Abstract
This study aimed to evaluate the prevalence and risk factors for undiagnosed age-related macular degeneration (AMD) in the Korean population. This cross-sectional study utilized data from the Korea National Health and Nutrition Examination Survey (KNHANES 2017–2020), which included a total of 13,737 subjects of 40 years or older. Cases in which AMD was identified through imaging interpretation of the KNHANES data, but the patients had not received a prior medical diagnosis of AMD, were classified as undiagnosed AMD. The prevalence and risk factors for undiagnosed AMD were analyzed. Among the patients identified to having AMD through KNHANES, the prevalence of undiagnosed AMD was 95.25% (95% confidence interval [CI], 94.13–96.37). Multivariate analysis revealed that a low level of education was significantly associated with a higher risk of undiagnosed AMD (p = 0.0066). A low level of education was also linked to a higher risk of undiagnosed early AMD (p = 0.0369) and neovascular AMD (p = 0.0399). Aging was strongly associated with an increased risk of undiagnosed geographic atrophy (p < 0.0001). Physical activity was associated with a lower risk of undiagnosed neovascular AMD (p = 0.0107). The high prevalence of undiagnosed AMD in the Korean population highlights the need for regular fundus examinations to facilitate accurate detection of AMD. Low education level emerged as a significant risk factor for undiagnosed AMD, emphasizing the importance of targeted interventions for this population to reduce the risk of visual impairment due to AMD.
Keywords: Age-related macular degeneration, Unrecognized, Risk factor, National Health and Nutrition Examination Survey
Subject terms: Macular degeneration, Retinal diseases, Epidemiology
Introduction
Age-related macular degeneration (AMD) is a progressive disease that is primarily caused by the aging of the eye1. In general, early and intermediate AMD do not lead to significant visual deterioration2. As a result, patients may remain unaware of AMD development unless they undergo a fundus examination. Furthermore, even with a fundus examination, primary eye care providers may sometimes fail to detect AMD3.
Late AMD, characterized by neovascular AMD and geographic atrophy (GA), can lead to severe visual impairment4,5. Currently, there is no cure for late AMD. For neovascular AMD, which requires ongoing treatment, the associated time, financial, and emotional burdens are substantial6,7. In the case of GA, complement inhibitor therapy has recently been introduced to slow disease progression. However, the high cost of the medication and the need for frequent intraocular injections8 pose significant challenges in achieving effective therapeutic outcomes.
Therefore, it is crucial to detect AMD at an early or intermediate stage and implement preventive measures9,10 to minimize its progression to late-stage AMD. This approach can potentially reduce the progression rate to late AMD and ultimately alleviate not only the disease burden on patients but also the socioeconomic burden associated with the condition. Furthermore, the early detection of late AMD is essential for timely intervention.
To date, numerous studies have reported the prevalence of AMD. However, the prevalence and risk factors for undiagnosed AMD remain to be elucidated, and such information would be invaluable for improving population health and developing strategies to promote the early detection of AMD.
Recently, a dataset from the Korea National Health and Nutrition Examination Survey (KNHANES) conducted between 2017 and 2020 became available. In the present study, we report the prevalence and risk factors of undiagnosed AMD in the Korean population based on the latest data.
Methods
This study was approved by the Institutional Review Board (IRB) of Kim’s Eye Hospital (approval number: 2023-03-011) and adhered to the principles of the Declaration of Helsinki. As the study did not entail the use of any personally identifiable information about the participants, the need for informed consent was waived by the IRB. The KNHANES protocol was conducted according to the guidelines of the Declaration of Helsinki and approved by the IRB (or Ethics Committee) of the Korea Centers for Disease Control and Prevention. Written informed consent was obtained from all participants involved in the KNHANES. Additionally, ethical approval was not required, as KNHANES provides anonymous, secondary data that are publicly available for scientific use. The KNHANES data collection protocol containing a comprehensive description of the methodology has been previously published11.
Study population
The data used in this study were obtained from the KNHANES, a nationwide cross-sectional survey conducted in South Korea between 2017 and 2020 by the Korean Centers for Disease Control and Prevention and the Korean Ministry of Health and Welfare. The survey included a health interview, a nutrition survey, and a health assessment survey. It was conducted using a multistage stratified cluster sampling technique based on National Census data.
Data collection
Demographic variables were obtained through a set of detailed questionnaires in health interview surveys. Blood tests, ophthalmic examinations, and routine urinalysis were performed as part of the physical examinations. A 45° non-mydriatic color fundus photograph was captured using a fundus camera (VISUCAM 224, Carl Zeiss Meditec), and optical coherence tomography (OCT) examinations were conducted using an OCT device (CIRRUS HD-OCT 500, Carl Zeiss Meditec, Dublin, CA, USA). For eyes with small pupils, fundus photographs were taken with a filed angle of 30°11.
Each fundus photograph was graded following the protocol defined by the International Age-Related Maculopathy Epidemiological Study Group12. Early AMD was defined as (1) the presence of soft, indistinct, or reticular drusen or (2) the presence of hard or soft distinct drusen with hyper- or hypopigmentary changes in the retinal pigment epithelium (RPE) in the macula without any sign of late AMD. Late dry AMD was defined as any sharply delineated, roughly round, or oval area of hypopigmentation, depigmentation, or apparent absence of the RPE, in which choroidal vessels are more visible than in surrounding areas that must be at least 175 µm in diameter. In the present study, the term "geographic atrophy (GA)" was used instead of the term "late dry AMD," which is less commonly used. Neovascular AMD was defined as RPE detachment, serous detachment of the sensory retina, the presence of subretinal pigment epithelial hemorrhages, and subretinal fibrous scars. The diagnosis of AMD was primarily based on fundus photography, with OCT images used as supplementary material. Even in cases where definitive neovascular AMD findings were not evident on fundus photography, eyes with early AMD features but with definitive OCT findings, such as pigment epithelial detachment or subretinal fluid, were considered to have neovascular AMD11.
In the KNHANES, AMD and other retinal diseases were diagnosed by experienced retinal specialists selected by the Korean Retina Society. Each retinal image was subjected to two separate examinations by an independent examiner. In cases of a discrepancy in the primary diagnosis, an independent senior reading committee was responsible for making the final diagnosis of the retinal diseases11.
Undiagnosed AMD
Participants identified as having AMD in the KNHANES but without a prior diagnosis of AMD by a physician were defined as having undiagnosed AMD. The participants were classified into four groups according to the type of undiagnosed AMD: total AMD, early AMD, GA, and neovascular AMD.
Analysis of risk factors of undiagnosed AMD
Factors associated with undiagnosed AMD were analyzed using the following variables: age, sex, BMI, abdominal obesity, educational level, household income, occupation, marital status, smoking, alcohol consumption, physical activity, diabetes, hypertension, hypercholesterolemia, and chronic kidney disease. The analysis was conducted separately for each type of AMD: total AMD, early AMD, GA, and neovascular AMD.
Statistical analyses
Continuous variables were reported as mean ± standard error (SE) or mean values with 95% confidence intervals (CIs). Statistical analyses were performed using a commercially available software (SAS version 9.4; SAS Institute Inc., Cary, NC, USA). Univariate and multivariate logistic regression analyses were performed to analyze the association between undiagnosed AMD and potential risk factors.
Results
Study participants
Between 2017 and 2020, 31,588 individuals were enrolled in the KNHANES. Among them, 18,760 individuals aged 40 years were included in this study. Of them, 16,154 underwent relevant ophthalmic examinations. After excluding participants with ocular evisceration, ungradable fundus photographs with undetermined diagnoses, and missing data, 13,737 participants were included in the final analysis (Fig. 1).
Fig. 1.
Participation flowchart for the Korea National Health and Nutrition Examination Survey (KNHANES) conducted between 2017 and 2020.
Among these participants, early AMD was diagnosed in 2085 individuals, GA in 38 individuals, and neovascular AMD in 99 individuals.
Prevalence of undiagnosed AMD
Among the total of 2222 patients identified with AMD in KNHANES, 2121 had never been diagnosed with AMD by a physician, while 101 had previously received a diagnosis of AMD. Table 1 presents the baseline characteristics of individuals without a prior diagnosis of AMD compared to those with a prior diagnosis.
Table 1.
Baseline characteristics of participants without a prior diagnosis of age-related macular degeneration (AMD) compared to those with a prior diagnosis of AMD.
| No | Undiagnosed AMD | Diagnosed AMD | P-value |
|---|---|---|---|
| 2121 | 101 | ||
| Age, ≥ 65 years | 47.74 (1.37) | 57.2 (6.43) | 0.1484 |
| Sex, Male | 51 (1.28) | 54.09 (5.8) | 0.5976 |
| BMI level | 0.8929 | ||
| < 18.5 | 1.85 (0.34) | 0.73 (0.57) | |
| < 23 | 37.1 (1.31) | 34.18 (5.62) | |
| < 25 | 26.03 (1.17) | 28.22 (5.32) | |
| < 30 | 31.67 (1.33) | 33.74 (5.93) | |
| ≥ 30 | 3.35 (0.4) | 3.13 (1.84) | |
| Abdominal obesity | 38.72 (1.33) | 45.95 (6.21) | 0.2258 |
| Education | 0.0891 | ||
| ≤ Elementary school | 30.32 (1.25) | 15.47 (3.72) | |
| Junior high | 16.92 (1) | 20.21 (5.72) | |
| Senior high | 32.64 (1.28) | 37.64 (6.1) | |
| ≥ University/college | 20.12 (1.18) | 26.68 (5.67) | |
| Household income | 0.256 | ||
| Q1 | 26.56 (1.33) | 29.61 (5.6) | |
| Q2 | 26.26 (1.21) | 19.04 (3.93) | |
| Q3 | 24.56 (1.29) | 32.91 (6.45) | |
| Q4 | 22.63 (1.26) | 18.44 (4.73) | |
| Occupation | 54.03 (1.36) | 42.86 (6.45) | 0.085 |
| Spouse | 0.5263 | ||
| Never married | 3.23 (0.53) | 5.37 (3.07) | |
| Cohabiting | 74.86 (1.17) | 76.03 (4.79) | |
| Separated or divorced | 21.91 (1.08) | 18.6 (4.13) | |
| Smoking | 0.3223 | ||
| Non | 56.6 (1.35) | 50.84 (5.86) | |
| Ex | 27.46 (1.17) | 36.2 (5.23) | |
| Current | 15.94 (1.01) | 12.96 (4.99) | |
| Drinking | 0.4163 | ||
| Non | 38.31 (1.34) | 34.85 (4.96) | |
| Mild | 54.1 (1.37) | 61.29 (5.3) | |
| Heavy | 7.59 (0.71) | 3.87 (2.94) | |
| Physical activity | 36.95 (1.28) | 48.11 (6.18) | 0.0712 |
| DM Stage | 0.6116 | ||
| Normal | 31.5 (1.26) | 26 (5.17) | |
| Pre DM | 47.15 (1.32) | 50.98 (6.01) | |
| DM | 21.34 (1.07) | 23.03 (4.9) | |
| HTN Stage | 0.19 | ||
| Normal | 25.75 (1.19) | 22.79 (5.22) | |
| Pre HTN | 26.25 (1.18) | 18.2 (4.91) | |
| HTN | 48 (1.31) | 59.02 (5.8) | |
| Hypercholesterolemia | 31.48 (1.21) | 43.19 (5.97) | 0.0408 |
| CKD | 4.92 (0.49) | 6.96 (2.55) | 0.3484 |
| Age, years | 64.21 ± 0.3 | 65.6 ± 1.45 | 0.3388 |
| Height, cm | 161.41 ± 0.25 | 163.8 ± 0.98 | 0.0158 |
| Weight, kg | 62.8 ± 0.31 | 66.41 ± 1.59 | 0.0195 |
| BMI, kg/m2 | 24.01 ± 0.08 | 24.6 ± 0.39 | 0.1284 |
| Waist Circumference, cm | 85.08 ± 0.23 | 88.24 ± 1.22 | 0.0082 |
| Systolic BP, mmHg | 125.38 ± 0.45 | 124.16 ± 2 | 0.5482 |
| Diastolic BP, mmHg | 75.86 ± 0.28 | 74.17 ± 1.17 | 0.1464 |
| Fasting glucose, mg/dL | 105.35 ± 0.64 | 103.9 ± 1.88 | 0.4585 |
| HbA1c, % | 5.94 ± 0.02 | 5.95 ± 0.07 | 0.8796 |
| Total Cholesterol, mg/dL | 186.95 ± 0.98 | 196.04 ± 5.54 | 0.1053 |
| HDL -C, mg/dL | 50.19 ± 0.34 | 50.7 ± 1.42 | 0.7267 |
AMD, age-related macular degeneration; BMI, body mass index; BP, blood pressure; CKD, chronic kidney disease; DM, diabetes mellitus; HDL-C, High-Density Lipoprotein Cholesterol; HTN, hypertension; No, number; Q, quartile.
Table 2 shows the prevalence of undiagnosed AMD categorized by age group. The weighted prevalence of undiagnosed total AMD was 95.25% (95% CI, 94.13–96.37). The prevalence was 94.55% (95% CI, 89.75–99.35) in the 40 s age group, 95.79% (95% CI, 93.20–98.38) in the 50 s age group, 96.33% (95% CI, 94.84–97.82) in the 60 s age group, 93.87% (95% CI, 91.75–95.99) in the 70 s age group, and 94.65% (95% CI, 91.51–97.79) in the ≥ 80 s age group.
Table 2.
Prevalence of unrecognized age-related macular degeneration.
| Age (years) | Unweighted no | Total | Male | Female | |||
|---|---|---|---|---|---|---|---|
| Unweighted no | % (SE) | Unweighted no | % (SE) | Unweighted no | % (SE) | ||
| Total | 2222 | 2121 | 95.25 (0.57) | 1007 | 94.98 (0.79) | 1114 | 95.54 (0.76) |
| 40–49 | 128 | 123 | 94.55 (2.45) | 83 | 94.99 (2.87) | 40 | 93.16 (4.68) |
| 50–59 | 438 | 427 | 95.79 (1.32) | 208 | 95.54 (1.91) | 219 | 96.09 (1.82) |
| 60–69 | 743 | 712 | 96.33 (0.76) | 323 | 96.03 (1.06) | 389 | 96.64 (1.05) |
| 70–79 | 702 | 660 | 93.87 (1.08) | 302 | 93.29 (1.52) | 358 | 94.33 (1.34) |
| ≥ 80 | 211 | 199 | 94.65 (1.60) | 91 | 93.70 (2.63) | 108 | 95.31 (2.02) |
No, number; SE, standard error.
Risk factors for undiagnosed AMD
Table 3 summarizes the association between the potential factors and unrecognized total AMD.
Table 3.
Univariate and multivariate logistic regression analyses for potential risk factors of undiagnosed age-related macular degeneration.
| Risk factors | Univariate | P-value | Multivariate | P-value |
|---|---|---|---|---|
| Age, years | 0.987 (0.959–1.015) | 0.3478 | 0.976 (0.939–1.014) | 0.2183 |
| Sex | 0.5984 | |||
| Male | 0.883 (0.557–1.402) | 1.537 (0.783–3.018) | 0.2109 | |
| Female | 1 (Ref.) | 1 (Ref.) | ||
| BMI, kg/m2 | 0.7823 | 0.8905 | ||
| < 18.5 | 2.337 (0.460–11.871) | 1.790 (0.310–10.329) | ||
| < 23 | 1 (Ref.) | 1 (Ref.) | ||
| < 25 | 0.850 (0.471–1.534) | 0.878 (0.429–1.797) | ||
| < 30 | 0.865 (0.483–1.550) | 1.068 (0.550–2.074) | ||
| ≥ 30 | 0.987 (0.285–3.417) | 1.261 (0.305–5.218) | ||
| Abdominal obesity | 0.2295 | 0.5004 | ||
| No | 1 (Ref.) | 1 (Ref.) | ||
| Yes | 0.743 (0.458–1.207) | 0.814 (0.447–1.482) | ||
| Education | 0.0277 | 0.0066 | ||
| ≤ Elementary school | 1 (Ref.) | 1 (Ref.) | ||
| Junior high | 0.427 (0.185–0.988) | 0.353 (0.145–0.858) | ||
| Senior high | 0.443 (0.235–0.833) | 0.304 (0.142–0.650) | ||
| ≥ University/college | 0.385 (0.189–0.783) | 0.242 (0.101–0.584) | ||
| Household income | 0.2586 | 0.2208 | ||
| Q1 | 1 (Ref.) | 1 (Ref.) | ||
| Q2 | 1.538 (0.832–2.840) | 1.672 (0.771–3.625) | ||
| Q3 | 0.832 (0.428–1.618) | 0.889 (0.394–2.008) | ||
| Q4 | 1.368 (0.659–2.843) | 1.542 (0.552–4.308) | ||
| Occupation | 0.0871 | 0.1968 | ||
| No | 1 (Ref.) | 1 (Ref.) | ||
| Yes | 1.567 (0.937–2.622) | 1.414 (0.835–2.395) | ||
| Spouse | 0.5444 | 0.3298 | ||
| Never married | 1 (Ref.) | 1 (Ref.) | ||
| Cohabiting | 1.640 (0.482–5.580) | 2.367 (0.557–10.065) | ||
| Separated or divorced | 1.962 (0.548–7.021) | 3.010 (0.672–13.490) | ||
| Smoking | 0.2233 | 0.2658 | ||
| Non | 1 (Ref.) | 1 (Ref.) | ||
| Ex | 0.681 (0.428–1.085) | 0.599 (0.322–1.114) | ||
| Current | 1.105 (0.447–2.730) | 0.722 (0.275–1.893) | ||
| Drinking | 0.4322 | 0.3424 | ||
| Non | 1 (Ref.) | 1 (Ref.) | ||
| Mild | 0.803 (0.514–1.254) | 0.768 (0.494–1.194) | ||
| Heavy | 1.785 (0.365–8.715) | 1.743 (0.338–8.985) | ||
| Physical activity | 0.074 | 0.1917 | ||
| No | 1 (Ref.) | 1 (Ref.) | ||
| Yes | 0.632 (0.382–1.046) | 0.716 (0.433–1.183) | ||
| DM stage | 0.617 | 0.8265 | ||
| Normal | 1 (Ref.) | 1 (Ref.) | ||
| Pre DM | 0.763 (0.428–1.363) | 0.834 (0.464–1.498) | ||
| DM | 0.765 (0.398–1.472) | 0.928 (0.495–1.742) | ||
| HTN stage | 0.1802 | 0.3176 | ||
| Normal | 1 (Ref.) | 1 (Ref.) | ||
| Pre HTN | 1.277 (0.568–2.870) | 1.338 (0.575–3.110) | ||
| HTN | 0.720 (0.39,− 1.314) | 0.793 (0.419–1.500) | ||
| Hypercholesterolemia | 0.0441 | 0.1484 | ||
| No | 1 (Ref.) | 1 (Ref.) | ||
| Yes | 0.604 (0.370–0.987) | 0.688 (0.415–1.143) | ||
| CKD | 0.3513 | 0.5936 | ||
| No | 1 (Ref.) | 1 (Ref.) | ||
| Yes | 0.692 (0.319–1.502) | 0.791 (0.333–1.877) |
Risk was expressed as odds ratio with 95% confidence intervals.
Abbreviations: AMD, age-related macular degeneration; BMI, body mass index; CKD, chronic kidney disease; DM, diabetes mellitus; HTN, hypertension; Q, quartile; Ref, reference.
Education level (p = 0.0277) and hypercholesterolemia (p = 0.0441) were significantly associated with a lower risk of undiagnosed total AMD in univariate logistic regression analysis, whereas education level (p = 0.0066) was the only significantly associated factor in multivariate analysis. More specifically, the risk of unrecognized total AMD was lower in individuals with middle school graduation (odds ratio [OR], 0.353; 95% CI, 0.145–0.858), high school graduation (OR, 0.304; 95% CI, 0.142–0.650), or university graduation or higher (OR, 0.242; 95% CI, 0.101–0.584), compared to individuals with an education level below elementary school graduation.
Tables 4 and 5 summarize the associations between potential factors and unrecognized early AMD and GA, respectively.In multivariate analysis, education level was significantly associated with the risk of unrecognized dry AMD (p = 0.0099), and age was significantly associated with the risk of unrecognized GA (p < 0.0001). Table 6 presents the associations between potential factors and unrecognized neovascular AMD. In the univariate analysis, BMI (p < 0.0001), smoking (p = 0.0267), and physical activity (p = 0.0090) were significantly associated with the risk of undiagnosed neovascular AMD. In multivariate analysis, age (p = 0.0131), BMI (p < 0.0001), household income (p = 0.0399), education level (p = 0.0399), and physical activity (p = 0.0107) were found to be significant factors. Specifically, individuals who engaged in physical activity had a lower risk of undiagnosed neovascular AMD (OR, 0.066; 95% CI, 0.008–0.518) compared to those who did not engage in physical activity.
Table 4.
Univariate and multivariate logistic regression analyses for potential risk factors of undiagnosed early age-related macular degeneration.
| Risk factors | Univariate | P-value | Multivariate | P-value |
|---|---|---|---|---|
| Age, years | 1.007 (0.970–1.045) | 0.7229 | 1.013 (0.962–1.065) | 0.6293 |
| Sex | 0.5641 | 0.4275 | ||
| Male | 1.190 (0.659–2.150) | 1.404 (0.607–3.247) | ||
| Female | 1 (Ref.) | 1 (Ref.) | ||
| BMI, kg/m2 | 0.8199 | 0.7893 | ||
| < 18.5 | 1.521 (0.292–7.931) | 0.974 (0.146–6.523) | ||
| < 23 | 1 (Ref.) | 1 (Ref.) | ||
| < 25 | 0.730 (0.350–1.526) | 0.696 (0.306–1.580) | ||
| < 30 | 1.027 (0.478–2.202) | 1.067 (0.462–2.467) | ||
| ≥ 30 | 0.704 (0.170–2.919) | 0.806 (0.186–3.489) | ||
| Abdominal obesity | 0.7948 | 0.8632 | ||
| No | 1 (Ref.) | 1 (Ref.) | ||
| Yes | 0.922 (0.500–1.700) | 0.934 (0.428–2.039) | ||
| Education | 0.034 | 0.0099 | ||
| ≤ Elementary school | 1 (Ref.) | 1 (Ref.) | ||
| Junior high | 0.334 (0.104–1.068) | 0.281 (0.085–0.932) | ||
| Senior high | 0.286 (0.123–0.662) | 0.206 (0.080–0.531) | ||
| ≥ University/college | 0.422 (0.165–1.081) | 0.297 (0.087–1.011) | ||
| Household income | 0.543 | 0.4832 | ||
| Q1 | 1 (Ref.) | 1 (Ref.) | ||
| Q2 | 1.837 (0.801–4.212) | 2.128 (0.827–5.470) | ||
| Q3 | 1.188 (0.483–2.926) | 1.500 (0.570–3.948) | ||
| Q4 | 1.321 (0.540–3.234) | 1.763 (0.551–5.644) | ||
| Occupation | 0.1116 | 0.0727 | ||
| No | 1 (Ref.) | 1 (Ref.) | ||
| Yes | 1.707 (0.883–3.299) | 1.696 (0.952–3.020) | ||
| Spouse | 0.5351 | 0.6758 | ||
| Never married | 1 (Ref.) | 1 (Ref.) | ||
| Cohabiting | 2.273 (0.521–9.921) | 2.478 (0.324–18.955) | ||
| Separated or divorced | 2.341 (0.508–10.789) | 2.477 (0.321–19.115) | ||
| Smoking | 0.8528 | 0.6953 | ||
| Non | 1 (Ref.) | 1 (Ref.) | ||
| Ex | 0.962 (0.539–1.715) | 0.723 (0.336–1.554) | ||
| Current | 1.446 (0.363–5.758) | 1.041 (0.246–4.404) | ||
| Drinking | – | – | ||
| Non | 1 (Ref.) | 1 (Ref.) | ||
| Mild | 0.720 (0.415–1.250) | 0.673 (0.399–1.136) | ||
| Heavy | – | – | ||
| Physical activity | 0.9522 | 0.8973 | ||
| No | 1 (Ref.) | 1 (Ref.) | ||
| Yes | 0.981 (0.517–1.860) | 1.041 (0.562–1.928) | ||
| DM stage | 0.8546 | 0.7223 | ||
| Normal | 1 (Ref.) | 1 (Ref.) | ||
| Pre DM | 0.832 (0.416–1.663) | 0.818 (0.403–1.661) | ||
| DM | 0.968 (0.441–2.124) | 1.093 (0.508–2.354) | ||
| HTN stage | 0.3835 | 0.2533 | ||
| Normal | 1 (Ref.) | 1 (Ref.) | ||
| Pre HTN | 1.526 (0.562–4.144) | 1.575 (0.540–4.594) | ||
| HTN | 0.867 (0.409–1.838) | 0.763 (0.339–1.716) | ||
| Hypercholesterolemia | 0.0595 | 0.1318 | ||
| No | 1 (Ref.) | 1 (Ref.) | ||
| Yes | 0.538 (0.282–1.025) | 0.612 (0.323–1.160) | ||
| CKD | 0.3905 | 0.4429 | ||
| No | 1 (Ref.) | 1 (Ref.) | ||
| Yes | 0.645 (0.236–1.758) | 0.637 (0.201–2.018) |
Risk was expressed as odds ratio with 95% confidence intervals.
The analysis results could not be determined because of insufficient sample size.
AMD, age-related macular degeneration; BMI, body mass index; CKD, chronic kidney disease; DM, diabetes mellitus; HTN, hypertension; Q, quartile; Ref, reference.
Table 5.
Univariate and multivariate logistic regression analyses for potential risk factors of undiagnosed geographic atrophy.
| Risk factors | Univariate | P-value | Multivariate | P-value |
|---|---|---|---|---|
| Age, years | 0.993 (0.900, 1.096) | 0.8838 | 3.046 (2.051, 4.523) | < .0001 |
| Sex | 0.803 | – | ||
| Male | 1.334 (0.108, 16.412) | – | ||
| Female | 1 (Ref.) | 1 (Ref.) | ||
| BMI, kg/m2 | – | – | ||
| < 18.5 | – | – | ||
| < 23 | 1 (Ref.) | 1 (Ref.) | ||
| < 25 | 2.179 (0.125, 37.930) | – | ||
| < 30 | 0.953 (0.084, 10.781) | – | ||
| ≥ 30 | – | |||
| Abdominal obesity | 0.6139 | – | ||
| No | 1 (Ref.) | 1 (Ref.) | ||
| Yes | 0.612 (0.075, 4.989) | – | ||
| Education | – | – | ||
| ≤ Elementary school | 1 (Ref.) | 1 (Ref.) | ||
| Junior high | – | – | ||
| Senior high | 0.786 (0.121, 5.116) | – | ||
| ≥ University/college | – | |||
| Household income | – | – | ||
| Q1 | 1 (Ref.) | 1 (Ref.) | ||
| Q2 | – | – | ||
| Q3 | – | – | ||
| Q4 | – | – | ||
| Occupation | – | – | ||
| No | 1 (Ref.) | 1 (Ref.) | ||
| Yes | – | – | ||
| Spouse | – | – | ||
| Never married | 0.1138 | – | ||
| Cohabiting | 1 (Ref.) | 1 (Ref.) | ||
| Separated or divorced | 0.145 (0.012, 1.738) | – | ||
| Smoking | – | – | ||
| Non | 1 (Ref.) | 1 (Ref.) | ||
| Ex | 0.756 (0.095, 6.039) | – | ||
| Current | – | |||
| Drinking | – | – | ||
| Non | 1 (Ref.) | 1 (Ref.) | ||
| Mild | 1.444 (0.192, 10.884) | – | ||
| Heavy | – | – | ||
| Physical activity | 0.6992 | – | ||
| No | 1 (Ref.) | 1 (Ref.) | ||
| Yes | 0.701 (0.096, 5.135) | – | ||
| DM stage | – | – | ||
| Normal | 1 (Ref.) | 1 (Ref.) | ||
| Pre DM | – | – | ||
| DM | 1.368 (0.193, 9.683) | |||
| HTN stage | – | – | ||
| Normal | 1 (Ref.) | 1 (Ref.) | ||
| Pre HTN | – | – | ||
| HTN | 0.198 (0.052, 0.754) | – | ||
| Hypercholesterolemia | 0.6575 | – | ||
| No | 1 (Ref.) | 1 (Ref.) | ||
| Yes | 0.649 (0.079, 5.345) | |||
| CKD | – | – | ||
| No | 1 (Ref.) | 1 (Ref.) | ||
| Yes | – | – |
Risk was expressed as odds ratio with 95% confidence intervals.
The analysis results could not be determined because of insufficient sample size.
AMD, age-related macular degeneration; BMI, body mass index; CKD, chronic kidney disease; DM, diabetes mellitus; HTN, hypertension; Q, quartile; Ref, reference.
Table 6.
Univariate and multivariate logistic regression analyses for potential risk factors of undiagnosed neovascular age-related macular degeneration.
| Risk factors | Univariate | P-value | Multivariate | P-value |
|---|---|---|---|---|
| Age, years | 0.971 (0.926, 1.019) | 0.2252 | 0.833 (0.722, 0.961) | 0.0131 |
| Sex | 0.2399 | 0.0101 | ||
| Male | 0.625 (0.283, 1.381) | – | ||
| Female | 1 (Ref.) | 1 (Ref.) | ||
| BMI, kg/m2 | < .0001 | < .0001 | ||
| < 18.5 | – | – | ||
| < 23 | 1 (Ref.) | 1 (Ref.) | ||
| < 25 | 1.188 (0.345, 4.095) | 0.253 (0.037, 1.742) | ||
| < 30 | 1.034 (0.313, 3.413) | 0.536 (0.008, 38.254) | ||
| ≥ 30 | 6.734 (0.623, 72.754) | – | ||
| Abdominal obesity | 0.9371 | 0.3728 | ||
| No | 1 (Ref.) | 1 (Ref.) | ||
| Yes | 1.038 (0.403, 2.675) | 4.719 (0.148, 150.367) | ||
| Education | 0.1135 | 0.0399 | ||
| ≤ Elementary school | 1 (Ref.) | 1 (Ref.) | ||
| Junior high | 0.237 (0.065, 0.869) | – | ||
| Senior high | 0.563 (0.158, 2.001) | – | ||
| ≥ University/college | 0.349 (0.107, 1.137) | – | ||
| Household income | 0.0839 | 0.003 | ||
| Q1 | 1 (Ref.) | 1 (Ref.) | ||
| Q2 | 0.549 (0.169, 1.785) | 3.014 (0.071, 127.752) | ||
| Q3 | 0.271 (0.072, 1.011) | 0.698 (0.020, 24.747) | ||
| Q4 | 1.434 (0.411, 5.001) | – | ||
| Occupation | 0.5486 | 0.7713 | ||
| No | 1 (Ref.) | 1 (Ref.) | ||
| Yes | 1.352 (0.497, 3.677) | 1.258 (0.260, 6.088) | ||
| Spouse | 0.1108 | 0.2261 | ||
| Never married | 1 (Ref.) | 1 (Ref.) | ||
| Cohabiting | 0.367 (0.035, 3.844) | 2.596 (0.071, 95.311) | ||
| Separated or divorced | 1.100 (0.132, 9.183) | – | ||
| Smoking | 0.0267 | 0.0555 | ||
| Non | 1 (Ref.) | 1 (Ref.) | ||
| Ex | 0.226 (0.078, 0.658) | – | ||
| Current | 0.560 (0.196, 1.600) | – | ||
| Drinking | 0.5502 | 0.1771 | ||
| Non | 1 (Ref.) | 1 (Ref.) | ||
| Mild | 1.222 (0.457, 3.264) | 1.576 (0.089, 27.861) | ||
| Heavy | 0.565 (0.137, 2.328) | – | ||
| Physical activity | 0.009 | 0.0107 | ||
| No | 1 (Ref.) | 1 (Ref.) | ||
| Yes | 0.273 (0.105, 0.714) | 0.066 (0.008, 0.518) | ||
| DM stage | 0.5524 | 0.1217 | ||
| Normal | 1 (Ref.) | 1 (Ref.) | ||
| Pre DM | 0.637 (0.176, 2.308) | 0.070 (0.006, 0.889) | ||
| DM | 0.495 (0.138, 1.777) | 0.197 (0.006, 6.660) | ||
| HTN stage | 0.992 | 0.5158 | ||
| Normal | 1 (Ref.) | 1 (Ref.) | ||
| Pre HTN | 0.925 (0.248, 3.443) | 0.653 (0.062, 6.923) | ||
| HTN | 0.958 (0.368, 2.496) | 0.268 (0.027, 2.667) | ||
| Hypercholesterolemia | 0.7233 | 0.4888 | ||
| No | 1 (Ref.) | 1 (Ref.) | ||
| Yes | 1.158 (0.508, 2.640) | 2.257 (0.217, 23.484) | ||
| CKD | 0.9116 | 0.4315 | ||
| No | 1 (Ref.) | 1 (Ref.) | ||
| Yes | 1.085 (0.251, 4.684) | 0.123 (< 0.001, 24.538) |
Risk was expressed as odds ratio with 95% confidence intervals.
The analysis results could not be determined because of insufficient sample size.
AMD, age-related macular degeneration; BMI, body mass index; CKD, chronic kidney disease; DM, diabetes mellitus; HTN, hypertension; Q, quartile; Ref, reference.
Discussion
In the present study, the prevalence of undiagnosed AMD was high, reaching 95.25% in the adult Korean population. The prevalence was similar regardless of age. AMD typically refers to changes in the macula that occur in individuals over 50 years of age, but it is also known to occur in younger age groups as well13–15. In addition, pachydrusen is relatively frequently found in Asians14. Considering the fact that pachydrusen can be present in individuals under the age of 50 years and may be misdiagnosed as drusen14, their presence may have contributed to the proportion of undiagnosed AMD cases observed in individuals in their 40 s.
Early detection of AMD is crucial for several reasons. In cases of early AMD, the risk of progression to late AMD can be mitigated through several known methods, such as smoking cessation, management of hyperlipidemia, adherence to a Mediterranean diet10, and intake of AREDS formulations9. Early implementation of these strategies can significantly reduce the risk of severe visual impairment associated with the progression to late AMD. In neovascular AMD, early detection and prompt treatment before severe visual impairment occurs are particularly important, as initial visual acuity is strongly associated with long-term visual outcomes16–18.
Anti-VEGF therapy is an effective treatment for neovascular AMD, with long-term studies spanning over 10 years reporting that approximately 20% of patients maintained vision of 70 or more ETDRS (Early Treatment Diabetic Retinopathy Study) letters18. However, real-world outcomes show significant variation among patients and treatment settings19, with some individuals still experiencing severe, irreversible vision loss, highlighting the need for early detection to reduce the risk of neovascularization.
Early detection of GA is also of significant importance. GA shows a progressive nature that is accompanied by slow but progressive visual loss5,20. Progression to severe GA can increase mortality by increasing the risk of falls and fracture21. While no effective treatment for GA has been available in the past, recent developments in complement inhibitors22,23 have introduced new possibilities for managing this condition. Pegcetacoplan is the first drug approved by the Food and Drug Administration for the treatment of GA, and clinical trials have demonstrated that it can reduce the GA growth rate by approximately 20–29%22. Although there is an increased risk of neovascularization as an adverse event, pegcetacoplan is considered a valuable treatment option given the lack of other alternatives for GA. More recently, avacincaptad pegol, another complement inhibitor, has been introduced. This drug has been shown to reduce the GA growth rate by 27.4–27.8%23. Similar to pegcetacoplan, the primary adverse event associated with avacincaptad pegol is an increased risk of neovascularization.
Since complement inhibitor therapy can help reduce the risk of progression from incomplete to complete retinal pigment epithelium and outer retinal atrophy24, the importance of early treatment has been emphasized25. From this perspective, early detection of GA emerges as a crucial issue in the era of GA treatment.
In this study, there was a tendency toward an increased risk of undiagnosed GA in the elderly population, suggesting the importance of accurately evaluating GA in this population. To accurately detect GA, OCT imaging would be of a great help26. Therefore, when conducting ocular examinations in elderly patients, it would be necessary to include OCT imaging to precisely identify the presence of GA.
The exact cause of unrecognized AMD is unclear. We postulate that the potential reasons for this trend are as follows: (1) individuals may not visit ophthalmologists unless they experience significant visual impairment, (2) they may not undergo regular ophthalmologic examinations, and (3) even after visiting an ophthalmologist, AMD may still go undiagnosed.
In the present study, the most important risk factor for undiagnosed AMD was a low education level. The potential reasons for this association can be hypothesized as follows. First, individuals with lower levels of education may be more likely to work in temporary jobs rather than regular positions in larger companies. In such cases, they may rely on national health screenings instead of regular health check-ups provided by their employers. Second, individuals with lower education levels and lower incomes may delay seeking ophthalmologic care until significant visual impairment occurs, often due to concerns about medical costs. In this study, low household income was closely associated with undiagnosed neovascular AMD. These findings are consistent with results from other population-based studies. In the U.S. population-based study by Cheng et al.27 on individuals aged 40 and older, those who did not speak English at home had lower awareness of the disease. Similarly, in a population-based study conducted by Thapa et al.28 in Nepal on individuals aged 60 and older, illiteracy was identified as a risk factor for disease awareness. These findings may support the potential association between undiagnosed AMD and the low education levels observed in our study.
The most effective method for early AMD detection, regardless of occupation, education level, or income, is through a national health-screening program. Such programs provide free access to eye examinations, enabling broader population coverage. Our findings suggest that incorporating fundus examinations into these programs is essential to reduce the incidence of undiagnosed AMD. In particular, targeted policies aimed at individuals with lower education levels are recommended. Furthermore, as self-monitoring and fundus examinations may have lower sensitivity than OCT in detecting neovascular AMD29, regular OCT scanning would be beneficial for individuals at high risk of developing AMD.
Neely et al.3 reported that approximately 25% of eyes considered normal based on dilated eye examinations by primary eye care physicians had macular characteristics indicative of AMD. This undiagnosed AMD was associated with older patient age, male sex, and less than a high school education3. We speculate that missed AMD diagnoses in primary eye care settings may contribute to the remarkably high prevalence of undiagnosed AMD observed in our study. This highlights the importance of continuous education for physicians in private practice to enhance their proficiency in diagnosing AMD accurately.
Previous population-based studies have also examined awareness of AMD. Cheng et al.27 using data from the 2005–2008 U.S. National Health and Nutrition Examination Survey, reported an AMD awareness rate of 17.5% among 5,553 individuals aged 40 and older, which is higher than the rate observed in our study. In a population-based cross-sectional study in the Bhaktapur district of Nepal, 7.6% of 1,000 individuals aged 60 and older were aware of AMD28. Awareness among the Korean population has been notably lower compared to these studies. Lee et al.30 utilizing data from the 5th KNHANES (2010–2012) with 7,403 participants aged 40 and older, found an average AMD awareness rate of 1.45%. However, in our study, which used data from the 2017–2020 KNHANES, the awareness rate increased to 4.75%, indicating some improvement over the years.
In the present study, various factors were closely associated with undiagnosed neovascular AMD. Among these factors, we focused on physical activity; the risk of undiagnosed neovascular AMD was markedly lower in individuals who engaged in physical activity than in those who did not. Previous studies have shown that physical activity is associated with a reduced risk of early and late AMD31. However, its impact on AMD awareness has not yet been elucidated. We hypothesize that individuals who engage in regular physical activity may be more attuned to visual abnormalities, leading to greater awareness of visual deterioration caused by neovascular AMD. Further controlled studies are needed to validate this hypothesis.
The primary strength of the present study is its large-scale population-based design, which utilized recently released data. Nevertheless, this study has several limitations. First, owing to the cross-sectional nature of the study, it was not possible to establish definitive causal relationships. Second, participants who declined ophthalmic examinations or could not undergo accurate fundus image evaluations were excluded from the analysis. Third, the presence of physician-diagnosed AMD was assessed through a questionnaire. Consequently, patients who had previously been diagnosed with AMD but could not accurately recall their diagnosis might have been misclassified as undiagnosed. This limitation could have led to an overestimation of the prevalence of undiagnosed AMD. Fourth, this study utilized data from the KNHANES, which is based on population-level data rather than clinic-based data. As a result, only non-invasive screening methods such as fundus photography and OCT, which are suitable for large-scale studies, were used. Therefore, advanced imaging techniques such as fluorescein angiography and indocyanine green angiography, which are crucial for detecting macular neovascularization (MNV) and diagnosing polypoidal choroidal vasculopathy (PCV), could not be employed. This limits the study’s diagnostic precision, especially in identifying MNV and PCV. Fifth, KNHANES provides individual researchers with the results analyzed by retina specialists, but does not provide the actual images. Therefore, in our study, we followed the classification method used by KNHANES and analyzed the data by categorizing AMD into three groups: early AMD, late dry AMD, and neovascular AMD. In general, dry AMD can be classified into early AMD and intermediate AMD32; however, for aforementioned reason, we were unable to further differentiate between early and intermediate AMD. Finally, all included subjects were Korean.
In conclusion, a considerable proportion of the participants identified with AMD in the KNHANES had never received a formal diagnosis of AMD from a healthcare professional. The primary risk factor for undiagnosed AMD was a low education level. These findings demonstrate the importance of regular fundus examinations, particularly for individuals with lower education levels, to accurately identify the presence of AMD. urthermore, additional research to identify the reasons for the higher rate of undiagnosed AMD among individuals with lower education levels would be valuable in developing targeted social policies to address this disparity.
Author contributions
Involved in conception and design (J.H.K.); acquisition of data (K.H., J.H.K.); analysis and interpretation (Y.K., K.H., J.H.K.); drafting the article (Y.K., K.H., J.H.K.); revising the article critically for important intellectual content (Y.K., K.H., J.H.K.); and final approval of the article (Y.K., K.H., J.H.K.). K.H. and J.H.K. contributed equally to this work and are considered co–corresponding authors.
Funding
This study was supported by Kim’s Eye Hospital Research Fund.
Data availability
The datasets generated and/or analyzed during the current study are available from the corresponding author upon reasonable request.
Declarations
Competing interest
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Kyungdo Han and Jae Hui Kim equally contributed as the corresponding authors.
Contributor Information
Kyungdo Han, Email: hkd917@naver.com.
Jae Hui Kim, Email: kimoph@gmail.com.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The datasets generated and/or analyzed during the current study are available from the corresponding author upon reasonable request.