- •Preface to the Second Edition
- •Contents
- •List of Abbreviations
- •1: Epidemiology of AMD
- •Core Messages
- •1.1 Introduction
- •1.3 Frequency
- •1.3.1 Prevalence
- •1.3.2 Incidence
- •1.4 Natural Course
- •1.5 Genetic Factors
- •1.5.1 The Complement Pathway Genes
- •1.5.1.1 Complement Factor H (CFH)
- •1.5.1.3 Complement Component 3 (C3)
- •1.5.1.4 Complement Factor I (CFI)
- •1.5.2 The ARMS2 (10q26) Locus
- •1.5.3.1 Apolipoprotein E (APOE)
- •1.5.4 Candidate Gene Association Studies
- •1.6 Environmental Factors
- •1.6.1 Smoking
- •1.6.2 Antioxidants
- •1.6.3 Body Mass Index (BMI)
- •1.6.4 Hypertension
- •1.6.5 Cataract Surgery
- •1.7 Interaction Between Risk Determinants
- •1.7.1 Combined Effects of CFH Y402H and Other Genetic and/or Environmental Factors
- •1.7.2 Combined Effects of 10q26 SNPs and Other Genetic and/or Environmental Factors
- •1.7.4 Combined Effects of the APOE Gene and Other Genetic and/or Environmental Factors
- •References
- •2: Genetics
- •Core Messages
- •2.1 Introduction
- •2.2 Identifying Risk Factors of a Common Disease
- •2.3 Early Findings
- •2.4.1 Functional Implications
- •2.5.1 Functional Implications
- •2.7 Prospects of Genetics in AMD Therapy and Prevention
- •Summary for the Clinician
- •References
- •Core Messages
- •3.1 Introduction
- •3.2 Cause and Consequences of Ageing
- •3.3 Clinical Changes Associated with Retinal Ageing
- •3.4 Ageing of the Neural Retina
- •3.5 Ageing of the RPE
- •3.5.1 Changes in RPE Cell Density
- •3.5.2 Subcellular Changes in the RPE
- •3.5.3 Accumulation of Lipofuscin
- •3.5.4 Melanosomes and Pigment Complexes
- •3.5.7 Antioxidant Capacity of the RPE
- •3.6 Ageing of Bruch’s Membrane
- •3.7 The Association Between Ageing and AMD
- •Summary for the Clinician
- •References
- •Core Messages
- •4.1 Introduction
- •4.2 The Complement System
- •4.3 Evidence for Involvement of the Complement System in AMD Pathogenesis
- •4.4.2 Complement Gene Variants and AMD Subtypes
- •4.4.3 Complement Gene Variants and Progression of AMD
- •4.4.5 Variations of Complement Genes and Response to Treatment: Pharmacogenetics
- •4.5 Emerging Pharmacological Intervention Targeting Complement Dysregulation
- •Conclusions
- •Summary for the Clinician
- •References
- •5: Histopathology
- •Core Messages
- •5.1 Retinal Pigment Epithelium
- •5.1.1 Structure and Function of the Retinal Pigment Epithelium
- •5.1.3 Deposits in the RPE
- •5.2 Bruch’s Membrane
- •5.2.1 Structure of Bruch’s Membrane
- •5.2.3 Deposits in Bruch’s Membrane, Drusen
- •5.3 Choroidal Neovascularization
- •5.4 Detachment of the Retinal Pigment Epithelium
- •5.5 Geographic Atrophy of the RPE
- •Summary for the Clinician
- •References
- •6: Early AMD
- •Core Messages
- •6.1 Introduction
- •6.2 Drusen
- •6.2.3 Fluorescence Angiography and Optical Coherence Tomography
- •6.3 Focal Hypopigmentation and Hyperpigmentation of the Retinal Pigment Epithelium
- •6.4 Abnormal Choroidal Perfusion
- •Summary for the Clinician
- •References
- •Core Messages
- •7.1 Introduction
- •7.2.1 Decreased Visual Acuity
- •7.2.2 Visual Distortion
- •7.2.3 Visual Field Defects
- •7.2.4 Miscellaneous Symptoms
- •7.3 Signs of Choroidal Neovascularization
- •7.3.1 Hemorrhage
- •7.3.2 Macular Edema and Subretinal Fluid
- •7.3.3 Retinal Pigment Epithelial Detachment
- •7.3.4 Miscellaneous Signs
- •7.4 Common Testing Modalities to Diagnose Choroidal Neovascularization
- •7.4.1 Fluorescein Angiography
- •7.4.2 Indocyanine Green Angiography
- •7.4.4 Optical Coherence Tomography
- •Summary for the Clinician
- •References
- •8: Geographic Atrophy
- •Core Messages
- •8.1 Introduction
- •8.3 Histology and Pathogenesis of Geographic Atrophy
- •8.5 Spectral Domain Optical Coherence Tomography in Geographic Atrophy
- •8.7 Risk Factors
- •8.7.1 Genetic Factors
- •8.7.2 Systemic Risk Factors
- •8.7.3 Ocular Risk Factors
- •8.8 Development of CNV in Eyes with GA
- •8.9 Visual Function in GA Patients
- •8.9.1 Measurement of Visual Acuity
- •8.9.2 Contrast Sensitivity
- •8.9.3 Reading Speed
- •8.9.4 Fundus Perimetry
- •8.10 Perspectives for Therapeutic Interventions
- •8.10.2 Complement Inhibition
- •8.10.3 Neuroprotection
- •8.10.4 Alleviation of Oxidative Stress
- •8.10.5 Serotonin-1A-Agonist
- •8.10.6 Perspective
- •Summary for the Clinician
- •References
- •9: Fundus Imaging of AMD
- •Core Messages
- •9.1 Introduction
- •9.2 Color Photography
- •9.3 Monochromatic Photography
- •9.5 Optical Coherence Tomography
- •9.5.2 Coherence Length
- •9.5.3 Time Domain Optical Coherence Tomography
- •9.5.4 Frequency Domain Optical Coherence Tomography
- •9.5.5 Increasing Depth of Imaging
- •9.5.6 General Optical Coherence Tomographic Imaging Characteristics of the Macular Region
- •9.6 Fundus Angiography
- •9.6.1 Fluorescein Dye Characteristics
- •9.6.2 Indocyanine Green Dye Characteristics
- •9.6.3 Cameras Used in Fluorescence Angiography
- •9.6.4 Patient Consent and Instruction
- •9.6.5 Fluorescein Injection
- •9.6.6 Fluorescein Technique
- •9.6.7 Indocyanine Green Technique
- •9.7 Fluorescein Angiographic Interpretation
- •9.7.1 Filling Sequence
- •9.7.2 The Macula
- •9.8 Deviations from Normal Angiographic Appearance
- •9.10.1 Drusen
- •9.12 Neovascular AMD
- •9.13 Retinal Pigment Epithelial Detachments
- •9.14 Retinal Vascular Contribution to the Exudative Process
- •9.15 Follow-up
- •9.15.1 Thermal Laser
- •9.15.2 Photodynamic Therapy
- •9.15.3 Anti-VEGF Therapy
- •Summary for the Clinician
- •References
- •10: Optical Coherence Tomography
- •10.1 Introduction
- •Core Messages
- •10.4 OCT in Geographic Atrophy
- •10.5 OCT in Exudative AMD
- •Summary for Clinician
- •References
- •11: Microperimetry
- •Core Messages
- •11.1 Introduction
- •11.2.1 From Manual to Automatic Microperimetry
- •11.2.2 Automatic Microperimetry
- •11.2.3 Microperimetry: The Examination
- •11.2.4 Microperimetry: Test Evaluation
- •11.2.5 Other Microperimeter
- •11.3 Microperimetry in AMD
- •11.3.1 Early AMD
- •11.3.2 Geographic Atrophy
- •11.3.3 Neovascular AMD
- •11.3.4 Neovascular AMD: Treatment
- •Summary for the Clinician
- •References
- •Core Messages
- •12.1 Introduction
- •12.2 Antioxidants and Zinc
- •12.3 Beta-Carotene
- •12.4 Macular Xanthophylls
- •12.6 Vitamin E
- •12.7 Vitamin C
- •12.8 Zinc
- •12.10 AREDS2
- •Summary for the Clinician
- •References
- •Core Messages
- •13.1 Introduction
- •13.2 Basic Principles
- •13.2.1 Clinical Background
- •13.2.2 Laser Photocoagulation
- •13.2.3 Photodynamic Therapy
- •13.3 Treatment Procedures
- •13.3.1 Laser Photocoagulation
- •13.3.2 Photodynamic Therapy
- •13.4 Study Results
- •13.4.1 Laser Photocoagulation
- •13.4.1.1 Extrafoveal CNV
- •13.4.1.2 Subfoveal CNV
- •13.4.1.3 Meta-analysis
- •13.4.2 Photodynamic Therapy
- •13.4.2.1 Predominantly Classic
- •13.4.2.2 Occult with No Classic Neovascularization
- •13.4.2.3 Minimally Classic
- •13.5 Safety and Adverse Events
- •13.5.1 Laser Photocoagulation
- •13.5.2 Photodynamic Therapy
- •13.6 Variations
- •13.6.1 Laser Photocoagulation: Different Wavelengths
- •13.6.2 Photodynamic Therapy
- •13.6.3 Combination Treatments
- •13.7 Present Guidelines
- •13.7.1 Laser Photocoagulation
- •13.7.2 Photodynamic Therapy
- •13.8 Perspectives
- •Summary for the Clinician
- •References
- •Core Messages
- •14.1 Introduction
- •14.2 Vascular Endothelial Growth Factor (VEGF)
- •14.3 Targets Within the VEGF Pathway
- •14.3.1 Sequestration of Released VEGF
- •14.3.2 Inhibition of VEGF and VEGF Receptor Synthesis by Small Interfering RNA (siRNA)
- •14.3.3 Inhibition of the Intracellular Signal Cascade
- •14.3.4 Natural VEGF Inhibitors
- •14.4 New Methods of Drug Delivery
- •14.5 Combined Strategies
- •Summary for the Clinician
- •References
- •Core Messages
- •15.1 Introduction
- •15.1.1 Anti-VEGF Therapies for NV-AMD
- •15.2.1 How Should Neovascular AMD be Diagnosed?
- •15.2.4.1 Results with Continuous Monthly Treatment
- •15.2.4.2 How Should Treatment be Started?
- •15.2.4.3 What Flexible Approaches Are Reported?
- •Fixed Quarterly Injection Studies
- •Flexible Dosing Regimens: Two Approaches
- •Flexible Dosing Regimens: ‘As Needed’ Approach
- •Flexible Dosing Regimens: ‘Treat-and-Extend’ Approach
- •Summary for the Clinician
- •References
- •Core Messages
- •16.1 Introduction
- •16.3 Current Limitation of Therapy in the Treatment of Exudative AMD
- •16.4 Rationale for Combination Therapy in the Treatment of Exudative AMD
- •16.5 Clinical Data Examining Combination Therapy for Exudative AMD
- •16.5.3 Triple Therapy for Exudative AMD
- •16.5.4 Combination Therapy with Radiation
- •Summary for the Clinician
- •References
- •Core Messages
- •17.1 Introduction
- •17.2 Current Treatment Options for Dry AMD
- •17.3 Targeting the Cause of AMD
- •17.4 Preclinical and Phase I Drugs in Development for Dry AMD
- •17.4.1 Clinical Trial Endpoints in Dry AMD
- •Trimetazidine
- •17.4.2.2 Neuroprotection
- •Ciliary Neurotrophic Factor (CNTF/NT-501)
- •AL-8309B (Tandospirone)
- •Brimonidine Tartrate Intravitreal Implant
- •17.4.2.3 Visual Cycle Modulators
- •Fenretinide
- •17.4.2.4 Other
- •17.4.3 Drugs to Prevent Injury from Oxidative Stress and Micronutrient Depletion
- •17.4.4.1 Complement Inhibition at C3
- •17.4.4.2 Complement Inhibition at C5
- •Eculizumab
- •17.4.4.3 Complement Inhibition of Factor D
- •FCFD4514S
- •Iluvien
- •Glatiramer Acetate (Copaxone)
- •17.5 Summary
- •Summary for the Clinician
- •References
- •18: Surgical Therapy
- •Core Messages
- •18.1 Maculoplasty
- •18.2 Macular Translocation
- •18.3 Single Cell Suspensions
- •18.5 Indications for Surgery
- •18.5.1 Non-responder
- •18.5.2 Pigment Epithelium Rupture
- •18.5.3 Massive Submacular Bleeding
- •18.5.5 Macula Dystrophies
- •Summary for the Clinician
- •References
- •19: Reading with AMD
- •Core Messages
- •19.1 Introduction
- •19.2 Physiological Principles
- •19.3 Reading with a Central Scotoma
- •19.3.1.2 The Reading Visual Field Related to the Fundus (Fig. 19.4b)
- •19.3.1.3 The Reading Visual Field Related to the Text (Fig. 19.4c)
- •19.3.1.4 Eccentric Fixation Related to the Globe (Fig. 19.5)
- •19.3.3 Examination of Fixation Behaviour
- •19.3.4 Motor Aspects
- •19.4 Methods to Examine Reading Ability
- •19.5 Rehabilitation Approaches to Improve Reading Ability
- •Summary for the Clinician
- •References
- •20: Low Vision Aids in AMD
- •Core Messages
- •20.2 Effects of Visual Impairment in AMD
- •20.5 Optical Magnifying Visual Aids for Distance
- •20.5.1 Aids for Watching Television
- •20.8 Electronic Reading Instruments
- •20.9 Additional Aids
- •20.10 Noteworthy Details for the Provision of Low Vision Aids
- •20.11 Basic Information on Prescription
- •Summary for the Clinician
- •References
- •Index
1 Epidemiology of AMD |
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documented [115, 116], whereas a functional consequence of L9H has not. In addition, one study with Anglo-Celtic ethnicity replicated the inverse association for the R32Q/IVS10 haplotype but not for the L9H/ E318D haplotype. The universality of the involvement of CFB R32Q in the pathogenesis of AMD is strengthened by the similar magnitude of the protective effect of this variant which is relatively common in both Caucasian and Indian populations. In addition, the protective R32Q/IVS10 haplotype seen in Caucasians was also validated in the Indian AMD cohort [112].
1.5.1.3 Complement Component 3 (C3)
Complement component C3 is the convergence point of all complement pathways (classical, lectin, and alternative). Activation of C3 is crucial for the formation of membrane attack complexes leading to cell lysis [119]. The C3 gene is located on chromosome 19p13.3–13.2. The amino acid changes caused by the C3 variants R102G and P314L may alter the binding capacity of C3 to pathogenic cell surfaces or other complement proteins [119–121]. A causal relation with AMD is plausible, since C3 mRNA is present in neural retina, choroid, and retinal pigment epithelium [118]; its cleavage product C3a is present in drusen [98, 122], and C3a can induce vascular endothelial growth factor expression and promote choroidal neovascularization [123].
Two functional variants in the C3 gene in high linkage disequilibrium, R102G (rs2230199) and P314L (rs1047286), have been identified as genetic risk factors for AMD in several studies in Caucasian populations [48, 124–130]. R102G has also been implicated in the progression from the earlier stages of AMD to late AMD [110]. The two initial case-control studies concluded from conditional analyses that R102G determined the association with AMD, and that neither P314L nor haplotypes in the region conferred additional risk [48, 125]. Other studies confirmed that R102G is more significant in AMD causality than P314L [124, 127, 131], and that no other nearby variation could explain the effect on disease risk [124, 127, 128, 131]. An allele-dose effect for R102G was observed in the various Caucasian studies with an increased risk of 1.5–1.7 for heterozygotes and 1.9–3.3 for homozygotes. The Rotterdam Study found associations of the C3 variants with early as well as late AMD, and reported that the risk increase was most prominent for the mixed type of AMD (both geographic atrophy
and neovascular AMD present) [127]. The effect of the C3 alleles is reportedly independent from the established genetic and environmental risk factors CFH Y402H, LOC387715 A69S, and smoking [127, 128].
Figure 1.7 presents a meta-analysis of all available studies up to date [48, 110, 124–130, 132]. The analysis resulted in a significant OR of 1.54 (95% CI, 1.42–1.67) for the R102G variant. In the Caucasian studies, the frequencies of the R102G varied between 23.7% and 30.0% in cases, and between 17.0% and 22.1% in controls. In the Asian studies, the R102G variant was not associated with AMD and uncommon with frequencies of 1.0–1.2% in cases, and 0.3–1.6% in controls [89, 132]. The population attributable risk (PAR) for R102G was 22% in the Scottish and English case-control study [48], 17% in the Caucasian-American family-based and casecontrol dataset [125], and 9.7% in the population-based Rotterdam Study [127]. These findings further support the notion of racial/ethnic differences in allele frequencies and genetic susceptibility to AMD.
1.5.1.4 Complement Factor I (CFI)
CFI is regulated by CFH and functions as a cofactor for the cleavage and inactivation of C3b. Recently, several variants near CFI have been associated with risk of AMD in Caucasian as well as Asian populations [133– 137]. In the Japanese study, rs10033900 had a protective effect with an OR of 0.28 (95% CI, 0.11–0.69) for homozygous carriers of the minor “C” allele. No association was found for heterozygous carriers (OR, 0.99; 95% CI, 0.61–1.62). A recent genome-wide association study found that the major “C” allele of rs2285714 was associated with an increased risk of 1.31 (95% CI, 1.18– 1.45). Ennis et al. reported significantly (P <0.05) protective effects for rs11728699, rs6854876, rs7439493, and rs13117504 with ORs ranging from 0.68 to 0.74 (P <0.05), and these SNPs also tagged significant protective (GCAG, OR 0.69) and causative (TGGC, OR 1.34) haplotypes [137]. Fagerness et al. previously identified a protective haplotype tagged by rs13117504 and rs10033900 (GC) with an OR of 0.72 [133].
1.5.2The ARMS2 (10q26) Locus
Linkage studies have initially identified a susceptibility locus at chromosome 10q26 as the second major contributor to the pathogenesis of AMD [52, 53, 55,
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L. Ho et al. |
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Late AMD |
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No AMD |
Odds Ratio |
Odds Ratio |
Study or Subgroup |
n |
N |
n |
N Weight IV, Random, 95% CI Year |
IV, Random, 95% CI |
Maller et al. |
703 |
2302 |
369 |
1766 |
16.9% |
1.66 |
[1.44, 1.92] |
Yates et al, Scott. group |
144 |
478 |
131 |
664 |
6.8% |
1.75 |
[1.33, 2.31] |
Yates et al., Engl. group |
332 |
1180 |
137 |
692 |
9.2% |
1.59 |
[1.27, 1.99] |
Park et al., AREDS |
619 |
2432 |
108 |
592 |
9.1% |
1.53 |
[1.22, 1.92] |
Pei et al. |
3 |
246 |
4 |
256 |
0.3% |
0.78 |
[0.17, 3.51] |
Francis et al., AREDS |
403 |
1344 |
126 |
644 |
9.2% |
1.76 |
[1.40, 2.21] |
Park et al., Mayo Clinic |
243 |
876 |
108 |
596 |
7.6% |
1.73 |
[1.34, 2.24] |
Despriet et al., RS |
88 |
340 |
1018 |
4874 |
7.8% |
1.32 |
[1.03, 1.70] |
Scholl et al. |
51 |
194 |
204 |
1168 |
4.4% |
1.69 |
[1.18, 2.40] |
Despriet et al., C-C |
124 |
494 |
53 |
336 |
4.3% |
1.79 |
[1.25, 2.56] |
Francis et al., C-C |
94 |
404 |
78 |
368 |
4.7% |
1.13 |
[0.80, 1.58] |
Francis et al., FC |
226 |
840 |
207 |
884 |
9.8% |
1.20 |
[0.97, 1.50] |
McKay et al. |
259 |
874 |
193 |
872 |
9.9% |
1.48 |
[1.19, 1.84] |
Cui et al. |
3 |
300 |
1 |
322 |
0.1% |
3.24 [0.34, 31.34] |
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Total (95% CI) |
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12304 |
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14034 |
100.0% |
1.54 |
[1.42, 1.67] |
Total events |
3292 |
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2737 |
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Heterogeneity: Tau2 = 0.00; Chi2 = 15.98, df = 13 (P = 0.25); I2 = 19% Test for overall effect: Z = 10.82 (P < 0.00001)
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2009 |
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2009 |
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2010 |
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2010 |
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0.2 |
0.5 |
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5 |
Fig. 1.7 Allele-based meta-analysis association studies investigating Complement Component C3 R102G and risk of late AMD, agerelated macular degeneration; AREDS, Age-related Eye Disease Study; C-C, Case-Control; CI, confidence interval; Engl., English; IV, inverse variance; n, number of risk alleles; N, total number of alleles;
Random, random effects model; RS, Rotterdam Study; Scott., Scottish ORs and 95% CIs were calculated using the random effects model of the DerSimoninian and Laird method. Scholl et al. only investigated associations with geographic atrophy, whereas Cui et al. and Pei et al. only investigated neovascular AMD
56, 138]; several studies have shown a strong correlation between AMD and multiple genetic variants in the 10q26 locus [62, 99, 139–152]. Nevertheless, controversy remains regarding the source of the genetic effect at this region due to the high linkage disequilibrium (LD) between three genes: the Pleckstrin Homology Domain-containing Protein family A member 1 (PLEKHA1), LOC387715 or age-related maculopathy susceptibility 2 (ARMS2) gene, and high temperature requirement factor A1 (HTRA1) gene. Multiple research disciplines support the involvement of both ARMS2 and HTRA1 in the causality of AMD with PARs of each gene up to 67% [46, 99, 125, 142, 153, 154], whereas PLEKHA1 appears at best to be only weakly associated with AMD [62, 99, 139–152].
Finding functional variants may elucidate which gene in the 10q26 locus is the major contributor in the causation of AMD. In the ARMS2 gene, rs10490924 has repeatedly been reported to increase risk of AMD up to 15 times [62, 99, 141–144, 147–151]. This functional SNP causes an A69S change, and has been described as the causal SNP that by itself could explain the bulk of the association between the 10q26 region and AMD [143]. In addition, a deletion-insertion
polymorphism (del443ins54; in/del) in the 3¢-UTR of ARMS2 was associated with AMD in Caucasian and Japanese studies [144, 150, 155, 156]. Fritsche et al. reported that the in/del was associated with rapid turnover of ARMS2 mRNA in placenta samples [144]. Conversely, Wang et al. found no correlation between the in/del and unstable ARMS2 mRNA in human retina and blood samples [150]. Moreover, they proposed that not the in/del but A69S confers AMD risk because of the strong LD between these variants. The precise function of ARMS2 in AMD remains to be elucidated. Earlier findings of disorganized mitochondrial membranes, as well as decreased number of mitochondria, in retinal pigment epithelium cells of AMD donors have provided evidence of mitochondrial dysfunction in AMD [157, 158]. This suggests that ARMS2 may jeopardize mitochondrial function, and consequently lead to the formation of reactive oxygen species, apoptosis, and AMD [144, 157–161]. Moreover, immunohistochemical studies located the ARMS2 protein to the mitochondrial outer membrane, in particular of rods and cones [143, 144]. However, its presence has also been reported in the cellular cytosol [162] and the extracellular matrix [163].
1 Epidemiology of AMD |
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A HTRA1 meta-analyses reported an increased risk of AMD for homozygous (OR, 7.46; 95% CI, 6.16– 9.04) as well as heterozygous (OR, 2.27; 95% CI, 2.02– 2.55) carriers of the rs11200638 risk allele compared to noncarriers [164]. Stratified analyses revealed that rs11200638 was significantly associated with CNV but not with GA, and that the causative effect was stronger in Caucasians than in Asians [164, 165]. Various lines of evidence supported the involvement of HTRA1 in AMD. The rs11200638 risk allele has been associated with higher levels of HTRA1 mRNA and protein in some studies [139, 151, 166, 167], although two other studies with larger datasets could not validate this finding in heterologous expression systems [143, 168]. Furthermore, HTRA1 may inhibit signaling of TGF-b proteins, which have been reported to act as negative growth regulators in the retina and RPE [169–171]. In addition, HTRA1 may stimulate the degradation of extracellular matrix through enhanced expression of matrix metalloproteases. Consequently, overexpression of HTRA1 may affect the integrity of Bruch’s membrane and RPE contributing to AMD development.
Recently, Richardson et al. found rs3793917 (located between ARMS2 and HTRA1) to be most associated with AMD (OR, 3.45; 95% CI, 2.36–5.05), and indicated that the intergenic region between this SNP and HTRA1 rs11200638 was most likely to confer AMD risk [152]. However, they could not distinguish rs3793917 from rs11200638 and rs10490924 to explain causality since they were all in high LD. Common haplotypes encompassing both the ARMS2 and the HTRA1 genes have also been linked to AMD. Gibbs et al. described a common haplotype TAT tagged by rs10490924, rs11200638, and rs2293870 that significantly predisposed to AMD (P = 2.70 × 10−9), and a haplotype GGG that was significantly protective against AMD (P = 0.003) [145]. Yang et al. also found a haplotype T-G-Wt-G tagged by rs2736911, rs10490924, in/del/Wt, and rs11200638, which was protective in Caucasian as well as Chinese populations (P < 0.007) [151]. They also observed that the in/del or the rs11200638 risk allele by itself was insufficient to alter HTRA1 expression, and found that a common disease haplotype including both the in/del and rs11200638 leads to upregulation of HTRA1 [151]. Hence, they proposed a binary model where downregulation of ARMS2 and concomitant upregulation of HTRA1 best explained the risk associated with the 10q26 locus. Further functional analyses in larger
datasets are warranted to conclude what the key genetic contributors in the 10q26 locus are.
Figure 1.8a presents meta-analyses of all presently available studies for A69S. This resulted in an overall allele-based OR of 2.41 (95% CI, 2.08–2.79) for late AMD. In the Caucasian studies, the frequencies of the A69S varied between 25.0% and 57.5% in cases, and between 18.8% and 37.0% in controls. In the Asian samples, the A69S variant was more common in both cases (61.9–73.9%) and controls (35.8–50.0%) compared to Caucasian samples. For GA, the pooled OR was 2.67 (95% CI, 2.22–3.22); for CNV, the OR was 2.99 (95% CI, 2.63–3.39); for early AMD, the OR was 1.68 (95% CI, 1.35–2.10; Fig. 1.8b). For HTRA1 rs11200638, the meta-analysis resulted in an overall OR of 2.49 (95% CI, 2.25–2.75). In the Caucasian studies, the frequencies of the A69S varied between 20.7% and 53.1% in cases, and between 18.2% and 28.9% in controls. In the Asian samples, the A69S variant was more common in both cases (42.5–77.2%) and controls (25.2–52.0%) compared to Caucasian samples. For GA, the pooled OR was 2.21 (95% CI, 1.77–2.75); for CNV, the OR was 2.92 (95% CI, 2.55–3.35). Only one study described the effects on early AMD with an OR of 1.89 (95% CI, 1.12–3.17).
1.5.3The Lipid-Related Genes
1.5.3.1 Apolipoprotein E (APOE)
Apolipoprotein E is a key regulator of lipid and cholesterol transport in the central nervous system [172], and has been linked to various neurodegenerative and cardiovascular diseases (e.g., Alzheimer’s disease and stroke) [173–175]. In the eye, APOE is expressed in photoreceptor cells, retinal ganglion cells, Müller cells, retinal pigment epithelium, Bruch’s membrane, choroid, and in the disease-associated lesions: drusen and basal laminar deposits [117, 118, 176–180]. There are three common allelic variants of the APOE gene: e2, e3, and e4, with e3 being the most prevalent [181, 182].
The majority of studies support a protective effect of the APOE e4 allele against AMD [180, 183–195], though in some reports, this inverse association failed to reach statistical significance [190–194]. Stratification of late AMD into GA and CNV showed that the greatest protective effect for the e3e4 genotype was in individuals with GA (OR 0.35, 95% CI 0.13–0.92) [195]. The APOE e2 allele has mainly been associated with a
14 L. Ho et al.
a
Study or Subgroup |
Late AMD |
No AMD |
|
Odds Ratio |
|
Odds Ratio |
||||||||||||||||||||||||
n |
N |
n |
N Weight IV, Random, 95% CI Year |
IV, Random, 95% CI |
||||||||||||||||||||||||||
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Rivera et al. |
580 |
1322 |
374 |
1844 |
5.5% |
3.07 [2.63, 3.60] |
2005 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Conley et al., AREDS |
619 |
1402 |
66 |
350 |
4.8% |
3.40 [2.55, 4.54] 2006 |
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
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|
|
|
|
|
Conley et al., CHS |
89 |
252 |
461 |
2102 |
4.8% |
1.94 [1.47, 2.57] |
2006 |
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Francis et al. |
465 |
1060 |
107 |
560 |
5.1% |
3.31 [2.59, 4.22] |
2007 |
|
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|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
Ross et al., AREDS, NEI |
320 |
798 |
124 |
658 |
5.1% |
2.88 [2.26, 3.67] 2007 |
|
|
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|
|
|
|
|
|
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|
|
|
|
|
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|
|
|
|
|
Ross et al., BMES |
148 |
556 |
211 |
1114 |
5.1% |
1.55 [1.22, 1.97] 2007 |
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Lin et al. |
139 |
190 |
90 |
180 |
3.9% |
2.73 [1.77, 4.21] |
2008 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Jakobsdottir et al. |
154 |
328 |
62 |
310 |
4.4% |
3.54 [2.49, 5.04] |
2008 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Kaur et al. |
239 |
386 |
113 |
316 |
4.7% |
2.92 [2.15, 3.98] |
2008 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Xu et al. |
157 |
242 |
114 |
264 |
4.3% |
2.43 [1.70, 3.48] |
2008 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Gibbs et al. |
287 |
684 |
109 |
430 |
4.9% |
2.13 [1.63, 2.78] |
2008 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Wang et al. |
148 |
556 |
211 |
1114 |
5.1% |
1.55 [1.22, 1.97] 2008 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Fritsche et al. |
644 |
1520 |
212 |
1098 |
5.4% |
3.07 [2.56, 3.68] |
2008 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Wang et al. |
380 |
912 |
123 |
468 |
5.0% |
2.00 [1.57, 2.56] 2009 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Losonczy et al. |
58 |
114 |
56 |
190 |
3.6% |
2.48 [1.53, 4.01] |
2009 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Marioli et al. |
69 |
120 |
85 |
230 |
3.8% |
2.31 [1.47, 3.62] |
2009 |
|
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|
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|
|
|
|
|
||||||||
Yang et al., Hopkins C-C |
221 |
884 |
208 |
868 |
5.2% |
1.06 [0.85, 1.32] |
2010 |
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
||||||||
Yang et al., Chinese C-C |
204 |
276 |
579 |
1182 |
4.8% |
2.95 [2.20, 3.95] |
2010 |
|
|
|
|
|
|
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|
|
|
|
|
|
|
||||||||
Richardson et al. |
402 |
804 |
55 |
238 |
4.5% |
3.33 [2.39, 4.64] |
2010 |
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
||||||||
Yang et al., Utah C-C |
536 |
1410 |
260 |
1300 |
5.4% |
2.45 [2.06, 2.92] |
2010 |
|
|
|
|
|
|
|
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|
|
|
|
|
|
||||||||
Hadley et al. |
210 |
582 |
88 |
382 |
4.8% |
1.89 [1.41, 2.53] |
2010 |
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
||||||||
Total (95% CI) |
14398 |
15198 100.0% |
2.41 [2.08, 2.79] |
|
|
|
|
|
|
|
|
|
|
|
|
Total events |
6069 |
3708 |
|
|
|
|
|
|
|
|
|
|
|
|
|
Heterogeneity: Tau2 = 0.10; Chi2 = 130.77, df = 20 (P < 0.00001); I2 = 85% |
0.2 |
0.5 |
1 |
2 |
5 |
||||||||||
Test for overall effect: Z = 11.74 (P < 0.00001) |
|
||||||||||||||
|
|
|
|
|
|
|
|
|
|
|
|
|
|||
b
|
Early AMD |
No AMD |
|
Odds Ratio |
Odds Ratio |
|
|
|
||||||||||
Study or Subgroup |
n |
N |
n |
N Weight |
IV, Random, 95% CI |
IV, Random, 95% CI |
|
|
|
|||||||||
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Losonczy et al. |
43 |
96 |
56 |
190 |
18.9% |
1.94 [1.17, 3.23] |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|||||||
Marioli et al. |
40 |
80 |
85 |
230 |
18.5% |
1.71 [1.02, 2.85] |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|||||||
Rivera et al. |
83 |
286 |
374 |
1844 |
62.6% |
1.61 [1.22, 2.13] |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|||||||
|
|
|
|
|
|
|
|
|
|
|
|
|||||||
Total (95% CI) |
462 |
2264 100.0% |
1.68 [1.35, 2.10] |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Total events |
166 |
515 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Heterogeneity: Tau2 = 0.00; Chi2 = 0.41, df = 2 (P = 0.81); I2 = 0% |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|||
0.5 |
0.7 |
1 |
1.5 |
2 |
|||||||||||||
Test for overall effect: Z = 4.62 (P < 0.00001) |
|||||||||||||||||
Favours experimental |
Favours control |
||||||||||||||||
|
|
|
|||||||||||||||
Fig. 1.8 Allele-based meta-analysis association studies investigating LOC387715 A69S and risk of (a) late AMD and (b) early AMD, age-related macular degeneration; CI, confidence interval; IV, inverse variance; n, number of risk alleles; N, total
number of alleles; Random, random effects model ORs and 95% CIs were calculated using the random effects model of the DerSimoninian and Laird method
non-significant but increased risk of AMD [180, 183– 185, 188, 191, 193, 195, 196].
The association between APOE and AMD may vary among different ethnic groups. The e4 allele was less common in the Chinese as well as in the Japanese population compared to the Caucasians (7.5% and 9.1% vs 10.5–30.6%), which may have contributed to the nonsignificant finding in Asians [190, 194]. However, their e2 allele frequency was comparable to that of Caucasians
(9.4% and 8.5% vs. 6.1–13.9%). In the Chinese study, e2-carriership correlated with a minor risk-increasing trend, whereas in the Japanese study, it associated with a risk-lowering trend, none statistically significant.
A recent study identified common haplotypes, which contained the e-alleles and four additional SNPs (rs405509 – rs440446 – rs769449 – rs769450 – e-alleles) and covered the entire APOE gene and its cis-regulatory region [197]. Two haplotypes associated significantly
1 Epidemiology of AMD |
15 |
|
|
a
|
Late AMD |
No AMD |
|
Odds Ratio |
|
|
|
|
|
|
|
|
|
Odds Ratio |
|||||||||||||||||||||||||||||
Study or Subgroup |
n |
N |
n |
N Weight IV, Random, 95% CI Year |
|
|
IV, Random, 95% CI |
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|
Klaver et al. |
12 |
124 |
281 |
1281 |
5.1% |
0.38 [0.21, 0.70] |
1998 |
|
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|
||||||||
Souied et al. |
17 |
181 |
50 |
258 |
5.5% |
0.43 [0.24, 0.78] |
1998 |
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|
||||||||
Pang et al. |
5 |
59 |
20 |
198 |
1.9% |
0.82 [0.30, 2.30] |
2000 |
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|
||||||||
Simonelli et al. |
5 |
137 |
259 |
1983 |
2.5% |
0.25 [0.10, 0.62] |
2001 |
|
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|
||||||||
Schmidt et al., UCLA |
16 |
136 |
16 |
110 |
3.5% |
0.78 [0.37, 1.65] |
2002 |
|
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|||||||||||
Schmidt et al., UP |
25 |
343 |
16 |
110 |
4.3% |
0.46 [0.24, 0.90] |
2002 |
|
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|||||||||||
Schultz et al., unrelated |
19 |
157 |
28 |
170 |
4.9% |
0.70 [0.37, 1.31] |
2003 |
|
|
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|||||||||||
Schultz et al., families |
64 |
384 |
59 |
299 |
10.9% |
0.81 [0.55, 1.20] |
2003 |
|
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|
|
|
|
|
|
|||||||||||
Baird et al. |
58 |
372 |
44 |
182 |
9.0% |
0.58 [0.37, 0.90] |
2004 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|||||||||||
Zareparsi et al. |
126 |
937 |
57 |
296 |
13.3% |
0.65 [0.46, 0.92] |
2004 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
||||||||
Gotoh et al. |
12 |
142 |
15 |
125 |
3.1% |
0.68 [0.30, 1.51] |
2004 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
||||||||
Schmidt et al. |
75 |
557 |
54 |
310 |
11.4% |
0.74 [0.50, 1.08] |
2005 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
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|
|
|
|
|
|
|
|
|
|
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|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
||||||||
Baird et al. |
16 |
112 |
37 |
249 |
4.8% |
0.95 [0.51, 1.80] |
2006 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
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|
|
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|
|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
||||||||
Bojanowski et al. |
35 |
302 |
81 |
418 |
9.5% |
0.55 [0.36, 0.84] |
2006 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|||||||||||
Tikellis et al., Cauc. |
4 |
34 |
383 |
2261 |
1.8% |
0.65 [0.23, 1.87] |
2007 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
||||||||
DeAngelis et al. |
22 |
160 |
30 |
158 |
5.3% |
0.68 [0.37, 1.24] |
2007 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|||||||||||
Losonczy et al. |
13 |
89 |
16 |
144 |
3.2% |
1.37 [0.62, 3.00] |
2009 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|||||||||||
Total (95% CI) |
4226 |
8552 100.0% |
0.64 [0.55, 0.74] |
|
|
|
|
|
|
|
|
|
|
Total events |
524 |
1446 |
|
|
|
|
|
|
|
|
|
|
|
Heterogeneity: Tau2 = 0.01; Chi2 = 18.02, df = 16 (P = 0.32); I2 = 11% |
|
|
|
|
|
|
|
|
|
||||
|
0.2 |
0.5 |
1 |
2 |
5 |
||||||||
Test for overall effect: Z = 6.03 (P < 0.00001) |
|
||||||||||||
|
|
|
|
|
|
|
|
|
|
||||
b
|
Late AMD |
|
No AMD |
Odds Ratio |
Odds Ratio |
Study or Subgroup |
n |
N |
n |
N Weight IV, Random, 95% CI Year |
IV, Random, 95% CI |
Klaver et al. |
22 |
134 |
162 |
1162 |
8.0% |
1.21 |
[0.75, 1.97] |
1998 |
Souied et al. |
23 |
187 |
21 |
229 |
5.9% |
1.39 |
[0.74, 2.60] |
1998 |
Pang et al. |
8 |
62 |
25 |
203 |
3.7% |
1.05 |
[0.45, 2.47] |
2000 |
Simonelli et al. |
17 |
149 |
151 |
1875 |
7.2% |
1.47 |
[0.86, 2.50] |
2001 |
Schmidt et al., UCLA |
24 |
144 |
12 |
106 |
4.6% |
1.57 |
[0.74, 3.30] |
2002 |
Schmidt et al., UP |
29 |
347 |
12 |
106 |
4.9% |
0.71 |
[0.35, 1.45] |
2002 |
Schultz et al., unrelated |
19 |
157 |
18 |
160 |
5.2% |
1.09 |
[0.55, 2.16] |
2003 |
Schultz et al., families |
49 |
369 |
38 |
278 |
8.6% |
0.97 |
[0.61, 1.53] |
2003 |
Baird et al. |
50 |
364 |
17 |
155 |
6.4% |
1.29 |
[0.72, 2.32] |
2004 |
Gotoh et al. |
9 |
139 |
14 |
124 |
3.6% |
0.54 |
[0.23, 1.30] |
2004 |
Zareparsi et al. |
113 |
924 |
33 |
272 |
9.4% |
1.01 |
[0.67, 1.53] |
2004 |
Schmidt et al. |
75 |
557 |
24 |
280 |
8.0% |
1.66 |
[1.02, 2.69] |
2005 |
Baird et al. |
15 |
111 |
21 |
233 |
5.0% |
1.58 |
[0.78, 3.19] |
2006 |
Bojanowski et al. |
18 |
285 |
46 |
373 |
6.7% |
0.48 |
[0.27, 0.85] |
2006 |
Tikellis et al., Cauc. |
10 |
40 |
264 |
2142 |
4.8% |
2.37 |
[1.15, 4.91] |
2007 |
DeAngelis et al. |
17 |
155 |
13 |
141 |
4.5% |
1.21 |
[0.57, 2.60] |
2007 |
Losonczy et al. |
8 |
84 |
19 |
147 |
3.6% |
0.71 |
[0.30, 1.70] |
2009 |
Total (95% CI) |
|
4208 |
|
7986 100.0% |
1.12 |
[0.93, 1.35] |
|
|
Total events |
506 |
|
890 |
|
|
|
|
|
Heterogeneity: Tau2 = 0.05; Chi2 = 24.59, df = 16 (P = 0.08); I2 = 35% Test for overall effect: Z = 1.23 (P = 0.22)
0.5 0.7 |
1 |
1.5 2 |
Fig. 1.9 Allele-based meta-analysis association studies investigating (a) APOe4 and (b) APOe2 and risk of late AMD. Allele-based meta-analysis of all currently available association studies investigating (c) APOe4 and (d) APOe2 and risk of early stages of AMD, age-related macular degeneration; Cauc., Caucasian; CI, confidence interval; IV, inverse variance; Random, random effects model; UCLA, University of California, Los Angeles; UP, University of Pittsburgh Event is the total number
of ε4 alleles; Total is the total number of ε4 (from ε4ε4, ε3ε4, and ε2ε4) and ε3 (from ε3ε3) alleles. ORs and 95% CIs comparing ε2 or ε4 allele carrier vs ε3/ε3 genotype as reference were calculated using the random effects model of the DerSimoninian and Laird method. Gotoh, DeAngelis, Losonczy and Souied et al. only studied neovascular AMD. Schmidt (2002, 2005), Schultz (families), and Zareparsi et al. included (signs of) early AMD in their analyses. Baird et al. (2004) studied progression of AMD
16 L. Ho et al.
c
|
Early AMD |
|
No AMD |
Odds Ratio |
Odds Ratio |
Study or Subgroup |
n |
N |
n |
N Weight IV, Random, 95% CI Year |
IV, Random, 95% CI |
Rotterdam Study |
137 |
833 |
666 |
2936 |
54.9% |
0.67 [0.55, 0.82] |
|
Pang et al. |
11 |
151 |
20 |
198 |
3.8% |
0.70 [0.32, 1.51] |
2000 |
Zareparsi et al. |
21 |
186 |
57 |
378 |
7.9% |
0.72 [0.42, 1.22] |
2004 |
Tikellis et al., Cauc. |
64 |
460 |
383 |
2261 |
27.5% |
0.79 [0.60, 1.05] |
2007 |
Tikellis et al., Afr. |
8 |
32 |
98 |
370 |
3.2% |
0.93 [0.40, 2.13] |
2007 |
Utheim et al. |
38 |
92 |
11 |
23 |
2.7% |
0.77 [0.31, 1.92] |
2008 |
Total (95% CI) |
|
1754 |
|
6166 |
100.0% |
0.72 [0.62, 0.83] |
|
Total events |
279 |
|
1235 |
|
|
|
|
Heterogeneity: Tau2 = 0.00; Chi2 = 1.27, df = 5 (P = 0.94); I2 = 0%
Test for overall effect: Z = 4.34 (P < 0.0001)
0.5 |
0.7 |
1 |
1.5 |
2 |
d
|
Early AMD |
No AMD |
|
Odds Ratio |
|
|
Odds Ratio |
|||||||||||||||||
Study or Subgroup |
n |
N |
n |
N |
Weight IV, Random, 95% CI Year |
IV, Random, 95% CI |
||||||||||||||||||
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Rotterdam Study |
102 |
798 |
328 |
2598 |
44.7% |
1.01 [0.80, 1.29] |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|||||||
Pang et al. |
19 |
159 |
25 |
203 |
7.9% |
0.97 [0.51, 1.83] |
2000 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
||||||||
Zareparsi et al. |
23 |
187 |
32 |
352 |
9.9% |
1.40 [0.79, 2.47] |
2004 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
||||||||
Tikellis et al., Cauc. |
53 |
449 |
264 |
2142 |
28.6% |
0.95 [0.70, 1.30] 2007 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
||||||||
Tikellis et al., Afr. |
14 |
38 |
78 |
350 |
6.5% |
2.03 [1.00, 4.12] |
2007 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
||||||||
Utheim et al. |
15 |
69 |
5 |
17 |
2.3% |
0.67 [0.20, 2.19] |
2008 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
||||||||
Total (95% CI) |
1700 |
5662 100.0% |
1.06 [0.88, 1.28] |
|
|
|
|
|
|
|
|
|
|
|
|
Total events |
226 |
732 |
|
|
|
|
|
|
|
|
|
|
|
|
|
Heterogeneity: Tau2 = 0.00; Chi2 = 5.45, df = 5 (P = 0.36); I2 = 8% |
0.5 0.7 |
1 |
1.5 |
2 |
|||||||||||
Test for overall effect: Z = 0.64 (P = 0.52) |
|
||||||||||||||
|
|
|
|
|
|
|
|
|
|
|
|
|
|
||
Fig. 1.9 (continued)
with AMD, namely G-G-G-G- e2 (OR, 1.59; 95% CI, 1.19–2.12) and T-G-A-G- e4 (OR, 0.76; 95% CI, 0.58– 0.99). An e3-haplotype, G-G-G-A- e3, showed a protective effect in homozygous carriers (OR, 0.65; 95% CI, 0.49–0.87). Furthermore, only 1 out of 4 possible e3/e4-haplotype combinations conferred protection, namely, T-G-A-G- e4/T-C-G-G- e3 (OR, 0.32; 95% CI, 0.20–0.51). This suggests that in addition to the known functional polymorphisms rs429358 and rs7412 (that define the e-alleles), cis-regulatory variants of the APOE gene may also influence AMD risk.
To date, alleleand genotype-based association tests showed a protective effect of up to 40% for e4 and a causative effect of up to 20% for e2 [180, 183–197]. All studies in the meta-analysis observed a reduced risk of late AMD for e4-carriers compared to persons with the e3e3-genotype (range ORs, 0.25–0.95), except for a Hungarian study where e4 was observed more often in cases than in controls (OR, 1.37; 95% CI, 0.62–3.00) [74]. Pooling the data increased the statistical power of the inverse association between e4 and
AMD, and yielded an overall significant OR of 0.64 (95% CI, 0.55–0.74; Fig. 1.9a). A meta-analysis of e2 allelic data yielded an OR of 1.12 (95% CI, 0.93–1.35; Fig. 1.9b). For early AMD, the meta-analysis also showed a reduced risk for carriers of e4 compared to persons with the e3e3-genotype (OR, 0.72; 95% CI, 0.62–0.83; Fig. 1.9c), whereas the e2 allele was not associated with early AMD (OR, 1.06; 95% CI, 0.88– 1.28; Fig. 1.9d).
The retina has high levels of oxygen, polyunsaturated fatty acids, and light exposure, which may cause oxidative damage and inflammation [198]. Cell damage and inflammation upregulates the influx of cholesterol, [199] but also the synthesis of APOE [200]. The APOE e4 variant inhibits dimerization of APOE that normally occurs with the e3 and e2 variants [201]. Therefore, the APOE e4 travels the interstitium more easily than the other APOE variants which are confined more intracellularly. The superior mobility of APOE e4 results in better transport of lipids, cholesterol, and RPE cell degradation products away from RPE cells
