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Keywords:

  • Risk factors;
  • age-related maculopathy;
  • incidence;
  • AMD;
  • ARM

Abstract.

  1. Top of page
  2. Abstract.
  3. Introduction
  4. Material and Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

Purpose: To examine the association between potential risk factors and the 14-year incidence of age-related maculopathy (ARM).

Design: Population-based cohort study.

Participants: At baseline, 946 volunteers participated in the study during 1986−88. These subjects were between 60 and 80 years of age and lived in the Østerbro district of Copenhagen. Excluding participants who had died since baseline, 359 subjects (97.3% of survivors) were re-examined 14 years later, during 2000−2002. A total of 31.8% (301/946) of the original material was included in the risk factor analyses.

Methods: Participants underwent an ophthalmological examination at Rigshos-pitalet, the National University Hospital of Copenhagen. Similar standardized protocols for physical examination were used at the baseline and follow-up examinations. Age-related maculopathy lesions were determined by the same grader grading colour fundus photographs from both examinations using a modification of the Wisconsin Age-related Maculopathy Grading System protocol.

Results: Of the 359 participants, 94 had incident early ARM and 52 had incident late ARM at follow-up in either eye. In logistic regression, the risk factors for early ARM or worse were as follows: cataract (odds ratio [OR] 2.8, 95% confidence interval [CI] 1.2–6.2); family history of ARM (OR 4.5, 95% CI 1.3–15.5), and alcohol consumption > 250 g/week (OR 4.6, 95% CI 1.1–19.2). High levels of apolipoprotein B (> 100 mg/l) decreased the risk of development of early ARM or worse (OR 0.4, 95% CI 0.2–0.8), while high levels of apolipoprotein A1 (≥ 150 mg/l) increased the risk of late ARM (OR 2.5, 95% CI 1.2–5.3). Advanced age at baseline was also associated with the incidence of late ARM (OR 2.0, 95% CI 1.4–2.9).

Conclusions: These findings indicate a direct correlation between age, cataract, family history, alcohol consumption, the apolipoproteins A1 and B and the 14-year incidence of ARM.


Introduction

  1. Top of page
  2. Abstract.
  3. Introduction
  4. Material and Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

Age-related maculopathy (ARM) is the most common cause of visual loss in most developed countries, including Denmark (Buch et al. 2001a, 2001b). Changing demographic profiles will lead to a further increase in the number of elderly people and increase the frequency of late ARM in the future. At present, there are few successful medical interventions which can prevent this disease (Age-related Eye Disease Study Group 2001). The treatment of ARM is limited to a few patients. However, the impact of the disease on the quality of life of our expanding elderly population is rising. For these reasons, further research needs to be directed at the primary and secondary prevention of ARM. Thus, the identification of modifiable factors related to this disease is hugely important to public health because it will facilitate and maximize the potential of appropriate interventions aimed at decreasing the prevalence and incidence of visual loss due to ARM.

Several studies have reported risk factors, some of which are modifiable while some are not, for early and late ARM in population-based and case-control studies (Kahn et al. 1977; Maltzman et al. 1979; Hyman et al. 1983; Goldberg et al. 1988; Eye Disease Case-control Study Group 1992; Vinding et al. 1992; Hirvela et al. 1996; Chaine et al. 1998; McCarty et al. 2001). Most of the epidemiological data come from cross-sectional studies and are inconsistent. No studies have previously reported on the association between risk factors and ARM with as long a follow-up period as in the present study. This study describes the associated risk factors for people with ARM in a longterm, population-based incidence study of elderly Danes.

Material and Methods

  1. Top of page
  2. Abstract.
  3. Introduction
  4. Material and Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

The study was approved by the Ethical Committee of Copenhagen and informed consent was obtained from each participant. The methods used to identify and describe this population have been published previously (Vinding 1995; Buch et al. 2005a). Briefly, the Copenhagen City Eye Study (CCES) is a population-based survey of vision and common eye diseases in a representative urban elderly population residing in the Copenhagen metropolitan area.

Study population

The study population consisted of an age- and sex-stratified, random subsample of 1000 subjects aged 60–80 years from the Copenhagen City Heart Study (CCHS) population, which comprised a random sample of 20 000 out of 90 000 citizens from a particular district in Copenhagen, Denmark (Schnohr et al. 1977; Vinding 1995). Of the 976 eligible subjects, 946 (96.9%) were examined at a baseline eye examination during 1986−1988 (Vinding 1995).

The mean and median times between the baseline and the 14-year follow-up examinations were 14.5 years and 14.7 years (range 12.6–16.1 years), respectively. During this period, a total of 577 of the original 946 participants died (61.0%). The follow-up study population was recruited from the surviving cohort of 369 subjects (39.0%). Of these survivors, 359 subjects (229 women, 130 men) participated in the 14-year follow-up examination between May 1st, 2000 and May 1st, 2002. Their mean age was 82.4 years (SD ± 4.63, median 81 years, range 75–95 years).

The age distribution is only borderline representative of that of the general Danish population in that the women in the study population were older. After excluding deceased individuals, we achieved a follow-up success rate of 97.3% (359/369).

Data on ARM status were based on available gradable fundus photographs. Of the 359 subjects who participated in both the baseline and follow-up studies, 313 subjects had retinal photographs without confounding lesions in at least one eye that could be evaluated at both examinations as described in detail previously (288 for both eyes, 25 for one eye) (Buch et al. 2005a). These 313 subjects were included in the incidence analysis. Of these, 301 subjects (i.e. 31.8% [301/946] of the original population) met the criteria for inclusion in the risk analyses, in that their fundus photographs could be graded into those showing no ARM, or incident early or late ARM.

Procedures

Similar questionnaires and eye examination procedures were used at the baseline and follow-up examinations (Vinding 1995; Buch et al. 2005a). A detailed questionnaire was administered covering medications, family history and medical history of diseases. An important part of the eye examinations at both visits involved the obtaining of 30-degree, colour fundus photo-graphs centred on the macula.

Age-related maculopathy grading, kappa values and quality assurance procedures have been reported elsewhere (Buch et al. 2005a). In summary, 30-degree, colour fundus photographs centred on the macula were assessed by a masked grading of ARM using a modified version of the Wisconsin Age-related Maculopathy Grading System (Klein et al. 1991) performed by the same grader. All questionable and late-stage lesions were assessed again by one of the authors, who was masked to the previous assessment.

Definitions

Age-related maculopathy

Definitions of ARM lesions closely followed the definitions developed by Klein et al. (1997b, 2002a). More detailed descriptions of these lesions have been presented elsewhere (Buch et al. 2005a). Early age-related maculopathy (early ARM) was defined as the presence in the macular area of either soft indistinct drusen (≥ 63 µm in diameter with decreasing density from the centre to periphery and fuzzy edges) or any distinct drusen plus pigmentary abnormalities (defined as retinal pigment epithelial depigmentation or increased retinal pigmentation). Late age-related maculopathy (late ARM) was defined as the presence of exudative age-related maculopathy or pure geographic atrophy. Exudative ARM comprised retinal pigment epithelial detachment, serous detachment of the sensory retina, subretinal haemorrhage, subretinal fibrous scars, or all of these. Pure geographic atrophy was defined by a retinal pigment epithelial atrophic area of ≥ 175 µm with visible choroidal vessels and/or sharp edges and/or a circular shape.

Age-related maculopathy status

When there was a discrepancy in the severity of lesions in the two respective eyes of a participant, the grade assigned to the participant pertained to that of the more severely affected eye (i.e. the worse eye). Further, when ARM lesions could not be graded in an eye, due, for example, to poor photographic quality or lack of a photograph, the participant was assigned a score equivalent to that of the other eye. Similarly, participants who did not have ARM in either eye at baseline and were then found to have developed it in at least one subfield in either eye at follow-up were assigned incidental ARM status.

Baseline variables

Age was defined as the age at the baseline examination. Specific ocular clinical variables, such as specific iris colour, hyperopia (spherical equivalent of + 1.00 D or more), cataract and a family history of ARM were recorded as at the baseline examination (Vinding 1995). Information on cataract surgery was obtained from data from the ophthalmological follow-up examination. These eye-specific variables were defined for the worse eyes of patients with incidents of unilateral ARM and for the right eyes of the remaining individuals.

Information on baseline cardiovascular risk factors and disease including information on baseline biochemical cardiovascular risk factors based on blood samples were obtained from the CCHS II and the CCHS III data files; the procedures used in CCHS II and III have been described previously (Appleyard et al. 1989; Schnohr et al. 2001). In brief, cardiovascular risk factors were assessed by a self-administered questionnaire and by various laboratory tests. The participants reported on smoking status, describing themselves as never having been smokers, ex-smokers or current smokers. Current smokers reported whether they inhaled. Arterial blood pressure was measured in the sitting position on the left upper arm after a 5-min rest and a London School of Hygiene sphygmomanometer was used. Hyper-tension was defined as systolic blood pressure > 140 mmHg and/or diastolic blood pressure > 85 mmHg, and/or use of antihypertensive drugs. Blood lipids were measured non-fasting. Total cholesterol and high density lipoprotein (HDL) cholesterol were measured using an enzymatic colorimetric method. High levels of total and HDL cholesterol were defined as > 6.5 mmol/l and ≥ 1.0 mmol/l, respectively. Additionally, triglycerides were measured enzymatically. A high level of triglyceride was defined as > 1.5 mmol/l. Apolipoproteins A1 and B (APO A1, APO B) were measured using a photometric method. High levels of APO A1 and APO B were defined as > 150 mg/l and > 100 mg/l, respectively. Cut-off points for blood lipids were chosen based on criteria for referral to a doctor while ensuring that the number of cases were appropriate for analyses. Body mass index (BMI) was calculated as weight divided by height squared (kg/m2), and obesity was defined as BMI ≥ 27 kg/m2. Diabetes was defined as self-reported disease, use of antidiabetic medicine or insulin, and/or non-fasting plasma glucose ≥ 11.1 mmol/l. Additionally, the participants gave information on any attacks of pain in the breast or legs when resting or exercising (angina pectoris and intermittent claudication). Physical activity in leisure time was classified as sedentary if the subject participated in < 2 hours of activity per week, moderate at 2−4 hours per week, and high at ≥ 5 hours per week. Cardiovascular disease included data on ischaemic heart disease (IHD) and stroke based on validated self-report by means of the National Patient Register.

Information on baseline socio-economic status variables was obtained from the CCHS II data files. Two socio-economic status variables were included: educational level (which was divided into three categories of < 8 years of schooling, 8–10 years of schooling, and ≥ 11 years of schooling, respectively), and household income (categorized as low at < 7000 Danish crone [DKR] per month, medium at DKR 7000–16 000 per month, and high at > DKR 16 000 per month).

Information on baseline alcohol intake was obtained from the CCHS II data files. Alcohol consumption was classified according to weekly intake in two categories: ≤ 250 g per week (∼ three drinks per day), and > 250 g per week, according to the WHO recommendations for men. Finally, data concerning regular intake of medicine, vitamins and other supplements were obtained from the CCHS III (1991−94) data files.

Statistical methods

All tabulations and statistical analyses were performed using SAS (Statistical Analysis System for Windows, Version 8.02; SAS Institute Inc., Cary, North Carolina, USA). The categorized data variables were represented by frequency distribution. The significance of variance between categorized data was analysed using Fisher's exact test when comparing baseline characteristics between participants with no ARM versus those with incident ARM.

To test the effect of risk factors on each ARM category, odds ratios (ORs) and 95% confidence intervals (CIs) were calculated from a logistic regression model. First, univariate logistic regression analyses were conducted that adjusted for gender and for age as a linear trend parameter. When testing for age we used a logistic regression model controlling for gender and vice versa.

Multivariate logistic regression analyses were then performed for the ‘early ARM or worse’ and the ‘late ARM’ categories, including only the significant (p < 0.05) and borderline significant (p < 0.1) univariate risk factors, which ensured that the number of explanatory variables included was adjusted to the number of events.

Criteria for inclusion in the analyses were no ARM, incident early or incident late ARM graded on fundus photographs according to a modified version of the Wisconsin Age-related Maculopathy Grading System (Buch et al. 2005a). The included cases were divided into three categories: 138 subjects who did not develop ARM in either eye and who were free from ARM at baseline (no ARM); 111 subjects who showed early ARM or worse in either eye at follow-up and who had not shown ARM at baseline (early ARM or worse), and 52 subjects who showed late ARM at follow-up in either eye and who had not shown late ARM at baseline (late ARM).

Results

  1. Top of page
  2. Abstract.
  3. Introduction
  4. Material and Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

Comparison between subjects with and without incident age-related maculopathy

The baseline characteristics of the patients are presented in Tables 1, 2 and 3. People with incident early ARM or worse had more cataract development and family histories of ARM but lower levels of APO A1 at baseline than those without ARM. Subjects with incident late ARM (i.e. pure geographic atrophy or exudative maculopathy) were older and more often had cataract and a history of cataract surgery, a family history of ARM, and significantly higher levels of APO A1 at baseline compared to those with no incident late ARM.

Table 1.  Baseline demographic and ocular characteristics of the follow-up study population according to age-related maculopathy status.
Baseline variablesAge-related maculopathy category
 No ARM* n = 138Early ARM or worse n = 111Late ARM n = 52
 %(No.)%(No.)%(No.)
  • *

    Cases were subjects who did not develop ARM in either eye and who were free from ARM at baseline.

  • Cases were subjects who developed early ARM or worse in either eye at follow-up and who had no ARM at baseline. The ‘Early ARM or worse’ category includes late ARM cases overlapping with the ‘Late ARM’ category.

  • Cases were subjects who developed late ARM at follow-up in either eye and who had no late ARM at baseline.

  • n = total number of cases in each category; No. = number of cases.

  • §

    p < 0.05;

  • **

    p < 0.01;

  • ‡‡

    p < 0.001.

Age groups (years)
 60–6441.3(57)33.3(37)15.4(8)‡‡
 65–6933.3(46)36.0(40)23.1(12)‡‡‡‡
 70–7421.1(29)18.9(21)36.5(19)‡‡
 75–804.35(6)11.7(13)25.0(13)‡‡
Men (%)40.6(56)33.3(37)30.8(16)
Ocular factors
 Xanthelasmata7.3(10)4.5(5)5.8(3)
 Corneal arcus31.9(44)30.6(34)38.5(20)
 Iris colour
  Blue54.7(75)52.3(58)46.2(24)
  Grey/green29.2(40)34.2(38)32.7(17)
  Brown16.6(22)13.5(15)21.2(11)
 Hyperopia (> 1.0 D)46.4(64)45.1(50)46.2(24)
 Cataract14.5(20)26.1(29)§36.5(19)**
 Cataract extraction18.1(25)23.4(26)34.6(18)§
Genetics
 Family history of late ARM4.8(6)14.9(15)§18.8(9)**
Table 2.  Baseline cardiovascular and lifestyle characteristics of the follow-up study population according to incident age-related maculopathy status.
Baseline variablesAge-related maculopathy category
 No ARM* n = 138Early ARM or worse n = 111Late ARM n = 52
 %(No.)%(No.)%(No.)
  • *

    Cases were subjects who did not develop ARM in either eye and who were free from ARM at baseline.

  • Cases were subjects who developed early ARM or worse in either eye at follow-up and who had no ARM at baseline. The ‘Early ARM or worse’ category includes late ARM cases overlapping with the ‘Late ARM’ category.

  • Cases were subjects who developed late ARM at follow-up in either eye and who had no late ARM at baseline.

  • n = total number of cases in each category; No. = number of cases.

  • DKK = Danish crone.

  • §

    p < 0.05;

  • **

    p < 0.01.

Cardiovascular risk factors and disease
 Hypertension (> 140/ > 85 mmg)62.3(86)69.4(77)69.2(36)
 Serum total cholesterol ≥ 6.5 mmol/l36.8(51)30.0(33)52.9(27)§
 Serum HDL cholesterol < 1.0 mmol/l33.8(46)26.6(29)23.5(12)
 Serum triglyceride ≥ 1.5 µmol/l60.0(66)55.9(52)46.3(19)
 Apolipoprotein A1 ≥ 150 mg/l34.2(39)45.2(42)59.5(25)**
 Apolipoprotein B ≥ 100 mg/l43.0(49)22.6(21)**26.2(11)
 Body mass index (per kg/m2) ≥ 2729.0(40)31.4(36)34.6(18)
 Angina pectoris and/or intermittent claudication34.1(47)41.4(46)36.5(19)
 Cardiovascular disease2.2(3)2.7(3)1.9(1)
 Diabetes1.5(2)0.9(1)0.0(0)
Smoking habits
 Never28.3(39)32.4(36)34.6(18)
 Former smoker22.5(31)26.1(29)28.9(15)
 Current
 Non-inhaling15.9(22)21.6(24)13.5(7)
 Inhaling33.3(46)19.8(22)23.1(12)
Physical activity (hours/week)
 < 29.4(13)12.6(14)11.5(6)
 2–455.8(77)50.5(56)38.5(20)
 ≥ 534.8(48)36.9(41)50.0(26)
Alcohol consumption
 None34.3(47)33.3(37)34.6(18)
 > 0 to ≤ 250 g/week60.6(83)57.7(64)53.9(28)
 > 250 g/week41.2(7)58.8(10)11.5(6)
Education
 ≤ 7 years42.8(59)49.6(55)51.9(27)
 8–10 years41.3(57)32.4(36)42.3(22)
 > 10 years15.9(22)18.0(20)5.8(3)
Income, DKK/month (amount in Euro)
 < 7.000 (< €940)26.3(35)29.4(32)45.1(23)
 7.000–16 000 (€940–2148)49.6(66)48.6(53)37.3(19)
 > 16 000 (> €2148)24.1(32)22.0(24)17.7(9)
Table 3.  Baseline medication and supplements characteristics of the follow-up study population according to incident age-related maculopathy status.
Baseline variablesAge-related maculopathy category
 No ARM* n = 138Early ARM or worse n = 111Late ARM n = 52
 %(No.)%(No.)%(No.)
  • *

    Cases were subjects who did not develop ARM in either eye and who were free from ARM at baseline.

  • Cases were subjects who developed early ARM or worse in either eye at follow-up and who had no ARM at baseline. The ‘Early ARM or worse’ category includes late ARM cases overlapping with the ‘Late ARM’ category.

  • Cases were subjects who developed late ARM at follow-up in either eye and who had no late ARM at baseline.

  • n = total number of cases in each category; No. = number of cases.

Ca + antagonists2.9(4)5.5(6)5.8(3)
Benzodiazepine use14.0(19)18.0(20)15.7(8)
Neuroleptics3.7(5)1.8(2)3.9(2)
Anticoagulants/salicylates20.2(23)24.2(23)23.3(10)
Multivitamins44.2(61)48.7(54)50.0(26)
Antioxidants5.8(8)6.3(7)7.7(4)

At the follow-up examination, only 15 individuals had incident pure geographic atrophy and 37 had incident exudative ARM. These groups were too small to allow for further statistical analyses.

Risk factors for the longterm incidence of age-related maculopathy

Risk factors associated with the 14-year incidence of early and late ARM corrected for age and gender are shown in Tables 4, 5 and 6.

Table 4.  Odds ratios of incident age-related maculopathy according to various baseline determinants in a univariate regression model.
Baseline variablesAge-related maculopathy category
 Early ARM or worseLate ARM
 %(No.)*OR(95% CI)%(No.)OR(95% CI)
  • *

    Cases were subjects who developed early ARM or worse in either eye with neither early nor late ARM at baseline. The ‘Early ARM or worse’ category includes late ARM cases overlapping with the ‘Late ARM’ category.

  • Cases were subjects who developed late ARM in either eye without late ARM at baseline.

  • Odds ratio, 95% confidence interval (CI) for the univariate analyses adjusted for age and gender.

  • No. = number at risk.

  • §

    p < 0.1;

  • **

    p < 0.05;

  • ††

    p < 0.01;

  • ‡‡

    p < 0.001.

Age groups (no age adjustment)
 60–64 years39.4(94) 17.4(108) 1
 65–69 years46.5(86)1.3(0.7–2.4)12.4(97)1.8(0.7–4.5)
 70–74 years42.0(50)1.1(0.5–2.2) §26.8(71)4.5(1.9–11.1)††
 75–80 years68.4(19)3.2(1.1–9.2)**40.6(32)8.3(3.0–22.8)‡‡
Gender (no sex adjustment)
 Women47.4(156) 118.7(193) 1
 Men40.0(93)0.76(0.4–1.3)13.9(115)0.8(0.4–1.6)
Ocular factors
 Xanthelasmata33.315)0.6(0.2–1.8)17.7(17)0.9(0.2–3.3)
 Corneal arcus43.6(78)0.9(0.5–1.6)20.4(98)1.2(0.6–2.3)
 Iris colour
  Blue43.6(133) 115.1(159) 1
  Grey/green48.7(78)0.8(0.4–1.7)17.5(97)0.9(0.4–1.9)
  Brown40.5(37)1.1(0.6–2.0)22.5(49)1.3(0.5–3.0)
 Hyperopia (> 1.0 D)43.8(114)0.9(0.6–1.6)17.0(141)1.1(0.6–2.1)
 Cataract59.2(49)1.9(1.0–3.6) §27.9(68)1.3(0.6–2.7)
 Cataract extraction51.0(51)1.3(0.7–2.4)26.1(69)1.6(0.8–3.2)
Family history of late ARM71.4(21)3.4(1.2–9.1)**29.0(31)1.9(0.8–4.7)
Table 5.  Odds ratios of the incidence of age-related maculopathy according to various baseline determinants in a univariate regression model.
Baseline variablesAge-related maculopathy category
 Early ARM or worseLate ARM
 %(No.)*OR(95% CI)%(No.)OR(95% CI)
  • *

    Cases were subjects who developed early ARM or worse in either eye with neither early nor late ARM at baseline. The ‘Early ARM or worse’ category includes late ARM cases overlapping with the ‘Late ARM’ category.

  • Cases were subjects who developed late ARM in either eye without late ARM at baseline.

  • Odds ratio, 95% confidence interval (CI) for the univariate analyses adjusted for age and gender.

  • No. = number at risk.

  • DKK = Danish crone.

  • §

    p < 0.1;

  • **

    p < 0.05;

  • ††

    p < 0.01;

  • ‡‡

    p < 0.001.

Cardiovascular risk factors and disease
 Hypertension (> 140/ > 85 mmg)47.2(163)1.3(0.8–2.3)17.8(202)0.98(0.5–1.9)
 Serum total cholesterol≥ 6.5 mmol/l39.8(83)0.6(0.4–1.1)24.3(111)2.1(1.1–4.1)**
 Serum HDL cholesterol < 1 mmol/l38.7(75)0.8(0.4–1.4)14.0(86)0.8(0.4–1.8)
 Serum triglyceride ≥ 1.5 mmol/l44.1(118)0.9(0.5–1.6)13.8(138)0.7(0.3–1.3)
 Apolipoprotein A1 ≥ 150 mg/l51.9(81)1.6(0.9–2.9)23.8(105)2.6(1.3–5.7)††
 Apolipoprotein B≥ 100 mg/l30.0(70)0.4(0.2–0.7)††13.4(81)0.7(0.3–1.5)
 Body mass index (per kg/m2) ≥ 2747.4(76)1.3(0.7–2.3)19.2(94)1.3(0.7–2.6)
 Angina pectoris/intermittent claudication49.5(93)1.4(0.8–2.4)17.1(111)1.1(0.6–2.1)
 Cardiovascular disease50.0(6)1.3(0.3–6.9)14.3(7)0.6(0.1–5.5)
 Diabetes33.3(3)0.6(0.1–6.8)0(3)0(0)
Smoking habits
 Never48.0(75) 119.4(93) 1
 Ex-smoker48.3(60)1.1(0.5–2.2)19.5(77)1.3(0.6–3.1)
 Current smoker: non-inhaling52.2(46)1.2(0.6–2.6)12.5(56)0.7(0.2–1.8)
 Current smoker: inhaling32.4(68)1.2(0.6–2.6)14.8(81)1.6(0.6–3.9)
Alcohol consumption
 None44.1(84) 116.8(107) 1
 > 0 –≤ 250 g/week43.5(147)1.2(0.7–2.2)15.9(176)1.2(0.6–2.4)
 > 250 g/week44.8(17)2.9(1.0–9.2) §25.0(24)2.8(0.8–9.9)
Physical activity (hours/week)
 < 251.8(27) 116.2(37) 1
 2–442.1(133)0.7(0.3–1.6)12.5(160)0.8(0.3–2.2)
 ≥ 546.1(89)0.8(0.3–1.9)23.4(111)1.5(0.5–4.3)
Education
 ≤ 7 years48.2(114) 119.0(142) 1
 8–10 years38.7(93)0.7(0.4–1.2)18.8(117)1.0(0.5–1.9)
 > 10 years47.6(42)1.1(0.5–2.2)6.1(49)0.4(0.1–1.2)
Income, DKK/month (amount in Euro)
 < 7000 (< €940)47.8(67) 125.6(90) 1
 7000–16 000 (€940–2148)44.5(119)1.2(0.6–2.4)13.1(145)1.0(0.4–1.9)
 > 16 000 (> €2148)42.9(56)1.2(0.5–2.6)13.8(65)1.0(0.4–2.5)
Table 6.  Odds ratios of the incidence of age-related maculopathy according to various baseline determinants in a univariate regression model.
Baseline variablesAge-related maculopathy category
 Early ARM or worseLate ARM
 %(No.)*OR(95% CI)%(No.)OR(95% CI)
  • *

    Cases were subjects who developed early ARM or worse in either eye with neither early nor late ARM at baseline. The ‘Early ARM or worse’ category includes late ARM cases overlapping with the ‘Late ARM’ category.

  • Cases were subjects who developed late ARM in either eye without late ARM at baseline.

  • Odds ratio, 95% confidence interval (CI) for the univariate analyses adjusted for age and gender.

  • No. = number at risk.

Ca + antagonists60.0(10)1.8(0.5–6.5)27.3(11)1.9(0.5–8.3)
Benzodiazepine use51.3(39)1.2(0.6–2.4)17.2(47)0.8(0.3–1.8)
Neuroleptics28.6(7)0.5(0.1–2.7)20.0(10)1.8(0.3–2.9)
Anticoagulants/salicylates50.0(46)1.2(0.6–2.4)16.1(62)0.7(0.3–1.6)
Multivitamins46.9(115)1.2(0.7–1.9)18.3(142)1.3(0.7–2.4)
Antioxidants46.7(15)1.0(0.4–3.0)22.2(18)1.6(0.5–5.3)

The significant (p < 0.05) and borderline significant (p < 0.1) univariate risk factors for early ARM or worse and late ARM categories, respectively, were placed into a multivariate logistic regression model (Table 7). The following risk factors were significantly associated with the incidence of early ARM or worse: cataract, excess alcohol consumption (> 250 g/week), a family history of ARM, and a low level of serum APO B (< 100 mg/l). The incidence of late ARM was significantly associated with age and a high level of APO A1 (> 150 mg/l). The association between high total serum cholesterol (≥ 6.5 mmol/l) and late ARM reached borderline significance in the multivariate analysis.

Table 7.  Odds ratios of the incidence of age-related maculopathy according to various baseline determinants in a univariate regression model.
Baseline variablesAge-related maculopathy category
 Early ARM or worse*
 OR(95% CI)p-value
Age1.20(0.85–1.70)0.298
Alcohol consumption > 250 g/week4.61(1.10–19.20)0.036
Apolipoprotein B ≥ 100 mg/l0.40(0.20–0.78)0.008
Cataract2.75(1.22–6.18)0.014
Family history of ARM4.54(1.33–15.52)0.016
 Late ARM§
 OR(95% CI)p-value
  • *Odds ratio (OR) and 95% confidence interval (CI) adjusted for gender and the significant and borderline significant factors in the univariate regression analyses for the early ARM or worse category (i.e. age, cataract, serum apolipoproteinB level, alcohol consumption and a family history of ARM).

  • Cases were those who developed early ARM or worse in either eye with neither early nor late ARM at baseline. The ‘Early ARM or worse’ category includes late ARM cases overlapping with the ‘Late ARM’ category.

  • OR and 95% CI adjusted for gender and the significant factors for the late ARM category in the univariate regression analyses (i.e. age, serum APO A1 and serum total cholesterol).

  • §

    Cases were those without late ARM at baseline who subsequently developed late ARM in either eye at follow-up.

Age2.02(1.40–2.94)< 0.001
Apolipoprotein A1 ≥ 150 mg/l2.53(1.21–5.30)0.005
Serum total cholesterol ≥ 6.5 µmol/l1.92(0.90–4.09)0.089

Discussion

  1. Top of page
  2. Abstract.
  3. Introduction
  4. Material and Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

Our study had the longest follow-up period to date. Therefore, it offered a unique opportunity to examine the relationships between ARM and potential risk factors.

We found that age was the most important risk factor for development of late ARM (p < 0.001). Subjects aged 75–80 years at baseline were more likely to develop late ARM than people who were 60–64 years of age (OR 8.3, 95% CI 3.0–22.8), a finding consistent with data from all other population-based incidence studies (Bressler et al. 1995; Klein et al. 1997b, 2002a; Sparrow et al. 1997; Klaver et al. 2001; Mitchell et al. 2002; van Leeuwen et al. 2003).

Several studies have reported a possible genetic link for ARM (Hyman et al. 1983; Heiba et al. 1994; Meyers 1994; Silvestri et al. 1994; de Jong et al. 1997, 2001; Seddon et al. 1997; Klaver et al. 1998a, 1998b; Klein et al. 1998a; Smith & Mitchell 1998; Allikmets 2000a, 2000b). In accordance with previous studies (Hyman et al. 1983; Smith & Mitchell 1998), we found that individuals reporting a family history of ARM are at risk of this disease. This finding is compatible with the hypothesis that susceptibility to ARM may be genetically determined. The identification of a gene for ARM could lead to screening programmes for early detection and treatment. However, previous studies point toward genetic heterogeneity (Gorin et al. 1999; Evans 2001; Tuo et al. 2004).

Higher levels of total serum cholesterol were previously reported to be associated with late ARM (Eye Disease Case-control Study Group 1992; Tomany et al. 2004). However, some studies have shown an inverse relationship (Goldberg et al. 1988; Klein et al. 1997a; Tomany et al. 2004), while others, including ours, have found no relationship (Klein et al. 1993, 2003; van Leeuwen et al. 2004). We also found that HDL was not related to either early or late ARM, contrary to recent findings elsewhere (van Leeuwen et al. 2004). A previous epidemiological prevalence study reported a strong association between APO A1, APO B and ARM (Klein et al. 1999). In our study the high serum level of APO A1 − a transport protein for HDL cholesterol− was an independent risk factor for late ARM (OR 2.5, 95% CI 1.2–5.3). Yet high concentrations of serum APO B − a transport protein for low density lipoprotein (LDL) cholesterol − seem to prevent or delay development of early ARM or worse (OR 0.4, 95% CI 0.2–0.8). These inverse effects of HDL and LDL cholesterol transport proteins on the incidence of ARM suggest that they may be related to the development of ARM and lipofuscin accumulation in the retinal pigment epithelium. On a molecular level our results support previous genetic findings (Klaver et al. 1998a; Souied et al. 1998; Baird et al. 2004; van Leeuwen et al. 2004; Zareparsi et al. 2004) that apolipoprotein E gene (APOE) polymorphism may be involved in the development of ARM. In previous studies the APOE ε2-allele has been associated with an increased risk of ARM (Klaver et al. 1998a; Baird et al. 2004). Because individuals with an APOE ε2-allele have high serum levels of APO A1 and low serum levels of APO B (Frikke-Schmidt 2000, 2000a, 2000b), they also were at increased risk of ARM according to our analyses. However, the discrepancies among studies concerning the relationships between serum lipids and the role of lipid transport proteins to the development of ARM are not clear (Smith et al. 1998, 2001; Klein et al. 2003).

Serum HDL cholesterol and, thus, APO A1 are directly associated with alcohol consumption (Linn et al. 1993). In our study, subjects who consumed > 250 g/week of alcohol were more likely to develop early ARM or worse. The association between high serum APO A1 and late ARM remained significant when we controlled for a history of greater alcohol consumption.

Excess alcohol consumption was independently associated with an increased risk of developing early ARM or worse, but the confidence intervals were large (OR 4.6, 95% CI 1.1–19.2). Age-related maculopathy was not associated with minimal or moderate alcohol consumption. Alcohol intake, which increases oxidant stress or affects a mechanism that protects against oxidative damage, has been associated with tissue damage in other organs (Rosenblum et al. 1989; Rikans & Gonzalez 1990) and the retina was found to be susceptible to oxidative damage in an animal study (Katz et al. 1982). Our finding that excess alcohol intake appears to be related to an increased risk of ARM is supported by previous studies (Maltzman et al. 1979; Eye Disease Case-control Study Group 1992; Smith & Mitchell 1996; Moss et al. 1998; Cho et al. 2000; Klein et al. 2002b).

Among clinical risk factors specific to the eye, cataract, but not cataract extraction, was a predictor of ARM in our population, as previously reported by some previous studies (Sperduto et al. 1981; West et al. 1989; Klein et al. 1994; Age-related Eye Disease Study Research Group 2000). This supports the hypothesis that cataract and ARM might occur together more frequently because they may share common pathogenetic factors. The risk factors that might be related to both conditions are age, environmental factors such as diet (Mares-Perlman et al. 1995, 1996) and light exposure (West et al. 1989; Cruickshanks et al. 1993, 2001), or genetic factors (Heiba et al. 1993, 1994). However, the results of studies evaluating the relationships between cataract, cataract extraction and ARM have been inconsistent. Some found no relationship (Hirvela et al. 1996; Klein et al. 1998b), while others reported that cataract served as a protective factor against the development of ARM by shielding the eye from light damage (Bellows 1971). Furthermore, recent studies found cataract extraction to increase the risk for late ARM (Klein et al. 1998b, 2002c; Wang et al. 2003). The differences between studies are not apparent. However, many factors may explain the lack of association between ARM and potential risk factors, such as cataract extraction, in this study.

Strengths and limitations of the study

The strengths of our study include its population-based nature, the duration of the follow-up period, the high follow-up success rate among survivors, and the use of standardized methods to analyse risk factors and ARM. However, any conclusions or explanations about associations or the lack of them must be drawn with caution.

Firstly, the main limitation concerns the significant number of subjects who died during the follow-up period. The fact that only a small percentage − 31.8% (301/946) − of the original material was included in the analyses introduces the possibility of sparse data biases. The possible influence of this large dropout on the conclusions may explain the lack of associations of certain risk factors with ARM and some of the unusual findings in the present study. The lack of association between ARM and smoking – known to be a risk factor from large scale studies (Tomany et al. 2004) – underlines the fact that our results should be considered with reservation as they are conditional on the subjects staying alive. Additionally, the unexplained protective effect of APO B, which is a risk factor for cardiovascular disease, may be a result of sparse data biases and chance findings. Annual follow-up examinations with recordings of the macular status at each visit would have been desirable.

Secondly, as about two-thirds of the original sample had died by follow-up, it is important to consider selective mortality. It is possible that no relationships were found between some risk factors, such as cardiovascular risk factors or disease, and ARM because individuals with these potential risk factors who developed ARM may have died before the follow-up examination. For example, because subjects with higher cholesterol levels are at higher risk of cardiovascular disease than those with normal cholesterol level (Manninen et al. 1988), a positive relationship would be obscured. In addition, failure to observe an association between smoking and the incidence of late ARM may be the result of deceased subjects having a higher prevalence of smoking and greater likelihood of developing late ARM. Lack of participation due to mortality is likely to have masked some associations. We note that the prevalence of early ARM at baseline (a risk factor for late ARM) was higher in subjects who died (31.9%) than in those who participated (17.6%). Additionally, the presence of ARM was associated with poorer survival in women in this study (Buch et al. 2005b), but not in other previous population-based studies (Klein et al. 1995; Taylor et al. 2000; Wang et al. 2001; Borger et al. 2003).

Thirdly, self-reported data have the potential for error. For example, data on family history is barely reliable and under-reporting of several risk factors, such as smoking habits in subjects at risk of developing ARM, would lead to an underestimation of the association between them.

Finally, the analyses conducted for this report included the examination of many associations. As with all studies, some statistically significant associations may be found due to chance, when in fact no such association exist. Thus, it is important to consider results in context, which includes other existing evidence or biological mechanisms that are likely to link the risk factors to the disease. Nevertheless, we found a few statistically significant associations and, as suggested by Rothman (1990), some of these relationships may be important and merit additional study.

In conclusion, the strongest risk factor for ARM was increasing age. Although this may reinforce the recommendation that elderly people have regular eye examinations, it clearly is not a modifiable risk factor. Our data also indicate relationships between alcohol, cataract, family history, and the cholesterol transport proteins APO A1 and APO B and the 14-year incidence of ARM. Our findings concerning APO A1 and B are difficult to explain and should be investigated further.

Finally, our findings, partly supported by those of other studies, suggest the need for further study of whether interventions such as reducing alcohol intake can decrease the incidence of late ARM.

Acknowledgements

  1. Top of page
  2. Abstract.
  3. Introduction
  4. Material and Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

This study was supported by the Carl and Nicoline Larsens Foundation, the Danish Eye Research Foundation and the Danish Velux Foundation of 1981.

The authors wish to thank senior grader Maria Swift for her assistance in Wisconsin Age-related Maculopathy Grading System manual training, and Dr Ronald Klein and Stacy Meuer for providing valuable advice, all affiliated with the Ocular Epidemiology Reading Centre as part of the Department of Ophthalmology and Visual Sciences at the University of Wisconsin. The authors also thank Henrik Scharling for extensive data handling and statistical assistance and the Copenhagen City Heart Study Group for providing data and facilities without which this study could not have been completed.

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  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
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