates a slowing in sensitivity recovery with increasing age, whereas the upwards shift along the y-axis indicates threshold elevation across the entire dark adaptation time course. Age-related changes affected all parameters of the dark adaptation curve and exhibited a slowing with increasing age. Per decade, the time to reach the RCB increased at a rate of 0.65 minutes and the S2 and S3 components of rod recovery decreased by 0.02 log unit/min and 0.01 log unit/min, respectively. The time constants of rhodopsin regeneration of S2 and S3 components increased by 0.14 minutes and 1 minute, respectively, per decade, which is consistent with rod densitometry data (Liem et al., 1991). The time taken to reach baseline scotopic sensitivity also increased by 2.76 minutes per decade.

35 0 8 0 s 7 0 s 1 6 0 s y t i 5 0 s v i

t 4 0 s i 2

s 3 0 s n

e 2 0 s s

3 g o L 4

5 0 1 0 2 0 3 0 4 0 5 0 T i m e ( m i n u t e s )

Figure 1.9: Dark adaptation as a function of age grouped in decades and ﬁtted with Lamb’s (1981) four-linear component model. Second component (S2) and third component (S3) refer to the regions of rod-mediated recovery. The functions shift rightwards with increasing age, indicating a gradual slowing of recovery, and upwards indicating threshold elevation throughout the dark adaptation time course. Plotted using data from Jackson et al. (1999).

The investigators concluded that age-related changes in Bruch’s membrane and RPE may be responsible for altered visual cycle and delayed dark adaptation, and as such, the potential of nutrition therapy to reverse this anomaly should be investigated.

1.2.3.2 Glare recovery

Glare refers to any bright light source (direct or indirect) which interferes with the quality of visual perception. There are three main types of glare: disability glare, discomfort glare and photostress recovery time (PSRT). Disability glare

Disability glare refers to the degradation of visual performance induced by surface light reﬂections or bright light sources (e.g. car headlights) which reduce the retinal image contrast. Disability glare is a consequence of increased forward light scatter within the eye (e.g. the lens and cornea) and has been shown to increase rapidly with age, particularly with the onset of cataract (Collins, 1989, Haegerstrom-Portnoy et al., 1999). Haegerstrom-Portnoy et al. (1999) found a 0.10 log unit (5 letters) loss per decade as a result of disability glare. Shorter (blue) wavelength light scatters to a greater extent than longer (red) wavelength light (Rayleigh scattering). The scattered light creates a

36 veiling luminance over the retinal image which impairs CS and reduces visual function (Abrahamsson and Sjöstrand, 1986). Increased glare sensitivity has been attributed to the onset of cataract (Abrahamsson and Sjöstrand, 1986) as well as age-related changes in the outer retina such as cellular debris deposition in RPE and thickening of Bruch’s membrane (Collins, 1989). Compared to the standard VA measurements, glare testing is more indicative of visual ability of a cataractous eye under outdoor conditions such as facing the sun (Rubin et al., 1994). Additionally, glare sensitivity in older adults is correlated with driving visual performance in scotopic conditions and night-time accidents (Rubin et al., 1994). Low contrast acuity loss with increasing age under glare conditions is about twice the rate of high contrast acuity loss without glare (Haegerstrom-Portnoy et al., 1999). Additionally, acuity in glare deteriorates ∼12 years before high contrast acuity without glare (Haegerstrom-Portnoy et al., 1999). Discomfort glare

Discomfort glare may also be caused by intense bright light, such as car headlights, causing distraction and/or discomfort but without any measurable eﬀect on visual performance. Of the two types of glare, disability glare is more commonly assessed both in clinical and research setting. Photostress recovery time

The PSRT test tracks the time taken to recover vision following exposure to a controlled glare source. The recovery of sensitivity to a predeﬁned visual task (such as baseline VA or CS) is typically measured in seconds or minutes. For normal healthy eyes the time taken to restore sensitivity to baseline VA and CS is 0.871 and 1.175 minutes, respectively (Neelam et al., 2009). PSRT increases with age as a result of age-related changes in the retina and the choriocapillaris (Haegerstrom-Portnoy et al., 1999).

1.3 Environmental factors in ageing

Given the increasing elderly population and the increased risk of developing visual function loss as a result of ageing and age-related disease, research into modiﬁable risk factors that can be intervened upon is needed. Evidence now exists to show a correlation between smoking, excessive alcohol consumption and sedentary lifestyle and the incidence of AMD, cataract and glaucoma (Klein et al., 2014). However, very few studies have examined the impact of modiﬁable lifestyle factors on visual function in normal ageing.

37 1.3.1 Smoking

Smoking is a significant risk factor for several cardiac, cerebrovascular, and respiratory diseases. It is also implicated in a number of eye diseases including bilateral optic neuropathy, thyroid ophthalmopathy, cataract, strabismus (when mother smokes during pregnancy) and colour vision defects (Uz et al., 2003). Additionally, after ageing, cigarette smoking is the second most consistent risk factor related with AMD (Velilla et al., 2013). Compared to non-smokers, non-symptomatic chronic cigarette smokers have reduced trace elements levels coupled with reduced CS (Uz et al., 2003). Trace elements such as zinc and magnesium play important roles in the functioning of the peripheral and central nervous systems. Within the retina, depletion of both magnesium and zinc, as a result of heavy cigarette smoking, leads to the accumulation of reactive oxygen species and compromised retinal function (Uz et al., 2003). Smoking has also been shown to decrease VA in the ageing population (Klein et al., 2014) and slow sensitivity recovery during dark adaptation (Calissendorff, 2009, Varghese et al., 2011). Recently, Klein et al. (2014) demonstrated an increase in the number of letters lost over a 20-year period in current and past smokers. It must be noted, however, that literature exploring the effect of smoking on dark adaptation is inconsistent with some studies showing no effect (Wiley, 1989, Hammond et al., 1998a) and others a positive effect (Troemel et al., 1951). One recent study (Varghese et al., 2011), has shown a significant decrease in the dark adapted b-wave electroretinogram intensity response after chewing nicotine gum in 10 healthy non-smoking adults. Reduced scotopic function in tobacco users may be due to a combination of increased blood viscosity, the vasoconstrictive action of nicotine, and tissue hypoxia caused by cigarette smoke-induced carbon monoxide displacement of oxygen from hemoglobin (Havelius and Hansen, 2005). Several other mechanisms related to a combination of toxic, angiogenic, neovascular and oxidative effects of cigarette smoke may also play a part in visual function decrease (Velilla et al., 2013). Additionally, smokers display lower plasma concentrations of antioxidants related to reduced intake of antioxidant-rich foods and increased utilization of available antioxidants to counter the smoke-induced free radicals (Ma et al., 2000). Smoking also tends to cluster with other unhealthy lifestyle habits such as alcohol abuse, physical inactivity, and poor diet lacking in antioxidant nutrients, which collectively place the retina at a higher risk of degeneration and disease (Ma et al., 2000).

38 1.3.2 Alcohol

Heavy alcohol intake has been associated with self reported near and distance visual impairment (Fan et al., 2012), decreased VA (Klein et al., 2014), and increased risk of AMD (Knudtson et al., 2007) and cataracts (Cumming and Mitchell, 1997). Moderate amounts of alcohol is also associated with an increase in the prevalence of retinopathy among diabetic patients (Emanuele et al., 1998). Alcohol promotes generation of reactive oxygen species leading to oxidative stress and reduced antioxidant capacity, particularly in the liver (Forman et al., 1995). A similar mechanism may occur within the retina. Alcohol consumption is also associated with reduced zinc and vitamin A levels resulting in abnormal dark adaptation. In animal studies, zinc deficiency has been shown to increase oxidative stress in the retina (Miceli et al., 1999), whilst in humans low levels of zinc is associated with increased risk of AMD (Mares-Perlman et al., 1996). Additionally, alcohol intake has been correlated with reduced plasma oxycarotenoid levels. After controlling for confounding factors such as smoking and dietary carotenoid intake, Forman et al. (1995) showed that plasma L and Z concentrations were significantly lower with alcohol ingestion. The inverse correlation between excessive alcohol consumption and reduced serum and retinal carotenoids is likely to be due to poor nutrient absorption by the liver, poor dietary intake of L amongst heavy drinkers and/or impaired transport within serum (Breslow et al., 2010). The possible reduction of MPOD with high alcohol intake deserves a thorough investigation particularly since alcohol abuse is becoming increasingly common in all age groups (Wilson et al., 2013). Furthermore, given that 18-40% of the elderly population are already micronutrient deficient (Beatty et al., 2001), excessive alcohol consumption in the elderly could be potentially catastrophic for retinal health.

1.3.3 Radiation exposure

Throughout life the human eye is continuously exposed to optical radiation between 300 nm in the UV to 1100 nm in the infrared spectrum. The cornea absorbs wavelengths below 295 nm, the lens absorbs wavelengths between 295 and 315 nm and the retina between 390 and 1400 nm. Physiological responses to photoreceptoral light absorption include phototransduction related events, discussed in Section 3.1.1.3. Radiation induced tissue damage can result from mechanical, thermal photocoagulation and photochemical processes. The latter

39 is most extensively studied since it occurs under ambient conditions and causes retinal damage increasing the risk of AMD. In an oxygen-rich environment, the light incident upon photoreceptors causes the production of reactive oxygen species including singlet oxygen, superoxide, hydrogen peroxide and hydroxyl radicals (Boulton et al., 2001). These compounds are highly toxic and can cause lipid peroxidation, protein oxidation and mutagenesis (Boulton et al., 2001). Reactive oxygen species formation in response to chronic light exposure play an important role in ageing. The two most susceptible retinal layers to photochemical damage are the photoreceptors outer segments and RPE due to the high concentration of visual pigment and production of lipofuscin within each of the structures (Boulton et al., 2001). UV exposure disrupts corneal metabolism by reducing oxygen uptake and increasing both glucose and glycogen levels, damages both the epithelial and endothelial layer of the cornea leading to corneal thinning, alters the endothelium and reduces corneal sensitivity with the most commonly observed diagnosis being photokeratitis (Golu et al., 2013). Cumulative exposure to UV has also been linked with eyelid erythema, cataract formation (see below) solar retinopathy and retinal degeneration (van Kuijk, 1991). Additionally, UV light has been implicated in the formation of vacuoles within the photoreceptor outer segment causing alteration in the lamellar structure and eventual detachment of the outer segment from the inner segment (Reme et al., 1999). The outer segments are then phagocytosed by the RPE resulting in further changes and eventual loss of the inner segments. Retinal light damage from fluorescent lamps has been demonstrated in several animal studies. In the mid-sixties, Noell et al. (1966) demonstrated photoreceptor cell degradation in a population of rats exposed to constant fluorescent lamp illumination and a later study replicated these results in monkeys (Sykes et al., 1981). UV exposure among fishermen has been correlated with increased risk of pterygium, pinguecula and keratopathy (Taylor et al., 1992). Corneal and conjunctival tumours as a consequence of excessive UV exposure have also been reported in adult contact lens wearers (Guex-Crosier and Herbort, 1993). Several studies have reported on the positive correlation between cataract incidence and higher altitudes and longer duration of UV exposure (Brilliant et al., 1983). The formation of cataracts is thought to be a result of accumulation of reactive oxygen species and free radicals which lead to alteration in lens proteins, photooxidation and disruption in lens biochemistry (Hejtmancika et al., 2004). Additionally, UV exposure

40 results in generation of ﬂuorescent chromophores which are associated with brunescence of the lens nucleus (van Kuijk, 1991).

1.3.4 Physical activity

A physically active lifestyle may play a role in reducing visual impairment in normal ageing (Bergman and Sjöstrand, 2002, Klein et al., 2014), whereas a sedentary lifestyle is associated with age-related diseases (Knudtson et al., 2006, Williams, 2008). Recently, Klein et al. (2014) found an association between reduced physical activity and visual impairment in a longitudinal, population-based cohort study. In another longitudinal study using a Swedish population aged between 70 and 97 years old, Bergman and Sjöstrand (2002) showed a propensity for reduced VA with lower levels of physical activity. People who do regular exercise are likely to be biologically younger compared to those with inactive lifestyles (Klein et al., 2014). For instance, regular physical exercise has been correlated with reduced blood pressure and abdominal fat, improved serum lipid lipoprotein proﬁles and lower incidence of systemic inﬂammation and endothelial dysfunction (Warburton, 2006). Given that cardiovascular disease and AMD share common risk factors, physical exercise may reduce the risk of AMD and lower the incidence of exudative AMD (Klein et al., 2003).

1.4 Ageing and age-related macular degeneration

Normal ageing processes, discussed earlier, can lead to structural and blood ﬂow alterations resulting in a increased risk of AMD. AMD is the leading cause of blindness amongst the elderly Western population (Klein et al., 1992). The early stage of AMD, sometimes referred to as age-related maculopathy (ARM), is characterized by formation of large drusen and pigmentary abnormalities. As the disease progresses it is subdivided into two types: dry AMD (or non exudative) and wet AMD (or exudative/neovascular) (Ehrlich et al., 2008). The dry form is more common than the wet form, accounting for ∼90% of cases, and is characterized by atrophic changes in the macula (Bird et al., 1995). The progression of dry AMD tends to be slower with better preservation of visual function than wet AMD. Wet AMD, characterized by the development of choroidal neovascularization, can progress rapidly resulting in severe loss of central vision. Neovascularization develops when new abnormal blood vessels originating from the choriocapillaris grow into the subretinal space through the Bruch’s membrane. The invading vessels have a tendency of leaking

41 and bleeding into the macula resulting in retinal detachment and disciform scar formation (Bird et al., 1995). Wet AMD accounts for the majority of cases of severe vision loss from AMD (Ehrlich et al., 2008). A combination of structural and vascular changes (see Figure 1.10), coupled with genetic and environmental risk factors pave the way for the development of AMD (Ehrlich et al., 2008). Although there is a significant overlap in the physiology of normal older and AMD eyes, there are also certain differences worthy of note. For example, in a study investigating the proteins in drusen in healthy and AMD donor eyes, Crabb et al. (2002) found that 65% of the proteins identified were found in drusen of both donors. However, oxidative protein modifications were found to be more abundant in AMD than in normal Bruch’s membranes. The authors concluded that oxidative protein modifications play a vital role in drusen formation and underlie the pathogenesis of AMD through oxidative injury. Currently there is no cure for either form of AMD, although photodynamic and anti- vascular endothelial growth factor therapy have been shown to attenuate choroidal neovascularization (Abdelsalam et al., 1999, Cheung et al., 2012). Given the lack of treatment options for dry AMD, the focus has fallen on nutritional modification of the MP, discussed in the next section.

Figure 1.10: Schematic showing the increased susceptibility to AMD resulting from age-related vascular and structural change, coupled with genetic and environmental risk factors (Ehrlich et al., 2008).

42 2 The macular pigment

MP is composed of three oxycarotenoids (L, Z and MZ) and is visually discernible as a yellow spot in the human macula (Nussbaum et al., 1981, Snodderly et al., 1984, Bone et al., 1988).

2.1 Bio- and stereochemistry of macular pigment

L, Z and MZ belong to a large family of carotenoids (around 600) which are the naturally occurring pigments in higher plants. There they play a role in plant photosynthesis and photoprotection (Nussbaum et al., 1981, Schalch, 1992, Snodderly, 1995). Carotenoids are divided into two groups: carotenes (e.g. β carotene, α carotene and lycopene) and xanthophylls (e.g. α and β cryptoxanthin, and astaxanthin). L, Z and MZ belong to the xanthophyll family of carotenoids which carry two additional oxygen atoms (C40H56O2) hence sometimes they are referred to as oxycarotenoids (or dihydrox- ycarotenoids). Carotenoids are water-insoluble and absorb in the short wavelength (blue) region of the visible spectrum (see Figure 2.3). Of the 40-50 carotenoids present in the human diet, approximately 14 (plus 12 isomers) are found in human blood samples (Beatty et al., 1999). Of these, only L, Z and MZ selectively accumulate within ocular structures with a peak at the fovea lutea and decrease in the periphery. As illustrated in Figure 2.1, Z is a mixture of three stereoisomers, (3R, 3’R), (3R, 3’S) and (3S, 3’S). The ﬁrst two are Z and MZ and are more numerous in the macula than the (3S, 3’S) isomer. L is a mixture of eight stereoisomers, however, only a single (3R, 3’R, 6’R) isomer is present in the macula (Bone et al., 1997). L and only the (3R, 3’R) Z stereoisomer have been detected in human blood which suggests that the other two, rather than a consequence of dietary intake, are synthesized in the retina (Bone et al., 1997). It is hypothesized that MZ (3R, 3’S) is isomerized from L at the site of the macula. This process may involve the migration of L 4’,5’ double bond to 5’,6’ position to form MZ, as illustrated in Figure 2.1 (Bone et al., 1997). Alternatively, MZ may be formed as a result of an enzymatic reduction pathway enabled by the metabolite hydrolutein.

43 a

Figure 2.1: a) Chemical structure of L and Z (Ahmed et al., 2005). b) Chemical structure of MZ. The position of L 4’,5’ double bond migrates to 5’,6’ position to form MZ (Bone et al., 1997).

2.2 Location of macular pigment

The majority of the MP is localized in the axons of the foveal cones (Henle’s ﬁbre layer) (Snodderly et al., 1984) and in plexiform layers (inner and outer) of the parafoveal region (Trieschmann et al., 2008) (see Figure 2.2). L and Z are found in the parafoveal rod outer segments where their concentration is 2.5 times higher than in the periphery (Sommerburg et al., 1999). Interestingly, Z is oriented perpendicularly to the plane of the membrane, whereas L resides in both parallel and perpendicular positions with respect to the plane (Sujak et al., 1999) yielding a larger surface area thus enhancing its ﬁltering ability (Junghans et al., 2001).

44 Figure 2.2: Photograph of a section through the macaque fovea showing the distribution of MP (dark region, top panel). Retinal layers are: PE, pigment epithelium; OS, receptor outer segments; IS, receptor inner segments; ON, outer nuclear layer; RA, receptor axons; IN, inner nuclear layer; IP, inner plexiform layer; GC, ganglion cell layer. Diﬀerence scans (middle panel) graphically superimposed on the foveal section outline showing the variation in the absorbance. Isodensity contours (bottom panel) derived from the diﬀerence scans in the panel above illustrating the anatomic distribution of MP (Snodderly et al., 1984).

High performance liquid chromatography (HPLC) studies reveal that MP is distributed throughout the entire retina, however psychophysically it is detectable only up to 8° of eccentricity (Bone et al., 1988, Hammond et al., 1997b, Snodderly et al., 1984). The retinal carotenoids, although densely concentrated in the central 20 mm² area of the retina, are also found in other ocular structures such as the cornea and sclera, albeit in smaller amounts, as shown in Table 2.1 (Bernstein et al., 2001). L and Z are also found in other tissues of the body such as the liver, pancreas, ovaries, kidneys, spleen, adrenals and testes (Hammond et al., 2002).

45 Table 2.1: Retinal carotenoid levels in human ocular tissues (Bernstein et al., 2001).

Ocular region Eyes Area Lutein (L) Zeaxanthin (Z) L/Z ratio examined (mm2) (ng per tissue ±SD) (ng per tissue ±SD)

Macular retina 14 20 13.98 ± 3.58 19.06 ± 4.50 0.7

Peripheral retina 19 ≈1000 64.18 ± 30.10 34.11 ± 16.83 1.9

Superior retina 78 20 1.68 ± 0.88 0.80 ± 0.62 2.1

Inferior retina 78 20 1.46 ± 0.71 0.63 ± 0.26 2.3

Nasal retina 7 20 1.76 ± 1.01 0.81 ± 0.53 2.2

Temporal retina 7 20 1.42 ± 0.90 0.65 ± 0.42 2.2

RPE/choroid 17 Whole 11.58 ± 5.99 5.89 ± 4.13 2.0

Superior RPE/choroid 78 20 0.63 ± 0.26 0.19 ± 0.09 3.3

Inferior RPE/choroid 78 20 0.53 ± 0.26 0.16 ± 0.09 3.3

Submacular RPE/choroid 25 20 0.77 ± 0.50 0.32 ± 0.20 2.4

Ciliary body 20 Whole 12.72 ± 7.90 5.98 ± 3.50 2.1

Iris 21 Whole 4.03 ± 1.98 1.54 ± 0.98 2.7

Lens 18 Whole 1.66 ± 1.09 1.43 ± 1.20 1.2

Cornea 3 Whole Trace Trace -

Sclera 5 20 Trace Trace -

Vitreous 3 0.5 ml Not detected Not detected -

2.3 The function of macular pigment

It has been hypothesized that the major roles of the MP lie in protecting the photoreceptors and RPE from oxidative damage (by quenching free radicals and singlet oxygen) and photochemical light damage (by absorbing high energy blue light) (Snod- derly, 1995, Davies and Morland, 2004). It may also play a role in improving visual performance by reducing the eﬀects of chromatic aberration (Nussbaum et al., 1981), discussed later. Additionally, L and Z have been found to protect against certain types of cancer (Holick et al., 2002), stroke (Ascherio et al., 1999), cardiovascular disease (Street et al., 1994, Connor et al., 2004) and UV light-induced skin damage (Gonzalez et al., 2003). The blue light hazard function describes the potential of a given wavelength of blue light to cause damage to retinal tissue and peaks at 450 nm (Schalch, 2001). This is remarkably close to the MP absorbance spectrum illustrated in Figure 2.3. The retinal

46 carotenoids, therefore, act as broad band ﬁlters by absorbing short wavelength light between 390 and 540 nm (Bone et al., 1992).

1 . 0 V i s i b l e S p e c t r u m ( 4 0 0 - 7 0 0 n m ) 0 . 9 B l u e l i g h t h a z a r d 0 . 8 4 0 0 4 5 0 5 0 0 5 5 0 6 0 0 6 5 0 7 0 0

y 0 . 7 t i U l t r a v i o l e t I n f r a r e d s 0 . 6 n e 0 . 5 d

a 0 . 4 c i t 0 . 3 p

O 0 . 2 0 . 1 0 . 0

3 5 0 4 0 0 4 5 0 5 0 0 5 5 0 6 0 0 6 5 0 7 0 0 7 5 0 W a v e l e n g t h ( n m )

Figure 2.3: MP absorption spectrum compared to the visible spectrum. Peak at 460 nm. Plotted using data from Beatty et al. (1999).

The majority of blue light is absorbed by the MP before neural stimulation (Snodderly et al., 1984) thus protecting the photoreceptors from short wavelength light damage. Short wavelength light is thought to be particularly damaging to the retina (Ham et al., 1976) and prone to chromatic aberration and scatter (Renzi and Hammond, 2010). The central pigmented area of the retina, however, is less susceptible to the damaging effects of blue light than the parafoveal area (Ham et al., 1978), further supporting the protective hypothesis of MP. It must be noted, however, that the crystalline lens also plays a role in protecting the retina from photochemical damage especially in older years. As discussed earlier, with age the lens gradually thickens and turns yellow leading to the formation of cataracts which are known to filter short wavelength light (Lerman and Borkman, 1976). There- fore, it may be that the ability of MP to filter blue light is more important in a younger eye with a clear lens, whereas in the older eye in the presence of lens opacities the importance shifts to the proposed antioxidant property of MP. The retina is a site of high metabolic activity and high blood flow. Reactive oxygen species such as free radicals, singlet oxygen and hydrogen peroxide form in the human retina as a result of photochemical reaction or as by-products of retinal metabolism

47 (Beatty et al., 1999). The rod outer segments contain high concentrations of polyunsaturated acids, particularly the docosahexaenoic acid (omega-3 fatty acid). Reactive oxygen species peroxidize these acids which compromises the integrity of the photoreceptor- RPE interface impeding photoreceptor phagocytosis and renewal (Schalch, 2001). Due to its antioxidant capacity, higher levels of MP may protect the retina from ageing- and disease-related degeneration. The ability of a xanthophyll to quench reactive oxygen species partly depends upon the number of conjugated double bonds it possesses. L has one less conjugated double bond than Z which reduces its ability to quench singlet oxygen (Bone et al., 1993). Also, in the proposed process of MZ formation (shown in Figure 2.1 (b)), the shift of the double bond in L yields a molecule more capable of quenching singlet oxygen than the original compound. It seems logical then that the centre of macula, an area of intense light insult and high reactive oxygen species production, should have higher quantities of MZ than L (Schalch, 2001). In vitro, studies have demonstrated that Z is twice as eﬀective at quenching reactive oxygen species than L, and that MZ has better protective capabilities against lipid peroxidation than Z . The shift towards the longer wavelengths of Z compared to L, as illustrated in Figure 2.3, increases the wavelength range over which the macula is protected against photooxidative damage (Bone et al., 2007).

1 . 2

n 1 . 0 o i t c

n 0 . 8 i t x e

0 . 6 e

v Z i L t 0 . 4 a l e

R 0 . 2

0 . 0 4 0 0 4 2 0 4 4 0 4 6 0 4 8 0 5 0 0 5 2 0 5 4 0 5 6 0 W a v e l e n g t h ( n m )

Figure 2.4: Individual xanthophyll absorption spectra. Plotted using data from van de Kraats et al. (2008).

L and Z are not the only antioxidants present in the layers of the central retina (Tri-

48 eschmann et al., 2008). Vitamins A, C and E have also been identiﬁed and their role in protecting against AMD is well supported (AREDS, 2001, AREDS2, 2013, Delcourt et al., 1999, Seddon et al., 1994, Smith et al., 1999).

2.3.1 The potential role of macular pigment in normal healthy eyes

MP has largely been studied in the context of AMD, however, its effect on visual performance in a normal human eye are poorly understood. Yet, its spatial location and spectral absorbance properties seem to be ideally suited to enhancing performance. First, it has been hypothesized that MP’s ability to absorb blue light reduces the effects of chromatic aberration (especially in the short wavelength component of longitudinal chromatic aberration) which may enhance VA, CS and reduce blue light scatter (Nuss- baum et al., 1981). However, the notion that MP counteracts chromatic aberration has been contested by McLellan et al. (2002) who concluded that the proposed detrimental effect of longitudinal chromatic aberration on vision is offset by the imperfect optics of the human eye. In a separate study on patients with cystic fibrosis who have markedly reduced MPOD and L and Z serum levels as a consequence of the disease, Schupp et al. (2004) found no adverse effects of chromatic aberration on visual function. Thus the mechanism behind the correlation between increased MP density and enhanced VA and CS (Loughman et al., 2012) remains unclear. Second, blue light absorption may reduce the effects of glare and photophobia by atten- uating the higher light energy and retinal irradiance leading to improved visual comfort (Loughman et al., 2007). Increased MP density also appears to have a positive effect on PSRT (Stringham and Hammond, 2007, 2008). Third, owing to the overlapping absorption spectra of MP and rhodopsin, under mesopic conditions, visual performance may be enhanced by the MP’s theoretical ability to attenuate the rod activity thus allowing the cones to operate at lower light levels (Kvansakul et al., 2006). Hammond et al. (1998b) investigated the effect of MPOD (as measured by HFP) on visual performance with increasing age. They measured photopic and scotopic sensitivity in healthy observers ranging from 24 to 84 years old. The results showed that MP levels were not correlated with visual sensitivity in younger observers (24-36). However, elderly observers (60-84) with low MPOD had significantly reduced sensitivity as compared to those with higher MPOD. Furthermore, older observers with higher MPOD had visual function similar to younger observers, suggesting that increased MP levels throughout adulthood may reduce age-related visual impairment.

49 Overall, although the mechanism behind the purported association between MPOD and visual performance in healthy eyes is unclear, it is likely to be related to a combination of biochemical (antioxidant) and optical proprieties of MP (Weale, 2007).

2.4 Characteristics of macular pigment

2.4.1 Composition

Using HPLC, Bone et al. (1988) demonstrated that the foveal (0 to 0.75 mm) MP is predominantly composed of Z (about twice the amount of L). The concentration of Z decreases with eccentricity more rapidly than L, thus in the periphery (beyond 2.5 mm) L is more dominant. The investigators also found an increase from central L:Z ratio of 1:2.4 to 2:1 in the periphery which is roughly proportional to increasing rod:cone ratio. This led to the hypothesis that L and Z may be associated with rods and cones, respectively (see Figure 2.5) and was later validated by Sommerburg et al. (1999) who found that L is present in the rod outer segment. However, it must be noted that in the same study Z was also found in rod outer segment of the peripheral retina. Indeed, between 10 (Rapp et al., 2000) and 25 percent (Sommerburg et al., 1999) of the total carotenoids are found in the rod outer segment.

2 . 5

2 . 0 n i h t

n 1 . 5 a x a e z

: 1 . 0 n i e t

u 0 . 5 L

0 . 0 0 5 1 0 1 5 2 0 2 5 3 0 R o d : c o n e

Figure 2.5: Linear relationship between rod:cone and average L:Z ratios. Plotted using data from Bone et al. (1988).

2.4.2 Distribution

MP distribution is complex (Delori et al., 2006), varies considerably between subjects (Robson et al., 2003, Trieschmann et al., 2008) and appears to be wider in subjects with

50 higher peak density (Hammond et al., 1997b). The broadening of the distribution has been attributed to ageing (Chen et al., 2001) and is more pronounced at the base of the profile and in females (Delori et al., 2006). Loane et al. (2007) hypothesized that this age-related broadening of MP distribution could be due to reduced retinal thickness with increasing age (Kanamori et al., 2003, Cavallotti et al., 2004, Budenz et al., 2007) which may lead to redistribution of MP within the retinal layers. On the contrary, subjects who are classed as heavy smokers tend to have very narrow MP spatial profile (Hammond et al., 1997b). Psychophysical (Hammond et al., 1997b, Wooten and Hammond, 2005) and reflectometry (from fundus photos) studies (Chen et al., 2001, Bour et al., 2002) have described the MP density distribution as circularly symmetrical. However, more recent studies, based on HFP, found very slight asymmetries between the temporal and nasal slopes (van der Veen et al., 2009). Additionally, Delori et al. (2006), using autofluorescence spectrometry, found the MP density distribution to be horizontally elliptical, rather than circularly symmetrical. From the fovea, the pigment density does not always decrease monotonically with eccentricity as previously suggested based on ex vivo studies (Handelman et al., 1991, Bone et al., 1997, Snodderly et al., 1984). Hammond et al. (1997b) and Wooten and Hammond (2005) found a deviation from the exponential fit in 40% of their subjects at around 1° retinal eccentricity using flicker photometry. Kirby et al. (2010) demonstrated a higher prevalence of central dips in MP profiles in older subjects and in cigarette smokers. Berendschot and van Norren (2006), using scanning laser ophthalmoscopy, observed a ringlike pattern of MP spatial distribution and found that in some subjects the area ∼0.7° away from the fovea had higher density than the foveal area. Delori et al. (2006) demonstrated a similar ring pattern in more than half of their subjects (more pronounced in women) at ∼0.8° from the fovea which was also observed entoptically through visualization of Maxwell’s spot. A study of Maxwell’s spot by Miles (1954) also confirms the inter-subject variability in MP density spatial profile and the existence of bimodal distribution as perceived when looking at a uniform blue-red screen. Deviations from the exponential decay in the form of bimodal and trimodal shapes (with peaks and valleys) have been demonstrated in human and monkey retinas (Williams et al., 1981, Snodderly et al., 1984, Hammond et al., 1997b, Snodderly et al., 1991, Delori et al., 2006). It has been hypothesized that these variations may be due to anatomical differences in the shape and size of the foveal depression (Delori et al., 2006, Snodderly et al., 1984). It is noteworthy, that detection of such distribution profiles using techniques based on flicker photometry can be difficult (due to poor sampling) and time consuming (around 50 minutes) (van der Veen et al., 2009).

51 2.4.3 Spatial proﬁle

The MP spatial profile (illustrated in Figure 2.6) can be described by logistic, polyno- mial, Gaussian and exponential functions (Moreland, 2004), with the latter generally yielding the best fit (Werner et al., 1987, Hammond et al., 1997b, Chen et al., 2001, Bour et al., 2002, Snodderly et al., 2004, Wooten and Hammond, 2005). The profile appears to be stable over time as does the MP density in individuals with stable dietary patterns (Hammond et al., 1997b).

y L P t 0 . 6 i s n e d

t 0 . 4 n e m g i

p 0 . 2

r a l u c

a 0 . 0

M N a s a l T e m p o r a l 8 4 0 4 8 R e t i n a l e c c e n t r i c i t y ( d e g r e e s )

Figure 2.6: A typical MP spatial proﬁle of a right eye for a young, healthy observer (LP) ﬁtted with an exponential function showing a decay with retinal eccentricity. MP density peaks at the fovea and declines to negligible levels by 8° eccentricity. Data obtained using HFP implemented in MPS 9000.

Psychophysical studies on MP have preferentially focused on the central peak characteristics (which are easier to measure) rather than the whole MP spatial profile (Pease et al., 1987, Landrum et al., 1997, Landrum et al., 1997, Beatty et al., 2000). This may, in part, be due to the length of time it takes to derive a MP distribution curve and coarse angular sampling with psychophysical methods. Alternative methods such as autofluorescence spectrometry provide measures over larger retinal areas therefore quickly yield MP spatial maps. It should be noted that the shape of the MP spatial profile cannot always be predicted from the central absorbance value (Robson et al., 2003). As such, Beatty et al. (2008) proposed that the total MP distribution (compared to a single central measure) better reflects an individual’s MP status. The value of measuring MP beyond the central point is highlighted by the evidence that parafoveal area is most susceptible to photochemical light damage (Ham et al., 1978). This may be due to individual variations in the MP profile. Hammond et al.

52 (1997b) demonstrated a variability in blue light transmittance (460 nm) of between 48 and 100% at 3° eccentricity. Furthermore, the region between 2 and 5° in AMD eyes is most susceptible to damage (Sarks et al., 1988, Swann and Lovie-Kitchin, 1991). According to Hammond et al. (1997b) the MP pigment density across the retina can be calculated using the following equation when MP density at one locus (i.e. within the central 2°) is known:

MP = A x 10−0.42x where A is the Y intercept, MP is the derived MPD, x is the eccentricity in degrees and −0.42 is the exponent derived from their experimental data. The equation does not work for all observers due to individual variations in spatial proﬁles discussed above. Therefore, the authors suggested measuring two locations, one within the central 2° and another between 2.5° and 3.5°, to calculate individual exponents based on the exponential curve ﬁts.

2.4.4 Density and assimilation

In common with all measures of optical density, MPOD is a ratio and therefore dimen- sionless. However, typically it is expressed in optical density units (DU). The reported MPOD values range between 0 (Bone and Sparrock, 1971) and 1.07 (Werner et al., 2000). The wide variation in MP peak density amongst individuals (Robson et al., 2003) is consistent with histological studies (Trieschmann et al., 2008) and may reflect heredi- tary differences in the ability of individual retinas to accumulate these two xanthophylls (Snodderly et al., 1991), differences in xanthophyll chemical concentration and molecular orientation, and differences in methodology employed to measure MP. For instance, psychophysical and autofluorescence spectrometry methods yield similar MPOD values between 0.3 and 0.5 (Pease et al., 1987, Werner et al., 1987, Hammond et al., 1997b, Delori et al., 2001), whereas reflectance procedures systematically yield smaller values between 0 and 0.45 (Wooten et al., 1999, Delori et al., 2001, Bour et al., 2002). Most human psychophysical studies report highly similar MPOD values for left and right eyes within the same subject (Hammond and Fuld, 1992, Nolan et al., 2004). However, Landrum et al. (1997) and Snodderly et al. (2004) reported small differences between fellow eyes which may be the result of sampling bias. The mechanism by which the retina incorporates xanthophylls into its tissue is poorly understood, although a specific xanthophyll-binding protein has been suggested as a mediator of this process (Yemelyanov et al., 2001, Beatty et al., 2008).

53 2.5 Factors aﬀecting macular pigment density

2.5.1 Age

L and Z are present in the prenatal and newborn human eyes (Bone et al., 1988) although they are not discernible by visual inspection until 6 months after birth. Prenatal and postnatal whole retinas up to about 2 years of age have higher L:Z ratio than older retinas (Bone et al., 1988), and after that period age-dependent changes do not affect the concentration and composition of L and Z (Bone et al., 1997). There is considerable disagreement amongst studies on the age dependency of MPOD. Some studies report no correlation (Werner et al., 1987, Bone et al., 1988, Chen et al., 2001, Broekmans et al., 2002, Mellerio et al., 2002, Wüstemeyer et al., 2003, Ciulla and Hammond, 2004, Berendschot and van Norren, 2005), whilst others demonstrate both an increase (Nussbaum et al., 1981, Liew, 2005) and a decrease (Kilbride et al., 1989, Hammond and Caruso-Avery, 2000, Beatty et al., 2001, Gellermann and Bernstein, 2004, Nolan et al., 2004, 2007) in MPOD with age. Delori et al. (2006) found that both age and gender affected the shape of MP density distribution. Subjects older than 60 years had a marked decrease in central MPOD (possibly as result of excessive stray light), while females displayed a significant increase in MPOD at eccentricities greater than 1° with age. There was no correlation between MPOD and degree of ametropia. A study on a large US population by van der Veen et al. (2009) found a mean MPOD of 0.33 (±0.19) which agrees with previous large-scale studies using HFP (Snodderly et al., 2004, Nolan et al., 2007). van der Veen and colleagues found that the older group on average had higher MPODs than the younger group. Younger males (20-30) had higher MPODs than females, which reversed in the older age group with the females having higher MPODs than males. The investigators hypothesized that the differences may be due to a healthier lifestyle amongst these groups. Berendschot and van Norren (2005), in their study on age dependency of MP (using optical methods), concluded that there is unlikely to be a link between the two variables in populations who are not using L and Z supplements, and that any age-related increase in MPOD is likely to be a result of group sample bias.

2.5.2 Gender

MP density has been found to be lower in females (Wooten et al., 1999, Hammond and Caruso-Avery, 2000, Mellerio et al., 2002). Hammond et al. (1996b) found that

54 their female subjects had 36% lower MPOD than males (despite similar serum L and Z) . Other studies, however, report no correlation between gender and MP (Bone and Sparrock, 1971, Delori et al., 2001, Berendschot et al., 2002, Wüstemeyer et al., 2003). The gender differences in MPOD may be due to different mechanisms of carotenoid uptake by the retina for males and females (Hammond et al., 2002). Hormonal changes (during the menstrual cycle and the menopause) may also affect L and Z metabolism (Curran-Celentano et al., 2001, Hammond et al., 2002).

2.5.3 Ocular factors

2.5.3.1 Iris pigmentation

The majority of studies on MP have demonstrated reduced MPOD in subjects with light iris pigment (Wooten et al., 1999, Hammond and Caruso-Avery, 2000, Mellerio et al., 2002). Hammond et al. (1996a) found that subjects with light iris pigment (blue and gray) had 34% less MP than subjects with dark irides (brown and black). However, some studies have found no correlation between ocular pigmentation and MP (Bone and Sparrock, 1971, Delori et al., 2001, 2006).

2.5.3.2 Crystalline lens optical density

Higher MP levels have previously been correlated with reduced lens optical density which may be due to increased retinal illumination and CFF thresholds (Stringham and Hammond, 2005). However, more recent reports have shown that MPOD as measured using MPS 9000 (which employs HFP) is not inﬂuenced by lens yellowing and ocular media optical density (Makridaki et al., 2009).

2.5.4 Non-ocular factors

2.5.4.1 Nutrition

MP is derived entirely from diet, therefore diet is probably the most important factor that aﬀects MP levels. L and Z are not synthesized by the body, rather they are derived entirely from dietary intake. Evidence for this comes from a study by Mali- now et al. (1980) in which macaques fed a xanthophyll-free diet displayed absence of or markedly reduced MP coupled with undetectable xanthophyll plasma level and appearance of drusen-like bodies at the RPE level. The xanthophyll deﬁcient monkeys,

55 despite a reduced cone electroretinogram amplitude, did not sustain significant visual disturbances. Until recently it was thought that because MZ is not present in the human diet and therefore human serum, it is synthesized in the macula as a result of the L to MZ conversion mechanism. However, recent supplementation studies demonstrated that MZ, although almost undetectable in a typical diet, is capable of being absorbed into the serum (Bone et al., 2007, Loughman et al., 2012). To date, the only foodstuff identified as containing MZ are certain fish species (Maoka et al., 1986) and eggs from chickens supplemented with MZ to enhance their hue (Rasmussen et al., 2012). Green leafy vegetables are often recommended as good sources of L and Z, however care must be taken as they are not present in equal quantities within one foodstuff. For instance, kiwi, pumpkin, spinach and butternut squash, although good sources of L, contain zero Z (see Table 2.2 for values of L and Z within foodstuffs). From Table 2.2, it should be clear that, overall, Z appears in smaller quantities in fruits and vegetables compared to L, which reflects the (∼7 times) higher L plasma levels compared to Z levels (Schalch, 2001). Eggs are the richest source of L and Z with a combined mole percentage of 89, closely followed by maize (86%). Maize is also the richest source of L (60%), whilst orange pepper is the highest supplier of Z (37%). Interestingly, spinach and broccoli contain zero percent Z, and carrots are very low in either micronutrient.

56 Table 2.2: Mole percentages of L and Z in foodstuﬀs measured using HPLC. Adapted from Sommerburg et al. (1998).

Vegetable/fruit Lutein and zeaxanthin Lutein Zeaxanthin

Egg yolk 89 54 35

Maize (corn) 86 60 25

Kiwi 54 54 0

Red seedless grapes 53 43 10

Zucchini squash 52 47 5

Pumpkin 49 49 0

Spinach 47 47 0

Orange pepper 45 8 37

Yellow squash 44 44 0

Cucumber 42 38 4

Pea 41 41 0

Green pepper 39 36 3

Red grape 37 33 4

Butternut squash 37 37 0

Orange juice 35 15 20

Honeydew 35 17 18

Celery (stalks, leaves) 34 32 2

Green grapes 31 25 7

Brussels sprouts 29 27 2

Scallions 29 27 3

Green beans 25 22 3

Orange 22 7 15

Broccoli 22 22 0

Apple (red delicious) 20 19 1

Mango 18 2 16

Green lettuce 15 15 0

Tomato juice 13 11 2

Peach 13 5 8

Yellow pepper 12 12 0

Nectarine 11 6 6

Red pepper 7 7 0

Tomato (fruit) 6 6 0

Carrots 2 2 0 2.5.4.2 Smoking

Smoking has been found to reduce MPOD (Wooten et al., 1999, Hammond and Caruso- Avery, 2000, Mellerio et al., 2002). Hammond et al. (1996c) found that smokers had a 50% reduction in MP compared to non smokers. Although no such correlation was reported by other workers (Curran-Celentano et al., 2002, Delori et al., 2006).

2.5.4.3 Obesity

A large fraction of L and Z (>80%) is stored in adipose tissue (Hammond et al., 2002). It has been proposed that L is preferentially absorbed by adipose tissue compared to the retina (Thomson et al., 2002). As such it might be expected that obese subjects have reduced MPOD due to low dietary carotenoid intake and competition between retinal and adipose tissue for these carotenoids. Indeed, studies have reported an inverse relationship between MPOD and adiposity and MPOD and body mass index (BMI) (Hammond et al., 2002, Nolan et al., 2004).

2.5.4.4 Light exposure

Previous researchers have referred to MP as non-bleaching or photostable (Snodderly et al., 1984, Werner et al., 1987, Kilbride et al., 1989), however more recent studies suggest that MP may be bleached or depleted by photolysis (Mortensen and Skibsted, 1997b,a) and exposure to pro-oxidants in vitro (Siems et al., 1999). In vitro, L and Z are photodegradable after 4 hours to sunlight exposure (Siems et al., 1999). Despite their similar bleaching rates in the presence of various pro-oxidants, compared to other carotenoids (such as lycopene and β carotene) L and Z bleach much slower. This may explain why these two carotenoids, in preference to others, accumulate at the site of the macula (Siems et al., 1999). Plasma carotenoid levels, in vivo, have also been shown to reduce in human subjects exposed to UV light (Roe, 1987, White et al., 1988), although the speciﬁc carotenoid implicated was not elucidated in either study. MP density was found to be lower in subjects exposed to daily natural sunlight or using sun beds (Hammond and Caruso- Avery, 2000, Mellerio et al., 2002). However, Ciulla et al. (2001) have reported no signiﬁcant correlation between MPOD and sun exposure.

58 2.5.4.5 Heredity

The subject of MP heredity was broached by Handelman et al. (1991) who suggested that MP characteristics may be inherited. Liew (2005) demonstrated a close correlation of MP proﬁles in monozygotic twins, whereas Hammond et al. (1995) found statistically signiﬁcant similarities in only half of his monozygotic twins suggesting that MP is not completely genetically driven.

2.5.4.6 Cultural diﬀerences in nutrition

L and Z intake is reported to vary amongst diﬀerent populations. For instance, non- Hispanic whites and Mexican-Americans consume fewer carotenoids than non-Hispanic blacks (Ma and Lin, 2010). The average daily intake of L and Z is reported to be low amongst US (1.7 mg), European (2.2 mg) and Australian (0.8 mg) populations (Alves- Rodrigues and Shao, 2004, Ma and Lin, 2010). These values are much lower than those reported to reduce the risk of AMD and cataracts (Seddon et al., 1994, Chasan- Taber et al., 1999, Brown et al., 1999). This discrepancy highlights the importance of increasing the consumption of foods high in L and Z and/or using supplements, and standardizing the recommended daily allowances for these micronutrients.

2.5.4.7 Ethnicity

To date, very few studies have investigated the eﬀect of ethnicity on MP characteristics. Wolf-Schnurrbusch et al. (2007) reported reduced MPOD in non-Hispanic white subjects compared to African observers, whereas Iannaccone et al. (2007) reported reverse ﬁndings. Tang et al. (2004) using HFP measured MP in young Chinese adults and found an average MPOD of 0.48 ±0.23 which is a similar value to that obtained amongst non-Hispanic white observers using similar techniques (van der Veen et al., 2009).

2.6 From plant to supplement

2.6.1 Xanthophyll extraction

Xanthophylls are usually extracted by solvent from marigold (Tagetes erecta L.) where they mostly exist in the form of fatty acid esters. The majority of the esterified oxycarotenoids available for use in supplements are derived from this flower (Breithaupt et al., 2002). Saponification allows the extraction of free (non-esterified) compounds,

59 however this method may result in chemical damage of the molecule (Oliver et al., 1998).

2.6.2 Supplement preparation

Supplements containing purified crystalline L have been commercially available for around 14 years (Alves-Rodrigues and Shao, 2004) with a more recent addition of Z and MZ. L, Z and MZ capsule supplements are available alone or in preparation with other vitamins and exist in esterified (powdery formulation from marigold flowers) and non- esterified forms (hard or soft gel capsules). Although the bioavailability of xanthophylls is reported to be dependent on the supplement formulation (Bone et al., 2007) with the non-esterified formulation showing higher bioavailability, studies on this subject produce inconclusive results (Bowen et al., 2002).

2.6.3 Recommended dosage

L, Z and MZ are not considered as essential nutrients (Alves-Rodrigues and Shao, 2004) and currently there are no protocols for optimal dosage of these carotenoids. Seddon et al. (1994) suggested a daily intake of L and Z of between 6-10 mg and 2 mg, respectively, to reduce the risk of AMD. However, some studies using L doses of less than 10 mg have found no effect on visual performance in both normal and diseased eyes (Bartlett and Eperjesi, 2007, 2008) suggesting that at least 10 mg of L is required to have an effect in either subject group. Additionally, recent research shows that in order to infer a substantial effect on visual function in normal healthy eyes all three carotenoids are required (Loughman et al., 2012).

2.6.4 Supplement safety

There have not been any reports of toxicity or adverse reactions with daily intake of 30 mg L for 5 months (Landrum et al., 1997), daily 40 mg L for 2 months (Dagnelie et al., 2000), daily 20 mg Z for 6 months (van de Kraats et al., 2008), or daily 10 mg MZ for 6 months (Loughman et al., 2012). Furthermore, Fijians are reported to consume 25 mg of L per day with no adverse effects. On the contrary, high L intake in this population leads to a reduced risk of lung cancer (Le Marchand et al., 1995). Studies investigating the potential side effects of both L and Z have reported no adverse effects and conclude that the two xanthophylls can safely be given to humans (Ma and

60 Lin, 2010). Likewise, MZ is reported to be safe and non-mutagenic with no known adverse side effects (Bone et al., 2007). All three carotenoids have achieved the generally recognized as safe status which has enabled them to be added to various foods and beverages (Alves-Rodrigues and Shao, 2004). The only known side effect resulting from L intake is carotenodermia (yellow-orange discolouration of the skin) which itself is harmless (Alves-Rodrigues and Shao, 2004). Other carotenoids have been shown to be harmful in certain circumstances. For example, studies supplementing smokers with high dose β carotenes report a higher incidence of lung cancer which is related to the provitamin A activity of this carotenoid. It must be noted that L, Z and MZ act differently to β carotene (a pre-cursor of vitamin A) in the retina in that they are incapable of improving vitamin A deficiency or deliver- ing retinol to the photoreceptors thus their functions appear independent of vitamin A (Schalch, 2001). Furthermore, the three retinal carotenoids do not posses provitamin A and therefore is not expected to procure the same pro-carcinogenic effect in smokers (Alves-Rodrigues and Shao, 2004).

2.7 Nutritional augmentation of macular pigment

2.7.1 Supplementation studies in healthy eyes

Several studies have demonstrated a positive relationship between MP augmentation (resulting from supplement use or by increasing the intake of spinach, corn or eggs) and increased MPOD with a concurrent increase in the carotenoid plasma levels (Lan- drum et al., 1997, Johnson et al., 2000, Bone et al., 2003, 2007). However, there is a considerable amount of variability among individuals in MP accumulation from foods (Edwards, 1996; Hammond et al. 1997a) which may reflect the varying bioavailability of retinal carotenoids from different foodstuffs. For instance, eating fruits and vegetables rich in these nutrients may not be as effective as eating eggs which have a higher bioavailability (see Section 2.8). The increase in MPOD following supplementation is reported to take anywhere between 4 weeks (Landrum et al., 1997) and 12 months (Nolan et al., 2011) and remains stable for several months after cessation despite the return of carotenoid plasma level to baseline (Hammond et al., 1997a). This suggests that the retinal carotenoid turnover is low and MPOD reflects cumulative carotenoid intake (Landrum et al., 1997). Supplementation studies (with up to 20 mg/day L) which used HFP to measure MPOD in healthy eyes report a significant increase in MPOD from baseline ranging from 0.025 to 0.09 (Bartlett et al., 2010a).

61 Of note, there is a subset of subjects referred to as nonresponders for whom serum carotenoids and/or MPOD do not increase as a result of supplementation. The nonresponders can be divided into two groups: retinal nonresponders (where serum carotenoid level is increased but not MPOD) and retinal and blood nonresponders (where neither the serum, nor MPOD respond to supplementation). It may be that carotenoid supplementation for these groups, rather than increasing the peak of the MP, alters the width of the proﬁle which is not easily measured with HFP (van der Veen et al., 2009).

2.7.1.1 Eﬀect on visual function

A summary of studies which have investigated the effect of MP augmentation on visual function in normal eyes is presented in Table 2.3. Several of the older studies, discussed in Nussbaum et al. (1981), have shown significant improvements in dark adaptation following intake of L dipalmitate. More recent supplementation studies, have demonstrated a positive effect on photophobia (Wenzel et al., 2006b), mesopic contrast acuity (Kvansakul et al., 2006), PSRT and grating visibility (Stringham and Hammond, 2008), and VA and CS (Loughman et al., 2012). It must be noted, however, that there are some methodology related issues worth mentioning in each of the studies reporting positive effects. First, Stringham and Hammond (2008) did not include a placebo group. Such a design makes it difficult to ascertain whether any effect is due to the treatment itself or to some other factor such as learning effects. Indeed, one previous study (Bartlett and Eperjesi, 2008) has reported a substantial and significant improvement in a number of visual parameters for the placebo group. This may have been due to learning effects as a result of test familiarity and repeated measurements which highlights the importance of a control group. Likewise, Wenzel et al. (2006b) did not have a placebo group and supplemented only four subjects. Second, Loughman et al. (2012) study was single-masked thus possibly introducing experimenter bias. Third, in the Kvansakul et al. (2006) study visual performance was not measured at baseline but only after 6 months of supplementation making the results difficult to interpret. By contrast, two recent and well-controlled studies of the effects of L on visual function in normal eyes have failed to show any effect on VA or CS (Bartlett and Eperjesi, 2008, Nolan et al., 2011). It must be noted, however, that these two studies used a multi-nutrient supplement as opposed to exclusively L, Z, MZ or a mixture of these used in the studies reporting positive results (Loughman et al., 2012). Theoretically, a multi-nutrient supplement should have no effect on xanthophyll absorption and retinal capture since carotenoids are known to work synergistically with vitamin C and E

62 (Blakely et al., 2003). It is more likely that the L content of the formulation used by Bartlett and Eperjesi (2008) was not suﬃcient to eﬀect a change in visual function, as discussed by the authors.

63 Table 2.3: Studies investigating the relationship between MP and visual performance in healthy eyes (modiﬁed from Loughman et al. 2010; *Data from Nussbaum et al. 1981).

64 2.7.2 Supplementation studies in ocular disease

A summary of all studies investigating the role of MP on visual performance in ocular disease is presented in Table 2.4. A variety of genetic and age-related diseases have been investigated including AMD, retinitis pigmentosa (RP) and cataract. The majority of research into the function of MP has been centered on its hypothesized role in AMD. The pathogenesis of AMD is not completely understood however long- term blue light damage and oxidative stress have been put forward as likely causes. Owing to MP spatial location, filtering and antioxidant properties, it seems ideally placed for protecting the retina against AMD. It is reported that AMD donor eyes versus non-AMD controls on average have 30% lower concentration of L and Z (Landrum et al., 1996). Additionally, high L and Z blood concentration as a result of high retinal carotenoid dietary intake is reported to reduce the risk of AMD (Seddon et al., 1994, Study, 1993). Conversely low L and Z blood concentration (Gale et al., 2003), along with low MP levels (Landrum et al., 1997, Landrum et al., 1997, Beatty et al., 1999, 2001, Bone et al., 2001) have been found to increase the risk of developing AMD. MPOD is also reported to be reduced in diabetic patients and is inversely related to increasing severity of diabetes induced maculopathy (Davies and Morland, 2002). The risk factors for AMD such as smoking, female gender and blue iris colour have also been correlated with low MPOD (Hammond et al., 1996c,a,b). Additionally, obesity is correlated with increased risk of AMD, disease progression (Nolan et al., 2004) and reduced MPOD (Hammond et al., 2002). Dietary modification through intake of 75 g of spinach two to four times a week has been shown to reduce the risk of AMD by 45%, and eating spinach daily can reduce this risk further (by 80%) (Seddon et al., 1994). Increasing spinach consumption (four times per week) has been shown to improve CS (Richer, 1999), whilst using supplements containing a variety of oxycarotenoids has been shown to improve VA, CS and dark adaptation (Richer et al., 2004, Berendschot et al., 2011, Sabour-Pickett et al., 2014, Murray et al., 2013). RP is an inherited, degenerative disorder of the RPE and photoreceptors which leads to progressive visual loss and eventually incurable blindness (Alves-Rodrigues and Shao, 2004). Although, treatment for this disease is limited, supplementation with vitamin A and L has been shown to slow down its progression, increase MPOD, and improve VA (Dagnelie et al., 2000, Aleman et al., 2001, Berson et al., 2010). L supplementation has also been show to augment MP serum and retinal levels (with no improvement to vision) in certain individuals suffering with choroideremia, another incurable genetically-linked disease of the retina (Duncan et al., 2002).

65 The crystalline lens accumulates L and Z to the exclusion of other carotenoids (Yeum et al., 1995), although in much smaller quantities than the macula. Nonetheless, these xanthophylls are hypothesized to play the same protective role (Alves-Rodrigues and Shao, 2004). A low dietary and serum carotenoid status has been linked to the development of age-related cataracts (Brown et al., 1999, Chasan-Taber et al., 1999). Furthermore, L supplementation has been shown to improve VA and glare sensitivity in subjects with existing age-related cataracts (Olmedilla et al. 2001; see Table 2.4). However, there is some disagreement amongst studies with regards to the role of L and Z in cataract and AMD development (Mares-Perlman et al., 1995a,c,b). For instance, one study failed to show any effect on CS in subjects with ARM and AMD following supplementation with a multi-nutrient formula containing low dose of L (6 mg) (Bartlett and Eperjesi, 2007). Yet another study reported an increased rate of AMD progression with higher intake of L, Z and omega-3 fatty acids estimated from food frequency questionnaires (Robman et al., 2007). The inconsistencies amongst studies may be due to differences in methodologies, supplement dose and delivery, severity of the disease and characteristics of the individual. It is worth noting that the negative finding in the Robman et al. study is an isolated case and that AMD progression was ascertained on the basis of fundus appearance rather than symptoms (e.g. VA decline). Additionally, the participants’ MPOD was not measured therefore it is difficult to ascertain how the intake of L and Z affected levels of the carotenoids at the macula, if at all. This is important since it is the change at the macula (i.e. increase in MPOD) that governs the extent of antioxidant protection and visual enhancing capabilities of the ingested carotenoids. Finally, the progression in AMD was seen among subjects who consumed a maximum of 1 mg/day of L and Z. These levels are far lower than those showing a beneficial effect of the carotenoids on retinal health, reduced risk of AMD and improvement in visual function in normal and diseased eyes. To date, there have been no reports of negative effects of L and Z intake on visual function or retinal integrity among normal, healthy subjects.

66 Table 2.4: Studies investigating the relationship between MP and visual performance in diseased eyes (modiﬁed from Loughman et al. 2010; *Data from Nussbaum et al. 1981).

67 2.8 Bioavailability of macular pigment

Bioavailability describes the fraction of the ingested nutrient that is capable of being absorbed and made available for use or storage. The bioavailability of L and Z from diﬀerent sources is variable and depends on dietary and non-dietary factors.

2.8.1 Dietary factors

Factors which affect availability of macular xanthophylls include the concurrent presence of dietary lipids, fibre and other carotenoids. For instance, the bioavailability of L can be enhanced by simultaneous consumption of lipids (Roodenburg et al., 2000, Unlu et al., 2005, van het Hof et al., 2000). Conversely, dietary fibre (Riedl et al., 1999) and β carotene (Kostic et al., 1995, van den Berg and van Vliet, 1998) may have inhibitory effects on L absorption. Bioavailability of L also depends on the nature of its source and digestibility of the vegetable matrix. For instance, bioavailability of L from green leafy vegetables is relatively poor (de Pee et al., 1998) and can be increased by disrupting the vegetable matrix through cooking and chopping (van het Hof et al., 1999b). Additionally, L obtained from eggs has been shown to have higher bioavailability (Handelman et al., 1999) than spinach or L ester supplements (Chung et al., 2004) as illustrated in Figure 2.7. This is likely to be due to the cholesterol content of egg yolk. Studies examining the effect of egg consumption on MP report significant increases in L and Z serum concentration (Surai et al., 2000, Chung et al., 2004, Goodrow et al., 2006, Herron et al., 2006, Handelman et al., 1999) and significant increases in MPOD (Wenzel et al., 2006a). However, a potential side effect of high egg intake may be an increase in low-density lipoprotein (LDL) cholesterol which is associated with increased risk of coronary heart disease. Although, eggs also increases high-density lipoprotein (HDL) cholesterol which is associated with reduced risk of coronary heart disease and it may be that the two cancel each other out. Indeed, several studies have found no significant increase in serum cholesterol or its subfractions following egg supplementation (Surai et al., 2000, Goodrow et al., 2006, Wenzel et al., 2006a).

68 8 0 ) L / l L u t e i n E s t e r o 6 0 L u t e i n m

n S p i n a c h ( E g g n i 4 0 e t u l

m u

r 2 0 e S

0 2 4 6 8 1 0 D a y s

Figure 2.7: Bioavailability of eggs, spinach, lutein and lutein ester. Plotted using data from Chung et al. (2004).

2.8.2 Supplement type

Purified crystalline L supplements have been shown to be more easily absorbed than L obtained from vegetables such as spinach (van het Hof et al., 1999a). However, both esterified and free L are reported to have lower bioavailability compared to L obtained from egg consumption, as shown in Figure 2.7. Given the characteristics of esterified L and that it must be hydrolyzed to free L before it is absorbed by the body, it was suggested that it is less bioavailable than the free form (Bowen et al., 2002). Additionally, unlike free L, the ability to hydrolyse L esters may decline with age (Chung et al., 2004). However, the hypothesis of lower L ester bioavailability has not been supported in empirical studies involving humans. For instance, Bowen et al. (2002) found that L diester formulation was more bioavailable than free L formulation for most individuals in their study. The authors concluded that formulation dissolution, rather than presence or absence of esterified fatty acids, is an important factor in L bioavailability which may me maximized by ingestion of sufficient fat in the meal proximate to supplement consumption.

2.8.3 Non-dietary factors

Non-dietary factors which can aﬀect carotenoid bioavailability include disease of the liver and kidneys, age, gender, alcohol and cigarette use, fat malabsorption, obesity, BMI and use of certain drugs, e.g. anti-obesity drugs which reduce fat absorption (Alves-Rodrigues and Shao, 2004).

69 A large fraction of L and Z (>80%) is stored in adipose tissue (Hammond et al., 2002). As such it has been speculated that obese subjects may have reduced MPOD due to reduced dietary carotenoid intake and competition between retinal and adipose tissue for these carotenoids. Several studies have reported an inverse relationship between MPOD and adiposity and MPOD and BMI, after correcting for dietary L and Z (Ham- mond et al., 2002, Nolan et al., 2004). These studies, along with the finding that L is preferentially absorbed by adipose tissue compared to the retina (Thomson et al., 2002), support the competition for carotenoids hypothesis. The inverse relationship between adiposity and MPOD may depend on BMI as reported in one study in which the relationship between the two variables was only significant for subjects with BMI over 29 and fat percentage of more than 27% (Hammond et al., 2002). Hammond et al. (2002) found that the relationship between MPOD and adiposity was irrespective of gender, whereas Nolan et al. (2004) found MPOD reduction amongst obese males only and inverse relationship between serum Z and percentage of body fat in females only. In a study on quails, Thomson et al. (2002) found that retinal L was inversely related to L fat concentration in females, whereas in males retinal Z was positively related to Z fat concentration. In other words, females exhibited enhanced uptake of Z by adipose tissue compared with males. These studies suggest that accumulation and stabilization of retinal carotenoids may be gender-dependent. Different carotenoids are associated with different plasma-lipoproteins (Clevidence and Bieri, 1993) which act as transport vehicles. For instance, L and Z are predominantly associated with HDL whereas hydrocarbon carotenoids (e.g β carotene) are transported by LDL (Alves-Rodrigues and Shao, 2004). Factors such as obesity affect individual lipoprotein profiles which may influence the transport of the macular xanthophylls to the retina and ultimately MP characteristics. Obese males are more likely to have reduced HDL-cholesterol than obese females which may in part explain why Nolan et al. (2004) found reduced MPOD amongst obese males only. Dietary fat intake has been found to be inversely related to serum L and Z but not MPOD (Nolan et al., 2004). This is consistent with the finding that anorexics, who tend to follow low fat diets and have low body fat content, exhibit a disproportionately high serum carotenoid levels which is unrelated to dietary intake (Curran-Celentano et al., 1985). Of note is the fact that BMI and adiposity are not always correlated (Nolan et al., 2004, Hammond et al., 2002) with females tending towards higher body fat percentage than males (irrespective of BMI). This highlights the importance of measuring BMI and adiposity separately when exploring the relationship between obesity and MPOD.

70 2.9 Measurement of macular pigment

MP density measurement is important when undertaking a study to evaluate the role of MP in human vision and the effect of oxycarotenoid supplementation on visual performance. The chosen technique to measure MPOD is equally important and has to be repeatable, reliable and suitable for naive observers of all age groups. This is because elderly observers and patients with ocular disease tend to have poorer fixation, increased media opacities and reduced motor skills (Delori et al., 2001) which may adversely affect the MP measurement. In vitro, MPOD can be determined using techniques such as HPLC (Bone et al., 1988) and microdensitometry (Snodderly et al., 1991). In vivo, both objective (optical) and subjective (psychophysical) methods have been employed. Objective techniques have utilized fundus reflectance (from photographic assessment (Bour et al., 2002, Kilbride et al., 1989), scanning laser ophthalmoscopy (Wüstemeyer et al., 2003) and densitometry (Berendschot et al., 2000)), autofluorescence spectrometry (Delori et al., 2001, 2006), and Raman spectroscopy (Bernstein et al., 1998, Ermakov et al., 2001, Bernstein et al., 2004). Optical methods generally require specialist equipment (Beatty et al., 2008), pupillary dilation, high light levels and accurate alignment (Snodderly et al., 2004). The majority are image-based apart from the signal-based Raman spectroscopy which requires a correction to account for pre-retinal absorption (Delori et al., 2001). The advantage of techniques such as reflectometry and autofluorescence spectrometry is that they rapidly yield a MP spatial profile, allow detection of ringlike structures (Berendschot and van Norren, 2006, Delori et al., 2001, 2006), and are less demanding on the patient (Robson et al., 2003). Amongst the psychophysical methods, the earlier MPOD measurements utilized techniques such as spectral sensitivity measurement (Pease et al., 1987), colour matching (Ruddock, 1963), visualization of Haidinger’s brushes (Naylor and Stanworth, 1954) and motion photometry (Moreland, 2004). In recent years, MPOD has been measured using a psychophysical technique based on HFP which requires the subject to adjust the luminance of two lights until they are equal thereby reducing flicker to a minimum (Beatty et al., 2000). This technique attempts to overcome the problems associated with ocular media absorption by assuming that when MPOD is measured at two retinal locations pre-retinal filters remain constant thus any difference is attributed to MP alone (Delori et al., 2006). Indeed, studies have shown that even in cases of dense cataracts and poor vision, MP measurement is largely unaffected and reliable (Wooten et al., 1999, Ciulla

71 et al., 2001, Wooten and Hammond, 2005). The general drive has been to encompass the HFP technique within a small, light and portable desktop device that is quick to administer (for novice operators and subjects) and can be used reliably in optometric practice as well as in clinical trials. Such instruments have been described by various investigators (Wooten et al., 1999, Beatty et al., 2000, Mellerio et al., 2002) and use light emitting diodes (LEDs) as light sources for the central and peripheral targets which emit near monochromatic light and have the advantage of being small (Mellerio et al., 2002). Another advantage is that these instruments generally do not depend on Maxwellian view optics, rather they employ a ‘free-viewing’ technique which avoids calibration and alignment problems (Beatty et al., 2000). A possible disadvantage of using a free-viewing set up as compared to Maxwellian view is that variations in pupil size may reduce the total retinal irradiance, however this was found no to be the case (Wooten et al., 1999). All HFP techniques require the observer to perform flicker matches using two wavelengths of light (blue and green) at two locations, foveal and extrafoveal. The foveal location, generally has the highest MP concentration and the peripheral (∼8°) is thought to be absent in MP. MPOD is calculated from the luminance ratio of the blue light at these two locations. The disadvantages of the psychophysical techniques are that an eccentric measure has to be made which is more demanding on the subject (Mellerio et al., 2002, Davies and Morland, 2004) and it can take up to 50 minutes to derive a spatial profile for each eye (Beatty et al., 2008, van der Veen et al., 2009). Potential sources of error with HFP may arise from criterion effects, eye movements, unstable fixation in the foveal region, possible rod intrusion with eccentricities beyond 7° (Robson et al., 2003) and Troxler effect in the parafoveal region where adaptation is rapid (Moreland, 2004). Generally, HFP is found to be reliable (Taylor, 1999, Snodderly et al., 2004, Wooten and Hammond, 2005) and yields MPODs that correlate well with chemical concentrations of L and Z (Handelman et al., 1991) and objective techniques such as autofluorescence spectrometry (Delori et al., 2001) and spectral fundus reflectance using a macular pigment reflectometer (van der Veen et al., 2009). HFP is a valid method for measuring MP in healthy subjects (Wooten and Hammond, 2005), and has been used successfully in patients with early AMD (Richer et al., 2002, Berendschot et al., 2011), advanced cataracts (Ciulla et al., 2001), choroideremia (Dun- can et al., 2002) and RP (Aleman et al., 2001). MP absorption spectra measured by HFP (and colour matching) have been validated against those obtained from in vitro studies (Ruddock, 1965, Hammond et al., 2005).

72 The MP data presented in this thesis is from the measurements taken using the MPS 9000 device (also know as Macular Pigment Optical Densitometer, M|POD, and Quan- tifEye) which is based on HFP. The algorithm of this device is described in the following methods section.

3 Development of methods

The majority of the psychophysical tests employed in the studies presented in this thesis (such as VA and CS) are well known and have been described extensively in previous literature. However, two tests designed by our lab and adapted speciﬁcally for the following experiments are new to the literature and merit further discussion. These are MP and dark adaptation measurement. As discussed earlier, photopic visual function (e.g. VA) remains well preserved in normal ageing and in early AMD (Haegerstrom-Portnoy et al., 1999). On the contrary, scotopic vision is highly sensitive to retinal ageing and disfunction (Jackson and Owsley, 2000). The majority of MP supplementation studies have focused on familiar clinical measures such as VA and CS. Given that rod degeneration predates cone degeneration, the emphasis of this thesis has been placed on dark adaptation recovery which is abnormal, both, in healthy ageing (Jackson et al., 1999, Patryas et al., 2013) and retinal disease (Owsley et al., 2000). In this section relevant background and preliminary results are presented which were thought superﬂuous to include in a journal manuscript but are nonetheless included here for completeness.

3.1 Dark adaptation

3.1.1 Introduction

The recovery of rods following a bleach is dependent on the number of photoreceptors present and rhodopsin regeneration rate (Neelam et al., 2009). The recovery of the visual pigment is affected by the kinetics of the visual cycle (see Section 3.1.1.3), in particular 11-cis retinal transportation from the RPE (Lamb and Pugh, 2004). The cone recovery is dependent on the bleach duration and intensity. Factors such as bleach magnitude and stimulus parameters also affect the time course and shape of the dark adaptation curve, now discussed. Hecht’s family of curves (illustrated in Figure 3.1) demonstrate the effect of bleaching intensity, size of the test stimulus, and retinal eccentricity on dark adaptation kinetics.

73 A low pre-adapting light intensity (263 trolands) results in a virtually absent cone phase, whereas large bleaches (>20,000 trolands) shift the curves rightwards indicating increased length of time to reach the RCB and RRB and the absolute threshold. Smaller stimulus sizes (2°) presented foveally produce curves pertaining to the cone phase only and with increasing size (≥5°) both cone and rod phases are visible. Simi- larly, a 2° stimulus presented centrally (0°) yields a rod-absent dark adaptation curve and with increasing retinal eccentricity, where anatomically both cones and rods are present, a typical biphasic curve is obtained.

6 o 4 0 0 , 0 0 0 b 2 a o 8 3 8 , 9 0 0 3 1 9 , 5 0 0 5 o 3 , 8 0 0 o 1 0

) 2 6 3 2 0 o s

t 4

r 6 e

m 4 a l i

l 2 l i

m 2 o

r 0 1 0 2 0 3 0 4 0 0 1 0 2 0 3 0 4 0 c i o 0 R I m o 8 ( 2 . 5 R I I c o d Y y 5

t o

i G 1 0 s 4 V

n W

e 6 t n i

g o

L 2 4

0 1 0 2 0 3 0 4 0 0 1 0 2 0 3 0 4 0 T i m e ( m i n u t e s )

Figure 3.1: Factors affecting dark adaptation kinetics. The effect of bleach magnitude in trolands (a), stimulus size in degrees for centrally fixated fields (b) and retinal eccentricity for a 2° field (c). Plotted using data from Hecht et al. (1935), Hecht (1937) and Hecht et al. (1937). (d) Effect of stimulus wavelength on dark adaptation kinetics. RI=extreme red (680 nm), RII=red (635 nm), Y=yellow (573 nm), G=green (520 nm), V=violet (485 nm) and W=white. Plotted using data from Bartlett (1965). See main text for description.

The wavelength of the test stimulus also aﬀects dark adaptation kinetics as illustrated in Figure 3.1, panel d. The two photoreceptor cells have diﬀerent absorption spectra, therefore using stimuli composed of long wavelength light (red), where the rod and cone spectral sensitivities overlap, results in the absence of the RCB. Shorter wavelengths (yellow through to violet) yield a typical biphasic function with a prominent RCB as rods are more sensitive to these wavelengths (Wyszecki and Stiles, 1982).

74 Lamb and Pugh (2004) demonstrated that the S2 regions of rod-mediated recovery share a common slope irrespective of bleach magnitude, provided that the bleach is greater than 20% as illustrated in Figure 3.2. The curves are progressively shifted rightwards with increasing bleach intensities indicating increased time to reach absolute threshold.

5 9 8 %

n 8 6 % o i

t 4 6 3 % a 3 9 % v

e 2 2 % l

e 3 8 %

l 4 %

o 2 % h 2

s 0 . 5 % e r h t

1 g o L 0

0 5 1 0 1 5 2 0 2 5 3 0 T i m e ( m i n u t e s )

Figure 3.2: Dark adaptation recovery following exposure to bleaches (4.7 - 7.6 log scotopic trolands) which were estimated to bleach between 0.5 and 98% of the rhodopsin. The straight lines indicate a common negative slope of ≈ 0.24 log cd.m-2.min-1 termed S2. Plotted using data from Lamb and Pugh (2004).

The slope of the S2 component is independent of the bleach intensity and for a normal observer estimated to be around 0.24 log cd.m-2.min-1 which corresponds to a time log (e) constant of 1 natural log unit per 1.8 minutes (= 10 , where e = 2.718) or 110 ψS2 seconds (s) (Lamb and Pugh, 2004). Furthermore, although the absolute rod threshold is reported to vary with retinal location, the time constants of the rod recovery remain similar (Hecht et al., 1935, Alexander and Fishman, 1984). For small bleaches (<20%) the S2 component recovery is exponential (follows first-order kinetics), however for larger bleaches the recovery is rate-limited. The limiting factor lies in the delivery of 11-cis retinal to the rod outer segments. The rate-limited recovery also applies to human cone photoreceptors. The time course of rhodopsin regeneration extracted from the dark adaptation curve agrees with the data obtained from reflection densitometry and electroretinogram a-wave (Lamb and Pugh, 2004). The S3 component of the final rod-mediated recovery is usually seen following larger bleaches (>20%), has a time constant of approximately 400 s, and is dependent on bleach intensity.

75 Lamb and Pugh (2004) concluded that the slow recovery following a bleach is due to a rate-limiting step in the delivery of 11-cis retinal to opsin which may be barrier- dependent in a normal eye, and enzyme-dependent in certain eye diseases. The removal of opsin (i.e. recombination with 11-cis retinal) is responsible for the recovery of scotopic threshold and rod circulating current, and regeneration of visual pigment in rods and cones. The investigators proposed a mathematical model, referred to as the Mahroo, Lamb and Pugh model which can accurately predict the kinetics of dark adaptation following exposure to bright light.

3.1.1.1 Retinal disease and dark adaptation

RP is a genetically inherited retinal disease which results in abnormal dark adaptation. Subjects with RP have elevated rod thresholds and prolonged rod recovery which is unrelated to the magnitude of rod sensitivity loss (Alexander and Fishman, 1984, Moore et al., 1992). Subjects with early AMD also display abnormalities in dark adaptation (illustrated in Figure 3.3) which aﬀect all parameters (Campbell and Rittler, 1969, Brown and Kitchin, 1983, Brown et al., 1986, Eisner et al., 1991, Jackson et al., 1998, 1999, Owsley et al., 2001, Jackson et al., 2006, Owsley et al., 2006, Dimitrov et al., 2008) but are more pronounced in the rod phase (Steinmetz et al., 1993, Owsley et al., 2000). This is consistent with histopathological data which show a preferential loss of rod photoreceptors, particularly in the parafoveal retina in early AMD (Curcio et al., 1996, 2000, Curcio, 2001, Jackson et al., 2002). Slowed rhodopsin regeneration as a result of age-related changes in Bruch’s membrane and RPE, which impede transport of vital nutrients to the rod outer segment, could account for this impairment in rod recovery (Owsley et al., 2000).

76 1

A g e - m a t c h e d n o r m a l s 2 A M D g r o u p y t i 3 v i t i s

n 4 e s

g 5 o L 6

7 0 2 0 4 0 6 0 8 0 T i m e ( m i n u t e s )

Figure 3.3: Dark adaptation curves for 20 subjects with early AMD compared with age-matched normals. Plotted using data from Makridaki (2010).

Several investigators have suggested that dark adaptometry is a valuable tool in di- agnosing early ARM in absence of retinal signs (Eisner et al., 1991, Lamb and Pugh, 2004). Dark adaptation abnormalities have also been reported in subjects who are at high risk of developing AMD (Eisner et al., 1987).

3.1.1.2 General health and dark adaptation

Vitamin A deficiency in healthy individuals associated with poor diet is nowadays relatively rare. Usually, there are other causative factors such as gastrointestinal disease or chronic liver disease which may stem from alcohol abuse, jaundice, hepatitis or biliary cirrhosis. Zinc deficiency in cases of alcohol abuse has also been reported (Vallee et al., 1957, Sullivan and Heaney, 1970) probably as a result of malnutrition and increased urinary excretion. Chronic alcoholics, without liver disease, also exhibit combined vitamin A and zinc deficiencies (McClain et al., 1979, Russell, 1980). Low vitamin A and zinc status can affect taste and smell sensation in patients with alcoholic liver disease contributing to poor appetite (Russell, 1980), further compounding the deficiency. In the human eye, retinol (vitamin A alcohol) is converted to the photochemically active substance retinal (vitamin A aldehyde) which is essential for rhodopsin synthesis. This conversion is zinc-dependent and involves the zinc metalloenzyme alcohol dehydrogenase (McClain et al., 1979). Patients with a low zinc status are likely to have a depressed level of this essential enzyme leading to abnormal dark adaptation. In their

77 work on laboratory rats, Huber and Gershoff (1975) found that the retina is much more sensitive to the depressed activity of alcohol dehydrogenase than the liver. Zinc and vitamin A deficiency have also been found to negatively affect synthesis and se- cretion of the retinol binding proteins from the liver, respectively (Morrison et al., 1978). One such protein identified in the human retina, the interphotoreceptor binding protein, is thought to be involved in the transport of retinol across the RPE-photoreceptor interface, rhodopsin regeneration and photoreceptor survival (Noy, 2000). Thus, both vitamin A and zinc play important roles in the visual cycle, and their deficiency is known to attenuate the speed of dark adaptation recovery and rhodopsin regeneration (Lamb and Pugh, 2004) leading to reduced scotopic sensitivity (night blindness) (Oomen, 1974). Dark adaptation impairment has been demonstrated in subjects with liver disease and appears to be accentuated in cases of alcoholic liver disease (Abbott-Johnson et al., 2010). This is due not only to vitamin A deficiency but also a low zinc status (Morrison et al., 1978). Psychophysically, a delay in the RCB, elevation in the final rod and cone thresholds, and reduced rate of rod recovery have been demonstrated in subjects with alcoholic liver disease. These people are usually unaware of their visual function deficits (Patek and Haig, 1939, Abbott-Johnson et al., 2010), which may pose a risk to their safety especially whilst night driving. In high doses, ethanol, a straight chain alcohol, competes with retinol for the enzyme alcohol dehydrogenase preventing the formation of retinal. This results in poor rod dark adaptation and night blindness. Russell (1980) found that a blood ethanol concentration of 250 mg/dl was sufficient to elevate the sensitivity threshold in one of his subjects. In the same study he also found a twofold increase in urinary zinc excretion (with a concurrent serum zinc reduction) during ethanol consumption as compared to the non-drinking period. Supplementation with vitamin A and zinc has been shown to return dark adaptation in liver disease and alcoholic patients towards normal values (Patek and Haig, 1939, Morrison et al., 1978, Russell, 1980, Abbott-Johnson et al., 2010). Specifically, zinc supplementation enhances the activity of the enzyme retinal dehydrogenase resulting in improved dark adaptation performance (Morrison et al., 1978, Russell, 1980). These beneficial effects become evident anywhere from minutes to months post supplementation depending on the supplement, vehicle of delivery and dose, and persist after cessation. In one study improved dark adaptation following vitamin A supplementation was maintained, in some cases, up to 9 months after discontinuation of therapy (Patek and Haig, 1939). It must be noted, however, that several studies failed to show the link between zinc supplementation or plasma zinc level and dark adaptation

78 (Weismann et al., 1979, Abbott-Johnson et al., 2010).

3.1.1.3 Photochemistry

Retinal rods undergo two important processes: phototransduction which is the conversion of incoming light into an electrical signal, and the retinoid (or visual) cycle which involves recycling of the photo-reactants along a biochemical pathway in order to re- generate rhodopsin (Jackson et al., 1999). It has been hypothesized that the geriatric scotopic sensitivity loss and prolonged dark adaptation may be the result of age-related changes in the biochemical pathway which underpin the visual cycle (Jackson et al., 1999). Phototransduction

Phototransduction takes place in the outer segments of the retinal photoreceptors. The rod photopigment, rhodopsin, belongs to the family of the G-protein linked receptors (GPLRs) which are responsible for intracellular signal transduction via two main path- ways: the cyclic adenosine monophosphate (cAMP) pathway and the phosphatidylinos- itol pathway. Rhodopsin is composed of opsin (a large GPLR) bound to a chromophore retinaldehyde (aka retinal or 11-cis retinal) via a Schiﬀ base forming a retinylidene protein. Opsin is manufactured within the rod inner segment (Deretic and Papermaster, 1993) whilst retinal is delivered to the outer segment discs via the interphotoreceptor binding protein originating from the interphotoreceptor matrix (Jones et al., 1989). Under scotopic conditions, the high levels of cyclic guanosine monophosphate (cGMP) facilitates a steady inward sodium current (referred to as dark current) through the open cGMP-gated sodium (Na+) channels. The dark current maintains a depolarized state of the rod photoreceptor cell at about -40 millivolts and keeps the voltage-gated calcium (Ca2+) channels open. Increased concentration of Ca2+ causes continuous neurotransmitter (glutamate) release into the synaptic cleft. In the rods, the bound chromophore (11-cis retinal) acts as an antagonist rendering rhodopsin inactive. Upon light absorption, 11-cis retinal is photoisomerized to all-trans (its active form) triggering a conformational change in the opsin GPLR. This leads to a shift in rhodopsin (now termed metarhodopsin II or M2) absorption spectrum to the UV range rendering the pigment colourless, or bleached (Lamb and Pugh, 2004). The conformational change also leads to the activation of the G-protein cascade (summarized in Figure 3.4) which depletes the levels of cGMP (a cytoplasmic messenger) causing closure of cGMP-gated sodium channels and hyperpolarization of the photoreceptor cell to about -70 millivolts.

79 Hyperpolarization causes closure of the voltage-gated Ca2+ channels which reduces the calcium concentration and consequently the glutamate release which depolarizes the rod On centre bipolar cells and signals the arrival of photons.

Figure 3.4: Molecular steps in the G-protein cascade of phototransduction. 1. Photon (hv) absorption causes a conformational change of rhodopsin (R) in the disk membrane to active rhodopsin (R*). 2. G-protein (G) is composed of α (Transducin), β and γ subunits. R* repeatedly contacts transducin catalyzing the exchange of GDP for GPT (which expels its β and γ subunits) resulting in Gα-GTP (G*). 3. Two G*s bind to the inhibitory γ subunits of the phosphodiesterase (PDE) activating its α and β subunits. 4. Activated PDE hydrolyzes the cyclic GMP (cGMP or cG). 5. Guanylyl cyclase (GC) synthesizes cGMP. Reduced concentration of cGMP results in closure of the cyclic nucleotide gated channels (CNGC) blocking inﬂux of sodium (Na+) and calcium (Ca2+), leading to reduced circulating electrical current and hyperpolarization. Adapted from Leskov et al. (2000).

The photoactivated rhodopsin (M2) is then inactivated through a series of reactions (see visual cycle below) before it is restored to its original functional state. Low calcium levels during phototransduction cause separation of recoverin (a calcium binding protein) and rhodopsin kinase. Rhodopsin kinase mediates phosphorylation of M2 (reducing its aﬃnity for transducin) and subsequent binding of arrestin to the phosphorylated metarhodopsin II rendering it completely inactive (Lamb and Pugh, 2004). These steps, although essential to deactivation of M2, do not directly aﬀect the recycling of the retinoid. The visual cycle

Retinoid(s) is a collective term for the metabolically active molecules arising from the dietary vitamin A. A speciﬁc retinoid which is vital to visual function is 11-cis retinal and its isomers. The retinoid cycle is composed of several biochemical processes which facilitate continual photoreceptor response to light. It involves vitamin A metabolism and retinoid traﬃcking between the photoreceptors and the RPE via the interphotoreceptor binding protein.

80 Photons captured by the 11-cis retinal chromophore within a photopigment molecule (rhodopsin) induce a photoisomeric change to its all-trans retinal form (Figure 3.5 (1)). This triggers a conformational change of rhodopsin into its active form (M2 or R*) which activates the G-protein cascade of phototransduction described above. The visual cycle, through a series of reactions, serves to restore the isomerized retinoid to its 11-cis form to recombine with opsin. The non-covalently bound all-trans is enzymatically reduced to all-trans retinol (Figure 3.5 (2)) which is transported to the RPE across the interphotoreceptor matrix (IPM) via the interphotoreceptor binding protein (IRBP) (Figure 3.5 (3)). There the cellular retinal-binding protein (CRBP-I) delivers all-trans retinol to lecithin-retinol acyltransferase (LRAT) for esterification leading to formation of all-trans retinyl esters (Sporn, 1994) (Figure 3.5 (4)). Equally, all-trans retinol (a form of vitamin A) bound to serum retinol-binding protein (RBP), enters the eye from the blood circulation via the RPE and may be esterified to all-trans retinyl esters by LRAT (Figure 3.5 (5)). The formation of 11-cis retinol may be catalyzed by isomerohydrolase enzyme (hydrolase) or retinyl ester hydrolase (REH) and all-trans retinol isomerase (isomerase) (Figure 3.5 (6)). 11-cis retinol may then be esterified by LRAT to form 11-cis retinyl esters and stored (Saari and Bredberg, 1988) (Figure 3.5 (7)) or oxidized by 11-cis retinol dehydrogenase (11cROLDH) to 11-cis retinal (Lion et al., 1975) (Figure 3.5 (8)). The cellular retinaldehyde binding protein (CRALBP) enhances the isomerase/hydrolase and the 11-cis ROLDH reactions. This protein along with interphotoreceptor binding protein and CRBP protect and chaperone the retinoid across the aqueous phase between membranes. 11-cis retinal then diffuses across the extracellular space via interphotoreceptor binding protein to the rod outer segment where it binds to opsin to form new and functional rhodopsin (Figure 3.5 (9)). It is believed that the retinoid delivery to the rod outer segment disc membranes across the outer segment cytoplasm is un-chaperoned, thus forming a barrier to diffusion (Lamb and Pugh, 2004).

81 5

8 6 4

7 3 10

9 1 2

Figure 3.5: Steps comprising the visual cycle. See main text for description. Adapted from Noy (2000).

The opsin cycle illustrated in Figure 3.5 (10) is additional to the main visual cycle. It is unidirectional because binding of the retinoid to the opsin protein is non-reversible. In normal human vision, all-trans retinyl ester is stored in the RPE and can readily be synthesized into 11-cis retinal, thus the recycling of retinoid released by the bleach appears to be unnecessary for the regeneration of visual pigment (Lamb and Pugh, 2004).

3.1.2 Study apparatus

The dark adaptation set up described in this thesis is based on that previously designed and used by our lab to investigate the eﬀect of MP augmentation on visual function (including dark adaptation) in subjects with early AMD (Makridaki, 2010). The initial test parameters such as testing location (11°) and stimulus wavelength (white light) were selected based on these preliminary studies. All dark adaptation data presented in the following experimental chapters were obtained using a calibrated and gamma-corrected high-resolution cathode ray tube (CRT) monitor (Sony GDM-F500R, Tokyo, Japan). In order to asses monitor accuracy, a range of monitor luminance values (1 - 70 cd.m-²) were veriﬁed against a PR1500 spot photometer (Photo Research, Calif. USA) and plotted as open circles in Figure 3.6 (a). Figure 3.6 (a) illustrates a linear relationship between the monitor output and the measured luminance. The semi-silvered mirror

82 was also calibrated to assess the level of luminance reduction. The measured values were plotted as open triangles in 3.6 and showed a 50% reduction in luminance. Since all dark adaptation thresholds were measured with the semi-silvered mirror in place, these were adjusted accordingly, as described later. Monitor stability was assessed by measuring luminances over a period of two hours from the moment the monitor was turned on. The results, presented graphically in Figure 3.6 (b), showed that the monitor required to be turned on for at least one hour before data collection in order to achieve luminance stability. Dark adaptation was run using the Visual Psychophysics Engine, written by Neil Parry, in conjunction with a VSG 2/5 card (Cambridge Research Systems, Rochester, UK). The CRT’s luminance output was modified using neutral density (ND) filters (Schott AG, Mainz, Germany) which were placed in front of the monitor and carefully calibrated to expose 5 log units of recovery, as illustrated in Figure 3.7. A similar technique was used by Dimitrov et al. (2008), however in their set up the observer was required to wear the ND filters mounted in goggles.

a 8 0 b 8 ) 2 m / W i t h o u t m i r r o r d

c W i t h m i r r o r ( 6 0 6 e c n a n i 4 0

m 4 u L

e 2 0 r u

s 2 a e

M 0 0 2 0 4 0 6 0 8 0 0 2 0 4 0 6 0 8 0 1 0 0 1 2 0 S e t L u m i n a n c e ( c d / m 2 ) T i m e ( m i n u t e s )

Figure 3.6: Monitor and semi-silvered mirror calibration. (a) Measured luminances for a range of preset luminance values without (circles) and with (triangles) the mirror. The black line is a line of unity. The dotted line represents 50% reduction in luminance. (b) Measured monitor luminance as a function of time for a preset luminance of 2 cd.m-². The monitor luminance stabilized at approximately 60 minutes.

3.1.3 Calculating the percentage bleach

It is important to know the percentage of rhodopsin bleached as this aﬀects many of the dark adaptation parameters. Additionally, in a clinical trial monitoring changes

83 over time in response to an intervention, it is vital to be able to achieve the exact same level of bleach across study visits. The percentage of rhodopsin bleached by a ﬂash can be measured using retinal densitometry (Rushton et al., 1955) or inferred from Rushton’s (1956) formula below provided that the ﬂash duration is less than 40 seconds:

h 1 i 0 log (log p0 ) = log (It ) - 7.3 where p0 is the percentage of unbleached pigment, t0 is the time in seconds and intensity (I) in scotopic trolands of the flash. Table 3.1 shows the expected percentage of rhodopsin bleached for a given pupil diameter and flash intensity setting on an electronic flash gun (Nikon Speedlight SB-800, Tokyo, Japan). In the following chapters, the percent bleach was obtained from the table below. For each participant the pupil of the test eye was recorded before data collection, whilst the flash setting remained constant.

Table 3.1: Expected percentage bleach for a given pupil diameter (mm) and ﬂash setting. Courtesy of Jeremiah Kelly.

Pupil 2 2.5 3 3.5 4 4.5 5 5.5 6 6.5 7 7.5 8

diameter

Flash setting

1/128 0.5 0.9 1.2 1.7 2.2 2.7 3.4 4.0 4.8 5.6 6.5 7.4 8.4

1/64 1.4 2.2 3.1 4.2 5.4 6.8 8.4 10.0 11.4 13.7 15.8 17.9 20.0

1/32 3.1 4.7 6.8 9.1 11.7 14.6 17.7 20.9 24.4 28.0 31.7 35.4 39.2

1/16 6.3 9.7 13.7 18.1 23.0 28.1 33.5 38.9 44.4 49.8 55.0 60.0 64.8

1/8 12.5 18.8 26.0 33.6 41.4 49.1 56.6 63.6 69.9 75.6 80.5 84.7 88.2

1/4 23.7 34.4 45.5 56.3 66.1 74.5 81.5 87.0 91.2 94.2 96.3 97.8 98.7

1/2 41.9 57.2 70.6 81.1 88.6 93.6 96.7 98.4 99.3 99.7 99.9 100.0 100.0

1/1 66.4 81.8 91.4 96.5 98.7 99.6 99.9 100.0 100.0 100.0 100.0 100.0 100.0

3.1.4 Procedure

All subjects were dark adapted for 5 minutes followed by a practice session for a further 5 minutes. Natural pupils were used throughout (unless stated, see Chapters 6, 7 and 8). A 1° circular test spot (1931 CIE x=0.31, y=0.316) temporally modulated at 1 Hertz (Hz) was presented at 5 or 11° in the inferior ﬁeld and viewed from 90 cm. The subject’s head was positioned in a chin/head rest. Subjects were asked to ﬁxate a red

84 cross (0.3°) throughout the entire test duration and were frequently reminded during the experiment to maintain their ﬁxation.

Figure 3.7: The experimental set up. A 1.2 ND filter covered the entire screen. An additional 2.4 ND filter covered the stimulus at position 2. A mask with four apertures the size of the stimulus and fixation cross covered the entire screen. The area to be tested was accurately bleached by aligning the bleaching flash (reflected in the semi- silvered) with the stimulus (seen through the semi-silvered mirror).

An electronic 0.9 ms ﬂash of white light was used to produce an estimated 30-98% bleach in the area to be tested (see Table 3.1). The large range in the percentage bleach is due to the variation in pupil size among the participants (pupils were not dilated). The ﬂash and the bleach area were precisely aligned through the use of a calibrated (see Figure 3.6) semi-silvered mirror placed at a 45° angle to the plane of the eye. An adjustment of 0.3 log units was made to all thresholds reported in the following chapters to compensate for the mirror (see Section 3.1.5). Monocular thresholds (the lowest light intensities of the stimulus just detectable by the subject) were measured immediately after bleaching and were set at approximately twice per minute using the method of adjustment. This was performed by the experimenter who adjusted the intensity (up and down in steps of 0.1 log units) until the subject could barely see the stimulus. Data were collected for a duration of 30 minutes. During all experiments the unstimulated eye wore a patch and the room was adequately ventilated to prevent loss of scotopic sensitivity as a result of oxygen deprivation (McFarland and Evans, 1939)

85 3.1.5 Correcting data for pre-retinal absorption

To distinguish between neuronal and non-neuronal structures (e.g. pupil and the lens) as causative factors for sensitivity loss in normal ageing (for data presented in Chapters 4 and 5), the rod thresholds were adjusted. Senile miosis is reported to elevate thresholds by 0.05 log units per decade (Birren and Shock, 1950, Pulos, 1989, Sturr et al., 1997). Consequently, based on the work of Sturr et al. (1997) the individual rod data presented in Chapters 4 and 5 were adjusted by 0.05 log units per decade for all subjects above the average age of the younger group according to the following equation 0.05/10 x (age - 26). The average correction for the older and younger groups (Chapter 4) was 0.16 ±0.03 log units and 0.02 ±0.02 log units, respectively. This procedure was not used to correct the data presented in Chapters 6, 7 and 8 since pupil dilation was employed to counter the effects of senile miosis. The published lens data from Pokorny et al. (1987) was used to compensate for the threshold-raising effect of increased lens density with age for data presented in Chapters 4 and 5. Since the stimulus was composed of a broad-band white light source (1931 CIE x=0.31, y=0.316), the lens density was first averaged across all wavelengths (400 nm - 650 nm) for each decade. The relative spectral power distribution of illuminant C is non-uniform across the visible spectrum, therefore the Pokorny values were adjusted to account for this and matched to rhodopsin absorbance spectrum as illustrated in Figure 3.8, panel c for a 60-year-old observer. For the data presented in Chapter 4, the average lens correction for the younger group was 0.20 ±0.02 log units and for the older group 0.32 ±0.05 log units. This procedure was not used for data in Chapters 6, 7 and 8 since all observers were 50 years old or above.

86 a - 3 . 0 b c U n c o r r e c t e d C o r r e c t e d I l l u m i n a n t C r ) 2 e 1 5 0 L e n s d e n s i t y a d j u s t e d m - 3 . 5 w /

o R h o d o p s i n a b s o r p t i o n w e i g h t e d d p c

( l

a d - 4 . 0 r l

t 1 0 0 o c h

e s p e - 4 . 5 s r

e h

t 5 0 v

i t g a

o - 5 . 0 l L e

R 0 - 5 . 5 0 2 0 4 0 6 0 8 0 0 2 0 4 0 6 0 8 0 4 0 0 4 5 0 5 0 0 5 5 0 6 0 0 6 5 0 A g e ( y e a r s ) W a v e l e n g t h ( n m )

Figure 3.8: Individual dark adapted rod thresholds as a function of age. (a) Thresholds before pre-retinal absorption correction and (b) thresholds after correction. Each point is a mean of two sessions. The lines represent linear regressions ﬁtted to the data. Panel c - Example of correcting threshold for a 60-year-old for lens density using illuminant C as a light source and weighting for rhodopsin absorption.

3.1.6 Modeling of dark adaptation data

Classically the recovery process has been described as biphasic (Hecht, 1937, Dowling, 1960, Rushton, 1961, Alpern et al., 1970) which can be modeled by a double exponential function. However, more precisely it appears to be triphasic as demonstrated by Lamb (1981) and can be described by a single-exponential, two-linear model. A typical dark adaptation curve fitted with a single-exponential, two-linear model (McGwin et al., 1999) is shown in Figure 3.9 and is produced by plotting log threshold (cd.m-²) as a function of time. There are two distinct regions of the dark adaptation curve, each one subserved by either the rods or the cones. The first component is mediated by the cones and is much faster (has a shorter time constant) than the rods, reaching a plateau at around 10 minutes. The point at which the rod sensitivity surpasses the cone sensitivity is termed RCB. This point is affected by both photoreceptor classes, therefore, when studying rod dynamics only the events after the RCB should be considered (Jackson et al., 1999) as these are mediated by the rods. The rod recovery phase is considerably slower than that of the cones, and forms the second region of the dark adaptation curve. This region can be further subdivided into two components: second (S2 in Figure 3.9) and third component (S3 in Figure 3.9) each with a rate of recovery dictated by the rate of rhodopsin regeneration (Rushton et al., 1955, Lamb, 1981). The second knee point occurs at the transition point between the S2 and S3 components as is referred to as the RRB.

87 1

) 2

- 0 C o n e t i m e c o n s t a n t m . d

c - 1 R o d - C o n e b r e a k g o l

( - 2 S 2 d l C o n e t h r e s h o l d o T h - 3 3 0 s e r

h - 4 T R o d - R o d b r e a k S 3 - 5 0 5 1 0 1 5 2 0 2 5 3 0 3 5 T i m e ( m i n u t e s )

Figure 3.9: Typical dark adaptation curve for a young, healthy observer (LP) measured at 11° eccentricity, using a 1° white light stimulus following an 82% bleach. The data points were ﬁtted with the McGwin model. The summed squared error is 0.3, r² = 0.9, the cone threshold is -1.42 log units, the slope of S2 is 0.22 log cd.m-2.min-1 (time constant of 1.97 minutes). The slope of S3 is 0.05 log cd.m-2.min-1, RCB = 8.79 minutes, RRB = 18.79 minutes. T30 is -4.2 log units.

Parameters such as time constants of the second and third components of the dark adaptation curve are associated with the visual cycle (Leibrock et al., 1998) and can be modeled mathematically. Mathematical models can be conducted on a batch basis allowing rapid analysis of data in large sample studies (McGwin et al., 1999). A few models have been proposed starting with Hecht’s classic double exponential component model (Hecht, 1937, Dimitrov et al., 2008), followed by the four-linear- component model (Lamb, 1981, Leibrock et al., 1998, Jackson et al., 1999), and a single-exponential (corresponding to the cone-mediated region of the curve), two-linear- component (corresponding to second and third component of the rod-mediated curve) model as described by McGwin et al. (1999); hereafter referred to as the McGwin model. This model provides multiple parameters of the dark adaptation curve according to the following formula:

y = ((Ct + k ∗ exp (-S1exp ∗ x))+(S2 ∗ ([max (x - α, 0)])))+(S3a ∗ ([max (x - β, 0)]))

where y is the log sensitivity as a function of time (x), Ct is the cone threshold (in log units), k is a constant, S1exp is the exponent which determines the slope of the cone- mediated component (smaller exponent values yield shallower slopes and vice versa), α is the RCB (in minutes), S2 is the slope of the second rod-mediated component

88 -2 -1 (log cd.m .min ), S3a added to S2 determines the slope of the third rod-mediated component S3 (log cd.m-2.min-1), and β is the RRB (in minutes). The McGwin model is accurate at predicting the data obtained from younger and older (healthy) observers, minimizes experimenter bias and lends itself to large-scale studies (McGwin et al., 1999). Furthermore, it has been used successfully in studies investigating dark adaptation kinetics in AMD subjects (Jackson et al., 2002, Owsley et al., 2000, 2001, 2007, Berendschot et al., 2011). The McGwin model was implemented in Matlab and used to analyse, on a batch basis, all dark adaptation data presented in the following experimental chapters.

3.2 Factors aﬀecting dark adaptation parameters: experimental data

Before any collection of data took place, the factors aﬀecting dark adaptation kinetics were examined in order to arrive at the most appropriate test conditions with regards to the following:

• Bleach intensity and frequency

• Pupil dilation

• Flash position

• Stimulus size and location

The following experiments were designed to investigate the stability of the dark adaptation curve, particularly the S2 parameter. The results were compared with those already published to assess the validity of our methodology. This ensured that appropriate test conditions were employed throughout the studies presented in this thesis. The protocol for testing dark adaptation in diseased eyes was developed by our lab for a previous supplementation study (Makridaki, 2010). This was modiﬁed and tested on two healthy individuals aged 29 and 59 years of age.

3.2.1 Bleach intensity

The following experiment replicated the work of Lamb and Pugh (2004) on dark adaptation kinetics. All experimental conditions were held constant apart from ﬂash intensity which produced bleaches ranging from 16 to 100%. As seen in Figure 3.10 (a), the recovery of the S2 component (parallel black lines) was independent of the percent

89 bleach. With the exception of the smallest bleach (16%), the slopes of S2 component did not differ substantially. The overall shape of the curve, however, changed since the bleach affected other curve parameters. Most noticeably, the rightward shift of the curve with larger bleaches indicated a prolonged cone phase, and delayed RCB and RRB points. The vertical shift demonstrated higher thresholds as larger bleaches take longer to recover from. The bottom trace in Figure 3.10 (a) could not be analyzed by the McGwin model because of the insufficient cone phase resulting from a small bleach. c

a b d ) e s

1 0 0 % t

n 1 . 0 0 a

L P i 9 6 % 1 5 L P r L P e m

( L a m b a n d P u g h

L a m b a n d P u g h n

8 1 % e y D i m i t r o v

r D i m i t r o v 0 . 8 - 1 5 5 % g e e d r 3 2 % v l

o o n

1 6 % i

c 1 0 h 0 . 6 s e s

- 2 r p

e r o n h d o t i

r 0 . 4 g h e

- 3 r t o

i 5 f r L o c

0 . 2 n o t o - 4 i e t c m a i 0 . 0

0 r T - 5 F 0 1 0 2 0 3 0 0 . 0 0 . 2 0 . 4 0 . 6 0 . 8 1 . 0 0 5 1 0 1 5 2 0 T i m e ( m i n u t e s ) F r a c t i o n b l e a c h e d T i m e ( m i n u t e s )

Figure 3.10: Dark adaptation curves for a young, healthy observer (LP) using a 1° stimulus following a range of bleaches (16 - 100%). (a) The parallel lines represent the constant rate of recovery of the S2 component across bleaches. The McGwin model was ﬁtted to each curve (except for the bottom trace) and returned highly similar S2 values (100% = 0.25, 96% = 0.23, 81% = 0.26, 55% = 0.26, 32% = 0.26 log cd.m-2.min-1). Average summed squared error = 0.28 ±0.06, all r² values above 0.9. The horizontal dashed line is the arbitrary criterion (-2.5 log units) used to plot the graph in panel b. (b) Linear relationship between fraction bleached (above 20%) and the rate of S2 recovery. (c) The rate-limited behaviour of rhodopsin regeneration as estimated from the dark adaptation curve. Transformed from panel b (see main text for explanation). The smooth curves in panels b and c represent the Mahroo, Lamb and Pugh model.

Figure 3.10 (panels b and c) demonstrates the rate-limited behaviour of the S2 component. For larger bleaches (above 20%) there was a linear relationship between the rate of recovery to criterion level and percent bleach (expressed as a fraction). The graph of rhodopsin regeneration rate (shown in panel c) was derived from panel b. The data was subtracted from that corresponding to a 100% bleach and the axes were reversed according to the work of Lamb and Pugh (2004). Figure 3.10 (c) demonstrates that rhodopsin regeneration does not recover exponen- tially, instead it recovers along a straight line up to ∼75%. The data from this experiment compared favourably with two previous studies (Lamb and Pugh, 2004, Dim- itrov et al., 2008) using psychophysical methods and also with the data obtained from ERG a-wave and retinal densitometry (not shown) (Lamb and Pugh, 2004). The CRT

90 method, presented in Dimitrov et al. (2008), has been validated against the conventional Goldmann-Weekers adaptometer.

3.2.2 Bleach frequency

The effect of repeated bleach was examined in order to distinguish between those changes that are due to neural mechanisms from those due to the possible cumulative effects of repeated bleach. Additionally, if for any reason the bleach was unsuccessful during a study visit (for instance, due to poor fixation), instead of asking the participant to return for another visit, it was thought helpful to establish the exact amount of time required to be able to bleach again within one testing session. In this experiment, the subject was initially dark adapted for 20 minutes before the onset of the first bleach. Thresholds were measured for 80 minutes, separated by three bleaches at 20 minute intervals. Figure 3.11 shows dark adaptation curves after repeated bleaching for two normal observers. All curve parameters were highly similar (see Table 3.2) suggesting that the recovery, even from a large bleach in healthy younger and older individuals, is not affected by successive bleaching.

a 1 b I J M L P F l a s h 1 0 F l a s h 2 d

l F l a s h 3

o F l a s h 4 h

s - 1 e r

h t - 2 g o L - 3

- 4 0 5 1 0 1 5 2 0 0 5 1 0 1 5 2 0 T i m e ( m i n u t e s )

Figure 3.11: Dark adaptation curves for a 1° stimulus following repeated bleach for an older observer IJM (panel a) and a younger observer LP (panel b). The bleaching (91%) was repeated every 20 minutes. All curve parameters were found to be highly similar conﬁrming that there is no cumulative eﬀect of bleach.

91 Table 3.2: Dark adaptation curve parameters for data presented in Figure 3.11. DA = dark adaptation, SSE = summed squared error, thld = threshold.

DA Cone thld RCB RRB S2 Model SSE Model r²

-2 -1 parameter (log units) (mins) (mins) (log cd.m .min )

IJM

Flash 1 -1.61 8.22 17.10 0.19 0.13 0.99

Flash 2 -1.60 6.81 16.84 0.17 0.11 0.99

Flash 3 -1.50 7.44 15.00 0.24 0.11 0.99

Flash 4 -1.36 5.33 15.33 0.20 0.32 0.99

Mean -1.52 6.95 16.07 0.20 0.18 0.99

SD 0.12 1.22 1.06 0.03 0.10 0.0

Flash 1 -1.41 7.92 15.00 0.23 0.06 0.99

Flash 2 -1.40 7.32 15.00 0.22 0.09 0.99

Flash 3 -1.40 7.94 15.00 0.25 0.10 0.99

Flash 4 -1.22 7.09 15.00 0.24 0.09 0.99

Mean -1.36 7.57 15.00 0.24 0.09 0.99

SD 0.09 0.43 0.00 0.01 0.02 0.0

3.2.3 Pupil dilation

Pupil dilation in studies involving human subjects can be perceived as an invasive procedure which can result in blurred vision for a considerable amount of time after the test and hinders the subject’s driving and overall visual performance. Therefore, the necessity of pupil dilation to examine scotopic function was investigated in the following experiment. Figure 3.12 shows the effect of pupil dilation on dark adaptation curve for an experienced observer. Left panel shows data for an undilated pupil and the right panel for a dilated pupil. Curve parameters are summarized in Table 3.3. The variability and larger SD values produced with a natural pupil resulted from inconsistent bleaching due to pupil diameter fluctuation. A dilated pupil (8mm) gave much more consistent results and lower SD values for all dark adaptation parameters. Therefore, when examining the effect of an intervention on the entirety of the dark adaptation curve (see Chapter 8), pupil dilation is necessary to limit potential changes resulting from experimental error.

92 1 b a L P U n d i l a t e d L P D i l a t e d F l a s h 1 F l a s h 1 0 F l a s h 2 F l a s h 2 F l a s h 3 F l a s h 3

d F l a s h 4 F l a s h 4 l

o - 1 h s e r

h - 2 t

g o

L - 3

- 4

0 5 1 0 1 5 2 0 0 5 1 0 1 5 2 0 T i m e ( m i n u t e s )

Figure 3.12: Dark adaptation curves for undilated (a) and dilated (b) pupils for a young, healthy observer (LP) and a 1° stimulus. The curves do not match exactly when natural pupils are used producing higher parameter variability.

Table 3.3: Dark adaptation curve parameters for data presented in Figure 3.12. DA = dark adaptation, SSE = summed squared error, thld = threshold.

DA Cone thld RCB RRB S2 Model SSE Model r²

-2 -1 parameter (log units) (mins) (mins) (log cd.m .min )

Undilated

Flash 1 -1.30 9.71 18.68 0.21 0.13 0.99

Flash 2 -1.51 7.26 15.00 0.21 0.05 0.99

Flash 3 -1.33 7.46 15.00 0.26 0.19 0.99

Flash 4 -1.27 5.92 15.00 0.25 0.21 0.99

Mean -1.35 7.59 15.92 0.23 0.15 0.99

SD 0.11 1.57 1.84 0.03 0.07 0.0

Dilated

Flash 1 -1.41 7.92 15.00 0.23 0.06 0.99

Flash 2 -1.40 7.32 15.00 0.22 0.09 0.99

Flash 3 -1.40 7.94 15.00 0.25 0.10 0.99

Flash 4 -1.22 7.09 15.00 0.24 0.09 0.99

Mean -1.36 7.57 15.00 0.24 0.09 0.99

SD 0.09 0.43 0.00 0.01 0.02 0.0

93 3.2.4 Flash position

Due to different characteristics and individual needs of each participant, as well as the eye tested (i.e. right or left), it may not always be possible to place the semi-silvered mirror and flash in exactly the same position. As such, the effect of varying positions of experimental apparatus on dark adaptation was examined. Figure 3.13 (panel a) shows the effect of flash and mirror position for a 1° stimulus following an 80% bleach. The curve parameters for the three conditions (10, 20 and 30 cm) are summarized in Table 3.4. All the parameters (cone threshold, RCB, RRB, S2 and S3) were highly similar for the three conditions suggesting that the distance of the flash and mirror did not affect the dark adaptation curve for a 1° stimulus. The average S2 was 0.25 ±0.01 log cd.m-2.min-1 which agrees with previous studies (Jackson et al., 1999, Dimitrov et al., 2008, Lamb and Pugh, 2004). When re-tested using a 4° stimulus, the position of the flash and mirror did influence dark adaptation kinetics. Figure 3.13 (panel b) shows that for the 4° stimulus condition and flash/mirror position at 30 cm (filled circles) the recovery was considerably faster with a slope of S2 of 0.41 log cd.m-2.min-1. This is not neural in origin and has arisen as an artifact due to incorrect flash/mirror position for this stimulus size. At 30 cm the bleach area produced was not sufficiently large to cover the stimulus. This caused noticeable light spillage (particularly during the rod phase) which artificially increased the rate of recovery. Moving the flash/mirror closer (10 cm) to the eye (open circles), increased the area of the bleach, and returned the slope of S2 to 0.24 log cd.m-2.min-1. From this it was concluded that the bleach area (as governed by the position of the bleaching apparatus) must be considerably larger than the stimulus to give reliable and repeatable results.

94 a 1 b L P 1 d e g s t i m u l u s L P 4 d e g s t i m u l u s 0 1 0 c m 3 0 c m 2 0 c m 1 0 c m

d 3 0 c m l - 1 o h s e

r - 2

h t

g - 3 o L

- 4

- 5 0 5 1 0 1 5 2 0 2 5 3 0 0 5 1 0 1 5 2 0 2 5 3 0 T i m e ( m i n u t e s )

Figure 3.13: Dark adaptation curves for a young, healthy observer (LP) for 1° (a) and 4° (b) stimuli following an 80% bleach. The distance of the flash and mirror to the front surface of the eye was varied from 10 to 30 cm. In panel a, the three curves are very similar suggesting no effect on dark adaptation parameters as a result of flash/mirror position. In panel b, bleaching with the far position of the flash and mirror produced artificially steeper slopes of S2 (filled circles). See main text for explanation.

Table 3.4: Dark adaptation curve parameters for data presented in Figure 3.13 (panel a). DA = dark adaptation, SSE = summed squared error, thld = threshold.

DA Cone thld RCB RRB S2 Model SSE Model r²

-2 -1 parameter (log units) (mins) (mins) (log cd.m .min )

condition

10 cm -1.30 9.33 16.95 0.25 0.07 0.99

20 cm -1.50 8.30 15.00 0.24 0.15 0.99

30 cm -1.30 9.99 17.55 0.25 0.15 0.99

Mean -1.36 9.21 16.50 0.25 0.12 0.99

SD 0.10 0.85 1.33 0.01 0.05 0.0

3.2.5 Stimulus size in the periphery: rod spatial summation

The typical stimulus size reported in dark adaptation literature is around 1°. This size was also used in previous dark adaptation experiments from our lab (Berendschot et al., 2011). However, it was deemed appropriate to test a variety of stimulus sizes. Figure 3.14 (a) shows the eﬀect of stimulus size on dark adaptation kinetics. The S2 parameter remained constant, whilst the cone and rod thresholds progressively improved with

95 increasing stimulus size resulting in a downward shift of the dark adaptation curve. In panel b, the individual rod and cone thresholds were plotted against stimulus size (as log area) and ﬁtted with a bi-linear function in Matlab.

1 o 0 . 2 5 b a L P o 0 . 5 0 C o n e t h r e s h o l d s o 0 0 . 7 5 R o d t h r e s h o l d s 1 o 1 . 5 o d l - 1 2 o o o h 3

s o

e 4

r - 2

h t

g - 3 o L

- 4

- 5 0 5 1 0 1 5 2 0 2 5 3 0 - 1 0 1 T i m e ( m i n u t e s ) L o g a r e a ( d e g r e e s 2 )

Figure 3.14: Dark adaptation curves for a range of stimulus sizes (0.25 - 4°) for a young healthy observer (LP) following a 90% bleach (panel a) and log threshold plotted against stimulus size (log area) (panel b). The cone thresholds are those obtained from the McGwin model ﬁtted to the data in panel a, and the rod thresholds are the last recorded thresholds for the curves in panel a at 30 minutes after the bleach. S2 values (log cd.m-2.min-1) in panel a are highly similar (0.25° = 0.21, 0.5° = 0.22, 0.75° = 0.25, 1° = 0.22, 1.5° = 0.26, 2° = 0.24, 3° = 0.22, 4° = 0.20). With increasing size the cone and rod threshold improve resulting in a vertical shift. The inﬂection point (arrow) of the bi-linear function for rod thresholds (open circles) in panel b corresponds to Ricco’s area of complete summation.

For rod thresholds (open circles), the slope of the first part of the function was set to -1 in accordance with Ricco’s law which states that total areal summation occurs when the combined stimulus area and intensity remain constant (Schefrin et al., 1998). The shallower slope of the second part (-0.5) indicates partial summation. The inflection point (arrow) of this function corresponds to Ricco’s area and was found to occur at 0.5 log area (deg²) which is approximately 2° of angular subtense. The cone thresholds (filled circles) were fitted with a slope of -0.5 to the first limb, which yielded an inflection point at 0.34 log area (deg²) and a slope of -0.2 for the second limb. Given the results from this experiment, a 1° stimulus was chosen as the most appropriate for adequately isolating, both, cone and rod function during dark adaptation.

96 3.2.6 Stimulus location

Typically, dark adaptation is measured at 11° in the periphery, whereas the area most vulnerable to damage in ARM is reported to be between 2 and 5° of eccentricity (Beatty et al., 1999). This experiment was designed to examine the stability of the dark adaptation curve, particularly the S2 parameter, at two different locations: 11 and 5°. The results showed that the S2 parameter was unaffected by the location tested (see Figure 3.15). The remaining parameters, however, were affected as shown in Table 3.5. Most notably, at 5° the cone threshold was much lower compared to 11°, resulting in a downward shift of the curve. This reflects a greater number of cone photoreceptors at 5° of eccentricity.

Figure 3.15: Dark adaptation curves for a 1° stimulus presented at 11° (open circles) and 5° (open triangles) in the periphery, for a young, healthy observer (LP). The parallel lines represent a constant rate of recovery of the S2 component for both conditions.

Table 3.5: Dark adaptation curve parameters for data presented in Figure 3.15. DA = dark adaptation, SSE = summed squared error, thld = threshold.

DA Cone thld RCB RRB S2 Model SSE Model r²

-2 -1 parameter (log units) (mins) (mins) (log cd.m .min )

Stimulus location

5° -1.92 7.41 17.41 0.20 0.36 0.99

11° -1.26 9.96 21.05 0.21 0.64 0.99

Mean -1.59 8.67 19.23 0.21 0.50 0.99

SD 0.47 1.80 2.57 0.00 0.20 0.0

97 3.2.7 Summary

Based on the preliminary experiments presented above, the following test conditions were chosen as the most appropriate to study dark adaptation kinetics in ageing (pilot study) and in response to MP augmentation (main supplementation study):

• Flashgun and semi-silvered mirror placed at 15 cm from the front surface of the eye and subtending an angle of 20.9° wide by 13.3° high

• 1° test stimulus ﬂickering at 1 Hz presented at 11° in the periphery (pilot study: Chapters 4 and 5) and 5° in the periphery for the supplementation study in Chapters 6, 7 and 8 (see justiﬁcation below)

• Undilated pupils to study the eﬀect of ageing on the S2 parameter of dark adaptation (pilot study presented in Chapter 4)

• Dilated pupils to examine the eﬀect of MP enhancement on all dark adaptation parameters in older, healthy observers (Chapters 6, 7 and 8)

The clinical utility of the CRT dark adaptometry technique was tested on 33 subjects aged between 15 and 68 years old, with a mean (±SD) age of 41 (±18) (see Chapters 4 and 5). This pilot study allowed the experimenter (LP) to gain experience in participant recruitment, data collection, analysis and dissemination. It was an invaluable experience which helped enormously in the undertaking of the clinical trial to investigate the eﬀect of MP augmentation on visual function in normal ageing, from which the results are presented in Chapters 6, 7 and 8. Following the pilot study, it was decided to measure dark adaptation in the clinical trial at 5° of eccentricity. This was for several reasons. First, some of the older observers (in the pilot study) said that the test target seemed too far in the periphery and that this made the task diﬃcult. Second, the area most vulnerable to damage in ARM is typically quoted as 2-5° of eccentricity where the density of MP is typically low (Beatty et al., 1999). Additionally, dark adapted sensitivity loss in subjects with AMD is greatest in the parafoveal region (2–8°) (Curcio et al., 1993, Brown et al., 1986) in agreement with histopathology studies showing the greatest age-related rod density loss between 0.5 and 3 mm eccentricity (Curcio et al., 1993).

98 3.3 Macular pigment apparatus

3.3.1 Procedure

MPOD was measured using MPS 9000 (Elektron Technology Ltd, UK) which utilizes the principle of HFP. Subjects were sat comfortably with their test eye level with the instrument eyepiece. The device was adjusted to enable the subject to rest the eye against the eyepiece whilst maintaining a steady fixation on the test target. Normal blinking was advised throughout the entire procedure. Prior to the beginning of the test, all observers received verbal explanation of the test and underwent a brief period of practice. This was done at each visit. The subject was given a response button and told to press it every time they perceived flicker. Subjects were frequently reminded to maintain correct fixation, particularly during the peripheral part of the test. The stimulus (two LEDs flickering in counter-phase) was presented in a 1° circular target (used for the central measurement), whilst the peripheral measurement had two static red, 3° circular fixation targets placed either side of the central stimulus at 8° retinal eccentricity (see Figure 3.16 (A)). The stimulus was initially presented at 60 Hz, a frequency above the CFF for human observers. For each green-blue ratio, the flicker frequency was reduced at a rate of 6 Hz/s until the subject pressed a button to indicate perception of flicker, thus reaching the equiluminant point for the two lights (see Figure 3.16 (E)). This procedure was used in both the pre-test and the main test. The stimulus was surrounded by a white area (of similar luminance) subtending 30° and viewed from 20 cm through a + 5 dioptre lens. The surround luminance was maintained at 250 cd.m-² which minimized the effect of senile miosis and media opacities on retinal illuminance (Winn et al., 1994).

99 Figure 3.16: Comparison between conventional method of HFP and that employed by MPS 9000. (A) Schematic of the MPS 9000 testing screen. (B) The luminance of both LEDs is yoked in order to maintain a constant luminance across the green-blue ratios, (C) Luminance profiles at varying green-blue ratios superimposed on a white pedestal, (D) Traditional HFP method where green-blue ratios are adjusted to obtain minimal flicker using a pre-set flicker rate, (E) MPS 9000 method where flicker rate is reduced at a constant rate and the subject presses a button to indicate flicker perception. Adapted from van der Veen et al. (2009).

Before the main test, the device determined individual ﬂicker sensitivity (centrally and peripherally) in order to adjust the luminance contrast of the white light pedestal (maximum luminance 350 cd.m-²) on which blue (peak λ 465 nm) and green (peak λ 530 nm) LEDs were superimposed (see Figure 3.16 (B)). This ensured that responses started in the centre of the subject’s ﬂicker sensitivity range (∼30 Hz) with optimum minimum value at around 15 Hz. In the main test (central and peripheral), the initial green-blue luminance ratio (Lr) was set according to the observer’s age with the green LED luminance above the blue. The green-blue luminance ratio then changed in 0.2 dB steps. The whole procedure is summarized in Figure 3.17 and yields a V-shaped function plotted on a semi-logarithmic scale of temporal frequency (Hz) versus green-blue luminance ratio (dB).

100 Figure 3.17: Algorithm of MPS 9000. Re-drawn from van der Veen et al. (2009)

3.3.2 MPOD calculation

MPOD was automatically calculated by the device according to the equation published in van der Veen et al. (2009) which is based on the assumption that in the centre the only blue light absorbing pigment is the MP and in the periphery MP absorption is zero.

h Lbc i h Lbp i MPOD = k log10 Lgc - k log10 Lgp

h Lbc ih Lbp i = k log10 Lgc Lgp

assume Lgc = Lgp, then

h Lbc i = k log10 Lbp

which can be re-written as

MPOD = 0.24 x (Lrcentre - Lrperiphery) where Lbc and Lbp are the luminance of the blue light (Lb) at minimum ﬂicker for central (c) and peripheral (p) viewing, Lgc and Lgp are the luminance of the green light (Lg) at minimum ﬂicker for central and peripheral viewing, and k is a correction factor of 1.2. Lrcentre and Lrperiphery are the green-blue ratios (in dB) at isoluminant point for centre and periphery, respectively, and 0.24 is a correction factor.

101 3.3.3 Repeatability

A clinically significant change in MPOD over time can be determined by calculating the level of measurement noise within a device inferred from the coefficient of repeatability (CoR). The CoR gives 95% confidence limits for the amount of difference between two sets of data and is calculated as 1.96 times the standard deviation (SD) of the differences between two measurements. The CoR of MPS 9000 has been assessed in several studies with values ranging from 0.09 to 0.33 depending on the protocol used (van der Veen et al., 2009, Bartlett et al., 2010b, Bartlett and Eperjesi, 2010, Murray et al., 2011, Howells et al., 2013). It has previously been suggested that acceptance of low quality V-shaped functions increases measurement error (Murray et al., 2011), whilst assessing the curves during the procedure and repeating or removing suboptimal data decreases measurement error (Howells et al., 2013). The protocol used to determine MPOD values reported in this thesis was similar but slightly more stringent than the one presented by Howells et al. (2013). First, curves with poorly-defined minima were rejected and the measurement was repeated until acceptable. Second, MPOD measurement was repeated until the difference in readings was ≤0.05. The two values within 0.05 of each other were averaged and taken as the final MPOD for that visit. For both dark adaptation and MP measurement subjects wore their best optical correction (where necessary) for the test distance. Viewing was monocular and the unstimulated eye wore a patch.

3.4 Study design and protocol

3.4.1 Pilot study

3.4.1.1 Study design

A case-controlled study conducted at the University of Manchester (UoM), UK to 1) assess CRT dark adaptometry repeatability, 2) investigate the eﬀect of ageing on scotopic function, and 3) examine the potential relationship between MPOD and dark adaptation. The results of this study are presented in Chapters 4 and 5.

102 3.4.1.2 Study protocol

Synopsis

Short title: Dark adaptation and ageing Methodology: Monocentre, case-control study Number of participants: 33 (divided into older [≥45 years old] and younger group [<45 years old]) Principle investigators: Laura Patryas and Ian Murray Location: UoM, UK Planned schedule:

• Proposed initiation: 2nd January 2011

• Proposed completion: 2nd June 2011

3.4.1.3 Objectives

Primary objectives:

• Assessment of CRT dark adaptometry repeatability

• Assessment of ageing-related deﬁcits in dark adaptation recovery, particularly S2

Secondary objectives:

• Assessment of the relationship between MPOD and dark adaptation kinetics, particularly S2

3.4.1.4 Selection of study population

Self-selected recruitment was facilitated via advertisements posted on university notice boards within the UoM Optometry department and internal bulletin circulated via email to UoM staﬀ.

3.4.1.5 Inclusion criteria

• Ability to provide written informed consent

• Male or female aged from 15 to 90 years old

• VA at least 0.2 logMAR

103 3.4.1.6 Exclusion criteria

• Ocular disease: e.g. diabetic retinopathy, AMD or glaucoma

• Systemic disease: e.g. diabetes and liver disease (cases of controlled hypertension and hyperlipidemia, and history of heart disease were allowed)

• Significant cataract (grade >3) according to a Lens Opacities Classification Sys- tem III (Chylack et al., 1993) shown in Figure 3.18 (the grading procedure was slightly modified from the one presented by Chylack et al. since pupils were not dilated)

• Signiﬁcant macular disturbance (grade >1) according to a macular grading scale (Jackson et al., 1998) shown in Table 3.6

• VA worse than 0.2 logMAR

• Inability of participant to understand the study procedures and provide informed consent

• Use of ocular nutritional supplements 6 months prior to study commencement

• Use of systemic medications known to be retinotoxic

• Smoker

Figure 3.18: Lens opacities classiﬁcation system III (Chylack et al., 1993).

104 Table 3.6: Macular grading system. Stage number represents macular disturbance severity. Stage 0 and 1 are considered normal. Stage 2 is classiﬁed as ARM and beyond 2 is classiﬁed as AMD (Jackson et al., 1998).

3.4.1.7 Ethical conduct of the study

This study was conducted in compliance with the ethical principles that have their origins in the Declaration of Helsinki (2008). Ethical approval for the study was obtained from the UoM Committee on the Ethics of Research on Human Beings (reference 09169). All participant data was held strictly confidential and the research team conformed to the Data Protection Act of 1998 with respect to data collection, storage and destruction. Data was kept on a password-protected computer in a lockable room at the UoM. Only the research team had access to the data. In cases where the results were published in a thesis or a scientific journal, no identifiable characteristic of the participant was disclosed (e.g. name, date of birth). The participants were free to withdraw from the study at any time (Declaration of Helsinki), without any obligations.

3.4.1.8 Patient information sheet

Volunteers who contacted the investigator were given a verbal explanation of the nature of the research along with an information sheet. The sheet explained the rationale of the study, the procedures and time involved, any potential risk or beneﬁt to the participant, ethical considerations (e.g. conﬁdentiality, withdrawal), and investigator contact details. Volunteers were given one week to decide whether they wanted to participate in the study.

105 3.4.1.9 Study procedure and visit schedule

Study investigations were conducted as per the schedule shown in Table 3.7. Each visit lasted approximately 1.5 hours with comfort breaks. Participants were asked to return for a follow up visit, of the same length, at least one week after the initial visit.

Table 3.7: Pilot study ﬂow chart

3.4.1.10 Statistical analysis

This pilot study was the ﬁrst of its kind to investigate the relationship between MPOD and dark adaptation in a healthy population aged from 15 to 90 years old. We estimated that approximately 15 participants were required for each group (30 in total). Dark adaptation analysis was implemented in Matlab (Mathworks, MA, USA). Graph plotting was done in Origin@ (Northampton, MA, USA) and statistical analyses were performed using SPSS software (version 20; SPSS Inc., Chicago, IL, USA).

3.4.2 Supplementation study

3.4.2.1 Study design

A prospective one-centre, 12 month, randomized, double-blind, placebo-controlled carotenoid supplementation trial conducted at the UoM, UK. The study was registered as a clinical trial on the international database (ref. number NCT02147171, http://www.clinicaltrials.gov). The registered title of the study is: “Carotenoid supplementation and normal ocular health”. The study investigated the eﬀect of daily consumption of a multi-nutrient supplement (Visionace®) containing 20 mg of L for a period of 12 months on MPOD levels and visual performance in normal older observers. The result of this study are presented in Chapters 6, 7 and 8.

106 3.4.2.2 Study protocol

Synopsis

Short title: Carotenoid supplementation and normal ocular health Clinical phase: II Methodology: Monocentre, double-masked, randomized, parallel comparison versus placebo Test product: Visionace® (commercially available and manufactured by Vitabiotics) Reference product: Placebo (consisting of dibasic calcium phosphate, starch and cellulose and manufactured by Vitabiotics) Number of participants: 2 x 40 participants (40 active, 40 placebo) Principle investigators: Laura Patryas and Ian Murray Location: UoM, UK Planned schedule:

• Proposed initiation: 1st November 2011

• Proposed completion: 1st February 2013

3.4.2.3 Objectives

Primary objectives:

• Signiﬁcant increase in MPOD in the active group, no change in placebo group

• Signiﬁcant increase in serum L in the active group, no change in placebo group

Secondary objectives:

• Signiﬁcant improvement in visual performance (VA and dark adaptation) in the active group, no change in placebo group

3.4.2.4 Selection of study population

Self-selected recruitment of subjects was facilitated through an editorial in the Manch- ester Evening News and from poster and internet advertisements within the university campus.

107 3.4.2.5 Inclusion criteria

• Ability to provide written informed consent by the patient

• Male or female aged from 50 to 90 years old

• VA at least 0.2 logMAR

3.4.2.6 Exclusion criteria

• Ocular disease: e.g. diabetic retinopathy, AMD, glaucoma or IOP ≥ 22 mm Hg

• Systemic disease: e.g. diabetes and liver disease (cases of controlled hypertension and hyperlipidemia, and history of heart disease were allowed)

• Signiﬁcant cataract (grade >3) according to a Lens Opacities Classiﬁcation Sys- tem III (Chylack et al., 1993) shown in Figure 3.18

• Signiﬁcant macular disturbance (grade >1) according to a macular grading scale (Jackson et al., 1998) shown in Table 3.6

• VA worse than 0.2 logMAR

• Body mass index >40

• Known allergic/hypersensitivity to one of the components of the study supplements

• Inability of participant to understand the study procedures and give informed consent

• Non compliant patient (e.g. not willing to attend the follow-up visits, way of life interfering with compliance)

• Participation in another clinical study within the last 3 months

• Already included once in this study

• Use of ocular nutritional supplements 6 months prior to study commencement

• Use of systemic medications known to be retinotoxic

108 3.4.2.7 Ethical conduct of the study

This study was conducted in compliance with the ethical principles that have their origins in the Declaration of Helsinki (2008). Ethical approval for the study was obtained from the North Greater Manchester Research Ethics Committee (reference 11/NW/0425). All participant data was held strictly confidential and the research team conformed to the Data Protection Act of 1998 with respect to data collection, storage and destruction. Data was kept on a password-protected computer in a lockable room at the UoM. Only the research team had access to the data. In cases where the results were published in a thesis or a scientific journal, no identifiable characteristic of the participant was disclosed (e.g. name, date of birth). The participants were free to withdraw from the study at any time they choose (Declaration of Helsinki), without any obligations.

3.4.2.8 Patient information sheet

3.4.2.9 Study procedure and visit schedule

Study investigations were conducted as per the schedule shown in Table 3.8. Each visit lasted approximately 2 hours with comfort breaks. Subjects were assessed at baseline (0 months, visit 1), 6 months (visit 2) and 12 months (visit 3).

109 Table 3.8: Supplementation study ﬂow chart

3.4.2.10 Study formulation and randomisation

Visionace® is a commercially available product and is manufactured by Vitabiotics who are the industrial partner of this BBSRC CASE award. Information about Visionace® is available to the public on the following website: http://www.vitabiotics.com/visionace/. Visionace® does not contain any drugs or hor- mones and is produced in accordance with high pharmaceutical standards of quality control, beyond those normally required for food supplements. The supplements (active and placebo) were provided in identical white tubs with tamper evident seals and child resistant lids. The study label on each tub, shown in Figure 3.19, speciﬁed participant number, directions for use, researcher contact details and tablet expiry date. Each tub contained 90 tablets (90 days supply).

110 Figure 3.19: Study label.

3.4.2.11 Active and placebo tablet formulation

The active and placebo tablets were identical in size and appearance. The formulation of each is presented in Tables 3.9 and 3.10.

111 Table 3.9: Active tablet formulation. Ingredient Quantity per tablet

Vitamin A 300 μm

Natural Mixed Carotenoids 3 mg

Vitamin E 60 mg

Vitamin C 150 mg

Vitamin D3 25 μm

Vitamin B6 10 mg

Thiamin (Vitamin B1) 12 mg

Riboﬂavin (Vitamin B2) 4.8 mg

Vitamin B12 9 μm

Folacin (Folic Acid) 400 μm

Pantothenic Acid 20 mg

Niacin (Vitamin B3) 18 mg

Zinc 15 mg

Iron 6 mg

Copper 1 mg

Magnesium 50 mg

Manganese 4 mg

Selenium 150 μm

Chromium 50 μm

Iodine 100 μm

Citrus Bioﬂavonoids 60% 15 mg

Bilberry Extract 60 mg

Lutein esters (from Marigold) 20 mg

Excipients

Microcrystalline Cellulose 100 mg

Dibasic Calcium Phosphate 90 mg

Crosslinked Cellulose Gum 35 mg

Crospovidone 30 mg

Polyvinylpyrrolidone 16 mg

Magnesium Stearate 15 mg

Pregelatinised Starch 15 mg

Stearic Acid 12 mg

Colloidal Anhydrous Silica 10 mg

Potato Starch 10 mg

Ethyl Cellulose 4.5 mg

Titanium Dioxide 2 mg

Tablet Coating

Instamoist Shield IC-MS-5950 13.5 mg

Wincoat WT-2022, White 20.2 mg

Opadry Yellow Powder (20A32137) 27 mg

112 Table 3.10: Placebo tablet formulation. Excipients Quantity per tablet

Dibasic Calcium Phosphate 950 mg

Potato Starch 215 mg

Polyvinylpyrrolidone 10 mg

Microcrystalline Cellulose 20 mg

Crosslinked Cellulose Gum 30 mg

Crospovidone 3 mg

Magnesium Stearate 2 mg

Pregelatinised Starch 5 mg

Stearic Acid 0.5 mg

Colloidal Anhydrous Silica 5 mg

Ethyl Cellulose 2 mg

Titanium Dioxide 1 mg

Tablet Coating

Puriﬁed Talc 15 mg

Instamoist Shield IC-MS-5950 15 mg

Wincoat WT-2022, White 20 mg

Opadry Yellow Powder (20A32137) 31 mg

3.4.2.12 Dispensation and randomisation

Each participant was allocated four tubs (360 tablets) with two tubs dispensed at the end of each visit and the remainder stored by the investigator in a dark, cool room. Each set of four tubs was assigned a number (1-90) using a random number generator by the supplement manufacturer. The randomisation code remained with the manufacturer until all data had been collected and analysed.

3.4.2.13 Directions for use

Subjects were instructed to take one tablet daily with a glass of water or a cold drink and taken with a main meal on a full stomach. Subjects were also advised not to chew the tablet or exceed the recommended intake.

3.4.2.14 Compliance

Compliance was assessed by unused tablet count, computed as a percentage (number of tablets consumed divided by the number that should have been consumed). Addi- tionally, subjects received regular phone calls.

113 3.4.2.15 Sample size calculation

Sample size calculations, performed in G*Power 3.0.10, were based on a series of RM ANOVAS which were used to analyse the results of the study. Values were derived from previous work from this lab (Makridaki, 2010) which involved subjects of similar age group (but with early AMD) and similar methodologies. This work has subsequently been published (Berendschot et al., 2011, Murray et al., 2013). In G*Power, the input parameters were: eﬀect size f (calculated according to Cohen (1988)), α error probability (0.05), power (90-95%), number of groups (2), number of measurements (3), correlation among repeated measures (0.5) and nonsphericity correction e (1). Serum lutein

Typical values are 200 – 250nM/L in normal non-supplemented individuals. Standard deviations across observers are of the order of 40nM/L. A power analysis for this variable is not required as such large eﬀects are readily detected in a single individual. Macular Pigment

From previous work, the change in MPOD in the active group was 0.16 ±0.20, whereas the change in the placebo group was 0.03 ±0.19 (Berendschot et al., 2011), yielding an eﬀect size (Cohen’s f ) of 0.34. Based on these values, the calculated sample size was 24 participants in each group to achieve 95% power . Visual Acuity

From previous work, the change in VA in the active group (in subjects with VA worse than 0.06 logMAR) was 0.07 ±0.11, whereas the placebo group slightly deteriorated by 0.01 ±0.12 (Murray et al., 2013), yielding an eﬀect size of 0.26. Based on these values, the calculated sample size was 34 participants in each group to achieve 90% power. Dark Adaptation

From previous work, the change in the slope of S2 in the active group was 0.03 ±0.07, whereas the placebo group slightly deteriorated by 0.01 ±0.07 (Berendschot et al., 2011), yielding an eﬀect size of 0.28. Based on these values, the calculated sample size was 36 participants in each group to achieve 95% power. Based on the above calculations a minimum of 80 participants were required to allow two groups of 36 observers with a 10% drop-out rate over the course of the study.

114 3.4.2.16 Statistical analysis

The statistical software SPSS version 20 (SPSS Inc., Chicago, IL) was used for data analysis. Kolmogorov-Smirnov tests with Lilliefors significance correction were used to check the data for normality of distribution. Normally distributed and not normally distributed data were analysed using parametric and nonparametric tests, respectively. Baseline measurements including demographic data were analyzed for differences between the two groups (active versus placebo) using independent t-tests and Mann- Whitney U tests. Anthropometric and lifestyle data were assessed for differences between visit 1 (baseline) and visit 3 (final) using paired t-tests. A two-way, mixed design repeated measures analysis of variance (RM ANOVA) was used to analyse all the normally distributed data to identify primary outcome effects of MPOD, VA and dark adaptation. The data were stratified by GROUP (placebo or active) and VISIT (n = 3). This parametric procedure was also used to analyse data which did not follow normal distribution as it is robust against mild violations of assumptions when the sample size is large (Ghasemi and Zahediasl, 2012). Where appropriate, the Greenhouse-Geisser correction for violation of sphericity was used. Individual active and placebo data sets were additionally analysed using one-way ANOVA (parametric and nonparametric [Friedman’s]). Means and ±SD are presented in text and tables. The 5% level of significance was used throughout the analysis, with adjustment for multiple testing.

3.4.2.17 Study protocol alterations

All the procedures were carried out according to the original protocol set out in this section and as assessed and approved by the ethics committee. However, we were unable to carry out blood L analysis for a variety of reasons including lack of funding and the possibility of hindering recruitment due to the invasive nature of sample collection.

115 4 Assessment of age changes and repeatability for computer based rod dark adaptation

Contributions

I designed this study in collaboration with my supervisors and co-authors. I was solely responsible for participant recruitment and data collection. I also analyzed the data with useful guidance from my supervisors and Dr Daniel Baker, who is the author of the Matlab script used to analyze the dark adaptation data. I wrote the manuscript with helpful comments from my supervisors and co-authors. Publications

Patryas L., Parry N.R.P., Carden D., Baker D.H., Kelly J.M., Aslam T., Murray I.J., 2013. Assessment of age changes and repeatability for computer-based rod dark adaptation. Graefes Arch Clin Exp Ophthalmol, 251(7):1821-7. Conference presentations

European Conference on Visual Perception, August 2011, Toulouse, France (poster). British Congress of Optometry and Vision Science, September 2011, Aston University, Birmingham (talk). Manchester Life Sciences PhD conference, May 2012, University of Manchester, Manch- ester (talk). Faculty of Life Sciences Research Symposium, September 2012, University of Manch- ester, Manchester (poster) Manchester Optometry Meeting, May 2013, Postgraduate Medical Centre, Manchester Royal Inﬁrmary (talk).

Acknowledgements

Supported by BBSRC grant (BB/F017227/1) and Vitabiotics CASE award. NRP and TA’s involvement was facilitated by the Manchester Biomedical Research Centre and the Greater Manchester Comprehensive Local Research Network. DHB was supported by EPSRC grant (EP/H000038/1).

116 4.1 Abstract

4.1.1 Purpose

To characterize the rate of rod-mediated sensitivity decline with age using a PC-driven cathode ray tube (CRT) monitor. To provide data regarding the repeatability of the technique.

4.1.2 Methods

Dark adaptation was monitored for 30 minutes following a minimum 30% pigment bleach, using a white 1° stimulus (modulated at 1 Hz), presented 11° below ﬁxation on a CRT monitor. 33 subjects with no ocular pathology and normal fundus photographs were divided into two groups: older (≥45, n = 16) and younger (<45, n = 17).

4.1.3 Results

Rod recovery was assessed using component S2 of dark adaptation. S2 was significantly slower in the older (0.19 ±0.03 log cd.m-2.min-1) compared with the younger group (0.23 ±0.03 log cd.m-2.min-1, t = -4.05, p < 0.0003), despite no difference in visual acuity and fundus appearance. Faster rates of S2 recovery were correlated with lower threshold at 30 minutes (T30) (r = -0.49). Correlation coefficients between first and second measurements for S2 and T30 were 0.49 (p < 0.009) and 0.84 (p < 0.0001), respectively. The coefficient of repeatability was 0.07 log cd.m-2.min-1 for S2 and 0.35 log cd.m-2 for

T30. The coeﬃcients of variation for S2 and T30 were 15% and 10%, respectively.

4.1.4 Conclusions

Dark adaptation is slowed in normal ageing. CRT-based dark adaptometry is easily implemented and highly repeatable. The technique described in this article would be useful for documenting visual changes in future clinical trials assessing retinal health in the older eye with and without ocular pathology.

117 4.2 Introduction

Dark adaptometry is considered a useful tool for investigating a variety of systemic and ocular diseases including vitamin A deficiency (Russell et al., 1973), liver disease (Abbott-Johnson et al., 2010), diabetes (Henson and North, 1979, Phipps, 2006), age- related macular degeneration (AMD) (Brown et al., 1986, Steinmetz et al., 1993, Owsley et al., 2000, 2001, 2007, Jackson et al., 2006), retinitis pigmentosa (Omar and Herse, 2004) and congenital stationary night blindness (Ruether et al., 1993). It has also been used to assess non-pathological mechanisms of ageing (Coile and Baker, 1992, Jackson et al., 1999). The term dark adaptation refers to the gradual recovery of visual sensitivity in total darkness following exposure to a bright light. The light bleaches the photoreceptor visual pigment resulting in its inactivation and a profound (∼5 log units) loss of sensitivity. Classically, the dark adaptation function has been described as biphasic and comprises an initial rapid phase served by the cones, followed by a slower phase served by the rods. In recent years, significant advances have been made in our understanding of the biological processes underpinning rod recovery (Lamb and Pugh, 2006). In terms of analysing and modeling dark adaptation data to obtain clinically useful parameters, the rod recovery can be partitioned into three partly overlapping components: S1, S2 and S3 (Lamb, 1981). Normally, S1 is obscured by cone recovery so that, in the standard dark adaptation curve, S2 is the first measurable sign of rod recovery. Slowed dark adaptation, particularly the rate of S2, is characteristic of ageing and AMD, and precedes retinal changes and cone-mediated visual function changes such as reduced visual acuity (VA) (Jackson et al., 1999, Owsley et al., 2001, 2000, Curcio et al., 2000, Dimitrov et al., 2008). Dark adaptometry is, therefore, likely to become the test of choice for investigating ageing and assessing efficacy of therapies and management strategies for early stage AMD. To that end, an inexpensive, readily available and repeatable technique for measuring dark adaptation kinetics will be essential if functional, as well as structural, features form part of the clinical outcomes. There have been many studies aimed at using dark adaptation to assess age-related ocular pathology, but few have provided data regarding the repeatability of the slope of S2. Accurate determination of this parameter’s repeatability is important because detecting small changes in the slope of S2 is of clinical significance. The problem of accurate determination of dark adaptation parameters is compounded by the fact that data obtained from elderly subjects, who may or may not have ocular pathology, are usually more variable than those produced by young, healthy individuals. Repeatability

118 and reliability of any technique will, therefore, be paramount to its applicability. Two recent studies employed cathode ray tube (CRT) technology to assess dark adaptation kinetics (Dimitrov et al., 2008, 2011). CRTs are ideally suited to, and used extensively in, visual psychophysics research. Their temporal and spatial characteristics are well documented and they are easily controlled by a computer. A major limitation of using computer monitors for dark adaptation, however, is that they have a limited dynamic range, but this problem can be avoided by the use of neutral density (ND) filters (Dimitrov et al., 2008). As far as we are aware, the coefficient of repeatability (CoR) for the rod parameters measured by CRT dark adaptometry has not yet been established. The CoR is important when evaluating the performance of an instrument that is used to detect clinically significant changes over the course of an intervention trial (Bland and Altman, 1986). In this study we use a customized version of commercially available software to investigate the ability of CRT-based dark adaptometry to quantify delays in rod-mediated recovery in ageing. We also provide data regarding the repeatability of this technique. We chose to focus on the measurement of the slope of S2 since rods are more vulnerable than cones in ageing and AMD (Owsley et al., 2000, Jackson et al., 2002, Owsley et al., 2001).

4.3 Methods

4.3.1 Subjects

Thirty three normal volunteers participated in this study and were divided into two groups. The older group (≥45 years old, age range 45-68, mean 57.44 ±7.98, n = 16) consisted of 8 males and 8 females. The younger group (<45 years old, age range 15-36, mean 25.12 ±6.08 n = 17) consisted of 10 males and 7 females. Younger subjects were primarily recruited from the University of Manchester under- graduate population and older subjects from university staﬀ. Informed consent was obtained. The tenets of the Declaration of Helsinki were followed. This study was approved by the University of Manchester Committee on the Ethics of Research on Human Beings. All subjects had recently had an eye examination (up to 12 months before recruitment), were free from any ocular disease (e.g. glaucoma, AMD, cataract) and were not taking nutritional supplements. Subjects with diabetes or liver disease, current smokers, and those using systemic medications known to be retinotoxic were excluded from the study.

119 On the day of testing all subjects underwent assessment of VA and dark adaptation. Fundus photographs were taken with a TRC-NW6S Non-Mydriatic Retinal Camera (Topcon, Tokyo, Japan). The VA was measured using an internally illuminated Early Treatment of Diabetic Retinopathy Study (ETDRS) chart (166.3 ±3.92 cd.m-2), and expressed as logarithm of the minimum angle of resolution (logMAR). The fundus photographs were processed using IMAGE Net 2000 software (Topcon, Tokyo, Japan) and viewed on a 20-inch monitor (1600 × 1200 pixels, 32 bits). The fundus images of all participants were graded by one of the authors (LP) according to a macular grading scale (Jackson et al., 1999). No subject had a grade beyond 1, thus all were classiﬁed as being normal.

4.3.2 Procedure

The stimuli were generated using a visual stimulus generator (VSG 2/5, Cambridge Re- search Systems, Rochester, UK) running Visual Psychophysics Engine software (Cam- bridge Research Systems, customized by NRP) and presented on a calibrated and gamma-corrected high-resolution CRT monitor (Sony GDM-F500R, Tokyo, Japan). A black cardboard mask with four apertures corresponding to the stimuli and fixation cross was placed over the monitor screen. One or more 1.2 log unit ND filters (#299; Lee Filters, Andover, UK) were placed in front of the test stimulus in the configuration illustrated in Figure 4.1.

120 Figure 4.1: The experimental set up. A mask with four apertures corresponding to the stimuli and fixation cross locations covered the entire screen. A 1.2 log unit ND filter was attached to the back of the mask at stimulus positions 1 and 2. When the filtered screen luminance fell below -2.3 log cd.m-2, the fixation cross and the stimulus were extinguished at position 1 and moved to position 2 where an additional smaller 2.4 log unit ND filter (attached to the back of the mask) exposed the remaining region of rod recovery. The retinal area to be tested was accurately bleached by aligning the flash with the stimulus (at position 1) through the use of a semi-silvered mirror.

The observer fixated a red cross (0.3°) at position 1 and responded to a 1° circular test spot (1931 CIE x = 0.31, y = 0.316), temporally modulated with a 1 Hz square wave and presented at 11° in the inferior field (a location typically used in the standard Goldmann-Weekers adaptometer). The dynamic range was sufficient (approximately 5.5 log units) to enable the measurement of the entire scotopic recovery function. A similar approach has been used previously with the filters mounted on goggles worn by the observer (Dimitrov et al., 2008, 2011). In our procedure, the expansion of the dynamic range by addition of further ND filters is fully automatic. In the absence of other visual cues (as the subject is in total darkness), the shift in location of the targets is rarely noticed. All subjects were dark adapted for 5 minutes followed by a practice session for a further 5 minutes which allowed the subject to become familiarized with the method of adjustment for setting thresholds. A localised 30-98% visual pigment bleach (Rushton and Powell, 1972) was then performed using an electronic 0.9 ms flash of white light (Nikon Speedlight SB-800, Tokyo, Japan). The flash intensity was 6.08 log cd.s.m-2 as mea-

121 sured using a PR1500 spot photometer (Photo Research, Burbank, Ca, USA). Since the rate of S2 is independent of the bleach magnitude provided the bleach is greater than 10-20% (Lamb and Pugh, 2006), we performed the measurements on natural pupils. The flashgun was positioned 15 cm from the eye and at this distance subtended an angle of 20.9° wide by 13.3° high. The flash and the bleach area were precisely aligned so that location of the test stimulus was centered on the bleached area of the retina. This was achieved by using a calibrated semi-silvered mirror, as illustrated in Figure 4.1, so that the subject observed the fixation mark when the flash was fired. An adjustment of 0.3 log units was made to all thresholds to compensate for the absorption characteristics of the mirror, which remained in place throughout the experiment. Thresholds (the lowest light intensities of the stimulus just detectable by the subject) were measured in complete darkness in a purpose-built room immediately after bleaching and were set at approximately twice per minute using the method of adjustment. This was performed by the experimenter who adjusted the intensity (up and down in steps of 0.1 log units) until the subject could barely see the stimulus. Data were collected for a duration of 30 minutes. The non-stimulated eye was patched during testing and the subjects wore their best optical correction for the test distance. The subject’s head was positioned in a chin/head rest. All participants repeated the dark adaptation measurement twice, separated by at least one week. The data presented hereafter are the means of two visits.

4.3.3 Data analysis

-2 Dark adaptation curves were plotted as log10 threshold in cd.m versus time in minutes. These were ﬁtted with a single exponential component to the cone phase and two linear components to the rod phase, as described by McGwin et al. (1999). The non- linear regression technique was implemented in Matlab (Mathworks, MA, USA) and yielded the following parameters of the dark adaptation curve: cone recovery rate, cone threshold, the rod-cone break (RCB), the slopes of the second (S2) and third (S3) rod components, the transition point between the two, and the threshold 30 minutes after the bleach (T30). Of these parameters, we were primarily interested in component S2 and T30. The latter was corrected for pre-retinal absorption (pupil diameter and media opacity) based on previous work (Birren and Shock, 1950, Pulos, 1989, Sturr et al., 1997, Pokorny et al., 1987). Kolmogorov-Smirnov tests were used to determine that the distributions of all dark adaptation parameters did not diﬀer from normal. Origin@ (Northampton, MA, USA) and Matlab were used for statistical analysis and graph plotting. Repeatability was

122 assessed using the standard correlation coefficient (Pearson’s r) and by calculating the CoR (1.96 multiplied by the standard deviation of the differences between test and retest data) and coefficients of variation (CoV, the ratio of the standard deviation to the mean multiplied by 100). Independent sample t-tests were used to make comparisons between groups (younger vs older, males vs females).

4.4 Results

4.4.1 Preliminary data

Figure 3.9 (a) depicts a classic dark adaptation function obtained with our CRT-based technique for a young, healthy observer (LP, one of the authors). An exponential- bilinear model partitioned the curve into three distinct phases of sensitivity recovery: a cone-mediated phase, followed by a rod-mediated phase divided into two linear regions. The two components of rod dark adaptation, S2 and S3, had negative slopes of 0.24 log cd.m-2.min-1 and 0.06 log cd.m-2.min-1, respectively.

Figure 4.2: Typical dark adaptation data measured with our CRT-based technique for a young, healthy observer (LP) measured inferiorly at 11° degrees eccentricity, using a 1° white light stimulus following an 82% bleach. The data points were ﬁtted with an exponential-bilinear model. S2 is the second rod component, S3 is the third rod component and T30 is the threshold 30 minutes after the bleach. Summed squared error, = 0.3, r2 = 0.9. b) Dark adaptation curves for the same observer using the same technique following a range of bleaches (16 – 100%). The parallel solid lines plot component S2 and demonstrate a constant rate of rod recovery across bleaches. The horizontal dashed line is an arbitrary criterion (-2.5 log units) used to plot the graph in panel c. c) Linear relationship between fraction bleached (above 20%) and the time required to reach a criterion recovery level for our data (LP) and those from previous studies.

The parallel lines in Figure 3.9 (b) plot component S2 for diﬀerent bleach intensities. There was no signiﬁcant correlation between bleach and slope of S2 (r = 0.65, p >

123 0.23) conﬁrming that this phase of rod recovery is independent of the bleach magnitude provided the bleach is greater than 10-20%. In Figure 3.9 (c), the time taken to reach an arbitrary threshold of -2.5 log units, extracted from Figure 3.9 (b), was re-plotted against the fraction bleached. The straight line ﬁt when plotted in semi-logarithmic co-ordinates for bleaches greater than 10-20% reveals the rate-limited behaviour of S2.

4.4.2 Repeatability

In order to quantify measurement error, repeated measurements were obtained on different days. The final 3 columns of Table 4.1 summarize correlation coefficients, CoRs and CoVs for the following parameters: RCB, S2, S3 and T30. Of these, we were primarily interested in S2 and T30. Correlation coefficients between first and second measurements for S2 and T30 were 0.49 (p < 0.009) and 0.84 (p < 0.0001), respectively. The average absolute change between sessions (dotted line in Figure 4.3) was -2 -1 -2 0.004 (±0.04) log cd.m .min for S2 and 0.05 (±0.23) log cd.m for T30 indicating only minimal bias. The CoR was 0.07 log cd.m-2.min-1 for S2 and 0.35 log cd.m-2 for

T30. The CoV was 15% for S2 and 10% for T30.

Table 4.1: Summary of statistical comparisons: older vs younger group and test-retest repeatability.

124 Figure 4.3: Test-retest diﬀerences versus means to assess the repeatability of dark adaptation curve parameters S2 (panel a) and T30 (panel b). The dotted line represents the bias (test-retest mean diﬀerences) and the dashed lines represent 95% limits of agreement.

4.4.3 Dark adaptation in older and younger eyes

VA in the test eye for all subjects was at least 0.2 logMAR and there was no diﬀerence in VA between the two groups (t = 1.00, p = 0.3). Figure 4 depicts rod dark adaptation kinetics (components S2 and S3), after the RCB, for the younger and the older group. Each subject’s curve was linearly shifted in x and y directions so that their individual RCBs were coincident. The group data were ﬁtted with a bilinear function. The older group (solid line, panel b) had a shallower slope of S2 compared with the younger group (dashed line) indicating slower rate of recovery. The vertical (upward) shift in the older group along the y-axis indicates threshold elevation across the entire rod-dominated region of sensitivity recovery.

125 Figure 4.4: Group data showing the S2 and S3 regions of rod recovery for younger (panel a) and older (panel b) subjects. A bilinear function was ﬁtted to each data set. The younger group model (dashed line) is superimposed onto the older group data in panel b to demonstrate slowing of the S2 region and elevated thresholds in the older group. Data have been shifted along the x and y axes so that the individual RCBs were coincident.

A summary of statistical comparisons between the two groups for RCB, S2, S3 and

T30 is presented in Table 4.1. The younger group had an average S2 of 0.23 ±0.03 log cd.m-2.min-1 with a time constant (τ = log10 (e) / S2) of 1.9 minutes. The older group was signiﬁcantly slower than the younger group (t = -4.05, p < 0.0003) with an average S2 of 0.19 ±0.03 log cd.m-2.min-1 (τ = 2.3 minutes). The negative correlation between S2 and age (r = -0.62, p < 0.0002) is shown in Figure 4.5 (a). The rate of recovery over the S2 region decreased 0.01 log units/min per decade. S2 was also correlated with T30 after corrections for media changes (r = -0.49), as illustrated in Figure 4.5 (b).

126 Figure 4.5: a) Scatter plot of S2 as a function of age. The line represents linear regression ﬁtted to the data (r = -0.62, p < 0.0002). b) Scatter plot with a line of best ﬁt illustrating negative correlation between T30 and S2 (r = -0.49). All data points are means of two sessions. Thresholds were corrected for lens density and pupil miosis.

Before pre retinal correction, T30 was elevated in the older group by 0.4 log units (t = 3.14, p < 0.004). However, after pre retinal correction the older group sustained a non-significant threshold elevation of 0.1 log units compared with the younger group (t = -0.48, p = 0.63). We did not observe any significant gender differences for S2 (t

= 0.28, p = 0.79) and T30 (t = 0.29, p = 0.77) in our cohort. As shown in Table 4.1, there was no signiﬁcant diﬀerence between the older and younger group for RCB (t = 0.23, p = 0.82) and S3 (t = -2.03, p = 0.05).

4.5 Discussion

The data presented in this paper demonstrate that our CRT-based dark adaptometry produces results that agree with previous studies (Lamb and Pugh, 2006, Dimitrov et al., 2008). The slowing of component S2 with increasing age found in this study was 0.01 log units/decade and reflects reduced rhodopsin regeneration rate, in agreement with other psychophysical (Jackson et al., 1999) and rod densitometry (Liem et al., 1991) data. The technique proved capable of differentiating between younger and older eyes (for the S2 parameter), despite no differences in VA and fundus appearance between the two groups. The power in this study to detect a difference between old and young eyes was 0.96 (calculated using G*Power 3.0.10). This is a good indicator of the ability of the technique to detect small changes in the slope of S2, either between two groups or in individuals in a longitudinal study.

127 Prolonged dark adaptation kinetics in older adults lead to difficulties with vision- oriented tasks in dim lighting and increase the risk of night-time falls and road traffic accidents. These problems have been confirmed in self-reporting surveys such as that described by Scilley et al. (2002) who used a questionnaire designed specifically for assessing low light visual problems. Difficulties arise, however, in establishing the exact contribution of impaired night vision to accidents because of the absence of a satis- factory test which can be used routinely under clinical conditions. It seems likely that those older observers with a healthy lifestyle and good nutrition can be expected to have relatively good scotopic recovery, but confirming such a hypothesis might be difficult because a technique for measuring the slope of S2 precisely is not generally available. The method described here could be used to quantitatively assess patient night vision with excellent reproducibility, enabling researchers to use scotopic recovery as a realistic outcome measure. The correlation we found between the rate of rod-mediated recovery (slope of S2) and

T30 is at odds with one previous study (Jackson and Owsley, 2000). In that previous study the absolute threshold was measured which may not be directly comparable to our measure of threshold after 30 minutes. Our correlation can be explained by geometry of the dark adaptation function and by the cellular model of recovery kinetics presented by Lamb and Pugh (2006). If the slope of S2 is steeper then the threshold at 30 minutes will be lower. Although the measurements were restricted to 30 minutes and some observers would have reached lower thresholds had the time been extended, it seems likely that the rate of S2 and T30 share the same cellular and molecular mechanisms (Lamb and Pugh, 2006). The CRT method has previously been compared with the conventional Goldmann- Weekers adaptometer (GWA) showing good agreement between the two methods on almost all parameters of the dark adaptation curve including cone recovery rate, RCB and S2 (Dimitrov et al., 2008). The general problem with the GWA is its poor repeatability for cone recovery. Gaffney et al. (2011) have shown a clinically unacceptable CoR for cone recovery time constant and concluded that the GWA would not be a useful instrument for documenting visual changes in future clinical trials. Christoforidis and Zhang (2011) also used GWA in a test-retest paradigm. They showed no learning effects and no statistically significant differences on repeated measures for any of the parameters of the scotopic recovery curve. Although their group mean S2 recovery rate of 0.15 log cd.m-2.min-1 is slower than ours and that typically reported in the literature for healthy subjects, their CoR for S2 of 0.06 log cd.m-2.min-1 is very similar to ours. Dimitrov et al. (2008) used a similar method to the one described in this article. How- ever, in that study, which also investigated AMD patients, the CoV for the rod param-

128 eters was not given. Their CoV for the RCB was 32% for normal and 44% for AMD subjects which is slightly higher than our CoV of 28%. Of note is the considerably larger CoV for RCB and S3 than for S2 and T30 in the present study. This could be due to the fact that, unlike S2, the RCB and S3 are largely dependent on the magnitude of the bleach (Lamb and Pugh, 2006) and highlights the importance of precise and uniform bleaching between visits for longitudinal clinical trials. In the present study we were able to accurately bleach the area to be tested by using a semi-silvered mirror. Although, we did not dilate the pupils because we were primarily interested in the slope of S2 (and used bleaches ≥30%) we would highly recommend pupil dilation in future clinical trials investigating multiple dark adaptation parameters. This is because dilation of pupils allows tighter control over the bleach. Finally, our technique readily elicited the third rod component (S3) unlike the protocol suggested by Dimitrov et al. (2008) using a single 2.6 log unit ND filter. Such a narrow range may pose problems in evaluating dark adaptation in ageing, particularly in subjects with good scotopic sensitivity due to its ceiling effect. In summary, the results of this study demonstrate the validity of using an easily implemented computer-based technique to explore scotopic sensitivity recovery in ageing using an automated and inexpensive method of expanding the luminance range with ND filters. The method is highly repeatable for the measurement of rod-mediated dark adaptation parameters (S2 and T30) without requiring pupil dilation. Because of its sound physiological basis, S2 is of particular interest. It seems likely that given its many advantages, dark adaptometry based on digital methods will become the method of choice for future work in assessing retinal health in the older eye with and without ocular pathology.

4.6 References

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129 Christoforidis, J. & Zhang, X., 2011. Learning eﬀect of dark adaptation among normal subjects. Graefe’s archive for clinical and experimental ophthalmology, 249(9), pp.1345–1352. Coile, D.C. & Baker, H.D., 1992. Foveal dark adaptation, photopigment regeneration, and aging. Visual Neuroscience, 8(1), pp.27–39. Curcio, C.A., Owsley, C. & Jackson, G.R., 2000. Spare the rods, save the cones in aging and age-related maculopathy. Investigative ophthalmology & visual science, 41(8), p.2015. Dimitrov, P.N. et al., 2008. Measuring rod and cone dynamics in age-related maculopathy. Investigative ophthalmology & visual science, 49(1), p.55. Dimitrov, P.N. et al., 2011. Visual function tests as potential biomarkers in age- related macular degeneration. Investigative Ophthalmology & Visual Science, 52(13), pp.9457–9469. Gaﬀney, A.J., Binns, A.M. & Margrain, T.H., 2011. The repeatability of the Goldmann- Weekers adaptometer for measuring cone adaptation. Documenta Ophthalmologica. Advances in Ophthalmology, 122(2), pp.71–75. Henson, D.B. & North, R.V., 1979. Dark adaptation in diabetes mellitus. The British Journal of Ophthalmology, 63(8), pp.539–541. Jackson, G.., Aleman, T.S. & Owsley, C., 2006. The Scotopic Sensitivity Tester-1 and the detection of early age-related macular degeneration. Ophthalmic & Physiological Optics: The Journal of the British College of Ophthalmic Opticians (Optometrists), 26(4), pp.431–437. Jackson, G.R. & Owsley, C., 2000. Scotopic sensitivity during adulthood. Vision Research, 40(18), pp.2467–2473. Jackson, G.R., Owsley, C. & Curcio, C.A., 2002. Photoreceptor degeneration and dysfunction in aging and age-related maculopathy. Ageing research reviews, 1(3), pp.381–396. Jackson, G.R., Owsley, C. & McGwinJr, G., 1999. Aging and dark adaptation. Vision research, 39(23), pp.3975–3982. Lamb, T.D., 1981. The involvement of rod photoreceptors in dark adaptation. Vision Research, 21(12), pp.1773–1782. Lamb, T.D. & Pugh, E.N., 2006. Phototransduction, dark adaptation, and rhodopsin regeneration the proctor lecture. Investigative ophthalmology & visual science, 47(12), p.5138.

130 Liem, A.T. et al., 1991. Rod densitometry in the aging human eye. Investigative ophthalmology & visual science, 32(10), p.2676. McGwin, G., Jackson, G.R. & Owsley, C., 1999. Using nonlinear regression to estimate parameters of dark adaptation. Behavior Research Methods, Instruments, & Computers: A Journal of the Psychonomic Society, Inc, 31(4), pp.712–717. Omar, R. & Herse, P., 2004. Quantification of dark adaptation dynamics in retinitis pigmentosa using non-linear regression analysis. Clinical & experimental optometry: journal of the Australian Optometrical Association, 87(6), pp.386–389. Owsley, C. et al., 2007. Cone- and rod-mediated dark adaptation impairment in age- related maculopathy. Ophthalmology, 114(9), pp.1728–1735. Owsley, C. et al., 2001. Delays in rod-mediated dark adaptation in early age-related maculopathy* 1. Ophthalmology, 108(7), pp.1196–1202. Owsley, C. et al., 2000. Psychophysical evidence for rod vulnerability in age-related macular degeneration. Investigative ophthalmology & visual science, 41(1), p.267. Phipps, J.A., 2006. Rod Photoreceptor Dysfunction in Diabetes: Activation, Deacti- vation, and Dark Adaptation. Investigative Ophthalmology & Visual Science, 47(7), pp.3187–3194. Pokorny, J., Smith, V.C. & Lutze, M., 1987. Aging of the human lens. Applied Optics, 26(8), p.1437. Pulos, E., 1989. Changes in rod sensitivity through adulthood. Investigative Ophthal- mology & Visual Science, 30(8), pp.1738–1742. Ruether, K., Apfelstedt-Sylla, E. & Zrenner, E., 1993. Clinical findings in patients with congenital stationary night blindness of the Schubert-Bornschein type. German journal of ophthalmology, 2(6), pp.429–435. Rushton, W.A.H. & Powell, D.S., 1972. The rhodopsin content and the visual threshold of human rods. Vision Research, 12(6), pp.1073–1081. Russell, R.M. et al., 1973. Dark-adaptation testing for diagnosis of subclinical vitamin- A deficiency and evaluation of therapy. The Lancet, 302(7839), pp.1161–1164. Scilley, K. et al., 2002. Early age-related maculopathy and self-reported visual difficulty in daily life. Ophthalmology, 109(7), pp.1235–1242. Steinmetz, R. et al., 1993. Symptomatic abnormalities of dark adaptation in patients with age-related Bruch’s membrane change. Br J Ophthalmol, 77(9), pp.549–54. Sturr, J.F. et al., 1997. Psychophysical Evidence for Losses in Rod Sensitivity in the Aging Visual System. Vision Research, 37(4), pp.475–481.

131 5 The association between dark adaptation and macular pigment optical density in healthy subjects

Contributions

I designed this study in collaboration with my supervisors and co-authors. I was solely responsible for participant recruitment and data collection. I also analysed the data with useful guidance from my supervisors and Dr. Daniel Baker, who is the author of the Matlab script used to analyse the dark adaptation data. I wrote the manuscript with helpful comments from my supervisors and co-authors. Publications

Patryas L., Parry N.R.P., Carden D., Aslam T., Murray I.J., 2014. The association between dark adaptation and macular pigment optical density in healthy subjects. Graefes Arch Clin Exp Ophthalmol, 252(4):657-63. Conference presentations

Acknowledgements

132 5.1 Abstract

5.1.1 Purpose

To investigate whether macular pigment optical density (MPOD) is related to dark adaptation in healthy subjects.

5.1.2 Methods

Dark adaptation was measured after a minimum 30% pigment bleach in 33 subjects (aged 15-68), using a white 1° stimulus presented 11° below fixation on a cathode ray tube monitor. The luminance range of the monitor was extended using neutral density filters. A heterochromatic flicker photometry based instrument (MPS 9000) was used to measure MPOD.

5.1.3 Results

The average MPOD for the whole group was 0.37 ±0.21 optical density units. Subjects with lighter irides had on average 40% lower MPOD compared to those with darker irides (0.3 ±0.20 vs 0.5 ±0.19). Group mean MPOD was weakly associated with second (r = 0.32, p = 0.07) and third rod-mediated recovery rates (r = 0.31, p = 0.08) and with the rod threshold (r = -0.24, p = 0.18) 30 minutes after the onset of bleach. MPOD was unrelated to cone time constant (r = -0.02, p = 0.91), cone threshold (r = -0.01, p = 0.96), rod-cone break (r = 0.13, p = 0.45) or the rod-rod break (r = 0.11, p = 0.52). The second rod-mediated recovery rate (S2) for the lower 10th percentile of MPOD (n = 4) was 0.18 log cd.m-2.min-1 and 0.24 log cd.m-2.min-1 for the upper 10th percentile (n = 4). The two groups were signiﬁcantly diﬀerent (t = 2.67, p = 0.037).

5.1.4 Conclusions

We report a statistically signiﬁcant diﬀerence between subjects falling in the 10th percentile extremes of MPOD and rod-mediated but not cone-mediated sensitivity recovery. Further investigation into the relationship between MPOD and rod function is warranted, particularly extending the work to encompass those with low MPOD and poor night vision.

133 5.2 Introduction

There is a growing body of evidence indicating the beneficial effects of the retinal carotenoids lutein (L), zeaxanthin (Z) and meso-zeaxanthin (MZ) on visual function (Loughman et al., 2007, 2010, Stringham and Hammond, 2007, 2008) and retinal integrity (Beatty et al., 1999, Seddon et al., 1994, Snodderly, 1995). L, Z and MZ are collectively referred to as macular pigment (MP). MP is a short-wavelength light filter and a powerful antioxidant, and is therefore believed to protect the photoreceptors and the retinal pigment epithelium (RPE) from oxidative damage (Davies and Morland, 2004, Snodderly, 1995). Several studies have investigated the effect of MP augmentation on visual performance in normal subjects and patients with age-related macular degeneration (AMD). In AMD, L supplementation has been shown to improve visual function (Berendschot et al., 2011, Murray et al., 2013, Richer, 1999, Richer et al., 2002, 2004). Studies investigating the effect of MP augmentation in normal, healthy eyes report conflicting results with some showing improvements in visual acuity (VA), contrast sensitivity, contrast acuity, glare disability and photostress recovery time (Kvansakul et al., 2006, Rodriguez-Carmona et al., 2006, Loughman et al., 2012, Stringham and Hammond, 2008) and others showing no effect on these visual function parameters (Bartlett and Eperjesi, 2008, Nolan et al., 2011). The bulk of research has focused on the effects of MP on central visual function as served by the cones and tested under photopic conditions. The relationship between MP and rod function has largely been ignored despite the fact that between 10 and 25% of the total retinal carotenoids are found in the membranes of rod outer segments (Rapp et al., 2000, Sommerburg et al., 1999). As has been suggested before, the presence of L and Z in the rod outer segment may be important for preserving scotopic retinal function (Hammond et al., 1998b). It may be argued that MP is unlikely to be linked to dark adaptation because it is concentrated in the central 6-8° and rods are more numerous in the periphery. However, this ignores two important points; first, L is present outside the central 6°, albeit at low concentrations, as indicated by van der Veen et al. (2009) and second, rods far outnumber cones in the central 6° of the normal retina (Curcio et al., 1990). The extreme retinal inhomogeneity of the human retina is important for the understanding of ageing and AMD; there is an equal number of rods and cones at the edge of the fovea and beyond this, rods increase in number with eccentricity so that there are around 9 times as many rods as cones in the macula (Curcio et al., 1990). Photoreceptor function can be assessed by measuring dark adaptation recovery, which has previously been used to investigate mechanisms of retinal ageing (Coile and Baker,

134 1992, Jackson et al., 1999, Patryas et al., 2013) and AMD (Brown et al., 1986, Jackson et al., 2002, Owsley et al., 2001, Steinmetz et al., 1993). One of the most clinically significant parameters of dark adaptation is the second component of rod-mediated recovery (Dimitrov et al., 2008). Numerous studies have shown this parameter, known as the S2 component (Lamb, 1981), to be significantly reduced in normal older versus younger eyes (Jackson et al., 1999, Patryas et al., 2013) and there are many studies showing rods (Curcio et al., 1996) and rod function (Dimitrov et al., 2008, Owsley et al., 2001, Steinmetz et al., 1993) to be abnormal in AMD. Given the increasing longevity of human populations and the self-reported difficulties in low light levels amongst older people (Haegerstrom-Portnoy et al., 1999), research into the mechanisms of photoreceptor degeneration in the normal older eye and in clinical cases, and its possible link with antioxidant supplementation could be of great clinical significance. As far as we are aware, only one previous study has compared MPOD and scotopic vision (Hammond et al., 1998b). Hammond et al. found that higher MPOD was correlated with improved sensitivity in their older subjects (Hammond et al., 1998b). However, in their study, only the absolute scotopic sensitivity was measured and not the rate of recovery. More recently, Berendschot et al. (2011) demonstrated a statistically significant improvement in the recovery rate in rod-mediated dark adaptation in a placebo-controlled study of the effects of L supplementation in 72 early AMD patients. This study raises the important question as to whether these functional benefits reflect an improvement in AMD patients only or whether they would apply to healthy older eyes. This distinction is very important and was one of the main reasons for conducting the present investigation. Given that dark adaptation is slowed in the normal older eye (Jackson et al., 1999, Patryas et al., 2013), it is timely to explore the possible reasons for this.

5.3 Methods

5.3.1 Subjects

Thirty three subjects participated in this study, aged between 15 and 68 years old, with a mean (±SD) age of 41 (±18). Sixteen of these observers were over 55. The group consisted of 18 males and 15 females. Subjects were recruited from the population of staﬀ and students at the University of Manchester. Informed consent was obtained from all participants and the tenets of the Declaration of Helsinki were followed. This study was approved by the University of Manchester Committee on the Ethics of Research on Human Beings.

135 All subjects were in good ocular and general health and potential participants with glaucoma, AMD, cataracts, diabetes and liver disease were excluded. Current smokers, those using systemic medications known to be retinotoxic and nutritional supplements were also excluded from the study.

5.3.2 Procedure

On the day of testing all subjects underwent assessment of VA, MPOD and dark adaptation. An internally illuminated Early Treatment of Diabetic Retinopathy Study (ET- DRS) chart was used to measure VA, expressed as logarithm of the minimum angle of resolution (logMAR). Fundus photographs were taken with a TRC-NW6S Non-Mydriatic Retinal Camera (Topcon, Tokyo, Japan) and graded according to the macular grading scale described in Jackson et al. (1999). No subject had a grade beyond 1, thus all were classified as being normal. A brief questionnaire was also completed for each subject to characterize demographic and lifestyle data such as diet, smoking, personal and family history of eye disease and use of dietary supplements. Iris pigmentation was determined by visual inspection. Dark adaptation was measured psychophysically on a cathode ray tube (CRT) monitor (Sony GDM-F500R, Tokyo, Japan) (Patryas et al., 2013). The subjects fixated a red cross throughout the entire test duration and responded to a 1° circular test spot (1931 CIE x = 0.31, y = 0.316), temporally modulated at 1 Hz and presented at 11° in the inferior field. Neutral density filters were used to extend the CRT’s dynamic range. Stimuli were controlled using a VSG 2/5 card (Cambridge Research Systems, Rochester, UK; see Patryas et al., 2013 for a full description of this technique). Subjects were seated 60 cm from the monitor and placed their heads on chin/head rest in a totally darkened room. They were dark adapted for 5 minutes followed by a practice session for a further 5 minutes. A 0.9 ms flash of white light (Nikon Speedlight SB-800, Tokyo, Japan) was used to produce an estimated 30-98% visual pigment bleach, depending on the pupil size (Rushton and Powell, 1972). The flashgun produced retinal bleach subtending an angle of 20.9° by 13.3° centered on the location of the test spot. Monocular thresholds were measured immediately after bleaching using the method of adjustment and were set at approximately twice per minute for a duration of 30 minutes. The non-stimulated eye was patched during testing and the subjects wore their best optical correction for the test distance. MPOD was measured using an MPS 9000 (Elektron Technology plc). The algorithm of this device and procedure is described extensively elsewhere (Makridaki et al., 2009,

136 van der Veen et al., 2009). Briefly, MPOD was determined using the principle of heterochromatic flicker photometry. The subjects reported the onset of flicker as the temporal frequency of blue-green flickering lights was reduced. The luminance ratio of these lights was plotted against temporal frequency for a series of blue-green ratios. A minimum is obtained when the blue and green lights are isoluminant. Flicker thresholds were obtained for central and peripheral viewing. The MPOD was calculated from the difference between the minima of central (0°) and peripheral (8°) curves and expressed in terms of optical density units (DU). All participants repeated MPOD and dark adaptation measurements twice; therefore the data presented hereafter are the means of two visits.

5.3.3 Data analysis

Non-linear regression analysis of the dark adaptation data was implemented in Matlab (Mathworks, MA, USA) as described previously (Patryas et al., 2013). The data were fitted with an exponential-bilinear model which yielded 7 parameters of the dark adaptation curve: cone time constant, cone threshold, rod-cone break (RCB), slopes of the second (S2) and third (S3) rod components, rod-rod break (RRB; the transition point between S2 and S3) and T30 (defined as the last threshold 30 minutes after the onset of bleach). The goodness of fit of the model was assessed by the sum of squared errors (SSE) and regression analysis (r2). Origin@ (Northampton, MA, USA) and Matlab were used for statistical analysis and graph plotting. The Kolmogorov-Smirnov test was used to determine that the distributions of all data did not differ from normal therefore parametric statistics were used. The relationship between variables was assessed by calculating correlation coefficients (Pearson’s r). In- dependent sample t-tests were used to make comparisons between subjects. P ≤ 0.05 was considered statistically significant. To facilitate the visual assessment of the scatter plots we fitted 95% confidence ellipses to the data.

5.4 Results

The VA in the test eye of all observers was at least 0.2 logMAR or better (mean VA 0.02 ±0.07). All subjects successfully completed dark adaptation measurements on two separate visits. All dark adaptation curves followed the classic function depicted in Figure 5.1. The model partitioned the curve into three distinct phases of sensitivity recovery: a cone-mediated phase, typically lasting around 10 minutes, followed by a

137 rod-mediated phase divided into two linear segments. The group averages and SDs for all dark adaptation parameters are summarized in Table 5.1.

) 2

- 0 C o n e t i m e c o n s t a n t m . d

c - 1 R o d - C o n e b r e a k g o l

( - 2 S 2 d l C o n e t h r e s h o l d o T h - 3 3 0 s e r

h - 4 T R o d - R o d b r e a k S 3 - 5 0 5 1 0 1 5 2 0 2 5 3 0 3 5 T i m e ( m i n u t e s )

Figure 5.1: Typical dark adaptation data for a young, healthy observer measured at 11° in the inferior ﬁeld, using a 1° white light stimulus following an 82% bleach. The data points were ﬁtted with an exponential-bilinear model. S2 is the second rod component, S3 is the third rod component and T30 is the threshold 30 minutes after the onset of bleach. Summed squared error = 0.3, r2 = 0.9.

Table 5.1: Summary of group dark adaptation parameters.

The mean (±SD) MPOD for our cohort was 0.37 ±0.21 DU with values ranging from 0.05 to 0.87. There was no significant correlation between MPOD and age (r = -0.22, p = 0.22). Females on average had higher MPOD values (0.41 ±0.17) than males (0.34 ±0.24), however this was not statistically significant (t = -0.86, p = 0.40). Subjects with dark (brown/hazel, n = 12) iris pigmentation had significantly higher (0.5 ±0.19) MPOD values than those with light (blue/green/gray, n = 21) iris pigmentation (0.3 ±0.20) (t = -2.35, p = 0.03). According to the data obtained from the questionnaire, there was no statistically significant difference in the dietary/nutritional habits between males and females (t = 1.40, p = 0.17).

138 The relationship between dark adaptation parameters and MPOD was analysed using Pearson’s correlation coeﬃcients and is summarized in Table 5.2. There was no statistically signiﬁcant relationship between MPOD and any of the cone parameters as illustrated in Figure 5.2.

Table 5.2: Summary of correlation coeﬃcients between MPOD and dark adaptation parameters.

Figure 5.2: Scatter plots showing the lack of relationship between MPOD and cone dark adaptation parameters. a) Cone time constant vs MPOD, r = -0.02, p = 0.91. b) Cone threshold vs MPOD, r = -0.01, p = 0.96. c) RCB vs MPOD, r = 0.13, p = 0.45. Each point is a mean of two sessions. To facilitate the visual assessment of the scatter plots we ﬁtted 95% conﬁdence ellipses to the data.

We found a positive association between MPOD and S2 (r = 0.32, p = 0.07) and MPOD and S3 (r = 0.31, p = 0.08) as illustrated in Figure 5.3 (a) and (b). Further analysis (paired t-test) showed that the average S2 for the lower 10th percentile of MPOD (0.18 log cd.m-2.min-1, n = 4) was signiﬁcantly slower (t = -2.67, p = 0.037) compared with the upper 10th percentile (0.24 log cd.m-2.min-1, n = 4). Figure 5.3 (c) and (d) show an association between MPOD and RRB (r = 0.11, p = 0.52) and MPOD and T30 (r = - 0.24, p = 0.18).

139 Figure 5.3: Scatter plots showing a weak association between MPOD and rod dark adaptation parameters. a) S2 vs MPOD, r = 0.32, p = 0.07. b) S3 vs MPOD, r = 0.31, p = 0.08. c) RRB vs MPOD, r = 0.11, p = 0.52. d) T30 vs MPOD, r = -0.24, p = 0.18. Each point is a mean of two sessions. To facilitate the visual assessment of the scatter plots we ﬁtted 95% conﬁdence ellipses to the data.

5.5 Discussion

MP has previously been studied in the context of photopic vision in normal, healthy adults, with inconclusive results (Bartlett and Eperjesi, 2008, Kvansakul et al., 2006, Rodriguez-Carmona et al., 2006, Nolan et al., 2011, Loughman et al., 2012). Given that up to 25% of the total retinal carotenoids are found in the membranes of the rod outer segments (Rapp et al., 2000, Sommerburg et al., 1999), it is reasonable to hypothesize that MP may also play a role in scotopic vision, particularly in older eyes. In this study we investigated the possible relationship between MPOD and dark adaptation parameters and found a statistically significant difference between subjects falling in the 10th percentile extremes of MPOD and rod-mediated sensitivity recovery. Importantly, no such relationship was evident in the cone-mediated parameters of the sensitivity recovery function. The average MPOD for our cohort (0.37 ±0.21) corresponds well with previous studies that used similar subject populations and HFP methods (Ciulla and Hammond, 2004, Nolan et al., 2004, van der Veen et al., 2009, Ciulla et al., 2001). The finding that

140 MPOD was significantly correlated with iris colour but not correlated with age is also in keeping with previous studies (Iannaccone et al., 2007, Hammond and Caruso-Avery, 2000, Hammond et al., 1996a, Mellerio et al., 2002, Wooten et al., 1999, Ciulla et al., 2001). The dark adaptation parameter estimates for our cohort are in good agreement with previous studies (Dimitrov et al., 2008, Jackson et al., 1999, Lamb and Pugh, 2006). The well-documented decrease in scotopic vision with age (Jackson et al., 1999, Sturr et al., 1997, Patryas et al., 2013) is indicative of reduced rhodopsin regeneration rate which affects visual performance under low light levels increasing the risk of night-time falls and road traffic accidents (McMurdo and Gaskell, 1991, Mortimer and Fell, 1989). Rhodopsin regeneration is dependent upon the availability and delivery of 11-cis retinal to recombine with opsin in the rod outer segment (Lamb and Pugh, 2004). Vitamin A is the precursor of 11-cis retinal, thus a scarcity of this nutrient results in slowed dark adaptation. Vitamin A deficiency at the ocular level may result from age-related changes in Bruch’s membrane which may impede the transport of vitamin A to the rod outer segment (Lamb and Pugh, 2004). Evidence for this hypothesis comes from studies using vitamin A supplementation to improve scotopic vision in subjects with and without retinal disease (Jacobson et al., 1995, Owsley et al., 2006). This raises the question of whether other antioxidants may be important for dark adaptation recovery in ageing and AMD and, if so, what their theoretical mechanism of action would be. In the present study we have demonstrated that subjects with lower 10% of MPOD had significantly slower rates of S2 rod recovery compared with the upper 10% of MPOD (p = 0.037). Two other recent studies have also hinted at a possible relationship between MPOD and scotopic vision. Hammond et al. (1998b) found that higher MPOD was correlated with improved sensitivity in their older subjects. In the CLEAR study, Berendschot et al. (2011) demonstrated an improvement in the rate of S2 rod recovery in subjects with early AMD following L supplementation. These findings are intriguing for several reasons. First, L and Z are not biochemically related to vitamin A and are therefore incapable of improving rhodopsin regeneration directly (Schalch, 2001). Second, the MPOD measured in the present and in the CLEAR study was in the central 1°, whereas dark adaptation was measured at 11°. The following lines of evidence give support to the idea that the MP may play a role in preserving scotopic visual function. First, rod photoreceptors are uniquely susceptible to oxidative damage due to their high concentration of polyunsaturated fatty acids and rhodopsin, and high oxygen tension (Rapp et al., 2000). It seems likely then that, if L and Z do indeed protect the retina from oxidative damage, it should be at the sites

141 of high rod photoreceptor concentration. Peroxidation of rod outer segment has been linked to accumulation of debris in Bruch’s membrane which not only increases the risk of AMD but may also impede the transfer of 11-cis retinal to the rods (Owsley et al., 2001). Higher MPOD throughout the life span may improve Bruch’s membrane function, in theory lessening the impact of ageing on dark adaptation kinetics. Second, correlating different retinal biomarkers measured at various eccentricities across the retina may be scientifically justified. Rapp et al. (2000) demonstrated that L and Z levels in the central (3 mm) retina correlate highly with those observed in the parafoveal (1.5 - 4 mm) and peripheral (beyond 4 mm) retina. Interestingly, they also found L and Z in the human RPE, albeit at low levels, and suggested that this may serve as a store to replenish the MP lost due to shedding of the rod outer segment tips. It is possible that supplementing with L not only increases MPOD centrally but also raises the peripheral levels, thus improving antioxidant capabilities throughout the whole of the retina and enhancing scotopic visual function. The data presented here suggest a weak association between MPOD and rod-mediated parameters (Figure 5.3), whereas we found no such association between MPOD and cone-mediated parameters (Figure 5.2). However, it cannot be concluded from this study that this association represents a causal effect. Furthermore, the results of the current investigation should be interpreted in light of its limitations which include large age range, small sample size and well nourished population. Given that scotopic vision deteriorates rapidly with increasing age and that healthy lifestyle and good nutrition may help to maintain the integrity of retinal structures, the association between MPOD and rod function is likely to be more substantial in older, undernourished populations. To our knowledge, this is the first report comparing rod kinetics with MPOD. The majority of observers in our study had normal MPOD values. Extending the work to encompass those with low MP levels and poor night vision may yield stronger correlations between MPOD and rod kinetics, since both are thought to be accurate bioassays of retinal health.

5.6 References

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144 Mellerio, J., Ahmadi-Lari, S., Van Kuijk, F., Pauleikhoff, D., Bird, A.C., Marshall, J., 2002. A portable instrument for measuring macular pigment with central fixation. Current eye research 25, 37–47. Mortimer, R.G., Fell, J.C., 1989. Older drivers: their night fatal crash involvement and risk. Accid Anal Prev 21, 273–282. Murray, I.J., Makridaki, M., van der Veen, R.L.P., Carden, D., Parry, N.R.A., Berend- schot, T.T.J.M., 2013. Lutein supplementation over a one-year period in early AMD might have a mild beneficial effect on visual acuity: the CLEAR study. Invest. Oph- thalmol. Vis. Sci. 54, 1781–1788. Nolan, J., O’Donovan, O., Kavanagh, H., Stack, J., Harrison, M., Muldoon, A., Mel- lerio, J., Beatty, S., 2004. Macular pigment and percentage of body fat. Investigative ophthalmology & visual science 45, 3940. Nolan, J.M. et al., 2011. The impact of macular pigment augmentation on visual performance in normal subjects: COMPASS. Vision research, 51(5), pp.459–469. Owsley, C., Jackson, G.R., White, M., Feist, R., Edwards, D., 2001. Delays in rod- mediated dark adaptation in early age-related maculopathy* 1. Ophthalmology 108, 1196–1202. Owsley, C., McGwin, G., Jackson, G.R., Heimburger, D.C., Piyathilake, C.J., Klein, R., White, M.F., Kallies, K., 2006. Effect of short-rerm, high-dose retinol on dark adaptation in aging and early age-related maculopathy. Investigative Ophthalmology & Visual Science 47, 1310 –1318. Patryas, L., Parry, N.R.., Carden, D., Baker, D.H., Kelly, J.M.F., Aslam, T., Murray, I.J., 2013. Assessment of age changes and repeatability for computer-based rod dark adaptation. Graefes Arch. Clin. Exp. Ophthalmol. Rapp, L.M., Maple, S.S., Choi, J.H., 2000. Lutein and zeaxanthin concentrations in rod outer segment membranes from perifoveal and peripheral human retina. Investigative Ophthalmology & Visual Science 41, 1200. Richer, S., 1999. ARMD–pilot (case series) environmental intervention data. J Am Optom Assoc 70, 24–36. Richer, S., Stiles, W., Statkute, L., Pei, K., Frankowski, J., Nyland, J., Pulido, J., Rudy, D., 2002. The lutein antioxidant supplementation trial. Investigative ophthalmology & visual science 43, 2542. Richer, S., Stiles, W., Statkute, L., Pulido, J., Frankowski, J., Rudy, D., Pei, K., Tsipursky, M., Nyland, J., 2004. Double-masked, placebo-controlled, randomized trial of lutein and antioxidant supplementation in the intervention of atrophic age-related

145 macular degeneration: the Veterans LAST study (Lutein Antioxidant Supplementation Trial). Optometry-Journal of the American Optometric Association 75, 216–229. Rodriguez-Carmona, M., Kvansakul, J., Harlow, J.A., Kopcke, W., Schalch, W., Bar- bur, J.L., 2006. The eﬀects of supplementation with lutein and/or zeaxanthin on human macular pigment density and colour vision. Ophthalmic and Physiological Optics 26, 137 – 147. Rushton, W.A.H., Powell, D.S., 1972. The rhodopsin content and the visual threshold of human rods. Vision Research 12, 1073–1081. Schalch, W., 2001. Importance of the carotenoids lutein and zeaxanthin for the human eye. Chimica Oggi-Chemistry Today 19, 12–45. Seddon, J.M., Ajani, U.A., Sperduto, R.D., Hiller, R., Blair, N., Burton, T.C., Farber, M.D., Gragoudas, E.S., Haller, J., Miller, D.T., 1994. Dietary carotenoids, vitamins A, C, and E, and advanced age-related macular degeneration. Eye Disease Case-Control Study Group. JAMA 272, 1413–1420. Snodderly, D.M., 1995. Evidence for protection against age-related macular degeneration by carotenoids and antioxidant vitamins. The American Journal of Clinical Nutrition 62, 1448S–1461S. Sommerburg, O.G., Siems, W.G., Hurst, J.S., Lewis, J.W., Kliger, D.S., van Kuijk, F., 1999. Lutein and zeaxanthin are associated with photoreceptors in the human retina. Curr Eye Res 19, 491 – 495. Steinmetz, R., Haimovici, R., Jubb, C., et al, 1993. Symptomatic abnormalities of dark adaptation in patients with age-related Bruch’s membrane change. Br J Ophthalmol 77, 549–54. Stringham, J.M., Hammond, B.R., 2007. The glare hypothesis of macular pigment function. Optometry & Vision Science 84, 859. Stringham, J.M., Hammond, B.R., 2008. Macular pigment and visual performance under glare conditions. Optometry & Vision Science 85, 82. Sturr, J.F., Zhang, L., Taub, H.A., Hannon, D.J., Jackowski, M.M., 1997. Psychophys- ical Evidence for Losses in Rod Sensitivity in the Aging Visual System. Vision Research 37, 475–481. Van der Veen, R.L.., Berendschot, T.T.J.., Hendrikse, F., Carden, D., Makridaki, M., Murray, I.J., 2009. A new desktop instrument for measuring macular pigment optical density based on a novel technique for setting ﬂicker thresholds. Ophthalmic and Physiological Optics 29, 127–137.

146 Wooten, B.R., Hammond Jr, B.R., Land, R.I., Snodderly, D.M., 1999. A practical method for measuring macular pigment optical density. Investigative ophthalmology & visual science 40, 2481.

147 6 Statin use may be associated with reduced retinal carotenoids and visual function in normal ageing

Contributions

Patryas L., Parry N.R.P., Carden D., Aslam T., Murray I.J., 2014. Statin use may be associated with reduced retinal carotenoids and visual function in normal ageing. Manuscript ready to submit. Conference presentations

None.

Acknowledgements

148 6.1 Abstract

6.1.1 Purpose

Previous studies have suggested an inverse relationship between statin use and macular pigment optical density (MPOD) in healthy individuals. Macular pigment is a biomarker of antioxidant status of the eye and retinal disease. Increasing the amount of the pigment has been correlated with improved visual function in normal and diseased eyes. We sought to investigate the relationship between statin use, MPOD and visual function in older adults.

6.1.2 Methods

25 subjects taking statins (mean age 70 ± 8.03 years) were identiﬁed from a larger study designed to investigate the eﬀect of MPOD augmentation on visual performance. These were matched with 25 participants not using statins for age and body mass index (mean age 69.24 ± 5.59). As well as MPOD, we measured a variety of visual parameters including visual acuity (VA), contrast sensitivity, photostress recovery time (PSRT) and dark adaptation.

6.1.3 Results

Statin users had a higher proportion of males (p = 0.04), and a higher prevalence of current smoking status (p = <0.0001), hypertension (p = 0.003), high cholesterol (p = <0.0001) and heart disease (p = 0.0005). According to a MANOVA, statin users had significantly reduced MPOD (p = 0.02) and PSRT (p = 0.045). Dark adaptation analysis revealed prolonged cone time constant (p = 0.03), rod-cone break (p = 0.02) and rod-rod break (p = 0.02) among statin users. Cone threshold, VA and other rod- related dark adaptation parameters were not significantly different between the two groups.

6.1.4 Conclusions

We report statistically significant reductions in a number of visual parameters including MPOD in subjects on lipid-modifying therapy. It is not clear whether these effects are a result of taking the medication, or the co-existing lifestyle and health factors among statin users (e.g. smoking and poorer general health). As expanding segments of the population are exposed to statins for prolonged periods, it will be important to gain a more complete understanding of the wide range of effects of these medications.

149 6.2 Introduction

Age is the main risk factor for the prevalent diseases of developed countries, such as cardiovascular disease (CVD) and age-related macular degeneration (AMD). Widespread increases in obesity amongst older persons have been shown to promote atherosclerosis, hypercholesterolemia and increase the prevalence of medical complications (Lavie et al., 2009). Since the late 1980s, hydroxymethylglutaryl coenzyme A reductase inhibitors, or statins, have been used in an attempt to tackle the growing problem of obesity-related CVD. The past two decades have seen a dramatic increase in the number of statin users, which is set to rise to 12 million in the United Kingdom (Lancet, 2014). Population ageing together with proposals for expanding the criteria for statin use will further increase these numbers emphasizing the need for more research into the potential side effects of these medications. The possible relationship between statin use and ocular health is largely unexplored, particularly in normal ageing and in the absence of obvious ocular disease. In recent years it has been hypothesized that the anti-atherosclerotic, antioxidant and anti- inflammatory properties of statins may protect against the development and progression of AMD (Peponis et al., 2010). Indeed, several studies have suggested a protective association between the use of statins and AMD (McGwin et al., 2003). However, there is much controversy surrounding the topic with some studies showing a protective effect and others not (see Peponis et al. 2010 for a review). One recent study has found that statin use of >12 months was associated with increased risk of developing exudative AMD (VanderBeek et al., 2013). Additionally, there is evidence that statin use is associated with increased risk of cataracts (Leuschen et al., 2013) and diabetes (Sattar et al., 2010). The latter affects retinal microvasculature and may lead to severe sight loss. Prolonged use of statins has also been shown to reduce levels of the retinal carotenoids lutein (L), zeaxanthin (Z) and meso-zeaxanthin (MZ) in healthy individuals (Renzi et al., 2012). Macular pigment (MP), located in the central retina of humans, is the collective term for L, Z and MZ. Its accumulation in photoreceptor axons is dependent on the levels of circulating lipoproteins, particularly high-density lipoproteins (HDL). These transport the MP constituents in serum and increase their bioavailability (Loane et al., 2010). Hence the statin-induced reduction of serum lipoprotein profile may interfere with the process of retinal carotenoid accumulation in the eye. MP is an efficient absorber of harmful blue light and has powerful antioxidant properties (Ahmed et al., 2005). Hence, low MP may expose the retina to unnecessarily high levels

150 of short wavelength light increasing photooxidative stress. It is for these reasons that low MP is epidemiologically linked to increased risk of AMD (Bone et al., 2001, Snodderly, 1995). Recent reports, have also shown that lower MP levels may be associated with reduced cognitive performance in older adults (Feeney et al., 2013, Vishwanathan et al., 2014). Given the controversy of a link between statin use and AMD and the strong eﬀect statins might be expected to have on retinal carotenoid bioavailability, it is important to further explore the link between these medications and MP. Higher levels of MP have previously been correlated with improved visual acuity (VA), contrast sensitivity (CS), photostress recovery time (PSRT) and scotopic vision in healthy and diseases eyes (Nussbaum et al., 1981, Hammond et al., 1998b, String- ham and Hammond, 2008, Berendschot et al., 2011, Loughman et al., 2012, Murray et al., 2013) therefore we included these measures in our investigation. Our aim was to examine the relationship between statin use, macular pigment optical density (MPOD) and visual function in a group of normal older subjects who are representative of those most likely to be taking statins.

6.3 Methods

6.3.1 Subjects

Eighty eight healthy participants between the ages of 50 to 90 years old volunteered for a study investigating the effect of retinal carotenoid supplementation on visual function in normal ageing. From this cohort, subjects taking statins were identified (n = 25, mean age 70 ±8.03 years, 18 males, 7 females) and matched with 25 participants not using statins for age and body mass index (BMI; mean age 69.24 ±5.59, 12 males, 13 females). Exclusion criteria for the main study were a history of ocular disease, diabetes, liver disease, significant cataract or macular disturbance, use of nutritional supplements 6 months prior to study commencement, BMI of more than 40 and VA worse than 0.2 logMAR. Cases of controlled hypertension and high cholesterol, and history of heart disease were allowed. Self-selected recruitment of subjects was facilitated through an editorial in the local newspaper and from poster and internet advertisements within the university campus. Informed consent was obtained and the tenets of the Declaration of Helsinki were followed. The study was approved by the Greater Manchester Research Ethics Committee.

151 6.3.2 Procedures

MPOD and visual performance were assessed using psychophysical tests including best- corrected visual acuity (BCVA), Mars letter CS, PSRT and dark adaptation. All tests were performed monocularly with natural pupils (except for dark adaptation) and with optimal refractive correction for the testing distance. Details of each test are described below.

6.3.2.1 Visual acuity

BCVA was determined using an internally illuminated Early Treatment of Diabetic Retinopathy Study chart (ETDRS) and recorded in logarithm of the minimum angle of resolution (logMAR) at a distance of 4 metres. The eye with the best acuity was chosen as the study eye. In cases where the acuity was the same in both eyes, the right eye was chosen.

6.3.2.2 Macular pigment optical density

MPOD was measured using a device (MPS 9000, Elektron Technology plc) based on the principles of heterochromatic flicker photometry (van der Veen et al., 2009). Subjects reported the onset of flicker (via a button press) as the temporal frequency of blue-green flickering lights was reduced. The luminance ratio of these lights was plotted against temporal frequency for a series of blue-green ratios yielding V-shaped functions. The minimum corresponds to the equalization of the blue and green luminance. Flicker matches were made at central (0°) and peripheral (8°) points in the retina. The optical density of the MP was calculated from the difference between the minima of the two retinal locations.

6.3.2.3 Contrast sensitivity

CS was measured using the Mars Letter CS Test (Mars Perceptrix Corporation, NY, USA). The standardized techniques and letter-by-letter scoring procedure were adopted as described by Arditi 2005. Subjects were tested on three separate charts giving a mean score of three measurements.

6.3.2.4 Photostress recovery time

The macular stop on a direct ophthalmoscope (Keeler Ltd, Windsor, UK) was used to produce 6.15 log trolands of retinal illuminance, as measured using a PR1500 spot

152 photometer (Photo Research, CA, USA). The light source (adjusted to full intensity) was held 1 cm from the pupil and directed towards the fovea for 30 seconds, resulting in a 98% cone photopigment bleach (Hollins and Alpern, 1973). The participants were instructed to look directly at the light source and correct fixation was monitored during light exposure by observing the foveal reflex. Before bleaching, each subject’s BCVA was determined using an ETDRS chart. PSRT was defined as the period measured in seconds until the subject reached 0.1 logMAR (one line) less than their BCVA. This method was chosen because it is easily performed and produces rapid and reliable results, comparable with a Maxwellian view optical system (Margrain and Thomson, 2002). The measurement is not affected by the pupil size due to the small image of the light filament and imaging in the pupil plane (Margrain and Thomson, 2002). Measurements were performed with natural pupils and repeated three times with 2 minute recovery breaks between bleaches.

6.3.2.5 Dark adaptation

Pupils were dilated with 1% tropicamide and 2.5% phenylephrine to achieve a minimum pupil diameter of 6 mm. Dark adaptation thresholds were measured psychophysically at 5° in the inferior ﬁeld on a cathode ray tube monitor, as described previously (Patryas et al., 2013). Example dark adaptation curve is shown in Figure 6.1.

153 1

) 2

- 0 C o n e t i m e c o n s t a n t m . d

c - 1 R o d - C o n e b r e a k g o l

( - 2 S 2 d l C o n e t h r e s h o l d o T h - 3 3 0 s e r

h - 4 T R o d - R o d b r e a k S 3 - 5 0 5 1 0 1 5 2 0 2 5 3 0 3 5 T i m e ( m i n u t e s )

Figure 6.1: Typical dark adaptation data for a young, healthy observer measured at 5° in the inferior ﬁeld, using a 1° white light stimulus following an 82% bleach. The data points were ﬁtted with an exponential-bilinear model. S2 is the second rod component, S3 is the third rod component and T30 is the threshold 30 minutes after the onset of bleach. Summed squared error = 0.3, r2 = 0.9.

-2 The functions were plotted as log10 threshold in cd.m versus time in minutes and ﬁtted with a single exponential component to the cone phase and two linear components to the rod phase (Patryas et al., 2013). Non-linear regression analysis was implemented in Matlab (Mathworks, MA, USA) to extract the following parameters of the dark adaptation curves: cone time constant, cone threshold, rod-cone break, two rod recovery rates (S2 and S3), rod-rod break, and rod threshold (T30).

6.3.2.6 Statistical analysis

According to Kolmogorov-Smirnov tests all visual function parameters exhibited normal distributions. A multivariate analysis of variance (MANOVA) was used to compare statin users with non-statin users for all visual parameters. SPSS version 20 (SPSS Inc., Chicago, IL) and Origin@ (Northampton, MA, USA) were used for statistical analysis and graph plotting. Binomial tests, implemented in R Statistical Software (Foundation for Statistical Computing, Vienna, Austria), were used for comparing the proportions (or percentages) between the two groups in terms of gender and medical status.

154 6.4 Results

The anthropometric, medical and lifestyle data for all subjects included in this study are presented in Table 6.1. All subjects were from a Caucasian ethnic background. Of the 25 subjects taking statin medications, 62% were taking simvastatin and 38% were taking atorvastatin, with a dose ranging from 5 to 80mg per day.

Table 6.1: Participant anthropometric, medical and lifestyle data.

No significant differences between the two groups were observed in terms of age (t = 0.45, p = 0.65) and BMI (t = 0.69, p = 0.49). In general, statin users had a higher proportion of males (72% vs 48%, p = 0.04), and a higher prevalence of current smoking status (24% vs 4%, p = <0.0001), hypertension (40% vs 16%, p = 0.003), high cholesterol (48% vs 4%, p = <0.0001) and heart disease (32% vs 8%, p = 0.0005). Females in the statin group had a higher BMI compared to males (29.36 ± 5.75 vs 26.71 ± 3.57), however this was not statistically significant (t = -1.66, p = 0.10). In the non-statin group, male and female BMI was very similar (26.76 ± 5.52 vs 26.52 ± 3.68, t = -0.12, p = 0.90). The mean group test scores and MANOVA p values are summarized in Table 6.2. Statin users had significantly reduced MPOD (F[1,48] = 5.82 p = 0.02) and PSRT (F[1,48] = 4.24, p = 0.045), as illustrated in Figure 6.2 in a box plot format for both groups. Dark adaptation analysis revealed prolonged cone time constant (F[1,48] = 4.87 p = 0.03), rod-cone break (F[1,48] = 5.91, p= 0.02), and rod-rod break (F[1,48] = 6.06, p= 0.02) among statin users (shown in Figure 6.3). Cone threshold, VA and other rod- related dark adaptation parameters were not significantly different between the two groups (see Table 6.2). We did not observe a significant association between BMI and MPOD in either of the groups: statin users (r = 0.16, p = 0.45), non-statin users (r = 0.26, p = 0.20).

155 Table 6.2: Summary of group means and p values obtained from MANOVAs.

a b 1.0 200 ) s t i

0.8 ) s n d u 150 n y t o

i 0.6 c s e n s e (

100 d T ( 0.4

R D S O P

P 50 0.2 M

0.0 0

Statin users Non-users Statin users Non-users

Figure 6.2: Box and whisker plots for MPOD (a) and PSRT (b) for statin and non-statin users. Error bars: 95% conﬁdence intervals.

156 a b c ) )

s 6 45

30 ) s e s t e t e u t u n

5 u 40 i n 25 i n i m ( m

( m t

4 ( 35

n 20 k a a t a e r s 3 e 30 r b n 15

b o

e c d n 2 25 o e o

10 r c - m - i d t d

1 o 20 o e

5 R R n

o 0 15 C 0

Statin users Non-users Statin users Non-users Statin users Non-users

Figure 6.3: Box and whisker plots for cone time constant (a), rod-cone break (b) and rod-rod break (c) for statin and non-statin users. Error bars: 95% conﬁdence intervals.

6.5 Discussion

The observation that AMD and CVD share important antecedents (Delcourt et al., 2001) together with the antioxidant, anti-atherosclerotic and anti-inflammatory properties of statins (Peponis et al., 2010) has led to the hypothesis that these medicines may have a protective effect against AMD. However, given the conflicting nature of past research in this area and reports of ocular adverse effects (Leuschen et al., 2013, Renzi et al., 2012) among statin users, additional work is warranted. This study aimed to investigate the effect of statin use on visual function in a group of older participants in reasonable general health and free from ocular disease. Our results showed an inverse association between statin use and MPOD. Subjects who reported statin use had, on average, 26% lower MPOD compared to non-users. This finding is consistent with Renzi et al. (2012), who demonstrated a statistically significant decline in MPOD in middle-aged healthy participants with long-term statin use. They also found a significant decrease in MPOD post atorvastatin intake in one mildly hyperlipidemic subject. The mechanism behind the statin-retinal carotenoid interaction is as yet unknown, but it is likely to be associated with individual serum lipoprotein profile. Statins reduce the circulating lipoprotein levels which may affect the delivery of carotenoids to the retina. Specifically, HDL is the major carrier for L and Z in serum thus a reduction of this lipoprotein could adversely affect retinal carotenoid bioavailability (Loane et al., 2010). Given the association between MP and AMD (Bone et al., 2001, Snodderly, 1995), patients with or at risk of developing AMD who are on lipid-modifying therapy may need to increase their intake of retinal carotenoids. Supplementing the diet with eggs,

157 for instance, has been shown to increase MPOD in subjects taking statins without adverse effects on lipoprotein status (Vishwanathan et al., 2009). To our knowledge, this is the first study to investigate the link between statin use and visual function in older subjects. We report statistically significant reductions in Mars letter CS, PSRT and three out of seven parameters of dark adaptation recovery among statin users. These findings are interesting but difficult to interpret since the effects may not be specifically related to statin use but to a number of other factors. Differences in lifestyle and health factors among older people could influence the risk of certain types of ageing-related visual function deficits (Owsley, 2011). For instance, cigarette smoking reduces antioxidant levels in the retina and increases oxidative stress which is implicated in the pathogenesis of atherosclerosis (Singh and Jialal, 2006). Atherosclerotic vascular changes may contribute to reduced nutrient supply and metabolic waste clearance between retinal layers. Accumulation of cholesterol and other debris with age in human Bruch’s membrane (Curcio, 2001) may also impede metabolite transport and impair visual function such as dark adaptation (Jackson et al., 1999, Owsley, 2011, Patryas et al., 2013). An alternative hypothesis is that lower retinal carotenoid levels, directly associated with statin use, diminish optical filtration and vision-enhancing capacity of the MP. The pre-receptoral location of the MP within the retina may influence the quality of visual performance by altering the spectral distribution of light incident on the photoreceptors (Loughman et al., 2010). Support for this hypothesis comes from studies which have shown an association between increased MP and reduced longitudinal chromatic aberration (Hammond et al., 2001) and glare (Stringham and Hammond, 2008), improved visibility (Wooten and Hammond, 2002), contrast acuity (Kvansakul et al., 2006), CS and VA (Loughman et al., 2012). Higher levels of MP may also be linked to improved rod function in healthy subjects (Hammond et al., 1998b, Patryas et al., 2014) as well and those with early AMD (Berendschot et al., 2011). Additionally, long-term effects of higher MP levels may help to preserve photoreceptor function in normal ageing and retard or prevent the onset of retinal disease. The retina is uniquely susceptible to photooxidative stress due to its high concentration of polyunsaturated fatty acids and chromophores, high oxygen tension and exposure to high-energy blue light (Rapp et al., 2000). Retinal carotenoids have demonstrable antioxidant abilities which help to protect the retina by quenching free radicals and singlet oxygen, and absorbing short wavelength light (Snodderly, 1995). If long-term statin use reduces MP, as is suggested in this study and as has been documented previously, this could lead to alterations in visual function and retinal integrity, and ultimately higher risk of age-related ocular disease.

158 Some limitations of our study should be mentioned. First, statin users were identified on the basis of self-report rather than from prescription records. This introduces the possibility of errors with regards to the type of statin used, the dose and duration of use. Likewise, participant health status was based on self-report rather than from medical records. Second, serum carotenoid and lipoprotein levels were not measured which prevented us from examining the effect of statins on serum concentrations of MP constituent carotenoids. In conclusion, this study suggests that the use of statins is associated with a reduction in visual function and MPOD in older subjects with normal retinal health. Alternatively, and given that statin users also had a higher prevalence of smoking and poorer general health, the observed deficits may be due to these factors. It seems likely, however, that the mode of action of these medications may have an effect on MPOD as shown in this and one previous study. More research is needed to explore the effect of these medications on serum carotenoids, their bioavailability and MP accumulation. It may be that the beneficial effects of statins in ageing and AMD are offset by their interference with carotenoid capture and stabilization at the macula. Given that obesity is on the increase, educating patients about their lifestyle choices and restricting statin use to approved indications will become more important. Furthermore, managing the intake of potentially counteracting compounds is paramount, particularly in cases of age-related ocular disease where, both, statins and retinal antioxidants are likely to be used by the patient.

6.6 References

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162 7 Photopic and scotopic visual function in the ageing eye: relationship to macular pigment, lifestyle factors and health status

Contributions

Patryas L., Parry N.R.P., Carden D., Aslam T., Murray I.J., 2014. Photopic and scotopic visual function in the ageing eye: relationship to macular pigment, lifestyle factors and health status. Manuscript ready to submit. Conference presentations

None.

Acknowledgements

163 7.1 Abstract

7.1.1 Purpose

To investigate the impact of unhealthy lifestyle behaviours and health status on a number of visual function parameters in the ageing eye.

7.1.2 Methods

74 subjects free of ocular disease (mean age 65.51) participated in this study. Macular pigment optical density (MPOD) and visual performance were assessed by psychophysical tests including best-corrected visual acuity (BCVA), contrast sensitivity (CS) and photostress recovery time (PSRT). The following parameters of dark adaptation were also assessed: cone time constant, cone threshold, rod-cone break, rod recovery rates (S2 and S3), rod-rod break, and ﬁnal rod threshold.

7.1.3 Results

After increasing age, current tobacco use had the greatest negative eﬀect on visual function. Smokers had reduced MPOD (p = 0.005) and slower S2 (p = 0.002). Smoking was also correlated with prevalence of high cholesterol (p = 0.009) and reduced fruit and vegetable consumption (p = 0.007). Subjects with cardiovascular disease had reduced PSRT (p = 0.003) and slower S3 (p = 0.04). Compared to normal weight subjects, obese subjects had signiﬁcantly reduced MPOD (p = 0.01).

7.1.4 Conclusions

We report statistically signiﬁcant visual function reductions among subjects who smoke, are obese and have health complications. To what extent this visual deﬁcit can be slowed or reversed by introducing health-promoting lifestyle behaviours remains to be determined.

164 7.2 Introduction

Human visual performance decreases with age, even in the absence of retinal disease, and is associated with reduced quality of life and increased risk of accidents (Jackson and Owsley, 2003). Reduced function, both under photopic and scotopic viewing conditions, stems from ageing-related changes in the optics of the eye and degradation of the visual neural pathway (Elliott and Hurst, 1990). Visual function and retinal health decline is further exacerbated by co-existent lifestyle factors such as cigarette smoking (Uz et al., 2003, Klein et al., 2014, Singh and Jialal, 2006), excessive alcohol consumption (Russell, 1980, Abbott-Johnson et al., 2010) and poor dietary antioxidant intake (Ma and Lin, 2010). Advancing age together with the aforementioned lifestyle factors (Chakravarthy et al., 2010, Chong et al., 2008) increase the risk of age-related macular degeneration (AMD); the leading cause of irreversible vision loss in the developed world (Curcio et al., 2000). Given the lack of treatment options for dry AMD, the emphasis has fallen on the possible preventative strategies. In recent years, a significant amount of research has been focusing on the role of macular pigment (MP), both in healthy eyes and AMD. MP is a yellow oily substance composed of lutein (L), zeaxanthin (Z) and meso-zeaxanthin (MZ) predominantly located in the photoreceptor axons (Snodderly et al., 1984) and in the plexiform layers of the parafoveal retina (Trieschmann et al., 2008). A substantial proportion (10-25%) of the total retinal carotenoids is found in the membranes of rod outer segments (Rapp et al., 2000, Sommerburg et al., 1999). L and Z are obtained through ingesting foods abundant in these carotenoids, whereas MZ is thought to occur as a result of bioconversion from L within the macula (Bone et al., 1997). A growing body of the literature indicates a reduction in AMD incidence (Bone et al., 2001, Snodderly, 1995) and improved visual function with increased MP (Richer, 1999, Richer et al., 2002, 2004, Berendschot et al., 2011, Murray et al., 2013). These effects are most likely due to a combination of blue-light filtration and antioxidant properties of the macular xanthophylls (Snodderly, 1995). In non-diseased eyes, increased MP has been correlated with reduced longitudinal chromatic aberration (Hammond et al., 2001) and glare (Stringham and Hammond, 2007), improved visibility (Wooten and Hammond, 2002), contrast acuity (Kvansakul et al., 2006), colour vision (Rodriguez-Carmona et al., 2006), contrast sensitivity and visual acuity (VA; Loughman et al. 2012). Higher levels of MP may also be linked to improved rod function in healthy subjects (Hammond et al., 1998b, Patryas et al., 2014) as well and those with early AMD (Berendschot et al., 2011). As the number of people 65 years and older increase, so will the incidence of age- and

165 disease-related visual deficits. Research into the effect of nutritional and lifestyle factors on ageing of the retina could potentially direct future interventions aiming to prevent avoidable visual impairment. Although there is a growing body of work on the effect of multiple lifestyle behaviours (such as tobacco use, excessive alcohol consumption and poor diet) on health and longevity (Khaw et al., 2008), there is a surprising lack of research on how these bahaviours impact on visual function. Recently Klein et al. (2014) published a study on the effects of modifiable behaviours on VA. In that study, after adjusting for a number of co-factors, current and past smokers were found to have greater losses in VA compared to never smokers. The aim of the present study was to assess whether certain lifestyle and health factors have any effect on multiple parameters of visual function. Here, we present the baseline data from a clinal trial investigating the effect of nutritional modulation on visual performance in normal ageing.

7.3 Methods

7.3.1 Subjects

Eighty eight participants between the ages of 50 to 90 years old volunteered for the study. Of these, seventy four (mean age 65.51 ±8.17 years) met the inclusion criteria and successfully completed the year-long study. Subjects were recruited through an editorial in the local newspaper and from poster and internet advertisements within the university campus. Informed consent was obtained and the tenets of the Declaration of Helsinki were followed. The study was approved by the Greater Manchester Research Ethics Committee. Exclusion criteria were a history of ocular disease, use of nutritional supplements 6 months prior to study commencement, body mass index (BMI) of more than 40 and VA worse than 0.2 logMAR. Lenticular opacities were graded according to a Lens Opacities Classification System III (Chylack et al., 1993). Fundus photographs were taken with a TRC-NW6S Non- Mydriatic Retinal Camera (Topcon, Tokyo, Japan) and graded according to a macular grading scale (Jackson et al., 1999). Subjects with significant cataract (grade >3) and significant macular disturbance (grade >1) were excluded from the study. A questionnaire was also completed for each subject to characterize anthropometric and lifestyle data such as BMI, iris colour, diet, smoking, personal or family history of eye

166 disease and use of dietary supplements. BMI was defined as weight (kg)/height (m)2. Subjects were classified into 1 of 3 categories of BMI: <25 kg/m2, normal weight; 25 to 29 kg/m2, overweight; and 30 to 40 kg/m2, obese. Units of alcohol, fruits, vegetables and fish were calculated based on the National Health Service (UK) guidelines.

7.3.2 Visual function assessment

7.3.2.1 Visual acuity

On the day of testing, the subjects’ monocular best-corrected visual acuity (BCVA) was determined using an internally illuminated Early Treatment of Diabetic Retinopathy Study chart. BCVA was recorded in logarithm of the minimum angle of resolution (logMAR) at a distance of 4 metres. The eye with the best acuity was chosen as the study eye. In cases where the acuity was the same in both eyes, the right eye was chosen.

7.3.2.2 Macular pigment optical density

Macular pigment optical density (MPOD) was measured using MPS 9000 (Elektron Technology plc). The algorithm of this device and procedure is described extensively elsewhere (van der Veen et al. 2009; Makridaki et al. 2009). Briefly, MPOD was determined using the principle of heterochromatic flicker photometry. The subjects reported the onset of flicker as the temporal frequency of blue-green flickering lights was reduced. The luminance ratio of these lights was plotted against temporal frequency for a series of blue-green ratios. A minimum is obtained when the blue and green lights are isoluminant. The MPOD was calculated from the difference between the minima of central (0°) and peripheral (8°) curves and expressed in terms of optical density units (DU).

7.3.2.3 Dark adaptation

Dark adaptation was measured psychophysically on a cathode ray tube (CRT) monitor, as described previously (Patryas et al., 2013). Neutral density ﬁlters were used to extend the CRT’s dynamic range. The chosen study eye was dilated with 1% tropicamide and 2.5% phenylephrine to achieve a minimum pupil diameter of 6 mm. Subjects were seated 60 cm from the monitor and placed their heads on chin/head rest in a totally darkened room. They were dark adapted for 5 minutes followed by a practice session for a further 5 minutes.

167 An electronic 0.9 ms flash of white light (Nikon Speedlight SB-800, Tokyo, Japan) was used to produce an estimated 98% visual pigment bleach (Rushton and Powell, 1972). The subjects fixated a red cross throughout the entire test duration and responded to a 1° circular test spot (1931 CIE x = 0.31, y = 0.316), temporally modulated at 1 Hz and presented at 5° in the inferior field. Monocular thresholds were measured immediately after bleaching using the method of adjustment and were set at approximately twice per minute for a duration of 30 minutes.

7.3.2.4 Contrast sensitivity

Contrast sensitivity (CS) was measured using the Mars Letter Contrast Sensitivity Test (Mars Perceptrix Corporation, NY, USA) using standardized techniques and letter-by- letter scoring procedure (Arditi, 2005). Subjects were tested on three separate charts giving a mean score of three measurements.

7.3.2.5 Photostress recovery time

The macular stop on a direct ophthalmoscope (Keeler Ltd, Windsor, UK) was used to produce 6.15 log trolands of retinal illuminance, as measured using a PR1500 spot photometer (Photo Research, CA, USA). The light source (adjusted to full intensity) was held 1 cm from the pupil and directed towards the fovea for 30 seconds, resulting in a 98% cone photopigment bleach (Hollins and Alpern, 1973). Subjects were instructed to look directly at the light source and correct fixation was monitored during light exposure by observing the foveal reflex. Before bleaching, the subject’s BCVA was measured on a distance logMar letter chart. The photostress recovery time (PSRT) was measured in seconds until the subjects reached 0.1 logMAR (one line) less than their BCVA. This method was chosen because it is easily performed and produces rapid and reliable results, comparable with a Maxwellian view optical system (Margrain and Thomson, 2002). The measurement is not affected by the pupil size due to the small image of the light filament and imaging in the pupil plane (Margrain and Thomson, 2002). As such, measurements were performed with natural pupils and repeated three times with 2 minute recovery breaks between bleaches. All tests were performed monocularly with natural pupils (except for dark adaptation), and with optimal refractive correction for the testing distance.

168 7.3.3 Statistical analysis

(T30), as shown in Figure 7.1.

) 2

- 0 C o n e t i m e c o n s t a n t m . d

c - 1 R o d - C o n e b r e a k g o l

( - 2 S 2 d l C o n e t h r e s h o l d o T h - 3 3 0 s e r

h - 4 T R o d - R o d b r e a k S 3 - 5 0 5 1 0 1 5 2 0 2 5 3 0 3 5 T i m e ( m i n u t e s )

Figure 7.1: Typical dark adaptation data for a young, healthy observer measured at 5° in the inferior ﬁeld, using a 1° white light stimulus following an 82% bleach. The data points were ﬁtted with an exponential-bilinear model. S2 is the second rod component, S3 is the third rod component and T30 is the threshold 30 minutes after the onset of bleach. Summed squared error = 0.3, r2 = 0.9.

Statistical analysis was performed using SPSS software (version 20; SPSS Inc., Chicago, IL, USA). Data were first tested for normality of distribution using Kolmogorov-Smirnov tests with Lilliefors significance correction. These showed that the majority of visual and non-visual data were not normally distributed. Therefore, non-parametric statistical tests were used throughout including Spearman’s rank correlation (rs), Mann-Whitney U test and Kruskal-Wallis one-way analysis of variance by ranks (with adjusted significance for multiple comparisons). Means and ± standard deviations (SD) are presented in text and tables. Origin@ (Northampton, MA, USA) was used for graph plotting.

169 7.4 Results

The anthropometric, lifestyle and medical data for all subjects included in this study are presented in Table 7.1. All subjects were from a Caucasian ethnic background with the majority (65%) having light iris pigmentation (blue/grey/green).

170 Table 7.1: Participant anthropometric, lifestyle and medical data.

171 7.4.1 Age and visual function

The group visual function parameter means, SDs and Spearman’s rank correlations with age are summarized in Table 7.2. All parameters measured were adversely aﬀected by increasing age with the exception of cone threshold, S3 and MPOD (see Table 7.2).

Table 7.2: Visual function group means and Spearman’s rank correlations with age.

7.4.2 Macular pigment optical density

The mean group MPOD was 0.37 (±0.17) and was unrelated to age (rs = -0.06, p = 0.64). Females, on average, tended to have higher MPOD than males (0.39 ± 0.18 vs 0.36 ± 0.16), however this was not significant (U = 613, p = 0.52). Iris colour did not have a significant effect on MPOD (U = 537, p = 0.85). There was no statistically significant relationship between MPOD and any of the visual function tests.

7.4.3 Lifestyle factors (smoking, nutrition and alcohol)

Current smoker status was signiﬁcantly associated with an increased prevalence of high cholesterol (rs = 0.3, p = 0.009). Compared to never-smokers, current tobacco users had signiﬁcantly reduced MPOD (H = 8.01, p = 0.005) and slower S2 recovery (H =

172 9.17, p = 0.002). We did not find an association between smoking status and BMI (rs = 0.17, p = 0.16) or age (rs = 0.15, p = 0.22). Dietary intake of the food groups assessed and alcohol intake was not significantly related to any of the visual function parameters. However, we did find an inverse association between current smoking status and fruit and vegetable consumption (rs = -0.31, p = 0.007). Those subjects classed as current smokers had significantly lower intake of fruits and vegetables (in units per week) compared to non-smokers (5.42 ±4.14 vs 8.27 ±3.68, U = 189, p = 0.007).

7.4.4 Health factors (cardiovascular health and BMI)

Cardiovascular disease was inversely correlated with the following visual parameters: PSRT (rs = 0.35, p = 0.003), RCB (rs = 0.24, p = 0.038) and S3 (rs = -0.24, p = 0.04). Compared to healthy subjects, those with cardiovascular disease had significantly reduced PSRT (48.18 ±22.63 vs 74.61 ±42.93, U = 164, p = 0.003) and S3 (-0.03 ±0.02 vs -0.05 ±0.01, U = 232, p = 0.04). According to BMI, 42% were normal weight (BMI <25), 34% were overweight (BMI 25-29) and 24% were obese (BMI 30-40). We found a significant inverse correlation between BMI and MPOD (rs = -0.37, p = 0.001), as shown in Figure 7.2. The average MPOD values for normal weight, overweight and obese subjects were 0.43 (±0.17), 0.35 (±0.18) and 0.33 (±0.15), respectively. There was a significant difference in the MPOD values between normal weight and obese subjects (U = 149, p = 0.01). We did not observe a difference between male and female BMI (U = 556, p = 0.27).

Figure 7.2: The relationship between MPOD and BMI.

173 7.5 Discussion

Ageing of the visual system and its function has been studied for centuries. However, to date, there is a surprising lack of research into how multiple lifestyle bahaviours (such as tobacco use, excessive alcohol consumption and poor diet) impact on visual performance and whether modifying these factors may help to slow down or even reverse age-related visual function decline. The aim of the present study was to examine the relationship between a variety of visual parameters and unhealthy lifestyle behaviours. As has been shown before, we found that both photopic and scotopic function declined among our subjects who were free of ocular disease. The strongest correlation with age was found for Mars CS (rs = 0.46). Previous studies have explained functional deficits in terms of optical and neuronal alterations with increasing age (Salvi, 2006). Our findings suggest that some of the losses are, in part, related to/or exacerbated by the presence of non-visual factors such as health status and tobacco use. After increasing age, current tobacco use had the greatest negative effect on visual function in agreement with one recent study (Klein et al., 2014). Compared to never smokers, current smokers had significantly reduced MPOD and S2. Additionally, these individuals were more likely to consume fewer fruits and vegetables in agreement with previous reports (Margetts and Jackson, 1993). In our study, we also observed a positive relationship between current tobacco use and increased prevalence of high cholesterol which is consistent with reports of higher intakes of saturated fat and altered serum lipoprotein levels among tobacco users (Dallongeville et al., 1996). Our data did not reveal a significant association between smoking status and BMI or age in agreement with past reports (Margetts and Jackson, 1993). Smoking has previously been shown to reduce plasma carotenoid levels (Handelman et al., 1996, Stryker et al., 1988) and MPOD (Hammond et al., 1996c). After controlling for various confounding factors, Hammond and colleagues found that non-smokers had over twice the MP level of smokers (0.34 vs 0.16). The underlying mechanism behind this is not fully established but is likely to be related to a combination of toxic, angiogenic, neovascular and oxidative effects of cigarette smoke (Velilla et al., 2013) which, coupled with reduced dietary intake of antioxidant nutrients, place the retina at a higher risk of degeneration and disease. Indeed, after ageing, cigarette smoking is the second most consistent risk factor related with AMD (Velilla et al., 2013). Reports on the effect of nicotine on dark adaptation are less consistent with some studies showing a negative effect (Calissendorff, 2009, Varghese et al., 2011), others no effect (Hammond et al., 1998a, Wiley, 1989), and still others a beneficial effect (Troemel et al., 1951). The inconsistencies in the findings are likely to be due to small

174 sample sizes, participant demographics and differences in methodologies. One recent study (Varghese et al., 2011), has shown a significant decrease in the dark adapted b-wave electroretinogram intensity response after chewing nicotine gum in 10 healthy non-smoking adults. Reduced scotopic function in tobacco users may be due to a combination of increased blood viscosity, the vasoconstrictive action of nicotine, and tissue hypoxia caused by cigarette smoke-induced carbon monoxide displacement of oxygen from hemoglobin (Havelius and Hansen, 2005). The inverse relationship between MPOD and BMI found in this study agrees with previous reports (Hammond et al., 2002, Nolan et al., 2004). Given that the majority of retinal carotenoids are stored in adipose tissue, it is plausible that the retina competes with this tissue for uptake of L and Z (Hammond et al., 2002). Low MP levels may also be related with the finding that subjects with higher BMI consume significantly fewer fruits and vegetables than those with lower BMI (Goss and Grubbs, 2005). A limitation of this study is that we have no quantification of serum carotenoid levels for our subjects. However, recent findings suggest that these measurements (along with skin tests) are not reliable biomarkers for MPOD (Richer et al., 2011). Additionally, health status was assessed on the basis of self-report rather than from participants’ medical records and food/drink intake was based on 1-week dietary recall instead of a food-frequency questionnaire. Despite the above limitations, our research fits in rather well with the wear and tear theory of ageing, which suggests that organs (including the eyes) are degraded by toxins in our diet and in the environment. Our findings also give support to the notion that unhealthy behaviours often occur in combination (Drieskens et al., 2010) and are linked to health complications such as hypercholesterolemia (Buck and Frosini, 2012). Currently 17% of the UK population is aged 65 and over, with the figure set to rise to 24% by 2037 (Demographic Analysis Unit, 2014). The overwhelming evidence on the spiralling health costs of an ageing population provide strong arguments for funding preventative approaches (Gray, 2005). To what extent declining visual performance in ageing can be slowed or reversed by introducing healthy lifestyle behaviours remains to be determined.

7.6 References

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180 8 Eﬀect of lutein and co-antioxidant supplementation on macular pigment and visual function in ageing

Contributions

Patryas L., Parry N.R.P., Carden D., Aslam T., Murray I.J., 2014. Eﬀect of lutein and co-antioxidant supplementation on macular pigment and visual function in ageing. Manuscript ready for submission. Conference presentations

None.

Acknowledgements

181 8.1 Abstract

8.1.1 Purpose

This study was designed to investigate the impact of macular pigment augmentation on photopic and scotopic visual function in normal older subjects.

8.1.2 Methods

74 subjects free of ocular disease (mean age 65.51) completed the 12 month randomized, double-blind, placebo-controlled study. Visual function was assessed by psychophysical tests including best-corrected visual acuity, contrast sensitivity, photostress recovery, resolution limit, macular pigment optical density (MPOD) and dark adaptation. Sub- jects were randomly assigned to either an active (n = 37) or a placebo (n = 37) group. Active formulation consisted of 20 mg lutein combined with vitamins and minerals. Data were collected at baseline, 6 months and 12 months.

8.1.3 Results

At the end of the trial, the active group mean MPOD significantly increased from 0.33 ± 0.15 to 0.41 ± 0.15 (p = 0.00), whereas the placebo group slightly decreased from 0.41 ± 0.18 to 0.38 ± 0.17 (p = 0.11). According to a mixed design, repeated measures ANOVA, and despite a 24% MPOD increase in the active group, there were no significant differences between the two groups over the three visits for any of the tested visual parameters.

8.1.4 Conclusions

Ingestion of lutein signiﬁcantly increases MPOD. Further research is needed to explore the potential eﬀect of MPOD increase on visual function, particularly in older populations engaging in several unhealthy lifestyle/dietary behaviours, those with low MPOD and suboptimal visual function.

182 8.2 Introduction

The human retina is a highly specialized organ well equipped to provide clear vision over a wide range of light intensities from 10-4 to 106 cd.m-2 (a luminance range of about 10 log units; Zhou et al. 2009). With increasing age, however, this tissue is subject to cumulative damage from short-wavelength light exposure and oxidative stress as a result of physiological processes, toxic lifestyle and environmental factors (Taylor, 1999). The central macula (abundant in cones responsible for acute vision and colour discrimination) along with mid-peripheral and peripheral macula (enriched in rods specializing in scotopic vision) is particularly vulnerable to ageing and disease-related processes (Jackson and Owsley, 2003). Both oxidative stress and blue light exposure have been associated with cellular dysfunction and impaired visual performance in ageing. These two mechanisms may also lead to an increased risk of age-related macular degeneration (AMD); the world’s leading cause of sight loss in the elderly population in the developed world (Friedman et al., 2004). Given the lack of treatment options for dry AMD, the emphasis has fallen on modifiable risk factors (Guymer and Chong, 2006), screening and early diagnosis (Kanagasingam et al., 2014) and nutritional therapy (Sin et al. 2013). Several studies have demonstrated a link between increased AMD risk and low antioxidant status (Gale et al., 2003), delayed disease progression (AREDS, 2001) and, in some cases, amelioration following dietary supplementation with antioxidants (Richer et al., 2002, Murray et al., 2013). In particular, three retinal carotenoids lutein (L), zeaxanthin (Z) and meso-zeaxanthin (MZ) have been the subject of intense investigation in recent years, both in patients with AMD and healthy participants (Sin et al., 2013, Loughman et al., 2010). These lipid-like molecules selectively accumulate within the macula where they are collectively referred to as the macular pigment (MP). L, Z and MZ are entirely of dietary origin and (with the exception of MZ) are abundant in foodstuffs such as egg yolks, green leafy vegetables (e.g. kale) and brightly coloured fruits and vegetables such as oranges, orange peppers and corn (Sommerburg et al., 1998). Higher levels of MP is thought to improve visual performance and comfort in healthy eyes due the ability of the carotenoids to filter blue light (resulting in reduced chromatic aberration and light scatter; Engles et al. 2007), absorb plane polarized light (Nolan et al., 2011), reduce wavefront aberrations (Kvansakul et al., 2006) and opti- mize neural efficiency (Renzi and Hammond, 2010). MP may also be correlated with cognitive function in ageing (Vishwanathan et al., 2014). Additionally, the biochemical

183 (antioxidant) and blue light filtration properties of MP may reduce photochemical and oxidative stress, which may protect the retina from degenerative diseases such as AMD (Ahmed et al., 2005). Several studies have reported on the potential association between MP level and a number of psychophysical measures such as glare disability and photostress recovery (Hammond et al., 2013), photophobia (Wenzel et al., 2006b), scotopic function (Ham- mond et al., 1998b, Patryas et al., 2014) and acuity (Engles et al., 2007). On the basis of these reports, L and/or antioxidant supplements are being prescribed to patients with dry AMD (Kaminski et al., 1993) and taken by healthy subjects in the hope of averting vision loss, improving function and preventing disease (Mares-Perlman, 1999). However, as far as we are aware, there has only been one double-blind, randomized, placebo-controlled trial of the effect of carotenoid supplementation on MP and visual function in healthy subjects (Nolan et al., 2011). Nolan et al. found a modest increase in MP with no effect on visual performance in a large cohort of younger subjects (<41 years old). Despite, an increasingly ageing population and higher prevalence of visual impairment among this group, the majority of interventional studies have focused on younger cohorts. In this study we concentrated on an exclusively older population. If visual function loss can be delayed or improved in the older eye as a result of nutritional supplementation, this would lead to improved quality of life and decreased healthcare costs. Our aims were twofold. First, we wanted to establish the amount of MP increase as a consequence of L and co-antioxidant supplementation, and second, we wanted to see whether a change in MP level has any effect on scotopic and photopic visual function in normal ageing.

8.3 Methods

8.3.1 Study design

We conducted a double-blind, randomized, placebo-controlled clinical trial investigating the eﬀect of daily ingestion of macular carotenoid (L) and co-antioxidants versus placebo. The study was approved by the Greater Manchester Research Ethics Com- mittee and registered on clinicaltrials.gov database (NCT02147171). Informed consent was obtained from all participants and the tenets of the Declaration of Helsinki were followed. All methods employed in this study have been published previously (Patryas et al., 2013, 2014). Baseline anthropometric, dietary and lifestyle data from this clinical trial have already been reported in a separate manuscript (Patryas et al., under review).

184 The study was conducted over a period of one year at the University of Manchester, UK.

8.3.2 Subjects

Self-selected recruitment of subjects was facilitated through an editorial in the local newspaper and from poster and internet advertisements within the university campus. Exclusion criteria were a history of ocular disease, use of nutritional supplements 6 months prior to study commencement, body mass index (BMI) of more than 40 and visual acuity (VA) worse than 0.2 logMAR. Eighty eight participants between the ages of 50 to 90 years old volunteered for the study, of which two participants did not meet the inclusion criteria. Of the 86 participants enrolled onto the study, 12 were lost to follow-up for a variety of reasons including diﬃculty swallowing the tablets (n = 2), health reasons unrelated to the study (n = 2), pyrosis post tablet ingestion (n = 1) and unspeciﬁed reasons (n = 7). Seventy four participants (mean age 65.51 ±8.17 years) who met the inclusion criteria and successfully completed the study, by chance, fell into two equal groups: active group (n = 37) and placebo group (n = 37). All reported analyses pertain to only these two groups.

8.3.3 Supplement speciﬁcations and randomisation

The supplements (active and placebo) were provided in identical white tubs with tamper evident seals and child resistant lids. The study label on each tub speciﬁed the participant number, directions for use, researcher contact details and tablet expiry date. Each tub contained 90 tablets (90 days supply). The active study tablets (see Table 8.1 for formulation) were a modiﬁed version of the commercially available Visionace® (Vitabiotics, UK). The placebo tablet was identical in size and appearance to the active tablet and consisted of dibasic calcium phosphate, starch and cellulose. Each participant was allocated four tubs (360 tablets) with two tubs dispensed at the end of each visit and the remainder stored by the investigator in a dark, cool room. Each set of four tubs was assigned a number (1-90) using a random number generator by the supplement manufacturer (Vitabiotics). The randomisation code remained with the manufacture until all data had been collected and analysed. Subjects were instructed to take one tablet per day with their main meal and advised not to alter their normal diet throughout the study period. Subjects were instructed to

185 keep unused supplements and to bring back the plastic tubs at each visit. Compliance was assessed by tablet counting and by regular telephone calls and emails.

Table 8.1: Active supplements study formulation.

8.3.4 Visual function tests

Visual function was assessed by psychophysical tests including best-corrected visual acuity (BCVA), contrast sensitivity (CS), photostress recovery time (PSRT), resolution limit, macular pigment optical density (MPOD) and dark adaptation. A typical study visit lasted approximately 2 hours. Before conducting each test, subjects were given a thorough explanation of the procedure and were allowed a period of practice to help reduce potential learning effects and improve performance. Subjects were assessed at baseline (visit 1), 6 months (visit 2) and 12 months (visit 3). Lenticular opacities were graded according to a Lens Opacities Classification System III (Chylack et al., 1993). Fundus photographs were taken with a TRC-NW6S Non- Mydriatic Retinal Camera (Topcon, Tokyo, Japan) and graded according to a macular grading scale (Jackson et al., 1999). Subjects with significant cataract (grade >3) and significant macular disturbance (grade >1) were excluded from the study. All tests were performed monocularly with natural pupils (except for dark adaptation), and with optimal refractive correction for the testing distance.

186 A questionnaire was also completed at baseline and ﬁnal visit for each subject to characterize anthropometric and lifestyle data such as body mass index (BMI), iris colour, diet, smoking, personal or family history of eye disease and use of dietary supplements (reported in Patryas et al., under review).

8.3.4.1 Best-corrected visual acuity

On the day of testing, the subjects’ monocular BCVA was determined using an internally illuminated Early Treatment of Diabetic Retinopathy Study (ETDRS) chart. BCVA was recorded in logarithm of the minimum angle of resolution (logMAR) at a distance of 4 metres. The eye with the best acuity was chosen as the study eye. In cases where the acuity was the same in both eyes, the right eye was chosen.

8.3.4.2 Contrast sensitivity

Stimuli were generated using a VSG 2/5 card (Cambridge Research Systems [CRS] Ltd., Cambridge, UK) driven by an Avantek personal computer and presented on a calibrated and gamma-corrected high-resolution cathode ray tube (CRT) monitor (Sony GDM- F500R, Tokyo, Japan). The Visual Psychophysics Engine software (CRS, Cambridge, UK) was used to render stimuli composed of horizontal sinusoidal gratings ﬂickering in counter-phase at 0.5 Hz. The patterns were viewed from 1.8 metres with natural pupils and subtended a visual angle of 7°. They were displayed in the centre of the monitor with a central black ﬁxation target subtending 0.54° of visual angle (see Figure 8.1).

Figure 8.1: Example contrast sensitivity stimulus composed of horizontal sinusoidal gratings.

Photopic CS was measured for 3 spatial frequencies: 1, 4 and 12 cycles per degree (cpd), whereas scotopic CS was only measured at 4 cpd. The background and mean

187 luminance were set to 50 and 2 cd.m-2 for photopic and scotopic viewing, respectively. Testing was performed under dark room conditions (10 lux; Isotech 13332, Taiwan). Contrast thresholds (in dB) were determined by method of adjustment using step sizes of 1dB. Five thresholds were measured for each spatial frequency. In a pilot study, the method of adjustment proved to be the most rapid method of attaining CS whilst remaining highly accurate and repeatable (coefficient of repeatability [CoR] = 8.45 dB, correlation coefficient [r] = 0.90 [p = <0.0001]). Contrast was defined as:

(Lmax – Lmin)/(Lmax + Lmin) x 100% where Lmax and Lmin refer to the maximum and minimum luminance on the screen, respectively. CS was additionally measured using the Mars Letter Contrast Sensitivity Test (Mars Perceptrix Corporation, NY, USA) using standardized techniques and letter-by-letter scoring procedure (Arditi 2005). Subjects were tested on three separate charts giving a mean score of three measurements.

8.3.4.3 Photostress recovery time

The macular stop on a direct ophthalmoscope (Keeler Ltd, Windsor, UK) was used to produce 6.15 log trolands of retinal illuminance, as measured using a PR1500 spot photometer (Photo Research, CA, USA). The light source (adjusted to full intensity) was held 1cm from the pupil and directed towards the fovea for 30 seconds, resulting in a 98% cone photopigment bleach (Hollins and Alpern, 1973). Subjects were instructed to look directly at the light source and correct fixation was monitored during light exposure by observing the foveal reflex. Before bleaching, the subject’s BCVA was measured on a distance logMar letter chart. Photostress recovery was measured in seconds until the subjects reached 0.1 logMAR (one line) less than their BCVA. This method was chosen because it is easily performed and produces rapid and reliable results, comparable with a Maxwellian view optical system (Margrain and Thomson, 2002). The measurement is not affected by the pupil size due to the small image of the light filament and imaging in the pupil plane (Margrain and Thomson, 2002). As such, measurements were performed with natural pupils and repeated three times with 2 minute recovery breaks between bleaches.

188 8.3.4.4 Resolution limit of gratings

The stimuli were generated with a TWG 501 function generator (Feedback Ltd, Sussex, UK) and presented on a 608 high-resolution monitor (Tektronix, OR, USA) with green P31 phosphor (mean luminance 87.5 cd.m-2). A horizontal sine wave grating pattern was viewed through an aperture of 1.6° of visual angle at a distance of 2 metres. Resolution limit was obtained by manually reducing the spatial frequency from above the maximum resolution limit (60 cpd) until the subject reported the presence of the grating. This was repeated ﬁve times.

8.3.4.5 Macular pigment optical density

MPOD was measured using MPS 9000 (Elektron Technology Ltd, UK). The algorithm of this device and procedure is described extensively elsewhere (van der Veen et al., 2009, Makridaki et al., 2009). Briefly, MPOD was determined using the principle of heterochromatic flicker photometry. The subjects reported the onset of flicker as the temporal frequency of blue-green flickering lights was reduced. The luminance ratio of these lights was plotted against temporal frequency for a series of blue-green ratios. A minimum is obtained when the blue and green lights are isoluminant. The MPOD was calculated from the difference between the minima of central (0°) and peripheral (8°) curves and expressed in terms of optical density units (DU).

8.3.4.6 Dark adaptation

Dark adaptation was measured psychophysically on a CRT monitor, as described previously (Patryas et al., 2013). Neutral density filters were used to extend the CRT’s dynamic range. The chosen study eye was dilated with 1% tropicamide and 2.5% phenylephrine to achieve a minimum pupil diameter of 6 mm. Subjects were seated 60 cm from the monitor and placed their heads on chin/head rest in a totally darkened room. They were dark adapted for 5 minutes followed by a practice session for a further 5 minutes. An electronic 0.9 ms flash of white light (Nikon Speedlight SB-800, Tokyo, Japan) was used to produce an estimated 98% visual pigment bleach (Rushton and Powell, 1972). The subjects fixated a red cross throughout the entire test duration and responded to a 1° circular test spot (1931 CIE x = 0.31, y = 0.316), temporally modulated at 1 Hz and presented at 5° in the inferior field. Monocular thresholds were measured immediately after bleaching using the method of adjustment and were set at approximately twice per minute for a duration of 30 minutes.

189 8.3.4.7 Statistical analysis

All statistical analyses were performed in SPSS version 20 (SPSS Inc., Chicago, IL). A minimum study sample size of 72 participants was calculated based on previously published work (Berendschot et al., 2011, Murray et al., 2013) in order to yield at least 90% power (for all outcome measures) at the 5% level of significance. Each data set was checked for normality of distribution using the Kolmogorov-Smirnov test with Lilliefors significance correction. A two-way, mixed design repeated measures analysis of variance (RM ANOVA) was used to analyse all the normally distributed data (MPOD, photopic CS at all spatial frequencies and resolution limit). The data were stratified by GROUP (placebo or active) and VISIT (n = 3). This parametric procedure was also used to analyse data which did not follow normal distribution (scotopic CS, BCVA, photostress recovery, Mars CS and all dark adaptation parameters) as it is robust against mild violations of assumptions when the sample size is large (Ghasemi and Zahediasl, 2012). Where appropriate, we used the Greenhouse-Geisser correction for violation of sphericity. Additionally, not normally distributed data were analysed using the non-parametric Friedman’s one-way ANOVA. Mann-Whitney U tests and t-tests were used to make comparisons between active and placebo group baseline data. Means and ± standard deviations (SD) are presented in text and tables. We used the 5% level of significance throughout our analysis, with adjustment for multiple testing. Dark adaptation data were fitted with a single exponential component to the cone phase and two linear components to the rod phase, as described previously (Patryas et al., 2013). The non-linear regression technique was implemented in Matlab (Mathworks, MA, USA) and yielded the following parameters of the dark adaptation curve: cone time constant, cone threshold, the rod-cone break (RCB), the slopes of the second (S2) and third (S3) rod components, rod-rod break (RRB), and the rod threshold (T30), as shown in Figure 8.2. Graphs were plotted in Origin@ (Northampton, MA, USA).

190 1

) 2

- 0 C o n e t i m e c o n s t a n t m . d

c - 1 R o d - C o n e b r e a k g o l

( - 2 S 2 d l C o n e t h r e s h o l d o T h - 3 3 0 s e r

h - 4 T R o d - R o d b r e a k S 3 - 5 0 5 1 0 1 5 2 0 2 5 3 0 3 5 T i m e ( m i n u t e s )

Figure 8.2: Typical dark adaptation data for a young, healthy observer measured at 5° in the inferior ﬁeld, using a 1° white light stimulus following an 82% bleach. The data points were ﬁtted with an exponential-bilinear model. S2 is the second rod component, S3 is the third rod component and T30 is the threshold 30 minutes after the onset of bleach. Summed squared error = 0.3, r2 = 0.9.

8.4 Results

Compliance was assessed by unused tablet count and computed as a percentage (number of tablets consumed divided by the number that should have been consumed). The mean rates of compliance were 79% and 81% for the active and placebo groups, respectively, with no significant difference between the two study arms (independent t-test, t = 1.23, p = 0.25). With the exception of one subject who complained of pyrosis the morning after ingesting the tablet, no other adverse events related to study supplements (active or placebo) were reported. All participant anthropometric and lifestyle data were assessed for differences between visit 1 (baseline) and visit 3 (final) using paired t-tests. There was no change between the study visits for BMI, diet, smoking history, alcohol use, intake of dietary supplements or use of medicines. Baseline measurements including anthropometric and lifestyle data (discussed in detail in a separate manuscript Patryas et al., under review), were analyzed for differences between the two groups (active versus placebo) using independent t-tests and Mann- Whitney U tests. With the exception of MPOD there were no significant differences between the two groups. As shown in Table 8.2, the mean placebo group MPOD was higher at baseline (0.41 ± 0.18) compared to the active group (0.33 ± 0.15; t = 1.69, p = 0.02).

191 A summary of all visual function parameters means, SDs and statistical comparisons is presented in Table 8.2. A two-way, mixed design, RM ANOVA of the MPOD data revealed a significant main effect of VISIT (F[2,144] = 3.07, p = 0.049) and a highly significant GROUP*VISIT interaction (F[2,144] = 13.73, p = <0.0005). A one-way repeated measures ANOVA of the active group showed a highly significant effect (F[2,72] = 14.02, p = <0.0005). A Bonferroni post hoc test showed statistically significant differences between visit 1 and visits 2 (p = 0.006) and 3 (p = <0.0005) but not between visit 2 and 3 (p = 0.098). A one-way RM ANOVA showed no change in MPOD for the placebo group (F[2,72] = 2.26, p = 0.1).

192 Table 8.2: Test parameter means, SDs and statistical comparisons. The MPOD data for baseline and final visits are illustrated in Figure 8.3 in box plot format for active and placebo groups. We calculated the change in MPOD between baseline and final visit for the two groups. As seen in Figure 8.3 (c), the active group MPOD increased on average by 0.08 (24% increase), whereas the placebo group MPOD decreased by 0.03. It is worth mentioning that, of the 37 participants in the active group, five did not respond to supplementation where the MPOD either did not change or decreased slightly. The mean MPOD of the nonresponders at baseline (0.44 ± 0.18) was higher compared to responders (0.31 ± 0.14) with one participant displaying a very high MPOD (0.76).

Figure 8.3: (a, b) Box and whisker plots for baseline and final visit MPOD for active and placebo groups. (c) Mean change in MPOD between baseline and final visit for active and placebo groups. Error bars: 95% confidence intervals.

One-way ANOVA (parametric and nonparametric) of the active group data showed significant improvements in BCVA, photopic CS at 1 and 12 cpd, Mars CS, cone threshold and rod threshold (T30). However, we also found significant improvements for the same visual parameters in the placebo group (see Table 8.2). Therefore, despite non- normality, it was deemed appropriate to perform a two-way, mixed design, RM ANOVA on these parameters. This analysis showed that overall, taking both groups into account, there were no significant differences between visits for these and the remaining visual parameters.

8.5 Discussion

To date only a handful of clinical trials have explored the eﬀect of MP augmentation on visual function in normal eyes (see Loughman et al. 2010 for a review), of which none, to our knowledge, examined exclusively older cohorts. The present study was designed to investigate the eﬀect of L and co-antioxidant supplementation on photopic

194 and scotopic visual function in ageing. The results suggest that, despite a statistically significant increase in MPOD in the active group, none of the tested parameters were significantly different between visits. Our findings are in agreement with two similar clinical trials involving younger cohorts. Bartlett and Eperjesi (2008) used a combination of nutrients including 6 mg L and vitamins A, C, E, zinc and copper, and found no improvement in any of the outcome measures over a period of 9 or 18 months. Unfortunately, MPOD was not measured in that study. Nolan et al. (2011), again, using a combined formulation and a much larger cohort, also reported no effect on visual performance despite a significant increase in MPOD. The results presented here, not only give support to the aforementioned studies, but further extend them by examining a wider range of psychophysical tests including dark adaptation. Interestingly, as reported in Nussbaum et al. (1981), several of the older studies found improvements in dark adaptation post L supplementation using doses ranging from 5-20 mg of the ester, taken over periods of 2-6 weeks. Recent reports on the association between improved scotopic function in subjects with higher MPOD (Hammond et al., 1998b), association between dark adaptation rod recovery and MPOD in normal older subjects (Patryas et al., 2014), and improvement in dark adaptation in subjects with AMD post L supplementation (Berendschot et al., 2011) lend credibility to these studies. However, in the present investigation, we were unable to replicate these results in a cohort of older subjects free of ocular disease. Our findings are also at odds with three recent interventional studies which reported improvements in mesopic CS (Kvansakul et al., 2006), grating visibility and photostress recovery (Stringham and Hammond, 2008), and VA and glare CS (Loughman et al., 2012) in normal healthy subjects. Reasons for the inconsistencies between these and our study may be related to methodological differences in terms of study design, choice of supplement and participant characteristics. For instance, in their design, Stringham and Hammond did not include a placebo group. Such a design makes it difficult to ascertain whether any effect is due to the treatment itself or to some other factor such as learning effects. Indeed, as previously reported (Bartlett and Eperjesi, 2008), we found a substantial and significant improvement in a number of visual parameters for the placebo group. Although care was taken to prevent learning effects, it is conceivable that this group improved as a result of test familiarity and repeated measurements which highlights the importance of a control group. The Loughman et al. study design was single-masked (thus possibly introducing bias) and in the Kvansakul et al. study visual performance was not measured at baseline but only after 6 months of supplementation. Additionally, all three studies investi-

195 gated smaller cohorts with a mean age of 51 years or less and used a xanthophyll-only supplement as opposed to a combined formulation used in the present investigation. Our modest 0.08 (24%) increase in MPOD in the active group is consistent with one previous (and similar) clinical trial (Nolan et al., 2011). More recently, studies using supplements comprising solely of xanthophylls have shown more substantial and rapid MPOD response compared to those using combined-nutrient formulations (Loughman et al., 2012). We chose to use a multi-nutrient supplement for several reasons. First, carotenoids (including L) are known to work synergistically with vitamin C and E (Blakely et al., 2003). Second, as with AMD, the ageing process of the retina is multi- factorial and thus may require a variety of nutrients. Indeed, the recommended formulation for the prevention of AMD progression is also a multi-nutrient supplement containing several vitamins and carotenoids (AREDS, 2001, AREDS2, 2013). Third, our supplement has the advantage of being widely available. It must be noted, however, that the L content was modified for this study to provide 20 mg L esters. This was based on previous work showing significant serum and retinal response, and improved visual function with 10-20 mg daily xanthophyll supplementation (Richer et al., 2004, Stringham and Hammond, 2008, Loughman et al., 2012) compared to no change observed in studies using lower doses (Bartlett and Eperjesi, 2008). In our study, there were five participants (13%) in the active group that failed to respond to supplementation. This is similar to previous reports in studies investigating AMD patients (Murray et al., 2013) and healthy subjects (Nolan et al., 2011). There have been several reasons proposed for serum and/or retinal non-responsiveness. First, a high baseline MPOD may already be at a saturation level rendering any further increase either slow or not significant (Bone et al., 2003). Indeed, our results show that the nonresponders had on average higher baseline MPOD than the responders. Second, it could be that non-responsiveness to carotenoid supplementation along with MP level may be genetically closely regulated (Hammond et al., 1997a). For instance, Landrum and Bone (2001) proposed genetic variations among individuals resulting in reduced ability of the xanthophyll-binding proteins and poor retinal response. Likewise, an inability to bio-convert retinal L to MZ (the predominant carotenoid in central macula), possibly as a result of increased age and cigarette smoking (Kirby et al., 2010), may also impede a central MPOD change. Third, non-responsiveness could be an artifact of the methodology employed to measure MPOD, which is typically constrained to only the central measurement (0.5°). One study has reported a 100% response rate following intake of 10 mg L diesters which the authors attributed to using scanning laser ophthalmoscopy-based technique for measuring MPOD (Berendschot et al., 2000). Finally, non-responsiveness may be attributable to tablet characteristics and mode of

196 delivery. For instance, different methods of tablet ingestion can affect its gastrointestinal absorption and retinal bioavailability. Our study participants were advised to take the supplements, which were hard coated, with their main meal to increase bioavailability. A recent study (Loughman et al., 2012), using a supplement suspended in oil, has demonstrated a rapid response, coupled with significant functional benefits, which may be related to improved bioavailability of xanthophylls for capture by the retina with this delivery method. Of note is the negative response among the active group which has also been documented before (Hammond et al., 1997a). To date, there is no clear explanation as to how and why this occurs but it may be that the augmented MP distribution, as opposed to increasing centrally, broadens out peripherally resulting in flattening of the peak. Indeed, supplementation with L, Z, and MZ has previously been shown to alter spatial distribution of MPOD resulting in the disappearance of “central dips” (Connolly et al., 2010). Recent reports, using all three carotenoids, have demonstrated a substantial and rapid MPOD increase (Connolly et al., 2010), coupled with significant visual function improvements (Loughman et al., 2012). In our study, a significant MPOD increase was observed at 6 months of supplementation. Additionally, even after a significant increase in MPOD at 6 or 12 months, we did not find any functional benefits. There are a number of possible explanations for the lack of significant findings in our study. First, over half of the subjects in the active group (60%) already had a medium to high MPOD and appreciable visual performance. As previously noted, higher MPOD (0.3 or greater) may be superfluous to visual function (Reading and Weale, 1974). Second, our non-invasive assessment of compliance may have over-estimated the true adherence rate leading to false negative results. Third, the supplement type, dose and method of delivery may have hindered serum and retinal bioavailability. In agreement with our results, previous studies have reported slow L retinal uptake (Bone et al., 2003, Johnson et al., 2000, Nolan et al., 2011), therefore, it is plausible that a longer follow up period was required to see any effect on visual function. Finally, studies which have shown functional changes post supplementation have reported significantly greater MPOD changes compared to ours (Stringham and Hammond, 2008, Loughman et al., 2012). A recently proposed hypothesis suggests that supplementing with all three carotenoids (including MZ), is important for, both, a substantial MPOD response and visual performance enhancement (Loughman et al., 2012). The implications of these findings, in terms of potential functional and biochemical benefits in exclusively older cohorts, warrant further study. There were some limitations in our study, which have, in part, been discussed. First,

197 we used a multi-nutrient formulation, which does not permit interpretation of the data on the basis of macular xanthophylls alone. Second, we did not perform serum carotenoid analysis. Although this additional measure was considered during study design, we chose to omit it due to lack of funds, observed poor correlation between serum carotenoid measurements and MPOD (Richer et al., 2011) and the possibility of hindering recruitment due to the invasive nature of sample collection. In conclusion, the current study adds to and further extends the existing literature by examining an exclusively older cohort and testing a multitude of visual parameters. The results of this study do not support a link between increased MPOD and improvement in visual function in healthy older eyes. Currently, there seems to be a lack of evidence to support functional beneﬁts resulting from MP supplementation, in older eyes at least. Therefore, the use of ocular-speciﬁc nutrients in older persons, who already have a healthy diet, may be redundant. Further research is warranted, particularly paying close attention to subjects engaging in several unhealthy lifestyle/dietary behaviours, those with low MPOD and suboptimal visual function.

8.6 References

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202 9 Conclusions and future work

9.1 Summary of key ﬁndings

The main purpose of this project was to assess the impact of MP augmentation on visual function in normal ageing. Given that rod deterioration precedes cone deterioration in healthy and diseased eyes, a big emphasis has been placed on scotopic visual function as assessed by dark adaptometry. Preliminary experiments in the Methods section were designed to test dark adaptation methodology and compare the results with those already published. The slope of the S2 component was found to be independent of many variables including stimulus size and location, bleach percentage (above 20%), bleach frequency and pupil size. In this respect, S2 is a unique parameter amongst those that can be extracted from the dark adaptation curve and can easily be compared between studies using different methodologies. The average slope of S2 for a young, healthy individual was found to be 0.24 log cd.m-2.min-1, in agreement with previous studies. A number of important conclusions can be drawn from this series of preliminary experiments. First, the CRT-based dark adaptometry technique is robust and produces results that are comparable with previous studies. Importantly, it is suitable for naive subjects of all age groups as demonstrated in the pilot study (Chapter 4). Second, repeated bleaching does not have a cumulative effect on pigment regeneration in healthy younger or older eyes. It is useful to know that any inaccuracies during bleaching can be corrected after a short wait (around 20 minutes) rather than rescheduling the appointment for another day. Finally, firm control of the pupil size is essential in achieving uniform bleaching across visits in supplementation studies assessing the change in the shape of the entire dark adaptation curve over time. The use of mydriatics is also essential when studying older eyes with senile miosis and hazy media, as these reduce the magnitude of the bleach. Chapter 4 explored the repeatability of CRT dark adaptometry and the extent of vision loss (particularly scotopic sensitivity) in healthy ageing. Dark adaptation and VA were measured in a group of 33 subjects with no ocular pathology aged between 15 and 68 years old. The results of this investigation showed that dark adaptation recovery slows with increasing age despite no significant change in VA or fundus appearance. The technique described had excellent repeatability, was easily implemented and correlated well with previous research. Therefore, it was deemed a useful tool for documenting visual changes in clinical trials assessing retinal health in the older eye with and without ocular pathology.

203 Chapter 5 evaluated the potential link between MP and dark adaptation. Dark adaptation and MPOD were measured in the same cohort as that presented in Chapter 4. The results showed that subjects with lighter irides had on average 40% lower MPOD than those with darker irides, which has been documented previously. The average MPOD for the whole group also correlated well with previous studies using similar populations. A new finding from this study was that the group mean MPOD was weakly associated with rod-mediated dark adaptation parameters. A paired t-test showed that the average S2 for the lower 10th percentile of MPOD was significantly slower compared with the upper 10th percentile. Although it cannot be concluded from this study that the association between MPOD and dark adaptation represents a causal effect, the results warrant further investigation, particularly since both parameters are thought to be accurate bioassays of retinal health. Chapter 6 investigated the effect of statin use on MPOD and visual function. A recent study has shown an inverse relationship between MPOD and statin use (Renzi et al., 2012). Furthermore, it might be expected that the statin-induced reduction of serum lipoprotein profile may interfere with the process of retinal carotenoid accumulation in the eye. In this investigation, subjects taking statins were identified (n = 25) and matched with 25 participants not using statins for age and BMI. The results showed that statin users had a higher proportion of males, higher prevalence of current smoking status and poorer general health (e.g. hypertension, high cholesterol and heart disease). Statin users also had significantly reduced MPOD, prolonged PSRT, and deficits in a number of dark adaptation parameters. It is not clear whether these effects are a result of taking the medication, or the co-existing lifestyle and health factors among statin users (e.g. smoking and poorer general health). Future studies exploring this association should aim to make that distinction, however it may be difficult finding subjects taking statins who are in perfect cardiovascular health. Chapter 7 examined the baseline data from the clinical trial (n = 74, mean age 65.51). The aim was to ascertain the effect of unhealthy lifestyle factors (e.g. smoking, alcohol consumption and poor diet) and health status (e.g. hypertension, cholesterol status and heart disease) on visual function. The results showed that after increasing age, smoking had the greatest negative effect on visual function. Smokers were found to have reduced MPOD, slower S2, higher prevalence of raised cholesterol and lower fruit and vegetable intake. MPOD was also reduced among obese subjects. Further research is needed in order to ascertain to what extent declining visual performance in ageing can be slowed or reversed by introducing healthy lifestyle behaviours. Chapter 8 investigated the impact of MP augmentation on visual function in normal older subjects (n = 74, mean age 65.51) in a 12 month randomized, double-blind,

204 placebo-controlled study. Active formulation consisted of 20 mg lutein combined with vitamins and minerals. Data were collected at baseline, 6 months and 12 months. The results showed that, despite a 24% MPOD increase in the active group, there were no significant differences between the two groups over the three visits for any of the visual parameters. These results are in agreement with two previous, well-controlled clinical trials using similar supplement formulations. Studies using different formulations and less stringent masking/placebo methodologies have shown positive effects on visual function in healthy eyes (Stringham and Hammond, 2008, Kvansakul et al., 2006, Loughman et al., 2012). Several factors may account for the lack of significant findings in our study such as supplement type. Indeed, Loughman et al. (2012) recently suggested that supplementing with all three carotenoids (including MZ), is important for, both, a substantial MPOD response and visual performance enhancement. In summary, the findings in this thesis reveal that dark adaptometry is a very sensitive marker of retinal health in normal ageing. The S2 parameter of dark adaptation is capable of distinguishing between younger and older observers despite no difference in VA or fundus appearance between the two groups. Additionally S2 may be correlated with MPOD. Given that both parameters are correlated with retinal function and antioxidant status, this potential link warrants further study. Importantly, the analysis of the clinical trial baseline data has shown that individual lifestyle habits, health status and certain medication use may further exacerbate retinal degeneration in normal ageing. These findings echo the wear and tear theory of ageing, which implicates toxins in our diet and in the environment as factors partly responsible for degeneration. The findings also give support to the notion that unhealthy behaviours often occur in combination and are linked to health complications such as hypercholesterolemia. The percentage of older people living in the UK is set to rise to 24% by 2037. The overwhelming evidence on the spiralling health costs of an ageing population provide strong arguments for funding preventive approaches. Additionally, the older population is more likely to be using statins. These medications are likely to have effects on serum carotenoids, their bioavailability and MP accumulation. Statin use has previously been associated with reduced MPOD (Renzi et al., 2012), as found in this study, as well as AMD progression (VanderBeek et al., 2013). For this reason, more research into these compounds is needed as well as more informed management of patients suffering from AMD who are also statin users. For these patients, education with regards to healthy lifestyle choices and restricting statin use to approved indications will become more important. Currently, a growing number of the healthy population is using ocular supplements

205 in the hope of averting vision loss, improving function and preventing disease (Mares- Perlman, 1999). The results of this study do not support a link between increased MPOD and improvement in visual function in healthy older eyes, therefore, the use of ocular-speciﬁc nutrients in older persons, who already have a healthy diet, may be redundant.

9.2 Suggestions for future work

This project has attempted to investigate the relationship between nutritional supplementation and visual function in normal ageing. Additionally, the effect of unhealthy lifestyle habits, health status and use of certain medications was examined. There were several limitations in each of the studies which have already been outlined. In brief, these limitations include large age range, small sample size and well nourished populations in the study presented in Chapter 5. In Chapters 6 and 7, statin users and participants’ health status were identified on the basis of self-report rather than from medical records. In Chapter 8, a multi-nutrient formulation was used which did not permit interpretation of the data on the basis of macular xanthophylls alone. Finally, in all the studies presented in this thesis, serum carotenoid and lipoprotein levels were not measured and food/drink intake was based on 1-week dietary recall instead of a food- frequency questionnaire. The remainder of the present thesis chapter aims to highlight areas for future investigation with regards to retinal ageing, lifestyle and health factors, and nutritional supplementation. The results of the Methods section showed that for healthy younger and older participants the washout period between bleaches was relatively short and did not have a significant effect on dark adaptation function. It would be interesting to examine whether the same occurs in AMD subjects. It could be that the recovery between bleaches for these individuals is protracted as a result of Bruch’s membrane-RPE-photoreceptor impairment. In Chapter 4, the S2 parameter was strongly associated with ageing. Since pupils were not dilated in that study, analysis of the effect of ageing on cone-mediated parameters was not possible due to bleaching inconsistencies resulting from wide variation in pupil sizes. A further study will be required to assess the effect of ageing on the rest of dark adaptation parameters using CRT-based methodology. In Chapter 5, there was a weak relationship between MPOD and S2. In this study the majority of observers had normal MPOD values. Future work in this area may need to focus on those with low MP levels and poor night vision in order to yield stronger correlations between MPOD and rod kinetics.

206 In Chapter 6, there was a relationship between lower MPOD and reduced visual function among statin users. However, since differences in lifestyle and health factors among older people may also relate to visual function deficits, future studies will need to focus on subjects who take statins but who are in good general health and do not engage in unhealthy lifestyle factors (e.g. smoking). However, it may be somewhat difficult to recruit statin users with no history of any health complications given that hypertension, increased cholesterol and/or vascular disease are the current criteria for prescribing statins. This may change in the near future, as there is a general trend towards prescribing statins to everyone over the age of 50, irrespective of health status (Lancet, 2014). Another interesting study would be to test the theory that “an apple a day is as good as a statin a day”, if not better, at reducing the risk of heart attacks, strokes and other major vascular events (Briggs et al., 2013); and to compare the effect of each intervention on MPOD. In Chapter 7, there were statistically significant visual function reductions among subjects who smoke, are obese and have health complications. It would be interesting to ascertain to what extent these visual deficits can be slowed or reversed by introducing health-promoting lifestyle behaviours. Physical activity was not measured in this study. Regular physical exercise has been correlated with reduced blood pressure and abdominal fat, improved serum lipid lipoprotein profiles and lower incidence of systemic inflammation and endothelial dysfunction (Warburton, 2006). Additionally, an active lifestyle is related with a reduced risk of age-related diseases (Knudtson et al., 2006, Williams, 2008, Klein et al., 2014). It would be interesting to assess the effect of exercise regime on visual function over a period of time in normal older populations. In Chapter 8, there were several participants who either failed to increase their MPOD or had a slight decrease. Although several reasons have been proposed for serum and/or retinal non-responsiveness, to date, no study has concentrated its efforts on this phenomenon. Future studies may need to concentrate on subjects with high versus low MPOD, specific genetic variations among individuals, effect of health and lifestyle factors (e.g. smoking, obesity), and the effect of methodology with respect to the technique used for measuring MPOD and the supplement type, dose and delivery. Additionally, a thorough investigation of the nonresponders’ entire MP spatial profile may show that, rather than increasing centrally, the profile broadens out peripherally resulting in flattening of the peak. Finally, a recently proposed hypothesis suggests that supplementing with all three carotenoids (including MZ), is important for, both, a substantial MPOD response and visual performance enhancement (Loughman et al., 2012). This hypothesis requires

207 further study involving an exclusively older cohort. In summary, the results of the studies presented in this thesis show that lifestyle, health status and certain medications can adversely aﬀect visual function in normal ageing. MP augmentation, however, had no eﬀect on visual function. Further research is warranted, particularly paying close attention to subjects engaging in several unhealthy lifestyle/dietary behaviours, statin users and those with low MPOD and suboptimal visual function. Given the increasing size of the older adult population in developed countries, research aimed at slowing or reversing age-related declines in vision is much needed both from an economical and psycho-social perspective.

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