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Open Access Research article

Identifying subtypes of patients with neovascular age-related macular degeneration by genotypic and cardiovascular risk characteristics

Michael Feehan1, John Hartman1, Richard Durante1, Margaux A Morrison23, Joan W Miller2, Ivana K Kim2 and Margaret M DeAngelis23*

Author Affiliations

1 Observant LLC, 1601 Trapelo Road, Waltham, MA, 02451, USA

2 Ocular Molecular Genetics Institute and the Retina Service, Department of Ophthalmology, Harvard Medical School, Massachusetts Eye and Ear Infirmary, 243 Charles Street, Boston, MA, 02114, USA

3 3 Ophthalmology and Visual Sciences, University of Utah, Moran Eye Center, 65 Mario Capecchi Drive, Salt Lake City, UT, 84132, USA

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BMC Medical Genetics 2011, 12:83  doi:10.1186/1471-2350-12-83


The electronic version of this article is the complete one and can be found online at: http://www.biomedcentral.com/1471-2350/12/83


Received:2 December 2010
Accepted:17 June 2011
Published:17 June 2011

© 2011 Feehan et al; licensee BioMed Central Ltd.

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Background

One of the challenges in the interpretation of studies showing associations between environmental and genotypic data with disease outcomes such as neovascular age-related macular degeneration (AMD) is understanding the phenotypic heterogeneity within a patient population with regard to any risk factor associated with the condition. This is critical when considering the potential therapeutic response of patients to any drug developed to treat the condition. In the present study, we identify patient subtypes or clusters which could represent several different targets for treatment development, based on genetic pathways in AMD and cardiovascular pathology.

Methods

We identified a sample of patients with neovascular AMD, that in previous studies had been shown to be at elevated risk for the disease through environmental factors such as cigarette smoking and genetic variants including the complement factor H gene (CFH) on chromosome 1q25 and variants in the ARMS2/HtrA serine peptidase 1 (HTRA1) gene(s) on chromosome 10q26. We conducted a multivariate segmentation analysis of 253 of these patients utilizing available epidemiologic and genetic data.

Results

In a multivariate model, cigarette smoking failed to differentiate subtypes of patients. However, four meaningfully distinct clusters of patients were identified that were most strongly differentiated by their cardiovascular health status (histories of hypercholesterolemia and hypertension), and the alleles of ARMS2/HTRA1 rs1049331.

Conclusions

These results have significant personalized medicine implications for drug developers attempting to determine the effective size of the treatable neovascular AMD population. Patient subtypes or clusters may represent different targets for therapeutic development based on genetic pathways in AMD and cardiovascular pathology, and treatments developed that may elevate CV risk, may be ill advised for certain of the clusters identified.

Background

The current medical literature is increasing weekly with studies identifying DNA variants and their possible interaction with environmental factors that may have impact on risk of disease. The growth of such studies has been spurred by the promise of understanding the genetic and environmental basis of complex diseases, and the possibility of identifying therapeutically responsive targets for drug development. Enormous numbers of DNA variants have been associated with diseases and traits and this number will only grow as it becomes economically feasible to sequence an individual patient's entire genome[1].

One key data interpretation challenge lies in how best to assess the phenotypic heterogeneity and risk factor heterogeneity within the affected patient population. Even in situations where the association between a risk factor and disease is highly significant, there are individuals with the disease who do not manifest all risk factors and those with risk factors who manifest no disease[2]. The presence of a risk factor is not a sufficient determinant of disease. This point is a critical consideration in drug development, as the effective size of the patient population that may be treated with a drug designed to target a particular genetic risk factor may in fact be much smaller than the total patient population. This has implications for the design of clinical trials that may incorporate genetic data, and ultimately for decisions on the feasibility of producing a medication.

Age-related macular degeneration (AMD) is an example of a complex disease that has been shown to have clear genetic and environmental antecedents. The leading cause of visual loss in the aging population, neovascular AMD is characterized by the growth of abnormal new blood vessels underlying the retina which can cause severe and rapid vision loss due to hemorrhage and exudation (for review please see Miller, 2008)[3].

The general population harbors both modifiable and non-modifiable characteristics associated with AMD, however, the current study examines the afflicted subsample of population rather than at the entire population. Prior epidemiologic characteristics shown to be associated with the risk of AMD include age, gender, elevated body mass index (BMI), hyperlipidemia, hypertension, and cigarette smoking[4-12]. These factors are all well-documented to be associated with the risk of cardiovascular disease, and events such as myocardial infarction and stroke. In terms of cardiovascular risk factors, several studies have found that cigarette smoking (perhaps through oxidative stress and injury) elevates the risk of AMD[13,14]. Another risk factor associated with cardiovascular disease is heavy alcohol consumption, which has also been shown to be associated with late-stage AMD, including neovascular AMD in one study, [7] but, other studies were unable to replicate this association[15-17]. Similarly, elevated BMI has been shown to be associated with AMD progression[18] and also elevated risk of AMD[19,20].

Cholesterol and lipid metabolism have been implicated in the pathogenesis of AMD,[21-32] and there is evidence both for and against the hypothesis that cholesterol lowering statin therapy may have a protective effect on the development of AMD[33-36]. In terms of hypertension, there is conflicting evidence supporting an association with neovascular AMD[6,19,37,38].

Several genes have been associated with all subtypes of AMD, including the advanced stages, with the most strongly associated variants seen within the complement factor H (CFH) gene on chromosome 1q25. The CFH gene is known to play a role in the immune/inflammatory system[39-43]. Additionally, other strongly associated variants with large influence on AMD risk, particularly the neovascular subtype, are found in the ARMS2/ HTRA1 genes on chromosome 10q26[44-49].

Nevertheless, the ability to predict AMD risk would be greatly enhanced if both the effects of genetic and environmental risk factors were considered collectively,[5] although the degree to which these factors interact in the risk of AMD or its progression is unclear. For example, although cigarette smoking has been shown to elevate the risk of AMD and its progression, significant interactions between smoking and CFH variants in predicting AMD risk have not been shown[50,51]. While there is one report of variation within ARMS2 and interaction with smoking,[52] others have not demonstrated this finding[46,49]. In terms of cardiovascular risk factors, when smoking was included in a multivariate model, alcohol consumption, hypertension, and BMI were no longer associated with neovascular AMD. Only history of cigarette smoking remained significantly associated with neovascular AMD, with each pack-year being associated with a 2% increase in the risk of disease[17]. Therefore it is important that presymptomatic diagnostic tests (and presumably any therapeutic agents in development) should be designed to take into account the assessment of all informative genetic variants along with documented disease associated environmental factors[2,51].

Recognizing that any patient population with the same disease phenotype will be heterogeneous to some degree for any single risk factor or collection of factors, it is critical that a multivariate or multifactor approach is used to consider risk. Another important consideration in interpreting measures of association is that although the association may be statistically significant, not all cases with the disease will have the risk factor. For example, our group has shown that having two copies of the risk allele (TT) at ARMS2/HTRA1 rs1049331 significantly increases risk of developing neovascular AMD when compared to individuals who are homozygous for the common allele (CC), with many times greater magnitude of effect than important non-genetic factors such as smoking[50]. However, it should be kept in mind that only 33% of the neovascular AMD patients evaluated actually carry the TT genotype, relative to 16% of their matched sibling controls.

Consequently, it is reasonable to hypothesize that appropriately designed studies may be able to identify meaningfully distinct subtypes or clusters of patients within the neovascular AMD population on the basis of genetic or environmental characteristics predictive of the risk of disease. If, for example, a pharmaceutical company was developing a drug specifically targeting neovascular AMD that focused on specific genetic and cardiovascular risk characteristics, the actual patient population that might be responsive or benefit from such an agent would actually be a subset of the total, comprising only those patients with that particular risk profile. There may well be overlapping pathophysiological antecedents between risk of cardiovascular disease and neovascular AMD[4,10-12,53,54].

In the present study we examine the genetic and cardiovascular risk characteristics of patients with neovascular AMD in a multivariate segmentation analysis to identify clusters of patients with distinct epidemiologic and genetic risk profiles. To do this, we leverage a clustering analytic approach, a multivariate method that yields groups of individuals who have underlying similarities across a number of different behavioral, attitudinal, and/or demographic characteristics. In the public health sector, standard clustering methods have been leveraged to identify relevant subgroups of individuals with a particular disorder. For example, three distinct subgroups of individuals with obsessive compulsive disorder have been identified. Each group was characterized by pathophysiologic mechanisms and different treatment outcomes, which may have significance in classifying and treating these patients. Other clustering studies have been conducted with suicidal psychiatric patients,[55] substance abusers,[56] Parkinson's Disease,[57] and caregivers of eating disorder patients[58] among others.

Methods

Patients

The protocol was reviewed and approved by the Institutional Review Boards at the Massachusetts Eye & Ear Infirmary (MEEI), Boston, Massachusetts and conforms to the tenets of the Declaration of Helsinki. Eligible patients were enrolled in this study after they gave informed consent either in person, over the phone, or through the mail, before answering questions to a standardized questionnaire and donating 10 to 50 ml of venous blood.

In this study of unrelated neovascular AMD, recruited patients all had a sibling with normal maculae. This is similar to what has been done in prior studies[55]. Details of the study design, and criteria for patient enrollment, are described elsewhere[17,49,50,59]. In brief, patients had the neovascular form of AMD in at least one eye, defined by subretinal hemorrhage, fibrosis, or fluorescein angiographic presence of neovascularization documented at the time of, or prior to, enrollment in the study. Disease status of every patient was confirmed by at least two investigators by evaluation of fundus photographs and fluorescein angiograms (JWM and IKK).

Measures

Smoking

Patients were administered a standardized questionnaire in person or via telephone to ascertain smoking exposure, with the age of the patient at the time entry into the study as the cutoff reference age for smoking exposure. Data captured included the age when they started smoking, the age when they quit smoking (if they did quit), and the number of packs of cigarettes smoked per day, on average. From these data the number of pack-years of cigarettes smoked was calculated for each smoker. A pack-year was defined as one pack of cigarettes per day for one year, with one pack defined as twenty cigarettes. To reduce the impact of any extreme outlying observations, a single patient's data were truncated to 140 pack-years.

Alcohol Consumption

Self reported alcohol consumption was measured as grams of alcohol consumed per week, with 1 can, glass, or bottle of beer considered equal to 12.8 g of ethanol, one 4oz glass of wine equal to 11.0 g of ethanol, and one drink or shot of liquor equal to 14.0 g of ethanol[16,17]. Alcohol consumption was coded for these analyses as the sum of its presence versus absence for each decade of life starting with the teen years until the decade of entry into the study. For example, a patient who consumed alcohol for three decades received a value of 3.

Body Mass Index (BMI)

Self reported weight in pounds was recorded decade by decade and then converted to kilograms, excluding years of pregnancy from the 20's until the decade of the patient's reference age. BMI was calculated as the current weight divided by the square of the self-reported height in meters at age 25 years. To reduce the impact of any extreme outlying observations in analyses, two patients' data were truncated to a BMI of 40.

History of High Cholesterol and/or Hypertension

To classify patients has having a history of either condition, self-reported medication use was captured. Patients were classified as having treated hypertension or hypercholesterolemia if they had any period of at least six months of regular use (at least twice per week) of an anti-hypertensive, or statin or other cholesterol lowering agent[17].

Genotypic Risk Characteristics

For the present analyses, two consistently associated AMD-risk genetic markers (SNPs) were selected for analysis. Their statistical association through family based association testing and conditional logistic regression with neovascular AMD risk has been described in detail previously[50,59]. We focused on variation in two genes (CFH and ARMS2/HTRA1), known independently to have the greatest influences on neovascular AMD risk overall. Of the several significant single nucleotide polymorphisms in each gene that have previously examined in this cohort,[49] we selected the inclusion of the SNPs with the highest genotype frequency among the 253 neovascular AMD patients included in this analysis. For CFH, the marker rs1061170 (Y402H) was selected, with genotype frequencies in the study population of CC 37.2%, CT 46.2%, and TT 16.6%. In prior research, the CC genotype has been shown to be strongly associated with neovascular AMD[50,60]. For ARMS2/HTRA1, the marker rs1049331 was selected, with genotype frequencies in the study population of CC 30.8%, TC 36.0%, and TT 33.2%. The TT genotype has also been shown to be strongly associated with elevated risk of neovascular AMD[60].

Segmentation Approach

The analytic approach used in the present study utilizes techniques that are standard to the pharmaceutical industry in the segmentation of physician and patient populations. In brief, cluster analysis groups data objects together based only on information describing their characteristics or relationships. The goal is that the objects within a group be similar (or related) to one another and different from (or unrelated to) the objects in other groups. Cluster analytic methods have a long history of use in the life sciences. Some forty years ago these methods were used to identify approximately similar subtypes in complex populations [61,62]. With the advent of advanced computational methods, the array of cluster analytic techniques has greatly expanded and clustering techniques have been adopted in disciplines as diverse as microarray image analysis;[63,64] the analysis of human populations [65] and to market research[66-69].

Traditional clustering methods fall into two broad categories: relocation and hierarchical. Relocation clustering methods -- such as k-means and EM (expectation-maximization) -- move records iteratively from one cluster to another, starting from an initial partition until an optimal set of clusters is identified. Hierarchical clustering methods proceed in steps -- producing a sequence of partitions in which each one nests into the next partition in the sequence. Hierarchical clustering can be either agglomerative or divisive. Agglomerative clustering starts with singleton clusters (clusters that contain only one record) and proceeds by successively merging the two "nearest neighbor" clusters at each stage. In contrast, divisive clustering methods begin with one single massive cluster that contains all records and then proceeds by successively separating the cluster into smaller ones.

In the present case, an industry standard two stage analytic process was used to identify meaningfully distinct clusters of patients. First, a variant of Zhang's BIRCH (Balanced Iterative Reducing and Clustering using Hierarchies) algorithm was used to create a preliminary group of clusters[70]. BIRCH is appropriate at this initial stage given that the dataset contains both nominal variables (e.g., sex) and ratio-scaled variables (e.g., BMI). The final patient clusters were identified from the BIRCH routine using a traditional agglomerative routine[71]. In this second phase, smaller clusters were merged into larger clusters, using change in the Bayesian Information Criterion (BIC) as a criterion for determining which clusters to join[72]. The standard BIC was augmented by a careful review of several solutions with larger and smaller numbers of clusters; none of these alterative solutions provided the level of cluster separation and ease of explanation. Similarly, solutions with very small segment outputs were also rejected as the final output should reflect meaningfully sized segments that could be reflected in the design of any future drug development trials.

Results

To be included in these analyses, all patients had to have complete data on all the variables under examination, resulting in a final N for analysis of 253 patients (out of an initial evaluation of 352). The group of 253 neovascular AMD patients had a mean age of 73.1 (SD = 7.4) years, and had a female majority (58.1%). The overall mean of total smoking in pack-years for the patient group was 26.7 (SD = 32.3). The overall patient mean decades of alcohol use was 3.3 (SD = 2.2). The overall patient mean for BMI was 26.5 (SD = 4.47). Across all patients, 63.6% were classified as having a history of hypertension and 48.2% were considered to have a history of hyperlipidemia.

The results of the segmentation modeling are presented in Table 1. The 253 patients were classified into four discrete and meaningfully different clusters based on heterogeneity in the distributions of both phenotypic and genotypic characteristics. In this multivariate model, the characteristics showing the greatest significant heterogeneity across clusters were the history of hypertension (F = 95.97, P < .001) and hypercholesterolemia (F = 89.68, P < .001). More modest but still significant differentiators were mean lifetime BMI (F = 4.58, P = .004) and mean age (F = 3.74, P = .01). Other risk factors did not significantly differentiate clusters among patients with the disease: alcohol consumption (F = 1.10, P = .351), gender (F = .605, P = .613), and smoking (F = .18, P = .910).

Table 1. Neovascular AMD Patient Clusters Defined by Phenotypic and Genotypic Risk Characteristics

In terms of the genetic markers, the distribution of genotypes for both genes of interest did significantly differentiate patients. The stronger differentiator was clearly the marker rs1049331 in ARMS2/HTRA1, with marked differentiation most evident for the risk genotype TT (F = 101.28, P < .001), though the CC and TC genotypes also significantly varied across clusters. The CFH marker rs1061170 (Y402H) was less sensitive in discriminating between segments. The risk genotype for this marker was only modestly differentiating (F = 5.06, P = .002) relative to the non-risk genotypes.

The first segment of patients, Cluster 1 (28.1%), is characterized as a group of patients where the clear majority have a history of both treated high blood pressure and hypercholesterolemia, who tend to be more overweight but slightly younger than the other clusters. Their genetic profile is mixed, with around half (52.1%) carrying the ARMS2/HTRA1 TT risk genotype, and a third (32.4) also carrying the CFH CC genotype (see Table 1).

Similarly, Cluster 2 patients (33.2%) are highly likely to have a history of treatment for hypertension (100%), though just under half have co-morbid hypercholesterolemia treatment histories (45.2%). They tend to be the oldest group of patients and are leaner than two of the other clusters. In terms of the genetic factors, they do not carry the ARMS2/HTRA1 rs1049331 TT risk genotype at all (0.0%), and less than half (41.7%) carry the CFH Y402H CC risk genotype.

Cluster 3 patients (22.1%) have much better cardiovascular profiles than those in Clusters 1 and 2, and when they have accompanying pathology it is more likely to be hypertension. Specifically, while just less than half have a history of high blood pressure treatment (42.9%), patients in cluster 3 are very unlikely to have been treated for hypercholesterolemia (1.8%). Patients in cluster 3 are highly likely to carry the ARMS2/HTRA1 rs1049331 TT risk genotype (83.9%), and a fifth carry the CFH Y402H CC genotype (21.4%).

The final group, Cluster 4 (16.6%), have the healthiest cardiovascular profile. They differ from Cluster 3 by having a tendency toward hypercholesterolemia rather than hypertension when there is accompanying pathology. None of the neovascular patients in Cluster 4 have a history of treated high blood pressure (0.0%), and only around a quarter (28.6%) have a history of hypercholesterolemia (28.6%). No one in this group carries the ARMS2/HTRA1 rs1049331 TT risk genotype (0.0%) but they are the group most likely to carry the CFH Y402H CC genotype (57.1%).

Discussion

In this analysis, various subtypes of patients with neovascular AMD were identified and the resulting segmentation was driven by both cardiovascular and genetic risk profiles. However, not all factors shared equal weight in creating the patient clusters. Importantly, several risk factors associated with developing neovascular AMD or its progression failed to differentiate clusters of patients with the disease itself. Clearly, modifiable risk factors such as alcohol consumption, body mass index, and cigarette smoking should continue to be a focus of preventive intervention efforts. However, our analysis suggests that once patients have end stage neovascular AMD, patient variability in cardiovascular health, specifically hypertension and hypercholesterolemia, tends to segregate with genotypic risk profiles (particularly markers of ARMS2/HTRA1). This has implications for the design of clinical trials, which may increasingly focus on the inclusion of genetic markers in their data collection protocols.

This heterogeneity may provide insights to eventual treatment development, or at the very least indicate subpopulations who may be more (or less) responsive to any potential agent targeting a vascular pathology and any genetic networks/pathways that include ARMS2/HTRA1 genotypes. Further, these results suggest that any manufacturer developing pharmacological treatments for the neovascular AMD population would need to consider that the market potential for such an agent may be limited. Any such therapy developed for a network that includes ARMS2/HTRA1 may be unlikely to affect Cluster 4, representing a reduction in market size by almost a fifth of all neovascular AMD patients (16.6%).

Similarly, if ARMS2/HTRA1 and/or the pathway it functions in (currently the pathway it functions in is unknown) became a focus of drug development, Cluster 2 patients represent a low-potential group that have poor cardiovascular health, but do not carry the ARMS2/HTRA1 homozygous risk TT genotype. However, Clusters 1 and 3 would represent a higher opportunity target sub-population of AMD patients, as they in total represent 50.2% of the neovascular AMD population, and tend to have high blood pressure in combination with a high likelihood of carrying the ARMS2/HTRA1 TT genotype.

Currently, anti-VEGF therapies delivered via injection (bevacizumab, pegaptanib, and ranibizumab) are the best treatments for neovascular AMD. However, it has been proposed that anti-VEGF therapies be contraindicated in those patients with cardiovascular risk factors - particularly high blood pressure (for review please see Enseleit et al 2010)[73]. One could also foresee a scenario where a potential new treatment that derives (even in part from ARMS2/HTRA1) may be only suitable for a smaller sample of the population. If, for example, a new treatment carried some risk of elevated cardiovascular events, then it may well be contraindicated for Clusters 1 and 2, and the real market potential may thus only be for Cluster 3.

Conclusions

In the future, it may important that efforts to identify druggable targets for potential AMD treatments look beyond bivariate tests of risk associations and take a multi-factorial approach, taking into consideration the fact that patients with the disease are very heterogeneous in their likelihood to have any genetic or cardiovascular profile of characteristics shown to elevate the risk of disease. The patient clusters identified here could reflect differential potential therapeutic targets for pharmaceuticals. Consideration of their profiles would allow drug developers to better design trials to reflect the heterogeneity of the AMD population, recognizing that subtypes of patients identified on the basis of genetic and epidemiological factors may be differentially responsive (or non-responsive or even adversely responsive) to any potential therapeutic agents in development.

Competing interests

The authors declare that they have no competing interests.

Authors' contributions

MF, JH, and RD conceived of the study, participated in its design and coordination, participated in the statistical analysis of data and helped to draft the manuscript. MAM participated in the study design and coordination, participated in the statistical analysis of data and helped to draft the manuscript. JWM and IKK participated in the study design and coordination, and helped to draft the manuscript. MMD conceived of the study, participated in its design and coordination, and helped to draft the manuscript. All authors read and approved the final manuscript.

Acknowledgements and Funding

Supported by grants from the Lincy Foundation, the Marion W. and Edward F. Knight Age-Related Macular Degeneration Fund, Genetics of Age-Related Macular Degeneration Fund, MEEI, Unrestricted grant from the Research to Prevent Blindness (University of Utah), The Edward N. and Della L. Thome Award, and NIH grant EY014458. We thank Liza Pliss and Michael Walsh from Observant LLC for support in these analyses, and we are especially grateful to the patients for their participation in this study.

References

  1. Ng PC, Murray SS, Levy S, Venter JC: An agenda for personalized medicine.

    Nature 2009, 461:724-726. PubMed Abstract | Publisher Full Text OpenURL

  2. Jakobsdottir J, Gorin MB, Conley YP, Ferrell RE, Weeks DE: Interpretation of genetic association studies: markers with replicated highly significant odds ratios may be poor classifiers.

    PLoS Genet 2009, 5:e1000337. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  3. Jager RD, Mieler WF, Miller JW: Age-related macular degeneration.

    N Engl J Med 2008, 358:2606-2617. PubMed Abstract | Publisher Full Text OpenURL

  4. Klein R, Deng Y, Klein BEK, Hyman L, Seddon J, Frank RN, Wallace RB, Hendrix SL, Kuppermann BD, Langer RD, Kuller L, Brunner R, Johnson KC, Thomas AM, Haan M: Cardiovascular disease, its risk factors and treatment, and age-related macular degeneration: Women's Health Initiative Sight Exam ancillary study.

    Am J Ophthalmol 2007, 143:473-483. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  5. Baird PN, Hageman GS, Guymer RH: New era for personalized medicine: the diagnosis and management of age-related macular degeneration.

    Clin Experiment Ophthalmol 2009, 37:814-821. PubMed Abstract | Publisher Full Text OpenURL

  6. Chaine G, Hullo A, Sahel J, Soubrane G, Espinasse-Berrod MA, Schutz D, Bourguignon C, Harpey C, Brault Y, Coste M, Moccatti D, Bourgeois H: Case-control study of the risk factors for age related macular degeneration. France-DMLA Study Group.

    Br J Ophthalmol 1998, 82:996-1002. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  7. Klein R, Klein BEK, Tomany SC, Moss SE: Ten-year incidence of age-related maculopathy and smoking and drinking: the Beaver Dam Eye Study.

    Am J Epidemiol 2002, 156:589-598. PubMed Abstract | Publisher Full Text OpenURL

  8. Klein R, Klein BE, Linton KL: Prevalence of age-related maculopathy. The Beaver Dam Eye Study.

    Ophthalmology 1992, 99:933-943. PubMed Abstract OpenURL

  9. Wong TY, Tikellis G, Sun C, Klein R, Couper DJ, Sharrett AR: Age-related macular degeneration and risk of coronary heart disease: the Atherosclerosis Risk in Communities Study.

    Ophthalmology 2007, 114:86-91. PubMed Abstract | Publisher Full Text OpenURL

  10. Nguyen-Khoa BA, Goehring EL, Werther W, Gower EW, Do DV, Jones JK: Hospitalized cardiovascular diseases in neovascular age-related macular degeneration.

    Arch Ophthalmol 2008, 126:1280-1286. PubMed Abstract | Publisher Full Text OpenURL

  11. McCarty CA, Dowrick A, Cameron J, McGrath B, Robman LD, Dimitrov P, Tikellis G, Nicolas C, McNeil J, Guymer R: Novel measures of cardiovascular health and its association with prevalence and progression of age-related macular degeneration: the CHARM Study.

    BMC Ophthalmol 2008, 8:25. PubMed Abstract | BioMed Central Full Text | PubMed Central Full Text OpenURL

  12. Sun C, Klein R, Wong TY: Age-related macular degeneration and risk of coronary heart disease and stroke: the Cardiovascular Health Study.

    Ophthalmology 2009, 116:1913-1919. PubMed Abstract | Publisher Full Text OpenURL

  13. Thornton J, Edwards R, Mitchell P, Harrison RA, Buchan I, Kelly SP: Smoking and age-related macular degeneration: a review of association.

    Eye (Lond) 2005, 19:935-944. OpenURL

  14. Cong R, Zhou B, Sun Q, Gu H, Tang N, Wang B: Smoking and the risk of age-related macular degeneration: a meta-analysis.

    Ann Epidemiol 2008, 18:647-656. PubMed Abstract | Publisher Full Text OpenURL

  15. Ajani UA, Christen WG, Manson JE, Glynn RJ, Schaumberg D, Buring JE, Hennekens CH: A prospective study of alcohol consumption and the risk of age-related macular degeneration.

    Ann Epidemiol 1999, 9:172-177. PubMed Abstract | Publisher Full Text OpenURL

  16. Cho E, Hankinson SE, Willett WC, Stampfer MJ, Spiegelman D, Speizer FE, Rimm EB, Seddon JM: Prospective study of alcohol consumption and the risk of age-related macular degeneration.

    Arch Ophthalmol 2000, 118:681-688. PubMed Abstract OpenURL

  17. DeAngelis MM, Lane AM, Shah CP, Ott J, Dryja TP, Miller JW: Extremely discordant sib-pair study design to determine risk factors for neovascular age-related macular degeneration.

    Arch Ophthalmol 2004, 122:575-580. PubMed Abstract | Publisher Full Text OpenURL

  18. Seddon JM, Cote J, Davis N, Rosner B: Progression of age-related macular degeneration: association with body mass index, waist circumference, and waist-hip ratio.

    Arch Ophthalmol 2003, 121:785-792. PubMed Abstract | Publisher Full Text OpenURL

  19. Risk factors associated with age-related macular degeneration. A case-control study in the age-related eye disease study: Age-Related Eye Disease Study Report Number 3

    Ophthalmology 2000, 107:2224-2232. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  20. Cheung N, Wong TY: Obesity and eye diseases.

    Surv Ophthalmol 2007, 52:180-195. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  21. Klein R, Klein BE, Jensen SC: The relation of cardiovascular disease and its risk factors to the 5-year incidence of age-related maculopathy: the Beaver Dam Eye Study.

    Ophthalmology 1997, 104:1804-1812. PubMed Abstract OpenURL

  22. Mullins RF, Russell SR, Anderson DH, Hageman GS: Drusen associated with aging and age-related macular degeneration contain proteins common to extracellular deposits associated with atherosclerosis, elastosis, amyloidosis, and dense deposit disease.

    FASEB J 2000, 14:835-846. PubMed Abstract | Publisher Full Text OpenURL

  23. Baker ML, Wang JJ, Rogers S, Klein R, Kuller LH, Larsen EK, Wong TY: Early age-related macular degeneration, cognitive function, and dementia: the Cardiovascular Health Study.

    Arch Ophthalmol 2009, 127:667-673. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  24. Tan JSL, Wang JJ, Flood V, Mitchell P: Dietary fatty acids and the 10-year incidence of age-related macular degeneration: the Blue Mountains Eye Study.

    Arch Ophthalmol 2009, 127:656-665. PubMed Abstract | Publisher Full Text OpenURL

  25. Yu AL, Lorenz RL, Haritoglou C, Kampik A, Welge-Lussen U: Biological effects of native and oxidized low-density lipoproteins in cultured human retinal pigment epithelial cells.

    Exp Eye Res 2009, 88:495-503. PubMed Abstract | Publisher Full Text OpenURL

  26. Chen W, Stambolian D, Edwards AO, Branham KE, Othman M, Jakobsdottir J, Tosakulwong N, Pericak-Vance MA, Campochiaro PA, Klein ML, Tan PL, Conley YP, Kanda A, Kopplin L, Li Y, Augustaitis KJ, Karoukis AJ, Scott WK, Agarwal A, Kovach JL, Schwartz SG, Postel EA, Brooks M, Baratz KH, Brown WL, Brucker AJ, Orlin A, Brown G, Ho A, Regillo C, Donoso L, Tian L, Kaderli B, Hadley D, Hagstrom SA, Peachey NS, Klein R, Klein BEK, Gotoh N, Yamashiro K, Ferris Iii F, Fagerness JA, Reynolds R, Farrer LA, Kim IK, Miller JW, Cortón M, Carracedo A, Sanchez-Salorio M, Pugh EW, Doheny KF, Brion M, Deangelis MM, Weeks DE, Zack DJ, Chew EY, Heckenlively JR, Yoshimura N, Iyengar SK, Francis PJ, Katsanis N, Seddon JM, Haines JL, Gorin MB, Abecasis GR, Swaroop A: Genetic variants near TIMP3 and high-density lipoprotein-associated loci influence susceptibility to age-related macular degeneration.

    Proc Natl Acad Sci USA 2010, 107:7401-7406. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  27. Neale BM, Fagerness J, Reynolds R, Sobrin L, Parker M, Raychaudhuri S, Tan PL, Oh EC, Merriam JE, Souied E, Bernstein PS, Li B, Frederick JM, Zhang K, Brantley MA, Lee AY, Zack DJ, Campochiaro B, Campochiaro P, Ripke S, Smith RT, Barile GR, Katsanis N, Allikmets R, Daly MJ, Seddon JM: Genome-wide association study of advanced age-related macular degeneration identifies a role of the hepatic lipase gene (LIPC).

    Proc Natl Acad Sci USA 2010, 107:7395-7400. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  28. Silveira AC, Morrison MA, Ji F, Xu H, Reinecke JB, Adams SM, Arneberg TM, Janssian M, Lee JE, Yuan Y, Schaumberg DA, Kotoula MG, Tsironi EE, Tsiloulis AN, Chatzoulis DZ, Miller JW, Kim IK, Hageman GS, Farrer LA, Haider NB, DeAngelis MM: Convergence of linkage, gene expression and association data demonstrates the influence of the RAR-related orphan receptor alpha (RORA) gene on neovascular AMD: a systems biology based approach.

    Vision Res 2010, 50:698-715. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  29. Schaumberg DA, Chasman D, Morrison MA, Adams SM, Guo Q, Hunter DJ, Hankinson SE, DeAngelis MM: Prospective study of common variants in the retinoic acid receptor-related orphan receptor α gene and risk of neovascular age-related macular degeneration.

    Arch Ophthalmol 2010, 128:1462-1471. PubMed Abstract | Publisher Full Text OpenURL

  30. Yamada Y, Tian J, Yang Y, Cutler RG, Wu T, Telljohann RS, Mattson MP, Handa JT: Oxidized low density lipoproteins induce a pathologic response by retinal pigmented epithelial cells.

    J Neurochem 2008, 105:1187-1197. PubMed Abstract | Publisher Full Text OpenURL

  31. Ding X, Patel M, Chan CC: Molecular pathology of age-related macular degeneration.

    Prog Retin Eye Res 2009, 28:1-18. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  32. Sallo FB, Bereczki E, Csont T, Luthert PJ, Munro P, Ferdinandy P, Sántha M, Lengyel I: Bruch's membrane changes in transgenic mice overexpressing the human biglycan and apolipoprotein b-100 genes.

    Exp Eye Res 2009, 89:178-186. PubMed Abstract | Publisher Full Text OpenURL

  33. Wilson HL, Schwartz DM, Bhatt HRF, McCulloch CE, Duncan JL: Statin and aspirin therapy are associated with decreased rates of choroidal neovascularization among patients with age-related macular degeneration.

    Am J Ophthalmol 2004, 137:615-624. PubMed Abstract | Publisher Full Text OpenURL

  34. McGwin G, Xie A, Owsley C: The use of cholesterol-lowering medications and age-related macular degeneration.

    Ophthalmology 2005, 112:488-494. PubMed Abstract | Publisher Full Text OpenURL

  35. Klein R, Klein BEK, Tomany SC, Danforth LG, Cruickshanks KJ: Relation of statin use to the 5-year incidence and progression of age-related maculopathy.

    Arch Ophthalmol 2003, 121:1151-1155. PubMed Abstract | Publisher Full Text OpenURL

  36. van Leeuwen R, Vingerling JR, Hofman A, de Jong PTVM, Stricker BHC: Cholesterol lowering drugs and risk of age related maculopathy: prospective cohort study with cumulative exposure measurement.

    BMJ 2003, 326:255-256. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  37. Hyman L, Schachat AP, He Q, Leske MC: Hypertension, cardiovascular disease, and age-related macular degeneration. Age-Related Macular Degeneration Risk Factors Study Group.

    Arch Ophthalmol 2000, 118:351-358. PubMed Abstract OpenURL

  38. Smith W, Assink J, Klein R, Mitchell P, Klaver CC, Klein BE, Hofman A, Jensen S, Wang JJ, de Jong PT: Risk factors for age-related macular degeneration: Pooled findings from three continents.

    Ophthalmology 2001, 108:697-704. PubMed Abstract | Publisher Full Text OpenURL

  39. Edwards AO, Ritter R, Abel KJ, Manning A, Panhuysen C, Farrer LA: Complement factor H polymorphism and age-related macular degeneration.

    Science 2005, 308:421-424. PubMed Abstract | Publisher Full Text OpenURL

  40. Hageman GS, Anderson DH, Johnson LV, Hancox LS, Taiber AJ, Hardisty LI, Hageman JL, Stockman HA, Borchardt JD, Gehrs KM, Smith RJH, Silvestri G, Russell SR, Klaver CCW, Barbazetto I, Chang S, Yannuzzi LA, Barile GR, Merriam JC, Smith RT, Olsh AK, Bergeron J, Zernant J, Merriam JE, Gold B, Dean M, Allikmets R: A common haplotype in the complement regulatory gene factor H (HF1/CFH) predisposes individuals to age-related macular degeneration.

    Proc Natl Acad Sci USA 2005, 102:7227-7232. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  41. Haines JL, Hauser MA, Schmidt S, Scott WK, Olson LM, Gallins P, Spencer KL, Kwan SY, Noureddine M, Gilbert JR, Schnetz-Boutaud N, Agarwal A, Postel EA, Pericak-Vance MA: Complement factor H variant increases the risk of age-related macular degeneration.

    Science 2005, 308:419-421. PubMed Abstract | Publisher Full Text OpenURL

  42. Klein RJ, Zeiss C, Chew EY, Tsai JY, Sackler RS, Haynes C, Henning AK, SanGiovanni JP, Mane SM, Mayne ST, Bracken MB, Ferris FL, Ott J, Barnstable C, Hoh J: Complement factor H polymorphism in age-related macular degeneration.

    Science 2005, 308:385-389. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  43. Zareparsi S, Branham KEH, Li M, Shah S, Klein RJ, Ott J, Hoh J, Abecasis GR, Swaroop A: Strong association of the Y402H variant in complement factor H at 1q32 with susceptibility to age-related macular degeneration.

    Am J Hum Genet 2005, 77:149-153. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  44. Jakobsdottir J, Conley YP, Weeks DE, Mah TS, Ferrell RE, Gorin MB: Susceptibility genes for age-related maculopathy on chromosome 10q26.

    Am J Hum Genet 2005, 77:389-407. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  45. Rivera A, Fisher SA, Fritsche LG, Keilhauer CN, Lichtner P, Meitinger T, Weber BHF: Hypothetical LOC387715 is a second major susceptibility gene for age-related macular degeneration, contributing independently of complement factor H to disease risk.

    Hum Mol Genet 2005, 14:3227-3236. PubMed Abstract | Publisher Full Text OpenURL

  46. Conley YP, Jakobsdottir J, Mah T, Weeks DE, Klein R, Kuller L, Ferrell RE, Gorin MB: CFH, ELOVL4, PLEKHA1 and LOC387715 genes and susceptibility to age-related maculopathy: AREDS and CHS cohorts and meta-analyses.

    Hum Mol Genet 2006, 15:3206-3218. PubMed Abstract | Publisher Full Text OpenURL

  47. Dewan A, Liu M, Hartman S, Zhang SS-M, Liu DTL, Zhao C, Tam POS, Chan WM, Lam DSC, Snyder M, Barnstable C, Pang CP, Hoh J: HTRA1 promoter polymorphism in wet age-related macular degeneration.

    Science 2006, 314:989-992. PubMed Abstract | Publisher Full Text OpenURL

  48. Yang Z, Camp NJ, Sun H, Tong Z, Gibbs D, Cameron DJ, Chen H, Zhao Y, Pearson E, Li X, Chien J, Dewan A, Harmon J, Bernstein PS, Shridhar V, Zabriskie NA, Hoh J, Howes K, Zhang K: A variant of the HTRA1 gene increases susceptibility to age-related macular degeneration.

    Science 2006, 314:992-993. PubMed Abstract | Publisher Full Text OpenURL

  49. Deangelis MM, Ji F, Adams S, Morrison MA, Harring AJ, Sweeney MO, Capone A, Miller JW, Dryja TP, Ott J, Kim IK: Alleles in the HtrA serine peptidase 1 gene alter the risk of neovascular age-related macular degeneration.

    Ophthalmology 2008, 115:1209-1215.e7. PubMed Abstract | Publisher Full Text OpenURL

  50. DeAngelis MM, Ji F, Kim IK, Adams S, Capone A, Ott J, Miller JW, Dryja TP: Cigarette smoking, CFH, APOE, ELOVL4, and risk of neovascular age-related macular degeneration.

    Arch Ophthalmol 2007, 125:49-54. PubMed Abstract | Publisher Full Text OpenURL

  51. Baird PN, Robman LD, Richardson AJ, Dimitrov PN, Tikellis G, McCarty CA, Guymer RH: Gene-environment interaction in progression of AMD: the CFH gene, smoking and exposure to chronic infection.

    Hum Mol Genet 2008, 17:1299-1305. PubMed Abstract | Publisher Full Text OpenURL

  52. Schmidt S, Hauser MA, Scott WK, Postel EA, Agarwal A, Gallins P, Wong F, Chen YS, Spencer K, Schnetz-Boutaud N, Haines JL, Pericak-Vance MA: Cigarette smoking strongly modifies the association of LOC387715 and age-related macular degeneration.

    Am J Hum Genet 2006, 78:852-864. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  53. Tan JSL, Mitchell P, Smith W, Wang JJ: Cardiovascular risk factors and the long-term incidence of age-related macular degeneration: the Blue Mountains Eye Study.

    Ophthalmology 2007, 114:1143-1150. PubMed Abstract | Publisher Full Text OpenURL

  54. Hogg RE, Woodside JV, Gilchrist SECM, Graydon R, Fletcher AE, Chan W, Knox A, Cartmill B, Chakravarthy U: Cardiovascular disease and hypertension are strong risk factors for choroidal neovascularization.

    Ophthalmology 2008, 115:1046-1052.e2. PubMed Abstract | Publisher Full Text OpenURL

  55. Ellis TE, Rudd MD, Rajab MH, Wehrly TE: Cluster analysis of MCMI scores of suicidal psychiatric patients: four personality profiles.

    J Clin Psychol 1996, 52:411-422. PubMed Abstract | Publisher Full Text OpenURL

  56. Quirk SW, McCormick RA: Personality subtypes, coping styles, symptom correlates, and substances of choice among a cohort of substance abusers.

    Assessment 1998, 5:157-169. PubMed Abstract | Publisher Full Text OpenURL

  57. van Rooden SM, Colas F, Martínez-Martín P, Visser M, Verbaan D, Marinus J, Chaudhuri RK, Kok JN, van Hilten JJ: Clinical subtypes of Parkinson's disease.

    Mov Disord 2010. OpenURL

  58. Jáuregui Lobera I, Garrido O, Santiago Fernández MJ, Alvarez Bautista E: Social comparison as a coping strategy among caregivers of eating disorder patients.

    J Psychiatr Ment Health Nurs 2010, 17:775-782. PubMed Abstract | Publisher Full Text OpenURL

  59. Zhang H, Morrison MA, Dewan A, Adams S, Andreoli M, Huynh N, Regan M, Brown A, Miller JW, Kim IK, Hoh J, Deangelis MM: The NEI/NCBI dbGAP database: genotypes and haplotypes that may specifically predispose to risk of neovascular age-related macular degeneration.

    BMC Med Genet 2008, 9:51. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  60. Andreoli MT, Morrison MA, Kim BJ, Chen L, Adams SM, Miller JW, DeAngelis MM, Kim IK: Comprehensive analysis of complement factor H and LOC387715/ARMS2/HTRA1 variants with respect to phenotype in advanced age-related macular degeneration.

    Am J Ophthalmol 2009, 148:869-874. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  61. Jardine N, Sibson R: Mathematical Taxonomy. John Wiley & Sons Ltd; 1971. OpenURL

  62. Winkel P, Paldam M, Tygstrup N: A numerical taxonomic analysis of symptoms and signs in 400 patients with cirrhosis of the liver.

    Comput Biomed Res 1970, 3:657-665. PubMed Abstract | Publisher Full Text OpenURL

  63. Bozinov D, Rahnenführer J: Unsupervised technique for robust target separation and analysis of DNA microarray spots through adaptive pixel clustering.

    Bioinformatics 2002, 18:747-756. PubMed Abstract | Publisher Full Text OpenURL

  64. Yang YH, Buckley MJ, Dudoit S, Speed TP: Comparison of Methods for Image Analysis on cDNA Microarray Data.

    Journal of Computational and Graphical Statistics 2002, 11:108-136. Publisher Full Text OpenURL

  65. Jakobsson M, Scholz SW, Scheet P, Gibbs JR, VanLiere JM, Fung HC, Szpiech ZA, Degnan JH, Wang K, Guerreiro R, Bras JM, Schymick JC, Hernandez DG, Traynor BJ, Simon-Sanchez J, Matarin M, Britton A, van de Leemput J, Rafferty I, Bucan M, Cann HM, Hardy JA, Rosenberg NA, Singleton AB: Genotype, haplotype and copy-number variation in worldwide human populations.

    Nature 2008, 451:998-1003. PubMed Abstract | Publisher Full Text OpenURL

  66. Phipps A, Hubert L: Cluster analysis in marketing research. In Advanced Methods of Marketing Research. Wiley; 1994:160-189. OpenURL

  67. Wedel M: Market segment derivation and profiling via a finite mixture model framework.

    Marketing Letters 2002, 13:17-25. Publisher Full Text OpenURL

  68. Wedel M, Kamakura WA: Market Segmentation: Conceptual and Methodological Foundations. 2nd edition. Kluwer Academic Publishers; 2000. OpenURL

  69. Punj G, Stewart DW: Cluster Analysis in Marketing Research: Review and Suggestions for Application.

    Journal of Marketing Research 1983, 20:134-148. Publisher Full Text OpenURL

  70. Zhang T, Ramakrishnon R, Livny M: BIRCH: An efficient data clustering method for very large databases.

    1996.

  71. Sneath PHA: Numerical Taxonomy: The Principles and Practice of Numerical Classification. W H Freeman & Co (Sd); 1973. OpenURL

  72. Han K, Kim S, Narayanan S: Strategies to Improve the Robustness of Agglomerative Hierarchical Clustering Under Data Source Variation for Speaker Diarization.

    Audio, Speech, and Language Processing, IEEE Transactions 2008, 16:1590-1601. OpenURL

  73. Enseleit F, Michels S, Ruschitzka F: Anti-VEGF therapies and blood pressure: more than meets the eye.

    Curr Hypertens Rep 2010, 12:33-38. PubMed Abstract | Publisher Full Text OpenURL

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