Visual difficulties in low illumination are frequently reported by elderly adults in the absence of ocular pathology1,2 and have been identified as a cause of trips and falls in these individuals.3 Correspondingly, a reduction in both photopic and scotopic visual sensitivity with age has been demonstrated.4–9 This may partially be attributed to the increased optical density of the ocular media and pupillary meiosis that occur with increasing age, causing a reduction in the amount of light reaching the retina.10,11 However, when the effects of these pre-retinal factors are controlled for, a reduction in scotopic sensitivity with increasing age is still evident.9 This indicates that retinal factors are also likely to contribute to diminishing visual sensitivity,12,13 and this may be related to the age-related reduction reported in the density of rod photoreceptors,13 or may be due to retinal ganglion cell loss.12,14
Although reduced sensitivity to light is a well-documented feature of aging, the effect of age on the rate at which the eye can adapt to a change in ambient illumination is less clear. Dark adaptation classically refers to the relatively slow recovery of visual threshold that occurs in the dark after exposure to a bright light that bleaches a significant portion of photopigment. Visual threshold during dark adaptation is initially mediated by the cones, and subsequently by the rods. In addition to reductions in dark-adapted retinal sensitivity,4–9 contemporary investigations have demonstrated an age-related decline in the rate of rod-mediated dark adaptation.15 However, data regarding the relationship between age and cone dark adaptation are sparse. Prolonged photostress or “glare recovery” with increasing age has been reported.16–20 However, only three studies have specifically examined the changes in cone dark adaptation dynamics with increasing age.7,8,21 Eisner et al. found the rate of dark adaptation, measured using a two-channel Maxwellian view device, to be independent of age in 122 subjects aged between 60 and 90 years.8 This finding was supported by earlier work, using the Hecht-Shlaer adaptometer, in 91 male subjects aged between 40 and 83 years.7 In contrast, Coile and Baker demonstrated a reduction in the rate of cone dark adaptation with increasing age in a cohort of 58 subjects aged between 10 and 78 years, using a modified retinal densitometer.21
The clinical significance of dark adaptation measurement is growing because there is an emerging body of evidence to suggest that it is a sensitive biomarker for age-related macular degeneration (AMD), the leading cause of visual impairment in the developed world.22–31 Recent studies that have examined the diagnostic potential of cone dark adaptation in participants with early AMD reported an area under the receiver operating characteristic curve in excess of 0.90.29–31 When measured alongside visual functions, such as color vision,24,30 flicker sensitivity,26,30 contrast sensitivity, and photopic and scotopic thresholds,25,30 dark adaptation appears to be the single most sensitive marker for AMD. In addition, cone dark adaptation may be measured in <10 min, making it particularly attractive to clinicians.
In light of the limited and seemingly contradictory evidence regarding the relationship between the dynamics of cone dark adaptation and age and the potential value of cone dark adaptation as a biomarker for AMD,26,29–31 this study aimed to explore the effect of age on cone dark adaptation in healthy adults.
Cone dark adaptation was recorded in 41 healthy adults aged between 20 and 83 years. The subjects provided informed written consent before participation, and all procedures adhered to the tenets of the Declaration of Helsinki. The study was approved by the South East Wales Research Ethics Committee.
Baseline examinations were completed at the start of the visit. These included patient history, visual acuity testing (Snellen), media opacity grading,32 and a binocular indirect fundus examination. All subjects included in the study had corrected visual acuity of 6/6 or better in the test eye, clear ocular media, normal retinal appearance, and no history of ocular or systemic disease known to affect visual function, such as diabetes or uncontrolled hypertension.
Apparatus and Psychophysical Methods
All stimuli were presented on a calibrated, high resolution computer monitor (Iiyama LS 902UT) driven by an 8-bit (nVIDIA Geforce 9) graphics board under software control (Matlab, Mathworks, Cambridge, UK). The luminance output of the monitor was γ-corrected33,34 and modified by a 1.2 log neutral density filter mounted on the screen to expose the full range of cone adaptation.
Dark adaptation was monitored using a psychophysical method, which was previously implemented by Jackson et al. (1999) using a modified Humphrey perimeter.15 The 4° white (9300K) stimulus was presented to the fovea for 200 ms, followed by a 600 ms response window, and then a randomly determined interstimulus delay of 0.9 to 2.4 s. If the subject responded to the stimulus within 600 ms, the luminance was reduced by 0.3 log units for the next presentation. Conversely, if the subject took longer than 600 ms to respond to the stimulus, or failed to respond at all, the intensity was increased by 0.1 log units for the following presentation. Threshold was recorded, approximately every 6.5 s, when the stimulus first became visible on an ascending staircase.
Before dark adaptation, subjects were dilated with one drop of 1.0% tropicamide in each eye. After a short familiarization trial, dark adaptation was monitored in the right eye of all subjects (the left eye was occluded). Participants viewed the computer screen at a distance of 55 cm, wearing their distance refractive correction.
A Maxwellian view optical system was used to deliver a 95% “bleach” (white, 5.78 log phot.Td for 60 s) of cone photopigment35 to the central 43.6° of the test eye. During light adaptation, the subject's head position was monitored by the examiner to ensure accurate fixation. In addition, the long duration adapting light was sufficient to produce an equilibrium level of cone photopigment bleach, which meant that all individuals should have reached the same level of photopigment bleach regardless of any momentary fixation losses. On cessation of the bleach, subjects placed their chin on the rest in front of the computer screen within 3 s; the computer program commenced immediately, and dark adaptation was monitored continuously for 5 min. Subjects were instructed to fixate the center of the computer monitor, marked by an achromatic fixation cross (0.8 log cd/m2), and to indicate perception of the stimulus via the computer keyboard.
The time constant of recovery (τ), initial cone thresholds, and final cone thresholds were determined by fitting a single exponential function (Eq. 1), on a least squares basis, to the threshold recovery data obtained from each participant, using Microsoft Excel (Redmond, WA).
where T is the threshold at time t after the bleach, F is the final dark adapted threshold, I is the initial threshold immediately after the cessation of the bleach, and τ is the time constant of recovery. Linear regression was performed to assess the relationship between age and the parameters of cone dark adaptation.
Cone dark adaptation functions were successfully recorded from all 41 subjects. Fig. 1 shows dark adaptation data obtained from typical subjects aged 23, 45, 65, and 83 years. A general trend toward slower dark adaptation with increasing age, but relatively stable final thresholds, can be observed.
Linear regression was used to evaluate the change in the parameters of cone dark adaptation with age (Fig. 2). There was no relationship between increasing age and initial threshold (p = 0.84) or final threshold (p = 0.82). In contrast, cone τ became significantly larger with increasing age (p < 0.0005), confirming that cone dark adaptation becomes progressively slower with advancing age (Fig. 2C). These data suggest that approximately half of the variation in cone τ measured here may be explained by this single parameter (R2 = 0.50). Cone τ increased by 16.4 s/decade of life.
The results show that cone dark adaptation kinetics become progressively slower throughout adulthood. Adults aged 20 to 30 years displayed an average cone τ that was approximately half that of adults aged >70 years. This indicates that older adults require substantially more time to adjust to darkness than their younger counterparts. These findings suggest that the performance of older individuals may be impaired during routine visual tasks that take them from very bright to dim lighting.
The increase in cone τ of 16.4 s/decade of life found here agrees with the increase in cone τ of 12.6 s/decade reported by Coile and Baker.21 The differences between these results and those of early work, in which no association between cone dark adaptation and age was reported,7,8 are likely to result from methodological differences. The key methodological differences are shown in Table 1. Notably, Eisner et al. measured cone dark adaptation after exposure to a pre-adapting light of markedly lower intensity to that used by the other investigators, and this may have contributed to the variability in their Fig. 3 data.8 The 20,000 phot.td bleach used in that study would have bleached only about 37% of cone photopigment. Although the use of a relatively modest bleach should not influence the measured exponential time constant, it does reduce the extent to which threshold is initially raised, and this may have made modeling an individual's recovery data more challenging.35 In addition, the effects of low intensity pre-adapting lights on the fraction of pigment bleached are more dependent on media changes than more substantial ones. For example, early media changes that reduce the retinal illuminance by a factor of 2 from 20,000 to 10,000 phot.td would reduce the percentage of photopigment bleached from 37% to just 22%, a 15% reduction. In contrast, in this study, the same media change would have reduced the percentage of photopigment bleach from 95% to 91%, a 4% reduction. Perhaps, just as importantly, Eisner et al.8 only studied older adults with a limited age range. That is, of the 122 subjects studied, all but five were in their 60s and 70s. Therefore, it is possible that over the limited age range studied, any effects of age were masked by variability in the data set.
Although the methodology used here minimized the impact of pre-retinal factors on the results, achromatic stimuli, such as those used in this study, are generally not well suited to the study of psychophysical thresholds in older participants because of age-related media changes. However, there are several reasons why media changes cannot explain the findings reported here. First, to obtain a good view of the retina, to rule out any pathology, we chose to include only those with “clear” ocular media (grade 3 or lower, LOCS-III32). Second, any reduction in retinal illuminance caused by media opacities would be unlikely to alter the cone dark adaptation time constant because, where “bleaches” are relatively substantial, recovery is independent of the fraction of pigment bleached. For example, Hollins and Alpern showed that 30%, 50%, 70%, and 100% cone pigment bleaches resulted in recoveries that had the same exponential time constant in one experienced observer.35 Third, although age-related media changes are associated with elevated visual thresholds, the vertical translation of threshold data that may result has no effect on the “shape” of the recovery, and hence no effect on dark adaptation time constant.35 Histological studies have shown that aging causes RPE cell loss and thickening of Bruch's membrane.12,36 These changes might be expected to impair photopigment regeneration, and arethereforelikely to contribute to delays in cone dark adaptation observed here.
There was no evidence of a relationship between age and final cone threshold within these data. This contrasts with previous work in which a modest increment in absolute cone threshold with increasing age (0.09 to 0.37 log cd/m2/decade) has been reported.7,8,21 However, unlike the current study, in which only participants classified as having “clear” ocular media (grade 3 or lower, LOCS-III32) were included in the sample, these studies did not use such criteria. Consequently, the changes in visual threshold reported previously may be attributed, at least in part, to age-related changes in the density of the ocular media.11 This notion is supported by Weale who showed that when cone threshold data collected from participants aged between 15 and 85 years were adjusted for age-related changes in lenticular absorption, there was no increment in cone threshold with increasing age.37 Additionally, histological evidence has shown that although there is a reduction in rod photoreceptor density throughout life,13 foveal cone density remains relatively stable throughout life.13 Although retinal ganglion cell density declines with increasing age,12,38–42 it has been suggested that large achromatic stimuli are relatively insensitive to this age-related neuronal loss.14 Consequently, there is no histological premise for a change in cone thresholds with advancing age, using the 4° diameter white stimulus used in this study.
Knowledge about the relationship between cone dark adaptation and age is clinically important because cone τ is potentially an important biomarker for early macular disease.22–31 Our observation that about 50% of the variance in cone τ may be attributed to age alone (R2 = 0.50) for our experimental conditions, suggests that the sensitivity and specificity of this biomarker could be improved by taking into account the significant age-related decline. To determine whether an individual's cone τ falls within the normal reference range for their age, it should be compared with the upper 95% prediction interval for the regression line, that is, (1.6346*age) + 90.41. A cone τ that is greater than this value is therefore “outside normal limits.”
In conclusion, this study has examined the relationship between age and the time course of cone dark adaptation in healthy adults and has provided evidence in support of an age-related slowing of cone dark adaptation after a full bleach. Older adults require up to twice as long to adapt to darkness as younger adults. Investigators proposing to use cone dark adaptation as a biomarker for AMD could optimize its diagnostic potential by comparing the results from individuals with age-adjusted norms.
Tom H. Margrain
School of Optometry and Vision Sciences
Cardiff University, Maindy Road
Cathays, Cardiff CF24 4LU
This study was funded by a research grant from the College of Optometrists, United Kingdom. We would like to thank Bryn Jones for his help with data collection.
1. Kline DW, Kline TJ, Fozard JL, Kosnik W, Schieber F, Sekuler R. Vision, aging, and driving: the problems of older drivers. J Gerontol 1992;47:P27–34.
2. McGregor LN, Chaparro A. Visual difficulties reported by low-vision and nonimpaired older adult drivers. Hum Factors 2005;47:469–78.
3. McMurdo ME, Gaskell A. Dark adaptation and falls in the elderly. Gerontology 1991;37:221–4.
4. Robertson GW, Yudkin J. Effect of age upon dark adaptation. J Physiol 1944;103:1–8.
5. Steven DM. Relation between dark adaptation and age. Nature 1946;157:376.
6. Birren JE, Bick MW, Fox C. Age changes in the light threshold of the dark adapted eye. J Gerontol 1948;3:267–71.
7. Birren JE, Shock NW. Age changes in rate and level of visual dark adaptation. J Appl Physiol 1950;2:407–11.
8. Eisner A, Fleming SA, Klein ML, Mauldin WM. Sensitivities in older eyes with good acuity: cross-sectional norms. Invest Ophthalmol Vis Sci 1987;28:1824–31.
9. Jackson GR, Owsley C, Cordle EP, Finley CD. Aging and scotopic sensitivity. Vision Res 1998;38:3655–62.
10. Birren JE, Casperson RC, Botwinick J. Age changes in pupil size. J Gerontol 1950;5:216–21.
11. Bron AJ, Vrensen GF, Koretz J, Maraini G, Harding JJ. The ageing lens. Ophthalmologica 2000;214:86–104.
12. Gao H, Hollyfield JG. Aging of the human retina. Differential loss of neurons and retinal pigment epithelial cells. Invest Ophthalmol Vis Sci 1992;33:1–17.
13. Curcio CA, Millican CL, Allen KA, Kalina RE. Aging of the human photoreceptor mosaic: evidence for selective vulnerability of rods in central retina. Invest Ophthalmol Vis Sci 1993;34:3278–96.
14. Pearson PM, Schmidt LA, Ly-Schroeder E, Swanson WH. Ganglion cell loss and age-related visual loss: a cortical pooling analysis. Optom Vis Sci 2006;83:444–54.
15. Jackson GR, Owsley C, McGwin G. Aging and dark adaptation. Vision Res 1999;39:3975–82.
16. Collins M. The onset of prolonged glare recovery with age. Ophthal Physiol Opt 1989;9:368–71.
17. Elliott DB, Whitaker D. Changes in macular function throughout adulthood. Doc Ophthalmol 1990;76:251–9.
18. Margrain TH, Thomson D. Sources of variability in the clinical photostress test. Ophthal Physiol Opt 2002;22:61–7.
19. Newsome DA, Negreiro M. Reproducible measurement of macular light flash recovery time using a novel device can indicate the presence and worsening of macular diseases. Curr Eye Res 2009;34:162–70.
20. Wood A, Margrain T, Binns A. The effect of bleach duration and age on the ERG photostress test. Graefes Arch Clin Exp Ophthalmol 2011;249:1359–65.
21. Coile DC, Baker HD. Foveal dark adaptation, photopigment regeneration, and aging. Vis Neurosci 1992;8:27–39.
22. Brown B, Kitchin JL. Dark adaptation and the acuity/luminance response in senile macular degeneration (SMD). Am J Optom Physiol Opt 1983;60:645–50.
23. Eisner A, Fleming SA, Klein ML, Mauldin WM. Sensitivities in older eyes with good acuity: eyes whose fellow eye has exudative AMD. Invest Ophthalmol Vis Sci 1987;28:1832–7.
24. Eisner A, Stoumbos VD, Klein ML, Fleming SA. Relations between fundus appearance and function. Eyes whose fellow eye has exudative age-related macular degeneration. Invest Ophthalmol Vis Sci 1991;32:8–20.
25. Owsley C, Jackson GR, White M, Feist R, Edwards D. Delays in rod-mediated dark adaptation in early age-related maculopathy. Ophthalmology 2001;108:1196–202.
26. Phipps JA, Guymer RH, Vingrys AJ. Loss of cone function in age-related maculopathy. Invest Ophthalmol Vis Sci 2003;44:2277–83.
27. Binns AM, Margrain TH. Evaluating retinal function in age-related maculopathy with the ERG photostress test. Invest Ophthalmol Vis Sci 2007;48:2806–13.
28. Owsley C, McGwin G Jr, Jackson GR, Kallies K, Clark M. Cone- and rod-mediated dark adaptation impairment in age-related maculopathy. Ophthalmology 2007;114:1728–35.
29. Dimitrov PN, Guymer RH, Zele AJ, Anderson AJ, Vingrys AJ. Measuring rod and cone dynamics in age-related maculopathy. Invest Ophthalmol Vis Sci 2008;49:55–65.
30. Dimitrov PN, Robman LD, Varsamidis M, Aung KZ, Makeyeva GA, Guymer RH, Vingrys AJ. Visual function tests as potential biomarkers in age-related macular degeneration. Invest Ophthalmol Vis Sci 2011;52:9457–69.
31. Gaffney AJ, Binns AM, Margrain TH. Topography of cone dark adaptation deficits in age-related maculopathy. Optom Vis Sci 2011;88:1080–7.
32. Chylack LT Jr, Wolfe JK, Singer DM, Leske MC, Bullimore MA, Bailey IL, Friend J, McCarthy D, Wu SY. The lens opacities classification system III. The longitudinal study of cataract study group. Arch Ophthalmol 1993;111:831–6.
33. Metha AM, Vingrys AJ, Badcock DR. Calibration of a color monitor for visual psychophysics. Behav Res Methods Instrum Comput 1993;25:371–83.
34. Brainard DH, Pelli DG, Robson T. Display characterization. In: Hornak JP, ed. The Encyclopaedia of Imaging Science and Technology, vol 18. New York: Wiley; 2001:172–88.
35. Hollins M, Alpern M. Dark adaptation and visual pigment regeneration in human cones. J Gen Physiol 1973;62:430–47.
36. Sarks SH. Ageing and degeneration in the macular region: a clinico-pathological study. Br J Ophthalmol 1976;60:324–41.
37. Weale RA. The Senescence of Human Vision. Oxford: Oxford Universtiy Press; 1992.
38. Balazsi AG, Rootman J, Drance SM, Schulzer M, Douglas GR. The effect of age on the nerve fiber population of the human optic nerve. Am J Ophthalmol 1984;97:760–6.
39. Johnson BM, Miao M, Sadun AA. Age-related decline of human optic nerve axon populations. Age 1987;10:5–9.
40. Curcio CA, Drucker DN. Retinal ganglion cells in Alzheimer's disease and aging. Ann Neurol 1993;33:248–57.
41. Harman A, Abrahams B, Moore S, Hoskins R. Neuronal density in the human retinal ganglion cell layer from 16–77 years. Anat Rec 2000;260:124–31.
42. Lovasik JV, Kergoat MJ, Justino L, Kergoat H. Neuroretinal basis of visual impairment in the very elderly. Graefes Arch Clin Exp Ophthalmol 2003;241:48–55.