PLASMA COMPLEMENT COMPONENTS AS EXPRESSION

MARKERS FOR AGE-RELATED MACULAR DEGENERATION

AND RELATED PHENOTYPES

BACKGROUND

Several genes encoding complement system components and fragments are associated with age-related macular degeneration (AMD). Alterations in circulating levels of these markers of complement activation and regulation were evaluated to determine whether they are also independently associated with advanced AMD and whether they are related to AMD genotypes.

SUMMARY

In one aspect, the invention provides a method for determining AMD risk in a patient, comprising: obtaining a patient blood sample and determining the serum or blood plasma levels of complement factor polypeptides, wherein elevated serum or plasma levels of one or more complement factor polypeptides are indicative of susceptibility for or an increased risk of developing AMD, or an increased risk of progression of AMD in the patient.

In one embodiment the complement factor polypeptides are C3, CFB, Factor I, CFH, Factor D, Bb, C3a, iC3b, C5a, SC5b-9 and related complement pathway polypeptides. In another embodiment, elevated serum or plasma levels of complement factor polypeptides is determined using an antibody to the complement factor polypeptides. In yet another embodiment elevated serum or plasma levels of complement factor polypeptides is determined using a radial immunodiffusion assay or an ELISA, and nephelometric methods.

In another aspect the invention provides a kit for determining AMD risk in a patient, comprising: an immunoassay having antibodies directed to one or more complement factor polypeptides, reference standards comprising physiological ranges of one or more of the complement factor polypeptides, suitable packaging and instructions for use. In one embodiment the complement factor polypeptides are C3, CFB, Factor I, CFH, Factor D, Bb, C3a, iC3b, C5a, SC5b-9 and related complement pathway polypeptides.

In yet another aspect the invention provides a diagnostic system comprising: an array, the array having reference locations and diagnostic locations, the reference locations having a known quantity of an antibody to a complement factor polypeptide at each location with the known quantity of antibody differing in quantity at each location, and the diagnostic locations having a known quantity of an antibody to a complement factor polypeptide at each location with the known quantity of antibody common to each location, the diagnostic system further comprising reference standards of one or more complement factor polypeptides, an array reader, an image processor, a database having data records and information records, a processor, and an information output; wherein the system compiles and processes patient data relative to the serum or plasma levels of complement factors in a patient, and where the system outputs information relating to the statistical probability of the patient having susceptibility for or an increased risk of developing AMD, or an increased risk of progression of AMD in the patient, based on the serum or plasma levels of complement factor polypeptides in the patient. In one embodiment the complement factor polypeptides are C3, CFB, Factor I, CFH, Factor D, Bb, C3a, iC3b, C5a, SC5b-9 and related complement pathway

polypeptides.

In still another aspect, the invention provides a method of using the system to predict AMD risk. The medical practitioner obtains a patient blood sample and determines the serum or plasma levels of complement factor polypeptides in the blood sample, wherein elevated serum or plasma levels of one or more complement factor polypeptides indicate a susceptibility for or an increased risk of developing AMD, or an increased risk of progression of AMD in the patient. In one embodiment, the method includes determining the presence or absence of a particular allele at a polymorphic site associated with one or more complement pathway genes, wherein the allele indicates a susceptibility to AMD, a protective phenotype for AMD, or a neutral genotype for AMD, thereby indicating AMD risk in the patient.

In one embodiment, the allele at a polymorphic site is a single nucleotide polymorphism associated with one or more complement pathway genes including rsl061170 (Factor H gene), rsl410996 (Factor H gene), rs9332739 (Complement Factor 2 gene), rs641153 (Factor B gene), rs2230199 (C3 gene); and rsl 0033900 (Complement Factor I), and other genes such as rsl 0490924 (at LOC387715/ARM5 on chromosome 10 region). In one embodiment, the method includes the presence or absence of a particular allele is detected by a hybridization. In one embodiment, the method includes an array of genes encoding one or more complement pathway proteins. The genes include single nucleotide polymorphism associated with one or more complement pathway genes including rsl 061170 (Factor H gene), rsl410996 (Factor H gene), rs9332739 (Complement Factor 2 gene), rs641153 (Factor B gene), rs2230199 (C3 gene); and rsl 0033900 (Complement Factor I) and other genes such as rsl0490924 (at LOC387715/ARM5 on chromosome 10 region)..

In one embodiment, the method includes contacting a subject sample to the diagnostic array under high stringency hybridization conditions; inputting patient information into the system; and obtaining from the system information relating to the statistical probability of the patient developing AMD.

In one embodiment, the method includes making the diagnostic array comprising: applying to a substrate at a plurality particular address on the substrate a sample of the individual purified polynucleotide compositions comprising rsl 061170 (Factor H gene), rsl 410996 (Factor H gene), rs9332739 (Complement Factor 2 gene), rs641153 (Factor B gene), rs2230199 (C3 gene); and rsl 0033900 (Complement Factor I) and other genes such as rsl 0490924 (at LOC387715/ARM5 on chromosome 10 region)..

In one embodiment, the method includes diagnosing AMD or a susceptibility to AMD in a subject comprising evaluating plasma levels of one or more of the complement pathway factor polypeptides of claim 2 and one or more of the gene polymorphisms of claim 11, and correlating the plasma levels of the polypeptides and the presence or absence of the gene polymorphisms, with medical, behavioral and environmental risk factors, thereby determining the patient's risk for AMD. Risk factors include hyperlipidemia, aberrant cholesterol levels, high blood pressure, obesity, smoking, vitamin and dietary supplement intake, patient use of alcohol or drugs, poor diet and sedentary lifestyle. High serum or plasma protein levels of complement factor polypeptides Bb, C3a, C5a and low serum or plasma protein levels of complement Factor H are indicative of susceptibility for or an increased risk of developing AMD, or an increased risk of progression of AMD in the patient. In one embodiment, the predictive value of complement factors Bb and C5a are positively associated with AMD with or without the adjustments for genotype, and complement Factor H has a reverse correlation with AMD without genotype adjustments but becomes non-significant after genotype adjustments. In one embodiment, the addition of complement factors to the polymorphism-based prediction models for AMD improve the statistical significance of the correlation with AMD. Susceptibility for or an increased risk of developing AMD, or an increased risk of progression of AMD increases when the positive risk factor of complement factor C5a is integrated with the positive risk factors of genetic polymorphisms in Factor H-, Factor I- and LOC, in associated genotype prediction models.

In one embodiment, the predictive value of complement factors that are markers of chronic complement activation are significantly elevated in AMD patients compared to non-AMD patients. The complement factors that increase patient risk include Ba, C3d and Factor D.

BRIEF DESCRIPTION OF THE DRAWING

The Figure is a graph showing risk scores for cases and controls based on age, gender, smoking, BMI, seven genetic variants, and C3a, Bb, and C5a fragments.

DETAILED DESCRIPTION

The present invention provides for systems and methods for determining susceptibility for AMD, an increased risk of developing AMD, or an increased risk of progression of AMD in a patient so evaluated using the techniques and diagnostic tools described herein. Certain genetic markers indicate a risk profile for AMD. These genetic risk factors, in combination with medical risk factors, behavioral and environmental risk factors are associated with are indicative of susceptibility for or an increased risk of developing AMD, or an increased risk of progression of AMD in the patient. Elevated levels of certain complement factor polypeptides, alone or in combination with the other risk factors, provide additional details about patient risk profiles. Accordingly, serum or plasma protein levels of various complement factors are determined, and correlated with the above genetic and other risk factors, to determine a risk of AMD for a particular patient, or to monitor progression of the disease. Analysis of serum or plasma protein levels of complement factors can be determined by common techniques in the medical arts, including ELISA, radial immunodiffusion assay or nephelometric methods. Similarly, genetic determinants for AMD susceptibility can be identified through common techniques such as PCR and nucleic acid sequencing. In one aspect, the invention comprises an array of polypeptide or

polynucleotides, particularly including those SNPs given below and probes for detecting the allele at the SNPs. Polynucleotide arrays provide a high throughput technique that can assay a large number of polynucleotide sequences in a single sample. This technology can be used, for example, as a diagnostic tool to assess the risk potential of developing AMD using the SNPs and probes of the invention. Polynucleotide arrays (for example, DNA or RNA arrays), include regions of usually different sequence polynucleotides arranged in a predetermined

configuration on a substrate, at defined x and y coordinates. These regions

(sometimes referenced as "features") are positioned at respective locations

("addresses") on the substrate. The arrays, when exposed to a sample, will exhibit an observed binding pattern. This binding pattern can be detected upon interrogating the array. For example all polynucleotide targets (for example, DNA) in the sample can be labeled with a suitable label (such as a fluorescent compound), and the fluorescence pattern on the array accurately observed following exposure to the sample. Assuming that the different sequence polynucleotides were correctly deposited in accordance with the predetermined configuration, then the observed binding pattern will be indicative of the presence and/or concentration of one or more polynucleotide components of the sample.

Arrays can be fabricated by depositing previously obtained biopolymers onto a substrate, or by in situ synthesis methods. The substrate can be any supporting material to which polynucleotide probes can be attached, including but not limited to glass, nitrocellulose, silicon, and nylon. Polynucleotides can be bound to the substrate by either covalent bonds or by non-specific interactions, such as hydrophobic interactions. The in situ fabrication methods include those described in U.S. Pat. No. 5,449,754 for synthesizing peptide arrays, and in U.S. Pat. No.

6,180,351 and WO 98/41531 and the references cited therein for synthesizing polynucleotide arrays. Further details of fabricating biopolymer arrays are described in U.S. Pat. No. 6,242,266; U.S. Pat. No. 6,232,072; U.S. Pat. No. 6,180,351 ; U.S. Pat. No. 6,171,797; EP No. 0 799 897; PCT No. WO 97/29212; PCT No. WO 97/27317; EP No. 0 785 280; PCT No. WO 97/02357; U.S. Pat. Nos. 5,593,839; 5,578,832; EP No. 0 728 520; U.S. Pat. No. 5,599,695; EP No. 0 721 016; U.S. Pat. No. 5,556,752; PCT No. WO 95/22058; and U.S. Pat. No. 5,631 ,734. Other techniques for fabricating biopolymer arrays include known light directed synthesis techniques. Commercially available polynucleotide arrays, such as Affymetrix GeneChipTM, can also be used. Use of the GeneChipTM, to detect gene expression is described, for example, in Lockhart et al., Nat. Biotechnol., 14:1675, 1996; Chee et al., Science, 274:610, 1996; Hacia et al, Nat. Gen., 14:441, 1996; and Kozal et al., Nat. Med., 2:753, 1996. Other types of polypeptide and polynucleotide arrays are known in the art, and are sufficient for developing an AMD diagnostic array of the present invention, for example an ELISAspot assay.To create the arrays, single- stranded polynucleotide probes can be spotted onto a substrate in a two-dimensional matrix or array. Each single-stranded polynucleotide probe can comprise at least 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, or 30 or more contiguous nucleotides selected from the nucleotide sequences of the SNPs provided herein, or the complement thereof. Preferred arrays comprise at least one single-stranded polynucleotide probe comprising at least 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, or 30 or more contiguous nucleotides selected from the nucleotide sequences or the complement thereof.

Tissue samples from a subject can be treated to form single-stranded polynucleotides, for example by heating or by chemical denaturation, as is known in the art. The single-stranded polynucleotides in the tissue sample can then be labeled and hybridized to the polynucleotide probes on the array. Detectable labels that can be used include but are not limited to radiolabels, biotinylated labels, fluorophors, and chemiluminescent labels. Double stranded polynucleotides, comprising the labeled sample polynucleotides bound to polynucleotide probes, can be detected once the unbound portion of the sample is washed away. Detection can be visual or with computer assistance. Preferably, after the array has been exposed to a sample, the array is read with a reading apparatus (such as an array "scanner") that detects the signals (such as a fluorescence pattern) from the array features. Such a reader preferably would have a very fine resolution (for example, in the range of five to twenty microns) for a array having closely spaced features.

The signal image resulting from reading the array can then be digitally processed to evaluate which regions (pixels) of read data belong to a given feature as well as to calculate the total signal strength associated with each of the features. The foregoing steps, separately or collectively, are referred to as "feature extraction" (U.S. Pat. No. 7,206,438). Using any of the feature extraction techniques so described, detection of hybridization of a patient derived polynucleotide sample with one of the AMD markers on the array given as SEQ ID NO: 1-7 identifies that subject as having or not having a genetic risk factor for AMD, as described above. In another aspect, the invention provides a system for compiling and processing patient data, and presenting a risk profile for developing AMD. A computer aided medical data exchange system is preferred. The system is designed to provide high-quality medical care to a patient by facilitating the management of data available to care providers. The care providers will typically include physicians, surgeons, nurses, clinicians, various specialists, and so forth. It should be noted, however, that while general reference is made to a clinician in the present context, the care providers may also include clerical staff, insurance companies, teachers and students, and so forth. The system provides an interface, which allows the clinicians to exchange data with a data processing system. The data processing system is linked to an integrated knowledge base and a database. The database may be software-based, and includes data access tools for drawing information from the various resources as described below, or coordinating or translating the access of such information. In general, the database will unify raw data into a useable form. Any suitable form may be employed, and multiple forms may be employed, where desired, including hypertext markup language (HTML) extended markup language (XML), Digital Imaging and Communications in Medicine (DICOM), Health Level Seven.TM. (HL7), and so forth. In the present context, the integrated knowledge base is considered to include any and all types of available medical data that can be processed by the data processing system and made available to the clinicians for providing the desired medical care. In general, data within the resources and knowledge base are digitized and stored to make the data available for extraction and analysis by the database and the data processing system. Even where more conventional data gathering resources are employed, the data is placed in a form that permits it to be identified and manipulated in the various types of analyses performed by the data processing system. The integrated knowledge base is intended to include one or more repositories of medical -related data in a broad sense, as well as interfaces and translators between the repositories, and processing capabilities for carrying out desired operations on the data, including analysis, diagnosis, reporting, display and other functions. The data itself may relate to patient-specific characteristics as well as to non-patient specific information, as for classes of persons, machines, systems and so forth. Moreover, the repositories may include devoted systems for storing the data, or memory devices that are part of disparate systems, such as imaging systems. As noted above, the repositories and processing resources making up the integrated knowledge base may be expandable and may be physically resident at any number of locations, typically linked by dedicated or open network links. Furthermore, the data contained in the integrated knowledge base may include both clinical data (e.g., data relating specifically to a patient condition) and non-clinical data. Examples of preferred clinical data include patient medical histories, patient serum and cellular antioxidant levels, and the identification of past or current environmental, lifestyle and other factors that predispose a patient to develop AMD. These include but are not limited to various risk factors such as obesity, smoking, vitamin and dietary supplement intake, use of alcohol or drugs, poor diet and a sedentary lifestyle. Nonclinical data may include more general information about the patient, such as residential address, data relating to an insurance carrier, and names and addresses or phone numbers of significant or recent practitioners who have seen or cared for the patient, including primary care physicians, specialists, and so forth.

The flow of information can include a wide range of types and vehicles for information exchange. In general, the patient can interface with clinicians through conventional clinical visits, as well as remotely by telephone, electronic mail, forms, and so forth. The patient can also interact with elements of the resources via a range of patient data acquisition interfaces that can include conventional patient history forms, interfaces for imaging systems, systems for collecting and analyzing tissue samples, body fluids, and so forth. Interaction between the clinicians and the interface can take any suitable form, depending upon the nature of the interface. Thus, the clinicians can interact with the data processing system through conventional input devices such as keyboards, computer mice, touch screens, portable or remote input and reporting devices. The links between the interface, data processing system, the knowledge base, the database and the resources typically include computer data exchange interconnections, network connections, local area networks, wide area networks, dedicated networks, virtual private network, and so forth.

In general, the resources can be patient-specific or patient-related, that is, collected from direct access either physically or remotely (e.g., via computer link) from a patient. The resource data can also be population-specific so as to permit analysis of specific patient risks and conditions based upon comparisons to known population characteristics. It should be noted that the resources can generally be thought of as processes for generating data. While many of the systems and resources will themselves contain data, these resources are controllable and can be prescribed to the extent that they can be used to generate data as needed for appropriate treatment of the patient. Exemplary controllable and prescribable resources include, for example, a variety of data collection systems designed to detect physiological parameters of patients based upon sensed signals. Such electrical resources can include, for example, electroencephalography resources (EEG), electrocardiography resources (ECG), electromyography resources (EMG), electrical impedance tomography resources (EIT), nerve conduction test resources, electronystagmography resources (ENG), and combinations of such resources. Various imaging resources can be controlled and prescribed as indicated at reference numeral. A number of modalities of such resources are currently available, such as, for example, X-ray imaging systems, magnetic resonance (MR) imaging systems, computed tomography (CT) imaging systems, positron emission tomography (PET) systems, fluorography systems, sonography systems, infrared imaging systems, nuclear imaging systems, thermoacoustic systems, and so forth. Imaging systems can draw information from other imaging systems, electrical resources can interface with imaging systems for direct exchange of information (such as for timing or coordination of image data generation, and so forth). In addition to such electrical and highly automated systems, various resources of a clinical and laboratory nature can be accessible. Such resources may include blood, urine, saliva and other fluid analysis resources, including

gastrointestinal, reproductive, and cerebrospinal fluid analysis system. Such resources can further include polymerase (PCR) chain reaction analysis systems, genetic marker analysis systems, radioimmunoassay systems, chromatography and similar chemical analysis systems, receptor assay systems and combinations of such systems. Histologic resources, somewhat similarly, can be included, such as tissue analysis systems, cytology and tissue typing systems and so forth. Other histologic resources can include immunocytochemistry and histopathological analysis systems. Similarly, electron and other microscopy systems, in situ hybridization systems, and so forth can constitute the exemplary histologic resources. Pharmacokinetic resources can include such systems as therapeutic drug monitoring systems, receptor characterization and measurement systems, and so forth. Again, while such data exchange can be thought of passing through the data processing system, direct exchange between the various resources can also be implemented.

Use of the present system involves a clinician obtaining a patient sample, and evaluation of the presence and amount of a complement factor protein or genetic marker in that patient indicating a predisposition (or not) for AMD, alone or in combination with other known risk factors. The clinician or their assistant also obtains appropriate clinical and non-clinical patient information, and inputs it into the system. The system then compiles and processes the data, and provides output information that includes a risk profile for the patient, of developing AMD.

The present invention thus provides for certain markers that have been correlated to AMD. These markers are useful as diagnostics, and are preferably used to fabricate an array, useful for screening patient samples. The array, in a currently most preferred embodiment, is used as part of a laboratory information management system, to store and process additional patient information in addition to the patient's genomic profile. As described herein, the system provides an assessment of the patient's risk for developing AMD, risk for disease progression, and likelihood of disease prevention based on patient controllable factors.

Plasma and DNA samples were selected from individuals in a previously described AMD registry who progressed to the advanced stages of AMD, including 58 with geographic atrophy and 62 with neovascular disease. Subjects without AMD of similar age and gender were included as controls (n=60). Plasma complement components (C3, CFB, CFI, CFH, Factor D) and activation fragments (Bb, C3a, C5a, iC3b, SC5b 9) were analyzed. DNA samples were genotyped for seven single nucleotide polymorphisms in six genes previously shown to be associated with AMD: CFB, CFH, C2, C3, CFI, and the LOC387715/ARMS2 gene region. Association between AMD and each complement biomarker was assessed using logistic regression, controlling for age, gender, and pro-inflammatory risk factors: smoking and body mass index. Functional genomic analyses were performed to assess the relationship between the complement markers and genotypes. Concordance or "C" statistics were calculated to assess the effect of complement components and activation fragments on our AMD gene-environment prediction model.

The highest quartiles of Bb and C5a were discovered to be significantly associated with advanced AMD compared with the lowest quartiles. In multivariate models without genetic variants, the odds ratio (OR) for Bb was 3.3 (95%

confidence interval (CI) 1.3-8.6) and the OR for C5a was 3.6 (95% CI 1.2-10.3). Controlling for genetic variants, these ORs were substantially higher. The alternative pathway regulator CFH was inversely associated with AMD in the model without genotypes (OR 0.3 ,p = 0.01). Positive associations were found between BMI and plasma C3, CFB, CFH, iC3b and C3 a. There were also significant associations between C5a fragment and LOC387715/ARMS2 and C3 genotypes (p for trend = 0.02, 0.04), respectively. C statistics for models with behavioral and genetic factors increased to 0.94 ± 0.20 with the addition of C3a, Bb and C5a.

Increased levels of activation fragments Bb and C5a are independently associated with AMD. Higher BMI is related to increased levels of complement components. C5a is associated with AMD genotypes. C statistics are stronger with the addition of C3a, Bb and C5a in predictive models. Results implicate ongoing activation of the alternative complement pathway in AMD pathogenesis.

In our analyses we found an independent relationship between activation fragments Bb and C5a and advanced AMD. Our study provides new information regarding the association between the complement system and AMD in several ways. We evaluated each complement component or fragment both with and without seven AMD genotypes, controlling for other covariates including age, gender, smoking, and BMI. We found a significant association between median levels of fragments Bb and C5a and advanced AMD as well as the GA group separately. There was a significant inverse association between CFH component and the total AMD group and GA group separately. C3a levels were significantly higher among cases but this association did not persist after controlling for other genetic and non- genetic factors.

Associations were found between higher BMI and activation fragments C3a, iC3b, and component CFH among controls, as well as associations between BMI and components C3, CFB, and CFH among the entire study population, while controlling for AMD. Higher BMI is a risk factor for developing AMD, has been shown to be related to higher CRP ₅ I and the independent association between BMI and complement components and fragments as disclosed herein are also noteworthy. A significant inverse association was found between C5a fragment and

LOC387715/ARMS2 genotypes, and significant positive trend between C3 genotypes and higher C5a fragment. ROC curves and C statistics were calculated for C3a, Bb, and C5a which included demographic and environmental covariates and seven SNPs. Significant increases in C statistics were observed when these fragments were added to the prediction model, especially when they were considered together, with discrimination between advanced AMD and a control increasing to as high as 94%.

For AMD there is increased complement deposition in Bruch's membrane and in drusen. Photo-oxidation of bis-retinoid lipofuscin in cultured RPE cells has been shown to lead to complement activation and release of fragments iC3b and C3a in vitro. This finding supports the theory that increased complement activation AMD might occur as a result of photo-oxidation within RPE. Having insufficient functional CFH to dampen the complement-induced injury in the outer retina may then lead to pathologic features in advanced AMD. This line of thinking also is supported by the inverse relationship found between CFH and AMD for analyses that were not controlled by genotype. The major predisposing effect is thus one of decreased regulation of activation of the complement cascade in the retina, such as through dysfunctional CFH gene protein or from insufficient production of protein. Whether there is systemic activation of the complement system or the elevated levels reflect systemically circulating fragments from local activation in the eye (as is observed in rheumatoid arthritis with complement activation in a joint) is not clear. These are not mutually exclusive possibilities in that both systemic activation and local (e.g., retinal) complement activation could play a role. The latter is established based on findings of complement deposition in drusen. Biologically the same outcome would occur if CFH was present in lower than normal amounts or was not fully functional. Complement activation, while probably not the initiating cause of the injury in AMD, can nevertheless substantially contribute to subsequent tissue damage. Animal models of laser induced damage to Bruch's membrane provide evidence for the importance of C5a and other components of alternative pathway in the development of choroidal neovascularization. Elevated plasma activation fragments Bb, C3a, and C5a are consistent with continuous, low level alternative pathway activation in patients with advanced AMD. Subtle alterations in the efficiency of activation and/or in regulatory capacity negatively influence a pathologic process that plays out over the years.

Activation of the complement system can lead to the formation the membrane attack complex (MAC or C5b-9). This terminal pathway begins with the cleavage of C5 to C5a and C5b. SC5b-9 is the fluid phase terminal complex which usually circulates bound to the inhibitor vitronectin. The quantity of SC5b-9 in the circulation is in part a reflection of the MAC that is generated locally. Thus, a fraction will deposit at the site and part will be in the fluid phase. Described herein are data showing increased activation of C5.

C5a levels in the blood and deposition of C5b-9 in the retina in AMD patients. C5a can bind to its receptor, C5aR, and has been shown to be important in models of AMD. C5b can be generated by activation of the alternative pathway or by direct cleavage by proteases including trypsin and thrombin. A recent report provided considerable evidence to indicate C5 activation by thrombin in a lung model of immune complex activation. As described herein, the SC5b-9 is not increased in AMD.

The association described herein between high BMI and elevated C3, CFB and CFH as well as increased concentrations of complement activation fragments derived from several components of the alternative pathway point to a role of the complement system in obesity. Adipose tissue is the source of Acylation

Stimulating Protein (ASP), which is also C3a desArg. C3a desArg is a C3 derived protein formed by removal of an arginine from the carboxy- terminus of C3a and is the major form of circulating C3a. Before the relationship to C3 was discovered, a role for lipogenic function of ASP/C3a desArg was discovered. Sivaprasad et al. studied C3a desArg as an indicator of C3 systemic complement activation among 84 cases of advanced AMD (42 GA, 42 NV), as well as in association with the

CFHrslOόl 170 genotype. They found no significant association between genotype and C3a desArg, but did find an increase in C3a desArg concentration among cases. Described herein are associations with C3a and AMD, as well as a second C3 split product, iC3b with BMI. Further, adipsin, an adipose specific factor linked to adipocyte differentiation, was shown to be Factor D of the alternative pathway of the complement system. No association was found with Factor D. Other noteworthy adipose-complement alterations include altered lipid clearance in C3 ^"A mice and partial lipodystrophy in children with an autoantibody that stabilizes the alternative pathway C3 convertase. Also, there are a few reports of elevated C3 concentrations in individuals with a high BMI. The data described herein support increased turnover and activation of the alternative pathway in individuals with high BMI, evidenced by increased activation fragments, Bb, C3a and iC3b. Enhanced alternative pathway activation in obesity and in AMD may cooperate to accelerate tissue damage.

Another noteworthy finding was the OR for the relationship between AMD and LOC387715/ARMS2 increased with the addition of C5a into the model. One possibility is that a tissue metalloproteinase directly activates C5 leading to formation of C5a and C5b, with former binding to its receptor and the latter beginning the MAC.

Regardless of known genotype, both Bb and C5a are strongly associated with increased risk of advanced AMD. Described herein are a new association between AMD and Bb and a new significant inverse association with median level of CFH. The case population consisted solely of advanced AMD cases, the majority of which were documented progressors to GA and NV disease. The GA and NV groups are in similar proportion and results are displayed for each subtype. The relationships between AMD and complement components and fragments has thereby been expanded by considering six genes (seven genetic loci) and covariates known to be related to AMD. The effect of these genetic factors was determined on function or levels of the complement factors. Data included herein also evidences increased BMI in association with complement activation fragments, suggesting that BMI itself increases the chronic activation of the complement system. The AMD prediction model with genetic, demographic, and environmental factors was improved with the addition of plasma complement markers. Results provide new evidence that the complement system, and particularly the alternative pathway, is chronically activated in AMD.

EXEMPLIFICATION

Several genes in the complement pathway are associated with age related macular degeneration (AMD) including: CFH, CFB, C2, C3, and CFI. Another gene in the LOC387715/ARMS2 region on chromosome 10 is related to AMD, although the mechanism is uncertain. Behavioral and modifiable factors, such as smoking and body mass index (BMI) that are related to AMD, influence levels of inflammatory biomarkers and also modify genetic susceptibility. The levels of complement components (C3, CFB, CFI, CFH, factor D) and activation fragments (Bb, C3a, C5a, iC3b, SC5b-9) in plasma samples from cases with advanced AMD and controls, were tested to further assess the role of the complement system and its association with AMD. The association of these biomarkers with behavioral factors related to AMD and their association with AMD genotypes were also assessed. The degree to which these components and activation fragments contribute to risk to predict the prevalence and incidence of advanced AMD was therefore identified. Study Population

Patients with and without AMD were enrolled in genetic epidemiologic studies using standardized clinical examinations, questionnaires and fundus photography. Grades were based on clinical and fundus photographic data using the Clinical Age-Related Maculopathy Grading System (CARMS). From the AMD registry, 180 unrelated Caucasian individuals with DNA and plasma samples were selected. The 120 cases (60 male and 60 female), were composed of 58 individuals with geographic atrophy (GA), and 62 with neovascular disease (NV). Among the cases, 108 had a baseline CARMS grade of 1 , 2, or 3 and progressed to either grade 4 - central or non-central GA (n = 48), or grade 5 with NV (n = 62) in one or both eyes. Controls (n = 60) had a CARMS grade of 1 in both eyes, and consisted of 30 males and 30 females. The mean ± standard deviations (SD) of ages of the case and control groups were 82 ± 6.9 years and 79 ± 4.4 years, respectively. This research followed the tenets of the Declaration of Helsinki, was approved by the institutional review board, and informed consent was obtained from all subjects.

Genotyping

DNA samples were genotyped for seven single nucleotide polymorphisms (SNPs) related to AMD: 1) Complement Factor H (CFH) Y402H (rs 1061170) in exon 9 of the CFH gene on chromosome Iq32, a change 1277T>C, resulting in a substitution of histidine for tyrosine at codon 402 of the CFH protein, 2) CFH rsl410996 an independently associated SNP variant within intron 14 of CFH, 3) LOC387715 A69S ARMS2 (rs 10490924 in the LOC387715/ARMS2 region of chromosome 10, a non-synonymous coding SNP variant in exon 1 of LOC387715), resulting in a substitution of the amino acid serine for alanine at codon 69, 4) Complement Factor 2 or C2 E318D (rs9332739), the non-synonymous coding SNP variant in exon 7 of C2 resulting in a substitution of aspartic acid for glutamic acid at codon 318, 5) Complement Factor B or CFB R32Q (rs641 153), the non- synonymous coding SNP variant in exon 2 of CFB resulting in the substitution of the amino acid glutamine for arginine at codon 32, 6) Complement Factor 3 or C3 R102G (rs2230199), the non- synonymous coding SNP variant in exon 3 of C3 resulting in the substitution of the amino acid glycine for arginine at codon 102, 7) Complement Factor I or CFI (rsl 0033900), an independently associated SNP located in the linkage peak region of chromosome 4, 2781 base pairs upstream of the 3 ' untranslated region of CFI.

The HTRAl gene, adjacent to the genetic variant on chromosome 10, LOC387715A69S, may in fact be the AMD-susceptibility gene on 10q26 as the relevant SNPs in these two genes have been reported to be nearly perfectly correlated. Thus, while the other SNP is a promising candidate variant, rsl 0490924 used in this study can be considered a surrogate for the causal variant that resides in this region. For the C2/CFB genes, there are two independent associations to the C2/CFB locus. Genotyping was performed using primer mass extension and MALDI-TOF MS analysis (MassEXTEND methodology of Sequenom, San Diego, CA).

Plasma Samples

Fasting plasma samples were drawn into Becton Dickinson tubes containing K ₂ EDTA, primarily at time of baseline grade. The blood was centrifuged and plasma separated within 30 minutes of collection. Samples were frozen and stored in liquid nitrogen until testing was performed. The following complement activation fragments and complement system proteins were analyzed: Bb, C3a, C5a, iC3b, SC5b-9, C3, Factor D, CFB, CFH, and CFI.

Complement Components and Activation Fragments

C3 was measured by standard clinical laboratory nephelometric methods.

CFB, CFH and CFI were measured by radial immunodiffusion (RID) using goat anti-human antibodies specific for the individual proteins in 1 % agarose gels. The CFH antiserum (Quidel, San Diego CA) was produced against purified human factor H. It is specific for CFH but has not been evaluated for cross-reactivity to the CFH- related proteins. The complement laboratory reference range for CFH is 160 - 412 μg/mL, in agreement with ranges used by other complement reference laboratories in the United States and Europe. The antibodies used for CFB and CFI are also goat polyclonal antisera specific for the given proteins (Quidel, San Diego CA). Purified CFB, CFH and CFI were used to standardize the assays (Quidel, San Diego CA and CompTech, Tyler TX). Appropriate controls were included with each batch of patient samples, and the results calculated from the area under the precipitin rings. Factor D was measured with a Quantikine ^® ELISA kit (R&D Systems, Minneapolis, MN) that uses a plate precoated with specific monoclonal antibody for Factor D. The standard provided with the kit is 40 ng of recombinant human complement factor D (CFD). The enzyme conjugate is polyclonal anti-CFD linked to horse-radish peroxidase. Three controls: high, medium and low are run with each set of samples. The reference range given by the manufacturer is 1468-3657 ng/mL for EDTA plasma. The reference range established by the complement laboratory is 1688 - 3076 ng/mL for EDTA plasma, which was converted to μg/mL: 1.69 - 3.08 (mean ± 2 SD) for reporting in this study.

Complement activation markers were measured by ELISAs using

commercially available kits with extra in-house controls. Bb, iC3b and SC5b-9 were done with kits from Quidel (San Diego, CA). The tests for C3a and C5a were performed using OptEIA® kits from BD-Pharmingen (San Diego, CA). Reference ranges for all of the complement fragments were established in the complement lab under stringent validation protocols. Reference ranges for the assays are as follows: C3a (305 - 1239 ng/mL), C5a (5.0 - 25.4 ng/mL), Bb (0.41 - 1.49 μg/mL), iC3b (0- 17.4 ng/mL), and SC5b-9 (72 - 244 ng/mL). The complement laboratory is accredited by the College of American Pathologists and the Clinical Laboratory of 1988 to perform tests of high complexity.

Covariates

Data on smoking were collected from a standardized risk factor

questionnaire. Smokers were defined as having smoked at least one cigarette per day for six months or longer. Pack years were calculated by multiplying number of cigarettes smoked per day by number of years smoked divided by twenty. Height and weight were measured at the time of the baseline grade to calculate BMI (weight in pounds multiplied by 703 divided by height in inches squared), or in a few instances by self - report.

Statistical analysis

Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated for covariates, and genotypes using logistic regression (controlling for age (60-79, 80+ and gender) to evaluate their association with each maculopathy group (GA and NV), and total AMD (GA and NV combined), with controls. T-tests were used to calculate p values for age between cases and controls. P values <0.05 were considered statistically significant for all analyses.

The Wilcoxon Rank Sum test was used to calculate p values to assess the relationship between the median plasma level of complement components and activation fragments and maculopathy group.

ORs and 95% CIs for total AMD were computed to compare the 4th quartile to the 1st quartile of component and activation fragment using logistic regression. In Model A, controls for age (60-79, 80+), gender, BMI (<25, 25-29.9, 30+) and smoking (ever, never) were considered. In Model B, controls for the same factors as in Model A were considered, plus all of the genotypes: CFB (CC, CT/TT),

CFHY402H (TT, CT, CC), CFH :rs 1410996 (TT, CT, CC), C2 (GG, CG/CC), LOC387715/ARMS2 (GG, GT/TT), C3 (CC, CG, GG), and CFI (CC, CT, TT).

ORs and 95% CIs for AMD were calculated for each genotype separately with one component or activation fragment at a time to assess whether the effect of genotype was mediated by that complement component or activation fragment. Log component and activation fragment values were used, because the distribution was slightly skewed, to assess associations with genotype among controls using linear regression. In addition, linear regression was done to test the relationship between each complement component or activation fragment and smoking and BMI, both a) among controls and b) among cases and controls combined. To see if the effects of genotype and complement components and activation fragments were dependent on one another, logistic regression was used to test for interaction effects on risk of AMD.

General linear model analysis was used to calculate the least square means to assess the relationship between mean level of components or fragments and genotype among cases and controls combined. In this model, controls for age, gender, AMD status and all the genotypes were used.

C Statistics were calculated to assess if activation fragments contribute to the predictability of developing advanced AMD. The area under the receiver operating characteristic (ROC) curve was obtained, and an age-adjusted concordant or "C" statistic based on the ROC curve was calculated. C statistics was calculated for six models with varying combinations of covariates, genotypes, and activation fragments to assess the probability that the risk score from a random case was higher than the corresponding risk score from a random control, based on the group of risk factors in each model, such that a perfect score would be 1.0, or 100% predictability. We obtained standard errors of estimated C statistics and compared C statistics from alternative risk prediction models using correlated ROC curve methods.

Table 1 Baseline Demographic, Behavioral, and Genetic Factors According to Maculopathy Group

Maculopathv Group

Controls GA OR (95% Cl)* p value NV OR (95% Cl)* p value Total AMD OR (95% Cl)* p value N <%) N (%) ^' N (%) N (%)

Characteristics (n = 60) (n = 58) (n = 62) ( n = 120)

Mean (SD) age, y 79 (44) 82 (79) 82 (57) 0003 ^T 82 (69) 0004 ^T

Female 30 (50) 30 (52) 10 30 (48) 10 60 (50) 10 Male 30 (50) 28 (48) 09(05-19) 085 32 (52) 09 (04 - 20) 086 60(50) 10(05-19) 091

Smoking Status

never 29 (48) 31 (52) 10 22 (35) 10 53 (44) 10 ever 31 (52) 27 (47) 08 (04 - 17) 054 40 (65) 16(08-35) 020 67 (56) 12 (06 - 22) 066

Pack Years

0 29 (48) 31 (54) 10 22 (36) 10 53 (45) 10 01-144 12(20) 11 (19) 08(03-22) 9(15) 09 (03 - 28) 20(17) 08(03-20)

145-33 10(17) 7(12) 06(02- 19) 15(25) 19 (07 - 54) 22(19) 13(05-31)

34+ 9(15) 8(14) 09(03-27) 15(25) 21 (07-61) 23(19) 13(05-34)

058 ^s 010 ^s 048 ^s

Body Mass Index

<25 35 (58) 25 (43) 10 24 (39) 10 49 (40) 1 0 25-299 14(23) 19(33) 16(07-36) 25 (40) 23(10-54) 44 (37) 1 8(09- 38) 30 or greater 11 (18) 14(24) 15(06-37) 13(21) 11 (04 - 28) 27 (23) 1 3(06- 28)

017 ^§

Genotypes

CFB rs641153(R32Q)

K) CC 46 (80) 50 (96) 10 47(92) 10 97 (94) 10

CT/TT 11(19) 2 (4) 02 (003 - 08) 003 4(8) 028 (008 - 10) 005 6 (6) 023 (008 - 070) 001

CFH rs1061170(Y402H)

TT 28 (50) 12(23) 10 8(16) 10 20(19) 10 CT 18(32) 21 (40) 25(10-63) 22 (43) 54(18 -160) 43 (42) 34(15-78) CC 10(18) 19(37) 44(16-126) 21 (41) 92 (28 - 300) 40 (39) 57 (23 - 144)

00002 ^s

CFH rs1410996

TT 6(11) 3 (6) 10 2(4) 10 5(5) 10

CT 32 (58) 12(23) 09(02-42) 11 (23) 11 (02- 73) 23 (23) 08(02- 31)

CC 17(31) 37(71) 45(10-21) 34 (72) 80 (13 - 50) 71 (72) 49(13- 181)

00002 ^§

C2 rs9332739 (E318D)

GG 48 (84) 52(100) 10 44 (88) 10 96 (94) 10

CG/CC 9(16) 0(0) O ¹ 6(12) 06 (02 - 18) 032 6 (6) 03 (01 -080) 002

LOC387715 rs10490924(A69S)

GG 33 (58) 20 (38) 10 15(31) 10 35 (35) 10

G T/T T 24 (42) 33(62) 22(10-47) 005 33 (69) 37(16-89) 0003 66 (65) 28(14-56) 0004

C3 rs2230199(R102H)

CC 36 (62) 21 (42) 10 23 (49) 10 44 (45) 10

CG 19(33) 22(44) 24(10-56) 19(40) 17(07-41) 41 (42) 21 (10-43)

GG 3 (5) 7 (14) 42(09-190) 5(11) 26(05-129) 12(12) 34(08-134)

002 ^s 013 ^§

CFI rs10033900

CC 20 (36) 19(36) 10 10(20) 10 29 (28) 10

CT 28(51) 20 (38) 08 (03 - 20) 30(61) 25 (09 - 66) 50 (49) 14(06-30)

TT 7(13) 14(26) 23(07-73) 9(18) 27 (07 - 99) 23 (23) 26 (09 - 73)

023 ^s 009 ^s 008 ^s

FreQuencv counts for some qenotvoes and pack vears mav not add UD to the total sample size

Table 1 shows associations between increasing age and all AMD groups. The NV only and total AMD groups were more likely to be smokers or heavy smokers and had higher BMI compared to controls, although these associations are not statistically significant. All genotypes are associated with both types of advanced AMD. There is a significant protective effect of genotype CFB (CT/TT) for GA and a trend for reduced risk of NV, compared with CC genotype. There are strong positive associations between CFH Y402H and CFHrsl410996 CC genotypes (vs TT) and both forms of advanced AMD, and a protective association of C2 CG/C (vs GG) with overall AMD and GA. LOC387715/ARMS2 GT/TT is associated with AMD compared with GG. The C3 GG genotype is positively associated with GA and a similar trend was seen for NV. For CFI, ORs are in the direction of increased risk for the T allele for both GA and NV.

Table 2 Levels of Plasma Complement Components/Fragments According to Maculopathy Group

Maculopathv Group

Controls Total AMD*

Complement Components/ (n = 60) (n = 58) (n = 62) ( n = 120)

Fragments

Median (10th 90th percentile) Median (10th 90th percentile) P value' Median (10th 90th percentile) P - value ¹ Median (10th 90th percentile) P value ¹

Bb(μg/mL) 083 (046-135) 095 (060 - 15) 084 (053 - 14) 03 093(056-144) 006 C3 (mg/dL) 110(86-155) 113(82-154) 115(86- 155) 085 114(83-155) 097 C3a(ng/mL) 1498(768-2154) 1567(959 2899) 1647(704-2613) 022 15935 (7575-27385) 005 C5a (ng/mL) 135(79199) 17(93-212) 16(86-24) 009 162(910-229) 002 CFB (μg/mL) 228(183-311) 249(182-352) 251(189-341) 019 251(185352) 014 ιC3b (ng/mL) 1002(469-2272) 11 (5-29) 10(6 25) 097 1037(524-2615) 063 SC5b-9 (ng/mL) 403(176-624) 332(164-716) 05 335(188 615) 026 333(184-652) 029 CFI (μg/mL) 399(272-641) 40(28-56) 43(22-57) 097 415(225-564) 082

4- CFH (μg/mL) 3115(2515-420) 289(231-417) 295 (246 - 389) 006 2935(238-3915) 0009 Factor D (μg/mL) 32(23-46) 34(26-47) 35(26-46) 025 34(26-46) 025

* AMD patients who had Geographic Atrophy (GA) or Neovascular (NV) disease in at least one eye

t P values calculated using Wilcoxon Rank Sum All p - values reflect comparison between

maculopathy group and controls

Table 2 shows that median levels of activation fragments, Bb, C3a, and C5a, are significantly higher in the

GA group compared with controls. CFH component is significantly lower in the GA group compared with controls

(median of 289 μg/mL vs 312 μg/mL, p= 0.008). Results for the total AMD group were similar to the GA group.

Table 3. Association Between Plasma Complement Components/Fragments and AMD, With and Without Adjustment for Genotypes

Model A Model B

Without genotypes* With genotypes*

OR (95% Cl)* p (trend) OR (95% Cl)* p (trend)

Quartile 4 vs Quartile 1 Quartile 4 vs Quartile 1

Plasma Complement

Component/Fragment

Bb 33(13-86) 001 54 (13 -229) 002

C3 08(03-21) 065 15 (04-62) 070

C3a 21 (08 - 56) 028 22 (05-94) 075

C5a 36(12-103) 001 252(37-1717) 00003

CFB 16(06-42) 018 33 (08-127) 008

ιC3b 08(03-21) 079 07 (02-27) 081

SC5b-9 07(03-18) 012 08 (02-26) 029

CFI 09(03-23) 090 09 (02 - 34) 092

CFH 03 (009 - 075) 001 06(01-26) 050

Factor D 14(05-37) 063 31 (07- 126) 021

^* Adjusted for age (60-79, 80+), gender, smoking (ever smoked vs never smoked), body mass index (<25, 25-299,>299)

t Adjusted for all variables in A plus CFB (CC, CT/TT), CFH Y402H (TT, CT, CC), CFHrs1410966 (TT, CT, CC), C2 (GG, CG/CC),

LOC387715 (GG, GT, TT), C3 (CC, CG, GG), CFI (CC, CT, TT)

+. Odds ratios (OR) reflect a comparison between cases and controls in the 4th quartile vs 1st quartile of

plasma complement factor

Table 3 shows a positive association between activation fragment Bb and the total AMD group, both with and without adjustments for genotype (OR =3.3, 95% CI 1.3-8.6; OR = 5.4, 95% CI 1.3-22.9, respectively). Similar results were found for C5a (OR = 3.6, 95% CI 1.2-10.3; OR = 25.2, 95% CI 3.7-171.7), with and without adjusting for genotypes, respectively. CFH component has a significant inverse association with AMD in the model that did not control for genotype (OR = 0.3, 95% CI 0.09-0.75, p = 0.01), which became non-significant after controlling for genotype.

Table 4. Evaluation of Change in Genotype - AMD Associations with Addition of Plasma Complement Components/Fragments*'

Plasma Complement Components/Fragments*

No Plasma

Complement Factors Bb C5a CFH

OR (95% Cl) p (trend) OR (95% Cl) p (trend) OR (95% Cl) p (trend) OR (95% Cl) p (trend)

Genotype

CFB: rs641153(R32Q)

CC 10 10 10 10

C T/ T T 026 (008 - 080) 028(009-091) 024 (008 - 074) 025(008-081)

002 003 001 002

CFH: rs1061170(Y402H)

TT 10 10 10 10

CT 37(16-87) 42(18-100) 43(17-106) 36(15-84)

CC 60(23-158) 59(22-157) 79 (28 - 224) 66(24-180)

00002 00002 <00001 00002

CFH: rs-1410996

TT 10 10 10 10

00 CT 09 (02 -37) 07(02-31) 10 (024 - 46) 10 (02- 36)

CC 52(12-214) 42(10-180) 74 (16 - 339) 49(12-204)

<00001 00002 <00001 <00001

C2. rs9332739(E318D)

GG 10 10 10 10

CG/CC 020 (006-067) 023 (007 - 078) 019(006-063) 022 (007 - 074)

0008 002 0007 001

LOC387715.rs10490924(A69S)

GG 10 10 10 10

GTfTT 26(13-53) 25 (12 - 52) 36 (17 - 77) 26(13-54)

0007 001 0001 0009

C3 rs2230199(R102H)

CC 10 10 10 10

CG 23 (11 -49) 21 (096 - 46) 20 (090 - 44) 22(10-48)

GG 38(09-158) 31 (086-146) 35(083-147) 52(12-232)

001 002 003 0008

CFI: rs10033900

CC 10 10 10 10

CT 16 (071 -34) 16 (071 - 35) 18 (078 - 40) 15 (068 - 34) TT 26 (090 - 77) 25 (082 - 74) 39(12-122) 21 (072 - 64

007 010 002 015

* Adjusted for age (60-79, 80+), gender, smoking (ever smoked vs never smoked), body mass index (<25, 25-299,>299)

t Odds Ratios (OR) reflect a comparison of all cases vs controls

t Quartiles

Table 4 shows the change in the association between genotype and AMD, with addition of complement factors Bb, C5a, and CFH to the models. The ORs for risk of AMD increase for both CFH loci, LOC, and CFI genotypes, with the addition of activation fragment C5a into the statistical models. There is also an increase in the OR for the C3 gene when CFH component was added to the model. The associations of the other genetic loci with AMD were not materially changed after inclusion of complement components and activation fragments one at a time.

Table 5 Associations Between Plasma Complement Components/Fragments, Smoking, and Body Mass Index

Smoking (Ever vs. Never)* Smoking (Ever vs. Never)* BMI ^§ BMlII

Controls (N = 60) All Subjects (N = 180) Controls (N = 60) All Subjects (N = 180)

Complement β ± SE* (p value)" β ± SE* (p value)" p interaction 0 ± SE* (p value) β ± SE* (p value)" p interaction Components/Fragments*

Bb 0 05 ± 0 11 (0 65) 0 02 ± 0 10 (0 15) 0 46 -0 02 ± 0 11 (0 83) -011 ±006 (008) 045

C3 - 0 03 ± 0 06 (0 59) 0 05 ± 0 04 (0 18) 0 23 0 09 ± 0 06 (0 17) 010 ±003 (0005) 093

C3a 0 001 ± 0 10 (0 99) 0 06 ± 0 07 (0 40) 0 75 0 25 ± 0 09 (0 01 ) 009 ±007 (017) 007

C5a -0 03 ± 0 09 (0 76) 0 02 ± 0 06 (0 77) 0 54 0 04 ± 0 09 (0 69) -003 ± 006 (062) 046

CFB 0 05 ± 0 07 (0 51) -0 006 ± 0 04 (0 88) 0 17 0 13 ± 0 07 (0 06) 012 ±004 (0001) 054 iC3b -0 13 + 0 16 (0 41) -0 12 ± 0 09 (0 17) 0 96 0 31 ± 0 16 (0 05) 010 ±009 (026) 011

SC5b-9 -0 15 ± 0 13 (0 24) -0 12 ± 0 07 (0 17) 0 55 0 11 ± 0 13 (0 39) -005 ± 008 (056) 041

O CFI -0 03 ± 0 09 (0 73) -0 005 ± 0 05 (0 92) 0 70 0 01 ± 0 09 (0 89) 004 ± 005 (047) 082

CFH -0 07 ± 0 05 (0 18) -0 02 ± 0 03 (0 60) 0 28 0 12 ± 0 05 (0 03) 012 ±003 (00006) 095

Factor D 0 09 ± 0 08 (0 25) 0 06 ± 0 04 (0 13) 0 50 0 08 ± 0 08 (0 34) 003 ± 004 (044) 038 p interaction is the interaction between smoking or BMI and AMD status

* Log values

t Controlling forage, gender

X Controlling forage, gender, and AMD status (total AMD, controls)

§ Body Mass Index (BMI) categoπes <25, 25+ Controlling for age and gender

I BMI categories <25, 25+ Controlling for age, gender, and AMD status (total AMD, controls)

# Beta ± Standard Error

^** P values for controls only were calculated by multiple regression of log complement component/

fragment on age 80+ vs <80 and gender P values for all subjects were caculated the same as controls, and

in addition controlled for AMD status

Significant associations were found between BMI >25 and increased levels of fragments C3a and iC3b, and component CFH among controls (Table 5). When the sample size was increased to include the entire study population to further assess these relationships and controlled for AMD status, CFH remained significant, CFB and C3 became significant (p = 0.001 and 0.005, respectively), whereas C3a and iC3b became non-significant. No significant association was found between complement components or fragments and smoking.

Interactions between genotypes and components and fragments were also assessed. A significant interaction was found between component CFH and the protective CFB genotype (p = teraction = 0.04). This indicates that the component CFH is inversely associated with risk of AMD among individuals with the CFB genotype CC, but is unrelated to risk of AMD in the presence of a protective allele CFB genotype of CT/TT.

Table 6. Associations Between Complement Components/Fragments and Genotype Among All Subjects

Complement Component/Fragment*

Bb C3a C5a CFH

LS Means + SE ⁺ (p value) LS Means + SE ⁺ (p value) LSMeBnSiSE ⁺ (p value) LS Means + SE ⁺ (p value)

Genotype

CFB:rs641153(R32Q)

CC -028 ± 008 741+009 279 + 008 579 + 006

C T/ T T -033 + 012 057 720 + 014 026 278 + 012 096 580 ± 007 080

CFH- rs1061170(Y402H)

TT -023 + 010 734 + 012 283 + 010 578 ± 006

CT -040 ± 011 732 + 012 277 + 010 578 ± 006

CC -029 ± 011 724 + 013 275 + 011 582 + 006

047* 043* 051* 052*

CFH rs1410996

TT -051 ±015 740 + 017 286 + 014 576 ± 008

CT -022±010 722 + 011 276 + 010 581+006

CC -019±010 729 + 011 273 + 010 581+005

016* 092 ¹ 045* 077*

C2: rs9332739 (E318D)

GG -020 ± 007 722 ± 008 270 ± 007 574 ± 004

CG/CC -042±014 013 739 + 016 021 287 + 014 016 585 ± 008 018

LOC387715:rs10490924(A69S)

GG -035±010 733 + 011 287 ± 009 577 ± 005

GT/TT -027 ± 009 040 727 + 011 049 270 ± 009 002 581 ± 005 030

C3: rs2230199 (R102H)

CC -033 ± 009 720 + 010 269 ±009 575 ± 005

CG -026 ± 009 747 + 010 282 + 009 571 ±005

GG -034 + 014 724 + 016 284 + 013 591 ±008

058* 007* 004 ¹ 015*

CFI: rs10033900

CC -032±010 739 ± 012 282 ±010 580 + 006

CT -030 + 009 730 + 010 282 + 009 580 ± 005

TT -030 + 011 722 + 013 272 + 011 577 ± 006

074* 007* 029* 065*

^* Log values

t Least square means + standard error, controlling for age, gender, AMD status, CFB (CC, CT/TT), CFH Y402H (TT, CT, CC), CFHrs1410966 (TT, CT, CC),

C2 (GG, CG/CC), LOC387715 (GG, GT, TT), C3 (CC, CG, GG), CFI (CC, CT, TT)

t P value for trend

Controlling for all genotypes, significant associations were found between fragment C5a and the LOC387715/ARMS2 and C3 genotypes although the differences were small (Table 6). For the LOC387715/ARMS2 genotype the least square (LS) mean for C5a was lower with the addition of the risk allele T. For the C3 genotype the trend for increasing C5a is significant with the addition of each risk allele. Bb LS means are negative because they are on a log scale and have values less than one in some instances.

Table 7. C Statistics for Age-Related Macular Degeneration Based on Models with Demographic, Behavioral, Genetic Factors, and C5a, Bb, and C3a

Model Variables C- statistic

1 Age, gender, smoking, BMI 0 612 ± 0 050

Age, gender, smoking, BMI, 0 832 + 0 036

CFB rs641153 (R32Q), CFH rs1061170 (Y402H),

CFH rs1410996, C2 rs9332739 (E318D),

LOC387715 rs10490924 (A69S), C3 rs2230199 (R102H),

CFI rs10033900

Age, gender, smoking, BMI, 0 844 ± 0 035

CFB rs641153 (R32Q), CFHrs1061170 (Y402H),

CFH rs1410996, C2 rs9332739 (E318D),

LOC387715 rs10490924 (A69S), C3 rs2230199 (R102H),

CFI rs10033900 , C3a fragment

Ul Age, gender, smoking, BMI,

CFB rs641153 (R32Q), CFH rs1061170 (Y402H), 0 870 ± 0 031

CFH rs1410996, C2 rs9332739 (E318D),

LOC387715 rs10490924 (A69S), C3 rs2230199 (R102H),

CFI rs10033900 , Bb fragment

Age, gender, smoking, BMI, 0 895 ± 0 028

CFB rs641153 (R32Q), CFH rs1061170 (Y402H),

CFH rs1410996, C2 rs9332739 (E318D),

LOC387715 rs10490924 (A69S), C3 rs2230199 (R102H),

CFI rs10033900 , C5a fragment

Age, gender, smoking, BMI, 0 944 ± 0 020

CFB rs641153 (R32Q), CFH rs1061170 (Y402H),

CFH rs1410996, C2 rs9332739 (E318D),

LOC387715 rs10490924 (A69S), C3 rs2230199 (R102H),

CFI rs10033900, C3a, Bb, C5a fragments

* p value (model 1 vs 2, p = 0 001 , 2 vs 3 = 0 63, 2 vs 4 = 0 043, 2 vs 5 = 0 029, 2 vs 6 = <0 001 )

C statistics for all cases versus controls were calculated for various models to assess the predictability of advanced AMD (Table 6): Model 1 (age, gender, AMD status, smoking, BMI); Model 2 (all variables in Model 1 plus genetic variants CFB, CFHY402H, CFH:rsl410996, C2 , LOC387715/ARMS2 , C3 , and CFI; and Models 3, 4, 5, 6 (Model 2 plus fragments C3a, Bb, and C5a, and all three markers, respectively). There were significant increases in C statistics upon adding Bb (0.870) and C5a (0.895) to Model 2 (p = 0.029, 0.043, respectively). Combining C3a, Bb, and C5a into the model resulted in a C statistic of 0.944, and a p value of <0.001 compared to Model 2. The frequency distribution of risk scores was plotted separately for cases and controls for Model 6, selecting a cutoff of 4 (risk score >0), yields a sensitivity of 88% and a specificity of 73% (Figure). In general, cases had higher risk scores than controls.

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