We evaluated automated OCT-derived drusen volume measures in a population-based study (n = 4,512) aged ≥40 years, and its correlation with conventional color fundus photographs (CFP)-derived early AMD features. Participants had protocol-based assessment to capture medical and ocular history, genotyping for SNPs in CFH, ARMS2, and CETP, CFP-based AMD grading and automated drusen volume based on SD-OCT using built-in software (Cirrus OCT advanced RPE analysis software). Significantly fewer eyes with early AMD features (drusen, hyperpigmentation, soft or reticular drusen) had drusen volume = 0 mm3 (p < 0.001). In eyes with drusen volume > 0 mm3, increasing AMD severity was associated with increase in drusen volume (correlation coefficient 0.17, p < 0.001). However 220 (59.14%) of 372 participants with AMD based on CFP grading had drusen volume = 0 mm3. Factors associated with drusen volume included age (OR 1.42 per 5 years, 95% confidence interval [CI] 2.76, 4.48), systolic blood pressure (OR1.00, 95% CI 1.00, 1.01), ethnic Malay (OR 1.54, 95% CI 1.29, 1.83) and Chinese (OR 1.66, 95% CI 1.37, 2.01) compared to Indian. The ARMS2 rs10490924 T allele was associated with increased drusen volume in subjects with AMD (multivariable adjusted OR1.54, 95% CI 1.08, 2.19). Automated OCT-derived drusen volume is correlated with CFP-based AMD grading in many, but not all subjects. However the agreement is not good. These two modalities provide complementary information and should be incorporated into future studies.
Drusen are extracellular deposits accumulating within Bruch’s membrane or between the retinal pigment epithelium (RPE) and Bruch’s membrane on the apical side of the RPE, and are considered the hallmark of age-related macular degeneration (AMD). Increasing severity of baseline drusen characteristics and pigmentary changes have been shown to be important predictors for progression of AMD[
Optical coherence tomography (OCT), particularly spectral domain OCT (SD-OCT) is increasingly used in clinical studies and trials to evaluate AMD[
To date, there have been no studies correlating fully automated OCT-derived drusen volume measurements with conventional CFP grading in population studies in Asians. Previous clinical and epidemiological studies have suggested that drusen characteristics based on CFP grading in Asians and the risk they pose on AMD progression may be different from those in white populations[
Data for this analysis were derived from the Singapore Epidemiology of Eye disease (SEED) program, a population-based cohort study of eye diseases in adults aged 40 years and older living in Singapore. Specifically, this report analyzes SD-OCT measurements, which were incorporated into the examination protocols since 2009. The SEED program was approved by the Institutional Review Board of the Singapore Eye Research Institute, Singapore and conducted in accordance with the Declaration of Helsinki. All participants provided written informed consent.
Detailed methodology and protocols for the examinations have been described elsewhere[
All participants had an interview, systemic examination, and laboratory investigations to determine socioeconomic, ocular, and systemic risk factors. After pupil dilation with 1.0% tropicamide, CFP were taken according to standardized protocol. Specifically, CFP of Early Treatment for Diabetic Retinopathy Study[
AMD status was classified per eye: early AMD was defined as presence of either any soft drusen (distinct or indistinct) plus pigmentary abnormalities, reticular drusen, or large soft drusen >125 µm in diameter with drusen area >500 µm-diameter. Late AMD was defined as the presence of neovascular AMD or geographic atrophy (GA). Neovascular AMD included serous or hemorrhagic detachment of the RPE or sensory retina, and the presence of subretinal or sub-RPE hemorrhages or subretinal fibrous scar tissue[
Each eye was scanned after pupil dilation with Cirrus OCT 4000 with 512 A-scans × 128 B-scans over a 6- × 6-mm
A detailed interviewer-administered questionnaire was used to collect information about medical history (including hypertension, diabetes, angina, myocardial infarction and stroke), cigarette smoking (defined as current, past and never). The following information was collected: demographic information, socioeconomic characteristics (education, income level and occupation), family and medical history, and lifestyle factors (smoking, alcohol). We defined age as the age at examination. We categorized cigarette smoking into current smokers, former smokers or nonsmokers. Alcohol consumption was categorized into drinkers (those who reported having drunk alcohol in the past 3 months, irrespective of quantity) and non-drinkers. The participant’s height was measured in centimeters using a wall-mounted measuring tape. Weight was measured in kilograms using a digital scale (SECA, model 7822321009: Vogel & Halke, Hamburg, Germany). We calculated body mass index (BMI) using weight divided by the square of body height. Blood pressure was taken with the participant seated and after 5 minutes of rest. Systolic and diastolic blood pressure and pulse rate were measured with a digital automatic blood pressure monitor (Dinamap model Pro Series DP110X-RW, 100V2; GE Medical Systems Information Technologies Inc., Milwaukee, USA). Blood pressure was measured on two occasions 5 minutes apart. If the blood pressures differed by more than 10 mmHg in systolic or 5 mmHg in diastolic, a third measurement was taken. The blood pressure of the participant was defined as the mean between the two closest readings. A 40 mL sample of non-fasting venous blood was collected for the assessment of Hemoglobin A1c (HbA1c), serum glucose and lipid levels. All serum biochemistry tests were performed in the Singapore General Hospital Laboratory on the same day.
We extracted DNA from venous blood samples from all participants. Genotyping was performed using Illumina Human OmniExpres or Human Hap610-Quad Beadchip. For the current analysis, we evaluated the following four SNPs: CFH rs900292, ARMS2 rs10490924, CETP rs3764261 and rs2303790 (D442G). Genetic ancestry was inferred using principal component (PC) analysis to account for spurious associations resulting from ancestral differences of individual SNPs. Details of the PC analysis is reported in detail elsewhere[
All the statistical analysis was performed using R software version 3.2.2[
We included 4,512 subjects of Chinese (n = 1, 294) and Malay (n = 1, 346) and Indian (n = 1, 872) ethnicity. The mean age was 59.1 years. Of the 4,521 participants, 348 had early AMD and 24 subjects had late AMD in the worse eye. Bilateral AMD was present in 92 participants. For CFH, 486 subjects (14.79%) were homozygous and 1, 552 subjects (47.25%) were heterozygous for rs800292_A respectively. For ARMS2, 479 subjects (14.69%) were homozygous and 1, 562 (47.91%) were heterozygous for the rs104909274_T. For CETP, 151 subjects (4.60%) were homozygous and 1, 027 subjects (31.24%) were heterozygous for rs3764261_A; whereas 1 subject (0.03%) was homozygous and 54 subjects (1.67%) were heterozygous for D442G. Detailed baseline characteristics are summarized in Table 1.
Characteristics of study participants.
Mean/Number (SD/%) AGE, years 59.1 (8.6) GENDER, Female 2307 (51.1%) Ethnicity Chinese 1294 (28.7%) Malay 1346 (29.8%) Indian 1872 (41.5%) Body Mass Index, kg/m2 25.79 (4.66) Total cholesterol, mmol/L 5.36 (1.18) Blood HDL Cholesterol 1.26 (0.34) Blood LDL Cholesterol 3.43 (0.98) Blood suger level(SD) 6.90 (3.22) Creatinine, 76.77 (42.42) HbA1c, % 6.25 (1.28) Systolic Blood pressure, mmHg 135.59 (18.52) Diastolic Blood pressure, mmHg 77.73 (9.86) Smoking status Never smoked 3151 (72.7%) Current smoker 656 (15.1%) Past smoker 525 (12.1%) Drinking alcohol, yes 384 (8.9%) History of cardiovascular disease, yes 393 (9.1%) Use anti-cholesterol medication, yes 1461 (34.4%) Genetic risk factors CFH rs900292 (*n = 3,285) CC 1247 (37.96%) AC 1552 (47.25%) AA 486 (14.79%) ARMS2 rs10490924 (*n = 3,260) GG 1221 (37.45%) GT 1562 (47.91%) TT 479 (14.69%) CETP rs3764261 (*n = 3,287) CC 2109 (64.16%) AC 1027 (31.24%) AA 151 (4.60%) D442G rs2303790 (*n = 3,286) TT 3231 (98.33%) GT 54 (1.64%) TT 1 (0.03%)
*Total number of subjects with genotype data available for each SNP studied.
Drusen volume was markedly skewed towards 0 mm
OCT-derived Drusen volume and color fundus photograph-derived AMD category and individual AMD Features.
Total N = 4512 Number (%) of eyes with drusen volume = 0 mm3 P-trend* of eyes with dichotamized drusen volume Drusen Volume P-trendϮ of eyes with DV > 0 mm3 P-trendϮ (Overall) (In 717 participants with DV > 0 mm3) 25% Percentile Median 75% Percentile AMD status No AMD 4141 3576 (86.36%) <0.001 0.002 0.007 0.024 0.036 <0.001 Early AMD 347 216 (62.25%) 0.002 0.006 0.019 Late AMD 24 3 (12.50%) 0.004 0.121 0.459 Any Drusen None 3924 3388 (86.34%) <0.001 0.002 0.008 0.02725 0.994 <0.001 Yes 588 407 (69.22%) 0.002 0.005 0.018 Hyperpigmentation None 3948 3427 (86.80%) <0.001 0.002 0.007 0.026 0.908 <0.001 Yes 564 368 (65.25%) 0.002 0.006 0.022 Soft drusen None 3147 2804 (89.10%) <0.001 0.002 0.007 0.023 0.492 <0.001 Intermediate 459 337 (73.42%) 0.001 0.005 0.018 Soft distinct 129 71 (55.04%) 0.002 0.006 0.022 Soft Indistinct 631 502 (79.56%) 0.002 0.006 0.026 Reticular drusen None 4439 3760 (84.70%) <0.001 0.002 0.007 0.023 0.005 <0.001 Yes 9 1 (11.11%) 0.017 0.022 0.118 Max Drusen size None 927 791 (85.33%) <0.001 0.002 0.007 0.028 0.909 <0.001 <63 µm 2190 1993 (91.00%) 0.002 0.007 0.021 ≥63-<125 µm 646 515 (79.72%) 0.002 0.006 0.025 ≥125-<250 µm 542 392 (72.32%) 0.001 0.004 0.017 ≥250 µm 43 13 (30.23%) 0.004 0.015 0.071 Drusen number None 926 790 (85.31%) <0.001 0.002 0.007 0.028 0.888 0.002 <10 2411 2128 (88.26%) 0.002 0.007 0.022 ≥10 1010 785 (77.72%) 0.002 0.006 0.022 Total drusen area <63 µm 939 797 (84.88%) <0.001 0.002 0.007 0.028 0.721 <0.001 63-125 µm 1876 1701 (90.67%) 0.002 0.007 0.021 125-175 µm 894 769 (86.02%) 0.003 0.007 0.024 175-350 µm 276 201 (72.83%) 0.001 0.004 0.018 350-650 µm 241 171 (70.95%) 0.001 0.004 0.01 <Half DA 65 38 (58.46%) 0.003 0.008 0.019 ≥Half and <1DA 23 15 (65.22%) 0.002 0.004 0.019 ≥1 DA 41 15 (36.59%) 0.005 0.018 0.073
Ptrend* of eyes with DV = 0 VS DV > 0 mm
Ptrend* of eyes with DV > 0 mm
P values < 0.05 are highlighted by bold font.
Next, the association of drusen volume with possible AMD risk factors was assessed using logistic regression. In the univariate regression model, drusen volume was associated with age, total cholesterol, LDL-cholesterol, anti- hyperlipidemia medication, glomerular filtration rate, systolic blood pressure, history of cardiovascular disease, and ethnic Malay (compared to Indian ethnicity) and presence of any AMD. (Table 3) In the multivariable regression model, higher drusen volume was associated with older age (OR 1.42 per 5 years, 95% CI 2.76, 4.48), systolic blood pressure (OR 1.00, 95% CI 1.00, 1.01), ethnic Malay (OR 1.54, 95% CI 1.29, 1.83) and Chinese (OR 1.66, 95% CI 1.37, 2.01) compared to Indian and presence of any AMD (OR 3.52, 95% CI 2.76, 4.48) remained significantly associated with higher drusen volume.
Factors influencing OCT-derived drusen volume.
Univariable-model Multivariable-model * OR (95% CI) P-val OR (95% CI) P-val Any AMD, yes 4.05 (3.27 5.02) <0.001 3.52 (2.76 4.48) <0.001 Age, every 5 years 1.48 (1.42 1.54) <0.001 1.42 (1.35 1.49) <0.001 Gender, reference to Male 1.00 (0.88 1.15) 0.954 1.14 (0.98 1.32) 0.101 Body mass index, kg/m2 1.00 (0.98 1.01) 0.558 — — Total cholesterol, mmol/l 0.89 (0.84 0.95) <0.001 — — HDL cholesterol, mmol/l 1.10 (0.9 1.34) 0.352 — — LDL cholesterol, mmol/l 0.83 (0.77 0.9) <0.001 0.97 (0.89 1.05) 0.412 Anti-hyperlipidaemia medication 1.47 (1.28 1.7) <0.001 1.01 (0.84 1.21) 0.922 Blood glucose, mmol/l 1.02 (1.00 1.04) 0.073 — — Serum Creatinine, mmol/l 1.00 (1.00 1.00) 0.001 1 (1 1) 0.979 Glomerular Filtration Rate, 0.98 (0.98 0.98) <0.001 1.00 (0.99 1.00) 0.546 HbA1c, % 1.05 (1.00 1.11) 0.056 — — Axial length, mm 1.02 (0.94 1.1) 0.641 — — Systolic blood pressure, mmHg 1.01 (1.01 1.02) <0.001 1.00 (1.00 1.01) 0.036 Diastolic blood pressure, mmHg 0.99 (0.99 1.00) 0.046 — — Alcohol consumption, yes 0.84 (0.66 1.08) 0.17 — — History of cardiovascular disease, yes 1.40 (1.14 1.74) 0.002 0.94 (0.74 1.21) 0.64 Non-smoker Reference Reference Reference Reference Past smoker 0.92 (0.76 1.11) 0.385 — — Current smoker 1.14 (0.93 1.40) 0.194 — — Indian Reference Reference Reference Reference Malay 1.49 (1.27 1.74) <0.001 1.54 (1.29 1.83) <0.001 Chinese 0.98 (0.82 1.16) 0.794 1.66 (1.37 2.01) <0.001
The analysis was done using logistic model for binary outcome of drusen volume based on: Drusen volume = 0 (n = 7372); Drusen volume > 0 (n = 1301).
*Multivariable models adjusted for age, gender, ethnicities, LDL, anti-cholesterol medication, history of cardio vascular disease, Glomerular Filtration Rate, blood pressure, and AMD.
P values < 0.05 are highlighted by bold font.
Amongst the AMD risk genotypes evaluated, presence of the ARMS2 rs10490924 T allele was associated with increased drusen volume (multivariable adjusted OR1.54, 95% CI 1.08, 2.19) in subjects with AMD (n = 347). (Table 4) None of the genotypes evaluated was significantly associated with increased drusen volume in subjects without AMD.
Genotype association with optical coherence tomography (OCT)-derived drusen volume in participants with AMD (n = 347).
Number (%) with Drusen volume = 0 Number (%) with Drusen volume > 0 Model 1* Model 2* OR (95% CI) P_val OR (95% CI) P_val CFHrs900292 0.82 (0.58 1.16) 0.251 0.78 (0.53 1.14) 0.203 CC 71(54.62%) 59(45.38%) AC 104(61.9%) 64(38.1%) AA 29(59.18%) 20(40.82%) ARMS2rs10490924 1.59 (1.14 2.24) 0.007 1.54 (1.08 2.19) 0.017 GG 83(68.6%) 38(31.4%) GT 96(58.18%) 69(41.82%) TT 25(40.98%) 36(59.02%) CETPrs3764261 0.97 (0.66 1.42) 0.864 0.97 (0.65 1.45) 0.885 CC 130(59.09%) 90(40.91%) AC 62(56.36%) 48(43.64%) AA 12(70.59%) 5(29.41%) D442Grs2303790 NA NA NA NA TT 203(58.67%) 143(41.33%) GT 1(100%) 0(0%) GG 0 0
AMD Age-related macular degeneration; DV drusen volume; OR Odds ratio; CI confidence intervals.
*Multivariable ordinal regression model adjusted for age, gender and principal components 1-3 from principal component analysis of genetic data.
In this population-based study using data from CFP and SD-OCT-derived drusen volume measured from 4512 subjects, we showed automated measurement of drusen volume increased with AMD severity based on traditional Wisconsin AMD grading parameters using CFP. We note that drusen volume was markedly skewed towards 0 mm
For several decades CFP-based grading has been the most widely-used imaging modality to assess AMD in population and clinical studies[
Regarding factors which may influence drusen volume, the strongest association was seen with the presence of any AMD (OR 3.52, p < 0.001). However, we also observed that increasing drusen volume was associated with older age, systolic blood pressure, ethnic Malay and Chinese compared to Indian ethnicity. These results are in keeping with previous report by Diniz et al. in non-AMD subjects aged 50 + years in which perifoveal drusen area was associated with increasing age, systolic blood pressure and years of smoking[
The pathogenesis of AMD is complex and multi-factorial. One of the key mechanisms involves thickening of the RPE-BM complex and deposition of extracellular materials above or below the RPE, which is thought to lead to reduced transport of nutrients and lowered oxygen tension. We hypothesize that increased drusen volume may be observed in eyes affected by this process, which may in turn be related to an individual’s genetic background. The association between OCT-derived drusen measurements with SNPs in eight AMD-associated genes (SYN3, LIPC, ARMS2, C3, CFB, CETP, CFI and CFH) was evaluated in 216 subjects (432 eyes) with intermediate AMD from the Amish population[
Strengths of the current study include its population-based sample, the adoption of standardized Wisconsin AMD grading methodology for CFP identical to those used in the landmark Beaver Dam and Blue Mountains Eye studies. The use of standardized questionnaires to capture baseline demographic data allowed us to evaluate the influence of a comprehensive list of medical and social risk factors on drusen volume. The availability of genotype data in a large sample size provided a unique opportunity to evaluate the influence of AMD-associated genetic polymorphisms on drusen volume. There are limitations, however, that need to be discussed. The automated drusen volume measurement algorithm used in this study is based on automatic SD-OCT segmentation of the outer border of the RPE layer and compared with the interpolated RPE floor which correspond to a virtual RPE layer in contact with Bruch’s membrane and free of deformation. Segmentation error may occur in cases of mild RPE distortion or low RPE reflectivity. As the minimum cutoff for detection of RPE elevation is 19.5μm, small drusen could be missed. Differences are likely to exist between several custom semi-automated and automated algorithms for drusen measurement. However, the use of a commercially available, FDA-approved automated drusen quantification tool in SD-OCT makes the current approach attractive and broadly applicable in clinical practice. Finally, to account for the highly right-skewed drusen volume distribution, we analyzed the relationship between drusen volume and CFP grading using non-parametric Cuzik’s trend test[
In conclusion, using population-based data with >8,000 eyes, we showed automated SD-OCT measurement of drusen volume using a commercial algorithm (Cirrus OCT; Carl Zeiss Meditec, Inc.) correlated closely with conventional CFP-derived AMD severity and individual AMD features. These two imaging modalities offer unique but complementary information and should be incorporated in future population and clinical studies. Our results suggest automated OCT-derived drusen volume can potentially be used in large scale population studies and big data analyses, such as UK Biobank, to screen for early AMD.
We thank Prof Cheung Yin Bun for his advice on statistical analysis. National Medical Research Council grants no. 0796/2003 and Biomedical Research Council Grant no. 501/25-5.
C.M.G. Cheung (literature review, study design, data analysis, writing of manuscript, decision to submit), Y.S. (data analysis). Y.C. Tham, C. Sabanayagam, K. Neelam (study design, data collection, review of manuscript), J.J. Wang, P. Mitchell, C.Y. Cheng, T.Y. Wong (study design, critical review of manuscript), C.Y.L. Cheung (study design, critical review of manuscript)
The authors declare no competing interests.
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By Chui Ming Gemmy Cheung; Yuan Shi; Yih Chung Tham; Charumathi Sabanayagam; Kumari Neelam; Jie Jin Wang; Paul Mitchell; Ching-Yu Cheng; Tien Yin Wong and Carol Yim Lui Cheung